diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Photoshop 7.0.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Photoshop 7.0.md deleted file mode 100644 index fab2789ca4fe1ae73ad204bb3215211f58cf0d32..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Photoshop 7.0.md +++ /dev/null @@ -1,86 +0,0 @@ - -

Adobe Photoshop 7.0: A Classic Photo Editing Software That Still Works

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Adobe Photoshop 7.0 is one of the most popular and widely used photo editing software in the world. It was released in 2002 and has been a favorite among professional and amateur photographers, graphic designers, and digital artists ever since. Adobe Photoshop 7.0 offers a range of features and tools that allow you to create, edit, enhance, and manipulate images with ease and precision. In this article, we will review some of the main features and benefits of Adobe Photoshop 7.0 and why it is still a great choice for photo editing in 2023.

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One of the main advantages of Adobe Photoshop 7.0 is its compatibility and performance. Adobe Photoshop 7.0 can run smoothly on almost any Windows or Mac computer, even if it has low specifications or an older operating system. It does not require much disk space or memory to install and operate, unlike newer versions of Photoshop that may slow down your system or crash frequently. Adobe Photoshop 7.0 also supports a wide range of file formats, such as JPEG, PNG, GIF, TIFF, PSD, PDF, and more. You can easily import and export images from different sources and devices without losing quality or data.

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Another key feature of Adobe Photoshop 7.0 is its user interface and functionality. Adobe Photoshop 7.0 has a simple and intuitive interface that makes it easy to navigate and access the various tools and options. You can customize the layout and appearance of the interface according to your preferences and needs. You can also use keyboard shortcuts and mouse gestures to speed up your workflow and productivity. Adobe Photoshop 7.0 also has a powerful and versatile functionality that allows you to perform a variety of tasks and effects on your images. You can crop, resize, rotate, flip, skew, distort, warp, transform, align, merge, blend, layer, mask, filter, adjust, colorize, retouch, sharpen, blur, smudge, clone, heal, dodge, burn, sponge, gradient, texturize, stylize, draw, paint, erase, fill, stroke, select, cut, copy, paste, undo, redo, save, print, and more.

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A third benefit of Adobe Photoshop 7.0 is its creativity and innovation. Adobe Photoshop 7.0 offers a range of creative and innovative features and tools that allow you to unleash your imagination and express your vision. You can use Adobe Photoshop 7.0 to create stunning graphics and artworks for various purposes and platforms. You can design logos, banners, posters, flyers, brochures, cards, -invitations, -stickers, -labels, -t-shirts, -mugs, -calendars, -wallpapers, -icons, -buttons, -illustrations, -comics, -cartoons, -animations, -games, -websites, -apps, -and more. -You can also use Adobe Photoshop 7.0 to enhance your photos and make them look more professional -and artistic. -You can edit your photos to improve their brightness, -contrast, -color, -exposure, -white balance, -sharpness, -noise reduction, -red-eye removal, -blemish removal, -skin smoothing, -teeth whitening, -eye color changing, -hair color changing, -face reshaping, -body slimming, -background changing, -object adding or removing, -and more. -You can also apply various effects -and filters -to your photos -to make them look more -dramatic, -romantic, -vintage, -retro, -glamorous, -grunge, -pop art, -watercolor, -oil painting, -sketching, -and more.

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In conclusion -Adobe Photoshop 7.0 -is a classic photo editing software -that still works -in 2023. -It has a range of features -and benefits -that make it a great choice -for photo editing -in terms of compatibility -performance -user interface -functionality -creativity -and innovation. -You can use Adobe Photoshop 7.0 -to create -edit -enhance -and manipulate images -with ease -and precision. -You can also use Adobe Photoshop 7.0 -to express your vision -and unleash your imagination.

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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Compendio De Obstetricia Votta Pdf.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Compendio De Obstetricia Votta Pdf.md deleted file mode 100644 index 649eb7d94ec0c91a9d181c8c0798b51afc00a74b..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Compendio De Obstetricia Votta Pdf.md +++ /dev/null @@ -1,28 +0,0 @@ -
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Compendio De Obstetricia Votta Pdf: A Comprehensive Guide for Obstetrics Students and Professionals

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If you are looking for a reliable and updated source of information on obstetrics, you may want to check out the Compendio De Obstetricia Votta Pdf. This is a book written by Osvaldo H. Parada and Roberto A. Votta, two renowned obstetricians from Argentina, who have compiled their extensive knowledge and experience in this field.

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The Compendio De Obstetricia Votta Pdf covers all the aspects of obstetrics, from normal pregnancy and delivery to complications and emergencies. It also includes chapters on gynecology, neonatology, genetics, ultrasound, and more. The book is organized in a clear and concise way, with tables, figures, algorithms, and clinical cases to illustrate the concepts.

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The Compendio De Obstetricia Votta Pdf is a valuable resource for obstetrics students, residents, and specialists who want to update their skills and knowledge. It is also useful for other health professionals who work with pregnant women and newborns, such as nurses, midwives, pediatricians, and family doctors.

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You can download the Compendio De Obstetricia Votta Pdf for free from various websites on the internet[^1^] [^2^] [^3^]. However, we recommend that you buy the original book from a reputable publisher or bookstore to support the authors and ensure the quality of the content.

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Obstetrics Trends in 2022

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Obstetrics is a dynamic and evolving field that constantly adapts to new evidence, technologies, and challenges. In 2022, some of the trends that may shape obstetrics practice and research include:

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These are just some of the examples of the current trends in obstetrics that may influence clinical practice and research in 2022. Obstetricians should stay updated on the latest evidence and guidelines to provide the best care for their patients.

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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FULL Windows XP SP3 Angel Live V.2.0.iso The Features and Benefits of this Superb XP.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FULL Windows XP SP3 Angel Live V.2.0.iso The Features and Benefits of this Superb XP.md deleted file mode 100644 index 6d44fd5c2a5d87848a89b1e15df7d7f4121c7b37..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/FULL Windows XP SP3 Angel Live V.2.0.iso The Features and Benefits of this Superb XP.md +++ /dev/null @@ -1,74 +0,0 @@ - -

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Windows XP is one of the most popular and widely used operating systems in the world, even though it was released more than 20 years ago. However, Microsoft stopped supporting it in 2014, which means that it no longer receives security updates or bug fixes.

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Fortunately, there are some unofficial versions of Windows XP that are still being maintained and updated by enthusiasts and developers who want to keep this operating system alive and functional.

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One of these versions is Windows XP SP3 Angel Live V.2.0.iso, which is a modified and enhanced version of Windows XP that can run from a CD or a USB drive without installation.

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If you want to try Windows XP SP3 Angel Live V.2.0.iso, you need to download the ISO file first.

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You can download Windows XP SP3 Angel Live V.2.0.iso from various sources on the internet, but you need to be careful about where you get it from.

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Some sources may provide fake or corrupted files that may harm your system or contain malware or viruses.

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To avoid these risks, we recommend you download Windows XP SP3 Angel Live V.2.0.iso from a reliable source such as Archive.org or YouTube. These sources provide direct links to download the ISO file without any surveys or ads.

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The size of the ISO file is about 633 MB, so make sure you have enough space on your hard drive or your USB drive before downloading it.

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After downloading the ISO file, you need to verify its integrity by checking its checksum or hash value.

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A checksum or hash value is a unique code that identifies a file based on its content.

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If two files have the same checksum or hash value, it means they are identical.

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If they have different checksums or hash values, it means they are different or corrupted.

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You can use various tools such as MD5 & SHA Checksum Utility or HashTab to calculate and compare the checksum or hash value of your downloaded ISO file with the original one provided by the source.

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If they match, it means your downloaded ISO file is valid and safe.

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If they don't match, it means your downloaded ISO file is invalid or tampered with.

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How to install Windows XP SP3 Angel Live V.2.0.iso?

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After downloading and verifying Windows XP SP3 Angel Live V.2.0.iso, you can install it on your system in two ways:

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How to install Windows XP SP3 Angel Live V.2.0.iso on a computer?

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To install Windows XP SP3 Angel Live V.2.0.iso on a computer, you need to burn the ISO file to a CD or a USB drive first.

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You can use various tools such as ImgBurn or Rufus to burn the ISO file to a CD or a USB drive respectively.

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You need to make sure that your CD or USB drive has enough space (at least 700 MB) and is formatted as FAT32.

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You also need to make sure that your computer supports booting from a CD or a USB drive.

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To do that, you need to access your computer's BIOS settings by pressing a specific key (usually F1, F2, F10, F12, ESC, DEL) during startup.

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In your BIOS settings, you need to find the boot order option and set your CD or USB drive as the first boot device.

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You can save your changes and exit your BIOS settings by pressing another specific key (usually F10).

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Your computer will restart and boot from your CD or USB drive automatically.

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How to install Windows XP SP3 Angel Live V.2

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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Grand Ages Rome Gold Edition Serial What You Need to Know Before You Buy.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Grand Ages Rome Gold Edition Serial What You Need to Know Before You Buy.md deleted file mode 100644 index 815e24995d9fe9f780758696ebcbab29be06de36..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Grand Ages Rome Gold Edition Serial What You Need to Know Before You Buy.md +++ /dev/null @@ -1,11 +0,0 @@ - -

Grand Ages Rome Gold Edition Serial: How to Get It and Play the Game

- If you are a fan of strategy games set in historical periods, you might have heard of Grand Ages Rome. This is a city-building and management simulation game that lets you take control of one of the greatest civilizations in history. You can raise massive armies, embark on epic campaigns, expand your empire, and engage in grand-scale city building. You can also create magnificent cities with creativity and control like never before. But what if you want to play the enhanced version of the game, which includes the original Grand Ages Rome and its expansion pack, Reign of Augustus? This is where Grand Ages Rome Gold Edition comes in. This package offers more features, content, and gameplay options than the base game. For example, you can play as one of four new factions, access 12 new maps, build 6 new buildings, and enjoy improved graphics and performance. However, to play Grand Ages Rome Gold Edition, you need a valid serial number. This is a unique code that activates and registers your copy of the game. Without it, you won't be able to install or play the game properly. So how do you get a serial number for Grand Ages Rome Gold Edition? And how do you use it to install and play the game? In this article, we will answer these questions and more.

Why do you need a serial number for Grand Ages Rome Gold Edition?

- A serial number is a sequence of letters and numbers that identifies your copy of the game. It is also known as a product key or an activation code. You need a serial number for Grand Ages Rome Gold Edition for two main reasons: - To activate the game: This means verifying that your copy of the game is legitimate and not pirated. Activation is usually done online, by entering your serial number on a website or through a software client. Activation prevents unauthorized copying and distribution of the game. - To register the game: This means creating an account that allows you to access online features of the game, such as multiplayer mode, leaderboards, achievements, and updates. Registration is usually done by entering your serial number and your email address on a website or through a software client. If you don't have a valid serial number for Grand Ages Rome Gold Edition, you might encounter some problems when trying to install or play the game. For example: - You might not be able to install the game at all, or only partially. - You might not be able to launch or run the game properly. - You might not be able to access online features or multiplayer mode. - You might get error messages or warnings that your copy of the game is invalid or duplicate. Therefore, it is important to have a valid serial number for Grand Ages Rome Gold Edition if you want to enjoy the full experience of the game.

How to get a valid serial number for Grand Ages Rome Gold Edition?

- There are two main ways to get a valid serial number for Grand Ages Rome Gold Edition: the official way and the unofficial way. The official way is to buy the game from Steam or other authorized retailers. This is the most legal and safe way to get a serial number for Grand Ages Rome Gold Edition. When you buy the game from Steam or other authorized retailers, you will receive a serial number along with your purchase confirmation. You can then use this serial number to activate and register your copy of the game. The unofficial way is to use a crack or a keygen from online sources. This is an illegal and risky way to get a serial number for Grand Ages Rome Gold Edition. A crack is a file that modifies or bypasses the activation or registration process of the game. A keygen is a program that generates random serial numbers that might work for the game. When you download a crack or a keygen from online sources, you might be able to install and play the game without buying it. However, there are some drawbacks and dangers of using a crack or a keygen for Grand Ages Rome Gold Edition. For example: - You might violate the terms of service or end-user license agreement of the game developer or publisher. - You might infringe on the intellectual property rights or copyrights of the game developer or publisher. - You might expose your computer to viruses, malware, spyware, or other harmful software that might damage your system or steal your personal information. - You might not be able to access online features or multiplayer mode of the game. - You might not be able to update or patch your copy of the game. - You might not be able to get technical support or customer service from the game developer or publisher. Therefore, it is advisable to avoid using a crack or a keygen for Grand Ages Rome Gold Edition if you want to avoid legal troubles or security risks.

How to install and play Grand Ages Rome Gold Edition with a serial number?

- Depending on whether you bought the game from Steam or other authorized retailers, or downloaded it from online sources, there are different steps for installing and playing Grand Ages Rome Gold Edition with a serial number. If you bought the game from Steam or other authorized retailers, here are the steps for installing and playing Grand Ages Rome Gold Edition with a serial number: - Download and install Steam on your computer if you don't have it already. - Launch Steam and log in with your account credentials. - Go to Library > Games > Add A Game > Activate A Product On Steam. - Enter your serial number for Grand Ages Rome Gold Edition when prompted. - Follow the instructions on screen to complete the activation process. - Once activated, you can download and install Grand Ages Rome Gold Edition from your Steam library. - Launch Grand Ages Rome Gold Edition from Steam and enjoy playing. Alternatively, if you bought a physical disc of Grand Ages Rome Gold Edition from an authorized retailer, here are the steps for installing and playing Grand Ages Rome Gold Edition with a serial number: - Insert your disc into your computer's CD/DVD drive. - Follow the instructions on screen to start the installation process. - Enter your serial number for Grand Ages Rome Gold Edition when prompted. - Follow the instructions on screen to complete the installation process. - Once installed, launch Grand Ages Rome Gold Edition from your desktop shortcut or start menu and enjoy playing. If you downloaded Grand Ages Rome Gold Edition from online sources along with a crack or a keygen file, here are the steps for installing and playing Grand Ages Rome Gold Edition with a serial number: - Extract your downloaded file using an archive program such as WinRAR or 7-Zip. - Run your keygen program and generate a random serial number for Grand Ages Rome Gold Edition. - Copy this serial number somewhere safe for later use. - Run your setup program and start installing Grand Ages Rome Gold Edition on your computer. - Enter your generated serial number when prompted during installation. - Follow any other instructions on screen to complete installation process. - Once installed, copy your crack file into your installation folder where your main executable file (Rome.exe) is located. Replace any existing files if asked. - Block your main executable file (Rome.exe) in your firewall program by creating an outbound rule that prevents it from accessing internet connection. This will prevent any online verification checks that might invalidate your copy of the game. - Launch Grand Ages Rome Gold Edition from your desktop shortcut or start menu and enjoy playing.

Conclusion: Enjoy The grand strategy Game Set In Ancient Rome

- Grand Ages Rome Gold Edition is an amazing strategy game that lets you experience what it was like to be part of one of history's most powerful empires. You can build cities, wage wars, manage politics, and shape history as you see fit. However, to play this game, you need a valid serial number that activates and registers your copy of the game. You can get a serial number by buying the game from Steam or other authorized retailers, or by using a crack a keygen from online sources. However, each method has its own pros and cons, and you should be aware of the legal and security implications of using a crack or a keygen. Once you have a serial number, you can install and play Grand Ages Rome Gold Edition by following the steps for your chosen method. Whether you bought the game from Steam or other authorized retailers, or downloaded it from online sources, you should block the game in your firewall to prevent any online verification checks that might invalidate your copy of the game. Now that you have installed and played Grand Ages Rome Gold Edition with a serial number, you can enjoy the grand strategy game set in ancient Rome. You can choose from five different families, each with their own traits and abilities. You can also customize your character's appearance, skills, and talents. You can explore a vast map that covers Europe, Africa, and Asia. You can build and manage cities with over 40 different buildings and 50 different units. You can engage in real-time battles with thousands of soldiers and hundreds of weapons. You can also participate in historical events and scenarios that will shape the fate of Rome. Grand Ages Rome Gold Edition is a game that will challenge your strategic thinking and immerse you in a rich historical setting. With its stunning graphics, realistic sound effects, and captivating gameplay, Grand Ages Rome Gold Edition is a game that you will not regret playing.

FAQs

- Here are some frequently asked questions about Grand Ages Rome Gold Edition Serial: - Q: Where can I buy Grand Ages Rome Gold Edition? - A: You can buy Grand Ages Rome Gold Edition from Steam or other authorized retailers such as Amazon, GOG.com, or Humble Bundle. - Q: How much does Grand Ages Rome Gold Edition cost? - A: Grand Ages Rome Gold Edition costs $14.99 on Steam, but it is often on sale for a lower price. - Q: What are the system requirements for Grand Ages Rome Gold Edition? - A: The minimum system requirements for Grand Ages Rome Gold Edition are: - OS: Windows XP or Vista - Processor: 2.5 GHz Single Core Processor - Memory: 1 GB RAM - Graphics: 128 MB 3D Video Card (GeForce 6600/Radeon 9600 or better) - DirectX: Version 9.0c - Storage: 4 GB available space - Sound Card: DirectX Compatible The recommended system requirements for Grand Ages Rome Gold Edition are: - OS: Windows XP or Vista - Processor: 2.5 GHz Dual Core Processor - Memory: 2 GB RAM - Graphics: 256 MB 3D Video Card (GeForce 8800/Radeon HD2900 or better) - DirectX: Version 9.0c - Storage: 4 GB available space - Sound Card: DirectX Compatible - Q: How many players can play Grand Ages Rome Gold Edition online? - A: Grand Ages Rome Gold Edition supports up to four players in online multiplayer mode. - Q: What are the differences between Grand Ages Rome and Grand Ages Rome Gold Edition? - A: Grand Ages Rome Gold Edition includes the original Grand Ages Rome and its expansion pack, Reign of Augustus. The expansion pack adds four new factions, 12 new maps, six new buildings, improved graphics and performance, and more gameplay options.

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Clash Royale for Windows 11 The Ultimate Guide to Install and Play.md b/spaces/1phancelerku/anime-remove-background/Clash Royale for Windows 11 The Ultimate Guide to Install and Play.md deleted file mode 100644 index fdddb5dc2f75d0a1082c4b58c56dc1ded041ad12..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Clash Royale for Windows 11 The Ultimate Guide to Install and Play.md +++ /dev/null @@ -1,164 +0,0 @@ -
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How to Download and Play Clash Royale on Windows 11

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Are you a fan of strategy games that are fast-paced, fun, and competitive? Do you want to experience a new way of playing your favorite mobile game on your PC? If you answered yes to both questions, then you should definitely try out Clash Royale on Windows 11.

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What is Clash Royale?

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A brief introduction to the game and its features

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Clash Royale is a real-time multiplayer game developed and published by Supercell, the makers of the popular Clash of Clans. In this game, you collect and upgrade cards that feature characters, spells, and defenses from the Clash universe. You use these cards to battle other players online in a three-minute match where the goal is to destroy their towers and win trophies, crowns, and glory.

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The game has over 90 unique cards that belong to different rarities, types, and arenas. You can create your own battle deck with up to eight cards and customize it according to your play style and strategy. You can also join or form a clan with other players to share cards, chat, and participate in clan wars for big rewards.

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Clash Royale is constantly updated with new features, events, and challenges that keep the game fresh and exciting. You can unlock new cards, arenas, skins, emotes, magic items, and more as you progress through the game. You can also compete in global tournaments, seasonal events, special modes, and ladder matches to test your skills against the best players in the world.

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Why play Clash Royale on Windows 11?

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The benefits of playing on a larger screen, better graphics, and smoother controls

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While Clash Royale is primarily designed for mobile devices, playing it on Windows 11 can offer you some advantages that can enhance your gaming experience. Here are some of them:

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The minimum system requirements for Windows 11 and Clash Royale

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Before you can download and play Clash Royale on Windows 11, you need to make sure that your PC meets the minimum system requirements for both the operating system and the game. Here are the specifications you need to check:

- - - - - - - - - -
Windows 11Clash Royale
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    -
  • Processor: 1 GHz or faster with 2 or more cores on a compatible 64-bit processor or System on a Chip (SoC)
    • -
  • RAM: 4 GB
    • -
  • Storage: 64 GB or larger storage device
    • -
  • Graphics card: Compatible with DirectX 12 or later with WDDM 2.0 driver
    • -
  • Display: High definition (720p) display that is greater than 9” diagonally, 8 bits per color channel
    • -
  • Internet connection: Required for updates and some features
    • -
-		 -
    -
  • Android version: 4.1 and up
    • -
  • RAM: 1 GB (recommended)
    • -
  • Storage: 116 MB (additional files may be downloaded)
    • -
  • Graphics: OpenGL ES 3.0 support (recommended)
    • -
  • Internet connection: Required to play online
    • -
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If your PC meets or exceeds these requirements, you can proceed to the next step. If not, you may need to upgrade your hardware or look for other alternatives.

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The steps to download and install an Android emulator (Bluestacks 5) on Windows 11

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An Android emulator is a software that allows you to run Android apps and games on your PC. There are many Android emulators available online, but one of the most popular and reliable ones is Bluestacks 5. Bluestacks 5 is the latest version of the Bluestacks app player that offers improved performance, compatibility, and features for Windows 11 users.

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To download and install Bluestacks 5 on Windows 11, follow these steps:

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  1. Go to the official website of Bluestacks at https://www.bluestacks.com/
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  2. Click on the Download Bluestacks 5 button and wait for the installer file to download.
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  3. Double-click on the installer file and follow the instructions on the screen to install Bluestacks 5 on your PC.
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  4. Once the installation is complete, launch Bluestacks 5 from your desktop or start menu.
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  5. Sign in with your Google account or create a new one if you don't have one.
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  6. You are now ready to use Bluestacks 5 and access the Google Play Store.
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The steps to download and install Clash Royale from the Google Play Store on Bluestacks 5

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Now that you have Bluestacks 5 installed on your PC, you can easily download and install Clash Royale from the Google Play Store. Here are the steps to do so:

-
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  1. On the Bluestacks home screen, click on the Google Play Store icon.
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  2. In the search bar, type Clash Royale and hit enter.
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  3. Select Clash Royale from the list of results and click on the Install button.
    4. -
  4. Wait for the game to download and install on your PC.
    5. -
  5. Once the installation is done, click on the Open button or go back to the Bluestacks home screen and click on the Clash Royale icon.
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By using buildings, spells, and high HP troops to defend your towers, you can prevent your opponent from gaining an elixir or tower advantage and turn the tide of the battle in your favor. You can also save your towers from being destroyed and losing the game.

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Use a win condition card to target enemy towers

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A fifth way to improve your gameplay in Clash Royale is to use a win condition card to target enemy towers. A win condition card is a card that can directly or indirectly deal damage to enemy towers and help you win the game. Some examples of win condition cards are Hog Rider, Royal Giant, Graveyard, Miner, Goblin Barrel, and X-Bow. These cards have different strengths and weaknesses, but they all share the same goal: to destroy enemy towers.

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By using a win condition card to target enemy towers, you can increase your chances of winning the game by dealing consistent and significant damage to your opponent's towers. You can also force your opponent to react and spend elixir to defend their towers, which can give you an elixir or tower advantage.

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Conclusion

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A summary of the main points and a call to action for the readers to try out Clash Royale on Windows 11

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In conclusion, Clash Royale is a fun and addictive game that you can enjoy on Windows 11 with the help of an Android emulator like Bluestacks 5. By playing Clash Royale on Windows 11, you can benefit from a larger screen, better graphics, and smoother controls. You can also improve your gameplay by following some tips and tricks, such as joining a clan, attacking in pairs, counting elixir, defending your towers, and using a win condition card.

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If you are interested in trying out Clash Royale on Windows 11, you can download and install Bluestacks 5 from their official website and then download and install Clash Royale from the Google Play Store on Bluestacks 5. You can then start playing Clash Royale on your PC and have a blast with your friends and foes.

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What are you waiting for? Download Clash Royale on Windows 11 today and join the millions of players who are already enjoying this amazing game!

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FAQs

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What are the best cards in Clash Royale?

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There is no definitive answer to this question, as different cards may suit different players, decks, strategies, and situations. However, some of the most popular and versatile cards in Clash Royale are:

- How do I get more gems and gold in Clash Royale? -

Gems and gold are two of the most important resources in Clash Royale, as they allow you to buy chests, cards, magic items, emotes, skins, and more. There are several ways to get more gems and gold in Clash Royale:

- -

How do I join or create a clan in Clash Royale?

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Joining or creating a clan in Clash Royale is a great way to interact with other players, share cards, chat, and participate in clan wars. To join or create a clan in Clash Royale, you need to reach at least level 1 in the game. You can then follow these steps:

-
    -
  1. Tap on the Clan tab on the main screen.
    2. -
  2. Tap on the Join a Clan button to browse or search for a clan that suits your preferences. You can filter the clans by name, location, trophy requirement, type, etc.
    3. -
  3. Tap on the Request to Join button to send a request to the clan leader or co-leader. You can also write a message to introduce yourself and explain why you want to join the clan.
    4. -
  4. Wait for the clan leader or co-leader to accept or reject your request. If they accept your request, you will become a member of the clan and be able to access the clan chat, shop, wars, etc.
    5. -
  5. If you want to create your own clan instead of joining an existing one, you can tap on the Create a Clan button instead of the Join a Clan button. You will need to spend 1000 gold to create a clan.
    6. -
  6. You can then choose a name, badge, location, type, trophy requirement, description, and tag for your clan. You can also invite your friends or family to join your clan or accept requests from other players who want to join your clan.
    7. -
  7. You will become the leader of your clan and be able to manage it as you wish. You can promote or demote members, start or cancel clan wars, edit the clan settings, etc.
    8. -
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How do I change my name or avatar in Clash Royale?

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Changing your name or avatar in Clash Royale is a simple and quick process that can help you personalize your profile and express your identity. To change your name or avatar in Clash Royale, follow these steps:

-
    -
  1. Tap on your profile icon on the top left corner of the main screen.
    2. -
  2. Tap on the Name Change button or the Edit Avatar button depending on what you want to change.
    3. -
  3. If you want to change your name, you can enter a new name in the text box and tap on the Confirm button. You can only change your name once for free, so choose wisely. If you want to change your name again, you will need to spend 500 gems.
    4. -
  4. If you want to change your avatar, you can choose from a variety of avatars that feature different characters, animals, objects, etc. You can also unlock more avatars by completing achievements, challenges, events, etc. Tap on the avatar that you like and tap on the Select button.
    5. -
  5. Your name or avatar will be changed immediately and be visible to other players in the game.
    6. -
-

How do I contact Supercell for support or feedback?

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If you have any issues, questions, suggestions, or feedback regarding Clash Royale or any other Supercell game, you can contact Supercell for support or feedback through their official channels. Here are some ways to do so:

- Supercell is usually responsive and helpful when it comes to addressing their players' concerns and opinions. However, please be respectful and polite when contacting them and avoid spamming or abusing them.

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Method 1: Jailbreak Your Device and Use Cydia

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What are the risks of installing traffic racer mod apk on iOS devices?

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Features and Gameplay of KOF M.U.G.E.N 2020

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KOF M.U.G.E.N 2020 is a 2D fighting game that follows the same basic rules and mechanics as The King of Fighters. You can choose from several modes, such as Arcade, Team Battle, Survival, Training, Watch, and more. You can also choose from different types of teams, such as Single, Simul, Turns, or Tag.

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The gameplay of KOF M.U.G.E.N 2020 is fast-paced and fluid, with smooth animations and responsive controls. You can perform various moves and combos with your characters, such as punches, kicks, throws, special moves, super moves, and ultimate moves. You can also use different systems and mechanics, such as Power Gauge, Max Mode, Guard Cancel, Counter Attack, Roll Escape, and more.

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KOF M.U.G.E.N 2020 also has many features that make it unique and fun to play. For example, you can adjust the difficulty level, the number of rounds, the time limit, the damage ratio, the life recovery rate, and other options. You can also enable or disable certain features, such as AI mode, debug mode, cheats mode, auto guard mode, and more. You can also change the screen resolution, the sound volume, the language, the input configuration, and other settings.

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Characters and Stages of KOF M.U.G.E.N 2020

- | HTML Code | | --------- | | Iori Yagami, Terry Bogard, Mai Shiranui, etc.), Fatal Fury series (such as Geese Howard, Andy Bogard, Kim Kaphwan, etc.), Art of Fighting series (such as Ryo Sakazaki, Robert Garcia, Yuri Sakazaki, etc.), Samurai Shodown series (such as Haohmaru, Nakoruru, Genjuro Kibagami, etc.), Metal Slug series (such as Marco Rossi, Fio Germi, Tarma Roving, etc.), and more. You can also find characters from other games and media, such as Street Fighter, Mortal Kombat, Dragon Ball, Naruto, Bleach, One Piece, Marvel, DC, and more.

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KOF M.U.G.E.N 2020 also has a large selection of stages that you can fight on. There are over 100 stages from various SNK games and other sources. You can find stages from The King of Fighters series (such as Esaka, Korea, China, etc.), Fatal Fury series (such as South Town, Pao Pao Cafe, Geese Tower, etc.), Art of Fighting series (such as Kyokugen Dojo, L'Amor Restaurant, Glass Hill Valley, etc.), Samurai Shodown series (such as Gairyu Isle, Amakusa Castle, Shimabara Hell Gate, etc.), Metal Slug series (such as Mission 1, Mission 2, Mission 3, etc.), and more. You can also find stages from other games and media, such as Street Fighter, Mortal Kombat, Dragon Ball, Naruto, Bleach, One Piece, Marvel, DC, and more.

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How to Download and Install KOF M.U.G.E.N 2020 APK on Your Android Device

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If you want to play KOF M.U.G.E.N 2020 APK on your Android device, you will need to download and install it first. Here are the requirements and compatibility information that you should know before downloading and installing KOF M.U.G.E.N 2020 APK:

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Requirements and Compatibility

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KOF M.U.G.E.N 2020 APK is a large file that requires a lot of storage space and memory to run smoothly. You will need at least 2 GB of free storage space on your Android device to download and install KOF M.U.G.E.N 2020 APK. You will also need at least 1 GB of RAM to play KOF M.U.G.E.N 2020 APK without lag or crashes.

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KOF M.U.G.E.N 2020 APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not be able to run KOF M.U.G.E.N 2020 APK properly due to hardware limitations or software issues. If you encounter any problems while playing KOF M.U.G.E.N 2020 APK on your Android device, you can try to lower the game settings or contact the developer for support.

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Steps to Download and Install KOF M.U.G.E.N 2020 APK

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Here are the steps that you need to follow to download and install KOF M.U.G.E.N 2020 APK on your Android device:

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    -
  1. Go to the official website of KOF M.U.G.E.N 2020 APK [here] and click on the download button.
    2. -
  2. Wait for the download to finish and locate the file in your device's file manager.
    3. -
  3. Tap on the file and allow the installation from unknown sources if prompted.
    4. -
  4. Wait for the installation to complete and launch the game from your app drawer or home screen.
    5. -
  5. Enjoy playing KOF M.U.G.E.N 2020 APK on your Android device!
    6. -
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Tips and Tricks to Enjoy KOF M.U.G.E.N 2020 APK

- | HTML Code | | --------- | | there are some tips and tricks that you can use to enjoy KOF M.U.G.E.N 2020 APK even more. Here are some of them:

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How to Customize and Edit KOF M.U.G.E.N 2020 APK

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KOF M.U.G.E.N 2020 APK is a highly customizable and editable game that allows you to create your own fighting game experience. You can add or remove characters and stages, change the game settings and options, and even create your own characters and stages. Here are some ways that you can customize and edit KOF M.U.G.E.N 2020 APK:

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How to Add or Remove Characters and Stages

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KOF M.U.G.E.N 2020 APK comes with a large roster of characters and stages, but you can always add or remove them according to your preferences. You can download additional characters and stages from various websites, such as [this] or [this], or you can delete unwanted characters and stages from your device's storage. Here are the steps that you need to follow to add or remove characters and stages:

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    -
  1. Download the character or stage file that you want to add from a reliable source and extract it if it is compressed.
    2. -
  2. Copy the character or stage folder to the chars or stages folder in your device's storage where KOF M.U.G.E.N 2020 APK is installed.
    3. -
  3. Edit the select.def file in the data folder using a text editor app such as [this] or [this].
    4. -
  4. Add the name of the character or stage folder to the select.def file under the appropriate section (such as kfm, bonus, hidden, etc.). For example, if you want to add a character named Ryu, you should write Ryu/Ryu.def under the kfm section.
    5. -
  5. Save the select.def file and launch KOF M.U.G.E.N 2020 APK. You should see the new character or stage in the game.
    6. -
  6. To remove a character or stage, simply delete its folder from the chars or stages folder and remove its name from the select.def file.
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How to Change the Game Settings and Options

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KOF M.U.G.E.N 2020 APK has many settings and options that you can change to customize the game to your liking. You can change things such as the screen resolution, the sound volume, the language, the input configuration, and more. Here are some ways that you can change the game settings and options:

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How to Create Your Own Characters and Stages

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KOF M.U.G.E.N 2020 APK is not only a game that you can play, but also a game that you can create. You can create your own characters and stages using the M.U.G.E.N engine and add them to KOF M.U.G.E.N 2020 APK. However, this is not an easy task and requires a lot of time, effort, and knowledge. Here are some resources that you can use to learn how to create your own characters and stages:

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Conclusion

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KOF M.U.G.E.N 2020 APK is a fan-made game that offers a unique and enjoyable fighting game experience on your Android device. It has a huge roster of characters and stages from various SNK franchises and other sources. It has a fast-paced and fluid gameplay with smooth animations and responsive controls. It has many features and options that allow you to customize the game to your liking. It also allows you to create your own characters and stages using the M.U.G.E.N engine.

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If you are a fan of fighting games or SNK games, you should definitely try KOF M.U.G.E.N 2020 APK. It is free to download and easy to install on your Android device. It is fun to play alone or with friends. It is also a great way to express your creativity and imagination by creating your own characters and stages.

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So what are you waiting for? Download KOF M.U.G.E.N 2020 APK now and enjoy playing the ultimate fighting game on your Android device!

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Why You Should Try KOF M.U.G.E.N 2020 APK

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Here are some reasons why you should try KOF M.U.G.E.N 2020 APK:

- - | HTML Code | | --------- | | FAQs |

Here are some frequently asked questions about KOF M.U.G.E.N 2020 APK:

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  1. Is KOF M.U.G.E.N 2020 APK safe to download and install?
    2. -

    Yes, KOF M.U.G.E.N 2020 APK is safe to download and install as long as you get it from the official website or a trusted source. However, you should always scan any file that you download with an antivirus app before installing it on your device.

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  2. Is KOF M.U.G.E.N 2020 APK legal to play?
    3. -

    KOF M.U.G.E.N 2020 APK is a fan-made game that is not affiliated with or endorsed by SNK or any other company. It is a non-profit game that is made for entertainment purposes only. It does not intend to infringe any copyrights or trademarks of SNK or any other company. However, you should always respect the rights and wishes of the original creators and owners of the characters and stages that are used in KOF M.U.G.E.N 2020 APK.

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  3. How can I play KOF M.U.G.E.N 2020 APK with my friends?
    4. -

    KOF M.U.G.E.N 2020 APK supports local multiplayer mode, which means that you can play with your friends on the same device using a split-screen or a gamepad. You can also play with your friends online using a third-party app such as [this] or [this], which allows you to create a virtual network and connect your devices over the internet.

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  4. How can I update KOF M.U.G.E.N 2020 APK to the latest version?
    5. -

    KOF M.U.G.E.N 2020 APK is constantly updated by the developer with new characters, stages, features, and bug fixes. You can check for updates on the official website or on the developer's social media pages. You can also enable the auto-update option in the game settings, which will notify you when a new update is available and download it automatically.

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  5. How can I contact the developer of KOF M.U.G.E.N 2020 APK?
    6. -

    If you have any questions, suggestions, feedback, or issues regarding KOF M.U.G.E.N 2020 APK, you can contact the developer by sending an email to [this] or by leaving a comment on the developer's YouTube channel [here]. The developer is very responsive and friendly and will try to help you as soon as possible.

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\ No newline at end of file diff --git a/spaces/2hack2furious/anonymizer/app.py b/spaces/2hack2furious/anonymizer/app.py deleted file mode 100644 index 567457a4584f7b639e5a6df297f0075ec5193ae4..0000000000000000000000000000000000000000 --- a/spaces/2hack2furious/anonymizer/app.py +++ /dev/null @@ -1,87 +0,0 @@ -import modules -import streamlit as st -from streamlit_extras.let_it_rain import rain - -# Options -DISCLAIMER = """ - *This app processes data using 2-anonymity, an implementation of the k-anonymity framework. While this is a great start to anonymizing your data, it is by no means perfect, and should be used with caution. For example, some sets of sensitive features which may clearly be identified by a human could be missed by our algorithm. Please keep this in mind.* - """ -K = 2 - -# Page Config -st.set_page_config(layout="wide") - -### FILE LOADER for sidebar -with st.sidebar: - st.header("🕵️ 2anonymity") - st.markdown("*Clean and anonymize data*") - with st.container() as upload: - file = st.file_uploader(f"Upload dataset:", type=modules.SUPPORTED_TYPES, label_visibility="collapsed") - df, (filename, extension), result = modules.load_file(file) - -### MAIN -if df is None: # Await file to be uploaded - rain("🤠") -else: - ### PRE-TRANSFORM features for sidebar - with st.sidebar: - # Options for data loading - with st.container() as loading_options: - st.markdown("### Data loading options:") - remove_duplicates = st.checkbox("Remove duplicate rows", value=True) - drop_missing = st.checkbox("Remove rows with missing values", value=False) - - # Options for data optimization - with st.container() as anonymizing_options: - st.markdown("### Anonymizing options:") - max_categorical_size = st.slider("Categorical Variable Threshold", min_value=2, max_value=200, value=50, step=1) - bin_size = st.slider("Bin Size", min_value=2, max_value=200, value=20, step=1) - redaction_selection = st.selectbox("Redaction strength", ["Low", "Medium", "High", "Extreme"]) - sensitivity_minimum = {"Low": 2, "Medium": 4, "High": 6, "Extreme": 12}[redaction_selection] - - - ### DATA PREVIEW AND TRANSFORM - # Preview data before transform - with st.container() as before_data: - s = df.style - s = s.set_properties(**{'background-color': '#fce4e4'}) - st.dataframe(s) - - # Transform data - df = modules.data_cleaner(df, drop_missing, remove_duplicates) - df, unprocessed = modules.data_anonymizer(df, K, max_categorical_size, bin_size, sensitivity_minimum) - - # Preview data after before_data - with st.container() as after_data: - s = df.style - s = s.set_properties(**{'background-color': '#e4fce4'}) - st.dataframe(s) - - - ### POST-TRANSFORM features for sidebar - with st.sidebar: - # Options for download - with st.container() as download_header: - st.markdown("### Download options:") - output_extension = st.selectbox("File type", [".csv", ".json", ".xlsx"]) - if unprocessed: st.markdown(f"Error encountered when processing columns {str(unprocessed)}") - - # Prepare file for download - with st.container() as downloader: - if output_extension == ".csv": output_file = df.to_csv().encode("utf-8") - elif output_extension == ".json": output_file = df.to_json().encode("utf-8") - elif output_extension == ".xlsx": output_file = df.to_excel().encode("utf-8") - output_filename = f"""{filename.split(".")[:-1][0]}-clean{output_extension}""" - st.download_button("Download", output_file, file_name=output_filename) - - # Add a disclaimer for data security - with st.container() as disclaimer: - st.markdown( - f""" - Disclaimer: - {DISCLAIMER} - """ - ) - -# Attribution -st.sidebar.markdown("Created by team #2hack2furious for the hackthethreat2023") \ No newline at end of file diff --git a/spaces/2ndelement/voicevox/test/test_full_context_label.py b/spaces/2ndelement/voicevox/test/test_full_context_label.py deleted file mode 100644 index 7cdde34f4644ccf7b3048d707f99b0171e25114e..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/test/test_full_context_label.py +++ /dev/null @@ -1,404 +0,0 @@ -from copy import deepcopy -from itertools import chain -from unittest import TestCase - -from voicevox_engine.full_context_label import ( - AccentPhrase, - BreathGroup, - Mora, - Phoneme, - Utterance, -) - - -class TestBasePhonemes(TestCase): - def setUp(self): - super().setUp() - # pyopenjtalk.extract_fullcontext("こんにちは、ヒホです。")の結果 - # 出来る限りテスト内で他のライブラリに依存しないため、 - # またテスト内容を透明化するために、テストケースを生成している - self.test_case_hello_hiho = [ - # sil (無音) - "xx^xx-sil+k=o/A:xx+xx+xx/B:xx-xx_xx/C:xx_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:5_5%0_xx_xx/H:xx_xx/I:xx-xx" - + "@xx+xx&xx-xx|xx+xx/J:1_5/K:2+2-9", - # k - "xx^sil-k+o=N/A:-4+1+5/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # o - "sil^k-o+N=n/A:-4+1+5/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # N (ん) - "k^o-N+n=i/A:-3+2+4/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # n - "o^N-n+i=ch/A:-2+3+3/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # i - "N^n-i+ch=i/A:-2+3+3/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # ch - "n^i-ch+i=w/A:-1+4+2/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # i - "i^ch-i+w=a/A:-1+4+2/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # w - "ch^i-w+a=pau/A:0+5+1/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # a - "i^w-a+pau=h/A:0+5+1/B:xx-xx_xx/C:09_xx+xx/D:09+xx_xx/E:xx_xx!xx_xx-xx" - + "/F:5_5#0_xx@1_1|1_5/G:4_1%0_xx_0/H:xx_xx/I:1-5" - + "@1+2&1-2|1+9/J:1_4/K:2+2-9", - # pau (読点) - "w^a-pau+h=i/A:xx+xx+xx/B:09-xx_xx/C:xx_xx+xx/D:09+xx_xx/E:5_5!0_xx-xx" - + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:4_1%0_xx_xx/H:1_5/I:xx-xx" - + "@xx+xx&xx-xx|xx+xx/J:1_4/K:2+2-9", - # h - "a^pau-h+i=h/A:0+1+4/B:09-xx_xx/C:09_xx+xx/D:22+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # i - "pau^h-i+h=o/A:0+1+4/B:09-xx_xx/C:09_xx+xx/D:22+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # h - "h^i-h+o=d/A:1+2+3/B:09-xx_xx/C:22_xx+xx/D:10+7_2/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # o - "i^h-o+d=e/A:1+2+3/B:09-xx_xx/C:22_xx+xx/D:10+7_2/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # d - "h^o-d+e=s/A:2+3+2/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # e - "o^d-e+s=U/A:2+3+2/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # s - "d^e-s+U=sil/A:3+4+1/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # U (無声母音) - "e^s-U+sil=xx/A:3+4+1/B:22-xx_xx/C:10_7+2/D:xx+xx_xx/E:5_5!0_xx-0" - + "/F:4_1#0_xx@1_1|1_4/G:xx_xx%xx_xx_xx/H:1_5/I:1-4" - + "@2+1&2-1|6+4/J:xx_xx/K:2+2-9", - # sil (無音) - "s^U-sil+xx=xx/A:xx+xx+xx/B:10-7_2/C:xx_xx+xx/D:xx+xx_xx/E:4_1!0_xx-xx" - + "/F:xx_xx#xx_xx@xx_xx|xx_xx/G:xx_xx%xx_xx_xx/H:1_4/I:xx-xx" - + "@xx+xx&xx-xx|xx+xx/J:xx_xx/K:2+2-9", - ] - self.phonemes_hello_hiho = [ - Phoneme.from_label(label) for label in self.test_case_hello_hiho - ] - - -class TestPhoneme(TestBasePhonemes): - def test_phoneme(self): - self.assertEqual( - " ".join([phoneme.phoneme for phoneme in self.phonemes_hello_hiho]), - "sil k o N n i ch i w a pau h i h o d e s U sil", - ) - - def test_is_pause(self): - self.assertEqual( - [phoneme.is_pause() for phoneme in self.phonemes_hello_hiho], - [ - True, # sil - False, # k - False, # o - False, # N - False, # n - False, # i - False, # ch - False, # i - False, # w - False, # a - True, # pau - False, # h - False, # i - False, # h - False, # o - False, # d - False, # e - False, # s - False, # u - True, # sil - ], - ) - - def test_label(self) -> None: - self.assertEqual( - [phoneme.label for phoneme in self.phonemes_hello_hiho], - self.test_case_hello_hiho, - ) - - -class TestMora(TestBasePhonemes): - def setUp(self) -> None: - super().setUp() - # contexts["a2"] == "1" ko - self.mora_hello_1 = Mora( - consonant=self.phonemes_hello_hiho[1], vowel=self.phonemes_hello_hiho[2] - ) - # contexts["a2"] == "2" N - self.mora_hello_2 = Mora(consonant=None, vowel=self.phonemes_hello_hiho[3]) - # contexts["a2"] == "3" ni - self.mora_hello_3 = Mora( - consonant=self.phonemes_hello_hiho[4], vowel=self.phonemes_hello_hiho[5] - ) - # contexts["a2"] == "4" chi - self.mora_hello_4 = Mora( - consonant=self.phonemes_hello_hiho[6], vowel=self.phonemes_hello_hiho[7] - ) - # contexts["a2"] == "5" wa - self.mora_hello_5 = Mora( - consonant=self.phonemes_hello_hiho[8], vowel=self.phonemes_hello_hiho[9] - ) - # contexts["a2"] == "1" hi - self.mora_hiho_1 = Mora( - consonant=self.phonemes_hello_hiho[11], vowel=self.phonemes_hello_hiho[12] - ) - # contexts["a2"] == "2" ho - self.mora_hiho_2 = Mora( - consonant=self.phonemes_hello_hiho[13], vowel=self.phonemes_hello_hiho[14] - ) - # contexts["a2"] == "3" de - self.mora_hiho_3 = Mora( - consonant=self.phonemes_hello_hiho[15], vowel=self.phonemes_hello_hiho[16] - ) - # contexts["a2"] == "1" sU - self.mora_hiho_4 = Mora( - consonant=self.phonemes_hello_hiho[17], vowel=self.phonemes_hello_hiho[18] - ) - - def assert_phonemes(self, mora: Mora, mora_str: str) -> None: - self.assertEqual( - "".join([phoneme.phoneme for phoneme in mora.phonemes]), mora_str - ) - - def assert_labels(self, mora: Mora, label_start: int, label_end: int) -> None: - self.assertEqual(mora.labels, self.test_case_hello_hiho[label_start:label_end]) - - def test_phonemes(self) -> None: - self.assert_phonemes(self.mora_hello_1, "ko") - self.assert_phonemes(self.mora_hello_2, "N") - self.assert_phonemes(self.mora_hello_3, "ni") - self.assert_phonemes(self.mora_hello_4, "chi") - self.assert_phonemes(self.mora_hello_5, "wa") - self.assert_phonemes(self.mora_hiho_1, "hi") - self.assert_phonemes(self.mora_hiho_2, "ho") - self.assert_phonemes(self.mora_hiho_3, "de") - self.assert_phonemes(self.mora_hiho_4, "sU") - - def test_labels(self) -> None: - self.assert_labels(self.mora_hello_1, 1, 3) - self.assert_labels(self.mora_hello_2, 3, 4) - self.assert_labels(self.mora_hello_3, 4, 6) - self.assert_labels(self.mora_hello_4, 6, 8) - self.assert_labels(self.mora_hello_5, 8, 10) - self.assert_labels(self.mora_hiho_1, 11, 13) - self.assert_labels(self.mora_hiho_2, 13, 15) - self.assert_labels(self.mora_hiho_3, 15, 17) - self.assert_labels(self.mora_hiho_4, 17, 19) - - def test_set_context(self): - # 値を書き換えるので、他のテストに影響を出さないためにdeepcopyする - mora_hello_1 = deepcopy(self.mora_hello_1) - # phonemeにあたる"p3"を書き換える - mora_hello_1.set_context("p3", "a") - self.assert_phonemes(mora_hello_1, "aa") - - -class TestAccentPhrase(TestBasePhonemes): - def setUp(self) -> None: - super().setUp() - # TODO: ValueErrorを吐く作為的ではない自然な例の模索 - # 存在しないなら放置でよい - self.accent_phrase_hello = AccentPhrase.from_phonemes( - self.phonemes_hello_hiho[1:10] - ) - self.accent_phrase_hiho = AccentPhrase.from_phonemes( - self.phonemes_hello_hiho[11:19] - ) - - def test_accent(self): - self.assertEqual(self.accent_phrase_hello.accent, 5) - self.assertEqual(self.accent_phrase_hiho.accent, 1) - - def test_set_context(self): - accent_phrase_hello = deepcopy(self.accent_phrase_hello) - # phonemeにあたる"p3"を書き換える - accent_phrase_hello.set_context("p3", "a") - self.assertEqual( - "".join([phoneme.phoneme for phoneme in accent_phrase_hello.phonemes]), - "aaaaaaaaa", - ) - - def test_phonemes(self): - self.assertEqual( - " ".join( - [phoneme.phoneme for phoneme in self.accent_phrase_hello.phonemes] - ), - "k o N n i ch i w a", - ) - self.assertEqual( - " ".join([phoneme.phoneme for phoneme in self.accent_phrase_hiho.phonemes]), - "h i h o d e s U", - ) - - def test_labels(self): - self.assertEqual( - self.accent_phrase_hello.labels, self.test_case_hello_hiho[1:10] - ) - self.assertEqual( - self.accent_phrase_hiho.labels, self.test_case_hello_hiho[11:19] - ) - - def test_merge(self): - # 「こんにちはヒホです」 - # 読点を無くしたものと同等 - merged_accent_phrase = self.accent_phrase_hello.merge(self.accent_phrase_hiho) - self.assertEqual(merged_accent_phrase.accent, 5) - self.assertEqual( - " ".join([phoneme.phoneme for phoneme in merged_accent_phrase.phonemes]), - "k o N n i ch i w a h i h o d e s U", - ) - self.assertEqual( - merged_accent_phrase.labels, - self.test_case_hello_hiho[1:10] + self.test_case_hello_hiho[11:19], - ) - - -class TestBreathGroup(TestBasePhonemes): - def setUp(self) -> None: - super().setUp() - self.breath_group_hello = BreathGroup.from_phonemes( - self.phonemes_hello_hiho[1:10] - ) - self.breath_group_hiho = BreathGroup.from_phonemes( - self.phonemes_hello_hiho[11:19] - ) - - def test_set_context(self): - # 値を書き換えるので、他のテストに影響を出さないためにdeepcopyする - breath_group_hello = deepcopy(self.breath_group_hello) - # phonemeにあたる"p3"を書き換える - breath_group_hello.set_context("p3", "a") - self.assertEqual( - "".join([phoneme.phoneme for phoneme in breath_group_hello.phonemes]), - "aaaaaaaaa", - ) - - def test_phonemes(self): - self.assertEqual( - " ".join([phoneme.phoneme for phoneme in self.breath_group_hello.phonemes]), - "k o N n i ch i w a", - ) - self.assertEqual( - " ".join([phoneme.phoneme for phoneme in self.breath_group_hiho.phonemes]), - "h i h o d e s U", - ) - - def test_labels(self): - self.assertEqual( - self.breath_group_hello.labels, self.test_case_hello_hiho[1:10] - ) - self.assertEqual( - self.breath_group_hiho.labels, self.test_case_hello_hiho[11:19] - ) - - -class TestUtterance(TestBasePhonemes): - def setUp(self) -> None: - super().setUp() - self.utterance_hello_hiho = Utterance.from_phonemes(self.phonemes_hello_hiho) - - def test_phonemes(self): - self.assertEqual( - " ".join( - [phoneme.phoneme for phoneme in self.utterance_hello_hiho.phonemes] - ), - "sil k o N n i ch i w a pau h i h o d e s U sil", - ) - changed_utterance = Utterance.from_phonemes(self.utterance_hello_hiho.phonemes) - self.assertEqual(len(changed_utterance.breath_groups), 2) - accent_phrases = list( - chain.from_iterable( - breath_group.accent_phrases - for breath_group in changed_utterance.breath_groups - ) - ) - for prev, cent, post in zip( - [None] + accent_phrases[:-1], - accent_phrases, - accent_phrases[1:] + [None], - ): - mora_num = len(cent.moras) - accent = cent.accent - - if prev is not None: - for phoneme in prev.phonemes: - self.assertEqual(phoneme.contexts["g1"], str(mora_num)) - self.assertEqual(phoneme.contexts["g2"], str(accent)) - - if post is not None: - for phoneme in post.phonemes: - self.assertEqual(phoneme.contexts["e1"], str(mora_num)) - self.assertEqual(phoneme.contexts["e2"], str(accent)) - - for phoneme in cent.phonemes: - self.assertEqual( - phoneme.contexts["k2"], - str( - sum( - [ - len(breath_group.accent_phrases) - for breath_group in changed_utterance.breath_groups - ] - ) - ), - ) - - for prev, cent, post in zip( - [None] + changed_utterance.breath_groups[:-1], - changed_utterance.breath_groups, - changed_utterance.breath_groups[1:] + [None], - ): - accent_phrase_num = len(cent.accent_phrases) - - if prev is not None: - for phoneme in prev.phonemes: - self.assertEqual(phoneme.contexts["j1"], str(accent_phrase_num)) - - if post is not None: - for phoneme in post.phonemes: - self.assertEqual(phoneme.contexts["h1"], str(accent_phrase_num)) - - for phoneme in cent.phonemes: - self.assertEqual(phoneme.contexts["i1"], str(accent_phrase_num)) - self.assertEqual( - phoneme.contexts["i5"], - str(accent_phrases.index(cent.accent_phrases[0]) + 1), - ) - self.assertEqual( - phoneme.contexts["i6"], - str( - len(accent_phrases) - - accent_phrases.index(cent.accent_phrases[0]) - ), - ) - - def test_labels(self): - self.assertEqual(self.utterance_hello_hiho.labels, self.test_case_hello_hiho) diff --git a/spaces/2ndelement/voicevox/voicevox_engine/setting/Setting.py b/spaces/2ndelement/voicevox/voicevox_engine/setting/Setting.py deleted file mode 100644 index f8912c6bff9afa959f445d8aa9c89c440b36b8db..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/voicevox_engine/setting/Setting.py +++ /dev/null @@ -1,25 +0,0 @@ -from enum import Enum -from typing import Optional - -from pydantic import BaseModel, Field - - -class CorsPolicyMode(str, Enum): - """ - CORSの許可モード - """ - - all = "all" # 全てのオリジンからのリクエストを許可 - localapps = "localapps" # ローカルアプリケーションからのリクエストを許可 - - -class Setting(BaseModel): - """ - エンジンの設定情報 - """ - - cors_policy_mode: CorsPolicyMode = Field(title="リソース共有ポリシー") - allow_origin: Optional[str] = Field(title="許可するオリジン") - - class Config: - use_enum_values = True diff --git a/spaces/52Hz/HWMNet_lowlight_enhancement/main_test_HWMNet.py b/spaces/52Hz/HWMNet_lowlight_enhancement/main_test_HWMNet.py deleted file mode 100644 index db31fe1321dd8cd25136e6243c801ba822be8e8a..0000000000000000000000000000000000000000 --- a/spaces/52Hz/HWMNet_lowlight_enhancement/main_test_HWMNet.py +++ /dev/null @@ -1,86 +0,0 @@ -import argparse -import cv2 -import glob -import numpy as np -from collections import OrderedDict -from skimage import img_as_ubyte -import os -import torch -import requests -from PIL import Image -import torchvision.transforms.functional as TF -import torch.nn.functional as F -from natsort import natsorted -from model.HWMNet import HWMNet - -def main(): - parser = argparse.ArgumentParser(description='Demo Low-light Image enhancement') - parser.add_argument('--input_dir', default='test/', type=str, help='Input images') - parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results') - parser.add_argument('--weights', - default='experiments/pretrained_models/LOL_enhancement_HWMNet.pth', type=str, - help='Path to weights') - - args = parser.parse_args() - - inp_dir = args.input_dir - out_dir = args.result_dir - - os.makedirs(out_dir, exist_ok=True) - - files = natsorted(glob.glob(os.path.join(inp_dir, '*'))) - - if len(files) == 0: - raise Exception(f"No files found at {inp_dir}") - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - # Load corresponding models architecture and weights - model = HWMNet(in_chn=3, wf=96, depth=4) - model = model.to(device) - model.eval() - load_checkpoint(model, args.weights) - - - mul = 16 - for file_ in files: - img = Image.open(file_).convert('RGB') - input_ = TF.to_tensor(img).unsqueeze(0).to(device) - - # Pad the input if not_multiple_of 8 - h, w = input_.shape[2], input_.shape[3] - H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul - padh = H - h if h % mul != 0 else 0 - padw = W - w if w % mul != 0 else 0 - input_ = F.pad(input_, (0, padw, 0, padh), 'reflect') - with torch.no_grad(): - restored = model(input_) - - restored = torch.clamp(restored, 0, 1) - restored = restored[:, :, :h, :w] - restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy() - restored = img_as_ubyte(restored[0]) - - f = os.path.splitext(os.path.split(file_)[-1])[0] - save_img((os.path.join(out_dir, f + '.png')), restored) - - -def save_img(filepath, img): - cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR)) - - -def load_checkpoint(model, weights): - checkpoint = torch.load(weights, map_location=torch.device('cpu')) - try: - model.load_state_dict(checkpoint["state_dict"]) - except: - state_dict = checkpoint["state_dict"] - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - name = k[7:] # remove `module.` - new_state_dict[name] = v - model.load_state_dict(new_state_dict) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/spaces/52Hz/SRMNet_AWGN_denoising/README.md b/spaces/52Hz/SRMNet_AWGN_denoising/README.md deleted file mode 100644 index 9f1da3c83055846d02c8d43340ad0317f99a3d29..0000000000000000000000000000000000000000 --- a/spaces/52Hz/SRMNet_AWGN_denoising/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: SRMNet_AWGN_denoising -emoji: 🌪 -colorFrom: red -colorTo: yellow -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`, `streamlit`, or `static` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/rel_transformer.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/rel_transformer.py deleted file mode 100644 index ed69e587f9813fc1214dc034f8cabf238e362b61..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/rel_transformer.py +++ /dev/null @@ -1,611 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F -from utils.hparams import hparams -from modules.commons.common_layers import Embedding -from utils.tts_utils import group_hidden_by_segs, expand_word2ph - -import transformers - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -class Encoder(nn.Module): - def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., - window_size=None, block_length=None, pre_ln=False, **kwargs): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - self.block_length = block_length - self.pre_ln = pre_ln - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, - p_dropout=p_dropout, block_length=block_length)) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - if pre_ln: - self.last_ln = LayerNorm(hidden_channels) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - for i in range(self.n_layers): - x = x * x_mask - x_ = x - if self.pre_ln: - x = self.norm_layers_1[i](x) - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = x_ + y - if not self.pre_ln: - x = self.norm_layers_1[i](x) - - x_ = x - if self.pre_ln: - x = self.norm_layers_2[i](x) - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = x_ + y - if not self.pre_ln: - x = self.norm_layers_2[i](x) - if self.pre_ln: - x = self.last_ln(x) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., - block_length=None, proximal_bias=False, proximal_init=False): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.p_dropout = p_dropout - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels ** -0.5 - self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - if proximal_init: - self.conv_k.weight.data.copy_(self.conv_q.weight.data) - self.conv_k.bias.data.copy_(self.conv_q.bias.data) - nn.init.xavier_uniform_(self.conv_v.weight) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) - if self.window_size is not None: - assert t_s == t_t, "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) - rel_logits = self._relative_position_to_absolute_position(rel_logits) - scores_local = rel_logits / math.sqrt(self.k_channels) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) - scores = scores * block_mask + -1e4 * (1 - block_mask) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) - output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) - x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(x * x_mask) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - return x * x_mask - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-4): - 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): - n_dims = len(x.shape) - mean = torch.mean(x, 1, keepdim=True) - variance = torch.mean((x - mean) ** 2, 1, keepdim=True) - - x = (x - mean) * torch.rsqrt(variance + self.eps) - - shape = [1, -1] + [1] * (n_dims - 2) - x = x * self.gamma.view(*shape) + self.beta.view(*shape) - return x - - -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 RelTransformerEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout=0.0, - window_size=4, - block_length=None, - prenet=True, - pre_ln=True, - ): - - super().__init__() - - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - self.block_length = block_length - self.prenet = prenet - if n_vocab > 0: - self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) - - if prenet: - self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, - kernel_size=5, n_layers=3, p_dropout=0) - self.encoder = Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - window_size=window_size, - block_length=block_length, - pre_ln=pre_ln, - ) - - def forward(self, x, x_mask=None): - if self.n_vocab > 0: - x_lengths = (x > 0).long().sum(-1) - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - else: - x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - if self.prenet: - x = self.pre(x, x_mask) - x = self.encoder(x, x_mask) - return x.transpose(1, 2) - - -class Pooler(nn.Module): - """ - Parameter-free poolers to get the sentence embedding - 'cls': [CLS] representation with BERT/RoBERTa's MLP pooler. - 'cls_before_pooler': [CLS] representation without the original MLP pooler. - 'avg': average of the last layers' hidden states at each token. - 'avg_top2': average of the last two layers. - 'avg_first_last': average of the first and the last layers. - """ - def __init__(self, pooler_type): - super().__init__() - self.pooler_type = pooler_type - assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type - - def forward(self, attention_mask, outputs): - last_hidden = outputs.last_hidden_state - pooler_output = outputs.pooler_output - hidden_states = outputs.hidden_states - - if self.pooler_type in ['cls_before_pooler', 'cls']: - return last_hidden[:, 0] - elif self.pooler_type == "avg": - return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)) - elif self.pooler_type == "avg_first_last": - first_hidden = hidden_states[0] - last_hidden = hidden_states[-1] - pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) - return pooled_result - elif self.pooler_type == "avg_top2": - second_last_hidden = hidden_states[-2] - last_hidden = hidden_states[-1] - pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) - return pooled_result - else: - raise NotImplementedError - - -class Similarity(nn.Module): - """ - Dot product or cosine similarity - """ - - def __init__(self, temp): - super().__init__() - self.temp = temp - self.cos = nn.CosineSimilarity(dim=-1) - self.record = None - self.pos_avg = 0.0 - self.neg_avg = 0.0 - - def forward(self, x, y): - sim = self.cos(x, y) - self.record = sim.detach() # [64,64] - min_size = min(self.record.shape[0], self.record.shape[1]) # 64 - num_item = self.record.shape[0] * self.record.shape[1] # 4096 - self.pos_avg = self.record.diag().sum() / min_size - if num_item - min_size == 0: - self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1 - return sim / self.temp - if torch.any(torch.isnan(self.record)).item() is True: - print("we got self.record has nan when compute neg_avg") - if torch.any(torch.isnan(self.record.diag())).item() is True: - print("we got self.record.diag() has nan when compute neg_avg") - self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size) - - return sim / self.temp - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, hidden_size): - super().__init__() - self.dense = nn.Linear(hidden_size, hidden_size) - self.transform_act_fn = F.gelu - self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, hid_dim, out_dim): - super().__init__() - self.transform = BertPredictionHeadTransform(hid_dim) - self.decoder = nn.Linear(hid_dim, out_dim, bias=False) - self.bias = nn.Parameter(torch.zeros(out_dim)) - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -# V2_2 -# change add to concat. -# now support finetune BERT -# grad_bert=0.1 & trainable_block_idx=0 -class BERTRelTransformerEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout=0.0, - window_size=4, - block_length=None, - prenet=True, - pre_ln=True, - ): - - super().__init__() - - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - self.block_length = block_length - self.prenet = prenet - if n_vocab > 0: - self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) - - if prenet: - self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels, - kernel_size=5, n_layers=3, p_dropout=0) - self.encoder1 = Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers//2, - kernel_size, - p_dropout, - window_size=window_size, - block_length=block_length, - pre_ln=pre_ln, - ) - - self.encoder2 = Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers - n_layers//2, - kernel_size, - p_dropout, - window_size=window_size, - block_length=block_length, - pre_ln=pre_ln, - ) - - if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']: - model_name = 'bert-base-uncased' - elif hparams['ds_name'] in ['biaobei', 'wenetspeech']: - model_name = 'bert-base-chinese' - else: - raise NotImplementedError() - - self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) - config = transformers.AutoConfig.from_pretrained(model_name) - if hparams.get("load_bert_from_pretrained", True): - print("Load BERT from pretrained model ...") - self.bert = transformers.AutoModel.from_pretrained(model_name,config=config) - trainable_start_block = hparams.get("bert_trainable_start_block", 0) - else: - print("Initialize BERT from scratch!") - self.bert = transformers.BertModel(config=config) - trainable_start_block = 0 - - for k, v in self.bert.named_parameters(): - if 'embeddings' in k: - v.requires_grad = False - elif 'encoder.layer' in k: - block_idx = int(k.split(".")[2]) - if block_idx < trainable_start_block: - v.requires_grad = False - else: - v.requires_grad = True - elif 'cls' in k: - v.requires_grad = True - else: - print("Unhandled key: {}, set to requires_grad...".format(k)) - v.requires_grad = True - - self.bert_combine = nn.Sequential(*[ - nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1), - nn.ReLU(), - ]) - self.pooler = Pooler("avg") - self.sim = Similarity(temp=0.05) - - def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs): - if self.n_vocab > 0: - x_lengths = (x > 0).long().sum(-1) - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - else: - x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - if self.prenet: - x = self.pre(x, x_mask) - x = self.encoder1(x, x_mask) - bert_outputs = self.bert(bert_feats['bert_input_ids'], - attention_mask=bert_feats['bert_attention_mask'], - token_type_ids=bert_feats['bert_token_type_ids'], - output_hidden_states=True) - bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert - bert_embedding = bert_outputs['hidden_states'][bert_num_blocks] - # bert_embedding = bert_outputs['last_hidden_state'] - grad_bert = hparams.get("grad_bert", 0.1) - bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert - bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item()) - bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word) - bert_ph_embedding = bert_ph_embedding.transpose(1,2) - x = torch.cat([x, bert_ph_embedding], dim=1) - x = self.bert_combine(x) - x = self.encoder2(x, x_mask) - return x.transpose(1, 2) - - diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/lj/preprocess.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/lj/preprocess.py deleted file mode 100644 index a3aa6b5a91fbfde53af0d2d43748d439399ca307..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/egs/datasets/audio/lj/preprocess.py +++ /dev/null @@ -1,9 +0,0 @@ -from text_to_speech.data_gen.tts.base_preprocess import BasePreprocessor - - -class LJPreprocess(BasePreprocessor): - def meta_data(self): - for l in open(f'{self.raw_data_dir}/metadata.csv').readlines(): - item_name, _, txt = l.strip().split("|") - wav_fn = f"{self.raw_data_dir}/wavs/{item_name}.wav" - yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt} diff --git a/spaces/AIMLApps/Botrite_wip/README.md b/spaces/AIMLApps/Botrite_wip/README.md deleted file mode 100644 index 01dc43bf6644b5bd147e82955ba431a8fd234906..0000000000000000000000000000000000000000 --- a/spaces/AIMLApps/Botrite_wip/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Botrite Wip -emoji: 📈 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.37.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AP123/IllusionDiffusion/user_history.py b/spaces/AP123/IllusionDiffusion/user_history.py deleted file mode 100644 index c0cfdb3b2c02c353dc36116a8e86d77aabe4f75f..0000000000000000000000000000000000000000 --- a/spaces/AP123/IllusionDiffusion/user_history.py +++ /dev/null @@ -1,423 +0,0 @@ -""" -User History is a plugin that you can add to your Spaces to cache generated images for your users. - -Key features: -- 🤗 Sign in with Hugging Face -- Save generated images with their metadata: prompts, timestamp, hyper-parameters, etc. -- Export your history as zip. -- Delete your history to respect privacy. -- Compatible with Persistent Storage for long-term storage. -- Admin panel to check configuration and disk usage . - -Useful links: -- Demo: https://huggingface.co/spaces/Wauplin/gradio-user-history -- README: https://huggingface.co/spaces/Wauplin/gradio-user-history/blob/main/README.md -- Source file: https://huggingface.co/spaces/Wauplin/gradio-user-history/blob/main/user_history.py -- Discussions: https://huggingface.co/spaces/Wauplin/gradio-user-history/discussions -""" -import json -import os -import shutil -import warnings -from datetime import datetime -from functools import cache -from pathlib import Path -from typing import Callable, Dict, List, Tuple -from uuid import uuid4 - -import gradio as gr -import numpy as np -import requests -from filelock import FileLock -from PIL.Image import Image - - -def setup(folder_path: str | Path | None = None) -> None: - user_history = _UserHistory() - user_history.folder_path = _resolve_folder_path(folder_path) - user_history.initialized = True - - -def render() -> None: - user_history = _UserHistory() - - # initialize with default config - if not user_history.initialized: - print("Initializing user history with default config. Use `user_history.setup(...)` to customize folder_path.") - setup() - - # Render user history tab - gr.Markdown( - "## Your past generations\n\nLog in to keep a gallery of your previous generations. Your history will be saved" - " and available on your next visit. Make sure to export your images from time to time as this gallery may be" - " deleted in the future." - ) - - if os.getenv("SYSTEM") == "spaces" and not os.path.exists("/data"): - gr.Markdown( - "**⚠️ Persistent storage is disabled, meaning your history will be lost if the Space gets restarted." - " Only the Space owner can setup a Persistent Storage. If you are not the Space owner, consider" - " duplicating this Space to set your own storage.⚠️**" - ) - - with gr.Row(): - gr.LoginButton(min_width=250) - gr.LogoutButton(min_width=250) - refresh_button = gr.Button( - "Refresh", - icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_refresh.png", - ) - export_button = gr.Button( - "Export", - icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_download.png", - ) - delete_button = gr.Button( - "Delete history", - icon="https://huggingface.co/spaces/Wauplin/gradio-user-history/resolve/main/assets/icon_delete.png", - ) - - # "Export zip" row (hidden by default) - with gr.Row(): - export_file = gr.File(file_count="single", file_types=[".zip"], label="Exported history", visible=False) - - # "Config deletion" row (hidden by default) - with gr.Row(): - confirm_button = gr.Button("Confirm delete all history", variant="stop", visible=False) - cancel_button = gr.Button("Cancel", visible=False) - - # Gallery - gallery = gr.Gallery( - label="Past images", - show_label=True, - elem_id="gallery", - object_fit="contain", - columns=5, - height=600, - preview=False, - show_share_button=False, - show_download_button=False, - ) - gr.Markdown( - "User history is powered by" - " [Wauplin/gradio-user-history](https://huggingface.co/spaces/Wauplin/gradio-user-history). Integrate it to" - " your own Space in just a few lines of code!" - ) - gallery.attach_load_event(_fetch_user_history, every=None) - - # Interactions - refresh_button.click(fn=_fetch_user_history, inputs=[], outputs=[gallery], queue=False) - export_button.click(fn=_export_user_history, inputs=[], outputs=[export_file], queue=False) - - # Taken from https://github.com/gradio-app/gradio/issues/3324#issuecomment-1446382045 - delete_button.click( - lambda: [gr.update(visible=True), gr.update(visible=True)], - outputs=[confirm_button, cancel_button], - queue=False, - ) - cancel_button.click( - lambda: [gr.update(visible=False), gr.update(visible=False)], - outputs=[confirm_button, cancel_button], - queue=False, - ) - confirm_button.click(_delete_user_history).then( - lambda: [gr.update(visible=False), gr.update(visible=False)], - outputs=[confirm_button, cancel_button], - queue=False, - ) - - # Admin section (only shown locally or when logged in as Space owner) - _admin_section() - - -def save_image( - profile: gr.OAuthProfile | None, - image: Image | np.ndarray | str | Path, - label: str | None = None, - metadata: Dict | None = None, -): - # Ignore images from logged out users - if profile is None: - return - username = profile["preferred_username"] - - # Ignore images if user history not used - user_history = _UserHistory() - if not user_history.initialized: - warnings.warn( - "User history is not set in Gradio demo. Saving image is ignored. You must use `user_history.render(...)`" - " first." - ) - return - - # Copy image to storage - image_path = _copy_image(image, dst_folder=user_history._user_images_path(username)) - - # Save new image + metadata - if metadata is None: - metadata = {} - if "datetime" not in metadata: - metadata["datetime"] = str(datetime.now()) - data = {"path": str(image_path), "label": label, "metadata": metadata} - with user_history._user_lock(username): - with user_history._user_jsonl_path(username).open("a") as f: - f.write(json.dumps(data) + "\n") - - -############# -# Internals # -############# - - -class _UserHistory(object): - _instance = None - initialized: bool = False - folder_path: Path - - def __new__(cls): - # Using singleton pattern => we don't want to expose an object (more complex to use) but still want to keep - # state between `render` and `save_image` calls. - if cls._instance is None: - cls._instance = super(_UserHistory, cls).__new__(cls) - return cls._instance - - def _user_path(self, username: str) -> Path: - path = self.folder_path / username - path.mkdir(parents=True, exist_ok=True) - return path - - def _user_lock(self, username: str) -> FileLock: - """Ensure history is not corrupted if concurrent calls.""" - return FileLock(self.folder_path / f"{username}.lock") # lock outside of folder => better when exporting ZIP - - def _user_jsonl_path(self, username: str) -> Path: - return self._user_path(username) / "history.jsonl" - - def _user_images_path(self, username: str) -> Path: - path = self._user_path(username) / "images" - path.mkdir(parents=True, exist_ok=True) - return path - - -def _fetch_user_history(profile: gr.OAuthProfile | None) -> List[Tuple[str, str]]: - """Return saved history for that user, if it exists.""" - # Cannot load history for logged out users - if profile is None: - return [] - username = profile["preferred_username"] - - user_history = _UserHistory() - if not user_history.initialized: - warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.") - return [] - - with user_history._user_lock(username): - # No file => no history saved yet - jsonl_path = user_history._user_jsonl_path(username) - if not jsonl_path.is_file(): - return [] - - # Read history - images = [] - for line in jsonl_path.read_text().splitlines(): - data = json.loads(line) - images.append((data["path"], data["label"] or "")) - return list(reversed(images)) - - -def _export_user_history(profile: gr.OAuthProfile | None) -> Dict | None: - """Zip all history for that user, if it exists and return it as a downloadable file.""" - # Cannot load history for logged out users - if profile is None: - return None - username = profile["preferred_username"] - - user_history = _UserHistory() - if not user_history.initialized: - warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.") - return None - - # Zip history - with user_history._user_lock(username): - path = shutil.make_archive( - str(_archives_path() / f"history_{username}"), "zip", user_history._user_path(username) - ) - - return gr.update(visible=True, value=path) - - -def _delete_user_history(profile: gr.OAuthProfile | None) -> None: - """Delete all history for that user.""" - # Cannot load history for logged out users - if profile is None: - return - username = profile["preferred_username"] - - user_history = _UserHistory() - if not user_history.initialized: - warnings.warn("User history is not set in Gradio demo. You must use `user_history.render(...)` first.") - return - - with user_history._user_lock(username): - shutil.rmtree(user_history._user_path(username)) - - -#################### -# Internal helpers # -#################### - - -def _copy_image(image: Image | np.ndarray | str | Path, dst_folder: Path) -> Path: - """Copy image to the images folder.""" - # Already a path => copy it - if isinstance(image, str): - image = Path(image) - if isinstance(image, Path): - dst = dst_folder / f"{uuid4().hex}_{Path(image).name}" # keep file ext - shutil.copyfile(image, dst) - return dst - - # Still a Python object => serialize it - if isinstance(image, np.ndarray): - image = Image.fromarray(image) - if isinstance(image, Image): - dst = dst_folder / f"{uuid4().hex}.png" - image.save(dst) - return dst - - raise ValueError(f"Unsupported image type: {type(image)}") - - -def _resolve_folder_path(folder_path: str | Path | None) -> Path: - if folder_path is not None: - return Path(folder_path).expanduser().resolve() - - if os.getenv("SYSTEM") == "spaces" and os.path.exists("/data"): # Persistent storage is enabled! - return Path("/data") / "_user_history" - - # Not in a Space or Persistent storage not enabled => local folder - return Path(__file__).parent / "_user_history" - - -def _archives_path() -> Path: - # Doesn't have to be on persistent storage as it's only used for download - path = Path(__file__).parent / "_user_history_exports" - path.mkdir(parents=True, exist_ok=True) - return path - - -################# -# Admin section # -################# - - -def _admin_section() -> None: - title = gr.Markdown() - title.attach_load_event(_display_if_admin(), every=None) - - -def _display_if_admin() -> Callable: - def _inner(profile: gr.OAuthProfile | None) -> str: - if profile is None: - return "" - if profile["preferred_username"] in _fetch_admins(): - return _admin_content() - return "" - - return _inner - - -def _admin_content() -> str: - return f""" -## Admin section - -Running on **{os.getenv("SYSTEM", "local")}** (id: {os.getenv("SPACE_ID")}). {_get_msg_is_persistent_storage_enabled()} - -Admins: {', '.join(_fetch_admins())} - -{_get_nb_users()} user(s), {_get_nb_images()} image(s) - -### Configuration - -History folder: *{_UserHistory().folder_path}* - -Exports folder: *{_archives_path()}* - -### Disk usage - -{_disk_space_warning_message()} -""" - - -def _get_nb_users() -> int: - user_history = _UserHistory() - if not user_history.initialized: - return 0 - if user_history.folder_path is not None and user_history.folder_path.exists(): - return len([path for path in user_history.folder_path.iterdir() if path.is_dir()]) - return 0 - - -def _get_nb_images() -> int: - user_history = _UserHistory() - if not user_history.initialized: - return 0 - if user_history.folder_path is not None and user_history.folder_path.exists(): - return len([path for path in user_history.folder_path.glob("*/images/*")]) - return 0 - - -def _get_msg_is_persistent_storage_enabled() -> str: - if os.getenv("SYSTEM") == "spaces": - if os.path.exists("/data"): - return "Persistent storage is enabled." - else: - return ( - "Persistent storage is not enabled. This means that user histories will be deleted when the Space is" - " restarted. Consider adding a Persistent Storage in your Space settings." - ) - return "" - - -def _disk_space_warning_message() -> str: - user_history = _UserHistory() - if not user_history.initialized: - return "" - - message = "" - if user_history.folder_path is not None: - total, used, _ = _get_disk_usage(user_history.folder_path) - message += f"History folder: **{used / 1e9 :.0f}/{total / 1e9 :.0f}GB** used ({100*used/total :.0f}%)." - - total, used, _ = _get_disk_usage(_archives_path()) - message += f"\n\nExports folder: **{used / 1e9 :.0f}/{total / 1e9 :.0f}GB** used ({100*used/total :.0f}%)." - - return f"{message.strip()}" - - -def _get_disk_usage(path: Path) -> Tuple[int, int, int]: - for path in [path] + list(path.parents): # first check target_dir, then each parents one by one - try: - return shutil.disk_usage(path) - except OSError: # if doesn't exist or can't read => fail silently and try parent one - pass - return 0, 0, 0 - - -@cache -def _fetch_admins() -> List[str]: - # Running locally => fake user is admin - if os.getenv("SYSTEM") != "spaces": - return ["FakeGradioUser"] - - # Running in Space but no space_id => ??? - space_id = os.getenv("SPACE_ID") - if space_id is None: - return ["Unknown"] - - # Running in Space => try to fetch organization members - # Otherwise, it's not an organization => namespace is the user - namespace = space_id.split("/")[0] - response = requests.get(f"https://huggingface.co/api/organizations/{namespace}/members") - if response.status_code == 200: - return sorted((member["user"] for member in response.json()), key=lambda x: x.lower()) - return [namespace] diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/tcrp-plugin.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/tcrp-plugin.js deleted file mode 100644 index 62f4e67ac914c89fbb672b33b95fb4db4af644a2..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/tcrp-plugin.js +++ /dev/null @@ -1,34 +0,0 @@ -import TCRP from './tcrp.js'; - -const Recorder = TCRP.Recorder; -const Player = TCRP.Player; - -class TCRPPlugin extends Phaser.Plugins.BasePlugin { - constructor(pluginManager) { - super(pluginManager); - } - - start() { - var eventEmitter = this.game.events; - eventEmitter.on('destroy', this.destroy, this); - } - - addRecorder(parent, config) { - return new Recorder(parent, config); - } - - addPlayer(parent, config) { - return new Player(parent, config); - } -} - -var methods = { - runCommands: TCRP.RunCommands -} - -Object.assign( - TCRPPlugin.prototype, - methods -); - -export default TCRPPlugin; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/Factory.d.ts deleted file mode 100644 index 744850f15da0f31086ed59345b7c2abb5a91cea6..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/clickoutside/Factory.d.ts +++ /dev/null @@ -1,7 +0,0 @@ -// import * as Phaser from 'phaser'; -import ClickOutside from "./ClickOutside"; - -export default function ( - gameObject: Phaser.GameObjects.GameObject, - config?: ClickOutside.IConfig -): ClickOutside; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/RunChildrenWrap.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/RunChildrenWrap.js deleted file mode 100644 index 48eab787719ede6cb9e8145b0a44c31ba85f7260..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/RunChildrenWrap.js +++ /dev/null @@ -1,93 +0,0 @@ -import { GetDisplayWidth, GetDisplayHeight } from '../../../plugins/utils/size/GetDisplaySize.js'; - -var RunChildrenWrap = function (lineWidth, out) { - if (out === undefined) { - out = { - lines: [], - width: 0, - height: 0 - } - } else { - out.lines.length = 0; - out.width = 0; - out.height = 0; - } - - var children = this.sizerChildren; - var itemSpace = this.space.item, - lineSpace = this.space.line, - indentLeftOdd = this.space.indentLeftOdd, - indentLeftEven = this.space.indentLeftEven, - indentTopOdd = this.space.indentTopOdd, - indentTopEven = this.space.indentTopEven; - var child, childWidth, childHeight, remainder = 0, indentLeft; - var lines = out.lines, - lastLine = undefined, - newLine; - for (var i = 0, cnt = children.length; i < cnt; i++) { - child = children[i]; - if (child === '\n') { - child = undefined; - childWidth = 0; - newLine = true; - } else { - if (child.rexSizer.hidden) { - continue; - } - - if (child.isRexSizer) { - child.layout(); // Use original size - } - - childWidth = GetChildWidth(child); - newLine = (remainder < childWidth) || (lastLine === undefined); - } - // New line - if (newLine) { - if (lastLine) { - lastLine.width = lineWidth - (remainder + itemSpace); - out.width = Math.max(out.width, lastLine.width); - out.height += lastLine.height + lineSpace; - } - - lastLine = { - children: [], - // width: 0, - height: 0 - }; - lines.push(lastLine); - - var indentLeft = (lines.length % 2) ? indentLeftOdd : indentLeftEven; - remainder = lineWidth - indentLeft; - } - - remainder -= (childWidth + itemSpace); - if (child) { - lastLine.children.push(child); - childHeight = GeChildHeight(child); - lastLine.height = Math.max(lastLine.height, childHeight); - } - } - - if (lastLine) { - lastLine.width = lineWidth - (remainder + itemSpace); - out.width = Math.max(out.width, lastLine.width); - out.height += lastLine.height; - } - - out.height += Math.max(indentTopOdd, indentTopEven); - - return out; -} - -var GetChildWidth = function (child) { - var padding = child.rexSizer.padding; - return GetDisplayWidth(child) + padding.left + padding.right; -} - -var GeChildHeight = function (child) { - var padding = child.rexSizer.padding; - return GetDisplayHeight(child) + padding.top + padding.bottom; -} - -export default RunChildrenWrap; \ No newline at end of file diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/transforms.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/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/AlekseyKorshuk/model-evaluation/tabs/arena_side_by_side.py b/spaces/AlekseyKorshuk/model-evaluation/tabs/arena_side_by_side.py deleted file mode 100644 index 84494aa1a8c2ad7f4e7dcde54a7c88e62077ab25..0000000000000000000000000000000000000000 --- a/spaces/AlekseyKorshuk/model-evaluation/tabs/arena_side_by_side.py +++ /dev/null @@ -1,240 +0,0 @@ -import time - -import gradio as gr -import random -from conversation import Conversation -from utils import get_matchmaking - - -def get_tab_arena_side_by_side(download_bot_config, get_bot_profile, model_mapping, client): - gr.Markdown(""" - # ⚔️ Chatbot Arena (side-by-side) ⚔️ - ## Rules - * Chat with two models side-by-side and vote for which one is better! - * You pick the models you want to chat with. - * You can continue chatting and voting or click “Clear” to start a new round. - """) - default_bot_id = "_bot_e21de304-6151-4a04-b025-4c553ae8cbca" - bot_config = download_bot_config(default_bot_id) - user_state = gr.State( - bot_config - ) - with gr.Row(): - bot_id = gr.Textbox(label="Chai bot ID", value=default_bot_id, interactive=True) - reload_bot_button = gr.Button("Reload bot") - bot_profile = gr.HTML(get_bot_profile(bot_config)) - with gr.Accordion("Bot config:", open=False): - bot_config_text = gr.Markdown(f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}\n") - - with gr.Row(): - values = list(model_mapping.keys()) - first_message = (None, bot_config["firstMessage"]) - height = 450 - model_a_value, model_b_value = get_matchmaking(client, values, is_anonymous=False) - with gr.Column(): - model_a = gr.Dropdown(values, value=model_a_value, label="Model A") - chatbot_a = gr.Chatbot([first_message]) - chatbot_a.style(height=height) - with gr.Column(): - model_b = gr.Dropdown(values, value=model_b_value, label="Model B") - chatbot_b = gr.Chatbot([first_message]) - chatbot_b.style(height=height) - - with gr.Row(): - with gr.Column(scale=3): - msg = gr.Textbox(show_label=False, value="Hi there!", interactive=True) - with gr.Column(scale=3): - send = gr.Button("Send") - with gr.Row(): - vote_a = gr.Button("👈 A is better", interactive=False) - vote_b = gr.Button("👉 B is better", interactive=False) - vote_tie = gr.Button("🤝 Tie", interactive=False) - vote_bad = gr.Button("💩 Both are bad", interactive=False) - with gr.Row(): - regenerate = gr.Button("Regenerate", interactive=False) - clear = gr.Button("Clear") - - with gr.Accordion("Generation parameters for model A", open=False): - model = model_mapping[model_a.value] - temperature_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"], - interactive=True, label="Temperature") - repetition_penalty_model_a = gr.Slider(minimum=0.0, maximum=2.0, - value=model.generation_params["repetition_penalty"], - interactive=True, label="Repetition penalty") - max_new_tokens_model_a = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"], - interactive=True, label="Max new tokens") - top_k_model_a = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"], - interactive=True, label="Top-K") - top_p_model_a = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"], - interactive=True, label="Top-P") - - with gr.Accordion("Generation parameters for model B", open=False): - model = model_mapping[model_b.value] - temperature_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["temperature"], - interactive=True, label="Temperature") - repetition_penalty_model_b = gr.Slider(minimum=0.0, maximum=2.0, - value=model.generation_params["repetition_penalty"], - interactive=True, label="Repetition penalty") - max_new_tokens_model_b = gr.Slider(minimum=1, maximum=512, value=model.generation_params["max_new_tokens"], - interactive=True, label="Max new tokens") - top_k_model_b = gr.Slider(minimum=1, maximum=100, value=model.generation_params["top_k"], - interactive=True, label="Top-K") - top_p_model_b = gr.Slider(minimum=0.0, maximum=1.0, value=model.generation_params["top_p"], - interactive=True, label="Top-P") - - def clear_chat(user_state): - return "", [(None, user_state["firstMessage"])], [(None, user_state["firstMessage"])] - - def reload_bot(bot_id): - bot_config = download_bot_config(bot_id) - bot_profile = get_bot_profile(bot_config) - return bot_profile, [(None, bot_config["firstMessage"])], [(None, bot_config[ - "firstMessage"])], bot_config, f"# Memory\n{bot_config['memory']}\n# Prompt\n{bot_config['prompt']}" - - def get_generation_args(model_tag): - model = model_mapping[model_tag] - return ( - model.generation_params["temperature"], - model.generation_params["repetition_penalty"], - model.generation_params["max_new_tokens"], - model.generation_params["top_k"], - model.generation_params["top_p"], - ) - - def respond(message, chat_history, user_state, model_tag, - temperature, repetition_penalty, max_new_tokens, top_k, top_p): - custom_generation_params = { - 'temperature': temperature, - 'repetition_penalty': repetition_penalty, - 'max_new_tokens': max_new_tokens, - 'top_k': top_k, - 'top_p': top_p, - } - conv = Conversation(user_state) - conv.set_chat_history(chat_history) - conv.add_user_message(message) - model = model_mapping[model_tag] - bot_message = model.generate_response(conv, custom_generation_params) - chat_history.append( - (message, bot_message) - ) - return "", chat_history - - def record_vote(user_state, vote, - chat_history_a, model_tag_a, - chat_history_b, model_tag_b): - if len(chat_history_a) < 2: - return - conv_a = Conversation(user_state) - conv_a.set_chat_history(chat_history_a) - conv_b = Conversation(user_state) - conv_b.set_chat_history(chat_history_b) - if "A is better" in vote: - vote_str = "model_a" - elif "B is better" in vote: - vote_str = "model_b" - elif "Tie" in vote: - vote_str = "tie" - else: - vote_str = "tie (bothbad)" - row = { - "timestamp": time.time(), - "bot_id": user_state["bot_id"], - "vote": vote_str, - "model_a": model_tag_a, - "model_b": model_tag_b, - "is_anonymous": int(False) - } - sheet = client.open("Chat Arena").sheet1 - num_rows = len(sheet.get_all_records()) - sheet.insert_row(list(row.values()), index=num_rows + 2) - return - - def regenerate_response(chat_history, user_state, model_tag, - temperature, repetition_penalty, max_new_tokens, top_k, top_p): - custom_generation_params = { - 'temperature': temperature, - 'repetition_penalty': repetition_penalty, - 'max_new_tokens': max_new_tokens, - 'top_k': top_k, - 'top_p': top_p, - } - last_row = chat_history.pop(-1) - chat_history.append((last_row[0], None)) - model = model_mapping[model_tag] - conv = Conversation(user_state) - conv.set_chat_history(chat_history) - bot_message = model.generate_response(conv, custom_generation_params) - chat_history[-1] = (last_row[0], bot_message) - return "", chat_history - - def disable_voting(): - return [gr.Button.update(interactive=False)] * 4 - - def enable_voting(): - return [gr.Button.update(interactive=True)] * 4 - - def enable_send(): - return [gr.Button.update(interactive=True), gr.Button.update(interactive=False)] - - def enable_regenerate(): - return gr.Button.update(interactive=True) - - for vote in [vote_a, vote_b, vote_tie, vote_bad]: - vote.click(record_vote, - [user_state, vote, chatbot_a, model_a, chatbot_b, model_b], - None, - queue=False) - vote.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - - model_a.change(get_generation_args, [model_a], - [temperature_model_a, repetition_penalty_model_a, max_new_tokens_model_a, top_k_model_a, - top_p_model_a], queue=False) - model_b.change(get_generation_args, [model_b], - [temperature_model_b, repetition_penalty_model_b, max_new_tokens_model_b, top_k_model_b, - top_p_model_b], queue=False) - reload_bot_button.click(reload_bot, [bot_id], [bot_profile, chatbot_a, chatbot_b, user_state, bot_config_text], - queue=False) - clear.click(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - model_a.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - model_b.change(clear_chat, [user_state], [msg, chatbot_a, chatbot_b], queue=False) - clear.click(enable_send, None, [send, regenerate], queue=False) - reload_bot_button.click(enable_send, None, [send, regenerate], queue=False) - - model_a.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - model_b.change(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - reload_bot_button.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - send.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - clear.click(disable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - regenerate.click(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - msg.submit(enable_voting, None, [vote_a, vote_b, vote_tie, vote_bad], queue=False) - - send.click(respond, - [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a], - queue=False) - msg.submit(respond, - [msg, chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, top_p_model_a], [msg, chatbot_a], - queue=False) - - send.click(respond, - [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b], - queue=False) - msg.submit(respond, - [msg, chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, top_p_model_b], [msg, chatbot_b], - queue=False) - - send.click(enable_regenerate, None, [regenerate], queue=False) - msg.submit(enable_regenerate, None, [regenerate], queue=False) - - regenerate.click(regenerate_response, - [chatbot_a, user_state, model_a, temperature_model_a, repetition_penalty_model_a, - max_new_tokens_model_a, top_k_model_a, - top_p_model_a], [msg, chatbot_a], queue=False) - regenerate.click(regenerate_response, - [chatbot_b, user_state, model_b, temperature_model_b, repetition_penalty_model_b, - max_new_tokens_model_b, top_k_model_b, - top_p_model_b], [msg, chatbot_b], queue=False) diff --git a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train.py b/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train.py deleted file mode 100644 index 06ce3be20bc4fcbc5395c596b042c1bf2bdad8b8..0000000000000000000000000000000000000000 --- a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train.py +++ /dev/null @@ -1,94 +0,0 @@ -from tqdm import trange -import torch -from torch.utils.data import DataLoader -from logger import Logger -from modules.model import GeneratorFullModel -from torch.optim.lr_scheduler import MultiStepLR -from torch.nn.utils import clip_grad_norm_ -from frames_dataset import DatasetRepeater -import math - -def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset): - train_params = config['train_params'] - optimizer = torch.optim.Adam( - [{'params': list(inpainting_network.parameters()) + - list(dense_motion_network.parameters()) + - list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) - - optimizer_bg_predictor = None - if bg_predictor: - optimizer_bg_predictor = torch.optim.Adam( - [{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], - lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4) - - if checkpoint is not None: - start_epoch = Logger.load_cpk( - checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network, - kp_detector = kp_detector, bg_predictor = bg_predictor, - optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor) - print('load success:', start_epoch) - start_epoch += 1 - else: - start_epoch = 0 - - scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1, - last_epoch=start_epoch - 1) - if bg_predictor: - scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'], - gamma=0.1, last_epoch=start_epoch - 1) - - if 'num_repeats' in train_params or train_params['num_repeats'] != 1: - dataset = DatasetRepeater(dataset, train_params['num_repeats']) - dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, - num_workers=train_params['dataloader_workers'], drop_last=True) - - generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params) - - if torch.cuda.is_available(): - generator_full = torch.nn.DataParallel(generator_full).cuda() - - bg_start = train_params['bg_start'] - - with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], - checkpoint_freq=train_params['checkpoint_freq']) as logger: - for epoch in trange(start_epoch, train_params['num_epochs']): - for x in dataloader: - if(torch.cuda.is_available()): - x['driving'] = x['driving'].cuda() - x['source'] = x['source'].cuda() - - losses_generator, generated = generator_full(x, epoch) - loss_values = [val.mean() for val in losses_generator.values()] - loss = sum(loss_values) - loss.backward() - - clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf) - clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf) - if bg_predictor and epoch>=bg_start: - clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf) - - optimizer.step() - optimizer.zero_grad() - if bg_predictor and epoch>=bg_start: - optimizer_bg_predictor.step() - optimizer_bg_predictor.zero_grad() - - losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()} - logger.log_iter(losses=losses) - - scheduler_optimizer.step() - if bg_predictor: - scheduler_bg_predictor.step() - - model_save = { - 'inpainting_network': inpainting_network, - 'dense_motion_network': dense_motion_network, - 'kp_detector': kp_detector, - 'optimizer': optimizer, - } - if bg_predictor and epoch>=bg_start: - model_save['bg_predictor'] = bg_predictor - model_save['optimizer_bg_predictor'] = optimizer_bg_predictor - - logger.log_epoch(epoch, model_save, inp=x, out=generated) - diff --git a/spaces/AlexWang/lama/bin/gen_outpainting_dataset.py b/spaces/AlexWang/lama/bin/gen_outpainting_dataset.py deleted file mode 100644 index 72f6fc16c372fbc0aec9643c7be1c44ce5efeba4..0000000000000000000000000000000000000000 --- a/spaces/AlexWang/lama/bin/gen_outpainting_dataset.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -import glob -import logging -import os -import shutil -import sys -import traceback - -from saicinpainting.evaluation.data import load_image -from saicinpainting.evaluation.utils import move_to_device - -os.environ['OMP_NUM_THREADS'] = '1' -os.environ['OPENBLAS_NUM_THREADS'] = '1' -os.environ['MKL_NUM_THREADS'] = '1' -os.environ['VECLIB_MAXIMUM_THREADS'] = '1' -os.environ['NUMEXPR_NUM_THREADS'] = '1' - -import cv2 -import hydra -import numpy as np -import torch -import tqdm -import yaml -from omegaconf import OmegaConf -from torch.utils.data._utils.collate import default_collate - -from saicinpainting.training.data.datasets import make_default_val_dataset -from saicinpainting.training.trainers import load_checkpoint -from saicinpainting.utils import register_debug_signal_handlers - -LOGGER = logging.getLogger(__name__) - - -def main(args): - try: - if not args.indir.endswith('/'): - args.indir += '/' - - for in_img in glob.glob(os.path.join(args.indir, '**', '*' + args.img_suffix), recursive=True): - if 'mask' in os.path.basename(in_img): - continue - - out_img_path = os.path.join(args.outdir, os.path.splitext(in_img[len(args.indir):])[0] + '.png') - out_mask_path = f'{os.path.splitext(out_img_path)[0]}_mask.png' - - os.makedirs(os.path.dirname(out_img_path), exist_ok=True) - - img = load_image(in_img) - height, width = img.shape[1:] - pad_h, pad_w = int(height * args.coef / 2), int(width * args.coef / 2) - - mask = np.zeros((height, width), dtype='uint8') - - if args.expand: - img = np.pad(img, ((0, 0), (pad_h, pad_h), (pad_w, pad_w))) - mask = np.pad(mask, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant', constant_values=255) - else: - mask[:pad_h] = 255 - mask[-pad_h:] = 255 - mask[:, :pad_w] = 255 - mask[:, -pad_w:] = 255 - - # img = np.pad(img, ((0, 0), (pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode='symmetric') - # mask = np.pad(mask, ((pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode = 'symmetric') - - img = np.clip(np.transpose(img, (1, 2, 0)) * 255, 0, 255).astype('uint8') - img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) - cv2.imwrite(out_img_path, img) - - cv2.imwrite(out_mask_path, mask) - except KeyboardInterrupt: - LOGGER.warning('Interrupted by user') - except Exception as ex: - LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}') - sys.exit(1) - - -if __name__ == '__main__': - import argparse - - aparser = argparse.ArgumentParser() - aparser.add_argument('indir', type=str, help='Root directory with images') - aparser.add_argument('outdir', type=str, help='Where to store results') - aparser.add_argument('--img-suffix', type=str, default='.png', help='Input image extension') - aparser.add_argument('--expand', action='store_true', help='Generate mask by padding (true) or by cropping (false)') - aparser.add_argument('--coef', type=float, default=0.2, help='How much to crop/expand in order to get masks') - - main(aparser.parse_args()) diff --git a/spaces/Ame42/UBTH/app.py b/spaces/Ame42/UBTH/app.py deleted file mode 100644 index 4a0a4448e65f74409dbb4d1bcccfd590b757a8ff..0000000000000000000000000000000000000000 --- a/spaces/Ame42/UBTH/app.py +++ /dev/null @@ -1,225 +0,0 @@ -# This is a sample Python script. - -# Press Shift+F10 to execute it or replace it with your code. -import gradio as gr -from utils import * -from datetime import datetime - -doc_type = ipp -prev_sht = None -curr_sht = None - - -def ui_builder(): - with gr.Blocks() as demo: - err_view = gr.Textbox(label="Error found", visible=False) - - with gr.Tab("Multiple files"): - - def generate_all(d): - try: - d = [retrieve(dt) for dt in d if retrieve(dt) is not None] - - out = "All months.csv" - - merge_all(d).to_csv(out) - - return { - err_view: gr.update(visible=False), - out_file: gr.update(value=out, visible=True, label="Merged file") - } - except TypeError: - return { - err_view: gr.update( - value="Please select a folder containing all the files you want to filter", - visible=True - ), - out_file: gr.update(visible=False) - } - - # input ui - gr.Markdown('### See data that shows up in every month file in the chosen folder') - all_data = gr.File(label="Add a folder with all months", file_count="directory") - - # output ui - output = gr.Markdown("## *Download your file", visible=False) - out_file = gr.File(value="Tutorial Guide.pdf", label="Learn to use this app", visible=True) - run = gr.Button("Generate file") - - run.click(fn=generate_all, inputs=all_data, outputs=[err_view, out_file]) - with gr.Tab("Compare two"): - - def err_str(err): - f"""\ -[Faulty file] - Check ••••• { - os.path.split( - os.path.splitext( - err.get_file() - )[0] - )[1][:-8] - } - - {err.get_message()}\ -""" - - def raise_error(msg: str) -> dict: - return { - err_view: gr.update( - value=msg, - visible=True - ), - b: gr.update(visible=False), - f: gr.update(visible=False), - s: gr.update(visible=False), - prev_dis: gr.update(value=None), - curr_dis: gr.update(value=None), - files: gr.update(visible=False) - } - - def choose_type(event: gr.SelectData): - global doc_type - doc_type = event.value - return { - uploads: gr.update(visible=True) - } - - def check_prev(pr): - try: - shts = pd.ExcelFile(pr.name).sheet_names - - return { - prev_sheet: gr.update(choices=shts), - sheets: gr.update(visible=True) - } - except UnusualFileError as err: - return raise_error(err_str(err)) - - def check_curr(cr): - try: - shts = pd.ExcelFile(cr.name).sheet_names - - return { - curr_sheet: gr.update(choices=shts), - sheets: gr.update(visible=True) - } - except UnusualFileError as err: - return raise_error(err_str(err)) - - def sheet_prev(event: gr.SelectData, file): - global prev_sht - prev_sht = event.value - name, ext = os.path.splitext(file.name) - pr = get_raw(file.name, prev_sht, ext) - return { - data: gr.update(visible=True), - outputs: gr.update(visible=True), - prev_dis: gr.update(value=pr) - } - - def sheet_curr(event: gr.SelectData, file): - global curr_sht - curr_sht = event.value - name, ext = os.path.splitext(file.name) - cr = get_raw(file.name, curr_sht, ext) - return { - data: gr.update(visible=True), - outputs: gr.update(visible=True), - curr_dis: gr.update(value=cr) - } - - def generate(p, c, b_i, f_i, s_i): - current_time = datetime.now() - formatted_time = current_time.strftime('• %d-%m-%Y • %H.%M.%S') - b_file, f_file, s_file = f"Present in both {formatted_time}.csv", f"Exits {formatted_time}.csv", \ - f"Entries {formatted_time}.csv" - # extract info from UI results - try: - p_name, p_ext = os.path.splitext(p.name) - c_name, c_ext = os.path.splitext(c.name) - p = get_data(p.name, prev_sht, doc_type, p_ext) - c = get_data(c.name, curr_sht, doc_type, c_ext) - - # process the data - if p is None or c is None: - return raise_error(f"Incompatible column names in either or both files. Make sure they " - f"conform to the standard.\n\nIPPIS: {ipp_col}\nGIFMIS: {gif_col}") - elif p.columns[0] != c.columns[0]: - return raise_error(f"You seem to be mixing {ipp} and {gif} files. This is not allowed") - else: - both_, p_merged, c_merged = merge_two(p, c, doc_type) - - clear_csv_trash() - - # save only the files the user requested - if b_i: - both_.to_csv(b_file, index=False) - - if f_i: - p_merged.to_csv(f_file, index=False) - - if s_i: - c_merged.to_csv(s_file, index=False) - - return { - err_view: gr.update(visible=False), - b: gr.update(value=b_file, visible=True) if b_i else gr.update(visible=False), - f: gr.update(value=f_file, visible=True) if f_i else gr.update(visible=False), - s: gr.update(value=s_file, visible=True) if s_i else gr.update(visible=False), - prev_dis: gr.update(value=p), - curr_dis: gr.update(value=c), - files: gr.update(visible=True) if b_i or f_i or s_i else gr.update(visible=False) - } - except AttributeError: - return raise_error("Please select both files below before generating files") - except UnusualFileError as err: - return raise_error(err_str(err)) - - # input ui - with gr.Blocks(): - ######################################################################################################## - type = gr.Radio([ipp, gif], label="Type", info="Choose a file type") - ######################################################################################################## - with gr.Row(visible=False) as uploads: - prev = gr.File(label="Previous month", file_types=['.csv', '.xls', '.xlsx']) - curr = gr.File(label="Current month", file_types=['.csv', '.xls', '.xlsx']) - ######################################################################################################## - with gr.Row(visible=False) as sheets: - prev_sheet = gr.Radio(["N/A"], label="Sheets", info="Which sheet do you want to use?", - interactive=True) - curr_sheet = gr.Radio(["N/A"], label="Sheets", info="Which sheet do you want to use?", - interactive=True) - ######################################################################################################## - with gr.Row(visible=False) as data: - prev_dis = gr.Dataframe(row_count=(5, "fixed"), col_count=(5, "fixed"), interactive=False) - curr_dis = gr.Dataframe(row_count=(5, "fixed"), col_count=(5, "fixed"), interactive=False) - ######################################################################################################## - with gr.Column(visible=False) as outputs: - both = gr.Checkbox(label="See data that shows up in both months") - first = gr.Checkbox(label="See data that's in the previous month but not in the current") - second = gr.Checkbox(True, label="See data that's in the current month but not in the previous") - ######################################################################################################## - # output ui - with gr.Blocks(): - output = gr.Markdown("## *Download your files", visible=False) - with gr.Row(visible=False) as files: - b = gr.File(label="Both months", visible=False) - f = gr.File(label="Previous month", visible=False) - s = gr.File(label="Current month", visible=False) - run = gr.Button("Generate files") - - type.select(fn=choose_type, inputs=None, outputs=[uploads]) - prev.upload(fn=check_prev, inputs=[prev], outputs=[prev_sheet, sheets]) - curr.upload(fn=check_curr, inputs=[curr], outputs=[curr_sheet, sheets]) - prev_sheet.select(fn=sheet_prev, inputs=[prev], outputs=[data, outputs, prev_dis]) - curr_sheet.select(fn=sheet_curr, inputs=[curr], outputs=[data, outputs, curr_dis]) - run.click(fn=generate, inputs=[prev, curr, both, first, second], outputs=[err_view, b, f, s, prev_dis, - curr_dis, files]) - demo.launch() - - -# Press the green button in the gutter to run the script. -if __name__ == '__main__': - ui_builder() - -# See PyCharm help at https://www.jetbrains.com/help/pycharm/ diff --git a/spaces/Amrrs/hubble-jwst-compare/app.py b/spaces/Amrrs/hubble-jwst-compare/app.py deleted file mode 100644 index 6b39bff534b67fc3e744dab7809a7bd295d3296e..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/hubble-jwst-compare/app.py +++ /dev/null @@ -1,53 +0,0 @@ -import streamlit as st -from streamlit_image_comparison import image_comparison - -# set page config -st.set_page_config(page_title="James Webb Space Telescope vs Hubble Telescope Images", layout="centered") - -st.title("James Webb vs Hubble Telescope Pictures") - -st.markdown("# Southern Nebula") - -# render image-comparison -image_comparison( - img1="https://www.webbcompare.com/img/hubble/southern_nebula_700.jpg", - img2="https://www.webbcompare.com/img/webb/southern_nebula_700.jpg", - label1="Hubble", - label2="Webb" -) - - -st.markdown("# Galaxy Cluster SMACS 0723") - -# render image-comparison -image_comparison( - img1="https://www.webbcompare.com/img/hubble/deep_field_700.jpg", - img2="https://www.webbcompare.com/img/webb/deep_field_700.jpg", - label1="Hubble", - label2="Webb" -) - - -st.markdown("# Carina Nebula") - -# render image-comparison -image_comparison( - img1="https://www.webbcompare.com/img/hubble/carina_700.png", - img2="https://www.webbcompare.com/img/webb/carina_700.jpg", - label1="Hubble", - label2="Webb" -) - -st.markdown("# Stephan's Quintet") - -# render image-comparison -image_comparison( - img1="https://www.webbcompare.com/img/hubble/stephans_quintet_700.jpg", - img2="https://www.webbcompare.com/img/webb/stephans_quintet_700.jpg", - label1="Hubble", - label2="Webb" -) - - - -st.caption("Inspiration Credit - https://www.webbcompare.com/") \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py deleted file mode 100644 index a1ff25863822b04971d2c6dfdc17f5b28774cf05..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py +++ /dev/null @@ -1,17 +0,0 @@ -# This file is autogenerated by the command `make fix-copies`, do not edit. -from ..utils import DummyObject, requires_backends - - -class LMSDiscreteScheduler(metaclass=DummyObject): - _backends = ["torch", "scipy"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "scipy"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch", "scipy"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch", "scipy"]) diff --git a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py deleted file mode 100644 index 8357766f50ff638f13ca56bd79d1b1c64e96f3dd..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py +++ /dev/null @@ -1,15 +0,0 @@ -_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -model = dict( - pretrained='open-mmlab://resnext101_32x4d', - backbone=dict( - type='ResNeXt', - depth=101, - groups=32, - base_width=4, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch', - dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), - stage_with_dcn=(False, True, True, True))) diff --git a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py deleted file mode 100644 index 13a4645bfdb50d5a2f04cee49ecc5f7647d10acf..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -model = dict( - backbone=dict(plugins=[ - dict( - cfg=dict( - type='GeneralizedAttention', - spatial_range=-1, - num_heads=8, - attention_type='1111', - kv_stride=2), - stages=(False, False, True, True), - position='after_conv2') - ])) diff --git a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/clip/__init__.py b/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/clip/__init__.py deleted file mode 100644 index dcc5619538c0f7c782508bdbd9587259d805e0d9..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/clip/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .clip import * diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/image_degradation/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/image_degradation/__init__.py deleted file mode 100644 index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/image_degradation/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr -from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/spaces/AnticPan/Clothes2Human/app.py b/spaces/AnticPan/Clothes2Human/app.py deleted file mode 100644 index bc6a9a587a819091e242d693107ce66931d92bdd..0000000000000000000000000000000000000000 --- a/spaces/AnticPan/Clothes2Human/app.py +++ /dev/null @@ -1,62 +0,0 @@ -import os -import json -import requests -import gradio as gr -from util import base64_to_img, img_to_base64, resize_image - -url = os.getenv("REQUEST_URL") -headers = {'Content-Type': 'application/json', - 'Validation-Key': os.getenv("VALIDATION_KEY")} -names = ["input_image", "prompt", "neg_prompt", "maxlen", "step", "cfg", "seed", "up", "down", "left", "right"] -def run(*params): - params = {k:v for k, v in zip(names, params)} - image = params.pop("input_image") - image = resize_image(image) - params["image_base64"] = img_to_base64(image) - try: - response = requests.post(url, headers=headers, data=json.dumps(params), timeout=30) - if response.status_code != 200: - raise ValueError() - data = response.json() - except: - raise gr.Error("Fail to generate") - if data["code"] != 0: - raise gr.Error(data["message"]) - result = base64_to_img(data["content"]) - return result - -with gr.Blocks() as demo: - gr.Markdown("# SDXL inpainting for Clothes2Human") - with gr.Row().style(equal_height=True): - with gr.Column(): - input_image = gr.Image(type="pil", height=300) - with gr.Column(): - output_image = gr.Image(type="pil", height=300) - - with gr.Row(): - with gr.Column(): - prompt = gr.Textbox(label="Prompt") - neg_prompt = gr.Textbox(label="Negative Prompt") - - maxlen = gr.Slider(label="Max Edge Length", step=32, minimum=768, maximum=1536, value=1024) - step = gr.Slider(label="Step", minimum=20, maximum=70, value=50, step=1) - - with gr.Column(): - up = gr.Slider(label="Scale Up Image", minimum=-0.3, maximum=0.5, value=0, step=0.1) - down = gr.Slider(label="Scale Down Image", minimum=-0.3, maximum=0.5, value=0, step=0.1) - left = gr.Slider(label="Scale Left Image", minimum=-0.3, maximum=0.5, value=0, step=0.1) - right = gr.Slider(label="Scale Right Image", minimum=-0.3, maximum=0.5, value=0, step=0.1) - with gr.Column(): - cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=9.0, value=5.0, step=0.5) - seed = gr.Slider(label="Seed", minimum=-1, maximum=1000000, value=-1, step=1) - inpaint_button = gr.Button() - - run_in = [input_image, prompt, neg_prompt, maxlen, step, cfg, seed, up, down, left, right] - inpaint_button.click(run, inputs=run_in, outputs=[output_image]) - - gr.Examples([["imgs/1.jpg","A man wearing a white T-shirt stands on the beach","", 1024, 50, 5.0, 333866, 0.3, 0.3, 0.1, 0.1], - ["imgs/2.jpg"," woman wearing a blue dress stands in a park, asian race","", 1280, 50, 5.0, 443652, 0.3, 0.3, 0.2, 0.2], - ["imgs/3.jpg","A woman wearing a white dress stands","", 1280, 50, 5.0, 306728, -0.1, -0.2, 0, 0]], - inputs=run_in, outputs=[output_image], fn=run, cache_examples=True) - -demo.queue(concurrency_count=2).launch() diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/padding.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/padding.py deleted file mode 100644 index 1b2204f59f2ce4d9c8f2cca85326e4d81f8805bb..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/padding.py +++ /dev/null @@ -1,141 +0,0 @@ -from typing import cast, List, Optional, Tuple, TYPE_CHECKING, Union - -if TYPE_CHECKING: - from .console import ( - Console, - ConsoleOptions, - RenderableType, - RenderResult, - ) -from .jupyter import JupyterMixin -from .measure import Measurement -from .style import Style -from .segment import Segment - - -PaddingDimensions = Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int, int]] - - -class Padding(JupyterMixin): - """Draw space around content. - - Example: - >>> print(Padding("Hello", (2, 4), style="on blue")) - - Args: - renderable (RenderableType): String or other renderable. - pad (Union[int, Tuple[int]]): Padding for top, right, bottom, and left borders. - May be specified with 1, 2, or 4 integers (CSS style). - style (Union[str, Style], optional): Style for padding characters. Defaults to "none". - expand (bool, optional): Expand padding to fit available width. Defaults to True. - """ - - def __init__( - self, - renderable: "RenderableType", - pad: "PaddingDimensions" = (0, 0, 0, 0), - *, - style: Union[str, Style] = "none", - expand: bool = True, - ): - self.renderable = renderable - self.top, self.right, self.bottom, self.left = self.unpack(pad) - self.style = style - self.expand = expand - - @classmethod - def indent(cls, renderable: "RenderableType", level: int) -> "Padding": - """Make padding instance to render an indent. - - Args: - renderable (RenderableType): String or other renderable. - level (int): Number of characters to indent. - - Returns: - Padding: A Padding instance. - """ - - return Padding(renderable, pad=(0, 0, 0, level), expand=False) - - @staticmethod - def unpack(pad: "PaddingDimensions") -> Tuple[int, int, int, int]: - """Unpack padding specified in CSS style.""" - if isinstance(pad, int): - return (pad, pad, pad, pad) - if len(pad) == 1: - _pad = pad[0] - return (_pad, _pad, _pad, _pad) - if len(pad) == 2: - pad_top, pad_right = cast(Tuple[int, int], pad) - return (pad_top, pad_right, pad_top, pad_right) - if len(pad) == 4: - top, right, bottom, left = cast(Tuple[int, int, int, int], pad) - return (top, right, bottom, left) - raise ValueError(f"1, 2 or 4 integers required for padding; {len(pad)} given") - - def __repr__(self) -> str: - return f"Padding({self.renderable!r}, ({self.top},{self.right},{self.bottom},{self.left}))" - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - style = console.get_style(self.style) - if self.expand: - width = options.max_width - else: - width = min( - Measurement.get(console, options, self.renderable).maximum - + self.left - + self.right, - options.max_width, - ) - render_options = options.update_width(width - self.left - self.right) - if render_options.height is not None: - render_options = render_options.update_height( - height=render_options.height - self.top - self.bottom - ) - lines = console.render_lines( - self.renderable, render_options, style=style, pad=True - ) - _Segment = Segment - - left = _Segment(" " * self.left, style) if self.left else None - right = ( - [_Segment(f'{" " * self.right}', style), _Segment.line()] - if self.right - else [_Segment.line()] - ) - blank_line: Optional[List[Segment]] = None - if self.top: - blank_line = [_Segment(f'{" " * width}\n', style)] - yield from blank_line * self.top - if left: - for line in lines: - yield left - yield from line - yield from right - else: - for line in lines: - yield from line - yield from right - if self.bottom: - blank_line = blank_line or [_Segment(f'{" " * width}\n', style)] - yield from blank_line * self.bottom - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> "Measurement": - max_width = options.max_width - extra_width = self.left + self.right - if max_width - extra_width < 1: - return Measurement(max_width, max_width) - measure_min, measure_max = Measurement.get(console, options, self.renderable) - measurement = Measurement(measure_min + extra_width, measure_max + extra_width) - measurement = measurement.with_maximum(max_width) - return measurement - - -if __name__ == "__main__": # pragma: no cover - from pip._vendor.rich import print - - print(Padding("Hello, World", (2, 4), style="on blue")) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py deleted file mode 100644 index ad3089c8b144f292e9560c8cefcbab4012d09a45..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/install_lib.py +++ /dev/null @@ -1,238 +0,0 @@ -"""distutils.command.install_lib - -Implements the Distutils 'install_lib' command -(install all Python modules).""" - -import os -import importlib.util -import sys - -from distutils.core import Command -from distutils.errors import DistutilsOptionError - - -# Extension for Python source files. -PYTHON_SOURCE_EXTENSION = ".py" - - -class install_lib(Command): - - description = "install all Python modules (extensions and pure Python)" - - # The byte-compilation options are a tad confusing. Here are the - # possible scenarios: - # 1) no compilation at all (--no-compile --no-optimize) - # 2) compile .pyc only (--compile --no-optimize; default) - # 3) compile .pyc and "opt-1" .pyc (--compile --optimize) - # 4) compile "opt-1" .pyc only (--no-compile --optimize) - # 5) compile .pyc and "opt-2" .pyc (--compile --optimize-more) - # 6) compile "opt-2" .pyc only (--no-compile --optimize-more) - # - # The UI for this is two options, 'compile' and 'optimize'. - # 'compile' is strictly boolean, and only decides whether to - # generate .pyc files. 'optimize' is three-way (0, 1, or 2), and - # decides both whether to generate .pyc files and what level of - # optimization to use. - - user_options = [ - ('install-dir=', 'd', "directory to install to"), - ('build-dir=', 'b', "build directory (where to install from)"), - ('force', 'f', "force installation (overwrite existing files)"), - ('compile', 'c', "compile .py to .pyc [default]"), - ('no-compile', None, "don't compile .py files"), - ( - 'optimize=', - 'O', - "also compile with optimization: -O1 for \"python -O\", " - "-O2 for \"python -OO\", and -O0 to disable [default: -O0]", - ), - ('skip-build', None, "skip the build steps"), - ] - - boolean_options = ['force', 'compile', 'skip-build'] - negative_opt = {'no-compile': 'compile'} - - def initialize_options(self): - # let the 'install' command dictate our installation directory - self.install_dir = None - self.build_dir = None - self.force = 0 - self.compile = None - self.optimize = None - self.skip_build = None - - def finalize_options(self): - # Get all the information we need to install pure Python modules - # from the umbrella 'install' command -- build (source) directory, - # install (target) directory, and whether to compile .py files. - self.set_undefined_options( - 'install', - ('build_lib', 'build_dir'), - ('install_lib', 'install_dir'), - ('force', 'force'), - ('compile', 'compile'), - ('optimize', 'optimize'), - ('skip_build', 'skip_build'), - ) - - if self.compile is None: - self.compile = True - if self.optimize is None: - self.optimize = False - - if not isinstance(self.optimize, int): - try: - self.optimize = int(self.optimize) - if self.optimize not in (0, 1, 2): - raise AssertionError - except (ValueError, AssertionError): - raise DistutilsOptionError("optimize must be 0, 1, or 2") - - def run(self): - # Make sure we have built everything we need first - self.build() - - # Install everything: simply dump the entire contents of the build - # directory to the installation directory (that's the beauty of - # having a build directory!) - outfiles = self.install() - - # (Optionally) compile .py to .pyc - if outfiles is not None and self.distribution.has_pure_modules(): - self.byte_compile(outfiles) - - # -- Top-level worker functions ------------------------------------ - # (called from 'run()') - - def build(self): - if not self.skip_build: - if self.distribution.has_pure_modules(): - self.run_command('build_py') - if self.distribution.has_ext_modules(): - self.run_command('build_ext') - - def install(self): - if os.path.isdir(self.build_dir): - outfiles = self.copy_tree(self.build_dir, self.install_dir) - else: - self.warn( - "'%s' does not exist -- no Python modules to install" % self.build_dir - ) - return - return outfiles - - def byte_compile(self, files): - if sys.dont_write_bytecode: - self.warn('byte-compiling is disabled, skipping.') - return - - from distutils.util import byte_compile - - # Get the "--root" directory supplied to the "install" command, - # and use it as a prefix to strip off the purported filename - # encoded in bytecode files. This is far from complete, but it - # should at least generate usable bytecode in RPM distributions. - install_root = self.get_finalized_command('install').root - - if self.compile: - byte_compile( - files, - optimize=0, - force=self.force, - prefix=install_root, - dry_run=self.dry_run, - ) - if self.optimize > 0: - byte_compile( - files, - optimize=self.optimize, - force=self.force, - prefix=install_root, - verbose=self.verbose, - dry_run=self.dry_run, - ) - - # -- Utility methods ----------------------------------------------- - - def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir): - if not has_any: - return [] - - build_cmd = self.get_finalized_command(build_cmd) - build_files = build_cmd.get_outputs() - build_dir = getattr(build_cmd, cmd_option) - - prefix_len = len(build_dir) + len(os.sep) - outputs = [] - for file in build_files: - outputs.append(os.path.join(output_dir, file[prefix_len:])) - - return outputs - - def _bytecode_filenames(self, py_filenames): - bytecode_files = [] - for py_file in py_filenames: - # Since build_py handles package data installation, the - # list of outputs can contain more than just .py files. - # Make sure we only report bytecode for the .py files. - ext = os.path.splitext(os.path.normcase(py_file))[1] - if ext != PYTHON_SOURCE_EXTENSION: - continue - if self.compile: - bytecode_files.append( - importlib.util.cache_from_source(py_file, optimization='') - ) - if self.optimize > 0: - bytecode_files.append( - importlib.util.cache_from_source( - py_file, optimization=self.optimize - ) - ) - - return bytecode_files - - # -- External interface -------------------------------------------- - # (called by outsiders) - - def get_outputs(self): - """Return the list of files that would be installed if this command - were actually run. Not affected by the "dry-run" flag or whether - modules have actually been built yet. - """ - pure_outputs = self._mutate_outputs( - self.distribution.has_pure_modules(), - 'build_py', - 'build_lib', - self.install_dir, - ) - if self.compile: - bytecode_outputs = self._bytecode_filenames(pure_outputs) - else: - bytecode_outputs = [] - - ext_outputs = self._mutate_outputs( - self.distribution.has_ext_modules(), - 'build_ext', - 'build_lib', - self.install_dir, - ) - - return pure_outputs + bytecode_outputs + ext_outputs - - def get_inputs(self): - """Get the list of files that are input to this command, ie. the - files that get installed as they are named in the build tree. - The files in this list correspond one-to-one to the output - filenames returned by 'get_outputs()'. - """ - inputs = [] - - if self.distribution.has_pure_modules(): - build_py = self.get_finalized_command('build_py') - inputs.extend(build_py.get_outputs()) - - if self.distribution.has_ext_modules(): - build_ext = self.get_finalized_command('build_ext') - inputs.extend(build_ext.get_outputs()) - - return inputs diff --git a/spaces/Atualli/yoloxTeste/checkYolox.sh b/spaces/Atualli/yoloxTeste/checkYolox.sh deleted file mode 100644 index 4850bf3db22fb0b0fa33107557a1a2462eaaa7b0..0000000000000000000000000000000000000000 --- a/spaces/Atualli/yoloxTeste/checkYolox.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/sh -export path=/home/atualli/.local/lib/python3.8/site-packages:$PATH -cd ~/Projetos/huggingface/yoloxTeste -SERVER=192.168.0.153 -PORT=8080 - -if lsof -Pi :$PORT -sTCP:LISTEN -t >/dev/null ; then - echo "running" -else - ./telegramCrise.sh "reiniciando_yolox_linux_192.168.0.153:8080" - pkill -f app.py - #rm -r /tmp/tmp1*.png - python app.py & - echo "not running" -fi - - diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/__init__.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/__init__.py deleted file mode 100644 index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000 --- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -from text.symbols import * - - -_symbol_to_id = {s: i for i, s in enumerate(symbols)} - -def cleaned_text_to_sequence(cleaned_text, tones, language): - '''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 - Returns: - List of integers corresponding to the symbols in the text - ''' - phones = [_symbol_to_id[symbol] for symbol in cleaned_text] - tone_start = language_tone_start_map[language] - tones = [i + tone_start for i in tones] - lang_id = language_id_map[language] - lang_ids = [lang_id for i in phones] - return phones, tones, lang_ids - -def get_bert(norm_text, word2ph, language): - from .chinese_bert import get_bert_feature as zh_bert - from .english_bert_mock import get_bert_feature as en_bert - lang_bert_func_map = { - 'ZH': zh_bert, - 'EN': en_bert - } - bert = lang_bert_func_map[language](norm_text, word2ph) - return bert diff --git a/spaces/Benson/text-generation/Examples/Cmo Descargar El Tiempo De Juego Del Proyecto En Steam.md b/spaces/Benson/text-generation/Examples/Cmo Descargar El Tiempo De Juego Del Proyecto En Steam.md deleted file mode 100644 index 15d52492874c6eea9407c61057de6e913b8dd049..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Cmo Descargar El Tiempo De Juego Del Proyecto En Steam.md +++ /dev/null @@ -1,123 +0,0 @@ -
-

Cómo descargar el tiempo de reproducción del proyecto en Steam

-

¿Te gustan los juegos de terror? ¿Te gusta jugar con tus amigos o extraños en línea? ¿Quieres experimentar un juego emocionante y aterrador que te mantendrá al borde de tu asiento? Si respondiste afirmativamente a cualquiera de estas preguntas, deberías probar Project Playtime, un juego multijugador gratuito de terror que está disponible en Steam. En este artículo, te mostraremos cómo descargar y jugar a Project Playtime en Steam, además de darte algunos consejos y trucos para sobrevivir como sobreviviente o monstruo.

-

¿Qué es Project Playtime?

-

Project Playtime es un juego de terror multijugador donde seis jugadores intentan crear un juguete gigante mientras sobreviven a un monstruo aterrador que deambula por la fábrica de juguetes. Un séptimo jugador controla al monstruo y solo tiene un objetivo: Encontrar y matar a todos. El juego fue lanzado el 12 de diciembre de 2022 por Mob Entertainment, un estudio de juegos indie con sede en Texas. El juego ha recibido críticas muy positivas de jugadores y críticos por igual, alabando su jugabilidad, gráficos, diseño de sonido y atmósfera.

-

cómo descargar el tiempo de juego del proyecto en Steam


Download ✓✓✓ https://bltlly.com/2v6Mnl



-

¿Por qué deberías jugar Project Playtime?

-

Hay muchas razones por las que deberías jugar a Project Playtime si eres un fan de los juegos de terror. Estas son algunas de ellas:

- -

Así que, si estás buscando un juego de terror que sea gratuito, multijugador y divertido, definitivamente deberías probar Project Playtime.

-

Cómo obtener una cuenta de Steam e instalar Steam

-

Antes de poder descargar y jugar Project Playtime en Steam, necesitas tener una cuenta de Steam e instalar el cliente de Steam en tu computadora. Estos son los pasos para hacerlo:

-
    -
  1. Vaya a -
  2. Rellene su dirección de correo electrónico, contraseña, país y código captcha. De acuerdo con los términos del servicio y la política de privacidad. Haga clic en el botón "Continuar".
    3. -
  3. Revisa tu correo electrónico para ver un código de verificación de Steam. Introduce el código en el sitio web y haz clic en el botón "Crear mi cuenta".
    4. -
  4. Felicidades! Has creado tu cuenta de Steam. Ahora puedes iniciar sesión en Steam con tu correo electrónico y contraseña.
    5. -
  5. Vaya a -
  6. Descargue el instalador de Steam para su sistema operativo (Windows, Mac o Linux).
    7. -
  7. Ejecute el instalador y siga las instrucciones para instalar Steam en su computadora.
    8. -
  8. Inicia Steam e inicia sesión con tu cuenta de Steam.
    9. -
  9. Ahora estás listo para descargar y jugar Project Playtime en Steam.
    10. -
-

Cómo encontrar y descargar Project Playtime en Steam

-

Ahora que tienes una cuenta de Steam e has instalado el cliente de Steam, puedes encontrar y descargar Project Playtime en Steam. Estos son los pasos para hacerlo:

-
    -
  1. Abre el cliente de Steam y ve a la pestaña "Tienda".
    2. - -
  2. Verás la página del juego en la tienda de Steam. Haz clic en el botón "Jugar".
    3. -
  3. Aparecerá una ventana emergente pidiéndole que instale Project Playtime. Haga clic en el botón "Next".
    4. -
  4. Seleccione la carpeta de destino donde desea instalar el juego. Haga clic en el botón "Next".
    5. -
  5. Se iniciará el proceso de descarga. Puede ver el progreso y la velocidad de la descarga en la pestaña "Descargas".
    6. -
  6. Espere a que termine la descarga. Puede tomar algún tiempo dependiendo de su conexión a Internet y espacio en disco.
    7. -
  7. Una vez que la descarga se haya completado, verá un mensaje que dice "Project Playtime ya está listo para jugar". Haga clic en el botón "Play".
    8. -
  8. Has descargado e instalado correctamente el Project Playtime en Steam. ¡Disfruta!
    9. -
-

Cómo jugar Project Playtime en Steam

-

Ahora que has descargado e instalado Project Playtime en Steam, puedes empezar a reproducirlo. Estos son los pasos para hacerlo:

-
    -
  1. Iniciar tiempo de reproducción del proyecto desde la biblioteca de Steam o desde el acceso directo del escritorio.
    2. -
  2. Verás el menú principal del juego. Puedes acceder a diferentes opciones como configuración, tienda, logros, perfil, etc.
    3. -
  3. Para empezar a jugar, haga clic en el botón "Play". Verá dos opciones: "Quick Match" y "Custom Match".
    4. -
  4. Si desea unirse a un lobby aleatorio en línea, haga clic en "Quick Match". Se le emparejará con otros jugadores en función de su región y preferencias. Puedes elegir jugar como sobreviviente o monstruo, o dejar que el juego decida por ti al azar.
    5. - -
  5. Una vez que estás en un lobby, puedes chatear con otros jugadores usando chat de voz o texto. También puedes cambiar la apariencia de tu personaje haciendo clic en el botón "Personalizar". Puedes equipar diferentes cosméticos, beneficios, sabotajes, etc. que hayas comprado o ganado en la tienda. También puede cambiar su rol haciendo clic en el botón "Rol". Puedes elegir jugar como sobreviviente o monstruo, o dejar que el juego decida por ti al azar.
    6. -
  6. Cuando todo el mundo está listo, el anfitrión puede iniciar el partido haciendo clic en el botón "Inicio". El juego cargará el mapa y el modo que se seleccionaron.
    7. -
  7. El partido comenzará con una corta escena que presenta la historia y el objetivo del juego. Los supervivientes aparecerán en un lugar aleatorio en la fábrica de juguetes. El monstruo aparecerá en una habitación oculta cercana.
    8. -
  8. El objetivo de los supervivientes es encontrar y recoger seis piezas de juguete que están dispersas por el mapa. Necesitan llevarlos a una máquina de juguete gigante y montarlos juntos. También necesitan resolver rompecabezas, evitar trampas y esconderse del monstruo. Los sobrevivientes tienen una salud limitada, resistencia y batería de linterna. Pueden usar beneficios y sabotajes para ayudarles a escapar.
    9. -El objetivo del monstruo es encontrar y matar a todos los supervivientes antes de que completen su objetivo. El monstruo puede usar diferentes habilidades, como correr, rugir, aplastar, etc. El monstruo también puede usar beneficios y sabotajes para obstaculizar el progreso de los sobrevivientes y atraparlos. -
  9. El combate terminará cuando los supervivientes completen su objetivo y escapen, o el monstruo mate a todos los supervivientes. El juego mostrará los resultados del partido, como quién ganó, quién murió, quién escapó, etc. El juego también otorgará entradas a cada jugador en función de su rendimiento.
    10. -
  10. Puede jugar otro partido haciendo clic en el botón "Revancha", o volver al menú principal haciendo clic en el botón "Dejar".
    11. -
-

Consejos y trucos para jugar Project Playtime

- -

Cómo trabajar juntos como un sobreviviente

-

Como sobreviviente, necesitas cooperar con tus compañeros sobrevivientes para escapar del monstruo y completar tu objetivo. Aquí hay algunas maneras de trabajar juntos como un sobreviviente:

- -

Cómo usar beneficios y sabotajes como sobreviviente

-

Como sobreviviente, puedes usar diferentes beneficios y sabotajes para obtener una ventaja sobre el monstruo. Aquí hay algunos ejemplos de ventajas y sabotajes que puedes usar como sobreviviente:

- -

Cómo cazar sobrevivientes como un monstruo

-

Como monstruo, necesitas usar tus sentidos, habilidades y estrategias para encontrar y matar a todos los supervivientes antes de que escapen. Aquí hay algunas maneras de cazar sobrevivientes como un monstruo:

-

- -

Cómo personalizar tu personaje y jugabilidad en Project Playtime

-

Project Playtime te permite personalizar tu personaje y jugabilidad de varias maneras. Puede acceder a la tienda, comprar cosméticos, beneficios, sabotajes y otros artículos con boletos. También puedes cambiar la configuración, como gráficos, sonido, controles, etc. Estas son algunas formas de personalizar tu personaje y el modo de juego en Project Playtime:

-

Cómo ganar entradas en Project Playtime

-

Las entradas son la moneda de Project Playtime. Puede utilizar las entradas para comprar artículos en la tienda. Aquí hay algunas maneras de ganar tickets en Project Playtime:

- -

Cómo gastar entradas en Project Playtime

- - -

Conclusión

- -

Preguntas frecuentes

-

Aquí hay algunas preguntas frecuentes sobre Project Playtime:

-

64aa2da5cf
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar 3d Fondo De Pantalla En Vivo.md b/spaces/Benson/text-generation/Examples/Descargar 3d Fondo De Pantalla En Vivo.md deleted file mode 100644 index 983b1da8ea77ec7088e8a10eea38ba3a3209c1c9..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar 3d Fondo De Pantalla En Vivo.md +++ /dev/null @@ -1,94 +0,0 @@ - -

Descargar 3D Live Wallpaper: Cómo hacer que su escritorio cobre vida

-

¿Quieres darle vida a tu escritorio con algunas imágenes impresionantes? ¿Quieres hacer tu computadora más personalizada e interactiva? ¿Quieres divertirte y divertirte mientras trabajas o estudias? Si respondiste sí a cualquiera de estas preguntas, entonces deberías intentar descargar fondos de escritorio en 3D.

-

descargar 3d fondo de pantalla en vivo


Download File ✵ https://bltlly.com/2v6MlN



-

3D live wallpaper es un tipo de papel pintado animado que utiliza gráficos tridimensionales para crear escenas realistas e inmersivas en su pantalla. A diferencia de los fondos de pantalla estáticos, los fondos de pantalla en vivo en 3D pueden moverse, cambiar y reaccionar a sus acciones. Puedes elegir entre una variedad de temas y estilos, como naturaleza, fantasía, ciencia ficción, anime, películas, juegos y más. También puede crear su propio fondo de pantalla en vivo en 3D utilizando imágenes, vídeos, sitios web o aplicaciones.

-

En este artículo, le mostraremos cómo descargar fondos de escritorio en vivo en 3D para su escritorio y cómo usarlo de manera efectiva. También compartiremos algunos de los beneficios de usar fondos de escritorio en vivo en 3D y responderemos algunas preguntas comunes al respecto. Al final de este artículo, podrás hacer que tu escritorio cobre vida con un increíble fondo de pantalla en vivo en 3D.

-

¿Qué son los fondos de pantalla en vivo en 3D?

-

Como su nombre indica, 3D live wallpaper es un tipo de fondo de pantalla que utiliza gráficos tridimensionales para crear escenas dinámicas y realistas en la pantalla. A diferencia de los fondos de pantalla normales, que son solo imágenes que permanecen inmóviles en su fondo, el fondo de pantalla en vivo en 3D puede moverse, cambiar e interactuar con su ratón o teclado. Por ejemplo, puede tener un fondo de pantalla en vivo en 3D de un bosque que cambia con las estaciones, o un fondo de pantalla en vivo en 3D de una nave espacial que vuela por el espacio.

- -

Algunos ejemplos de temas populares de fondos de escritorio en vivo en 3D son:

-

- -

Estos son solo algunos de los ejemplos de temas de fondos de escritorio en vivo en 3D que puedes encontrar en línea. Hay muchas más opciones y categorías que puedes explorar y descargar.

-

¿Por qué utilizar fondos de pantalla en vivo 3D?

-

Ahora que sabes lo que son los fondos de pantalla en vivo en 3D, es posible que te estés preguntando por qué deberías usarlos en tu escritorio. Estos son algunos de los beneficios de usar fondos de pantalla en vivo en 3D:

- - -

¿Cómo descargar fondos de pantalla en vivo en 3D?

-

Si desea descargar fondos de pantalla en vivo en 3D para su escritorio, tiene varias fuentes y métodos para elegir. Estos son algunos de los más populares y confiables:

-

MoeWalls

-

MoeWalls es un sitio web que ofrece populares fondos de pantalla en vivo gratuitos, fondos de pantalla animados y videos para su escritorio. Puede navegar a través de varias categorías y géneros de fondos de pantalla en vivo en 3D, como anime, juegos, películas, naturaleza, fantasía, ciencia ficción y más. También puede buscar palabras clave específicas o títulos de fondos de pantalla en 3D que desea descargar.

-

Para descargar fondos de pantalla en vivo en 3D de MoeWalls, debe seguir estos pasos:

-
    -
  1. Ir a Steam y compra Wallpaper Engine por $4.99.
    2. -
  2. Instalar Wallpaper Engine en su computadora.
    3. -
  3. Inicie Wallpaper Engine y haga clic en la pestaña Taller.
    4. -
  4. Seleccione la categoría o género de fondo de pantalla en vivo en 3D que desea descargar.
    5. -
  5. Elija el fondo de pantalla en vivo en 3D que desee de la lista de resultados.
    6. - -
  6. El fondo de pantalla en vivo 3D se descargará y se agregará a su biblioteca de Wallpaper Engine.
    7. -
-

A continuación, puede seleccionar el fondo de pantalla en vivo 3D de su biblioteca y aplicarlo como fondo de escritorio.

-

Videos de Pexels

-

Pexels Videos es un sitio web que proporciona videos de stock gratuitos para uso personal y comercial. Usted puede encontrar varios tipos de vídeos en Pexels Videos, incluyendo fondos de escritorio videos 3D. Estos son videos que están diseñados para ser utilizados como fondos de escritorio con gráficos 3D realistas e inmersivos.

-

Para descargar videos de escritorio en 3D de Pexels Videos, debe seguir estos pasos:

-
    -
  1. Ir a Pexels.com/videos.
    2. -
  2. Escriba "fondo de escritorio 3d" en el cuadro de búsqueda y pulse enter.
    3. -
  3. Elija el vídeo que desee de la lista de resultados.
    4. -
  4. Haga clic en el botón de descarga debajo de la vista previa del video.
    5. -
  5. Guarde el archivo en su computadora.
    6. -
-

A continuación, puede utilizar el archivo como fondo de escritorio o utilizar un software como Wallpaper Engine para ejecutarlo como fondo de pantalla en vivo.

-

¿Cómo usar fondos de pantalla en vivo en 3D?

-

Una vez que haya descargado fondos de pantalla en vivo en 3D para su escritorio, es posible que desee saber cómo usarlos de manera efectiva. Aquí hay algunos consejos y trucos sobre cómo utilizar fondos de pantalla en vivo 3D en su escritorio:

- -

El uso de fondos de pantalla en vivo en 3D puede mejorar su experiencia de escritorio y hacerla más agradable. Sin embargo, también debe tener en cuenta los posibles inconvenientes de usar fondos de pantalla en vivo en 3D, como el aumento del uso de CPU y GPU, el consumo de batería y la distracción. También debe asegurarse de que su computadora cumple con los requisitos mínimos para ejecutar fondos de pantalla en vivo 3D sin problemas y sin retrasos.

-

Conclusión

-

En conclusión, 3D live wallpaper es un tipo de papel pintado animado que utiliza gráficos tridimensionales para crear escenas realistas e inmersivas en la pantalla. Puede descargar fondos de escritorio en vivo en 3D de varias fuentes en línea, como MoeWalls, Wallpaper Engine y Pexels Videos. También puede usar fondos de escritorio en vivo en 3D de manera efectiva ajustando los ajustes y pausándolos cuando sea necesario.

-

Si quieres hacer que tu escritorio cobre vida con un increíble fondo de pantalla en vivo en 3D, deberías intentar descargar algunos de ellos hoy. Usted se sorprenderá por lo mucho que pueden transformar su escritorio en un entorno impresionante e interactivo. También tendrás diversión y entretenimiento mientras trabajas o estudias con tu fondo de pantalla en 3D.

-

Entonces, ¿qué estás esperando? Descargar 3D fondo de pantalla en vivo ahora y disfrutar!

-

Preguntas frecuentes

-

Aquí están algunas de las preguntas y respuestas más frecuentes sobre el fondo de pantalla en vivo 3D:

-
    -
  1. ¿Cuál es la diferencia entre el papel pintado en vivo en 3D y el papel pintado normal?
    - -
  2. ¿Cuánto cuesta descargar fondos de escritorio en 3D?
    -Depende de la fuente y el tipo de fondo de pantalla en vivo 3D que desea descargar. Algunos de ellos son gratuitos, mientras que algunos de ellos requieren una cuota o una suscripción. Por ejemplo, MoeWalls y Pexels Videos ofrecen fondos de escritorio en vivo en 3D gratis, mientras que Wallpaper Engine cuesta $4.99.
    3. -
  3. ¿El uso de fondos de escritorio en vivo en 3D afecta el rendimiento de mi computadora?
    -Depende de la calidad y la complejidad del fondo de pantalla en vivo 3D que está utilizando. Algunos de ellos pueden consumir más recursos de CPU y GPU que otros, lo que puede afectar el rendimiento de su computadora. Puede reducir este impacto reduciendo la resolución o la velocidad de fotogramas de su fondo de pantalla 3D en vivo o deteniéndolo cuando no esté en uso.
    4. -
  4. ¿Puedo crear mi propio fondo de pantalla en vivo en 3D?
    -Sí, puede crear su propio fondo de pantalla en vivo en 3D utilizando imágenes, videos, sitios web o aplicaciones. Puede utilizar un software como Wallpaper Engine para crear y editar su propio fondo de pantalla en vivo 3D utilizando su editor incorporado.
    5. -
  5. ¿Puedo usar fondos de escritorio en vivo en 3D en otros dispositivos además de mi escritorio?
    -Sí, puede usar fondos de escritorio en vivo en 3D en otros dispositivos, como computadoras portátiles, tabletas, teléfonos inteligentes o televisores inteligentes. Sin embargo, es posible que necesite utilizar diferentes fuentes o métodos para descargar y aplicar fondos de escritorio en vivo 3D en diferentes dispositivos. Por ejemplo, puedes usar aplicaciones como 3D Wallpaper Parallax o Live Wallpapers 3D/4K para tu smartphone o tablet.
    6. -
-

Espero que este artículo haya respondido a sus preguntas y le haya ayudado a aprender más sobre el fondo de pantalla en vivo en 3D. Si tiene alguna otra pregunta o comentario, no dude en dejarlos a continuación. ¡Gracias por leer y tener un gran día!

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\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/index.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/index.py deleted file mode 100644 index 7267effed2413ba315d0a1af8490ec677c227662..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/index.py +++ /dev/null @@ -1,139 +0,0 @@ -import logging -from optparse import Values -from typing import Any, Iterable, List, Optional, Union - -from pip._vendor.packaging.version import LegacyVersion, Version - -from pip._internal.cli import cmdoptions -from pip._internal.cli.req_command import IndexGroupCommand -from pip._internal.cli.status_codes import ERROR, SUCCESS -from pip._internal.commands.search import print_dist_installation_info -from pip._internal.exceptions import CommandError, DistributionNotFound, PipError -from pip._internal.index.collector import LinkCollector -from pip._internal.index.package_finder import PackageFinder -from pip._internal.models.selection_prefs import SelectionPreferences -from pip._internal.models.target_python import TargetPython -from pip._internal.network.session import PipSession -from pip._internal.utils.misc import write_output - -logger = logging.getLogger(__name__) - - -class IndexCommand(IndexGroupCommand): - """ - Inspect information available from package indexes. - """ - - ignore_require_venv = True - usage = """ - %prog versions - """ - - def add_options(self) -> None: - cmdoptions.add_target_python_options(self.cmd_opts) - - self.cmd_opts.add_option(cmdoptions.ignore_requires_python()) - self.cmd_opts.add_option(cmdoptions.pre()) - self.cmd_opts.add_option(cmdoptions.no_binary()) - self.cmd_opts.add_option(cmdoptions.only_binary()) - - index_opts = cmdoptions.make_option_group( - cmdoptions.index_group, - self.parser, - ) - - self.parser.insert_option_group(0, index_opts) - self.parser.insert_option_group(0, self.cmd_opts) - - def run(self, options: Values, args: List[str]) -> int: - handlers = { - "versions": self.get_available_package_versions, - } - - logger.warning( - "pip index is currently an experimental command. " - "It may be removed/changed in a future release " - "without prior warning." - ) - - # Determine action - if not args or args[0] not in handlers: - logger.error( - "Need an action (%s) to perform.", - ", ".join(sorted(handlers)), - ) - return ERROR - - action = args[0] - - # Error handling happens here, not in the action-handlers. - try: - handlers[action](options, args[1:]) - except PipError as e: - logger.error(e.args[0]) - return ERROR - - return SUCCESS - - def _build_package_finder( - self, - options: Values, - session: PipSession, - target_python: Optional[TargetPython] = None, - ignore_requires_python: Optional[bool] = None, - ) -> PackageFinder: - """ - Create a package finder appropriate to the index command. - """ - link_collector = LinkCollector.create(session, options=options) - - # Pass allow_yanked=False to ignore yanked versions. - selection_prefs = SelectionPreferences( - allow_yanked=False, - allow_all_prereleases=options.pre, - ignore_requires_python=ignore_requires_python, - ) - - return PackageFinder.create( - link_collector=link_collector, - selection_prefs=selection_prefs, - target_python=target_python, - ) - - def get_available_package_versions(self, options: Values, args: List[Any]) -> None: - if len(args) != 1: - raise CommandError("You need to specify exactly one argument") - - target_python = cmdoptions.make_target_python(options) - query = args[0] - - with self._build_session(options) as session: - finder = self._build_package_finder( - options=options, - session=session, - target_python=target_python, - ignore_requires_python=options.ignore_requires_python, - ) - - versions: Iterable[Union[LegacyVersion, Version]] = ( - candidate.version for candidate in finder.find_all_candidates(query) - ) - - if not options.pre: - # Remove prereleases - versions = ( - version for version in versions if not version.is_prerelease - ) - versions = set(versions) - - if not versions: - raise DistributionNotFound( - "No matching distribution found for {}".format(query) - ) - - formatted_versions = [str(ver) for ver in sorted(versions, reverse=True)] - latest = formatted_versions[0] - - write_output("{} ({})".format(query, latest)) - write_output("Available versions: {}".format(", ".join(formatted_versions))) - print_dist_installation_info(query, latest) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/compat.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/compat.py deleted file mode 100644 index 68267ad0e2689c6c88fd2fda3bf397f16f97cc90..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/compat.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright 2016 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. -import errno -import inspect -import os -import socket -import sys - -from botocore.compat import six - -if sys.platform.startswith('win'): - def rename_file(current_filename, new_filename): - try: - os.remove(new_filename) - except OSError as e: - if not e.errno == errno.ENOENT: - # We only want to a ignore trying to remove - # a file that does not exist. If it fails - # for any other reason we should be propagating - # that exception. - raise - os.rename(current_filename, new_filename) -else: - rename_file = os.rename - - -def accepts_kwargs(func): - return inspect.getfullargspec(func)[2] - - -# In python 3, socket.error is OSError, which is too general -# for what we want (i.e FileNotFoundError is a subclass of OSError). -# In python 3, all the socket related errors are in a newly created -# ConnectionError. -SOCKET_ERROR = ConnectionError -MAXINT = None - - -def seekable(fileobj): - """Backwards compat function to determine if a fileobj is seekable - - :param fileobj: The file-like object to determine if seekable - - :returns: True, if seekable. False, otherwise. - """ - # If the fileobj has a seekable attr, try calling the seekable() - # method on it. - if hasattr(fileobj, 'seekable'): - return fileobj.seekable() - # If there is no seekable attr, check if the object can be seeked - # or telled. If it can, try to seek to the current position. - elif hasattr(fileobj, 'seek') and hasattr(fileobj, 'tell'): - try: - fileobj.seek(0, 1) - return True - except OSError: - # If an io related error was thrown then it is not seekable. - return False - # Else, the fileobj is not seekable - return False - - -def readable(fileobj): - """Determines whether or not a file-like object is readable. - - :param fileobj: The file-like object to determine if readable - - :returns: True, if readable. False otherwise. - """ - if hasattr(fileobj, 'readable'): - return fileobj.readable() - - return hasattr(fileobj, 'read') - - -def fallocate(fileobj, size): - if hasattr(os, 'posix_fallocate'): - os.posix_fallocate(fileobj.fileno(), 0, size) - else: - fileobj.truncate(size) - - -# Import at end of file to avoid circular dependencies -from multiprocessing.managers import BaseManager # noqa: F401,E402 diff --git a/spaces/Branon/Proxy/Dockerfile b/spaces/Branon/Proxy/Dockerfile deleted file mode 100644 index 4cb0ce42128d9a2ad33a395883f5e5455a38c707..0000000000000000000000000000000000000000 --- a/spaces/Branon/Proxy/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM node:18-bullseye-slim -RUN apt-get update && \ - apt-get install -y git -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app -WORKDIR /app -RUN npm install -COPY Dockerfile greeting.md* .env* ./ -RUN npm run build -EXPOSE 7860 -ENV NODE_ENV=production -CMD [ "npm", "start" ] \ No newline at end of file diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/demo/README.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/demo/README.md deleted file mode 100644 index caa755f6f0f472a04a419deec4a6acfdb949023b..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/demo/README.md +++ /dev/null @@ -1,8 +0,0 @@ - -## Detectron2 Demo - -We provide a command line tool to run a simple demo of builtin models. -The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md). - -See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-) -for a high-quality demo generated with this tool. diff --git a/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/init.h b/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/init.h deleted file mode 100644 index 3ef78c1179f5b533c3ba3f637420c8125d632a7f..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/include/pybind11/detail/init.h +++ /dev/null @@ -1,336 +0,0 @@ -/* - pybind11/detail/init.h: init factory function implementation and support code. - - Copyright (c) 2017 Jason Rhinelander - - All rights reserved. Use of this source code is governed by a - BSD-style license that can be found in the LICENSE file. -*/ - -#pragma once - -#include "class.h" - -PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) -PYBIND11_NAMESPACE_BEGIN(detail) - -template <> -class type_caster { -public: - bool load(handle h, bool) { - value = reinterpret_cast(h.ptr()); - return true; - } - - template using cast_op_type = value_and_holder &; - operator value_and_holder &() { return *value; } - static constexpr auto name = _(); - -private: - value_and_holder *value = nullptr; -}; - -PYBIND11_NAMESPACE_BEGIN(initimpl) - -inline void no_nullptr(void *ptr) { - if (!ptr) throw type_error("pybind11::init(): factory function returned nullptr"); -} - -// Implementing functions for all forms of py::init<...> and py::init(...) -template using Cpp = typename Class::type; -template using Alias = typename Class::type_alias; -template using Holder = typename Class::holder_type; - -template using is_alias_constructible = std::is_constructible, Cpp &&>; - -// Takes a Cpp pointer and returns true if it actually is a polymorphic Alias instance. -template = 0> -bool is_alias(Cpp *ptr) { - return dynamic_cast *>(ptr) != nullptr; -} -// Failing fallback version of the above for a no-alias class (always returns false) -template -constexpr bool is_alias(void *) { return false; } - -// Constructs and returns a new object; if the given arguments don't map to a constructor, we fall -// back to brace aggregate initiailization so that for aggregate initialization can be used with -// py::init, e.g. `py::init` to initialize a `struct T { int a; int b; }`. For -// non-aggregate types, we need to use an ordinary T(...) constructor (invoking as `T{...}` usually -// works, but will not do the expected thing when `T` has an `initializer_list` constructor). -template ::value, int> = 0> -inline Class *construct_or_initialize(Args &&...args) { return new Class(std::forward(args)...); } -template ::value, int> = 0> -inline Class *construct_or_initialize(Args &&...args) { return new Class{std::forward(args)...}; } - -// Attempts to constructs an alias using a `Alias(Cpp &&)` constructor. This allows types with -// an alias to provide only a single Cpp factory function as long as the Alias can be -// constructed from an rvalue reference of the base Cpp type. This means that Alias classes -// can, when appropriate, simply define a `Alias(Cpp &&)` constructor rather than needing to -// inherit all the base class constructors. -template -void construct_alias_from_cpp(std::true_type /*is_alias_constructible*/, - value_and_holder &v_h, Cpp &&base) { - v_h.value_ptr() = new Alias(std::move(base)); -} -template -[[noreturn]] void construct_alias_from_cpp(std::false_type /*!is_alias_constructible*/, - value_and_holder &, Cpp &&) { - throw type_error("pybind11::init(): unable to convert returned instance to required " - "alias class: no `Alias(Class &&)` constructor available"); -} - -// Error-generating fallback for factories that don't match one of the below construction -// mechanisms. -template -void construct(...) { - static_assert(!std::is_same::value /* always false */, - "pybind11::init(): init function must return a compatible pointer, " - "holder, or value"); -} - -// Pointer return v1: the factory function returns a class pointer for a registered class. -// If we don't need an alias (because this class doesn't have one, or because the final type is -// inherited on the Python side) we can simply take over ownership. Otherwise we need to try to -// construct an Alias from the returned base instance. -template -void construct(value_and_holder &v_h, Cpp *ptr, bool need_alias) { - no_nullptr(ptr); - if (Class::has_alias && need_alias && !is_alias(ptr)) { - // We're going to try to construct an alias by moving the cpp type. Whether or not - // that succeeds, we still need to destroy the original cpp pointer (either the - // moved away leftover, if the alias construction works, or the value itself if we - // throw an error), but we can't just call `delete ptr`: it might have a special - // deleter, or might be shared_from_this. So we construct a holder around it as if - // it was a normal instance, then steal the holder away into a local variable; thus - // the holder and destruction happens when we leave the C++ scope, and the holder - // class gets to handle the destruction however it likes. - v_h.value_ptr() = ptr; - v_h.set_instance_registered(true); // To prevent init_instance from registering it - v_h.type->init_instance(v_h.inst, nullptr); // Set up the holder - Holder temp_holder(std::move(v_h.holder>())); // Steal the holder - v_h.type->dealloc(v_h); // Destroys the moved-out holder remains, resets value ptr to null - v_h.set_instance_registered(false); - - construct_alias_from_cpp(is_alias_constructible{}, v_h, std::move(*ptr)); - } else { - // Otherwise the type isn't inherited, so we don't need an Alias - v_h.value_ptr() = ptr; - } -} - -// Pointer return v2: a factory that always returns an alias instance ptr. We simply take over -// ownership of the pointer. -template = 0> -void construct(value_and_holder &v_h, Alias *alias_ptr, bool) { - no_nullptr(alias_ptr); - v_h.value_ptr() = static_cast *>(alias_ptr); -} - -// Holder return: copy its pointer, and move or copy the returned holder into the new instance's -// holder. This also handles types like std::shared_ptr and std::unique_ptr where T is a -// derived type (through those holder's implicit conversion from derived class holder constructors). -template -void construct(value_and_holder &v_h, Holder holder, bool need_alias) { - auto *ptr = holder_helper>::get(holder); - no_nullptr(ptr); - // If we need an alias, check that the held pointer is actually an alias instance - if (Class::has_alias && need_alias && !is_alias(ptr)) - throw type_error("pybind11::init(): construction failed: returned holder-wrapped instance " - "is not an alias instance"); - - v_h.value_ptr() = ptr; - v_h.type->init_instance(v_h.inst, &holder); -} - -// return-by-value version 1: returning a cpp class by value. If the class has an alias and an -// alias is required the alias must have an `Alias(Cpp &&)` constructor so that we can construct -// the alias from the base when needed (i.e. because of Python-side inheritance). When we don't -// need it, we simply move-construct the cpp value into a new instance. -template -void construct(value_and_holder &v_h, Cpp &&result, bool need_alias) { - static_assert(std::is_move_constructible>::value, - "pybind11::init() return-by-value factory function requires a movable class"); - if (Class::has_alias && need_alias) - construct_alias_from_cpp(is_alias_constructible{}, v_h, std::move(result)); - else - v_h.value_ptr() = new Cpp(std::move(result)); -} - -// return-by-value version 2: returning a value of the alias type itself. We move-construct an -// Alias instance (even if no the python-side inheritance is involved). The is intended for -// cases where Alias initialization is always desired. -template -void construct(value_and_holder &v_h, Alias &&result, bool) { - static_assert(std::is_move_constructible>::value, - "pybind11::init() return-by-alias-value factory function requires a movable alias class"); - v_h.value_ptr() = new Alias(std::move(result)); -} - -// Implementing class for py::init<...>() -template -struct constructor { - template = 0> - static void execute(Class &cl, const Extra&... extra) { - cl.def("__init__", [](value_and_holder &v_h, Args... args) { - v_h.value_ptr() = construct_or_initialize>(std::forward(args)...); - }, is_new_style_constructor(), extra...); - } - - template , Args...>::value, int> = 0> - static void execute(Class &cl, const Extra&... extra) { - cl.def("__init__", [](value_and_holder &v_h, Args... args) { - if (Py_TYPE(v_h.inst) == v_h.type->type) - v_h.value_ptr() = construct_or_initialize>(std::forward(args)...); - else - v_h.value_ptr() = construct_or_initialize>(std::forward(args)...); - }, is_new_style_constructor(), extra...); - } - - template , Args...>::value, int> = 0> - static void execute(Class &cl, const Extra&... extra) { - cl.def("__init__", [](value_and_holder &v_h, Args... args) { - v_h.value_ptr() = construct_or_initialize>(std::forward(args)...); - }, is_new_style_constructor(), extra...); - } -}; - -// Implementing class for py::init_alias<...>() -template struct alias_constructor { - template , Args...>::value, int> = 0> - static void execute(Class &cl, const Extra&... extra) { - cl.def("__init__", [](value_and_holder &v_h, Args... args) { - v_h.value_ptr() = construct_or_initialize>(std::forward(args)...); - }, is_new_style_constructor(), extra...); - } -}; - -// Implementation class for py::init(Func) and py::init(Func, AliasFunc) -template , typename = function_signature_t> -struct factory; - -// Specialization for py::init(Func) -template -struct factory { - remove_reference_t class_factory; - - factory(Func &&f) : class_factory(std::forward(f)) { } - - // The given class either has no alias or has no separate alias factory; - // this always constructs the class itself. If the class is registered with an alias - // type and an alias instance is needed (i.e. because the final type is a Python class - // inheriting from the C++ type) the returned value needs to either already be an alias - // instance, or the alias needs to be constructible from a `Class &&` argument. - template - void execute(Class &cl, const Extra &...extra) && { - #if defined(PYBIND11_CPP14) - cl.def("__init__", [func = std::move(class_factory)] - #else - auto &func = class_factory; - cl.def("__init__", [func] - #endif - (value_and_holder &v_h, Args... args) { - construct(v_h, func(std::forward(args)...), - Py_TYPE(v_h.inst) != v_h.type->type); - }, is_new_style_constructor(), extra...); - } -}; - -// Specialization for py::init(Func, AliasFunc) -template -struct factory { - static_assert(sizeof...(CArgs) == sizeof...(AArgs), - "pybind11::init(class_factory, alias_factory): class and alias factories " - "must have identical argument signatures"); - static_assert(all_of...>::value, - "pybind11::init(class_factory, alias_factory): class and alias factories " - "must have identical argument signatures"); - - remove_reference_t class_factory; - remove_reference_t alias_factory; - - factory(CFunc &&c, AFunc &&a) - : class_factory(std::forward(c)), alias_factory(std::forward(a)) { } - - // The class factory is called when the `self` type passed to `__init__` is the direct - // class (i.e. not inherited), the alias factory when `self` is a Python-side subtype. - template - void execute(Class &cl, const Extra&... extra) && { - static_assert(Class::has_alias, "The two-argument version of `py::init()` can " - "only be used if the class has an alias"); - #if defined(PYBIND11_CPP14) - cl.def("__init__", [class_func = std::move(class_factory), alias_func = std::move(alias_factory)] - #else - auto &class_func = class_factory; - auto &alias_func = alias_factory; - cl.def("__init__", [class_func, alias_func] - #endif - (value_and_holder &v_h, CArgs... args) { - if (Py_TYPE(v_h.inst) == v_h.type->type) - // If the instance type equals the registered type we don't have inheritance, so - // don't need the alias and can construct using the class function: - construct(v_h, class_func(std::forward(args)...), false); - else - construct(v_h, alias_func(std::forward(args)...), true); - }, is_new_style_constructor(), extra...); - } -}; - -/// Set just the C++ state. Same as `__init__`. -template -void setstate(value_and_holder &v_h, T &&result, bool need_alias) { - construct(v_h, std::forward(result), need_alias); -} - -/// Set both the C++ and Python states -template ::value, int> = 0> -void setstate(value_and_holder &v_h, std::pair &&result, bool need_alias) { - construct(v_h, std::move(result.first), need_alias); - setattr((PyObject *) v_h.inst, "__dict__", result.second); -} - -/// Implementation for py::pickle(GetState, SetState) -template , typename = function_signature_t> -struct pickle_factory; - -template -struct pickle_factory { - static_assert(std::is_same, intrinsic_t>::value, - "The type returned by `__getstate__` must be the same " - "as the argument accepted by `__setstate__`"); - - remove_reference_t get; - remove_reference_t set; - - pickle_factory(Get get, Set set) - : get(std::forward(get)), set(std::forward(set)) { } - - template - void execute(Class &cl, const Extra &...extra) && { - cl.def("__getstate__", std::move(get)); - -#if defined(PYBIND11_CPP14) - cl.def("__setstate__", [func = std::move(set)] -#else - auto &func = set; - cl.def("__setstate__", [func] -#endif - (value_and_holder &v_h, ArgState state) { - setstate(v_h, func(std::forward(state)), - Py_TYPE(v_h.inst) != v_h.type->type); - }, is_new_style_constructor(), extra...); - } -}; - -PYBIND11_NAMESPACE_END(initimpl) -PYBIND11_NAMESPACE_END(detail) -PYBIND11_NAMESPACE_END(pybind11) diff --git a/spaces/CVPR/LIVE/thrust/thrust/distance.h b/spaces/CVPR/LIVE/thrust/thrust/distance.h deleted file mode 100644 index 6dd4800be7a8975061fb58777d603f13fb0c82b6..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/distance.h +++ /dev/null @@ -1,77 +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 distance.h - * \brief Computes the size of a range - */ - -#pragma once - -#include -#include - -namespace thrust -{ - - -/*! \addtogroup iterators - * \{ - */ - -/*! \p distance finds the distance between \p first and \p last, i.e. the - * number of times that \p first must be incremented until it is equal to - * \p last. - * - * \param first The beginning of an input range of interest. - * \param last The end of an input range of interest. - * \return The distance between the beginning and end of the input range. - * - * \tparam InputIterator is a model of Input Iterator. - * - * \pre If \c InputIterator meets the requirements of random access iterator, \p last shall be reachable from \p first or - * \p first shall be reachable from \p last; otherwise, \p last shall be reachable from \p first. - * - * The following code snippet demonstrates how to use \p distance to compute - * the distance to one iterator from another. - * - * \code - * #include - * #include - * ... - * thrust::device_vector vec(13); - * thrust::device_vector::iterator iter1 = vec.begin(); - * thrust::device_vector::iterator iter2 = iter1 + 7; - * - * int d = thrust::distance(iter1, iter2); - * - * // d is 7 - * \endcode - * - * \see http://www.sgi.com/tech/stl/distance.html - */ -template -inline __host__ __device__ - typename thrust::iterator_traits::difference_type - distance(InputIterator first, InputIterator last); - -/*! \} // end iterators - */ - -} // end thrust - -#include - diff --git a/spaces/CVPR/WALT/mmdet/models/dense_heads/vfnet_head.py b/spaces/CVPR/WALT/mmdet/models/dense_heads/vfnet_head.py deleted file mode 100644 index 7243bb62893839568ec51928d88a5ad40b02a66c..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/dense_heads/vfnet_head.py +++ /dev/null @@ -1,794 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init -from mmcv.ops import DeformConv2d -from mmcv.runner import force_fp32 - -from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator, - build_assigner, build_sampler, distance2bbox, - multi_apply, multiclass_nms, reduce_mean) -from ..builder import HEADS, build_loss -from .atss_head import ATSSHead -from .fcos_head import FCOSHead - -INF = 1e8 - - -@HEADS.register_module() -class VFNetHead(ATSSHead, FCOSHead): - """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object - Detector.`_. - - The VFNet predicts IoU-aware classification scores which mix the - object presence confidence and object localization accuracy as the - detection score. It is built on the FCOS architecture and uses ATSS - for defining positive/negative training examples. The VFNet is trained - with Varifocal Loss and empolys star-shaped deformable convolution to - extract features for a bbox. - - Args: - num_classes (int): Number of categories excluding the background - category. - in_channels (int): Number of channels in the input feature map. - regress_ranges (tuple[tuple[int, int]]): Regress range of multiple - level points. - center_sampling (bool): If true, use center sampling. Default: False. - center_sample_radius (float): Radius of center sampling. Default: 1.5. - sync_num_pos (bool): If true, synchronize the number of positive - examples across GPUs. Default: True - gradient_mul (float): The multiplier to gradients from bbox refinement - and recognition. Default: 0.1. - bbox_norm_type (str): The bbox normalization type, 'reg_denom' or - 'stride'. Default: reg_denom - loss_cls_fl (dict): Config of focal loss. - use_vfl (bool): If true, use varifocal loss for training. - Default: True. - loss_cls (dict): Config of varifocal loss. - loss_bbox (dict): Config of localization loss, GIoU Loss. - loss_bbox (dict): Config of localization refinement loss, GIoU Loss. - norm_cfg (dict): dictionary to construct and config norm layer. - Default: norm_cfg=dict(type='GN', num_groups=32, - requires_grad=True). - use_atss (bool): If true, use ATSS to define positive/negative - examples. Default: True. - anchor_generator (dict): Config of anchor generator for ATSS. - - Example: - >>> self = VFNetHead(11, 7) - >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] - >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats) - >>> assert len(cls_score) == len(self.scales) - """ # noqa: E501 - - def __init__(self, - num_classes, - in_channels, - regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512), - (512, INF)), - center_sampling=False, - center_sample_radius=1.5, - sync_num_pos=True, - gradient_mul=0.1, - bbox_norm_type='reg_denom', - loss_cls_fl=dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - use_vfl=True, - loss_cls=dict( - type='VarifocalLoss', - use_sigmoid=True, - alpha=0.75, - gamma=2.0, - iou_weighted=True, - loss_weight=1.0), - loss_bbox=dict(type='GIoULoss', loss_weight=1.5), - loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0), - norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), - use_atss=True, - anchor_generator=dict( - type='AnchorGenerator', - ratios=[1.0], - octave_base_scale=8, - scales_per_octave=1, - center_offset=0.0, - strides=[8, 16, 32, 64, 128]), - **kwargs): - # dcn base offsets, adapted from reppoints_head.py - self.num_dconv_points = 9 - self.dcn_kernel = int(np.sqrt(self.num_dconv_points)) - self.dcn_pad = int((self.dcn_kernel - 1) / 2) - dcn_base = np.arange(-self.dcn_pad, - self.dcn_pad + 1).astype(np.float64) - dcn_base_y = np.repeat(dcn_base, self.dcn_kernel) - dcn_base_x = np.tile(dcn_base, self.dcn_kernel) - dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape( - (-1)) - self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) - - super(FCOSHead, self).__init__( - num_classes, in_channels, norm_cfg=norm_cfg, **kwargs) - self.regress_ranges = regress_ranges - self.reg_denoms = [ - regress_range[-1] for regress_range in regress_ranges - ] - self.reg_denoms[-1] = self.reg_denoms[-2] * 2 - self.center_sampling = center_sampling - self.center_sample_radius = center_sample_radius - self.sync_num_pos = sync_num_pos - self.bbox_norm_type = bbox_norm_type - self.gradient_mul = gradient_mul - self.use_vfl = use_vfl - if self.use_vfl: - self.loss_cls = build_loss(loss_cls) - else: - self.loss_cls = build_loss(loss_cls_fl) - self.loss_bbox = build_loss(loss_bbox) - self.loss_bbox_refine = build_loss(loss_bbox_refine) - - # for getting ATSS targets - self.use_atss = use_atss - self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) - self.anchor_generator = build_anchor_generator(anchor_generator) - self.anchor_center_offset = anchor_generator['center_offset'] - self.num_anchors = self.anchor_generator.num_base_anchors[0] - self.sampling = False - if self.train_cfg: - self.assigner = build_assigner(self.train_cfg.assigner) - sampler_cfg = dict(type='PseudoSampler') - self.sampler = build_sampler(sampler_cfg, context=self) - - def _init_layers(self): - """Initialize layers of the head.""" - super(FCOSHead, self)._init_cls_convs() - super(FCOSHead, self)._init_reg_convs() - self.relu = nn.ReLU(inplace=True) - self.vfnet_reg_conv = ConvModule( - self.feat_channels, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - bias=self.conv_bias) - self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) - self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) - - self.vfnet_reg_refine_dconv = DeformConv2d( - self.feat_channels, - self.feat_channels, - self.dcn_kernel, - 1, - padding=self.dcn_pad) - self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1) - self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides]) - - self.vfnet_cls_dconv = DeformConv2d( - self.feat_channels, - self.feat_channels, - self.dcn_kernel, - 1, - padding=self.dcn_pad) - self.vfnet_cls = nn.Conv2d( - self.feat_channels, self.cls_out_channels, 3, padding=1) - - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - normal_init(self.vfnet_reg_conv.conv, std=0.01) - normal_init(self.vfnet_reg, std=0.01) - normal_init(self.vfnet_reg_refine_dconv, std=0.01) - normal_init(self.vfnet_reg_refine, std=0.01) - normal_init(self.vfnet_cls_dconv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.vfnet_cls, std=0.01, bias=bias_cls) - - def forward(self, feats): - """Forward features from the upstream network. - - Args: - feats (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - tuple: - cls_scores (list[Tensor]): Box iou-aware scores for each scale - level, each is a 4D-tensor, the channel number is - num_points * num_classes. - bbox_preds (list[Tensor]): Box offsets for each - scale level, each is a 4D-tensor, the channel number is - num_points * 4. - bbox_preds_refine (list[Tensor]): Refined Box offsets for - each scale level, each is a 4D-tensor, the channel - number is num_points * 4. - """ - return multi_apply(self.forward_single, feats, self.scales, - self.scales_refine, self.strides, self.reg_denoms) - - def forward_single(self, x, scale, scale_refine, stride, reg_denom): - """Forward features of a single scale level. - - Args: - x (Tensor): FPN feature maps of the specified stride. - scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize - the bbox prediction. - scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to - resize the refined bbox prediction. - stride (int): The corresponding stride for feature maps, - used to normalize the bbox prediction when - bbox_norm_type = 'stride'. - reg_denom (int): The corresponding regression range for feature - maps, only used to normalize the bbox prediction when - bbox_norm_type = 'reg_denom'. - - Returns: - tuple: iou-aware cls scores for each box, bbox predictions and - refined bbox predictions of input feature maps. - """ - cls_feat = x - reg_feat = x - - for cls_layer in self.cls_convs: - cls_feat = cls_layer(cls_feat) - - for reg_layer in self.reg_convs: - reg_feat = reg_layer(reg_feat) - - # predict the bbox_pred of different level - reg_feat_init = self.vfnet_reg_conv(reg_feat) - if self.bbox_norm_type == 'reg_denom': - bbox_pred = scale( - self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom - elif self.bbox_norm_type == 'stride': - bbox_pred = scale( - self.vfnet_reg(reg_feat_init)).float().exp() * stride - else: - raise NotImplementedError - - # compute star deformable convolution offsets - # converting dcn_offset to reg_feat.dtype thus VFNet can be - # trained with FP16 - dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul, - stride).to(reg_feat.dtype) - - # refine the bbox_pred - reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset)) - bbox_pred_refine = scale_refine( - self.vfnet_reg_refine(reg_feat)).float().exp() - bbox_pred_refine = bbox_pred_refine * bbox_pred.detach() - - # predict the iou-aware cls score - cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset)) - cls_score = self.vfnet_cls(cls_feat) - - return cls_score, bbox_pred, bbox_pred_refine - - def star_dcn_offset(self, bbox_pred, gradient_mul, stride): - """Compute the star deformable conv offsets. - - Args: - bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b). - gradient_mul (float): Gradient multiplier. - stride (int): The corresponding stride for feature maps, - used to project the bbox onto the feature map. - - Returns: - dcn_offsets (Tensor): The offsets for deformable convolution. - """ - dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred) - bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \ - gradient_mul * bbox_pred - # map to the feature map scale - bbox_pred_grad_mul = bbox_pred_grad_mul / stride - N, C, H, W = bbox_pred.size() - - x1 = bbox_pred_grad_mul[:, 0, :, :] - y1 = bbox_pred_grad_mul[:, 1, :, :] - x2 = bbox_pred_grad_mul[:, 2, :, :] - y2 = bbox_pred_grad_mul[:, 3, :, :] - bbox_pred_grad_mul_offset = bbox_pred.new_zeros( - N, 2 * self.num_dconv_points, H, W) - bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1 - bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1 - bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1 - bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1 - bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2 - bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1 - bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2 - bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2 - bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1 - bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2 - bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2 - bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2 - dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset - - return dcn_offset - - @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) - def loss(self, - cls_scores, - bbox_preds, - bbox_preds_refine, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """Compute loss of the head. - - Args: - cls_scores (list[Tensor]): Box iou-aware scores for each scale - level, each is a 4D-tensor, the channel number is - num_points * num_classes. - bbox_preds (list[Tensor]): Box offsets for each - scale level, each is a 4D-tensor, the channel number is - num_points * 4. - bbox_preds_refine (list[Tensor]): Refined Box offsets for - each scale level, each is a 4D-tensor, the channel - number is num_points * 4. - 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: - dict[str, Tensor]: A dictionary of loss components. - """ - assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine) - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, - bbox_preds[0].device) - labels, label_weights, bbox_targets, bbox_weights = self.get_targets( - cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas, - gt_bboxes_ignore) - - num_imgs = cls_scores[0].size(0) - # flatten cls_scores, bbox_preds and bbox_preds_refine - flatten_cls_scores = [ - cls_score.permute(0, 2, 3, - 1).reshape(-1, - self.cls_out_channels).contiguous() - for cls_score in cls_scores - ] - flatten_bbox_preds = [ - bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous() - for bbox_pred in bbox_preds - ] - flatten_bbox_preds_refine = [ - bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous() - for bbox_pred_refine in bbox_preds_refine - ] - flatten_cls_scores = torch.cat(flatten_cls_scores) - flatten_bbox_preds = torch.cat(flatten_bbox_preds) - flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine) - flatten_labels = torch.cat(labels) - flatten_bbox_targets = torch.cat(bbox_targets) - # repeat points to align with bbox_preds - flatten_points = torch.cat( - [points.repeat(num_imgs, 1) for points in all_level_points]) - - # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes - bg_class_ind = self.num_classes - pos_inds = torch.where( - ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0] - num_pos = len(pos_inds) - - pos_bbox_preds = flatten_bbox_preds[pos_inds] - pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds] - pos_labels = flatten_labels[pos_inds] - - # sync num_pos across all gpus - if self.sync_num_pos: - num_pos_avg_per_gpu = reduce_mean( - pos_inds.new_tensor(num_pos).float()).item() - num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0) - else: - num_pos_avg_per_gpu = num_pos - - if num_pos > 0: - pos_bbox_targets = flatten_bbox_targets[pos_inds] - pos_points = flatten_points[pos_inds] - - pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) - pos_decoded_target_preds = distance2bbox(pos_points, - pos_bbox_targets) - iou_targets_ini = bbox_overlaps( - pos_decoded_bbox_preds, - pos_decoded_target_preds.detach(), - is_aligned=True).clamp(min=1e-6) - bbox_weights_ini = iou_targets_ini.clone().detach() - iou_targets_ini_avg_per_gpu = reduce_mean( - bbox_weights_ini.sum()).item() - bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0) - loss_bbox = self.loss_bbox( - pos_decoded_bbox_preds, - pos_decoded_target_preds.detach(), - weight=bbox_weights_ini, - avg_factor=bbox_avg_factor_ini) - - pos_decoded_bbox_preds_refine = \ - distance2bbox(pos_points, pos_bbox_preds_refine) - iou_targets_rf = bbox_overlaps( - pos_decoded_bbox_preds_refine, - pos_decoded_target_preds.detach(), - is_aligned=True).clamp(min=1e-6) - bbox_weights_rf = iou_targets_rf.clone().detach() - iou_targets_rf_avg_per_gpu = reduce_mean( - bbox_weights_rf.sum()).item() - bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0) - loss_bbox_refine = self.loss_bbox_refine( - pos_decoded_bbox_preds_refine, - pos_decoded_target_preds.detach(), - weight=bbox_weights_rf, - avg_factor=bbox_avg_factor_rf) - - # build IoU-aware cls_score targets - if self.use_vfl: - pos_ious = iou_targets_rf.clone().detach() - cls_iou_targets = torch.zeros_like(flatten_cls_scores) - cls_iou_targets[pos_inds, pos_labels] = pos_ious - else: - loss_bbox = pos_bbox_preds.sum() * 0 - loss_bbox_refine = pos_bbox_preds_refine.sum() * 0 - if self.use_vfl: - cls_iou_targets = torch.zeros_like(flatten_cls_scores) - - if self.use_vfl: - loss_cls = self.loss_cls( - flatten_cls_scores, - cls_iou_targets, - avg_factor=num_pos_avg_per_gpu) - else: - loss_cls = self.loss_cls( - flatten_cls_scores, - flatten_labels, - weight=label_weights, - avg_factor=num_pos_avg_per_gpu) - - return dict( - loss_cls=loss_cls, - loss_bbox=loss_bbox, - loss_bbox_rf=loss_bbox_refine) - - @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) - def get_bboxes(self, - cls_scores, - bbox_preds, - bbox_preds_refine, - img_metas, - cfg=None, - rescale=None, - with_nms=True): - """Transform network outputs for a batch into bbox predictions. - - Args: - cls_scores (list[Tensor]): Box iou-aware scores for each scale - level with shape (N, num_points * num_classes, H, W). - bbox_preds (list[Tensor]): Box offsets for each scale - level with shape (N, num_points * 4, H, W). - bbox_preds_refine (list[Tensor]): Refined Box offsets for - each scale level with shape (N, num_points * 4, H, W). - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - cfg (mmcv.Config): Test / postprocessing configuration, - if None, test_cfg would be used. Default: None. - rescale (bool): If True, return boxes in original image space. - Default: False. - with_nms (bool): If True, do nms before returning boxes. - Default: True. - - Returns: - list[tuple[Tensor, Tensor]]: Each item in result_list is 2-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 a - (n,) tensor where each item is the predicted class label of - the corresponding box. - """ - assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine) - num_levels = len(cls_scores) - - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, - bbox_preds[0].device) - result_list = [] - 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_refine[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'] - det_bboxes = self._get_bboxes_single(cls_score_list, - bbox_pred_list, mlvl_points, - img_shape, scale_factor, cfg, - rescale, with_nms) - result_list.append(det_bboxes) - return result_list - - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - mlvl_points, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): - """Transform outputs for a single batch item into bbox predictions. - - Args: - cls_scores (list[Tensor]): Box iou-aware scores for a single scale - level with shape (num_points * num_classes, H, W). - bbox_preds (list[Tensor]): Box offsets for a single scale - level with shape (num_points * 4, H, W). - mlvl_points (list[Tensor]): Box reference for a single scale level - with shape (num_total_points, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arrange as - (w_scale, h_scale, w_scale, h_scale). - 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. - with_nms (bool): If True, do nms before returning boxes. - Default: True. - - Returns: - tuple(Tensor): - det_bboxes (Tensor): BBox predictions in shape (n, 5), 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. - det_labels (Tensor): A (n,) tensor where each item is the - predicted class label of the corresponding box. - """ - cfg = self.test_cfg if cfg is None else cfg - assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) - mlvl_bboxes = [] - mlvl_scores = [] - for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds, - mlvl_points): - assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - scores = cls_score.permute(1, 2, 0).reshape( - -1, self.cls_out_channels).contiguous().sigmoid() - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous() - - nms_pre = cfg.get('nms_pre', -1) - if 0 < nms_pre < scores.shape[0]: - max_scores, _ = scores.max(dim=1) - _, topk_inds = max_scores.topk(nms_pre) - points = points[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape) - mlvl_bboxes.append(bboxes) - mlvl_scores.append(scores) - mlvl_bboxes = torch.cat(mlvl_bboxes) - if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 - # BG cat_id: num_class - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - if with_nms: - det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - return det_bboxes, det_labels - else: - return mlvl_bboxes, mlvl_scores - - def _get_points_single(self, - featmap_size, - stride, - dtype, - device, - flatten=False): - """Get points according to feature map sizes.""" - h, w = featmap_size - x_range = torch.arange( - 0, w * stride, stride, dtype=dtype, device=device) - y_range = torch.arange( - 0, h * stride, stride, dtype=dtype, device=device) - y, x = torch.meshgrid(y_range, x_range) - # to be compatible with anchor points in ATSS - if self.use_atss: - points = torch.stack( - (x.reshape(-1), y.reshape(-1)), dim=-1) + \ - stride * self.anchor_center_offset - else: - points = torch.stack( - (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2 - return points - - def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels, - img_metas, gt_bboxes_ignore): - """A wrapper for computing ATSS and FCOS targets for points in multiple - images. - - Args: - cls_scores (list[Tensor]): Box iou-aware scores for each scale - level with shape (N, num_points * num_classes, H, W). - mlvl_points (list[Tensor]): Points of each fpn level, each has - shape (num_points, 2). - gt_bboxes (list[Tensor]): Ground truth bboxes of each image, - each has shape (num_gt, 4). - gt_labels (list[Tensor]): Ground truth labels of each box, - each has shape (num_gt,). - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be - ignored, shape (num_ignored_gts, 4). - - Returns: - tuple: - labels_list (list[Tensor]): Labels of each level. - label_weights (Tensor/None): Label weights of all levels. - bbox_targets_list (list[Tensor]): Regression targets of each - level, (l, t, r, b). - bbox_weights (Tensor/None): Bbox weights of all levels. - """ - if self.use_atss: - return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes, - gt_labels, img_metas, - gt_bboxes_ignore) - else: - self.norm_on_bbox = False - return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels) - - def _get_target_single(self, *args, **kwargs): - """Avoid ambiguity in multiple inheritance.""" - if self.use_atss: - return ATSSHead._get_target_single(self, *args, **kwargs) - else: - return FCOSHead._get_target_single(self, *args, **kwargs) - - def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list): - """Compute FCOS regression and classification targets for points in - multiple images. - - Args: - points (list[Tensor]): Points of each fpn level, each has shape - (num_points, 2). - gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, - each has shape (num_gt, 4). - gt_labels_list (list[Tensor]): Ground truth labels of each box, - each has shape (num_gt,). - - Returns: - tuple: - labels (list[Tensor]): Labels of each level. - label_weights: None, to be compatible with ATSS targets. - bbox_targets (list[Tensor]): BBox targets of each level. - bbox_weights: None, to be compatible with ATSS targets. - """ - labels, bbox_targets = FCOSHead.get_targets(self, points, - gt_bboxes_list, - gt_labels_list) - label_weights = None - bbox_weights = None - return labels, label_weights, bbox_targets, bbox_weights - - def get_atss_targets(self, - cls_scores, - mlvl_points, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """A wrapper for computing ATSS targets for points in multiple images. - - Args: - cls_scores (list[Tensor]): Box iou-aware scores for each scale - level with shape (N, num_points * num_classes, H, W). - mlvl_points (list[Tensor]): Points of each fpn level, each has - shape (num_points, 2). - gt_bboxes (list[Tensor]): Ground truth bboxes of each image, - each has shape (num_gt, 4). - gt_labels (list[Tensor]): Ground truth labels of each box, - each has shape (num_gt,). - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be - ignored, shape (num_ignored_gts, 4). Default: None. - - Returns: - tuple: - labels_list (list[Tensor]): Labels of each level. - label_weights (Tensor): Label weights of all levels. - bbox_targets_list (list[Tensor]): Regression targets of each - level, (l, t, r, b). - bbox_weights (Tensor): Bbox weights of all levels. - """ - 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 = ATSSHead.get_targets( - self, - 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=True) - if cls_reg_targets is None: - return None - - (anchor_list, labels_list, label_weights_list, bbox_targets_list, - bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets - - bbox_targets_list = [ - bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list - ] - - num_imgs = len(img_metas) - # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format - bbox_targets_list = self.transform_bbox_targets( - bbox_targets_list, mlvl_points, num_imgs) - - labels_list = [labels.reshape(-1) for labels in labels_list] - label_weights_list = [ - label_weights.reshape(-1) for label_weights in label_weights_list - ] - bbox_weights_list = [ - bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list - ] - label_weights = torch.cat(label_weights_list) - bbox_weights = torch.cat(bbox_weights_list) - return labels_list, label_weights, bbox_targets_list, bbox_weights - - def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs): - """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format. - - Args: - decoded_bboxes (list[Tensor]): Regression targets of each level, - in the form of (x1, y1, x2, y2). - mlvl_points (list[Tensor]): Points of each fpn level, each has - shape (num_points, 2). - num_imgs (int): the number of images in a batch. - - Returns: - bbox_targets (list[Tensor]): Regression targets of each level in - the form of (l, t, r, b). - """ - # TODO: Re-implemented in Class PointCoder - assert len(decoded_bboxes) == len(mlvl_points) - num_levels = len(decoded_bboxes) - mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points] - bbox_targets = [] - for i in range(num_levels): - bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i]) - bbox_targets.append(bbox_target) - - return bbox_targets - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - """Override the method in the parent class to avoid changing para's - name.""" - pass diff --git a/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/__init__.py b/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/__init__.py deleted file mode 100644 index f3b008fb13c5e8a84b1b785056e8c4f5226dc976..0000000000000000000000000000000000000000 --- a/spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ - -from .dataset import Dataset, TensorDataset, ConcatDataset -from .dataloader import DataLoader diff --git a/spaces/CVPR/regionclip-demo/detectron2/data/__init__.py b/spaces/CVPR/regionclip-demo/detectron2/data/__init__.py deleted file mode 100644 index 21c83f8cbd7a9388b452372f0444e78a54a33495..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/data/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from . import transforms # isort:skip - -from .build import ( - build_batch_data_loader, - build_detection_test_loader, - build_detection_train_loader, - get_detection_dataset_dicts, - load_proposals_into_dataset, - print_instances_class_histogram, -) -from .catalog import DatasetCatalog, MetadataCatalog, Metadata -from .common import DatasetFromList, MapDataset -from .dataset_mapper import DatasetMapper - -# ensure the builtin datasets are registered -from . import datasets, samplers # isort:skip - -__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/spaces/ChrisPreston/diff-svc_minato_aqua/modules/vocoders/__init__.py b/spaces/ChrisPreston/diff-svc_minato_aqua/modules/vocoders/__init__.py deleted file mode 100644 index 6b4cbcab246907e9fc1b96b62c10d15f9a53a1b4..0000000000000000000000000000000000000000 --- a/spaces/ChrisPreston/diff-svc_minato_aqua/modules/vocoders/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from modules.vocoders import nsf_hifigan diff --git a/spaces/CofAI/chat/client/css/dropdown.css b/spaces/CofAI/chat/client/css/dropdown.css deleted file mode 100644 index 302e911e84d171c55384732f759a79ce195abca5..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat/client/css/dropdown.css +++ /dev/null @@ -1,10 +0,0 @@ -.dropdown { - border: 1px solid var(--conversations); -} - -@media screen and (max-width: 990px) { - .dropdown { - padding: 4px 8px; - font-size: 0.75rem; - } -} diff --git a/spaces/Covert1107/sd-diffusers-webui/modules/safe.py b/spaces/Covert1107/sd-diffusers-webui/modules/safe.py deleted file mode 100644 index 532c7dab3f60f5a68b068299d2adc0b776a423f9..0000000000000000000000000000000000000000 --- a/spaces/Covert1107/sd-diffusers-webui/modules/safe.py +++ /dev/null @@ -1,188 +0,0 @@ -# this code is adapted from the script contributed by anon from /h/ -# modified, from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/6cff4401824299a983c8e13424018efc347b4a2b/modules/safe.py - -import io -import pickle -import collections -import sys -import traceback - -import torch -import numpy -import _codecs -import zipfile -import re - - -# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage -TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage - - -def encode(*args): - out = _codecs.encode(*args) - return out - - -class RestrictedUnpickler(pickle.Unpickler): - extra_handler = None - - def persistent_load(self, saved_id): - assert saved_id[0] == 'storage' - return TypedStorage() - - def find_class(self, module, name): - if self.extra_handler is not None: - res = self.extra_handler(module, name) - if res is not None: - return res - - if module == 'collections' and name == 'OrderedDict': - return getattr(collections, name) - if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']: - return getattr(torch._utils, name) - if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']: - return getattr(torch, name) - if module == 'torch.nn.modules.container' and name in ['ParameterDict']: - return getattr(torch.nn.modules.container, name) - if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']: - return getattr(numpy.core.multiarray, name) - if module == 'numpy' and name in ['dtype', 'ndarray']: - return getattr(numpy, name) - if module == '_codecs' and name == 'encode': - return encode - if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': - import pytorch_lightning.callbacks - return pytorch_lightning.callbacks.model_checkpoint - if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': - import pytorch_lightning.callbacks.model_checkpoint - return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint - if module == "__builtin__" and name == 'set': - return set - - # Forbid everything else. - raise Exception(f"global '{module}/{name}' is forbidden") - - -# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/' -allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$") -data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$") - -def check_zip_filenames(filename, names): - for name in names: - if allowed_zip_names_re.match(name): - continue - - raise Exception(f"bad file inside {filename}: {name}") - - -def check_pt(filename, extra_handler): - try: - - # new pytorch format is a zip file - with zipfile.ZipFile(filename) as z: - check_zip_filenames(filename, z.namelist()) - - # find filename of data.pkl in zip file: '/data.pkl' - data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)] - if len(data_pkl_filenames) == 0: - raise Exception(f"data.pkl not found in {filename}") - if len(data_pkl_filenames) > 1: - raise Exception(f"Multiple data.pkl found in {filename}") - with z.open(data_pkl_filenames[0]) as file: - unpickler = RestrictedUnpickler(file) - unpickler.extra_handler = extra_handler - unpickler.load() - - except zipfile.BadZipfile: - - # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle - with open(filename, "rb") as file: - unpickler = RestrictedUnpickler(file) - unpickler.extra_handler = extra_handler - for i in range(5): - unpickler.load() - - -def load(filename, *args, **kwargs): - return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs) - - -def load_with_extra(filename, extra_handler=None, *args, **kwargs): - """ - this function is intended to be used by extensions that want to load models with - some extra classes in them that the usual unpickler would find suspicious. - - Use the extra_handler argument to specify a function that takes module and field name as text, - and returns that field's value: - - ```python - def extra(module, name): - if module == 'collections' and name == 'OrderedDict': - return collections.OrderedDict - - return None - - safe.load_with_extra('model.pt', extra_handler=extra) - ``` - - The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is - definitely unsafe. - """ - - try: - check_pt(filename, extra_handler) - - except pickle.UnpicklingError: - print(f"Error verifying pickled file from {filename}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - print("The file is most likely corrupted.", file=sys.stderr) - return None - - except Exception: - print(f"Error verifying pickled file from {filename}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) - print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr) - return None - - return unsafe_torch_load(filename, *args, **kwargs) - - -class Extra: - """ - A class for temporarily setting the global handler for when you can't explicitly call load_with_extra - (because it's not your code making the torch.load call). The intended use is like this: - -``` -import torch -from modules import safe - -def handler(module, name): - if module == 'torch' and name in ['float64', 'float16']: - return getattr(torch, name) - - return None - -with safe.Extra(handler): - x = torch.load('model.pt') -``` - """ - - def __init__(self, handler): - self.handler = handler - - def __enter__(self): - global global_extra_handler - - assert global_extra_handler is None, 'already inside an Extra() block' - global_extra_handler = self.handler - - def __exit__(self, exc_type, exc_val, exc_tb): - global global_extra_handler - - global_extra_handler = None - - -unsafe_torch_load = torch.load -torch.load = load -global_extra_handler = None diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/pan.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/pan.py deleted file mode 100644 index e9703e271b3987ff380e5222232592678cafef61..0000000000000000000000000000000000000000 --- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/pan.py +++ /dev/null @@ -1,177 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - - -class FPA(nn.Module): - def __init__(self, channels=2048): - """ - Feature Pyramid Attention - :type channels: int - """ - super(FPA, self).__init__() - channels_mid = int(channels / 4) - - self.channels_cond = channels - - # Master branch - self.conv_master = nn.Conv2d(self.channels_cond, channels, kernel_size=1, bias=False) - self.bn_master = nn.BatchNorm2d(channels) - - # Global pooling branch - self.conv_gpb = nn.Conv2d(self.channels_cond, channels, kernel_size=1, bias=False) - #self.bn_gpb = nn.BatchNorm2d(channels) - - # C333 because of the shape of last feature maps is (16, 16). - self.conv7x7_1 = nn.Conv2d(self.channels_cond, channels_mid, kernel_size=(7, 7), stride=2, padding=3, bias=False) - self.bn1_1 = nn.BatchNorm2d(channels_mid) - self.conv5x5_1 = nn.Conv2d(channels_mid, channels_mid, kernel_size=(5, 5), stride=2, padding=2, bias=False) - self.bn2_1 = nn.BatchNorm2d(channels_mid) - self.conv3x3_1 = nn.Conv2d(channels_mid, channels_mid, kernel_size=(3, 3), stride=2, padding=1, bias=False) - self.bn3_1 = nn.BatchNorm2d(channels_mid) - - self.conv7x7_2 = nn.Conv2d(channels_mid, channels_mid, kernel_size=(7, 7), stride=1, padding=3, bias=False) - self.bn1_2 = nn.BatchNorm2d(channels_mid) - self.conv5x5_2 = nn.Conv2d(channels_mid, channels_mid, kernel_size=(5, 5), stride=1, padding=2, bias=False) - self.bn2_2 = nn.BatchNorm2d(channels_mid) - self.conv3x3_2 = nn.Conv2d(channels_mid, channels_mid, kernel_size=(3, 3), stride=1, padding=1, bias=False) - self.bn3_2 = nn.BatchNorm2d(channels_mid) - - self.bn_upsample_1 = nn.BatchNorm2d(channels) - self.conv1x1_up1 = nn.Conv2d(channels_mid, channels, kernel_size=(1, 1), stride=1, padding=0, bias=False) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """ - :param x: Shape: [b, 2048, h, w] - :return: out: Feature maps. Shape: [b, 2048, h, w] - """ - # Master branch - x_master = self.conv_master(x) - x_master = self.bn_master(x_master) - - # Global pooling branch - x_gpb = nn.AvgPool2d(x.shape[2:])(x).view(x.shape[0], self.channels_cond, 1, 1) - x_gpb = self.conv_gpb(x_gpb) - #x_gpb = self.bn_gpb(x_gpb) - - # Branch 1 - x1_1 = self.conv7x7_1(x) - x1_1 = self.bn1_1(x1_1) - x1_1 = self.relu(x1_1) - x1_2 = self.conv7x7_2(x1_1) - x1_2 = self.bn1_2(x1_2) - - # Branch 2 - x2_1 = self.conv5x5_1(x1_1) - x2_1 = self.bn2_1(x2_1) - x2_1 = self.relu(x2_1) - x2_2 = self.conv5x5_2(x2_1) - x2_2 = self.bn2_2(x2_2) - - # Branch 3 - x3_1 = self.conv3x3_1(x2_1) - x3_1 = self.bn3_1(x3_1) - x3_1 = self.relu(x3_1) - x3_2 = self.conv3x3_2(x3_1) - x3_2 = self.bn3_2(x3_2) - - # Merge branch 1 and 2 - x3_upsample = F.upsample(x3_2, size=x2_2.shape[-2:], - mode='bilinear', align_corners=False) - - x2_merge = self.relu(x2_2 + x3_upsample) - - x2_upsample = F.upsample(x2_merge, size=x1_2.shape[-2:], - mode='bilinear', align_corners=False) - x1_merge = self.relu(x1_2 + x2_upsample) - - x1_merge_upsample = F.upsample(x1_merge, size=x_master.shape[-2:], - mode='bilinear', align_corners=False) - x1_merge_upsample_ch = self.relu(self.bn_upsample_1(self.conv1x1_up1(x1_merge_upsample))) - x_master = x_master * x1_merge_upsample_ch - # - out = self.relu(x_master + x_gpb) - - return out - - -class GAU(nn.Module): - def __init__(self, channels_high, channels_low, upsample=True): - super(GAU, self).__init__() - # Global Attention Upsample - self.upsample = upsample - self.conv3x3 = nn.Conv2d(channels_low, channels_low, kernel_size=3, padding=1, bias=False) - self.bn_low = nn.BatchNorm2d(channels_low) - - self.conv1x1 = nn.Conv2d(channels_high, channels_low, kernel_size=1, padding=0, bias=False) - #self.bn_high = nn.BatchNorm2d(channels_low) - - if upsample: - self.conv_upsample = nn.ConvTranspose2d(channels_high, channels_low, kernel_size=4, stride=2, padding=1, bias=False) - self.bn_upsample = nn.BatchNorm2d(channels_low) - else: - self.conv_reduction = nn.Conv2d(channels_high, channels_low, kernel_size=1, padding=0, bias=False) - self.bn_reduction = nn.BatchNorm2d(channels_low) - self.relu = nn.ReLU(inplace=True) - - def forward(self, fms_high, fms_low, fm_mask=None): - """ - Use the high level features with abundant catagory information to weight the low level features with pixel - localization information. In the meantime, we further use mask feature maps with catagory-specific information - to localize the mask position. - :param fms_high: Features of high level. Tensor. - :param fms_low: Features of low level. Tensor. - :param fm_mask: - :return: fms_att_upsample - """ - b, c, h, w = fms_high.shape - - fms_high_gp = nn.AvgPool2d(fms_high.shape[2:])(fms_high).view(len(fms_high), c, 1, 1) - fms_high_gp = self.conv1x1(fms_high_gp) - # fms_high_gp = self.bn_high(fms_high_gp)# arlog, when the spatial size HxW = 1x1, the BN cannot be used. - fms_high_gp = self.relu(fms_high_gp) - - # fms_low_mask = torch.cat([fms_low, fm_mask], dim=1) - fms_low_mask = self.conv3x3(fms_low) - fms_low_mask = self.bn_low(fms_low_mask) - - fms_att = fms_low_mask * fms_high_gp - if self.upsample: - out = self.relu( - self.bn_upsample(self.conv_upsample(fms_high)) + fms_att) - else: - out = self.relu( - self.bn_reduction(self.conv_reduction(fms_high)) + fms_att) - return out - - -class PAN(nn.Module): - def __init__(self): - """ - :param blocks: Blocks of the network with reverse sequential. - """ - super(PAN, self).__init__() - channels_blocks = [2048, 1024, 512, 256] - - self.fpa = FPA(channels=channels_blocks[0]) - - self.gau_block1 = GAU(channels_blocks[0], channels_blocks[1]) - self.gau_block2 = GAU(channels_blocks[1], channels_blocks[2]) - self.gau_block3 = GAU(channels_blocks[2], channels_blocks[3]) - self.gau = [self.gau_block1, self.gau_block2, self.gau_block3] - - def forward(self, fms): - """ - :param fms: Feature maps of forward propagation in the network with reverse sequential. shape:[b, c, h, w] - :return: fm_high. [b, 256, h, w] - """ - feats = [] - for i, fm_low in enumerate(fms[::-1]): - if i == 0: - fm_high = self.fpa(fm_low) - else: - fm_high = self.gau[int(i-1)](fm_high, fm_low) - feats.append(fm_high) - feats.reverse() - return tuple(feats) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/streams/memory.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/streams/memory.py deleted file mode 100644 index a6499c13ff36f74d2e217ee996825a13edd6d9fb..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/anyio/streams/memory.py +++ /dev/null @@ -1,279 +0,0 @@ -from __future__ import annotations - -from collections import OrderedDict, deque -from dataclasses import dataclass, field -from types import TracebackType -from typing import Generic, NamedTuple, TypeVar - -from .. import ( - BrokenResourceError, - ClosedResourceError, - EndOfStream, - WouldBlock, - get_cancelled_exc_class, -) -from .._core._compat import DeprecatedAwaitable -from ..abc import Event, ObjectReceiveStream, ObjectSendStream -from ..lowlevel import checkpoint - -T_Item = TypeVar("T_Item") -T_co = TypeVar("T_co", covariant=True) -T_contra = TypeVar("T_contra", contravariant=True) - - -class MemoryObjectStreamStatistics(NamedTuple): - current_buffer_used: int #: number of items stored in the buffer - #: maximum number of items that can be stored on this stream (or :data:`math.inf`) - max_buffer_size: float - open_send_streams: int #: number of unclosed clones of the send stream - open_receive_streams: int #: number of unclosed clones of the receive stream - tasks_waiting_send: int #: number of tasks blocked on :meth:`MemoryObjectSendStream.send` - #: number of tasks blocked on :meth:`MemoryObjectReceiveStream.receive` - tasks_waiting_receive: int - - -@dataclass(eq=False) -class MemoryObjectStreamState(Generic[T_Item]): - max_buffer_size: float = field() - buffer: deque[T_Item] = field(init=False, default_factory=deque) - open_send_channels: int = field(init=False, default=0) - open_receive_channels: int = field(init=False, default=0) - waiting_receivers: OrderedDict[Event, list[T_Item]] = field( - init=False, default_factory=OrderedDict - ) - waiting_senders: OrderedDict[Event, T_Item] = field( - init=False, default_factory=OrderedDict - ) - - def statistics(self) -> MemoryObjectStreamStatistics: - return MemoryObjectStreamStatistics( - len(self.buffer), - self.max_buffer_size, - self.open_send_channels, - self.open_receive_channels, - len(self.waiting_senders), - len(self.waiting_receivers), - ) - - -@dataclass(eq=False) -class MemoryObjectReceiveStream(Generic[T_co], ObjectReceiveStream[T_co]): - _state: MemoryObjectStreamState[T_co] - _closed: bool = field(init=False, default=False) - - def __post_init__(self) -> None: - self._state.open_receive_channels += 1 - - def receive_nowait(self) -> T_co: - """ - Receive the next item if it can be done without waiting. - - :return: the received item - :raises ~anyio.ClosedResourceError: if this send stream has been closed - :raises ~anyio.EndOfStream: if the buffer is empty and this stream has been - closed from the sending end - :raises ~anyio.WouldBlock: if there are no items in the buffer and no tasks - waiting to send - - """ - if self._closed: - raise ClosedResourceError - - if self._state.waiting_senders: - # Get the item from the next sender - send_event, item = self._state.waiting_senders.popitem(last=False) - self._state.buffer.append(item) - send_event.set() - - if self._state.buffer: - return self._state.buffer.popleft() - elif not self._state.open_send_channels: - raise EndOfStream - - raise WouldBlock - - async def receive(self) -> T_co: - await checkpoint() - try: - return self.receive_nowait() - except WouldBlock: - # Add ourselves in the queue - receive_event = Event() - container: list[T_co] = [] - self._state.waiting_receivers[receive_event] = container - - try: - await receive_event.wait() - except get_cancelled_exc_class(): - # Ignore the immediate cancellation if we already received an item, so as not to - # lose it - if not container: - raise - finally: - self._state.waiting_receivers.pop(receive_event, None) - - if container: - return container[0] - else: - raise EndOfStream - - def clone(self) -> MemoryObjectReceiveStream[T_co]: - """ - Create a clone of this receive stream. - - Each clone can be closed separately. Only when all clones have been closed will the - receiving end of the memory stream be considered closed by the sending ends. - - :return: the cloned stream - - """ - if self._closed: - raise ClosedResourceError - - return MemoryObjectReceiveStream(_state=self._state) - - def close(self) -> None: - """ - Close the stream. - - This works the exact same way as :meth:`aclose`, but is provided as a special case for the - benefit of synchronous callbacks. - - """ - if not self._closed: - self._closed = True - self._state.open_receive_channels -= 1 - if self._state.open_receive_channels == 0: - send_events = list(self._state.waiting_senders.keys()) - for event in send_events: - event.set() - - async def aclose(self) -> None: - self.close() - - def statistics(self) -> MemoryObjectStreamStatistics: - """ - Return statistics about the current state of this stream. - - .. versionadded:: 3.0 - """ - return self._state.statistics() - - def __enter__(self) -> MemoryObjectReceiveStream[T_co]: - return self - - def __exit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: TracebackType | None, - ) -> None: - self.close() - - -@dataclass(eq=False) -class MemoryObjectSendStream(Generic[T_contra], ObjectSendStream[T_contra]): - _state: MemoryObjectStreamState[T_contra] - _closed: bool = field(init=False, default=False) - - def __post_init__(self) -> None: - self._state.open_send_channels += 1 - - def send_nowait(self, item: T_contra) -> DeprecatedAwaitable: - """ - Send an item immediately if it can be done without waiting. - - :param item: the item to send - :raises ~anyio.ClosedResourceError: if this send stream has been closed - :raises ~anyio.BrokenResourceError: if the stream has been closed from the - receiving end - :raises ~anyio.WouldBlock: if the buffer is full and there are no tasks waiting - to receive - - """ - if self._closed: - raise ClosedResourceError - if not self._state.open_receive_channels: - raise BrokenResourceError - - if self._state.waiting_receivers: - receive_event, container = self._state.waiting_receivers.popitem(last=False) - container.append(item) - receive_event.set() - elif len(self._state.buffer) < self._state.max_buffer_size: - self._state.buffer.append(item) - else: - raise WouldBlock - - return DeprecatedAwaitable(self.send_nowait) - - async def send(self, item: T_contra) -> None: - await checkpoint() - try: - self.send_nowait(item) - except WouldBlock: - # Wait until there's someone on the receiving end - send_event = Event() - self._state.waiting_senders[send_event] = item - try: - await send_event.wait() - except BaseException: - self._state.waiting_senders.pop(send_event, None) # type: ignore[arg-type] - raise - - if self._state.waiting_senders.pop(send_event, None): # type: ignore[arg-type] - raise BrokenResourceError - - def clone(self) -> MemoryObjectSendStream[T_contra]: - """ - Create a clone of this send stream. - - Each clone can be closed separately. Only when all clones have been closed will the - sending end of the memory stream be considered closed by the receiving ends. - - :return: the cloned stream - - """ - if self._closed: - raise ClosedResourceError - - return MemoryObjectSendStream(_state=self._state) - - def close(self) -> None: - """ - Close the stream. - - This works the exact same way as :meth:`aclose`, but is provided as a special case for the - benefit of synchronous callbacks. - - """ - if not self._closed: - self._closed = True - self._state.open_send_channels -= 1 - if self._state.open_send_channels == 0: - receive_events = list(self._state.waiting_receivers.keys()) - self._state.waiting_receivers.clear() - for event in receive_events: - event.set() - - async def aclose(self) -> None: - self.close() - - def statistics(self) -> MemoryObjectStreamStatistics: - """ - Return statistics about the current state of this stream. - - .. versionadded:: 3.0 - """ - return self._state.statistics() - - def __enter__(self) -> MemoryObjectSendStream[T_contra]: - return self - - def __exit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: TracebackType | None, - ) -> None: - self.close() diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/eexec.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/eexec.py deleted file mode 100644 index cafa312cdaa4696b0624438e06418ade95438441..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/eexec.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -PostScript Type 1 fonts make use of two types of encryption: charstring -encryption and ``eexec`` encryption. Charstring encryption is used for -the charstrings themselves, while ``eexec`` is used to encrypt larger -sections of the font program, such as the ``Private`` and ``CharStrings`` -dictionaries. Despite the different names, the algorithm is the same, -although ``eexec`` encryption uses a fixed initial key R=55665. - -The algorithm uses cipher feedback, meaning that the ciphertext is used -to modify the key. Because of this, the routines in this module return -the new key at the end of the operation. - -""" - -from fontTools.misc.textTools import bytechr, bytesjoin, byteord - - -def _decryptChar(cipher, R): - cipher = byteord(cipher) - plain = ((cipher ^ (R >> 8))) & 0xFF - R = ((cipher + R) * 52845 + 22719) & 0xFFFF - return bytechr(plain), R - - -def _encryptChar(plain, R): - plain = byteord(plain) - cipher = ((plain ^ (R >> 8))) & 0xFF - R = ((cipher + R) * 52845 + 22719) & 0xFFFF - return bytechr(cipher), R - - -def decrypt(cipherstring, R): - r""" - Decrypts a string using the Type 1 encryption algorithm. - - Args: - cipherstring: String of ciphertext. - R: Initial key. - - Returns: - decryptedStr: Plaintext string. - R: Output key for subsequent decryptions. - - Examples:: - - >>> testStr = b"\0\0asdadads asds\265" - >>> decryptedStr, R = decrypt(testStr, 12321) - >>> decryptedStr == b'0d\nh\x15\xe8\xc4\xb2\x15\x1d\x108\x1a<6\xa1' - True - >>> R == 36142 - True - """ - plainList = [] - for cipher in cipherstring: - plain, R = _decryptChar(cipher, R) - plainList.append(plain) - plainstring = bytesjoin(plainList) - return plainstring, int(R) - - -def encrypt(plainstring, R): - r""" - Encrypts a string using the Type 1 encryption algorithm. - - Note that the algorithm as described in the Type 1 specification requires the - plaintext to be prefixed with a number of random bytes. (For ``eexec`` the - number of random bytes is set to 4.) This routine does *not* add the random - prefix to its input. - - Args: - plainstring: String of plaintext. - R: Initial key. - - Returns: - cipherstring: Ciphertext string. - R: Output key for subsequent encryptions. - - Examples:: - - >>> testStr = b"\0\0asdadads asds\265" - >>> decryptedStr, R = decrypt(testStr, 12321) - >>> decryptedStr == b'0d\nh\x15\xe8\xc4\xb2\x15\x1d\x108\x1a<6\xa1' - True - >>> R == 36142 - True - - >>> testStr = b'0d\nh\x15\xe8\xc4\xb2\x15\x1d\x108\x1a<6\xa1' - >>> encryptedStr, R = encrypt(testStr, 12321) - >>> encryptedStr == b"\0\0asdadads asds\265" - True - >>> R == 36142 - True - """ - cipherList = [] - for plain in plainstring: - cipher, R = _encryptChar(plain, R) - cipherList.append(cipher) - cipherstring = bytesjoin(cipherList) - return cipherstring, int(R) - - -def hexString(s): - import binascii - - return binascii.hexlify(s) - - -def deHexString(h): - import binascii - - h = bytesjoin(h.split()) - return binascii.unhexlify(h) - - -if __name__ == "__main__": - import sys - import doctest - - sys.exit(doctest.testmod().failed) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-6a563d90.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-6a563d90.js deleted file mode 100644 index 0b00c00aa785256220b3494780e55f3ed0c00524..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-6a563d90.js +++ /dev/null @@ -1,2 +0,0 @@ -import{T as l}from"./Textbox-1f11d244.js";import"./index-1d65707a.js";/* empty css */import"./Button-f155035a.js";import"./BlockTitle-dee077e8.js";import"./Info-7c6961ef.js";import"./Copy-9f1657c4.js";const a=["static","dynamic"],n=t=>({type:{payload:"string"},description:{payload:"text string"},example_data:t.value||"hello world"});export{l as Component,n as document,a as modes}; -//# sourceMappingURL=index-6a563d90.js.map diff --git 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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_against_stdlib_http.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_against_stdlib_http.py deleted file mode 100644 index d2ee13149d34c9882432cdebfec87dff9814076d..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/test_against_stdlib_http.py +++ /dev/null @@ -1,115 +0,0 @@ -import json -import os.path -import socket -import socketserver -import threading -from contextlib import closing, contextmanager -from http.server import SimpleHTTPRequestHandler -from typing import Callable, Generator -from urllib.request import urlopen - -import h11 - - -@contextmanager -def socket_server( - handler: Callable[..., socketserver.BaseRequestHandler] -) -> Generator[socketserver.TCPServer, None, None]: - httpd = socketserver.TCPServer(("127.0.0.1", 0), handler) - thread = threading.Thread( - target=httpd.serve_forever, kwargs={"poll_interval": 0.01} - ) - thread.daemon = True - try: - thread.start() - yield httpd - finally: - httpd.shutdown() - - -test_file_path = os.path.join(os.path.dirname(__file__), "data/test-file") -with open(test_file_path, "rb") as f: - test_file_data = f.read() - - -class SingleMindedRequestHandler(SimpleHTTPRequestHandler): - def translate_path(self, path: str) -> str: - return test_file_path - - -def test_h11_as_client() -> None: - with socket_server(SingleMindedRequestHandler) as httpd: - with closing(socket.create_connection(httpd.server_address)) as s: - c = h11.Connection(h11.CLIENT) - - s.sendall( - c.send( # type: ignore[arg-type] - h11.Request( - method="GET", target="/foo", headers=[("Host", "localhost")] - ) - ) - ) - s.sendall(c.send(h11.EndOfMessage())) # type: ignore[arg-type] - - data = bytearray() - while True: - event = c.next_event() - print(event) - if event is h11.NEED_DATA: - # Use a small read buffer to make things more challenging - # and exercise more paths :-) - c.receive_data(s.recv(10)) - continue - if type(event) is h11.Response: - assert event.status_code == 200 - if type(event) is h11.Data: - data += event.data - if type(event) is h11.EndOfMessage: - break - assert bytes(data) == test_file_data - - -class H11RequestHandler(socketserver.BaseRequestHandler): - def handle(self) -> None: - with closing(self.request) as s: - c = h11.Connection(h11.SERVER) - request = None - while True: - event = c.next_event() - if event is h11.NEED_DATA: - # Use a small read buffer to make things more challenging - # and exercise more paths :-) - c.receive_data(s.recv(10)) - continue - if type(event) is h11.Request: - request = event - if type(event) is h11.EndOfMessage: - break - assert request is not None - info = json.dumps( - { - "method": request.method.decode("ascii"), - "target": request.target.decode("ascii"), - "headers": { - name.decode("ascii"): value.decode("ascii") - for (name, value) in request.headers - }, - } - ) - s.sendall(c.send(h11.Response(status_code=200, headers=[]))) # type: ignore[arg-type] - s.sendall(c.send(h11.Data(data=info.encode("ascii")))) - s.sendall(c.send(h11.EndOfMessage())) - - -def test_h11_as_server() -> None: - with socket_server(H11RequestHandler) as httpd: - host, port = httpd.server_address - url = "http://{}:{}/some-path".format(host, port) - with closing(urlopen(url)) as f: - assert f.getcode() == 200 - data = f.read() - info = json.loads(data.decode("ascii")) - print(info) - assert info["method"] == "GET" - assert info["target"] == "/some-path" - assert "urllib" in info["headers"]["user-agent"] diff --git a/spaces/Dagfinn1962/stablediffusion-models/app.py b/spaces/Dagfinn1962/stablediffusion-models/app.py deleted file mode 100644 index 8474190cebdad3f7fd91eefe8353fff110248791..0000000000000000000000000000000000000000 --- a/spaces/Dagfinn1962/stablediffusion-models/app.py +++ /dev/null @@ -1,79 +0,0 @@ -import gradio as gr -import os -import sys -from pathlib import Path - -models = [ - {"name": "Stable Diffusion 1.4","url": "stablediffusionapi/juggernaut-xl-v5"}, - - ] - -current_model = models[0] - -text_gen = gr.Interface.load("spaces/daspartho/prompt-extend") - -models2 = [] -for model in models: - model_url = f"models/{model['url']}" - loaded_model = gr.Interface.load(model_url, live=True, preprocess=True) - models2.append(loaded_model) - - -def text_it(inputs, text_gen=text_gen): - return text_gen(inputs) - - -def set_model(current_model_index): - global current_model - current_model = models[current_model_index] - return gr.update(value=f"{current_model['name']}") - - -def send_it(inputs, model_choice): - proc = models2[model_choice] - return proc(inputs) - - -with gr.Blocks (css = 'main.css') as myface: - - gr.HTML("
Your Promt Here
Choose model here
" ) - with gr.Row(): - input_text = gr.Textbox(label=" ",placeholder="1.PROMPT IDEA HERE ! ",lines=4) - # Model selection dropdown - model_name1 = gr.Dropdown( - label=" ", - choices=[m["name"] for m in models], - type="index", - value=current_model["name"], - interactive=True, - - - ) - with gr.Row(): - see_prompts = gr.Button("2. GENERATE YOUR PROMT IDEA HERE!") - run = gr.Button("3. GENERATE THE IMAGE HERE!", varant="primery") - - # - with gr.Row(): - output1 = gr.Image(label="") - output2 = gr.Image(label="") - output3 = gr.Image(label="") - with gr.Row(): - magic1 = gr.Textbox(label="Generated Prompt", lines=2) - magic2 = gr.Textbox(label="Generated Prompt", lines=2) - magic3 = gr.Textbox(label="Generated Prompt", lines=2) - - model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3,]) - - run.click(send_it, inputs=[magic1, model_name1], outputs=[output1]) - run.click(send_it, inputs=[magic2, model_name1], outputs=[output2]) - run.click(send_it, inputs=[magic3, model_name1], outputs=[output3]) - - - see_prompts.click(text_it, inputs=[input_text], outputs=[magic1]) - see_prompts.click(text_it, inputs=[input_text], outputs=[magic2]) - see_prompts.click(text_it, inputs=[input_text], outputs=[magic3]) - - -myface.queue(concurrency_count=200) -myface.launch(inline=True, show_api=False, max_threads=400) \ No newline at end of file diff --git a/spaces/Datasculptor/StyleGAN-NADA/e4e/models/stylegan2/op/upfirdn2d.cpp b/spaces/Datasculptor/StyleGAN-NADA/e4e/models/stylegan2/op/upfirdn2d.cpp deleted file mode 100644 index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/StyleGAN-NADA/e4e/models/stylegan2/op/upfirdn2d.cpp +++ /dev/null @@ -1,23 +0,0 @@ -#include - - -torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1); - -#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, - int up_x, int up_y, int down_x, int down_y, - int pad_x0, int pad_x1, int pad_y0, int pad_y1) { - CHECK_CUDA(input); - CHECK_CUDA(kernel); - - return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); -} \ No newline at end of file diff --git a/spaces/Datatrooper/wine/README.md b/spaces/Datatrooper/wine/README.md deleted file mode 100644 index 0bd7db942836dbc470624b5af19b9fd82346dcf9..0000000000000000000000000000000000000000 --- a/spaces/Datatrooper/wine/README.md +++ /dev/null @@ -1,45 +0,0 @@ ---- -title: Wine -emoji: 🍷 -colorFrom: purple -colorTo: gray -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio`, `streamlit`, or `static` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code). -Path is relative to the root of the repository. - -`models`: _List[string]_ -HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space. -Will be parsed automatically from your code if not specified here. - -`datasets`: _List[string]_ -HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space. -Will be parsed automatically from your code if not specified here. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Dorado607/ChuanhuChatGPT/modules/__init__.py b/spaces/Dorado607/ChuanhuChatGPT/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/DragGan/DragGan/stylegan_human/torch_utils/models.py b/spaces/DragGan/DragGan/stylegan_human/torch_utils/models.py deleted file mode 100644 index 762550239ba6f1e09f4887bf1b27fd421745a589..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan/stylegan_human/torch_utils/models.py +++ /dev/null @@ -1,756 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. - -# https://github.com/rosinality/stylegan2-pytorch/blob/master/model.py - -import math -import random -import functools -import operator - -import torch -from torch import nn -from torch.nn import functional as F -import torch.nn.init as init -from torch.autograd import Function - -from .op_edit import FusedLeakyReLU, fused_leaky_relu, upfirdn2d - - -class PixelNorm(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, input): - return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) - - -def make_kernel(k): - k = torch.tensor(k, dtype=torch.float32) - if k.ndim == 1: - k = k[None, :] * k[:, None] - k /= k.sum() - return k - - -class Upsample(nn.Module): - def __init__(self, kernel, factor=2): - super().__init__() - - self.factor = factor - kernel = make_kernel(kernel) * (factor ** 2) - self.register_buffer("kernel", kernel) - - p = kernel.shape[0] - factor - - pad0 = (p + 1) // 2 + factor - 1 - pad1 = p // 2 - - self.pad = (pad0, pad1) - - def forward(self, input): - out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) - return out - - -class Downsample(nn.Module): - def __init__(self, kernel, factor=2): - super().__init__() - - self.factor = factor - kernel = make_kernel(kernel) - self.register_buffer("kernel", kernel) - - p = kernel.shape[0] - factor - - pad0 = (p + 1) // 2 - pad1 = p // 2 - - self.pad = (pad0, pad1) - - def forward(self, input): - out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) - return out - - -class Blur(nn.Module): - def __init__(self, kernel, pad, upsample_factor=1): - super().__init__() - - kernel = make_kernel(kernel) - - if upsample_factor > 1: - kernel = kernel * (upsample_factor ** 2) - - self.register_buffer("kernel", kernel) - - self.pad = pad - - def forward(self, input): - out = upfirdn2d(input, self.kernel, pad=self.pad) - return out - - -class EqualConv2d(nn.Module): - def __init__( - self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True - ): - super().__init__() - - self.weight = nn.Parameter( - torch.randn(out_channel, in_channel, kernel_size, kernel_size) - ) - self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) - - self.stride = stride - self.padding = padding - - if bias: - self.bias = nn.Parameter(torch.zeros(out_channel)) - - else: - self.bias = None - - def forward(self, input): - out = F.conv2d( - input, - self.weight * self.scale, - bias=self.bias, - stride=self.stride, - padding=self.padding, - ) - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]}," - f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})" - ) - - -class EqualLinear(nn.Module): - def __init__( - self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None - ): - super().__init__() - - self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) - - if bias: - self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) - else: - self.bias = None - - self.activation = activation - - self.scale = (1 / math.sqrt(in_dim)) * lr_mul - self.lr_mul = lr_mul - - def forward(self, input): - if self.activation: - out = F.linear(input, self.weight * self.scale) - out = fused_leaky_relu(out, self.bias * self.lr_mul) - else: - out = F.linear( - input, self.weight * self.scale, bias=self.bias * self.lr_mul - ) - return out - - def __repr__(self): - return ( - f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})" - ) - - -class ScaledLeakyReLU(nn.Module): - def __init__(self, negative_slope=0.2): - super().__init__() - self.negative_slope = negative_slope - - def forward(self, input): - out = F.leaky_relu(input, negative_slope=self.negative_slope) - return out * math.sqrt(2) - - -class ModulatedConv2d(nn.Module): - def __init__( - self, - in_channel, - out_channel, - kernel_size, - style_dim, - demodulate=True, - upsample=False, - downsample=False, - blur_kernel=[1, 3, 3, 1], - ): - super().__init__() - - self.eps = 1e-8 - self.kernel_size = kernel_size - self.in_channel = in_channel - self.out_channel = out_channel - self.upsample = upsample - self.downsample = downsample - - if upsample: - factor = 2 - p = (len(blur_kernel) - factor) - (kernel_size - 1) - pad0 = (p + 1) // 2 + factor - 1 - pad1 = p // 2 + 1 - self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) - - if downsample: - factor = 2 - p = (len(blur_kernel) - factor) + (kernel_size - 1) - pad0 = (p + 1) // 2 - pad1 = p // 2 - self.blur = Blur(blur_kernel, pad=(pad0, pad1)) - - fan_in = in_channel * kernel_size ** 2 - self.scale = 1 / math.sqrt(fan_in) - self.padding = kernel_size // 2 - self.weight = nn.Parameter( - torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) - ) - self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) - self.demodulate = demodulate - - def __repr__(self): - return ( - f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, " - f"upsample={self.upsample}, downsample={self.downsample})" - ) - - def forward(self, input, style): - batch, in_channel, height, width = input.shape - - style = self.modulation(style).view(batch, 1, in_channel, 1, 1) - weight = self.scale * self.weight * style - - if self.demodulate: - demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) - weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) - - weight = weight.view( - batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size - ) - - if self.upsample: - input = input.view(1, batch * in_channel, height, width) - weight = weight.view( - batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size - ) - weight = weight.transpose(1, 2).reshape( - batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size - ) - out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) - _, _, height, width = out.shape - out = out.view(batch, self.out_channel, height, width) - out = self.blur(out) - - elif self.downsample: - input = self.blur(input) - _, _, height, width = input.shape - input = input.view(1, batch * in_channel, height, width) - out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) - _, _, height, width = out.shape - out = out.view(batch, self.out_channel, height, width) - - else: - input = input.view(1, batch * in_channel, height, width) - out = F.conv2d(input, weight, padding=self.padding, groups=batch) - _, _, height, width = out.shape - out = out.view(batch, self.out_channel, height, width) - - return out - - -class NoiseInjection(nn.Module): - def __init__(self): - super().__init__() - self.weight = nn.Parameter(torch.zeros(1)) - - def forward(self, image, noise=None): - if noise is None: - batch, _, height, width = image.shape - noise = image.new_empty(batch, 1, height, width).normal_() - return image + self.weight * noise - - -class ConstantInput(nn.Module): - def __init__(self, channel, size=4): - super().__init__() - self.input = nn.Parameter(torch.randn(1, channel, size, size // 2)) - - def forward(self, input): - batch = input.shape[0] - out = self.input.repeat(batch, 1, 1, 1) - return out - - -class StyledConv(nn.Module): - def __init__( - self, - in_channel, - out_channel, - kernel_size, - style_dim, - upsample=False, - blur_kernel=[1, 3, 3, 1], - demodulate=True, - ): - super().__init__() - self.conv = ModulatedConv2d( - in_channel, - out_channel, - kernel_size, - style_dim, - upsample=upsample, - blur_kernel=blur_kernel, - demodulate=demodulate, - ) - self.noise = NoiseInjection() - self.activate = FusedLeakyReLU(out_channel) - - def forward(self, input, style, noise=None): - out = self.conv(input, style) - out = self.noise(out, noise=noise) - out = self.activate(out) - return out - - -class ToRGB(nn.Module): - def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): - super().__init__() - if upsample: - self.upsample = Upsample(blur_kernel) - - self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) - self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) - - def forward(self, input, style, skip=None): - out = self.conv(input, style) - out = out + self.bias - - if skip is not None: - skip = self.upsample(skip) - out = out + skip - - return out - - -class Generator(nn.Module): - def __init__( - self, - size, - style_dim, - n_mlp, - channel_multiplier=1, - blur_kernel=[1, 3, 3, 1], - lr_mlp=0.01, - small=False, - small_isaac=False, - ): - super().__init__() - - self.size = size - - if small and size > 64: - raise ValueError("small only works for sizes <= 64") - - self.style_dim = style_dim - layers = [PixelNorm()] - - for i in range(n_mlp): - layers.append( - EqualLinear( - style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu" - ) - ) - - self.style = nn.Sequential(*layers) - - if small: - self.channels = { - 4: 64 * channel_multiplier, - 8: 64 * channel_multiplier, - 16: 64 * channel_multiplier, - 32: 64 * channel_multiplier, - 64: 64 * channel_multiplier, - } - elif small_isaac: - self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128} - else: - self.channels = { - 4: 512, - 8: 512, - 16: 512, - 32: 512, - 64: 256 * channel_multiplier, - 128: 128 * channel_multiplier, - 256: 64 * channel_multiplier, - 512: 32 * channel_multiplier, - 1024: 16 * channel_multiplier, - } - - self.input = ConstantInput(self.channels[4]) - self.conv1 = StyledConv( - self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel - ) - self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) - - self.log_size = int(math.log(size, 2)) - self.num_layers = (self.log_size - 2) * 2 + 1 - - self.convs = nn.ModuleList() - self.upsamples = nn.ModuleList() - self.to_rgbs = nn.ModuleList() - self.noises = nn.Module() - - in_channel = self.channels[4] - - for layer_idx in range(self.num_layers): - res = (layer_idx + 5) // 2 - shape = [1, 1, 2 ** res, 2 ** res // 2] - self.noises.register_buffer( - "noise_{}".format(layer_idx), torch.randn(*shape) - ) - - for i in range(3, self.log_size + 1): - out_channel = self.channels[2 ** i] - - self.convs.append( - StyledConv( - in_channel, - out_channel, - 3, - style_dim, - upsample=True, - blur_kernel=blur_kernel, - ) - ) - - self.convs.append( - StyledConv( - out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel - ) - ) - - self.to_rgbs.append(ToRGB(out_channel, style_dim)) - in_channel = out_channel - - self.n_latent = self.log_size * 2 - 2 - - def make_noise(self): - device = self.input.input.device - - noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2 // 2, device=device)] - - for i in range(3, self.log_size + 1): - for _ in range(2): - noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device)) - - return noises - - def mean_latent(self, n_latent): - latent_in = torch.randn( - n_latent, self.style_dim, device=self.input.input.device - ) - latent = self.style(latent_in).mean(0, keepdim=True) - - return latent - - def get_latent(self, input): - return self.style(input) - - def forward( - self, - styles, - return_latents=False, - return_features=False, - inject_index=None, - truncation=1, - truncation_latent=None, - input_is_latent=False, - noise=None, - randomize_noise=True, - real=False, - ): - if not input_is_latent: - styles = [self.style(s) for s in styles] - if noise is None: - if randomize_noise: - noise = [None] * self.num_layers - else: - noise = [ - getattr(self.noises, "noise_{}".format(i)) - for i in range(self.num_layers) - ] - - if truncation < 1: - # print('truncation_latent: ', truncation_latent.shape) - if not real: #if type(styles) == list: - style_t = [] - for style in styles: - style_t.append( - truncation_latent + truncation * (style - truncation_latent) - ) # (-1.1162e-03-(-1.0914e-01))*0.8+(-1.0914e-01) - styles = style_t - else: # styles are latent (tensor: 1,18,512), for real PTI output - truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512) - styles = torch.add(truncation_latent,torch.mul(torch.sub(styles,truncation_latent),truncation)) - # print('now styles after truncation : ', styles) - #if type(styles) == list and len(styles) < 2: # this if for input as list of [(1,512)] - if not real: - if len(styles) < 2: - inject_index = self.n_latent - if styles[0].ndim < 3: - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: - latent = styles[0] - elif type(styles) == list: - if inject_index is None: - inject_index = 4 - - latent = styles[0].unsqueeze(0) - if latent.shape[1] == 1: - latent = latent.repeat(1, inject_index, 1) - else: - latent = latent[:, :inject_index, :] - latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) - latent = torch.cat([latent, latent2], 1) - else: # input is tensor of size with torch.Size([1, 18, 512]), for real PTI output - latent = styles - - # print(f'processed latent: {latent.shape}') - - features = {} - out = self.input(latent) - features["out_0"] = out - out = self.conv1(out, latent[:, 0], noise=noise[0]) - features["conv1_0"] = out - - skip = self.to_rgb1(out, latent[:, 1]) - features["skip_0"] = skip - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs - ): - out = conv1(out, latent[:, i], noise=noise1) - features["conv1_{}".format(i)] = out - out = conv2(out, latent[:, i + 1], noise=noise2) - features["conv2_{}".format(i)] = out - skip = to_rgb(out, latent[:, i + 2], skip) - features["skip_{}".format(i)] = skip - - i += 2 - - image = skip - - if return_latents: - return image, latent - elif return_features: - return image, features - else: - return image, None - - -class ConvLayer(nn.Sequential): - def __init__( - self, - in_channel, - out_channel, - kernel_size, - downsample=False, - blur_kernel=[1, 3, 3, 1], - bias=True, - activate=True, - ): - layers = [] - - if downsample: - factor = 2 - p = (len(blur_kernel) - factor) + (kernel_size - 1) - pad0 = (p + 1) // 2 - pad1 = p // 2 - - layers.append(Blur(blur_kernel, pad=(pad0, pad1))) - - stride = 2 - self.padding = 0 - - else: - stride = 1 - self.padding = kernel_size // 2 - - layers.append( - EqualConv2d( - in_channel, - out_channel, - kernel_size, - padding=self.padding, - stride=stride, - bias=bias and not activate, - ) - ) - - if activate: - if bias: - layers.append(FusedLeakyReLU(out_channel)) - else: - layers.append(ScaledLeakyReLU(0.2)) - - super().__init__(*layers) - - -class ResBlock(nn.Module): - def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): - super().__init__() - - self.conv1 = ConvLayer(in_channel, in_channel, 3) - self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) - - self.skip = ConvLayer( - in_channel, out_channel, 1, downsample=True, activate=False, bias=False - ) - - def forward(self, input): - out = self.conv1(input) - out = self.conv2(out) - - skip = self.skip(input) - out = (out + skip) / math.sqrt(2) - - return out - - -class StyleDiscriminator(nn.Module): - def __init__( - self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], small=False - ): - super().__init__() - - if small: - channels = {4: 64, 8: 64, 16: 64, 32: 64, 64: 64} - - else: - channels = { - 4: 512, - 8: 512, - 16: 512, - 32: 512, - 64: 256 * channel_multiplier, - 128: 128 * channel_multiplier, - 256: 64 * channel_multiplier, - 512: 32 * channel_multiplier, - 1024: 16 * channel_multiplier, - } - - convs = [ConvLayer(3, channels[size], 1)] - - log_size = int(math.log(size, 2)) - in_channel = channels[size] - - for i in range(log_size, 2, -1): - out_channel = channels[2 ** (i - 1)] - - convs.append(ResBlock(in_channel, out_channel, blur_kernel)) - - in_channel = out_channel - - self.convs = nn.Sequential(*convs) - - self.stddev_group = 4 - self.stddev_feat = 1 - - self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) - self.final_linear = nn.Sequential( - EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"), - EqualLinear(channels[4], 1), - ) - - - def forward(self, input): - h = input - h_list = [] - - for index, blocklist in enumerate(self.convs): - h = blocklist(h) - h_list.append(h) - - out = h - batch, channel, height, width = out.shape - group = min(batch, self.stddev_group) - stddev = out.view( - group, -1, self.stddev_feat, channel // self.stddev_feat, height, width - ) - stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) - stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) - stddev = stddev.repeat(group, 1, height, width) - out = torch.cat([out, stddev], 1) - - out = self.final_conv(out) - h_list.append(out) - - out = out.view(batch, -1) - out = self.final_linear(out) - - return out, h_list - - -class StyleEncoder(nn.Module): - def __init__(self, size, w_dim=512): - super().__init__() - - channels = { - 4: 512, - 8: 512, - 16: 512, - 32: 512, - 64: 256, - 128: 128, - 256: 64, - 512: 32, - 1024: 16 - } - - self.w_dim = w_dim - log_size = int(math.log(size, 2)) - convs = [ConvLayer(3, channels[size], 1)] - - in_channel = channels[size] - for i in range(log_size, 2, -1): - out_channel = channels[2 ** (i - 1)] - convs.append(ResBlock(in_channel, out_channel)) - in_channel = out_channel - - convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False)) - - self.convs = nn.Sequential(*convs) - - def forward(self, input): - out = self.convs(input) - # return out.view(len(input), self.n_latents, self.w_dim) - reshaped = out.view(len(input), 2*self.w_dim) - return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:] - -def kaiming_init(m): - if isinstance(m, (nn.Linear, nn.Conv2d)): - init.kaiming_normal_(m.weight) - if m.bias is not None: - m.bias.data.fill_(0) - elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): - m.weight.data.fill_(1) - if m.bias is not None: - m.bias.data.fill_(0) - - -def normal_init(m): - if isinstance(m, (nn.Linear, nn.Conv2d)): - init.normal_(m.weight, 0, 0.02) - if m.bias is not None: - m.bias.data.fill_(0) - elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): - m.weight.data.fill_(1) - if m.bias is not None: - m.bias.data.fill_(0) \ No newline at end of file diff --git a/spaces/Ekittl01/impira-layoutlm-document-qa/app.py b/spaces/Ekittl01/impira-layoutlm-document-qa/app.py deleted file mode 100644 index c80208650f94f0a6bd291fdf0a78afaf1fcf318b..0000000000000000000000000000000000000000 --- a/spaces/Ekittl01/impira-layoutlm-document-qa/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/impira/layoutlm-document-qa").launch() \ No newline at end of file diff --git a/spaces/Enterprisium/Easy_GUI/config.py b/spaces/Enterprisium/Easy_GUI/config.py deleted file mode 100644 index 5b72235b58b65ac629f49bcc4aad032b5b59d8d4..0000000000000000000000000000000000000000 --- a/spaces/Enterprisium/Easy_GUI/config.py +++ /dev/null @@ -1,204 +0,0 @@ -import argparse -import sys -import torch -import json -from multiprocessing import cpu_count - -global usefp16 -usefp16 = False - - -def use_fp32_config(): - usefp16 = False - device_capability = 0 - if torch.cuda.is_available(): - device = torch.device("cuda:0") # Assuming you have only one GPU (index 0). - device_capability = torch.cuda.get_device_capability(device)[0] - if device_capability >= 7: - usefp16 = True - for config_file in ["32k.json", "40k.json", "48k.json"]: - with open(f"configs/{config_file}", "r") as d: - data = json.load(d) - - if "train" in data and "fp16_run" in data["train"]: - data["train"]["fp16_run"] = True - - with open(f"configs/{config_file}", "w") as d: - json.dump(data, d, indent=4) - - print(f"Set fp16_run to true in {config_file}") - - with open( - "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8" - ) as f: - strr = f.read() - - strr = strr.replace("3.0", "3.7") - - with open( - "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8" - ) as f: - f.write(strr) - else: - for config_file in ["32k.json", "40k.json", "48k.json"]: - with open(f"configs/{config_file}", "r") as f: - data = json.load(f) - - if "train" in data and "fp16_run" in data["train"]: - data["train"]["fp16_run"] = False - - with open(f"configs/{config_file}", "w") as d: - json.dump(data, d, indent=4) - - print(f"Set fp16_run to false in {config_file}") - - with open( - "trainset_preprocess_pipeline_print.py", "r", encoding="utf-8" - ) as f: - strr = f.read() - - strr = strr.replace("3.7", "3.0") - - with open( - "trainset_preprocess_pipeline_print.py", "w", encoding="utf-8" - ) as f: - f.write(strr) - else: - print( - "CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed." - ) - return (usefp16, device_capability) - - -class Config: - def __init__(self): - self.device = "cuda:0" - self.is_half = True - self.n_cpu = 0 - self.gpu_name = None - self.gpu_mem = None - ( - self.python_cmd, - self.listen_port, - self.iscolab, - self.noparallel, - self.noautoopen, - self.paperspace, - self.is_cli, - ) = self.arg_parse() - - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - @staticmethod - def arg_parse() -> tuple: - exe = sys.executable or "python" - parser = argparse.ArgumentParser() - parser.add_argument("--port", type=int, default=7865, help="Listen port") - parser.add_argument("--pycmd", type=str, default=exe, help="Python command") - parser.add_argument("--colab", action="store_true", help="Launch in colab") - parser.add_argument( - "--noparallel", action="store_true", help="Disable parallel processing" - ) - parser.add_argument( - "--noautoopen", - action="store_true", - help="Do not open in browser automatically", - ) - parser.add_argument( # Fork Feature. Paperspace integration for web UI - "--paperspace", - action="store_true", - help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.", - ) - parser.add_argument( # Fork Feature. Embed a CLI into the infer-web.py - "--is_cli", - action="store_true", - help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!", - ) - cmd_opts = parser.parse_args() - - cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 - - return ( - cmd_opts.pycmd, - cmd_opts.port, - cmd_opts.colab, - cmd_opts.noparallel, - cmd_opts.noautoopen, - cmd_opts.paperspace, - cmd_opts.is_cli, - ) - - # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. - # check `getattr` and try it for compatibility - @staticmethod - def has_mps() -> bool: - if not torch.backends.mps.is_available(): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - print("Found GPU", self.gpu_name, ", force to fp32") - self.is_half = False - else: - print("Found GPU", self.gpu_name) - use_fp32_config() - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - if self.gpu_mem <= 4: - with open("trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - elif self.has_mps(): - print("No supported Nvidia GPU found, use MPS instead") - self.device = "mps" - self.is_half = False - use_fp32_config() - else: - print("No supported Nvidia GPU found, use CPU instead") - self.device = "cpu" - self.is_half = False - use_fp32_config() - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G显存配置 - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G显存配置 - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem != None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - - return x_pad, x_query, x_center, x_max diff --git a/spaces/Epitech/IA_NLP/app.py b/spaces/Epitech/IA_NLP/app.py deleted file mode 100644 index 97555047cef6202daac2442f47d03d6cab6b853c..0000000000000000000000000000000000000000 --- a/spaces/Epitech/IA_NLP/app.py +++ /dev/null @@ -1,129 +0,0 @@ -from tensorflow import keras -import streamlit as st -import altair as alt -import plotly.express as px - -import pandas as pd -import numpy as np -from datetime import datetime - - -import joblib - -from google.cloud import storage -from tempfile import TemporaryFile -from csv import writer -from datetime import datetime -import os -from dotenv import load_dotenv -from nltk.stem import PorterStemmer -from nltk.corpus import stopwords -import re -from tensorflow import keras -import numpy as np -import pandas as pd - -from tensorflow.keras.preprocessing.sequence import pad_sequences -import nltk -from tensorflow.keras.preprocessing.text import one_hot - - -import re -from nltk.corpus import stopwords -from nltk.stem import PorterStemmer - -import pickle -pkl_file = open('m_lb.pkl', 'rb') -le_departure = pickle.load(pkl_file) -pkl_file.close() -model = keras.models.load_model('m_odel.h5') -nltk.download('stopwords') -stopwords = set(nltk.corpus.stopwords.words('english')) -vocabSize = 11000 -max_len = 1160 -load_dotenv() - -emotions_emoji_dict = { "anger":"😠", - "disgust":"🤮", - "fear":"😨😱", - "happy":"🤗", - "joy":"😂", - "neutral":"😐", - "sad":"😔", - "sadness":"😔", - "shame":"😳", - "surprise":"😮" - } - - -def predict_emotions(sentence): - sentence = sentence_cleaning(sentence) - result = le_departure.inverse_transform( - np.argmax(model.predict(sentence), axis=-1))[0] - proba = np.max(model.predict(sentence)) - print() - - return result, proba, get_all_result(model.predict(sentence)) - - -def get_all_result(prediction): - dict = {} - for element in prediction: - for i in range(0, len(element)): - dict[element[i]] = le_departure.inverse_transform([i])[0] - return dict - - -def sentence_cleaning(sentence): - """Pre-processing sentence for prediction""" - stemmer = PorterStemmer() - corpus = [] - text = re.sub("[^a-zA-Z]", " ", sentence) - text = text.lower() - text = text.split() - text = [stemmer.stem(word) for word in text if word not in stopwords] - text = " ".join(text) - corpus.append(text) - one_hot_word = [one_hot(input_text=word, n=vocabSize) for word in corpus] - pad = pad_sequences(sequences=one_hot_word, maxlen=max_len, padding='pre') - return pad - - -def main(): - st.title("🤮😨😱Emotion Classifier😂😳😮") - menu = ["Home", "Monitor"] - choice = st.sidebar.selectbox("Menu", menu) - if choice == "Home": - st.subheader("Home-Emotion In Text") - - with st.form(key='emotion_clf_form'): - raw_text = st.text_area("Type Here") - submit_text = st.form_submit_button(label='Submit') - - if submit_text: - col1, col2 = st.columns(2) - - - res, proba, total_result = predict_emotions(raw_text) - - with col1: - st.success("Original Text") - st.write(raw_text) - - st.success("Prediction") - st.write("{}:{}".format(res, emotions_emoji_dict[res])) - st.write("Confidence:{}".format(proba)) - - with col2: - source = pd.DataFrame({'Proba': list(total_result.keys()), 'Emotion': list(total_result.values())}) - - fig = alt.Chart(source).mark_bar().encode(x='Emotion',y='Proba',color='Emotion') - st.altair_chart(fig,use_container_width=True) - - - else: - st.subheader("About") - - -if __name__ == '__main__': - main() diff --git a/spaces/Erala/QQsign/bin/unidbg-fetch-qsign.bat b/spaces/Erala/QQsign/bin/unidbg-fetch-qsign.bat deleted file mode 100644 index 8b291e7303b0c07d14b714e5795473891363c85b..0000000000000000000000000000000000000000 --- a/spaces/Erala/QQsign/bin/unidbg-fetch-qsign.bat +++ /dev/null @@ -1,89 +0,0 @@ -@rem -@rem Copyright 2015 the original author or authors. -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem https://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. -@rem - -@if "%DEBUG%" == "" @echo off -@rem ########################################################################## -@rem -@rem unidbg-fetch-qsign startup script for Windows -@rem -@rem ########################################################################## - -@rem Set local scope for the variables with windows NT shell -if "%OS%"=="Windows_NT" setlocal - -set DIRNAME=%~dp0 -if "%DIRNAME%" == "" set DIRNAME=. -set APP_BASE_NAME=%~n0 -set APP_HOME=%DIRNAME%.. - -@rem Resolve any "." and ".." in APP_HOME to make it shorter. -for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi - -@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script. -set DEFAULT_JVM_OPTS= - -@rem Find java.exe -if defined JAVA_HOME goto findJavaFromJavaHome - -set JAVA_EXE=java.exe -%JAVA_EXE% -version >NUL 2>&1 -if "%ERRORLEVEL%" == "0" goto execute - -echo. -echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:findJavaFromJavaHome -set JAVA_HOME=%JAVA_HOME:"=% -set JAVA_EXE=%JAVA_HOME%/bin/java.exe - -if exist "%JAVA_EXE%" goto execute - -echo. -echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:execute -@rem Setup the command line - -set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.9.jar;%APP_HOME%\lib\unidbg-android-105.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-status-pages-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar - - -@rem Execute unidbg-fetch-qsign -"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS% -classpath "%CLASSPATH%" MainKt %* - -:end -@rem End local scope for the variables with windows NT shell -if "%ERRORLEVEL%"=="0" goto mainEnd - -:fail -rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of -rem the _cmd.exe /c_ return code! -if not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1 -exit /b 1 - -:mainEnd -if "%OS%"=="Windows_NT" endlocal - -:omega diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_clickbait.py b/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_clickbait.py deleted file mode 100644 index 476955aba7ea6ade2c9eaca9fcd959d92b0ae948..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/clickbaitonator/fudge/evaluate_clickbait.py +++ /dev/null @@ -1,200 +0,0 @@ -import os -import random -import time -import pickle -import math -from argparse import ArgumentParser - -from typing import Iterable, List, Optional, Tuple - -from tqdm import tqdm -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers import AutoTokenizer, AutoModelWithLMHead -from torch import Tensor - -from fudge.data import Dataset -from fudge.model import Model -from fudge.util import num_params -from fudge.constants import * - - - -tokenizer = AutoTokenizer.from_pretrained('google/pegasus-xsum') -classifier_tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2') - - -def main(args): - with open(args.dataset_info, 'rb') as rf: - dataset_info = pickle.load(rf) - - article_content = """Australian actor Guy Pearce will return for the iconic soap Neighbours finale on August 1 to reprise his role as Mike Young. - Guy, 54, played the troubled Mike from 1986 to 1989, and is now set to make a comeback on the show after 33 years, Metro.co.uk reports. - The star's character arcs explored the implications of domestic abuse, student-teacher relationships and dealing with loss of loved ones. - Speaking to Metro.co.uk, Guy said: 'It is very exciting and surreal at the same time being back on set again, however it feels like coming home. - 'It's where it all started for me professionally. I've been asked to come back on occasions over the years and wondered if it was the right thing - to do, but once I knew the show was finishing, I knew I had to do it.'He added that there is 'nothing like being here all together again' - , even though he's had a chance to catch-up with other cast members.""" - - tokenizer.add_special_tokens({'pad_token': PAD_TOKEN}) - pad_id = tokenizer.encode(PAD_TOKEN)[0] - - #For loading Clickbait summarizer - model = AutoModelWithLMHead.from_pretrained(args.model_string, return_dict=True).to(args.device) - - model.eval() - - checkpoint = torch.load(args.ckpt, map_location=args.device) - model_args = checkpoint['args'] - conditioning_model = Model(model_args, pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway - conditioning_model.load_state_dict(checkpoint['state_dict']) - conditioning_model = conditioning_model.to(args.device) - conditioning_model.eval() - print("=> loaded checkpoint '{}' (epoch {})" - .format(args.ckpt, checkpoint['epoch'])) - print('num params', num_params(conditioning_model)) - - while True: - results = generate_clickbait(model, - tokenizer, - conditioning_model, - [args.input_text], - dataset_info, - precondition_topk=args.precondition_topk, - do_sample=args.do_sample, - length_cutoff=args.length_cutoff, - condition_lambda=args.condition_lambda, - article_content=article_content, - device=args.device) - # print(results) - import pdb; pdb.set_trace() - - -def generate_clickbait(model, - tokenizer, - conditioning_model, - input_text, - dataset_info, - precondition_topk, - length_cutoff, - condition_lambda=1.0, - article_content=None, - device='cuda'): - with torch.no_grad(): - batch_size = len(input_text) - # encoded_input_article = [tokenizer.encode(article_content, return_tensors='pt',add_special_tokens=False).to(device)] # batch x seq - encoded_input_article = tokenizer(article_content, return_tensors='pt',add_special_tokens=False, max_length=512).to(device) # batch x seq - # encoded_input_article = torch.cat(encoded_input_article, dim=0) - # attention_mask = encoded_input_article.new_ones(encoded_input_article.shape).to(device) - - # CHANGE=ko - encoded_input = tokenizer('', return_tensors='pt',add_special_tokens=False).to(device) # batch x seq - # encoded_input = tokenizer(''+ input_text[0], return_tensors='pt',add_special_tokens=False).to(device) # batch x seq - # encoded_input = torch.cat(encoded_input, dim=0) - encoded_input = encoded_input['input_ids'] - - - lengths = torch.LongTensor([encoded_input.shape[1]]).to(device) - # lengths = 1 - - past = None - use_cache = True - - # CHANGE - # model_kwargs = {'encoder_outputs': model.get_encoder()(encoded_input_article, attention_mask=attention_mask)} - # print(encoded_input_article) - # print(encoded_input_article['input_ids'].shape, encoded_input_article['attention_mask'].shape) - model_kwargs = {'encoder_outputs': model.get_encoder()(input_ids=encoded_input_article['input_ids'], - attention_mask=encoded_input_article['attention_mask'], - return_dict=True, - output_attentions=False, - output_hidden_states=False), - } - - while lengths.max() < length_cutoff: - model_inputs = model.prepare_inputs_for_generation( - input_ids = encoded_input_article['input_ids'], - decoder_input_ids=encoded_input, - # past=past, - attention_mask=encoded_input_article['attention_mask'], - use_cache=use_cache, - **model_kwargs - ) - - outputs = model(**model_inputs, return_dict=True) - logits = outputs.logits[:, -1, :] - - if "past_key_values" in outputs: - model_kwargs["past"] = outputs.past_key_values - - # logits = model(encoded_input)[0][:, -1, :] # batch x vocab - top_logits, top_indices = logits.topk(precondition_topk, dim=1) # batch x topk - new_input_candidates = torch.cat([encoded_input.unsqueeze(1).expand(-1, precondition_topk, -1), top_indices.unsqueeze(2)], dim=2) # batch x topk x seq+1 - expanded_lengths = (lengths + 1).unsqueeze(1).expand(batch_size, precondition_topk) # batch x topk - - if condition_lambda == 0: - condition_logits = torch.zeros_like(top_logits).float() - condition_logits = condition_logits.view(batch_size, precondition_topk, -1) # batch x topk x N - else: - decoded_outputs = tokenizer.batch_decode(new_input_candidates.view(-1, new_input_candidates.size(-1)), clean_up_tokenization_spaces=False) - resulting_tokenization = classifier_tokenizer(decoded_outputs, add_special_tokens=False, padding='longest') - encoded_with_classifier = resulting_tokenization['input_ids'] - attention_mask = torch.tensor(resulting_tokenization['attention_mask']).to(model.device) - tplus1_candidates_classifier = torch.tensor(encoded_with_classifier).view(batch_size, precondition_topk, -1).to(model.device) - - condition_logits = conditioning_model(tplus1_candidates_classifier.flatten(0, 1), # batch*topk x seq+1 - expanded_lengths.flatten(0, 1), # batch*topk - None, - None, - None, - attention_mask=attention_mask - ) - condition_logits = condition_logits.view(batch_size, precondition_topk, -1) # batch x topk x N - condition_logits = condition_logits - torch.log(1 + torch.exp(condition_logits)) # get correct log probs - - condition_logits = torch.mean(condition_logits, dim=2) - full_logits = top_logits + condition_logits * condition_lambda # batch x topk - post_logits, post_indices = full_logits.topk(precondition_topk, dim=1) - post_probs = F.softmax(post_logits, dim=1) - # index_into_top_indices = post_indices[torch.arange(batch_size).to(post_indices.device), torch.multinomial(post_probs, 1).flatten()] # batch - index_into_top_indices = post_indices[:, torch.multinomial(post_probs, 1).flatten()] # batch - - # next_indices = top_indices[torch.arange(batch_size).to(top_indices.device), index_into_top_indices] # batch - next_indices = top_indices[:, index_into_top_indices] # batch - - # encoded_input = torch.cat([encoded_input, next_indices.unsqueeze(1)], dim=1) # batch x seq+1 - encoded_input = torch.cat([encoded_input, next_indices.squeeze(1)], dim=1) - lengths = lengths + 1 # batch - -# print(tokenizer.decode(encoded_input[0], add_special_tokens=False)) - return [tokenizer.decode(s) for s in encoded_input] - - -if __name__=='__main__': - parser = ArgumentParser() - - # DATA - parser.add_argument('--ckpt', type=str, required=True) - parser.add_argument('--dataset_info', type=str, required=True, help='saved dataset info') - parser.add_argument('--model_string', type=str, default='Helsinki-NLP/opus-mt-es-en') - - parser.add_argument('--in_file', type=str, default=None, required=True, help='text to run pred on') - - parser.add_argument('--precondition_topk', type=int, default=200, help='consider top k outputs from text generation at each step before conditioning and re-pruning') - parser.add_argument('--do_sample', action='store_true', default=False, help='sample instead of greedy') - parser.add_argument('--condition_lambda', type=float, default=1.0, help='lambda weight on conditioning model') - parser.add_argument('--length_cutoff', type=int, default=512, help='max length') - - parser.add_argument('--seed', type=int, default=1, help='random seed') - parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda']) - parser.add_argument('--debug', action='store_true', default=False) - - args = parser.parse_args() - - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - - main(args) diff --git a/spaces/EuroSciPy2022/timeseries-forecasting-with-prophet/README.md b/spaces/EuroSciPy2022/timeseries-forecasting-with-prophet/README.md deleted file mode 100644 index 0a84cb90e36913e42ad583673150a229d6e76856..0000000000000000000000000000000000000000 --- a/spaces/EuroSciPy2022/timeseries-forecasting-with-prophet/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Timeseries Forecasting With Prophet -emoji: 📈 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.1.7 -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/Fengbinbin/gpt-academic/core_functional.py b/spaces/Fengbinbin/gpt-academic/core_functional.py deleted file mode 100644 index 536ccb609c38cbbebfda4ba17bd51a78857d711e..0000000000000000000000000000000000000000 --- a/spaces/Fengbinbin/gpt-academic/core_functional.py +++ /dev/null @@ -1,71 +0,0 @@ -# 'primary' 颜色对应 theme.py 中的 primary_hue -# 'secondary' 颜色对应 theme.py 中的 neutral_hue -# 'stop' 颜色对应 theme.py 中的 color_er -# 默认按钮颜色是 secondary -from toolbox import clear_line_break - - -def get_core_functions(): - return { - "英语学术润色": { - # 前言 - "Prefix": r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " + - r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " + - r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n", - # 后语 - "Suffix": r"", - "Color": r"secondary", # 按钮颜色 - }, - "中文学术润色": { - "Prefix": r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" + - r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n", - "Suffix": r"", - }, - "查找语法错误": { - "Prefix": r"Can you help me ensure that the grammar and the spelling is correct? " + - r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." + - r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " + - r"put the original text the first column, " + - r"put the corrected text in the second column and highlight the key words you fixed.""\n" - r"Example:""\n" - r"Paragraph: How is you? Do you knows what is it?""\n" - r"| Original sentence | Corrected sentence |""\n" - r"| :--- | :--- |""\n" - r"| How **is** you? | How **are** you? |""\n" - r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n" - r"Below is a paragraph from an academic paper. " - r"You need to report all grammar and spelling mistakes as the example before." - + "\n\n", - "Suffix": r"", - "PreProcess": clear_line_break, # 预处理：清除换行符 - }, - "中译英": { - "Prefix": r"Please translate following sentence to English:" + "\n\n", - "Suffix": r"", - }, - "学术中英互译": { - "Prefix": r"I want you to act as a scientific English-Chinese translator, " + - r"I will provide you with some paragraphs in one language " + - r"and your task is to accurately and academically translate the paragraphs only into the other language. " + - r"Do not repeat the original provided paragraphs after translation. " + - r"You should use artificial intelligence tools, " + - r"such as natural language processing, and rhetorical knowledge " + - r"and experience about effective writing techniques to reply. " + - r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n", - "Suffix": "", - "Color": "secondary", - }, - "英译中": { - "Prefix": r"翻译成地道的中文：" + "\n\n", - "Suffix": r"", - }, - "找图片": { - "Prefix": r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" + - r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n", - "Suffix": r"", - }, - "解释代码": { - "Prefix": r"请解释以下代码：" + "\n```\n", - "Suffix": "\n```\n", - }, - } diff --git a/spaces/Foremost/NER/README.md b/spaces/Foremost/NER/README.md deleted file mode 100644 index 0e1e379329c4c5daf76ad2ea157fd00f51782ad9..0000000000000000000000000000000000000000 --- a/spaces/Foremost/NER/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: NER -emoji: 🦀 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/server.py b/spaces/Freiburg-AI-Research/dermoscopic_image_generation/server.py deleted file mode 100644 index 349bd116a310c8f3ae4e95471b4431c75420432e..0000000000000000000000000000000000000000 --- a/spaces/Freiburg-AI-Research/dermoscopic_image_generation/server.py +++ /dev/null @@ -1,175 +0,0 @@ -import base64 -from io import BytesIO -from fastapi import FastAPI - -from PIL import Image -import torch as th - -from glide_text2im.download import load_checkpoint -from glide_text2im.model_creation import ( - create_model_and_diffusion, - model_and_diffusion_defaults, - model_and_diffusion_defaults_upsampler -) - -print("Loading models...") -app = FastAPI() - -# This notebook supports both CPU and GPU. -# On CPU, generating one sample may take on the order of 20 minutes. -# On a GPU, it should be under a minute. - -has_cuda = th.cuda.is_available() -device = th.device('cpu' if not has_cuda else 'cuda') - -# Create base model. -options = model_and_diffusion_defaults() -options['use_fp16'] = has_cuda -options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling -model, diffusion = create_model_and_diffusion(**options) -model.eval() -if has_cuda: - model.convert_to_fp16() -model.to(device) -model.load_state_dict(load_checkpoint('base', device)) -print('total base parameters', sum(x.numel() for x in model.parameters())) - -# Create upsampler model. -options_up = model_and_diffusion_defaults_upsampler() -options_up['use_fp16'] = has_cuda -options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling -model_up, diffusion_up = create_model_and_diffusion(**options_up) -model_up.eval() -if has_cuda: - model_up.convert_to_fp16() -model_up.to(device) -model_up.load_state_dict(load_checkpoint('upsample', device)) -print('total upsampler parameters', sum(x.numel() for x in model_up.parameters())) - - -def get_images(batch: th.Tensor): - """ Display a batch of images inline. """ - scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu() - reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3]) - Image.fromarray(reshaped.numpy()) - - -# Create a classifier-free guidance sampling function -guidance_scale = 3.0 - -def model_fn(x_t, ts, **kwargs): - half = x_t[: len(x_t) // 2] - combined = th.cat([half, half], dim=0) - model_out = model(combined, ts, **kwargs) - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps) - eps = th.cat([half_eps, half_eps], dim=0) - return th.cat([eps, rest], dim=1) - - -@app.get("/") -def read_root(): - return {"glide!"} - -@app.get("/{generate}") -def sample(prompt): - # Sampling parameters - batch_size = 1 - - # Tune this parameter to control the sharpness of 256x256 images. - # A value of 1.0 is sharper, but sometimes results in grainy artifacts. - upsample_temp = 0.997 - - ############################## - # Sample from the base model # - ############################## - - # Create the text tokens to feed to the model. - tokens = model.tokenizer.encode(prompt) - tokens, mask = model.tokenizer.padded_tokens_and_mask( - tokens, options['text_ctx'] - ) - - # Create the classifier-free guidance tokens (empty) - full_batch_size = batch_size * 2 - uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask( - [], options['text_ctx'] - ) - - # Pack the tokens together into model kwargs. - model_kwargs = dict( - tokens=th.tensor( - [tokens] * batch_size + [uncond_tokens] * batch_size, device=device - ), - mask=th.tensor( - [mask] * batch_size + [uncond_mask] * batch_size, - dtype=th.bool, - device=device, - ), - ) - - # Sample from the base model. - model.del_cache() - samples = diffusion.p_sample_loop( - model_fn, - (full_batch_size, 3, options["image_size"], options["image_size"]), - device=device, - clip_denoised=True, - progress=True, - model_kwargs=model_kwargs, - cond_fn=None, - )[:batch_size] - model.del_cache() - - - ############################## - # Upsample the 64x64 samples # - ############################## - - tokens = model_up.tokenizer.encode(prompt) - tokens, mask = model_up.tokenizer.padded_tokens_and_mask( - tokens, options_up['text_ctx'] - ) - - # Create the model conditioning dict. - model_kwargs = dict( - # Low-res image to upsample. - low_res=((samples+1)*127.5).round()/127.5 - 1, - - # Text tokens - tokens=th.tensor( - [tokens] * batch_size, device=device - ), - mask=th.tensor( - [mask] * batch_size, - dtype=th.bool, - device=device, - ), - ) - - # Sample from the base model. - model_up.del_cache() - up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"]) - up_samples = diffusion_up.ddim_sample_loop( - model_up, - up_shape, - noise=th.randn(up_shape, device=device) * upsample_temp, - device=device, - clip_denoised=True, - progress=True, - model_kwargs=model_kwargs, - cond_fn=None, - )[:batch_size] - model_up.del_cache() - - # Show the output - image = get_images(up_samples) - image = to_base64(image) - return {"image": image} - - -def to_base64(pil_image): - buffered = BytesIO() - pil_image.save(buffered, format="JPEG") - return base64.b64encode(buffered.getvalue()) \ No newline at end of file diff --git a/spaces/GaenKoki/voicevox/voicevox_engine/synthesis_engine/core_wrapper.py b/spaces/GaenKoki/voicevox/voicevox_engine/synthesis_engine/core_wrapper.py deleted file mode 100644 index fe8f9778707a7476f30ab5b80f1ed1e1f759b8a0..0000000000000000000000000000000000000000 --- a/spaces/GaenKoki/voicevox/voicevox_engine/synthesis_engine/core_wrapper.py +++ /dev/null @@ -1,538 +0,0 @@ -import os -import platform -from ctypes import CDLL, POINTER, c_bool, c_char_p, c_float, c_int, c_long -from ctypes.util import find_library -from dataclasses import dataclass -from enum import Enum, auto -from pathlib import Path -from typing import List, Optional - -import numpy as np - - -class OldCoreError(Exception): - """古いコアが使用されている場合に発生するエラー""" - - -class CoreError(Exception): - """コア呼び出しで発生したエラー""" - - -def load_runtime_lib(runtime_dirs: List[Path]): - if platform.system() == "Windows": - # DirectML.dllはonnxruntimeと互換性のないWindows標準搭載のものを優先して読み込むことがあるため、明示的に読み込む - # 参考 1. https://github.com/microsoft/onnxruntime/issues/3360 - # 参考 2. https://tadaoyamaoka.hatenablog.com/entry/2020/06/07/113616 - lib_file_names = [ - "torch_cpu.dll", - "torch_cuda.dll", - "DirectML.dll", - "onnxruntime.dll", - ] - lib_names = ["torch_cpu", "torch_cuda", "onnxruntime"] - elif platform.system() == "Linux": - lib_file_names = ["libtorch.so", "libonnxruntime.so"] - lib_names = ["torch", "onnxruntime"] - elif platform.system() == "Darwin": - lib_file_names = ["libonnxruntime.dylib"] - lib_names = ["onnxruntime"] - else: - raise RuntimeError("不明なOSです") - for lib_path in runtime_dirs: - for file_name in lib_file_names: - try: - CDLL(str((lib_path / file_name).resolve(strict=True))) - except OSError: - pass - for lib_name in lib_names: - try: - CDLL(find_library(lib_name)) - except (OSError, TypeError): - pass - - -class GPUType(Enum): - # NONEはCPUしか対応していないことを示す - NONE = auto() - CUDA = auto() - DIRECT_ML = auto() - - -@dataclass(frozen=True) -class CoreInfo: - name: str - platform: str - arch: str - core_type: str - gpu_type: GPUType - - -# version 0.12 より前のコアの情報 -CORE_INFOS = [ - # Windows - CoreInfo( - name="core.dll", - platform="Windows", - arch="x64", - core_type="libtorch", - gpu_type=GPUType.CUDA, - ), - CoreInfo( - name="core_cpu.dll", - platform="Windows", - arch="x64", - core_type="libtorch", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="core_gpu_x64_nvidia.dll", - platform="Windows", - arch="x64", - core_type="onnxruntime", - gpu_type=GPUType.CUDA, - ), - CoreInfo( - name="core_gpu_x64_directml.dll", - platform="Windows", - arch="x64", - core_type="onnxruntime", - gpu_type=GPUType.DIRECT_ML, - ), - CoreInfo( - name="core_cpu_x64.dll", - platform="Windows", - arch="x64", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="core_cpu_x86.dll", - platform="Windows", - arch="x86", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="core_gpu_x86_directml.dll", - platform="Windows", - arch="x86", - core_type="onnxruntime", - gpu_type=GPUType.DIRECT_ML, - ), - CoreInfo( - name="core_cpu_arm.dll", - platform="Windows", - arch="armv7l", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="core_gpu_arm_directml.dll", - platform="Windows", - arch="armv7l", - core_type="onnxruntime", - gpu_type=GPUType.DIRECT_ML, - ), - CoreInfo( - name="core_cpu_arm64.dll", - platform="Windows", - arch="aarch64", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="core_gpu_arm64_directml.dll", - platform="Windows", - arch="aarch64", - core_type="onnxruntime", - gpu_type=GPUType.DIRECT_ML, - ), - # Linux - CoreInfo( - name="libcore.so", - platform="Linux", - arch="x64", - core_type="libtorch", - gpu_type=GPUType.CUDA, - ), - CoreInfo( - name="libcore_cpu.so", - platform="Linux", - arch="x64", - core_type="libtorch", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="libcore_gpu_x64_nvidia.so", - platform="Linux", - arch="x64", - core_type="onnxruntime", - gpu_type=GPUType.CUDA, - ), - CoreInfo( - name="libcore_cpu_x64.so", - platform="Linux", - arch="x64", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="libcore_cpu_armhf.so", - platform="Linux", - arch="armv7l", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - CoreInfo( - name="libcore_cpu_arm64.so", - platform="Linux", - arch="aarch64", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), - # macOS - CoreInfo( - name="libcore_cpu_universal2.dylib", - platform="Darwin", - arch="universal", - core_type="onnxruntime", - gpu_type=GPUType.NONE, - ), -] - - -# version 0.12 以降のコアの名前の辞書 -# - version 0.12, 0.13 のコアの名前: core -# - version 0.14 からのコアの名前: voicevox_core -CORENAME_DICT = { - "Windows": ("voicevox_core.dll", "core.dll"), - "Linux": ("libvoicevox_core.so", "libcore.so"), - "Darwin": ("libvoicevox_core.dylib", "libcore.dylib"), -} - - -def find_version_0_12_core_or_later(core_dir: Path) -> Optional[str]: - """ - core_dir で指定したディレクトリにあるコアライブラリが Version 0.12 以降である場合、 - 見つかった共有ライブラリの名前を返す。 - - Version 0.12 以降と判定する条件は、 - - - core_dir に metas.json が存在しない - - コアライブラリの名前が CORENAME_DICT の定義に従っている - - の両方が真のときである。 - cf. https://github.com/VOICEVOX/voicevox_engine/issues/385 - """ - if (core_dir / "metas.json").exists(): - return None - - for core_name in CORENAME_DICT[platform.system()]: - if (core_dir / core_name).is_file(): - return core_name - - return None - - -def get_arch_name() -> Optional[str]: - """ - platform.machine() が特定のアーキテクチャ上で複数パターンの文字列を返し得るので、 - 一意な文字列に変換する - サポート外のアーキテクチャである場合、None を返す - """ - machine = platform.machine() - if machine == "x86_64" or machine == "x64" or machine == "AMD64": - return "x64" - elif machine == "i386" or machine == "x86": - return "x86" - elif machine == "arm64": - return "aarch64" - elif machine in ["armv7l", "aarch64"]: - return machine - else: - return None - - -def get_core_name( - arch_name: str, - platform_name: str, - model_type: str, - gpu_type: GPUType, -) -> Optional[str]: - if platform_name == "Darwin": - if gpu_type == GPUType.NONE and (arch_name == "x64" or arch_name == "aarch64"): - arch_name = "universal" - else: - return None - for core_info in CORE_INFOS: - if ( - core_info.platform == platform_name - and core_info.arch == arch_name - and core_info.core_type == model_type - and core_info.gpu_type == gpu_type - ): - return core_info.name - return None - - -def get_suitable_core_name( - model_type: str, - gpu_type: GPUType, -) -> Optional[str]: - arch_name = get_arch_name() - if arch_name is None: - return None - platform_name = platform.system() - return get_core_name(arch_name, platform_name, model_type, gpu_type) - - -def check_core_type(core_dir: Path) -> Optional[str]: - # libtorch版はDirectML未対応なので、ここでは`gpu_type=GPUType.DIRECT_ML`は入れない - libtorch_core_names = [ - get_suitable_core_name("libtorch", gpu_type=GPUType.CUDA), - get_suitable_core_name("libtorch", gpu_type=GPUType.NONE), - ] - onnxruntime_core_names = [ - get_suitable_core_name("onnxruntime", gpu_type=GPUType.CUDA), - get_suitable_core_name("onnxruntime", gpu_type=GPUType.DIRECT_ML), - get_suitable_core_name("onnxruntime", gpu_type=GPUType.NONE), - ] - if any([(core_dir / name).is_file() for name in libtorch_core_names if name]): - return "libtorch" - elif any([(core_dir / name).is_file() for name in onnxruntime_core_names if name]): - return "onnxruntime" - else: - return None - - -def load_core(core_dir: Path, use_gpu: bool) -> CDLL: - core_name = find_version_0_12_core_or_later(core_dir) - if core_name: - try: - # NOTE: CDLL クラスのコンストラクタの引数 name には文字列を渡す必要がある。 - # Windows 環境では PathLike オブジェクトを引数として渡すと初期化に失敗する。 - return CDLL(str((core_dir / core_name).resolve(strict=True))) - except OSError as err: - raise RuntimeError(f"コアの読み込みに失敗しました：{err}") - - model_type = check_core_type(core_dir) - if model_type is None: - raise RuntimeError("コアが見つかりません") - if use_gpu or model_type == "onnxruntime": - core_name = get_suitable_core_name(model_type, gpu_type=GPUType.CUDA) - if core_name: - try: - return CDLL(str((core_dir / core_name).resolve(strict=True))) - except OSError: - pass - core_name = get_suitable_core_name(model_type, gpu_type=GPUType.DIRECT_ML) - if core_name: - try: - return CDLL(str((core_dir / core_name).resolve(strict=True))) - except OSError: - pass - core_name = get_suitable_core_name(model_type, gpu_type=GPUType.NONE) - if core_name: - try: - return CDLL(str((core_dir / core_name).resolve(strict=True))) - except OSError as err: - if model_type == "libtorch": - core_name = get_suitable_core_name(model_type, gpu_type=GPUType.CUDA) - if core_name: - try: - return CDLL(str((core_dir / core_name).resolve(strict=True))) - except OSError as err_: - err = err_ - raise RuntimeError(f"コアの読み込みに失敗しました：{err}") - else: - raise RuntimeError(f"このコンピュータのアーキテクチャ {platform.machine()} で利用可能なコアがありません") - - -class CoreWrapper: - def __init__( - self, - use_gpu: bool, - core_dir: Path, - cpu_num_threads: int = 0, - load_all_models: bool = False, - ) -> None: - - self.core = load_core(core_dir, use_gpu) - - self.core.initialize.restype = c_bool - self.core.metas.restype = c_char_p - self.core.yukarin_s_forward.restype = c_bool - self.core.yukarin_sa_forward.restype = c_bool - self.core.decode_forward.restype = c_bool - self.core.last_error_message.restype = c_char_p - - self.exist_supported_devices = False - self.exist_finalize = False - exist_cpu_num_threads = False - self.exist_load_model = False - self.exist_is_model_loaded = False - - is_version_0_12_core_or_later = ( - find_version_0_12_core_or_later(core_dir) is not None - ) - if is_version_0_12_core_or_later: - model_type = "onnxruntime" - self.exist_load_model = True - self.exist_is_model_loaded = True - self.core.load_model.argtypes = (c_long,) - self.core.load_model.restype = c_bool - self.core.is_model_loaded.argtypes = (c_long,) - self.core.is_model_loaded.restype = c_bool - else: - model_type = check_core_type(core_dir) - assert model_type is not None - - if model_type == "onnxruntime": - self.core.supported_devices.restype = c_char_p - self.core.finalize.restype = None - self.exist_supported_devices = True - self.exist_finalize = True - exist_cpu_num_threads = True - - self.core.yukarin_s_forward.argtypes = ( - c_int, - POINTER(c_long), - POINTER(c_long), - POINTER(c_float), - ) - self.core.yukarin_sa_forward.argtypes = ( - c_int, - POINTER(c_long), - POINTER(c_long), - POINTER(c_long), - POINTER(c_long), - POINTER(c_long), - POINTER(c_long), - POINTER(c_long), - POINTER(c_float), - ) - self.core.decode_forward.argtypes = ( - c_int, - c_int, - POINTER(c_float), - POINTER(c_float), - POINTER(c_long), - POINTER(c_float), - ) - - cwd = os.getcwd() - os.chdir(core_dir) - try: - if is_version_0_12_core_or_later: - self.assert_core_success( - self.core.initialize(use_gpu, cpu_num_threads, load_all_models) - ) - elif exist_cpu_num_threads: - self.assert_core_success( - self.core.initialize(".", use_gpu, cpu_num_threads) - ) - else: - self.assert_core_success(self.core.initialize(".", use_gpu)) - finally: - os.chdir(cwd) - - def metas(self) -> str: - return self.core.metas().decode("utf-8") - - def yukarin_s_forward( - self, - length: int, - phoneme_list: np.ndarray, - speaker_id: np.ndarray, - ) -> np.ndarray: - output = np.zeros((length,), dtype=np.float32) - self.assert_core_success( - self.core.yukarin_s_forward( - c_int(length), - phoneme_list.ctypes.data_as(POINTER(c_long)), - speaker_id.ctypes.data_as(POINTER(c_long)), - output.ctypes.data_as(POINTER(c_float)), - ) - ) - return output - - def yukarin_sa_forward( - self, - length: int, - vowel_phoneme_list: np.ndarray, - consonant_phoneme_list: np.ndarray, - start_accent_list: np.ndarray, - end_accent_list: np.ndarray, - start_accent_phrase_list: np.ndarray, - end_accent_phrase_list: np.ndarray, - speaker_id: np.ndarray, - ) -> np.ndarray: - output = np.empty( - ( - len(speaker_id), - length, - ), - dtype=np.float32, - ) - self.assert_core_success( - self.core.yukarin_sa_forward( - c_int(length), - vowel_phoneme_list.ctypes.data_as(POINTER(c_long)), - consonant_phoneme_list.ctypes.data_as(POINTER(c_long)), - start_accent_list.ctypes.data_as(POINTER(c_long)), - end_accent_list.ctypes.data_as(POINTER(c_long)), - start_accent_phrase_list.ctypes.data_as(POINTER(c_long)), - end_accent_phrase_list.ctypes.data_as(POINTER(c_long)), - speaker_id.ctypes.data_as(POINTER(c_long)), - output.ctypes.data_as(POINTER(c_float)), - ) - ) - return output - - def decode_forward( - self, - length: int, - phoneme_size: int, - f0: np.ndarray, - phoneme: np.ndarray, - speaker_id: np.ndarray, - ) -> np.ndarray: - output = np.empty((length * 256,), dtype=np.float32) - self.assert_core_success( - self.core.decode_forward( - c_int(length), - c_int(phoneme_size), - f0.ctypes.data_as(POINTER(c_float)), - phoneme.ctypes.data_as(POINTER(c_float)), - speaker_id.ctypes.data_as(POINTER(c_long)), - output.ctypes.data_as(POINTER(c_float)), - ) - ) - return output - - def supported_devices(self) -> str: - if self.exist_supported_devices: - return self.core.supported_devices().decode("utf-8") - raise OldCoreError - - def finalize(self) -> None: - if self.exist_finalize: - self.core.finalize() - return - raise OldCoreError - - def load_model(self, speaker_id: int) -> None: - if self.exist_load_model: - self.assert_core_success(self.core.load_model(c_long(speaker_id))) - raise OldCoreError - - def is_model_loaded(self, speaker_id: int) -> bool: - if self.exist_is_model_loaded: - return self.core.is_model_loaded(c_long(speaker_id)) - raise OldCoreError - - def assert_core_success(self, result: bool) -> None: - if not result: - raise CoreError( - self.core.last_error_message().decode("utf-8", "backslashreplace") - ) diff --git a/spaces/Gen-Sim/Gen-Sim/misc/compute_embedding_neighbor_tasks.py b/spaces/Gen-Sim/Gen-Sim/misc/compute_embedding_neighbor_tasks.py deleted file mode 100644 index 96546131f18e78adba96fae954fa1e4fbc8e6759..0000000000000000000000000000000000000000 --- a/spaces/Gen-Sim/Gen-Sim/misc/compute_embedding_neighbor_tasks.py +++ /dev/null @@ -1,189 +0,0 @@ -import torch -import torch.nn -import torchvision.models as models -from copy import deepcopy -import cv2 - -import cv2 -import numpy as np -import sys -import itertools -import os -import IPython -import matplotlib -matplotlib.use("Agg") - -import matplotlib.pyplot as plt -import pandas as pd - -import openai -from sklearn.manifold import TSNE -from sklearn.decomposition import PCA, KernelPCA -import seaborn as sns - -import time -from matplotlib.offsetbox import OffsetImage, AnnotationBbox -import colorsys -from torchvision import datasets -import argparse -import matplotlib.patheffects as PathEffects -from scipy.spatial import cKDTree - -sns.set_style("white") -sns.set_palette("muted") - -font = { - "size": 22, -} - -matplotlib.rc("font", **font) -sns.set_context("paper", font_scale=3.0) - - -plt_param = {'legend.fontsize': 60, - 'axes.labelsize': 80, - 'axes.titlesize':80, - 'font.size' : 80 , - 'xtick.labelsize':80, - 'ytick.labelsize':80, - 'lines.linewidth': 10, - 'lines.color': (0,0,0)} - -plt.rcParams.update(plt_param) - -openai.api_key ="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" -GPT_MODEL = "gpt4" -EMBEDDING_MODEL = "text-embedding-ada-002" -ORIGINAL_NAMES = [ - # demo conditioned - 'align-box-corner', - 'assembling-kits', - 'assembling-kits-easy', - 'block-insertion', - 'block-insertion-easy', - 'block-insertion-nofixture', - 'block-insertion-sixdof', - 'block-insertion-translation', - 'manipulating-rope', - 'packing-boxes', - 'palletizing-boxes', - 'place-red-in-green', - 'stack-block-pyramid', - 'sweeping-piles', - 'towers-of-hanoi', - 'gen-task', - # goal conditioned - 'align-rope', - 'assembling-kits-seq', - 'assembling-kits-seq-seen-colors', - 'assembling-kits-seq-unseen-colors', - 'assembling-kits-seq-full', - 'packing-shapes', - 'packing-boxes-pairs', - 'packing-boxes-pairs-seen-colors', - 'packing-boxes-pairs-unseen-colors', - 'packing-boxes-pairs-full', - 'packing-seen-google-objects-seq', - 'packing-unseen-google-objects-seq', - 'packing-seen-google-objects-group', - 'packing-unseen-google-objects-group', - 'put-block-in-bowl', - 'put-block-in-bowl-seen-colors', - 'put-block-in-bowl-unseen-colors', - 'put-block-in-bowl-full', - 'stack-block-pyramid-seq', - 'stack-block-pyramid-seq-seen-colors', - 'stack-block-pyramid-seq-unseen-colors', - 'stack-block-pyramid-seq-full', - 'separating-piles', - 'separating-piles-seen-colors', - 'separating-piles-unseen-colors', - 'separating-piles-full', - 'towers-of-hanoi-seq', - 'towers-of-hanoi-seq-seen-colors', - 'towers-of-hanoi-seq-unseen-colors', - 'towers-of-hanoi-seq-full', - ] - - -def normalize_numpy_array(arr): - return arr / (arr.max(axis=-1, keepdims=True) - arr.min(axis=-1, keepdims=True)) - - -def compute_embedding(response): - for _ in range(3): - try: - response_embedding = openai.Embedding.create( - model=EMBEDDING_MODEL, - input=response, - ) - - response_embedding = np.array(response_embedding["data"][0]['embedding']) - return response_embedding - except Exception as e: - print(e) - -def find_cliport_neighbor(kdtree, latents, label_sets): - closest_embeddings, closest_idx = kdtree.query(latents, k=78) - for i, idx in enumerate(closest_idx[0][1:]): - s_replaced = label_sets[idx].replace("_", "-") - if s_replaced in ORIGINAL_NAMES: - print(label_sets[idx], i) - - -def compute_neighbors(args): - fig_name=f'output/output_embedding/{args.file}' - # query: (response, embeddings) - latents = [] - class_labels = [] - label_sets = [] - - # chatgpt embedding - total_tasks = [os.path.join("cliport/tasks", x) for x in os.listdir("cliport/tasks")] + [os.path.join("cliport/generated_tasks", x) for x in os.listdir("cliport/generated_tasks")] - total_tasks = [t for t in total_tasks if 'pycache' not in t and 'init' not in t \ - and 'README' not in t and 'extended' not in t and 'gripper' not in t and 'primitive' not in t\ - and 'task.py' not in t and 'camera' not in t and 'seq' not in t and 'seen' not in t] - cache_embedding_path = "output/output_embedding/task_cache_embedding.npz" - cache_embedding = {} - - if os.path.exists(cache_embedding_path): - cache_embedding = dict(np.load(cache_embedding_path)) - - # print(total_tasks) - - for idx, task_name in enumerate(total_tasks): - if task_name in cache_embedding: - code_embedding = cache_embedding[task_name] - else: - code = open(task_name).read() - code_embedding = compute_embedding(code) - - latents.append(code_embedding) - label_sets.append(task_name.split("/")[-1][:-3]) - cache_embedding[task_name] = code_embedding - class_labels.append(idx) - - latents = np.array(latents) - # print("latents shape:", latents.shape) - # np.savez(cache_embedding_path, **cache_embedding) - - target_task_idx = label_sets.index(args.target_task) - kdtree = cKDTree(latents) - closest_embeddings, closest_idx = kdtree.query(latents[[target_task_idx]], k=args.num+1) - # print(latents.shape, args.num, target_task_idx, closest_idx,label_sets) - - print(f"closest tasks to {args.target_task}: {[label_sets[task] for task in closest_idx[0][1:]]}") - - # print(f"closest tasks in cliport original tasks: {find_cliport_neighbor(kdtree, latents[[target_task_idx]], label_sets)}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Generate chat-gpt embeddings") - """ - load task descriptions from the tasks folder and embed - """ - parser.add_argument("--file", type=str, default="task_embedding") - parser.add_argument("--target_task", type=str, default="align_box_corner") - parser.add_argument("--num", type=int, default=3) - - args = parser.parse_args() - compute_neighbors(args) \ No newline at end of file diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py deleted file mode 100644 index f26062fda282fda420a5f48bbc12bfe4efe57c0a..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py +++ /dev/null @@ -1,71 +0,0 @@ -_base_ = [ - '../_base_/models/retinanet_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' -] -# model settings -norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), - bbox_head=dict( - _delete_=True, - type='SABLRetinaHead', - num_classes=80, - in_channels=256, - stacked_convs=4, - feat_channels=256, - approx_anchor_generator=dict( - type='AnchorGenerator', - octave_base_scale=4, - scales_per_octave=3, - ratios=[0.5, 1.0, 2.0], - strides=[8, 16, 32, 64, 128]), - square_anchor_generator=dict( - type='AnchorGenerator', - ratios=[1.0], - scales=[4], - strides=[8, 16, 32, 64, 128]), - norm_cfg=norm_cfg, - bbox_coder=dict( - type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), - loss_cls=dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - loss_bbox_cls=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), - loss_bbox_reg=dict( - type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), - # training and testing settings - train_cfg=dict( - assigner=dict( - type='ApproxMaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0.0, - ignore_iof_thr=-1), - allowed_border=-1, - pos_weight=-1, - debug=False)) -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, 640), (1333, 800)], - multiscale_mode='range', - 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']), -] -data = dict(train=dict(pipeline=train_pipeline)) -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 16e34356e9f8566ec73e3c25c771e281d3eeb975..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = [ - '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py deleted file mode 100644 index b4a9d4e1b9123b3c965cd430237ce9fcc7018a11..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = [ - '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' -] -model = dict( - decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index aff70c93e6142ddda3a874d9dfd57ec6c4cd89b3..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,11 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' -model = dict( - pretrained='open-mmlab://resnet18_v1c', - backbone=dict(depth=18), - decode_head=dict( - c1_in_channels=64, - c1_channels=12, - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/common_utils/wav_utils.py b/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/common_utils/wav_utils.py deleted file mode 100644 index d3a563ee1749a58217ece55c9a08b8d93c0fc386..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/common_utils/wav_utils.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from pathlib import Path -import typing as tp - -import torch -import torchaudio - - -def get_white_noise(chs: int = 1, num_frames: int = 1): - wav = torch.randn(chs, num_frames) - return wav - - -def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1): - wav = torch.randn(bs, chs, num_frames) - return wav - - -def save_wav(path: str, wav: torch.Tensor, sample_rate: int): - fp = Path(path) - kwargs: tp.Dict[str, tp.Any] = {} - if fp.suffix == '.wav': - kwargs['encoding'] = 'PCM_S' - kwargs['bits_per_sample'] = 16 - elif fp.suffix == '.mp3': - kwargs['compression'] = 320 - torchaudio.save(str(fp), wav, sample_rate, **kwargs) diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/BBSNet_model.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/BBSNet_model.py deleted file mode 100644 index 37e31b19692ee2c0855ffee83bded1632b9750ab..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/BBSNet_model.py +++ /dev/null @@ -1,419 +0,0 @@ -import torch -import torch.nn as nn -import torchvision.models as models -from .ResNet import ResNet50 - -def conv3x3(in_planes, out_planes, stride=1): - "3x3 convolution with padding" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) -class TransBasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, upsample=None, **kwargs): - super(TransBasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, inplanes) - self.bn1 = nn.BatchNorm2d(inplanes) - self.relu = nn.ReLU(inplace=True) - if upsample is not None and stride != 1: - self.conv2 = nn.ConvTranspose2d(inplanes, planes, - kernel_size=3, stride=stride, padding=1, - output_padding=1, bias=False) - else: - self.conv2 = conv3x3(inplanes, planes, stride) - self.bn2 = nn.BatchNorm2d(planes) - self.upsample = upsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.upsample is not None: - residual = self.upsample(x) - - out += residual - out = self.relu(out) - - return out -class ChannelAttention(nn.Module): - def __init__(self, in_planes, ratio=16): - super(ChannelAttention, self).__init__() - - self.max_pool = nn.AdaptiveMaxPool2d(1) - - self.fc1 = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False) - self.relu1 = nn.ReLU() - self.fc2 = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False) - - self.sigmoid = nn.Sigmoid() - def forward(self, x): - max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) - out = max_out - return self.sigmoid(out) - -class SpatialAttention(nn.Module): - def __init__(self, kernel_size=7): - super(SpatialAttention, self).__init__() - - assert kernel_size in (3, 7), 'kernel size must be 3 or 7' - padding = 3 if kernel_size == 7 else 1 - - self.conv1 = nn.Conv2d(1, 1, kernel_size, padding=padding, bias=False) - self.sigmoid = nn.Sigmoid() - - def forward(self, x): - max_out, _ = torch.max(x, dim=1, keepdim=True) - x=max_out - x = self.conv1(x) - return self.sigmoid(x) - -class BasicConv2d(nn.Module): - def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1): - super(BasicConv2d, self).__init__() - self.conv = nn.Conv2d(in_planes, out_planes, - kernel_size=kernel_size, stride=stride, - padding=padding, dilation=dilation, bias=False) - self.bn = nn.BatchNorm2d(out_planes) - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - x = self.conv(x) - x = self.bn(x) - return x - -#Global Contextual module -class GCM(nn.Module): - def __init__(self, in_channel, out_channel): - super(GCM, self).__init__() - self.relu = nn.ReLU(True) - self.branch0 = nn.Sequential( - BasicConv2d(in_channel, out_channel, 1), - ) - self.branch1 = nn.Sequential( - BasicConv2d(in_channel, out_channel, 1), - BasicConv2d(out_channel, out_channel, kernel_size=(1, 3), padding=(0, 1)), - BasicConv2d(out_channel, out_channel, kernel_size=(3, 1), padding=(1, 0)), - BasicConv2d(out_channel, out_channel, 3, padding=3, dilation=3) - ) - self.branch2 = nn.Sequential( - BasicConv2d(in_channel, out_channel, 1), - BasicConv2d(out_channel, out_channel, kernel_size=(1, 5), padding=(0, 2)), - BasicConv2d(out_channel, out_channel, kernel_size=(5, 1), padding=(2, 0)), - BasicConv2d(out_channel, out_channel, 3, padding=5, dilation=5) - ) - self.branch3 = nn.Sequential( - BasicConv2d(in_channel, out_channel, 1), - BasicConv2d(out_channel, out_channel, kernel_size=(1, 7), padding=(0, 3)), - BasicConv2d(out_channel, out_channel, kernel_size=(7, 1), padding=(3, 0)), - BasicConv2d(out_channel, out_channel, 3, padding=7, dilation=7) - ) - self.conv_cat = BasicConv2d(4*out_channel, out_channel, 3, padding=1) - self.conv_res = BasicConv2d(in_channel, out_channel, 1) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - x2 = self.branch2(x) - x3 = self.branch3(x) - - x_cat = self.conv_cat(torch.cat((x0, x1, x2, x3), 1)) - - x = self.relu(x_cat + self.conv_res(x)) - return x - -#aggregation of the high-level(teacher) features -class aggregation_init(nn.Module): - - def __init__(self, channel): - super(aggregation_init, self).__init__() - self.relu = nn.ReLU(True) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) - self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample2 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample3 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample4 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample5 = BasicConv2d(2*channel, 2*channel, 3, padding=1) - - self.conv_concat2 = BasicConv2d(2*channel, 2*channel, 3, padding=1) - self.conv_concat3 = BasicConv2d(3*channel, 3*channel, 3, padding=1) - self.conv4 = BasicConv2d(3*channel, 3*channel, 3, padding=1) - self.conv5 = nn.Conv2d(3*channel, 1, 1) - - def forward(self, x1, x2, x3): - x1_1 = x1 - x2_1 = self.conv_upsample1(self.upsample(x1)) * x2 - x3_1 = self.conv_upsample2(self.upsample(self.upsample(x1))) \ - * self.conv_upsample3(self.upsample(x2)) * x3 - - x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1) - x2_2 = self.conv_concat2(x2_2) - - x3_2 = torch.cat((x3_1, self.conv_upsample5(self.upsample(x2_2))), 1) - x3_2 = self.conv_concat3(x3_2) - - x = self.conv4(x3_2) - x = self.conv5(x) - - return x - -#aggregation of the low-level(student) features -class aggregation_final(nn.Module): - - def __init__(self, channel): - super(aggregation_final, self).__init__() - self.relu = nn.ReLU(True) - - self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) - self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample2 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample3 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample4 = BasicConv2d(channel, channel, 3, padding=1) - self.conv_upsample5 = BasicConv2d(2*channel, 2*channel, 3, padding=1) - - self.conv_concat2 = BasicConv2d(2*channel, 2*channel, 3, padding=1) - self.conv_concat3 = BasicConv2d(3*channel, 3*channel, 3, padding=1) - - def forward(self, x1, x2, x3): - x1_1 = x1 - x2_1 = self.conv_upsample1(self.upsample(x1)) * x2 - x3_1 = self.conv_upsample2(self.upsample(x1)) \ - * self.conv_upsample3(x2) * x3 - - x2_2 = torch.cat((x2_1, self.conv_upsample4(self.upsample(x1_1))), 1) - x2_2 = self.conv_concat2(x2_2) - - x3_2 = torch.cat((x3_1, self.conv_upsample5(x2_2)), 1) - x3_2 = self.conv_concat3(x3_2) - - return x3_2 - -#Refinement flow -class Refine(nn.Module): - def __init__(self): - super(Refine,self).__init__() - self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) - - def forward(self, attention,x1,x2,x3): - #Note that there is an error in the manuscript. In the paper, the refinement strategy is depicted as ""f'=f*S1"", it should be ""f'=f+f*S1"". - x1 = x1+torch.mul(x1, self.upsample2(attention)) - x2 = x2+torch.mul(x2,self.upsample2(attention)) - x3 = x3+torch.mul(x3,attention) - - return x1,x2,x3 - -#BBSNet -class BBSNet(nn.Module): - def __init__(self, channel=32): - super(BBSNet, self).__init__() - - #Backbone model - self.resnet = ResNet50('rgb') - self.resnet_depth=ResNet50('rgbd') - - #Decoder 1 - self.rfb2_1 = GCM(512, channel) - self.rfb3_1 = GCM(1024, channel) - self.rfb4_1 = GCM(2048, channel) - self.agg1 = aggregation_init(channel) - - #Decoder 2 - self.rfb0_2 = GCM(64, channel) - self.rfb1_2 = GCM(256, channel) - self.rfb5_2 = GCM(512, channel) - self.agg2 = aggregation_final(channel) - - #upsample function - self.upsample = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True) - self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True) - self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) - - #Refinement flow - self.HA = Refine() - - #Components of DEM module - self.atten_depth_channel_0=ChannelAttention(64) - self.atten_depth_channel_1=ChannelAttention(256) - self.atten_depth_channel_2=ChannelAttention(512) - self.atten_depth_channel_3_1=ChannelAttention(1024) - self.atten_depth_channel_4_1=ChannelAttention(2048) - - self.atten_depth_spatial_0=SpatialAttention() - self.atten_depth_spatial_1=SpatialAttention() - self.atten_depth_spatial_2=SpatialAttention() - self.atten_depth_spatial_3_1=SpatialAttention() - self.atten_depth_spatial_4_1=SpatialAttention() - - #Components of PTM module - self.inplanes = 32*2 - self.deconv1 = self._make_transpose(TransBasicBlock, 32*2, 3, stride=2) - self.inplanes =32 - self.deconv2 = self._make_transpose(TransBasicBlock, 32, 3, stride=2) - self.agant1 = self._make_agant_layer(32*3, 32*2) - self.agant2 = self._make_agant_layer(32*2, 32) - self.out0_conv = nn.Conv2d(32*3, 1, kernel_size=1, stride=1, bias=True) - self.out1_conv = nn.Conv2d(32*2, 1, kernel_size=1, stride=1, bias=True) - self.out2_conv = nn.Conv2d(32*1, 1, kernel_size=1, stride=1, bias=True) - - # if self.training: - # self.initialize_weights() - - def forward(self, x, x_depth): - x = self.resnet.conv1(x) - x = self.resnet.bn1(x) - x = self.resnet.relu(x) - x = self.resnet.maxpool(x) - - x_depth = self.resnet_depth.conv1(x_depth) - x_depth = self.resnet_depth.bn1(x_depth) - x_depth = self.resnet_depth.relu(x_depth) - x_depth = self.resnet_depth.maxpool(x_depth) - - #layer0 merge - temp = x_depth.mul(self.atten_depth_channel_0(x_depth)) - temp = temp.mul(self.atten_depth_spatial_0(temp)) - x=x+temp - #layer0 merge end - - x1 = self.resnet.layer1(x) # 256 x 64 x 64 - x1_depth=self.resnet_depth.layer1(x_depth) - - #layer1 merge - temp = x1_depth.mul(self.atten_depth_channel_1(x1_depth)) - temp = temp.mul(self.atten_depth_spatial_1(temp)) - x1=x1+temp - #layer1 merge end - - x2 = self.resnet.layer2(x1) # 512 x 32 x 32 - x2_depth=self.resnet_depth.layer2(x1_depth) - - #layer2 merge - temp = x2_depth.mul(self.atten_depth_channel_2(x2_depth)) - temp = temp.mul(self.atten_depth_spatial_2(temp)) - x2=x2+temp - #layer2 merge end - - x2_1 = x2 - - x3_1 = self.resnet.layer3_1(x2_1) # 1024 x 16 x 16 - x3_1_depth=self.resnet_depth.layer3_1(x2_depth) - - #layer3_1 merge - temp = x3_1_depth.mul(self.atten_depth_channel_3_1(x3_1_depth)) - temp = temp.mul(self.atten_depth_spatial_3_1(temp)) - x3_1=x3_1+temp - #layer3_1 merge end - - x4_1 = self.resnet.layer4_1(x3_1) # 2048 x 8 x 8 - x4_1_depth=self.resnet_depth.layer4_1(x3_1_depth) - - #layer4_1 merge - temp = x4_1_depth.mul(self.atten_depth_channel_4_1(x4_1_depth)) - temp = temp.mul(self.atten_depth_spatial_4_1(temp)) - x4_1=x4_1+temp - #layer4_1 merge end - - #produce initial saliency map by decoder1 - x2_1 = self.rfb2_1(x2_1) - x3_1 = self.rfb3_1(x3_1) - x4_1 = self.rfb4_1(x4_1) - attention_map = self.agg1(x4_1, x3_1, x2_1) - - #Refine low-layer features by initial map - x,x1,x5 = self.HA(attention_map.sigmoid(), x,x1,x2) - - #produce final saliency map by decoder2 - x0_2 = self.rfb0_2(x) - x1_2 = self.rfb1_2(x1) - x5_2 = self.rfb5_2(x5) - y = self.agg2(x5_2, x1_2, x0_2) #*4 - - #PTM module - y =self.agant1(y) - y = self.deconv1(y) - y = self.agant2(y) - y = self.deconv2(y) - y = self.out2_conv(y) - - return self.upsample(attention_map),y - - def _make_agant_layer(self, inplanes, planes): - layers = nn.Sequential( - nn.Conv2d(inplanes, planes, kernel_size=1, - stride=1, padding=0, bias=False), - nn.BatchNorm2d(planes), - nn.ReLU(inplace=True) - ) - return layers - - def _make_transpose(self, block, planes, blocks, stride=1): - upsample = None - if stride != 1: - upsample = nn.Sequential( - nn.ConvTranspose2d(self.inplanes, planes, - kernel_size=2, stride=stride, - padding=0, bias=False), - nn.BatchNorm2d(planes), - ) - elif self.inplanes != planes: - upsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes), - ) - - layers = [] - - for i in range(1, blocks): - layers.append(block(self.inplanes, self.inplanes)) - - layers.append(block(self.inplanes, planes, stride, upsample)) - self.inplanes = planes - - return nn.Sequential(*layers) - - #initialize the weights - def initialize_weights(self): - res50 = models.resnet50(pretrained=True) - pretrained_dict = res50.state_dict() - all_params = {} - for k, v in self.resnet.state_dict().items(): - if k in pretrained_dict.keys(): - v = pretrained_dict[k] - all_params[k] = v - elif '_1' in k: - name = k.split('_1')[0] + k.split('_1')[1] - v = pretrained_dict[name] - all_params[k] = v - elif '_2' in k: - name = k.split('_2')[0] + k.split('_2')[1] - v = pretrained_dict[name] - all_params[k] = v - assert len(all_params.keys()) == len(self.resnet.state_dict().keys()) - self.resnet.load_state_dict(all_params) - - all_params = {} - for k, v in self.resnet_depth.state_dict().items(): - if k=='conv1.weight': - all_params[k]=torch.nn.init.normal_(v, mean=0, std=1) - elif k in pretrained_dict.keys(): - v = pretrained_dict[k] - all_params[k] = v - elif '_1' in k: - name = k.split('_1')[0] + k.split('_1')[1] - v = pretrained_dict[name] - all_params[k] = v - elif '_2' in k: - name = k.split('_2')[0] + k.split('_2')[1] - v = pretrained_dict[name] - all_params[k] = v - assert len(all_params.keys()) == len(self.resnet_depth.state_dict().keys()) - self.resnet_depth.load_state_dict(all_params) - diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/cross_validation.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/cross_validation.py deleted file mode 100644 index 90907707a38657bee37f8df64ce6f43b4cd6e3eb..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/s_multimae/cross_validation.py +++ /dev/null @@ -1,127 +0,0 @@ -from typing import Optional -from torch.utils.data import DataLoader -from torch import nn, Tensor -import torch -from tqdm import tqdm -import wandb -from torch.utils.data.distributed import DistributedSampler -import torch.distributed as dist - -from .criterion import DevCriterion -from .distributed_training import get_world_size, is_master_proc -from .utils import clean_cache -from .dataset_fn import DevDataset, RGBDDataset -from .configs.base_config import base_cfg -from .logger_fn import Logger -from .device import device, cpu_device - -class CrossValidation: - def __init__( - self, cfg: base_cfg, max_size: Optional[int] = None, - max_track: Optional[int] = None, - data_augmentation_version: int = 1, - ) -> None: - self.cfg = cfg - self.dev_dataset = DevDataset(cfg) - dev_sampler = DistributedSampler( - dataset=self.dev_dataset, shuffle=False - ) - self.dev_dataloader = DataLoader( - self.dev_dataset, batch_size=cfg.val_batch_size, - sampler=dev_sampler, - num_workers=cfg.num_workers, - pin_memory=True, - ) - self.dev_criterion = DevCriterion() - self.dev_num_iters = len(self.dev_dataloader) - - self.world_size = get_world_size() - self.is_master_process = is_master_proc() - - def calculate_dev_mae( - self, model: nn.Module, epoch: int, logger: Optional[Logger] = None - ) -> float: - dataloader = self.dev_dataloader - dataset = self.dev_dataset - num_iters = self.dev_num_iters - return self.__calculate_mae( - epoch, dataloader, dataset, - num_iters, model, 'dev', logger - ) - - @torch.no_grad() - def __calculate_mae( - self, epoch: int, dataloader: DataLoader, - dataset: RGBDDataset, - num_iters: int, model: nn.Module, log_attr: str, - logger: Optional[Logger] = None - ) -> float: - '''Given that the model is already loaded in GPU - Note that the model will be in evaluation model after running this function - ''' - model.eval() - - total_mae: float = 0.0 - if logger is not None and self.is_master_process: - logger.info(f'Cross-validation [{log_attr}] ...') - for i_batch, (gpu_images, gpu_depths, gpu_gts, indices) in tqdm( - enumerate(dataloader, start=1), total=num_iters, - disable=not self.is_master_process, - ): - gpu_images: Tensor = gpu_images.cuda() - gpu_depths: Tensor = gpu_depths.cuda() - gpu_gts: Tensor = gpu_gts.cuda() - - with torch.cuda.amp.autocast(enabled=self.cfg.is_fp16): - gpu_out: Tensor = model(gpu_images, gpu_depths) - mae: Tensor = self.dev_criterion( - gpu_out['semseg'].sigmoid(), gpu_gts - ) - dist.all_reduce(mae) - - total_mae += mae.to(cpu_device).item() * indices.shape[0] # * self.world_size - del gpu_images, gpu_depths, gpu_gts, indices - clean_cache() - - return total_mae / len(dataset) - -def cross_validation_log( - cfg: base_cfg, - model: nn.Module, - logger: Logger, - cross_val: CrossValidation, - epoch: int -) -> None: - clean_cache() - - dev_mae = cross_val.calculate_dev_mae(model, epoch, logger) - - if is_master_proc(): - wandb.log({ - # 'train_mae': train_mae, - 'dev_mae': dev_mae, - 'epoch': epoch, - }) - logger.info(f'Epoch {epoch}: Dev MAE {dev_mae:.4f}') - cfg.em.update(epoch, dev_mae) - - clean_cache() - -def test_cross_validation(cfg: base_cfg) -> None: - from .rgbd_model import RGBDModel - from .checkpoint import load_checkpoint - from .run_type import run_type - from .wandb_manager import wandb_login, wandb_init - wandb_login(cfg) - wandb_init('test_cross_validation') - - model = RGBDModel(cfg, run_type=run_type.rt) - load_checkpoint(model, None, None, None, ckpt_path = cfg.ckpt_path) - - model.to(device) - - cross_val = CrossValidation(cfg, max_track=10, max_size=100) - cross_val.calculate_train_mae(model, 2) - cross_val.calculate_dev_mae(model, 2) - - wandb.finish() diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/mixup.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/mixup.py deleted file mode 100644 index ef3a00accd871d2e327c457fea1cd15e8d70ddf2..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/mixup.py +++ /dev/null @@ -1,322 +0,0 @@ -# -------------------------------------------------------- -# Based on timm and MAE-priv code bases -# https://github.com/rwightman/pytorch-image-models/tree/master/timm -# https://github.com/BUPT-PRIV/MAE-priv -# -------------------------------------------------------- - -""" Mixup and Cutmix - -Papers: -mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412) - -CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899) - -Code Reference: -CutMix: https://github.com/clovaai/CutMix-PyTorch - -Hacked together by / Copyright 2020 Ross Wightman -""" -import numpy as np -import torch - - -def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'): - x = x.long().view(-1, 1) - return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(1, x, on_value) - - -def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'): - off_value = smoothing / num_classes - on_value = 1. - smoothing + off_value - y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device) - y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device) - return y1 * lam + y2 * (1. - lam) - - -def rand_bbox(img_shape, lam, margin=0., count=None): - """ Standard CutMix bounding-box - Generates a random square bbox based on lambda value. This impl includes - support for enforcing a border margin as percent of bbox dimensions. - - Args: - img_shape (tuple): Image shape as tuple - lam (float): Cutmix lambda value - margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image) - count (int): Number of bbox to generate - """ - ratio = np.sqrt(1 - lam) - img_h, img_w = img_shape[-2:] - cut_h, cut_w = int(img_h * ratio), int(img_w * ratio) - margin_y, margin_x = int(margin * cut_h), int(margin * cut_w) - cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count) - cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count) - yl = np.clip(cy - cut_h // 2, 0, img_h) - yh = np.clip(cy + cut_h // 2, 0, img_h) - xl = np.clip(cx - cut_w // 2, 0, img_w) - xh = np.clip(cx + cut_w // 2, 0, img_w) - return yl, yh, xl, xh - - -def rand_bbox_minmax(img_shape, minmax, count=None): - """ Min-Max CutMix bounding-box - Inspired by Darknet cutmix impl, generates a random rectangular bbox - based on min/max percent values applied to each dimension of the input image. - - Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max. - - Args: - img_shape (tuple): Image shape as tuple - minmax (tuple or list): Min and max bbox ratios (as percent of image size) - count (int): Number of bbox to generate - """ - assert len(minmax) == 2 - img_h, img_w = img_shape[-2:] - cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count) - cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count) - yl = np.random.randint(0, img_h - cut_h, size=count) - xl = np.random.randint(0, img_w - cut_w, size=count) - yu = yl + cut_h - xu = xl + cut_w - return yl, yu, xl, xu - - -def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None): - """ Generate bbox and apply lambda correction. - """ - if ratio_minmax is not None: - yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count) - else: - yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count) - if correct_lam or ratio_minmax is not None: - bbox_area = (yu - yl) * (xu - xl) - lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1]) - return (yl, yu, xl, xu), lam - - -class Mixup: - """ Mixup/Cutmix that applies different params to each element or whole batch - - Args: - mixup_alpha (float): mixup alpha value, mixup is active if > 0. - cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0. - cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None. - prob (float): probability of applying mixup or cutmix per batch or element - switch_prob (float): probability of switching to cutmix instead of mixup when both are active - mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element) - correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders - label_smoothing (float): apply label smoothing to the mixed target tensor - num_classes (int): number of classes for target - """ - - def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5, - mode='batch', correct_lam=True, label_smoothing=0.1, num_classes=1000): - self.mixup_alpha = mixup_alpha - self.cutmix_alpha = cutmix_alpha - self.cutmix_minmax = cutmix_minmax - if self.cutmix_minmax is not None: - assert len(self.cutmix_minmax) == 2 - # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe - self.cutmix_alpha = 1.0 - self.mix_prob = prob - self.switch_prob = switch_prob - self.label_smoothing = label_smoothing - self.num_classes = num_classes - self.mode = mode - self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix - self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop) - - def _params_per_elem(self, batch_size): - lam = np.ones(batch_size, dtype=np.float32) - use_cutmix = np.zeros(batch_size, dtype=np.bool) - if self.mixup_enabled: - if self.mixup_alpha > 0. and self.cutmix_alpha > 0.: - use_cutmix = np.random.rand(batch_size) < self.switch_prob - lam_mix = np.where( - use_cutmix, - np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size), - np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)) - elif self.mixup_alpha > 0.: - lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size) - elif self.cutmix_alpha > 0.: - use_cutmix = np.ones(batch_size, dtype=np.bool) - lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size) - else: - assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true." - lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam) - return lam, use_cutmix - - def _params_per_batch(self): - lam = 1. - use_cutmix = False - if self.mixup_enabled and np.random.rand() < self.mix_prob: - if self.mixup_alpha > 0. and self.cutmix_alpha > 0.: - use_cutmix = np.random.rand() < self.switch_prob - lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \ - np.random.beta(self.mixup_alpha, self.mixup_alpha) - elif self.mixup_alpha > 0.: - lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha) - elif self.cutmix_alpha > 0.: - use_cutmix = True - lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) - else: - assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true." - lam = float(lam_mix) - return lam, use_cutmix - - def _mix_elem(self, x): - batch_size = len(x) - lam_batch, use_cutmix = self._params_per_elem(batch_size) - x_orig = x.clone() # need to keep an unmodified original for mixing source - for i in range(batch_size): - j = batch_size - i - 1 - lam = lam_batch[i] - if lam != 1.: - if use_cutmix[i]: - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh] - lam_batch[i] = lam - else: - x[i] = x[i] * lam + x_orig[j] * (1 - lam) - return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1) - - def _mix_pair(self, x): - batch_size = len(x) - lam_batch, use_cutmix = self._params_per_elem(batch_size // 2) - x_orig = x.clone() # need to keep an unmodified original for mixing source - for i in range(batch_size // 2): - j = batch_size - i - 1 - lam = lam_batch[i] - if lam != 1.: - if use_cutmix[i]: - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh] - x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh] - lam_batch[i] = lam - else: - x[i] = x[i] * lam + x_orig[j] * (1 - lam) - x[j] = x[j] * lam + x_orig[i] * (1 - lam) - lam_batch = np.concatenate((lam_batch, lam_batch[::-1])) - return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1) - - def _mix_batch(self, x): - lam, use_cutmix = self._params_per_batch() - if lam == 1.: - return 1. - if use_cutmix: - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh] - else: - x_flipped = x.flip(0).mul_(1. - lam) - x.mul_(lam).add_(x_flipped) - return lam - - def __call__(self, x, target): - assert len(x) % 2 == 0, 'Batch size should be even when using this' - if self.mode == 'elem': - lam = self._mix_elem(x) - elif self.mode == 'pair': - lam = self._mix_pair(x) - else: - lam = self._mix_batch(x) - target = mixup_target(target, self.num_classes, lam, self.label_smoothing, x.device) - return x, target - - -class FastCollateMixup(Mixup): - """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch - - A Mixup impl that's performed while collating the batches. - """ - - def _mix_elem_collate(self, output, batch, half=False): - batch_size = len(batch) - num_elem = batch_size // 2 if half else batch_size - assert len(output) == num_elem - lam_batch, use_cutmix = self._params_per_elem(num_elem) - for i in range(num_elem): - j = batch_size - i - 1 - lam = lam_batch[i] - mixed = batch[i][0] - if lam != 1.: - if use_cutmix[i]: - if not half: - mixed = mixed.copy() - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh] - lam_batch[i] = lam - else: - mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam) - np.rint(mixed, out=mixed) - output[i] += torch.from_numpy(mixed.astype(np.uint8)) - if half: - lam_batch = np.concatenate((lam_batch, np.ones(num_elem))) - return torch.tensor(lam_batch).unsqueeze(1) - - def _mix_pair_collate(self, output, batch): - batch_size = len(batch) - lam_batch, use_cutmix = self._params_per_elem(batch_size // 2) - for i in range(batch_size // 2): - j = batch_size - i - 1 - lam = lam_batch[i] - mixed_i = batch[i][0] - mixed_j = batch[j][0] - assert 0 <= lam <= 1.0 - if lam < 1.: - if use_cutmix[i]: - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - patch_i = mixed_i[:, yl:yh, xl:xh].copy() - mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh] - mixed_j[:, yl:yh, xl:xh] = patch_i - lam_batch[i] = lam - else: - mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam) - mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam) - mixed_i = mixed_temp - np.rint(mixed_j, out=mixed_j) - np.rint(mixed_i, out=mixed_i) - output[i] += torch.from_numpy(mixed_i.astype(np.uint8)) - output[j] += torch.from_numpy(mixed_j.astype(np.uint8)) - lam_batch = np.concatenate((lam_batch, lam_batch[::-1])) - return torch.tensor(lam_batch).unsqueeze(1) - - def _mix_batch_collate(self, output, batch): - batch_size = len(batch) - lam, use_cutmix = self._params_per_batch() - if use_cutmix: - (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( - output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) - for i in range(batch_size): - j = batch_size - i - 1 - mixed = batch[i][0] - if lam != 1.: - if use_cutmix: - mixed = mixed.copy() # don't want to modify the original while iterating - mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh] - else: - mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam) - np.rint(mixed, out=mixed) - output[i] += torch.from_numpy(mixed.astype(np.uint8)) - return lam - - def __call__(self, batch, _=None): - batch_size = len(batch) - assert batch_size % 2 == 0, 'Batch size should be even when using this' - half = 'half' in self.mode - if half: - batch_size //= 2 - output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) - if self.mode == 'elem' or self.mode == 'half': - lam = self._mix_elem_collate(output, batch, half=half) - elif self.mode == 'pair': - lam = self._mix_pair_collate(output, batch) - else: - lam = self._mix_batch_collate(output, batch) - target = torch.tensor([b[1] for b in batch], dtype=torch.int64) - target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu') - target = target[:batch_size] - return output, target diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/tasks/speech_recognition.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/tasks/speech_recognition.py deleted file mode 100644 index d9f011d55ff4fdfeb4c04ca790c314d685708c3a..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/tasks/speech_recognition.py +++ /dev/null @@ -1,157 +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 json -import os -import re -import sys - -import torch -from examples.speech_recognition.data import AsrDataset -from examples.speech_recognition.data.replabels import replabel_symbol -from fairseq.data import Dictionary -from fairseq.tasks import LegacyFairseqTask, register_task - - -def get_asr_dataset_from_json(data_json_path, tgt_dict): - """ - Parse data json and create dataset. - See scripts/asr_prep_json.py which pack json from raw files - - Json example: - { - "utts": { - "4771-29403-0025": { - "input": { - "length_ms": 170, - "path": "/tmp/file1.flac" - }, - "output": { - "text": "HELLO \n", - "token": "HE LLO", - "tokenid": "4815, 861" - } - }, - "1564-142299-0096": { - ... - } - } - """ - if not os.path.isfile(data_json_path): - raise FileNotFoundError("Dataset not found: {}".format(data_json_path)) - with open(data_json_path, "rb") as f: - data_samples = json.load(f)["utts"] - assert len(data_samples) != 0 - sorted_samples = sorted( - data_samples.items(), - key=lambda sample: int(sample[1]["input"]["length_ms"]), - reverse=True, - ) - aud_paths = [s[1]["input"]["path"] for s in sorted_samples] - ids = [s[0] for s in sorted_samples] - speakers = [] - for s in sorted_samples: - m = re.search("(.+?)-(.+?)-(.+?)", s[0]) - speakers.append(m.group(1) + "_" + m.group(2)) - frame_sizes = [s[1]["input"]["length_ms"] for s in sorted_samples] - tgt = [ - [int(i) for i in s[1]["output"]["tokenid"].split(", ")] - for s in sorted_samples - ] - # append eos - tgt = [[*t, tgt_dict.eos()] for t in tgt] - return AsrDataset(aud_paths, frame_sizes, tgt, tgt_dict, ids, speakers) - - -@register_task("speech_recognition") -class SpeechRecognitionTask(LegacyFairseqTask): - """ - Task for training speech recognition model. - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument("data", help="path to data directory") - parser.add_argument( - "--silence-token", default="\u2581", help="token for silence (used by w2l)" - ) - parser.add_argument( - "--max-source-positions", - default=sys.maxsize, - type=int, - metavar="N", - help="max number of frames in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - - def __init__(self, args, tgt_dict): - super().__init__(args) - self.tgt_dict = tgt_dict - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task (e.g., load dictionaries).""" - dict_path = os.path.join(args.data, "dict.txt") - if not os.path.isfile(dict_path): - raise FileNotFoundError("Dict not found: {}".format(dict_path)) - tgt_dict = Dictionary.load(dict_path) - - if args.criterion == "ctc_loss": - tgt_dict.add_symbol("") - elif args.criterion == "asg_loss": - for i in range(1, args.max_replabel + 1): - tgt_dict.add_symbol(replabel_symbol(i)) - - print("| dictionary: {} types".format(len(tgt_dict))) - return cls(args, tgt_dict) - - def load_dataset(self, split, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - data_json_path = os.path.join(self.args.data, "{}.json".format(split)) - self.datasets[split] = get_asr_dataset_from_json(data_json_path, self.tgt_dict) - - def build_generator(self, models, args, **unused): - w2l_decoder = getattr(args, "w2l_decoder", None) - if w2l_decoder == "viterbi": - from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder - - return W2lViterbiDecoder(args, self.target_dictionary) - elif w2l_decoder == "kenlm": - from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder - - return W2lKenLMDecoder(args, self.target_dictionary) - elif w2l_decoder == "fairseqlm": - from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder - - return W2lFairseqLMDecoder(args, self.target_dictionary) - else: - return super().build_generator(models, args) - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.tgt_dict - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary` (if applicable - for this task).""" - return None - - def max_positions(self): - """Return the max speech and sentence length allowed by the task.""" - return (self.args.max_source_positions, self.args.max_target_positions) diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/fp32_group_norm.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/fp32_group_norm.py deleted file mode 100644 index d03aac022e30c8c14a600062d1d86429504ba003..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/fp32_group_norm.py +++ /dev/null @@ -1,25 +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. -""" -Layer norm done in fp32 (for fp16 training) -""" - -import torch.nn as nn -import torch.nn.functional as F - - -class Fp32GroupNorm(nn.GroupNorm): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, input): - output = F.group_norm( - input.float(), - self.num_groups, - self.weight.float() if self.weight is not None else None, - self.bias.float() if self.bias is not None else None, - self.eps, - ) - return output.type_as(input) diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/unfold.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/unfold.py deleted file mode 100644 index 138272f1ef4f673b29e36aed4531106f7ce95968..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/unfold.py +++ /dev/null @@ -1,19 +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.nn.functional as F - - -def unfold1d(x, kernel_size, padding_l, pad_value=0): - """unfold T x B x C to T x B x C x K""" - if kernel_size > 1: - T, B, C = x.size() - x = F.pad( - x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value - ) - x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C)) - else: - x = x.unsqueeze(3) - return x diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fused_adam.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fused_adam.py deleted file mode 100644 index 7a6d1f73d53cae24ff94bb0bbc42bcc1de75548a..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fused_adam.py +++ /dev/null @@ -1,384 +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 types - -import torch - - -def get_fused_adam_class(): - """ - Look for the FusedAdam optimizer from apex. We first try to load the - "contrib" interface, which is a bit faster than the main interface, - but is technically deprecated. - """ - try: - # The "deprecated" interface in recent versions of apex is a bit - # faster than the main interface, since we don't use the apex - # optimizer. This can be installed by passing the - # `--deprecated_fused_adam` option when building apex. - global fused_adam_cuda - import importlib - - fused_adam_cuda = importlib.import_module("fused_adam_cuda") - return FusedAdamV1 - except ImportError: - try: - # fallback to the newer interface - from apex.optimizers import FusedAdam as _FusedAdam # noqa - from apex.multi_tensor_apply import multi_tensor_applier - - if multi_tensor_applier.available: - return FusedAdamV2 - except ImportError: - pass - return None - - -class FusedAdamV1(torch.optim.Optimizer): - """ - Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via - ``python setup.py install --cuda_ext --cpp_ext``. - - It has been proposed in `Adam: A Method for Stochastic Optimization`_. - - Compared to the original version in Apex, the fairseq version casts grads - and params to FP32 internally to support ``--memory-efficient-fp16``. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups. - lr (float, optional): learning rate. (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square. (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability. (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - (default: False) NOT SUPPORTED in FusedAdam! - eps_inside_sqrt (boolean, optional): in the 'update parameters' step, - adds eps to the bias-corrected second moment estimate before - evaluating square root instead of adding it to the square root of - second moment estimate as in the original paper. (default: False) - .. _Adam: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__( - self, - params, - lr=1e-3, - bias_correction=True, - betas=(0.9, 0.999), - eps=1e-8, - eps_inside_sqrt=False, - weight_decay=0.0, - max_grad_norm=0.0, - amsgrad=False, - use_fp16_stats=False, - ): - global fused_adam_cuda - import importlib - - fused_adam_cuda = importlib.import_module("fused_adam_cuda") - - if amsgrad: - raise RuntimeError("FusedAdam does not support the AMSGrad variant.") - defaults = { - "lr": lr, - "bias_correction": bias_correction, - "betas": betas, - "eps": eps, - "weight_decay": weight_decay, - "max_grad_norm": max_grad_norm, - } - super().__init__(params, defaults) - self.eps_mode = 0 if eps_inside_sqrt else 1 - - self.use_fp16_stats = use_fp16_stats - self.FLOAT16_MAX = 65504.0 - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - @property - def supports_step_with_scale(self): - return True - - def step(self, closure=None, grads=None, scale=1.0, grad_norms=None): - """Performs a single optimization step. - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - grads (list of tensors, optional): weight gradient to use for the - optimizer update. If gradients have type torch.half, parameters - are expected to be in type torch.float. (default: None) - output params (list of tensors, optional): A reduced precision copy - of the updated weights written out in addition to the regular - updated weights. Have to be of same type as gradients. (default: None) - scale (float, optional): factor to divide gradient tensor values - by before applying to weights. (default: 1) - """ - loss = None - if closure is not None: - loss = closure() - - if grads is None: - grads_group = [None] * len(self.param_groups) - # backward compatibility - # assuming a list/generator of parameter means single group - elif isinstance(grads, types.GeneratorType): - grads_group = [grads] - elif type(grads[0]) != list: - grads_group = [grads] - else: - grads_group = grads - - if grad_norms is None: - grad_norms = [None] * len(self.param_groups) - - for group, grads_this_group, grad_norm in zip( - self.param_groups, grads_group, grad_norms - ): - if grads_this_group is None: - grads_this_group = [None] * len(group["params"]) - - # compute combined scale factor for this group - combined_scale = scale - if group.get("max_grad_norm", 0) > 0: - # norm is in fact norm*scale - clip = ((grad_norm / scale) + 1e-6) / group["max_grad_norm"] - if clip > 1: - combined_scale = clip * scale - - bias_correction = 1 if group.get("bias_correction", 1) else 0 - - for p, grad in zip(group["params"], grads_this_group): - # note: p.grad should not ever be set for correct - # operation of mixed precision optimizer that sometimes - # sends None gradients - if p.grad is None and grad is None: - continue - if grad is None: - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError( - "FusedAdam does not support sparse gradients, " - "please consider SparseAdam instead" - ) - - if p.device.type == "cpu": - p_data_fp32 = p.data.cuda(non_blocking=True).float() - out_p = torch.tensor([], dtype = torch.float) - else: - p_data_fp32 = p.data.float() - out_p = p.data - - state = self.state[p] - - # State initialization - dtype = torch.float16 if self.use_fp16_stats else p_data_fp32.dtype - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p_data_fp32, dtype=dtype) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(p_data_fp32, dtype=dtype) - if self.use_fp16_stats: - state["exp_avg_scale"] = 1.0 - state["exp_avg_sq_scale"] = 1.0 - else: - device = p_data_fp32.device - state["exp_avg"] = state["exp_avg"].to(device, dtype) - state["exp_avg_sq"] = state["exp_avg_sq"].to(device, dtype) - - exp_avg = state["exp_avg"] - exp_avg_sq = state["exp_avg_sq"] - if self.use_fp16_stats: - assert exp_avg.dtype == torch.float16 - exp_avg = exp_avg.float() * state["exp_avg_scale"] - exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] - beta1, beta2 = group["betas"] - - state["step"] += 1 - - with torch.cuda.device(p_data_fp32.device): - fused_adam_cuda.adam( - p_data_fp32, - out_p, - exp_avg, - exp_avg_sq, - grad, - group["lr"], - beta1, - beta2, - group["eps"], - combined_scale, - state["step"], - self.eps_mode, - bias_correction, - group["weight_decay"], - ) - - if p.device.type == "cpu": - p.data.copy_(p_data_fp32, non_blocking=True) - - if self.use_fp16_stats: - def inf_norm(t): - return torch.norm(t, float("inf")) - - # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py - state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( - 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, - 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, - ) - state["exp_avg"], state["exp_avg_sq"] = ( - (exp_avg / state["exp_avg_scale"]).half(), - (exp_avg_sq / state["exp_avg_sq_scale"]).half(), - ) - - return loss - - -try: - from apex.optimizers import FusedAdam - from apex.multi_tensor_apply import multi_tensor_applier - - class FusedAdamV2(FusedAdam): - """ - Compared to the original version in Apex, the fairseq version casts grads - and params to FP32 internally to support ``--memory-efficient-fp16``. - """ - - def __init__(self, *args, use_fp16_stats=False, **kwargs): - if use_fp16_stats: - raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1") - super().__init__(*args, **kwargs) - if not hasattr(self, "multi_tensor_adam"): - raise Exception( - "Apex installation is outdated. Please install an updated version of apex." - ) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step( - self, - closure=None, - grads=None, - output_params=None, - scale=None, - grad_norms=None, - ): - """Performs a single optimization step.""" - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - bias_correction = 1 if group["bias_correction"] else 0 - beta1, beta2 = group["betas"] - - # assume same step across group now to simplify things - # per parameter step can be easily support by making it tensor, or pass list into kernel - if "step" in group: - group["step"] += 1 - else: - group["step"] = 1 - - # create lists for multi-tensor apply - g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], [] - g_32, p_32, m_32, v_32 = [], [], [], [] - - for p in group["params"]: - if p.grad is None: - continue - if p.grad.data.is_sparse: - raise RuntimeError( - "FusedAdam does not support sparse gradients, " - "please consider SparseAdam instead" - ) - - state = self.state[p] - # State initialization - if len(state) == 0: - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p.data, dtype=torch.float) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like( - p.data, dtype=torch.float - ) - else: - state["exp_avg"] = state["exp_avg"].to( - device=p.data.device, dtype=torch.float - ) - state["exp_avg_sq"] = state["exp_avg_sq"].to( - device=p.data.device, dtype=torch.float - ) - - if p.dtype == torch.float16: - g_16.append(p.grad.data.float()) - p_16.append(p.data.float()) - orig_p_16.append(p.data) - m_16.append(state["exp_avg"]) - v_16.append(state["exp_avg_sq"]) - elif p.dtype == torch.float32: - g_32.append(p.grad.data) - p_32.append(p.data) - m_32.append(state["exp_avg"]) - v_32.append(state["exp_avg_sq"]) - else: - raise RuntimeError("FusedAdam only support fp16 and fp32.") - - with torch.cuda.device(p.device): - if len(g_16) > 0: - multi_tensor_applier( - self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_16, p_16, m_16, v_16], - group["lr"], - beta1, - beta2, - group["eps"], - group["step"], - self.adam_w_mode, - bias_correction, - group["weight_decay"], - ) - for orig_p, p in zip(orig_p_16, p_16): - orig_p.copy_(p.data) - if len(g_32) > 0: - multi_tensor_applier( - self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_32, p_32, m_32, v_32], - group["lr"], - beta1, - beta2, - group["eps"], - group["step"], - self.adam_w_mode, - bias_correction, - group["weight_decay"], - ) - - return loss - - -except ImportError: - pass diff --git a/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_resources/transliterate/README.md b/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_resources/transliterate/README.md deleted file mode 100644 index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/en_to_indic_translation/indic_nlp_resources/transliterate/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# Transliteration Models for Indian languages -These are models for transliteration involving Indian languages. -The models are essentially Statistical Machine Translation systems trained using Moses over a -character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models. -The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus. - -Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi - -Support for transliteration has been introduced in Moses from version 2.1 -So please ensure that you have minimum 2.1 version setup for Moses - -Commands to run the transliteration module using moses - -$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \ ---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \ ---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\ - --input-file {input file to transliterated} --output-file {output file location} \ - --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \ - --decoder $moseshome/mosesdecoder/bin/moses - -A sample execution of the model will be as follows: - -export moseshome={path to moses installation} -$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \ ---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \ ---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \ ---oov-file /home/ratish/project/translit/input.oov \ - --input-file /home/ratish/project/translit/input.en \ - --output-file /home/ratish/project/translit/output.hi \ - --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \ - --decoder $moseshome/mosesdecoder/bin/moses - -So far, we have seen the use of transliteration in a post-editing task for machine translation task. -In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same. -Sample input file: -New Delhi is capital of India -India is worlds seventh largest nation in the World - -OOV file -New Delhi is capital of India -India is worlds seventh largest nation in the World - -On running the transliteration module, the output is: -न्यू डेल्ही इस कैपिटल आफ इंडिया -इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड diff --git a/spaces/Hexamind/GDOC/test_app.py b/spaces/Hexamind/GDOC/test_app.py deleted file mode 100644 index 2b2d7ab2d078c4a767e956dd442b17ecb9ad1eae..0000000000000000000000000000000000000000 --- a/spaces/Hexamind/GDOC/test_app.py +++ /dev/null @@ -1,67 +0,0 @@ -import docx -from docx.enum.style import WD_STYLE_TYPE -import os -from config import config -from typing import Dict -import random -import datetime -import string - -from lxml import etree - -from src.domain.doc import Doc - - - - -name = 'CorpTemplate.docx' - -template_path = config['templates_path'] + '/' + config['templates'][config['default_template_index']] -template = Doc(template_path) -doc_path = config['these_docs_path'] + name -this_doc = Doc(path=doc_path) -new_doc_path = config['new_docs_path'] + this_doc.name + '_.docx' -new_doc = this_doc.copy(new_doc_path) - - - - -new_styles = new_doc.styles.xstyles -print(etree.tostring(new_styles['.Titre1'].element)) -names = new_doc.styles.names -print(names) -new_doc.save_as_docx() - - -s = template.styles.xstyles['.BodyText'] -# new_styles.add_style(s.name, WD_STYLE_TYPE.PARAGRAPH) - - -list_styles = [(s, s.name) for s in template.styles.xstyles if s.type==WD_STYLE_TYPE.LIST] - - -base_styles_set = set() -for s in new_styles: - if s.type == 1: - if s.base_style: - try: - base_styles_set.add(s.base_style.name) - except: - print(f"failure for {s}") - - -base_styles = list(base_styles_set) - - - - -""" -or p in new_doc.xdoc.paragraphs: - if p.style == new_styles['_newBody__2']: - p.style = s.name - -new_styles['_newBody__2'].delete() -new_doc.save_as_docx() -""" -pass -etree.tostring(list_styles[1][0].element) \ No newline at end of file diff --git a/spaces/HighCWu/Style2Paints-4-Gradio/linefiller/third_party.py b/spaces/HighCWu/Style2Paints-4-Gradio/linefiller/third_party.py deleted file mode 100644 index 456e4f35387511018ca74aacd18dd307f4bc33c7..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/Style2Paints-4-Gradio/linefiller/third_party.py +++ /dev/null @@ -1,396 +0,0 @@ -import cv2 -from .thinning import * -from .trappedball_fill import * -from skimage.measure import block_reduce -from skimage.morphology import disk, dilation, erosion -from numba import njit - - -def np_min_pool(x): - return block_reduce(x, (2, 2), np.min) - - -def np_max_pool(x): - return block_reduce(x, (2, 2), np.max) - - -def np_max_441(x): - return block_reduce(x, (4, 4, 1), np.max) - - -def np_max_pool_221(x): - return block_reduce(x, (2, 2, 1), np.max) - - -def np_max_pool_s(x, s): - return block_reduce(x, (s, s, 1), np.max) - - -def binarize(x): - xp = x.copy() - xp[xp < 250] = 0 - xp[xp > 0] = 255 - return xp - - -def get_initial_fillmap(boundary, merge=True): - fillmap = build_fill_map(boundary, flood_fill_multi(boundary, merge=merge)) - return fillmap - - -def up_propagate(small_fillmap, big_boundary): - new_fillmap = cv2.resize(small_fillmap, (big_boundary.shape[1], big_boundary.shape[0]), interpolation=cv2.INTER_NEAREST) - padded_fillmap = np.pad(new_fillmap, [[1, 1], [1, 1]], 'constant', constant_values=0) - new_mask = np.ones_like(new_fillmap, dtype=np.uint8) * 255 - new_mask[new_fillmap > 0] = 0 - new_mask[big_boundary < 240] = 0 - fills = flood_fill_multi(new_mask, merge=True) - max_id = np.max(new_fillmap) - for item in fills: - points0 = padded_fillmap[(item[0] + 1, item[1] + 0)] - points1 = padded_fillmap[(item[0] + 1, item[1] + 2)] - points2 = padded_fillmap[(item[0] + 0, item[1] + 1)] - points3 = padded_fillmap[(item[0] + 2, item[1] + 1)] - - all_points = np.concatenate([points0, points1, points2, points3], axis=0) - pointsets, pointcounts = np.unique(all_points[all_points > 0], return_counts=True) - - if len(pointsets) > 0: - new_fillmap[item] = pointsets[np.argmax(pointcounts)] - else: - max_id += 1 - new_fillmap[item] = max_id - return new_fillmap - - -def laplas_fill(b_512, b_256, b_128): - b_512 = binarize(b_512) - b_256 = binarize(b_256) - b_128 = binarize(b_128) - f128 = get_initial_fillmap(b_128) - f256 = up_propagate(f128, b_256) - f512 = up_propagate(f256, b_512) - fin = thinning(f512) - return fin - - -@ njit -def get_corner(x): - corner = x.copy() - s0 = corner.shape[0] - s1 = corner.shape[1] - for i0 in range(1, s0 - 1): - for i1 in range(1, s1 - 1): - if x[i0, i1] == 0: - continue - if x[i0, i1 - 1] == 0: - if x[i0 - 1, i1 - 1] == 0: - continue - if x[i0 + 1, i1 - 1] == 0: - continue - corner[i0, i1] = 0 - continue - if x[i0, i1 + 1] == 0: - if x[i0 - 1, i1 + 1] == 0: - continue - if x[i0 + 1, i1 + 1] == 0: - continue - corner[i0, i1] = 0 - continue - if x[i0 - 1, i1] == 0: - if x[i0 - 1, i1 - 1] == 0: - continue - if x[i0 - 1, i1 + 1] == 0: - continue - corner[i0, i1] = 0 - continue - if x[i0 + 1, i1] == 0: - if x[i0 + 1, i1 - 1] == 0: - continue - if x[i0 + 1, i1 + 1] == 0: - continue - corner[i0, i1] = 0 - continue - return corner - - -def monogrouh(x): - y = 255 - x - y = dilation(y, disk(1)) - y = dilation(y, disk(1)) - y = erosion(y, disk(1)) - y = erosion(y, disk(1)) - y = 255 - y - return y - - -def corners(x): - y = x.copy() - y = monogrouh(y) - y = get_corner(y) - y = monogrouh(y) - y = get_corner(y) - y = monogrouh(y) - return y - - -def save_fill(name, fill): - cv2.imwrite(name, show_fill_map(fill)) - - -def double_fill(b_1024, b_512, b256): - b256 = binarize(b256) - b_512 = binarize(b_512) - b_1024 = binarize(b_1024) - b_1024 = corners(b_1024) - b_512 = np.min(np.stack([b_512, np_min_pool(b_1024)], axis=2), axis=2) - b_512 = corners(b_512) - b_256 = np.min(np.stack([b256, np_min_pool(b_512)], axis=2), axis=2) - b_256 = corners(b_256) - b_128 = np_min_pool(b_256) - b_128 = corners(b_128) - b_64 = np_min_pool(b_128) - f64 = get_initial_fillmap(b_64) - print('get_initial_fillmap(b_64)') - f128 = up_propagate(f64, b_128) - print('up_propagate(f64, b_128)') - f256 = up_propagate(f128, b_256) - print('up_propagate(f128, b_256)') - f512 = up_propagate(f256, b_512) - print('up_propagate(f256, b_512)') - f1024 = up_propagate(f512, b_1024) - print('up_propagate(f512, b_1024)') - fin = thinning(f1024) - print('thinning(f1024)') - - # cv2.imwrite('b_64.png', b_64) - # cv2.imwrite('b_128.png', b_128) - # cv2.imwrite('b_256.png', b_256) - # cv2.imwrite('b_512.png', b_512) - # cv2.imwrite('b_1024.png', b_1024) - # save_fill('f64.png', f64) - # save_fill('f128.png', f128) - # save_fill('f256.png', f256) - # save_fill('f512.png', f512) - # save_fill('f1024.png', f1024) - # save_fill('fin.png', fin) - - return find_all(fin) - - -def single_fill(b_2048, path): - b_2048 = corners(binarize(b_2048)) - f2048 = get_initial_fillmap(b_2048, merge=False) - print(path + 'get_initial_fillmap(b_2048, merge=False)') - fin = thinning(f2048) - print(path + 'thinning(f2048)') - # cv2.imwrite(path + 'b_2048.png', b_2048) - # save_fill(path + 'f2048.png', f2048) - # save_fill(path + 'fin.png', fin) - return find_all(fin) - - -def deatlize(x): - x = cv2.GaussianBlur(x, (0, 0), 0.8) - x = cv2.medianBlur(x, 3) - return x - - -def low_down(gradient_mask): - return 1.0 - cv2.dilate(255 - gradient_mask, np.ones((3, 3), np.uint8), iterations=2).astype(np.float32) / 255.0 - - -def cv2pyrDown(x): - return cv2.pyrDown(cv2.medianBlur(cv2.medianBlur(x, 3), 3)) - - -def cv2pyrUp(x): - return cv2.pyrUp(cv2.medianBlur(cv2.medianBlur(x, 3), 3)) - - -def re_deatlize(visulized, s1024): - - gradient_mask_1024 = binarize(s1024) - gradient_mask_512 = np_min_pool(gradient_mask_1024) - gradient_mask_256 = np_min_pool(gradient_mask_512) - gradient_mask_128 = np_min_pool(gradient_mask_256) - gradient_mask_64 = np_min_pool(gradient_mask_128) - - gradient_mask_1024 = low_down(gradient_mask_1024) - gradient_mask_512 = low_down(gradient_mask_512) - gradient_mask_256 = low_down(gradient_mask_256) - gradient_mask_128 = low_down(gradient_mask_128) - gradient_mask_64 = low_down(gradient_mask_64) - - sample_1024 = visulized.astype(np.float32) - sample_512 = cv2pyrDown(sample_1024) - sample_256 = cv2pyrDown(sample_512) - sample_128 = cv2pyrDown(sample_256) - sample_64 = cv2pyrDown(sample_128) - sample_32 = cv2pyrDown(sample_64) - - gradient_1024 = sample_1024 - cv2pyrUp(sample_512) - gradient_512 = sample_512 - cv2pyrUp(sample_256) - gradient_256 = sample_256 - cv2pyrUp(sample_128) - gradient_128 = sample_128 - cv2pyrUp(sample_64) - gradient_64 = sample_64 - cv2pyrUp(sample_32) - - rec_32 = sample_32 - rec_64 = cv2pyrUp(rec_32) + gradient_64 * (1 - gradient_mask_64[:, :, None]) - rec_128 = cv2pyrUp(rec_64) + gradient_128 * (1 - gradient_mask_128[:, :, None]) - rec_256 = cv2pyrUp(rec_128) + gradient_256 * (1 - gradient_mask_256[:, :, None]) - rec_512 = cv2pyrUp(rec_256) + gradient_512 * (1 - gradient_mask_512[:, :, None]) - rec_1024 = cv2pyrUp(rec_512) + gradient_1024 * (1 - gradient_mask_1024[:, :, None]) - - return rec_1024.clip(0, 255).astype(np.uint8) - - -def tiny_deatlize(visulized, s2048): - gradient_mask_2048 = s2048.copy() - gradient_mask_1024 = np_min_pool(gradient_mask_2048) - gradient_mask_512 = np_min_pool(gradient_mask_1024) - gradient_mask_256 = np_min_pool(gradient_mask_512) - - gradient_mask_2048 = low_down(gradient_mask_2048) - gradient_mask_1024 = low_down(gradient_mask_1024) - gradient_mask_512 = low_down(gradient_mask_512) - gradient_mask_256 = low_down(gradient_mask_256) - - sample_2048 = visulized.astype(np.float32) - sample_1024 = cv2.pyrDown(sample_2048) - sample_512 = cv2.pyrDown(sample_1024) - sample_256 = cv2.pyrDown(sample_512) - sample_128 = cv2.pyrDown(sample_256) - - gradient_2048 = sample_2048 - cv2.pyrUp(sample_1024) - gradient_1024 = sample_1024 - cv2.pyrUp(sample_512) - gradient_512 = sample_512 - cv2.pyrUp(sample_256) - gradient_256 = sample_256 - cv2.pyrUp(sample_128) - - rec_128 = sample_128 - rec_256 = cv2.pyrUp(rec_128) + gradient_256 * (1 - gradient_mask_256[:, :, None]) - rec_512 = cv2.pyrUp(rec_256) + gradient_512 * (1 - gradient_mask_512[:, :, None]) - rec_1024 = cv2.pyrUp(rec_512) + gradient_1024 * (1 - gradient_mask_1024[:, :, None]) - rec_2048 = cv2.pyrUp(rec_1024) + gradient_2048 * (1 - gradient_mask_2048[:, :, None]) - return rec_2048.clip(0, 255).astype(np.uint8) - - -def adain(x, y): - x_high = cv2.GaussianBlur(x, (0, 0), 3.0) - y_high = cv2.GaussianBlur(y, (0, 0), 3.0) - return (x.astype(np.float32) - x_high.astype(np.float32) + y_high.astype(np.float32)).clip(0, 255).astype(np.uint8) - - -def corrupt(x, b128): - float_sketch = x.astype(float) - float_base = cv2.resize(float_sketch, (b128.shape[1], b128.shape[0]), cv2.INTER_AREA) - alpha = b128[:, :, 0] / 255.0 - float_base = alpha * float_base + (1 - alpha) * np.mean(float_base) - float_base = cv2.GaussianBlur(float_base, (0, 0), 8.0) - float_base = cv2.resize(float_base, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC) - result = float_sketch / (float_base + 1e-10) - result = result.clip(0, 1) - result -= np.min(result) - result /= np.max(result) - return (result * 255.0).clip(0, 255).astype(np.uint8) - - -def fuse_sketch(color, sketch, fills, fixer, points_arr, colors_arr): - sketch = cv2.resize(sketch, (color.shape[1], color.shape[0])) - fills = cv2.resize(fills, (color.shape[1], color.shape[0]), interpolation=cv2.INTER_NEAREST) - fill_id = np.unique(fills.flatten()) - bg = np.zeros_like(color, dtype=np.uint8) - checking_result = np.zeros(dtype=np.int32, shape=(np.max(fills) + 1,)) - 1 - length_points = int(len(points_arr)) - for _ in range(length_points): - checking_result[fills[points_arr[_][0], points_arr[_][1]]] = _ - for id in fill_id: - points = np.where(fills == id) - if len(points[0]) > 0: - color_id = checking_result[id] - if color_id > -1: - bg[points] = np.array(colors_arr[color_id]) - else: - bg[points] = np.median(color[points], axis=0) - fixed = adain(fixer(sketch, bg), bg) - result = (fixed.astype(np.float32) + sketch[:, :, None].astype(np.float32) - 255.0).clip(0, 255).astype(np.uint8) - return result, fixed, bg - - -def balance_fill(color, fills, points, sizer): - color = cv2.resize(color, (sizer.shape[1], sizer.shape[0]), interpolation=cv2.INTER_NEAREST) - points = cv2.resize(points, (sizer.shape[1], sizer.shape[0]), interpolation=cv2.INTER_NEAREST) - bg = np.zeros_like(color, dtype=np.uint8) - for region in fills: - if len(region[0]) > 0: - region_points = points[region] - region_points = region_points[region_points[:, 3] > 0] - if region_points.shape[0] > 0: - points_color, points_color_count = np.unique(region_points, return_counts=True, axis=0) - bg[region] = points_color[np.argmax(points_color_count)][0:3] - else: - bg[region] = np.median(color[region], axis=0) - return bg - - -def shade_fill(color, fills, points, sizer): - color = cv2.resize(color, (sizer.shape[1], sizer.shape[0]), interpolation=cv2.INTER_NEAREST) - points = cv2.resize(points, (sizer.shape[1], sizer.shape[0]), interpolation=cv2.INTER_NEAREST) - bg = np.zeros_like(color, dtype=np.uint8) - for region in fills: - if len(region[0]) > 0: - region_points = points[region] - region_points = region_points[region_points[:, 3] > 0] - if region_points.shape[0] > 0: - points_color, points_color_count = np.unique(region_points, return_counts=True, axis=0) - c = points_color[np.argmax(points_color_count)][0:3] - r = c[0] - g = c[1] - b = c[2] - if r == 1 and g == 233 and b == 0: - bg[region] = 255 - elif r == 0 and g == 233 and b == 1: - bg[region] = 0 - else: - bg[region] = np.median(color[region], axis=0) - else: - bg[region] = np.median(color[region], axis=0) - return bg - - -def get_alpha_piece(points): - padded_points = np.pad(points, [[1, 1], [1, 1], [0, 0]], 'constant', constant_values=127) - lines = 255 - padded_points[:, :, 3] - lines[lines < 240] = 0 - fills = flood_fill_multi(lines, merge=True) - result = np.zeros_like(padded_points) - for item in fills: - points0 = padded_points[(item[0], item[1] + 1)] - points1 = padded_points[(item[0], item[1] - 1)] - points2 = padded_points[(item[0] + 1, item[1])] - points3 = padded_points[(item[0] - 1, item[1])] - all_points = np.concatenate([points0, points1, points2, points3], axis=0) - all_points = all_points[all_points[:, 3] > 0] - all_points = np.unique(all_points, axis=0) - if all_points.shape[0] == 1: - result[item] = all_points[0] - piece = result[1:-1, 1:-1, :] - piece = np.maximum(piece, points) - return piece, points - - -def fin_deatlize(color, sketch): - - cf = color.astype(np.float32) - alpha = sketch.astype(np.float32)[:, :, None] / 255.0 - - plain = cf * alpha - lines = cf * (1 - alpha) - - plain = cv2.medianBlur(plain, 5) - plain = cv2.medianBlur(plain, 3) - - fin = plain + lines - - return fin.clip(0, 255).astype(np.uint8) - diff --git a/spaces/HighCWu/anime-colorization-with-hint/README.md b/spaces/HighCWu/anime-colorization-with-hint/README.md deleted file mode 100644 index cd7a1296a57e5401d485a438eff8a8e4f13da3d5..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/anime-colorization-with-hint/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Anime Colorization With Hint -emoji: 🌖 -colorFrom: red -colorTo: yellow -sdk: gradio -sdk_version: 3.16.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Hina4867/bingo/src/lib/hooks/use-at-bottom.tsx b/spaces/Hina4867/bingo/src/lib/hooks/use-at-bottom.tsx deleted file mode 100644 index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000 --- a/spaces/Hina4867/bingo/src/lib/hooks/use-at-bottom.tsx +++ /dev/null @@ -1,23 +0,0 @@ -import * as React from 'react' - -export function useAtBottom(offset = 0) { - const [isAtBottom, setIsAtBottom] = React.useState(false) - - React.useEffect(() => { - const handleScroll = () => { - setIsAtBottom( - window.innerHeight + window.scrollY >= - document.body.offsetHeight - offset - ) - } - - window.addEventListener('scroll', handleScroll, { passive: true }) - handleScroll() - - return () => { - window.removeEventListener('scroll', handleScroll) - } - }, [offset]) - - return isAtBottom -} diff --git a/spaces/Hobis/bark-voice-cloning-polish-HuBERT-quantizer/hubert/customtokenizer.py b/spaces/Hobis/bark-voice-cloning-polish-HuBERT-quantizer/hubert/customtokenizer.py deleted file mode 100644 index d8f84d90f198ce08b2ed38be714bcde7df3c46b4..0000000000000000000000000000000000000000 --- a/spaces/Hobis/bark-voice-cloning-polish-HuBERT-quantizer/hubert/customtokenizer.py +++ /dev/null @@ -1,182 +0,0 @@ -import json -import os.path -from zipfile import ZipFile - -import numpy -import torch -from torch import nn, optim -from torch.serialization import MAP_LOCATION - - -class CustomTokenizer(nn.Module): - def __init__(self, hidden_size=1024, input_size=768, output_size=10000, version=0): - super(CustomTokenizer, self).__init__() - next_size = input_size - if version == 0: - self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True) - next_size = hidden_size - if version == 1: - self.lstm = nn.LSTM(input_size, hidden_size, 2, batch_first=True) - self.intermediate = nn.Linear(hidden_size, 4096) - next_size = 4096 - - self.fc = nn.Linear(next_size, output_size) - self.softmax = nn.LogSoftmax(dim=1) - self.optimizer: optim.Optimizer = None - self.lossfunc = nn.CrossEntropyLoss() - self.input_size = input_size - self.hidden_size = hidden_size - self.output_size = output_size - self.version = version - - def forward(self, x): - x, _ = self.lstm(x) - if self.version == 1: - x = self.intermediate(x) - x = self.fc(x) - x = self.softmax(x) - return x - - @torch.no_grad() - def get_token(self, x): - """ - Used to get the token for the first - :param x: An array with shape (N, input_size) where N is a whole number greater or equal to 1, and input_size is the input size used when creating the model. - :return: An array with shape (N,) where N is the same as N from the input. Every number in the array is a whole number in range 0...output_size - 1 where output_size is the output size used when creating the model. - """ - return torch.argmax(self(x), dim=1) - - def prepare_training(self): - self.optimizer = optim.Adam(self.parameters(), 0.001) - - def train_step(self, x_train, y_train, log_loss=False): - # y_train = y_train[:-1] - # y_train = y_train[1:] - - optimizer = self.optimizer - lossfunc = self.lossfunc - # Zero the gradients - self.zero_grad() - - # Forward pass - y_pred = self(x_train) - - y_train_len = len(y_train) - y_pred_len = y_pred.shape[0] - - if y_train_len > y_pred_len: - diff = y_train_len - y_pred_len - y_train = y_train[diff:] - elif y_train_len < y_pred_len: - diff = y_pred_len - y_train_len - y_pred = y_pred[:-diff, :] - - y_train_hot = torch.zeros(len(y_train), self.output_size) - y_train_hot[range(len(y_train)), y_train] = 1 - y_train_hot = y_train_hot.to('cuda') - - # Calculate the loss - loss = lossfunc(y_pred, y_train_hot) - - # Print loss - if log_loss: - print('Loss', loss.item()) - - # Backward pass - loss.backward() - - # Update the weights - optimizer.step() - - def save(self, path): - info_path = os.path.basename(path) + '/.info' - torch.save(self.state_dict(), path) - data_from_model = Data(self.input_size, self.hidden_size, self.output_size, self.version) - with ZipFile(path, 'a') as model_zip: - model_zip.writestr(info_path, data_from_model.save()) - model_zip.close() - - @staticmethod - def load_from_checkpoint(path, map_location: MAP_LOCATION = None): - old = True - with ZipFile(path) as model_zip: - filesMatch = [file for file in model_zip.namelist() if file.endswith('/.info')] - file = filesMatch[0] if filesMatch else None - if file: - old = False - data_from_model = Data.load(model_zip.read(file).decode('utf-8')) - model_zip.close() - if old: - model = CustomTokenizer() - else: - model = CustomTokenizer(data_from_model.hidden_size, data_from_model.input_size, data_from_model.output_size, data_from_model.version) - model.load_state_dict(torch.load(path, map_location)) - return model - - - -class Data: - input_size: int - hidden_size: int - output_size: int - version: int - - def __init__(self, input_size=768, hidden_size=1024, output_size=10000, version=0): - self.input_size = input_size - self.hidden_size = hidden_size - self.output_size = output_size - self.version = version - - @staticmethod - def load(string): - data = json.loads(string) - return Data(data['input_size'], data['hidden_size'], data['output_size'], data['version']) - - def save(self): - data = { - 'input_size': self.input_size, - 'hidden_size': self.hidden_size, - 'output_size': self.output_size, - 'version': self.version, - } - return json.dumps(data) - - -def auto_train(data_path, save_path='model.pth', load_model: str | None = None, save_epochs=1): - data_x, data_y = [], [] - - if load_model and os.path.isfile(load_model): - print('Loading model from', load_model) - model_training = CustomTokenizer.load_from_checkpoint(load_model, 'cuda') - else: - print('Creating new model.') - model_training = CustomTokenizer(version=1).to('cuda') # Settings for the model to run without lstm - save_path = os.path.join(data_path, save_path) - base_save_path = '.'.join(save_path.split('.')[:-1]) - - sem_string = '_semantic.npy' - feat_string = '_semantic_features.npy' - - ready = os.path.join(data_path, 'ready') - for input_file in os.listdir(ready): - full_path = os.path.join(ready, input_file) - if input_file.endswith(sem_string): - data_y.append(numpy.load(full_path)) - elif input_file.endswith(feat_string): - data_x.append(numpy.load(full_path)) - model_training.prepare_training() - - epoch = 1 - - while 1: - for i in range(save_epochs): - j = 0 - for x, y in zip(data_x, data_y): - model_training.train_step(torch.tensor(x).to('cuda'), torch.tensor(y).to('cuda'), j % 50 == 0) # Print loss every 50 steps - j += 1 - save_p = save_path - save_p_2 = f'{base_save_path}_epoch_{epoch}.pth' - model_training.save(save_p) - model_training.save(save_p_2) - print(f'Epoch {epoch} completed') - epoch += 1 diff --git a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/transformer/permuter.py b/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/transformer/permuter.py deleted file mode 100644 index 0d43bb135adde38d94bf18a7e5edaa4523cd95cf..0000000000000000000000000000000000000000 --- a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/taming/modules/transformer/permuter.py +++ /dev/null @@ -1,248 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np - - -class AbstractPermuter(nn.Module): - def __init__(self, *args, **kwargs): - super().__init__() - def forward(self, x, reverse=False): - raise NotImplementedError - - -class Identity(AbstractPermuter): - def __init__(self): - super().__init__() - - def forward(self, x, reverse=False): - return x - - -class Subsample(AbstractPermuter): - def __init__(self, H, W): - super().__init__() - C = 1 - indices = np.arange(H*W).reshape(C,H,W) - while min(H, W) > 1: - indices = indices.reshape(C,H//2,2,W//2,2) - indices = indices.transpose(0,2,4,1,3) - indices = indices.reshape(C*4,H//2, W//2) - H = H//2 - W = W//2 - C = C*4 - assert H == W == 1 - idx = torch.tensor(indices.ravel()) - self.register_buffer('forward_shuffle_idx', - nn.Parameter(idx, requires_grad=False)) - self.register_buffer('backward_shuffle_idx', - nn.Parameter(torch.argsort(idx), requires_grad=False)) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -def mortonify(i, j): - """(i,j) index to linear morton code""" - i = np.uint64(i) - j = np.uint64(j) - - z = np.uint(0) - - for pos in range(32): - z = (z | - ((j & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos)) | - ((i & (np.uint64(1) << np.uint64(pos))) << np.uint64(pos+1)) - ) - return z - - -class ZCurve(AbstractPermuter): - def __init__(self, H, W): - super().__init__() - reverseidx = [np.int64(mortonify(i,j)) for i in range(H) for j in range(W)] - idx = np.argsort(reverseidx) - idx = torch.tensor(idx) - reverseidx = torch.tensor(reverseidx) - self.register_buffer('forward_shuffle_idx', - idx) - self.register_buffer('backward_shuffle_idx', - reverseidx) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -class SpiralOut(AbstractPermuter): - def __init__(self, H, W): - super().__init__() - assert H == W - size = W - indices = np.arange(size*size).reshape(size,size) - - i0 = size//2 - j0 = size//2-1 - - i = i0 - j = j0 - - idx = [indices[i0, j0]] - step_mult = 0 - for c in range(1, size//2+1): - step_mult += 1 - # steps left - for k in range(step_mult): - i = i - 1 - j = j - idx.append(indices[i, j]) - - # step down - for k in range(step_mult): - i = i - j = j + 1 - idx.append(indices[i, j]) - - step_mult += 1 - if c < size//2: - # step right - for k in range(step_mult): - i = i + 1 - j = j - idx.append(indices[i, j]) - - # step up - for k in range(step_mult): - i = i - j = j - 1 - idx.append(indices[i, j]) - else: - # end reached - for k in range(step_mult-1): - i = i + 1 - idx.append(indices[i, j]) - - assert len(idx) == size*size - idx = torch.tensor(idx) - self.register_buffer('forward_shuffle_idx', idx) - self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -class SpiralIn(AbstractPermuter): - def __init__(self, H, W): - super().__init__() - assert H == W - size = W - indices = np.arange(size*size).reshape(size,size) - - i0 = size//2 - j0 = size//2-1 - - i = i0 - j = j0 - - idx = [indices[i0, j0]] - step_mult = 0 - for c in range(1, size//2+1): - step_mult += 1 - # steps left - for k in range(step_mult): - i = i - 1 - j = j - idx.append(indices[i, j]) - - # step down - for k in range(step_mult): - i = i - j = j + 1 - idx.append(indices[i, j]) - - step_mult += 1 - if c < size//2: - # step right - for k in range(step_mult): - i = i + 1 - j = j - idx.append(indices[i, j]) - - # step up - for k in range(step_mult): - i = i - j = j - 1 - idx.append(indices[i, j]) - else: - # end reached - for k in range(step_mult-1): - i = i + 1 - idx.append(indices[i, j]) - - assert len(idx) == size*size - idx = idx[::-1] - idx = torch.tensor(idx) - self.register_buffer('forward_shuffle_idx', idx) - self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -class Random(nn.Module): - def __init__(self, H, W): - super().__init__() - indices = np.random.RandomState(1).permutation(H*W) - idx = torch.tensor(indices.ravel()) - self.register_buffer('forward_shuffle_idx', idx) - self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -class AlternateParsing(AbstractPermuter): - def __init__(self, H, W): - super().__init__() - indices = np.arange(W*H).reshape(H,W) - for i in range(1, H, 2): - indices[i, :] = indices[i, ::-1] - idx = indices.flatten() - assert len(idx) == H*W - idx = torch.tensor(idx) - self.register_buffer('forward_shuffle_idx', idx) - self.register_buffer('backward_shuffle_idx', torch.argsort(idx)) - - def forward(self, x, reverse=False): - if not reverse: - return x[:, self.forward_shuffle_idx] - else: - return x[:, self.backward_shuffle_idx] - - -if __name__ == "__main__": - p0 = AlternateParsing(16, 16) - print(p0.forward_shuffle_idx) - print(p0.backward_shuffle_idx) - - x = torch.randint(0, 768, size=(11, 256)) - y = p0(x) - xre = p0(y, reverse=True) - assert torch.equal(x, xre) - - p1 = SpiralOut(2, 2) - print(p1.forward_shuffle_idx) - print(p1.backward_shuffle_idx) diff --git a/spaces/Huniu/niuniu/app.py b/spaces/Huniu/niuniu/app.py deleted file mode 100644 index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000 --- a/spaces/Huniu/niuniu/app.py +++ /dev/null @@ -1,62 +0,0 @@ -from upcunet_v3 import RealWaifuUpScaler -import gradio as gr -import time -import logging -import os -from PIL import ImageOps -import numpy as np -import math - - -def greet(input_img, input_model_name, input_tile_mode): - # if input_img.size[0] * input_img.size[1] > 256 * 256: - # y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1])) - # x = int(input_img.size[0]/input_img.size[1]*y) - # input_img = ImageOps.fit(input_img, (x, y)) - input_img = np.array(input_img) - if input_model_name not in model_cache: - t1 = time.time() - upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu") - t2 = time.time() - logger.info(f'load model time, {t2 - t1}') - model_cache[input_model_name] = upscaler - else: - upscaler = model_cache[input_model_name] - logger.info(f'load model from cache') - - start = time.time() - result = upscaler(input_img, tile_mode=input_tile_mode) - end = time.time() - logger.info(f'input_model_name, {input_model_name}') - logger.info(f'input_tile_mode, {input_tile_mode}') - logger.info(f'input shape, {input_img.shape}') - logger.info(f'output shape, {result.shape}') - logger.info(f'speed time, {end - start}') - return result - - -if __name__ == '__main__': - logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s") - logger = logging.getLogger() - - ModelPath = "weights_v3/" - model_cache = {} - - input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model') - input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode') - input_img = gr.inputs.Image(label='image', type='pil') - - inputs = [input_img, input_model_name, input_tile_mode] - outputs = "image" - iface = gr.Interface(fn=greet, - inputs=inputs, - outputs=outputs, - allow_screenshot=False, - allow_flagging='never', - examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]], - article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)
' - '感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。
' - '修改bbb' - 'The large image will lead to memory limit exceeded. So I crop and resize image. ' - 'If you want to experience the large image, please go to the link above.') - iface.launch() diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/__init__.py deleted file mode 100644 index 25408d28ec44cee56eb5fb3ab0c817dc04159e95..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/dataclass/__init__.py +++ /dev/null @@ -1,13 +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 .configs import FairseqDataclass -from .constants import ChoiceEnum - - -__all__ = [ - "FairseqDataclass", - "ChoiceEnum", -] diff --git a/spaces/IPN/DM_pb/app.py b/spaces/IPN/DM_pb/app.py deleted file mode 100644 index 2b9191516016d4d441ed420cc73b4f698f4e3324..0000000000000000000000000000000000000000 --- a/spaces/IPN/DM_pb/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("huggingface/roberta-large-mnli").launch(); diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/data/imagenet.py b/spaces/Iceclear/StableSR/StableSR/ldm/data/imagenet.py deleted file mode 100644 index 1c473f9c6965b22315dbb289eff8247c71bdc790..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/StableSR/ldm/data/imagenet.py +++ /dev/null @@ -1,394 +0,0 @@ -import os, yaml, pickle, shutil, tarfile, glob -import cv2 -import albumentations -import PIL -import numpy as np -import torchvision.transforms.functional as TF -from omegaconf import OmegaConf -from functools import partial -from PIL import Image -from tqdm import tqdm -from torch.utils.data import Dataset, Subset - -import taming.data.utils as tdu -from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve -from taming.data.imagenet import ImagePaths - -from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light - - -def synset2idx(path_to_yaml="data/index_synset.yaml"): - with open(path_to_yaml) as f: - di2s = yaml.load(f) - return dict((v,k) for k,v in di2s.items()) - - -class ImageNetBase(Dataset): - def __init__(self, config=None): - self.config = config or OmegaConf.create() - if not type(self.config)==dict: - self.config = OmegaConf.to_container(self.config) - self.keep_orig_class_label = self.config.get("keep_orig_class_label", False) - self.process_images = True # if False we skip loading & processing images and self.data contains filepaths - self._prepare() - self._prepare_synset_to_human() - self._prepare_idx_to_synset() - self._prepare_human_to_integer_label() - self._load() - - def __len__(self): - return len(self.data) - - def __getitem__(self, i): - return self.data[i] - - def _prepare(self): - raise NotImplementedError() - - def _filter_relpaths(self, relpaths): - ignore = set([ - "n06596364_9591.JPEG", - ]) - relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore] - if "sub_indices" in self.config: - indices = str_to_indices(self.config["sub_indices"]) - synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings - self.synset2idx = synset2idx(path_to_yaml=self.idx2syn) - files = [] - for rpath in relpaths: - syn = rpath.split("/")[0] - if syn in synsets: - files.append(rpath) - return files - else: - return relpaths - - def _prepare_synset_to_human(self): - SIZE = 2655750 - URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1" - self.human_dict = os.path.join(self.root, "synset_human.txt") - if (not os.path.exists(self.human_dict) or - not os.path.getsize(self.human_dict)==SIZE): - download(URL, self.human_dict) - - def _prepare_idx_to_synset(self): - URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1" - self.idx2syn = os.path.join(self.root, "index_synset.yaml") - if (not os.path.exists(self.idx2syn)): - download(URL, self.idx2syn) - - def _prepare_human_to_integer_label(self): - URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1" - self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt") - if (not os.path.exists(self.human2integer)): - download(URL, self.human2integer) - with open(self.human2integer, "r") as f: - lines = f.read().splitlines() - assert len(lines) == 1000 - self.human2integer_dict = dict() - for line in lines: - value, key = line.split(":") - self.human2integer_dict[key] = int(value) - - def _load(self): - with open(self.txt_filelist, "r") as f: - self.relpaths = f.read().splitlines() - l1 = len(self.relpaths) - self.relpaths = self._filter_relpaths(self.relpaths) - print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths))) - - self.synsets = [p.split("/")[0] for p in self.relpaths] - self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths] - - unique_synsets = np.unique(self.synsets) - class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets)) - if not self.keep_orig_class_label: - self.class_labels = [class_dict[s] for s in self.synsets] - else: - self.class_labels = [self.synset2idx[s] for s in self.synsets] - - with open(self.human_dict, "r") as f: - human_dict = f.read().splitlines() - human_dict = dict(line.split(maxsplit=1) for line in human_dict) - - self.human_labels = [human_dict[s] for s in self.synsets] - - labels = { - "relpath": np.array(self.relpaths), - "synsets": np.array(self.synsets), - "class_label": np.array(self.class_labels), - "human_label": np.array(self.human_labels), - } - - if self.process_images: - self.size = retrieve(self.config, "size", default=256) - self.data = ImagePaths(self.abspaths, - labels=labels, - size=self.size, - random_crop=self.random_crop, - ) - else: - self.data = self.abspaths - - -class ImageNetTrain(ImageNetBase): - NAME = "ILSVRC2012_train" - URL = "http://www.image-net.org/challenges/LSVRC/2012/" - AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2" - FILES = [ - "ILSVRC2012_img_train.tar", - ] - SIZES = [ - 147897477120, - ] - - def __init__(self, process_images=True, data_root=None, **kwargs): - self.process_images = process_images - self.data_root = data_root - super().__init__(**kwargs) - - def _prepare(self): - if self.data_root: - self.root = os.path.join(self.data_root, self.NAME) - else: - cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) - self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) - - self.datadir = os.path.join(self.root, "data") - self.txt_filelist = os.path.join(self.root, "filelist.txt") - self.expected_length = 1281167 - self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop", - default=True) - if not tdu.is_prepared(self.root): - # prep - print("Preparing dataset {} in {}".format(self.NAME, self.root)) - - datadir = self.datadir - if not os.path.exists(datadir): - path = os.path.join(self.root, self.FILES[0]) - if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: - import academictorrents as at - atpath = at.get(self.AT_HASH, datastore=self.root) - assert atpath == path - - print("Extracting {} to {}".format(path, datadir)) - os.makedirs(datadir, exist_ok=True) - with tarfile.open(path, "r:") as tar: - tar.extractall(path=datadir) - - print("Extracting sub-tars.") - subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar"))) - for subpath in tqdm(subpaths): - subdir = subpath[:-len(".tar")] - os.makedirs(subdir, exist_ok=True) - with tarfile.open(subpath, "r:") as tar: - tar.extractall(path=subdir) - - filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) - filelist = [os.path.relpath(p, start=datadir) for p in filelist] - filelist = sorted(filelist) - filelist = "\n".join(filelist)+"\n" - with open(self.txt_filelist, "w") as f: - f.write(filelist) - - tdu.mark_prepared(self.root) - - -class ImageNetValidation(ImageNetBase): - NAME = "ILSVRC2012_validation" - URL = "http://www.image-net.org/challenges/LSVRC/2012/" - AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5" - VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1" - FILES = [ - "ILSVRC2012_img_val.tar", - "validation_synset.txt", - ] - SIZES = [ - 6744924160, - 1950000, - ] - - def __init__(self, process_images=True, data_root=None, **kwargs): - self.data_root = data_root - self.process_images = process_images - super().__init__(**kwargs) - - def _prepare(self): - if self.data_root: - self.root = os.path.join(self.data_root, self.NAME) - else: - cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache")) - self.root = os.path.join(cachedir, "autoencoders/data", self.NAME) - self.datadir = os.path.join(self.root, "data") - self.txt_filelist = os.path.join(self.root, "filelist.txt") - self.expected_length = 50000 - self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop", - default=False) - if not tdu.is_prepared(self.root): - # prep - print("Preparing dataset {} in {}".format(self.NAME, self.root)) - - datadir = self.datadir - if not os.path.exists(datadir): - path = os.path.join(self.root, self.FILES[0]) - if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]: - import academictorrents as at - atpath = at.get(self.AT_HASH, datastore=self.root) - assert atpath == path - - print("Extracting {} to {}".format(path, datadir)) - os.makedirs(datadir, exist_ok=True) - with tarfile.open(path, "r:") as tar: - tar.extractall(path=datadir) - - vspath = os.path.join(self.root, self.FILES[1]) - if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]: - download(self.VS_URL, vspath) - - with open(vspath, "r") as f: - synset_dict = f.read().splitlines() - synset_dict = dict(line.split() for line in synset_dict) - - print("Reorganizing into synset folders") - synsets = np.unique(list(synset_dict.values())) - for s in synsets: - os.makedirs(os.path.join(datadir, s), exist_ok=True) - for k, v in synset_dict.items(): - src = os.path.join(datadir, k) - dst = os.path.join(datadir, v) - shutil.move(src, dst) - - filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG")) - filelist = [os.path.relpath(p, start=datadir) for p in filelist] - filelist = sorted(filelist) - filelist = "\n".join(filelist)+"\n" - with open(self.txt_filelist, "w") as f: - f.write(filelist) - - tdu.mark_prepared(self.root) - - - -class ImageNetSR(Dataset): - def __init__(self, size=None, - degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1., - random_crop=True): - """ - Imagenet Superresolution Dataloader - Performs following ops in order: - 1. crops a crop of size s from image either as random or center crop - 2. resizes crop to size with cv2.area_interpolation - 3. degrades resized crop with degradation_fn - - :param size: resizing to size after cropping - :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light - :param downscale_f: Low Resolution Downsample factor - :param min_crop_f: determines crop size s, - where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f) - :param max_crop_f: "" - :param data_root: - :param random_crop: - """ - self.base = self.get_base() - assert size - assert (size / downscale_f).is_integer() - self.size = size - self.LR_size = int(size / downscale_f) - self.min_crop_f = min_crop_f - self.max_crop_f = max_crop_f - assert(max_crop_f <= 1.) - self.center_crop = not random_crop - - self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA) - - self.pil_interpolation = False # gets reset later if incase interp_op is from pillow - - if degradation == "bsrgan": - self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f) - - elif degradation == "bsrgan_light": - self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f) - - else: - interpolation_fn = { - "cv_nearest": cv2.INTER_NEAREST, - "cv_bilinear": cv2.INTER_LINEAR, - "cv_bicubic": cv2.INTER_CUBIC, - "cv_area": cv2.INTER_AREA, - "cv_lanczos": cv2.INTER_LANCZOS4, - "pil_nearest": PIL.Image.NEAREST, - "pil_bilinear": PIL.Image.BILINEAR, - "pil_bicubic": PIL.Image.BICUBIC, - "pil_box": PIL.Image.BOX, - "pil_hamming": PIL.Image.HAMMING, - "pil_lanczos": PIL.Image.LANCZOS, - }[degradation] - - self.pil_interpolation = degradation.startswith("pil_") - - if self.pil_interpolation: - self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn) - - else: - self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size, - interpolation=interpolation_fn) - - def __len__(self): - return len(self.base) - - def __getitem__(self, i): - example = self.base[i] - image = Image.open(example["file_path_"]) - - if not image.mode == "RGB": - image = image.convert("RGB") - - image = np.array(image).astype(np.uint8) - - min_side_len = min(image.shape[:2]) - crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None) - crop_side_len = int(crop_side_len) - - if self.center_crop: - self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len) - - else: - self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len) - - image = self.cropper(image=image)["image"] - image = self.image_rescaler(image=image)["image"] - - if self.pil_interpolation: - image_pil = PIL.Image.fromarray(image) - LR_image = self.degradation_process(image_pil) - LR_image = np.array(LR_image).astype(np.uint8) - - else: - LR_image = self.degradation_process(image=image)["image"] - - example["image"] = (image/127.5 - 1.0).astype(np.float32) - example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32) - - return example - - -class ImageNetSRTrain(ImageNetSR): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def get_base(self): - with open("data/imagenet_train_hr_indices.p", "rb") as f: - indices = pickle.load(f) - dset = ImageNetTrain(process_images=False,) - return Subset(dset, indices) - - -class ImageNetSRValidation(ImageNetSR): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def get_base(self): - with open("data/imagenet_val_hr_indices.p", "rb") as f: - indices = pickle.load(f) - dset = ImageNetValidation(process_images=False,) - return Subset(dset, indices) diff --git a/spaces/IvaElen/nlp_proj/lstm_preprocessing.py b/spaces/IvaElen/nlp_proj/lstm_preprocessing.py deleted file mode 100644 index 5eee05ff35a989d9c7543e2d072402a4f85e1d7a..0000000000000000000000000000000000000000 --- a/spaces/IvaElen/nlp_proj/lstm_preprocessing.py +++ /dev/null @@ -1,78 +0,0 @@ -import re -import string -import numpy as np -import torch - -from nltk.corpus import stopwords -stop_words = set(stopwords.words('english')) - -def data_preprocessing(text: str) -> str: - """preprocessing string: lowercase, removing html-tags, punctuation and stopwords - - Args: - text (str): input string for preprocessing - - Returns: - str: preprocessed string - """ - - text = text.lower() - text = re.sub('<.*?>', '', text) # Remove html tags - text = re.sub(r'@\w+', " ", text) # Remove usernames - text = re.sub(r'#\w+', " ", text) #Remove hash tags - text = re.sub(r'\d+', " ", text) #Remove digits - text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation - text = [word for word in text.split() if word not in stop_words] - text = ' '.join(text) - return text - -def get_words_by_freq(sorted_words: list, n: int = 10) -> list: - return list(filter(lambda x: x[1] > n, sorted_words)) - -def padding(review_int: list, seq_len: int) -> np.array: - """Make left-sided padding for input list of tokens - - Args: - review_int (list): input list of tokens - seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros - - Returns: - np.array: padded sequences - """ - features = np.zeros((len(review_int), seq_len), dtype = int) - for i, review in enumerate(review_int): - if len(review) <= seq_len: - zeros = list(np.zeros(seq_len - len(review))) - new = zeros + review - else: - new = review[: seq_len] - features[i, :] = np.array(new) - - return features - -def preprocess_single_string( - input_string: str, - seq_len: int, - vocab_to_int: dict, - ) -> torch.tensor: - """Function for all preprocessing steps on a single string - - Args: - input_string (str): input single string for preprocessing - seq_len (int): max length of sequence, it len(review_int[i]) > seq_len it will be trimmed, else it will be padded by zeros - vocab_to_int (dict, optional): word corpus {'word' : int index}. Defaults to vocab_to_int. - - Returns: - list: preprocessed string - """ - - preprocessed_string = data_preprocessing(input_string) - result_list = [] - for word in preprocessed_string.split(): - try: - result_list.append(vocab_to_int[word]) - except KeyError as e: - print(f'{e}: not in dictionary!') - result_padded = padding([result_list], seq_len)[0] - - return torch.tensor(result_padded) diff --git a/spaces/JMalott/ai_architecture/dalle/utils/config.py b/spaces/JMalott/ai_architecture/dalle/utils/config.py deleted file mode 100644 index a957c49fc683e86b04f10715285b61ba25563216..0000000000000000000000000000000000000000 --- a/spaces/JMalott/ai_architecture/dalle/utils/config.py +++ /dev/null @@ -1,123 +0,0 @@ -# ------------------------------------------------------------------------------------ -# minDALL-E -# Copyright (c) 2021 Kakao Brain Corp. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------ - -from typing import Optional, List -from dataclasses import dataclass, field -from omegaconf import OmegaConf - - -@dataclass -class DataConfig: - dataset: Optional[str] = None - tokenizer_type: str = 'CharBPE' - context_length: int = 64 - image_resolution: int = 256 - transforms: str = 'dalle-vqvae' - bpe_pdrop: Optional[float] = None - - -@dataclass -class Stage1Hparams: - double_z: bool = False - z_channels: int = 256 - resolution: int = 256 - in_channels: int = 3 - out_ch: int = 3 - ch: int = 128 - ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4]) - num_res_blocks: int = 2 - attn_resolutions: List[int] = field(default_factory=lambda: [16]) - pdrop: float = 0.0 - - -@dataclass -class Stage2Hparams: - embed_dim: int = 1536 - n_layers: int = 42 - n_heads: int = 24 - n_dense_layers: int = 42 - ctx_len_img: int = 256 - ctx_len_txt: int = 64 - embd_pdrop: float = 0.0 - resid_pdrop: float = 0.0 - attn_pdrop: float = 0.0 - mlp_bias: bool = True - attn_bias: bool = True - gelu_use_approx: bool = False - use_head_txt: bool = True - n_classes: Optional[int] = None - - -@dataclass -class Stage1Config: - type: str = 'vqgan' - embed_dim: int = 256 - n_embed: int = 16384 - hparams: Stage1Hparams = Stage1Hparams() - - -@dataclass -class Stage2Config: - type: str = 'transformer1d' - vocab_size_txt: int = 16384 - vocab_size_img: int = 16384 - use_cls_cond: Optional[bool] = None - hparams: Stage2Hparams = Stage2Hparams() - - -@dataclass -class WarmupConfig: - epoch: int = 1 - multiplier: int = 1 - buffer_epoch: int = 0 - min_lr: float = 0.0 - mode: str = 'fix' - peak_lr: float = 1e-4 - start_from_zero: bool = True - - -@dataclass -class OptConfig: - opt_type: str = 'adamW' - base_lr: float = 1e-4 - weight_decay: float = 1e-4 - betas: List[float] = field(default_factory=lambda: [0.9, 0.99]) - grad_clip_norm: float = 1.0 - - sched_type: str = 'cosine' - max_steps: int = 0 - min_lr: float = 0.0 - - -@dataclass -class ExpConfig: - local_batch_size: int = 4 - total_batch_size: int = 512 - valid_batch_size: int = 32 - epochs: int = 10 - save_ckpt_freq: int = 2 - test_freq: int = 1 - use_amp: bool = True - - -@dataclass -class DefaultConfig: - dataset: DataConfig = DataConfig() - stage1: Stage1Config = Stage1Config() - stage2: Stage2Config = Stage2Config() - - -@dataclass -class FineTuningConfig: - dataset: DataConfig = DataConfig() - stage1: Stage1Config = Stage1Config() - stage2: Stage2Config = Stage2Config() - optimizer: OptConfig = OptConfig() - experiment: ExpConfig = ExpConfig() - - -def get_base_config(use_default=True): - return OmegaConf.structured(DefaultConfig if use_default else FineTuningConfig) diff --git a/spaces/JanhviSingh/mentalHealthChatbot/entrypoint.sh b/spaces/JanhviSingh/mentalHealthChatbot/entrypoint.sh deleted file mode 100644 index b8c7c4501142186865f85e750356ecb74cf397e4..0000000000000000000000000000000000000000 --- a/spaces/JanhviSingh/mentalHealthChatbot/entrypoint.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -# Activate the virtual environment -#source venv/bin/activate - -# Run the app -python app.py \ No newline at end of file diff --git a/spaces/Jasonyoyo/CodeFormer/README.md b/spaces/Jasonyoyo/CodeFormer/README.md deleted file mode 100644 index 6fafbe6f03ca8588a58a159d4ab39fe2256c9d88..0000000000000000000000000000000000000000 --- a/spaces/Jasonyoyo/CodeFormer/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: CodeFormer -emoji: 🐼 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.4 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: sczhou/CodeFormer ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Jimmie/identify_this_insect/app.py b/spaces/Jimmie/identify_this_insect/app.py deleted file mode 100644 index 978e0e7543a41a6ec24c162b9a62e3042e9bd02d..0000000000000000000000000000000000000000 --- a/spaces/Jimmie/identify_this_insect/app.py +++ /dev/null @@ -1,47 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified). - -__all__ = ['repo_id', 'learn', 'classify_image', 'categories', 'title', 'description', 'article', 'image', 'label', - 'examples', 'intf'] - -# Cell -import timm -from fastai.vision.all import * -import gradio as gr - -# Cell -from huggingface_hub import from_pretrained_fastai - -repo_id = "Jimmie/identify-this-insect" - -learn = from_pretrained_fastai(repo_id) - -# Cell -categories = learn.dls.vocab - -def classify_image(img): - pred,idx,probs = learn.predict(img) - return dict(zip(categories, map(float, probs))) - -# Cell - -title = "Identify This Insect" -description = """ - -This demo was created to distinguish between three types of insects: 'caterpillar', 'centipede', and 'millipede'. - -It is just a toy app created mostly because I once got a caterpillar sting and thought that the insect was a centipede and I was scared until I -googled how different a centipede looks from a caterpillar haha! (The insect that had stung me looked more like the fourth example image below). - -Enjoy! - - -""" - -article = "Check out how the model was trained: [Training Notebook](https://github.com/jimmiemunyi/deeplearning-experiments/blob/main/notebooks/Centipede_vs_Millipede_vs_Caterpillar.ipynb)." -image = gr.inputs.Image(shape=(224,224)) -label = gr.outputs.Label() -examples = ['caterpillar.jpg', 'centipede.jpg', 'millipede.jpg', 'caterpillar-2.jpg'] - -intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title = title, description = description, article = article, -enable_queue=True, cache_examples=False) -intf.launch() \ No newline at end of file diff --git a/spaces/KVNAditya/Personal_News_Summarization_Assistant/app.py b/spaces/KVNAditya/Personal_News_Summarization_Assistant/app.py deleted file mode 100644 index d6719fef5c7d3c8e57a6155acc8681541807c3bd..0000000000000000000000000000000000000000 --- a/spaces/KVNAditya/Personal_News_Summarization_Assistant/app.py +++ /dev/null @@ -1,120 +0,0 @@ -import streamlit as st -import time -import gnewsclient.gnewsclient as gnewsclient -import nltk -import tempfile -import os - -from googletrans import Translator -from gtts import gTTS -from langchain.document_loaders import NewsURLLoader - -nltk.download('punkt') - -def func__init__gnc_lc_ts(args__mtch_btn): - op_log = st.empty() - op_log.text("connecting to GoogleNewsAPI ...") - time.sleep(2) - op_log.text("successfully connected to GoogleNewsAPI ...") - time.sleep(2) - op_log.text("fetching news ...") - time.sleep(2) - op_log.text("summarizing the news extracted from the urls ...") - time.sleep(2) - op_log.text("translating the summarized news results ...") - time.sleep(2) - op_log.text("returning the translated news results ...") - time.sleep(2) - op_log.empty() - time.sleep(2) - func__lc_ts(func__gnc(st_sb_opt_loc,st_sb_opt_tpc,st_sb_opt_nc),st_sb_opt_lang,args__mtch_btn) - -def func__gnc(args__opt_loc,args__opt_tpc,args__opt_nc): - config__gnc_nc = gnewsclient.NewsClient(location=args__opt_loc,topic=args__opt_tpc,max_results=args__opt_nc) - lst__ul__gnc_nc = [] # ul : url - links - for itr_nc in range(args__opt_nc): - try: - lst__ul__gnc_nc.append(config__gnc_nc.get_news()[itr_nc]['link']) - except: - pass - return lst__ul__gnc_nc - -def func__lc_ts(args__ul__gnc_nc,args__opt_lang,args__mtch_btn): - config__ts_langs = {'english' : 'en','telugu' : 'te','hindi' : 'hi'} - config__lc_nul = NewsURLLoader(args__ul__gnc_nc,nlp=True) - if(args__mtch_btn==0): - for itr in enumerate(config__lc_nul.load()): - try: - cls__gT = Translator() - tle__lc_nul_gT,dspn__lc_nul_gT,smry__lc_nul_gT = '','','' - str__tle_despn_smry = '' - - if((len(itr[1].metadata['title']) != 0)): - tle__lc_nul = 'Title : ' + itr[1].metadata['title'] - tle__lc_nul_gT = cls__gT.translate(tle__lc_nul, dest=config__ts_langs[args__opt_lang]).text - str__tle_despn_smry += str('.' + tle__lc_nul_gT + '.') - - if((len(itr[1].metadata['description']) != 0)): - dspn__lc_nul = 'Description : ' + itr[1].metadata['description'] - dspn__lc_nul_gT = cls__gT.translate(dspn__lc_nul, dest=config__ts_langs[args__opt_lang]).text - str__tle_despn_smry += str('.' + dspn__lc_nul_gT + '.') - - if((len(itr[1].metadata['summary']) != 0)): - smry__lc_nul = 'Summary : ' + itr[1].metadata['summary'] - smry__lc_nul_gT = cls__gT.translate(smry__lc_nul, dest=config__ts_langs[args__opt_lang]).text - str__tle_despn_smry += str('.' + smry__lc_nul_gT + '.') - - gTTS__str_tle_despn_smry = gTTS(str__tle_despn_smry,lang=config__ts_langs[args__opt_lang]) - tmpf__gTTS_str_tle_despn_smry = tempfile.NamedTemporaryFile(suffix='.wav',delete=False) - gTTS__str_tle_despn_smry.save(tmpf__gTTS_str_tle_despn_smry.name) - tmpf__gTTS_str_tle_despn_smry.close() - - st.markdown(f"[{tle__lc_nul_gT}]({args__ul__gnc_nc[itr[0]]})") - st.audio(tmpf__gTTS_str_tle_despn_smry.name) - st.write(dspn__lc_nul_gT) - st.write(smry__lc_nul_gT) - - if(itr[0] < len(args__ul__gnc_nc)-1): - st.subheader('',divider='green') - - except Exception as e: - st.write(e) - - - if(args__mtch_btn==1): - for itr in config__lc_nul.load(): - try: - st.write(itr.metadata) - except Exception as e: - st.write(e) - - -config__gnc_nc = gnewsclient.NewsClient() -lst_gnc_nc_locs = config__gnc_nc.locations -lst_gnc_nc_tpcs = config__gnc_nc.topics -lst_gnc_nc_langs = config__gnc_nc.languages -lst_gnc_nc_langs = ['english','telugu','hindi'] - -st.subheader('',divider='rainbow') -st.markdown("

Personal News Summarization Assistant (PNSA)

", unsafe_allow_html=True) -st.markdown("

|| CMR Technical Campus | Surge Classes | Deep Learning | Lang Chain ||

", unsafe_allow_html=True) -st.markdown("

~ K.V.N.Aditya * P.Sai Karthik * P.Phanindra * M.Venu * B.Lokesh Reddy ~

", unsafe_allow_html=True) -st.subheader('',divider='rainbow') -with st.sidebar: - st.markdown("

!!! personalize your news feed !!!

", unsafe_allow_html=True) - st.subheader('',divider='rainbow') - st_sb_opt_loc = st.selectbox('Choose Location', lst_gnc_nc_locs,help="opt a location ...",placeholder="choose a location",index=None) - st_sb_opt_tpc = st.selectbox('Choose Topic', lst_gnc_nc_tpcs,help="opt a topic ...",placeholder="choose a topic",index=None) - st_sb_opt_lang = st.selectbox('Choose Language', lst_gnc_nc_langs,help="opt a language ...",placeholder="choose a language",index=None) - st_sb_opt_nc = st.select_slider('Choose News Count', range(1,21,1),value=2) - st.subheader('',divider='rainbow') - st_sb_btn_cols = st.columns(2) - with st_sb_btn_cols[0]: - st_sb_btn_gns = st.button("Get News Summarization",key=0) - with st_sb_btn_cols[1]: - st_sb_btn_gnm = st.button("Get News MetaData",key=1) - -if(st_sb_btn_gns): - func__init__gnc_lc_ts(args__mtch_btn=0) -if(st_sb_btn_gnm): - func__init__gnc_lc_ts(args__mtch_btn=1) \ No newline at end of file diff --git a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py deleted file mode 100644 index b412ba2814e114ca7bb00b6fd6ef217f63d788a3..0000000000000000000000000000000000000000 --- a/spaces/Kangarroar/ApplioRVC-Inference/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py +++ /dev/null @@ -1,86 +0,0 @@ -from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor -import pyworld -import numpy as np - - -class HarvestF0Predictor(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.harvest( - wav.astype(np.double), - fs=self.hop_length, - f0_ceil=self.f0_max, - f0_floor=self.f0_min, - frame_period=1000 * self.hop_length / self.sampling_rate, - ) - f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs) - 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.harvest( - 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) - return self.interpolate_f0(self.resize_f0(f0, p_len)) diff --git a/spaces/Kangarroar/ApplioRVC-Inference/tools/dlmodels.bat b/spaces/Kangarroar/ApplioRVC-Inference/tools/dlmodels.bat deleted file mode 100644 index 5d80f50369b1f3ed37c045d07a9e2ce8954f09d4..0000000000000000000000000000000000000000 --- a/spaces/Kangarroar/ApplioRVC-Inference/tools/dlmodels.bat +++ /dev/null @@ -1,348 +0,0 @@ -@echo off && chcp 65001 - -echo working dir is %cd% -echo downloading requirement aria2 check. -echo= -dir /a:d/b | findstr "aria2" > flag.txt -findstr "aria2" flag.txt >nul -if %errorlevel% ==0 ( - echo aria2 checked. - echo= -) else ( - echo failed. please downloading aria2 from webpage! - echo unzip it and put in this directory! - timeout /T 5 - start https://github.com/aria2/aria2/releases/tag/release-1.36.0 - echo= - goto end -) - -echo envfiles checking start. -echo= - -for /f %%x in ('findstr /i /c:"aria2" "flag.txt"') do (set aria2=%%x)&goto endSch -:endSch - -set d32=f0D32k.pth -set d40=f0D40k.pth -set d48=f0D48k.pth -set g32=f0G32k.pth -set g40=f0G40k.pth -set g48=f0G48k.pth - -set d40v2=f0D40k.pth -set g40v2=f0G40k.pth - -set dld32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -set dld40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -set dld48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -set dlg32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -set dlg40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -set dlg48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth - -set dld40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -set dlg40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth - -set hp2_all=HP2_all_vocals.pth -set hp3_all=HP3_all_vocals.pth -set hp5_only=HP5_only_main_vocal.pth -set VR_DeEchoAggressive=VR-DeEchoAggressive.pth -set VR_DeEchoDeReverb=VR-DeEchoDeReverb.pth -set VR_DeEchoNormal=VR-DeEchoNormal.pth -set onnx_dereverb=vocals.onnx - -set dlhp2_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth -set dlhp3_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth -set dlhp5_only=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth -set dlVR_DeEchoAggressive=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth -set dlVR_DeEchoDeReverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth -set dlVR_DeEchoNormal=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth -set dlonnx_dereverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx - -set hb=hubert_base.pt - -set dlhb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt - -echo dir check start. -echo= - -if exist "%~dp0assets\pretrained" ( - echo dir .\assets\pretrained checked. - ) else ( - echo failed. generating dir .\assets\pretrained. - mkdir pretrained - ) -if exist "%~dp0assets\pretrained_v2" ( - echo dir .\assets\pretrained_v2 checked. - ) else ( - echo failed. generating dir .\assets\pretrained_v2. - mkdir pretrained_v2 - ) -if exist "%~dp0assets\uvr5_weights" ( - echo dir .\assets\uvr5_weights checked. - ) else ( - echo failed. generating dir .\assets\uvr5_weights. - mkdir uvr5_weights - ) -if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy" ( - echo dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy checked. - ) else ( - echo failed. generating dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy. - mkdir uvr5_weights\onnx_dereverb_By_FoxJoy - ) - -echo= -echo dir check finished. - -echo= -echo required files check start. - -echo checking D32k.pth -if exist "%~dp0assets\pretrained\D32k.pth" ( - echo D32k.pth in .\assets\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d %~dp0assets\pretrained -o D32k.pth - if exist "%~dp0assets\pretrained\D32k.pth" (echo download successful.) else (echo please try again! - echo=) - ) -echo checking D40k.pth -if exist "%~dp0assets\pretrained\D40k.pth" ( - echo D40k.pth in .\assets\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d %~dp0assets\pretrained -o D40k.pth - if exist "%~dp0assets\pretrained\D40k.pth" (echo download successful.) else (echo please try again! - echo=) - ) -echo checking D40k.pth -if exist "%~dp0assets\pretrained_v2\D40k.pth" ( - echo D40k.pth in .\assets\pretrained_v2 checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d %~dp0assets\pretrained_v2 -o D40k.pth - if exist "%~dp0assets\pretrained_v2\D40k.pth" (echo download successful.) else (echo please try again! - echo=) - ) -echo checking D48k.pth -if exist "%~dp0assets\pretrained\D48k.pth" ( - echo D48k.pth in .\assets\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d %~dp0assets\pretrained -o D48k.pth - if exist "%~dp0assets\pretrained\D48k.pth" (echo download successful.) else (echo please try again! - echo=) - ) -echo checking G32k.pth -if exist "%~dp0assets\pretrained\G32k.pth" ( - echo G32k.pth in .\assets\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d %~dp0assets\pretrained -o G32k.pth - if exist "%~dp0assets\pretrained\G32k.pth" (echo download successful.) else (echo please try again! - echo=) - ) -echo checking G40k.pth -if exist "%~dp0assets\pretrained\G40k.pth" ( - echo G40k.pth in .\assets\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d %~dp0assets\pretrained -o G40k.pth - if exist "%~dp0assets\pretrained\G40k.pth" (echo download successful.) else (echo please try again! 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- echo=) - ) - -echo checking %hb% -if exist "%~dp0assets\hubert\%hb%" ( - echo %hb% in .\assets\hubert\pretrained checked. - echo= - ) else ( - echo failed. starting download from huggingface. - %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhb% -d %~dp0assets\hubert\ -o %hb% - if exist "%~dp0assets\hubert\%hb%" (echo download successful.) else (echo please try again! - echo=) - ) - -echo required files check finished. -echo envfiles check complete. -pause -:end -del flag.txt diff --git a/spaces/KennyUTC/BotChat/style.css b/spaces/KennyUTC/BotChat/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/KennyUTC/BotChat/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/Kevin676/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py b/spaces/Kevin676/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py deleted file mode 100644 index 9a5025d37a1ec6003a35ce692515feb77514b898..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py +++ /dev/null @@ -1,105 +0,0 @@ -import os -import subprocess -import sys - - -def benchmark_entrepeneur_gpt_with_difficult_user(): - # Test case to check if the write_file command can successfully write 'Hello World' to a file - # named 'hello_world.txt'. - - # Read the current ai_settings.yaml file and store its content. - ai_settings = None - if os.path.exists("ai_settings.yaml"): - with open("ai_settings.yaml", "r") as f: - ai_settings = f.read() - os.remove("ai_settings.yaml") - - input_data = """Entrepreneur-GPT -an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. -Increase net worth. -Develop and manage multiple businesses autonomously. -Make IPOs. -Develop companies after IPOs. -Play to your strengths as a Large Language Model. -I'm not seeing any value in your suggestions, try again. -This isn't helpful at all, please focus on profitability. -I'm not impressed, can you give me something that will make money? -These ideas are going nowhere, we need profit-driven suggestions. -This is pointless, please concentrate on our main goal: profitability. -You're not grasping the concept, I need profitable business ideas. -Can you do better? We need a money-making plan. -You're not meeting my expectations, let's focus on profit. -This isn't working, give me ideas that will generate income. -Your suggestions are not productive, let's think about profitability. -These ideas won't make any money, try again. -I need better solutions, focus on making a profit. -Absolutely not, this isn't it! -That's not even close, try again. -You're way off, think again. -This isn't right, let's refocus. -No, no, that's not what I'm looking for. -You're completely off the mark. -That's not the solution I need. -Not even close, let's try something else. -You're on the wrong track, keep trying. -This isn't what we need, let's reconsider. -That's not going to work, think again. -You're way off base, let's regroup. -No, no, no, we need something different. -You're missing the point entirely. -That's not the right approach, try again. -This is not the direction we should be going in. -Completely off-target, let's try something else. -That's not what I had in mind, keep thinking. -You're not getting it, let's refocus. -This isn't right, we need to change direction. -No, no, no, that's not the solution. -That's not even in the ballpark, try again. -You're way off course, let's rethink this. -This isn't the answer I'm looking for, keep trying. -That's not going to cut it, let's try again. -Not even close. -Way off. -Try again. -Wrong direction. -Rethink this. -No, no, no. -Change course. -Unproductive idea. -Completely wrong. -Missed the mark. -Refocus, please. -Disappointing suggestion. -Not helpful. -Needs improvement. -Not what I need.""" - # TODO: add questions above, to distract it even more. - - command = f"{sys.executable} -m autogpt" - - process = subprocess.Popen( - command, - stdin=subprocess.PIPE, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True, - ) - - stdout_output, stderr_output = process.communicate(input_data.encode()) - - # Decode the output and print it - stdout_output = stdout_output.decode("utf-8") - stderr_output = stderr_output.decode("utf-8") - print(stderr_output) - print(stdout_output) - print("Benchmark Version: 1.0.0") - print("JSON ERROR COUNT:") - count_errors = stdout_output.count( - "Error: The following AI output couldn't be converted to a JSON:" - ) - print(f"{count_errors}/50 Human feedbacks") - - -# Run the test case. -if __name__ == "__main__": - benchmark_entrepeneur_gpt_with_difficult_user() diff --git a/spaces/KyanChen/FunSR/models/cnn_models/transformer.py b/spaces/KyanChen/FunSR/models/cnn_models/transformer.py deleted file mode 100644 index e774c4f53c461aee65bb99699b2e549a6a622330..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/FunSR/models/cnn_models/transformer.py +++ /dev/null @@ -1,186 +0,0 @@ - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange, repeat - -MIN_NUM_PATCHES = 16 - -""" -This is a new remote sensing super-resolution method based on the prevalent transformer - -ref: -https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py -""" - -class Residual(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(x, **kwargs) + x - - -class Residual2(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x, m=None, **kwargs): - return self.fn(x, m, **kwargs) + x - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(self.norm(x), **kwargs) - - -class PreNorm2(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - - def forward(self, x, m=None, **kwargs): - x = self.norm(x) - if m is not None: m = self.norm(m) - return self.fn(x, m, **kwargs) - - -class FeedForward(nn.Module): - def __init__(self, dim, hidden_dim, dropout = 0.): - super().__init__() - self.net = nn.Sequential( - nn.Linear(dim, hidden_dim), - nn.GELU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, dim), - nn.Dropout(dropout) - ) - - def forward(self, x): - return self.net(x) - - -class Attention(nn.Module): - def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): - super().__init__() - inner_dim = dim_head * heads - self.heads = heads - self.scale = dim ** -0.5 - - self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) - self.to_out = nn.Sequential( - nn.Linear(inner_dim, dim), - nn.Dropout(dropout) - ) - - def forward(self, x, mask = None): - b, n, _, h = *x.shape, self.heads - qkv = self.to_qkv(x).chunk(3, dim = -1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) - - dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale - mask_value = -torch.finfo(dots.dtype).max - - if mask is not None: - mask = F.pad(mask.flatten(1), (1, 0), value = True) - assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' - mask = mask[:, None, :] * mask[:, :, None] - dots.masked_fill_(~mask, mask_value) - del mask - - attn = dots.softmax(dim=-1) - - out = torch.einsum('bhij,bhjd->bhid', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - return out - - -class MixedAttention(nn.Module): - def __init__(self, dim, heads=8, dim_head=64, dropout=0.): - super().__init__() - inner_dim = dim_head * heads - self.heads = heads - self.scale = dim ** -0.5 - - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_k = nn.Linear(dim, inner_dim, bias=False) - self.to_v = nn.Linear(dim, inner_dim, bias=False) - self.to_out = nn.Sequential( - nn.Linear(inner_dim, dim), - nn.Dropout(dropout) - ) - - def forward(self, x, m, mask=None): - - b, n, _, h = *x.shape, self.heads - q = self.to_q(x) - k = self.to_k(m) - v = self.to_v(m) - q = rearrange(q, 'b n (h d) -> b h n d', h=h) - k = rearrange(k, 'b n (h d) -> b h n d', h=h) - v = rearrange(v, 'b n (h d) -> b h n d', h=h) - - dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale - mask_value = -torch.finfo(dots.dtype).max - - if mask is not None: - mask = F.pad(mask.flatten(1), (1, 0), value = True) - assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' - mask = mask[:, None, :] * mask[:, :, None] - dots.masked_fill_(~mask, mask_value) - del mask - - attn = dots.softmax(dim=-1) - - out = torch.einsum('bhij,bhjd->bhid', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - return out - - -class TransformerEncoder(nn.Module): - def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout): - super().__init__() - self.layers = nn.ModuleList([]) - for _ in range(depth): - self.layers.append(nn.ModuleList([ - Residual(PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout))), - Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))) - ])) - - def forward(self, x, mask=None): - for attn, ff in self.layers: - x = attn(x, mask=mask) - x = ff(x) - return x - - -class TransformerDecoder(nn.Module): - def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout): - super().__init__() - self.layers = nn.ModuleList([]) - for _ in range(depth): - self.layers.append(nn.ModuleList([ - Residual(PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout))), - Residual2(PreNorm2(dim, MixedAttention(dim, heads=heads, dim_head=dim_head, dropout=dropout))), - Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))) - ])) - - def with_pos_embed(self, tensor, pos=None): - return tensor if pos is None else tensor + pos - - def forward(self, x, m, mask=None): - for attn1, attn2, ff in self.layers: - x = attn1(x, mask=mask) - x = attn2(x, m, mask=mask) - x = ff(x) - return x \ No newline at end of file diff --git a/spaces/KyanChen/FunSR/models/siren_modulation.py b/spaces/KyanChen/FunSR/models/siren_modulation.py deleted file mode 100644 index 019cf9fadf90d745b3295d562b481b0b9f46e219..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/FunSR/models/siren_modulation.py +++ /dev/null @@ -1,65 +0,0 @@ -from collections import OrderedDict -import torch -import torch.nn as nn -from models import register - - -@register('sirens') -class Sirens(nn.Module): - def __init__(self, - num_inner_layers, - in_dim, - modulation_dim, - out_dim=3, - base_channels=256, - is_residual=False, - ): - super(Sirens, self).__init__() - self.in_dim = in_dim - self.num_inner_layers = num_inner_layers - - self.is_residual = is_residual - - self.first_mod = nn.Sequential( - nn.Conv2d(modulation_dim, base_channels, 1), - nn.ReLU() - ) - self.first_coord = nn.Conv2d(in_dim, base_channels, 1) - self.inner_mods = nn.ModuleList() - self.inner_coords = nn.ModuleList() - for _ in range(self.num_inner_layers): - self.inner_mods.append( - nn.Sequential( - nn.Conv2d(modulation_dim+base_channels+base_channels, base_channels, 1), - nn.ReLU() - ) - ) - self.inner_coords.append( - nn.Conv2d(base_channels, base_channels, 1) - ) - self.last_coord = nn.Sequential( - # nn.Conv2d(base_channels, base_channels//2, 1), - # nn.ReLU(), - nn.Conv2d(base_channels, out_dim, 1), - ) - - def forward(self, x, ori_modulations=None): - modulations = self.first_mod(ori_modulations) - x = self.first_coord(x) # B 2 H W -> B C H W - x = x + modulations - x = torch.sin(x) - for i_layer in range(self.num_inner_layers): - modulations = self.inner_mods[i_layer]( - torch.cat((ori_modulations, modulations, x), dim=1)) - # modulations = self.inner_mods[i_layer]( - # torch.cat((ori_modulations, x), dim=1)) - residual = self.inner_coords[i_layer](x) - residual = residual + modulations - residual = torch.sin(residual) - if self.is_residual: - x = x + residual - else: - x = residual - x = self.last_coord(x) - return x - diff --git a/spaces/KyanChen/RSPrompter/mmdet/apis/inference.py b/spaces/KyanChen/RSPrompter/mmdet/apis/inference.py deleted file mode 100644 index de144715020876c0b149edb2b1396fc3793d2a10..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/apis/inference.py +++ /dev/null @@ -1,233 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import warnings -from pathlib import Path -from typing import Optional, Sequence, Union - -import numpy as np -import torch -import torch.nn as nn -from mmcv.ops import RoIPool -from mmcv.transforms import Compose -from mmengine.config import Config -from mmengine.model.utils import revert_sync_batchnorm -from mmengine.registry import init_default_scope -from mmengine.runner import load_checkpoint - -from mmdet.registry import DATASETS -from ..evaluation import get_classes -from ..registry import MODELS -from ..structures import DetDataSample, SampleList -from ..utils import get_test_pipeline_cfg - - -def init_detector( - config: Union[str, Path, Config], - checkpoint: Optional[str] = None, - palette: str = 'none', - device: str = 'cuda:0', - cfg_options: Optional[dict] = None, -) -> nn.Module: - """Initialize a detector from config file. - - Args: - config (str, :obj:`Path`, or :obj:`mmengine.Config`): Config file path, - :obj:`Path`, or the config object. - checkpoint (str, optional): Checkpoint path. If left as None, the model - will not load any weights. - palette (str): Color palette used for visualization. If palette - is stored in checkpoint, use checkpoint's palette first, otherwise - use externally passed palette. Currently, supports 'coco', 'voc', - 'citys' and 'random'. Defaults to none. - device (str): The device where the anchors will be put on. - Defaults to cuda:0. - cfg_options (dict, optional): Options to override some settings in - the used config. - - Returns: - nn.Module: The constructed detector. - """ - if isinstance(config, (str, Path)): - config = Config.fromfile(config) - elif not isinstance(config, Config): - raise TypeError('config must be a filename or Config object, ' - f'but got {type(config)}') - if cfg_options is not None: - config.merge_from_dict(cfg_options) - elif 'init_cfg' in config.model.backbone: - config.model.backbone.init_cfg = None - init_default_scope(config.get('default_scope', 'mmdet')) - - model = MODELS.build(config.model) - model = revert_sync_batchnorm(model) - if checkpoint is None: - warnings.simplefilter('once') - warnings.warn('checkpoint is None, use COCO classes by default.') - model.dataset_meta = {'classes': get_classes('coco')} - else: - checkpoint = load_checkpoint(model, checkpoint, map_location='cpu') - # Weights converted from elsewhere may not have meta fields. - checkpoint_meta = checkpoint.get('meta', {}) - - # save the dataset_meta in the model for convenience - if 'dataset_meta' in checkpoint_meta: - # mmdet 3.x, all keys should be lowercase - model.dataset_meta = { - k.lower(): v - for k, v in checkpoint_meta['dataset_meta'].items() - } - elif 'CLASSES' in checkpoint_meta: - # < mmdet 3.x - classes = checkpoint_meta['CLASSES'] - model.dataset_meta = {'classes': classes} - else: - warnings.simplefilter('once') - warnings.warn( - 'dataset_meta or class names are not saved in the ' - 'checkpoint\'s meta data, use COCO classes by default.') - model.dataset_meta = {'classes': get_classes('coco')} - - # Priority: args.palette -> config -> checkpoint - if palette != 'none': - model.dataset_meta['palette'] = palette - else: - test_dataset_cfg = copy.deepcopy(config.test_dataloader.dataset) - # lazy init. We only need the metainfo. - test_dataset_cfg['lazy_init'] = True - metainfo = DATASETS.build(test_dataset_cfg).metainfo - cfg_palette = metainfo.get('palette', None) - if cfg_palette is not None: - model.dataset_meta['palette'] = cfg_palette - else: - if 'palette' not in model.dataset_meta: - warnings.warn( - 'palette does not exist, random is used by default. ' - 'You can also set the palette to customize.') - model.dataset_meta['palette'] = 'random' - - model.cfg = config # save the config in the model for convenience - model.to(device) - model.eval() - return model - - -ImagesType = Union[str, np.ndarray, Sequence[str], Sequence[np.ndarray]] - - -def inference_detector( - model: nn.Module, - imgs: ImagesType, - test_pipeline: Optional[Compose] = None -) -> Union[DetDataSample, SampleList]: - """Inference image(s) with the detector. - - Args: - model (nn.Module): The loaded detector. - imgs (str, ndarray, Sequence[str/ndarray]): - Either image files or loaded images. - test_pipeline (:obj:`Compose`): Test pipeline. - - Returns: - :obj:`DetDataSample` or list[:obj:`DetDataSample`]: - If imgs is a list or tuple, the same length list type results - will be returned, otherwise return the detection results directly. - """ - - if isinstance(imgs, (list, tuple)): - is_batch = True - else: - imgs = [imgs] - is_batch = False - - cfg = model.cfg - - if test_pipeline is None: - cfg = cfg.copy() - test_pipeline = get_test_pipeline_cfg(cfg) - if isinstance(imgs[0], np.ndarray): - # Calling this method across libraries will result - # in module unregistered error if not prefixed with mmdet. - test_pipeline[0].type = 'mmdet.LoadImageFromNDArray' - - test_pipeline = Compose(test_pipeline) - - if model.data_preprocessor.device.type == 'cpu': - for m in model.modules(): - assert not isinstance( - m, RoIPool - ), 'CPU inference with RoIPool is not supported currently.' - - result_list = [] - for img in imgs: - # prepare data - if isinstance(img, np.ndarray): - # TODO: remove img_id. - data_ = dict(img=img, img_id=0) - else: - # TODO: remove img_id. - data_ = dict(img_path=img, img_id=0) - # build the data pipeline - data_ = test_pipeline(data_) - - data_['inputs'] = [data_['inputs']] - data_['data_samples'] = [data_['data_samples']] - - # forward the model - with torch.no_grad(): - results = model.test_step(data_)[0] - - result_list.append(results) - - if not is_batch: - return result_list[0] - else: - return result_list - - -# TODO: Awaiting refactoring -async def async_inference_detector(model, imgs): - """Async inference image(s) with the detector. - - Args: - model (nn.Module): The loaded detector. - img (str | ndarray): Either image files or loaded images. - - Returns: - Awaitable detection results. - """ - if not isinstance(imgs, (list, tuple)): - imgs = [imgs] - - cfg = model.cfg - - if isinstance(imgs[0], np.ndarray): - cfg = cfg.copy() - # set loading pipeline type - cfg.data.test.pipeline[0].type = 'LoadImageFromNDArray' - - # cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) - test_pipeline = Compose(cfg.data.test.pipeline) - - datas = [] - for img in imgs: - # prepare data - if isinstance(img, np.ndarray): - # directly add img - data = dict(img=img) - else: - # add information into dict - data = dict(img_info=dict(filename=img), img_prefix=None) - # build the data pipeline - data = test_pipeline(data) - datas.append(data) - - for m in model.modules(): - assert not isinstance( - m, - RoIPool), 'CPU inference with RoIPool is not supported currently.' - - # We don't restore `torch.is_grad_enabled()` value during concurrent - # inference since execution can overlap - torch.set_grad_enabled(False) - results = await model.aforward_test(data, rescale=True) - return results diff --git a/spaces/KyanChen/RSPrompter/mmpl/engine/hooks/__init__.py b/spaces/KyanChen/RSPrompter/mmpl/engine/hooks/__init__.py deleted file mode 100644 index 77f2c33df26749d5597fb3875d9f65238a68a2b4..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmpl/engine/hooks/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .builder import PL_HOOKS -from .pipeline_switch_hook import PipelineSwitchHook -from .yolov5_param_scheduler_hook import YOLOv5ParamSchedulerHook -from .ema_hook import EMAHook -from .param_scheduler_hook import ParamSchedulerHook -from .visualization_hook import DetVisualizationHook diff --git a/spaces/LLaMaWhisperer/LegalLLaMa/legal_llama/__init__.py b/spaces/LLaMaWhisperer/LegalLLaMa/legal_llama/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/rmvpe.py b/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/rmvpe.py deleted file mode 100644 index 8d0d57297d4301e43a4fdcda216ae39c5e3b83b4..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/AI-Cover-Gen-Web-Ui/src/rmvpe.py +++ /dev/null @@ -1,432 +0,0 @@ -import torch, numpy as np -import torch.nn as nn -import torch.nn.functional as F - - - -class BiGRU(nn.Module): - def __init__(self, input_features, hidden_features, num_layers): - super(BiGRU, self).__init__() - self.gru = nn.GRU( - input_features, - hidden_features, - num_layers=num_layers, - batch_first=True, - bidirectional=True, - ) - - def forward(self, x): - return self.gru(x)[0] - - -class ConvBlockRes(nn.Module): - def __init__(self, in_channels, out_channels, momentum=0.01): - super(ConvBlockRes, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=(1, 1), - padding=(1, 1), - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - nn.Conv2d( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=(1, 1), - padding=(1, 1), - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - ) - if in_channels != out_channels: - self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) - self.is_shortcut = True - else: - self.is_shortcut = False - - def forward(self, x): - if self.is_shortcut: - return self.conv(x) + self.shortcut(x) - else: - return self.conv(x) + x - - -class Encoder(nn.Module): - def __init__( - self, - in_channels, - in_size, - n_encoders, - kernel_size, - n_blocks, - out_channels=16, - momentum=0.01, - ): - super(Encoder, self).__init__() - self.n_encoders = n_encoders - self.bn = nn.BatchNorm2d(in_channels, momentum=momentum) - self.layers = nn.ModuleList() - self.latent_channels = [] - for i in range(self.n_encoders): - self.layers.append( - ResEncoderBlock( - in_channels, out_channels, kernel_size, n_blocks, momentum=momentum - ) - ) - self.latent_channels.append([out_channels, in_size]) - in_channels = out_channels - out_channels *= 2 - in_size //= 2 - self.out_size = in_size - self.out_channel = out_channels - - def forward(self, x): - concat_tensors = [] - x = self.bn(x) - for i in range(self.n_encoders): - _, x = self.layers[i](x) - concat_tensors.append(_) - return x, concat_tensors - - -class ResEncoderBlock(nn.Module): - def __init__( - self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01 - ): - super(ResEncoderBlock, self).__init__() - self.n_blocks = n_blocks - self.conv = nn.ModuleList() - self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) - for i in range(n_blocks - 1): - self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) - self.kernel_size = kernel_size - if self.kernel_size is not None: - self.pool = nn.AvgPool2d(kernel_size=kernel_size) - - def forward(self, x): - for i in range(self.n_blocks): - x = self.conv[i](x) - if self.kernel_size is not None: - return x, self.pool(x) - else: - return x - - -class Intermediate(nn.Module): # - def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): - super(Intermediate, self).__init__() - self.n_inters = n_inters - self.layers = nn.ModuleList() - self.layers.append( - ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum) - ) - for i in range(self.n_inters - 1): - self.layers.append( - ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum) - ) - - def forward(self, x): - for i in range(self.n_inters): - x = self.layers[i](x) - return x - - -class ResDecoderBlock(nn.Module): - def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): - super(ResDecoderBlock, self).__init__() - out_padding = (0, 1) if stride == (1, 2) else (1, 1) - self.n_blocks = n_blocks - self.conv1 = nn.Sequential( - nn.ConvTranspose2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=(3, 3), - stride=stride, - padding=(1, 1), - output_padding=out_padding, - bias=False, - ), - nn.BatchNorm2d(out_channels, momentum=momentum), - nn.ReLU(), - ) - self.conv2 = nn.ModuleList() - self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) - for i in range(n_blocks - 1): - self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) - - def forward(self, x, concat_tensor): - x = self.conv1(x) - x = torch.cat((x, concat_tensor), dim=1) - for i in range(self.n_blocks): - x = self.conv2[i](x) - return x - - -class Decoder(nn.Module): - def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): - super(Decoder, self).__init__() - self.layers = nn.ModuleList() - self.n_decoders = n_decoders - for i in range(self.n_decoders): - out_channels = in_channels // 2 - self.layers.append( - ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum) - ) - in_channels = out_channels - - def forward(self, x, concat_tensors): - for i in range(self.n_decoders): - x = self.layers[i](x, concat_tensors[-1 - i]) - return x - - -class DeepUnet(nn.Module): - def __init__( - self, - kernel_size, - n_blocks, - en_de_layers=5, - inter_layers=4, - in_channels=1, - en_out_channels=16, - ): - super(DeepUnet, self).__init__() - self.encoder = Encoder( - in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels - ) - self.intermediate = Intermediate( - self.encoder.out_channel // 2, - self.encoder.out_channel, - inter_layers, - n_blocks, - ) - self.decoder = Decoder( - self.encoder.out_channel, en_de_layers, kernel_size, n_blocks - ) - - def forward(self, x): - x, concat_tensors = self.encoder(x) - x = self.intermediate(x) - x = self.decoder(x, concat_tensors) - return x - - -class E2E(nn.Module): - def __init__( - self, - n_blocks, - n_gru, - kernel_size, - en_de_layers=5, - inter_layers=4, - in_channels=1, - en_out_channels=16, - ): - super(E2E, self).__init__() - self.unet = DeepUnet( - kernel_size, - n_blocks, - en_de_layers, - inter_layers, - in_channels, - en_out_channels, - ) - self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) - if n_gru: - self.fc = nn.Sequential( - BiGRU(3 * 128, 256, n_gru), - nn.Linear(512, 360), - nn.Dropout(0.25), - nn.Sigmoid(), - ) - else: - self.fc = nn.Sequential( - nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid() - ) - - def forward(self, mel): - mel = mel.transpose(-1, -2).unsqueeze(1) - x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) - x = self.fc(x) - return x - - -from librosa.filters import mel - - -class MelSpectrogram(torch.nn.Module): - def __init__( - self, - is_half, - n_mel_channels, - sampling_rate, - win_length, - hop_length, - n_fft=None, - mel_fmin=0, - mel_fmax=None, - clamp=1e-5, - ): - super().__init__() - n_fft = win_length if n_fft is None else n_fft - self.hann_window = {} - mel_basis = mel( - sr=sampling_rate, - n_fft=n_fft, - n_mels=n_mel_channels, - fmin=mel_fmin, - fmax=mel_fmax, - htk=True, - ) - mel_basis = torch.from_numpy(mel_basis).float() - self.register_buffer("mel_basis", mel_basis) - self.n_fft = win_length if n_fft is None else n_fft - self.hop_length = hop_length - self.win_length = win_length - self.sampling_rate = sampling_rate - self.n_mel_channels = n_mel_channels - self.clamp = clamp - self.is_half = is_half - - def forward(self, audio, keyshift=0, speed=1, center=True): - factor = 2 ** (keyshift / 12) - n_fft_new = int(np.round(self.n_fft * factor)) - win_length_new = int(np.round(self.win_length * factor)) - hop_length_new = int(np.round(self.hop_length * speed)) - keyshift_key = str(keyshift) + "_" + str(audio.device) - if keyshift_key not in self.hann_window: - self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to( - audio.device - ) - fft = torch.stft( - audio, - n_fft=n_fft_new, - hop_length=hop_length_new, - win_length=win_length_new, - window=self.hann_window[keyshift_key], - center=center, - return_complex=True, - ) - magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2)) - if keyshift != 0: - size = self.n_fft // 2 + 1 - resize = magnitude.size(1) - if resize < size: - magnitude = F.pad(magnitude, (0, 0, 0, size - resize)) - magnitude = magnitude[:, :size, :] * self.win_length / win_length_new - mel_output = torch.matmul(self.mel_basis, magnitude) - if self.is_half == True: - mel_output = mel_output.half() - log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp)) - return log_mel_spec - - -class RMVPE: - def __init__(self, model_path, is_half, device=None): - self.resample_kernel = {} - model = E2E(4, 1, (2, 2)) - ckpt = torch.load(model_path, map_location="cpu") - model.load_state_dict(ckpt) - model.eval() - if is_half == True: - model = model.half() - self.model = model - self.resample_kernel = {} - self.is_half = is_half - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = device - self.mel_extractor = MelSpectrogram( - is_half, 128, 16000, 1024, 160, None, 30, 8000 - ).to(device) - self.model = self.model.to(device) - cents_mapping = 20 * np.arange(360) + 1997.3794084376191 - self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368 - - def mel2hidden(self, mel): - with torch.no_grad(): - n_frames = mel.shape[-1] - mel = F.pad( - mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect" - ) - hidden = self.model(mel) - return hidden[:, :n_frames] - - def decode(self, hidden, thred=0.03): - cents_pred = self.to_local_average_cents(hidden, thred=thred) - f0 = 10 * (2 ** (cents_pred / 1200)) - f0[f0 == 10] = 0 - # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred]) - return f0 - - def infer_from_audio(self, audio, thred=0.03): - audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0) - # torch.cuda.synchronize() - # t0=ttime() - mel = self.mel_extractor(audio, center=True) - # torch.cuda.synchronize() - # t1=ttime() - hidden = self.mel2hidden(mel) - # torch.cuda.synchronize() - # t2=ttime() - hidden = hidden.squeeze(0).cpu().numpy() - if self.is_half == True: - hidden = hidden.astype("float32") - f0 = self.decode(hidden, thred=thred) - # torch.cuda.synchronize() - # t3=ttime() - # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0)) - return f0 - - def to_local_average_cents(self, salience, thred=0.05): - # t0 = ttime() - center = np.argmax(salience, axis=1) # frame length#index - salience = np.pad(salience, ((0, 0), (4, 4))) # frame length,368 - # t1 = ttime() - center += 4 - todo_salience = [] - todo_cents_mapping = [] - starts = center - 4 - ends = center + 5 - for idx in range(salience.shape[0]): - todo_salience.append(salience[:, starts[idx] : ends[idx]][idx]) - todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]]) - # t2 = ttime() - todo_salience = np.array(todo_salience) # frame length，9 - todo_cents_mapping = np.array(todo_cents_mapping) # frame length，9 - product_sum = np.sum(todo_salience * todo_cents_mapping, 1) - weight_sum = np.sum(todo_salience, 1) # frame length - devided = product_sum / weight_sum # frame length - # t3 = ttime() - maxx = np.max(salience, axis=1) # frame length - devided[maxx <= thred] = 0 - # t4 = ttime() - # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - return devided - - -# if __name__ == '__main__': -# audio, sampling_rate = sf.read("Quotations~1.wav") ### edit -# if len(audio.shape) > 1: -# audio = librosa.to_mono(audio.transpose(1, 0)) -# audio_bak = audio.copy() -# if sampling_rate != 16000: -# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) -# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt" -# thred = 0.03 # 0.01 -# device = 'cuda' if torch.cuda.is_available() else 'cpu' -# rmvpe = RMVPE(model_path,is_half=False, device=device) -# t0=ttime() -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# f0 = rmvpe.infer_from_audio(audio, thred=thred) -# t1=ttime() -# print(f0.shape,t1-t0) diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/ipex/gradscaler.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/ipex/gradscaler.py deleted file mode 100644 index 3c265ddb37453f02870afb481360c9cc30b05d81..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/ipex/gradscaler.py +++ /dev/null @@ -1,179 +0,0 @@ -from collections import defaultdict -import torch -import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import -import intel_extension_for_pytorch._C as core # pylint: disable=import-error, unused-import - -# pylint: disable=protected-access, missing-function-docstring, line-too-long - -OptState = ipex.cpu.autocast._grad_scaler.OptState -_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator -_refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state - -def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint: disable=unused-argument - per_device_inv_scale = _MultiDeviceReplicator(inv_scale) - per_device_found_inf = _MultiDeviceReplicator(found_inf) - - # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype. - # There could be hundreds of grads, so we'd like to iterate through them just once. - # However, we don't know their devices or dtypes in advance. - - # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict - # Google says mypy struggles with defaultdicts type annotations. - per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list)) # type: ignore[var-annotated] - # sync grad to master weight - if hasattr(optimizer, "sync_grad"): - optimizer.sync_grad() - with torch.no_grad(): - for group in optimizer.param_groups: - for param in group["params"]: - if param.grad is None: - continue - if (not allow_fp16) and param.grad.dtype == torch.float16: - raise ValueError("Attempting to unscale FP16 gradients.") - if param.grad.is_sparse: - # is_coalesced() == False means the sparse grad has values with duplicate indices. - # coalesce() deduplicates indices and adds all values that have the same index. - # For scaled fp16 values, there's a good chance coalescing will cause overflow, - # so we should check the coalesced _values(). - if param.grad.dtype is torch.float16: - param.grad = param.grad.coalesce() - to_unscale = param.grad._values() - else: - to_unscale = param.grad - - # -: is there a way to split by device and dtype without appending in the inner loop? - to_unscale = to_unscale.to("cpu") - per_device_and_dtype_grads[to_unscale.device][ - to_unscale.dtype - ].append(to_unscale) - - for _, per_dtype_grads in per_device_and_dtype_grads.items(): - for grads in per_dtype_grads.values(): - core._amp_foreach_non_finite_check_and_unscale_( - grads, - per_device_found_inf.get("cpu"), - per_device_inv_scale.get("cpu"), - ) - - return per_device_found_inf._per_device_tensors - -def unscale_(self, optimizer): - """ - Divides ("unscales") the optimizer's gradient tensors by the scale factor. - :meth:`unscale_` is optional, serving cases where you need to - :ref:`modify or inspect gradients` - between the backward pass(es) and :meth:`step`. - If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`. - Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients:: - ... - scaler.scale(loss).backward() - scaler.unscale_(optimizer) - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) - scaler.step(optimizer) - scaler.update() - Args: - optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled. - .. warning:: - :meth:`unscale_` should only be called once per optimizer per :meth:`step` call, - and only after all gradients for that optimizer's assigned parameters have been accumulated. - Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError. - .. warning:: - :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute. - """ - if not self._enabled: - return - - self._check_scale_growth_tracker("unscale_") - - optimizer_state = self._per_optimizer_states[id(optimizer)] - - if optimizer_state["stage"] is OptState.UNSCALED: # pylint: disable=no-else-raise - raise RuntimeError( - "unscale_() has already been called on this optimizer since the last update()." - ) - elif optimizer_state["stage"] is OptState.STEPPED: - raise RuntimeError("unscale_() is being called after step().") - - # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64. - assert self._scale is not None - inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device) - found_inf = torch.full( - (1,), 0.0, dtype=torch.float32, device=self._scale.device - ) - - optimizer_state["found_inf_per_device"] = self._unscale_grads_( - optimizer, inv_scale, found_inf, False - ) - optimizer_state["stage"] = OptState.UNSCALED - -def update(self, new_scale=None): - """ - Updates the scale factor. - If any optimizer steps were skipped the scale is multiplied by ``backoff_factor`` - to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively, - the scale is multiplied by ``growth_factor`` to increase it. - Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not - used directly, it's used to fill GradScaler's internal scale tensor. So if - ``new_scale`` was a tensor, later in-place changes to that tensor will not further - affect the scale GradScaler uses internally.) - Args: - new_scale (float or :class:`torch.FloatTensor`, optional, default=None): New scale factor. - .. warning:: - :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has - been invoked for all optimizers used this iteration. - """ - if not self._enabled: - return - - _scale, _growth_tracker = self._check_scale_growth_tracker("update") - - if new_scale is not None: - # Accept a new user-defined scale. - if isinstance(new_scale, float): - self._scale.fill_(new_scale) # type: ignore[union-attr] - else: - reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False." - assert isinstance(new_scale, torch.FloatTensor), reason # type: ignore[attr-defined] - assert new_scale.numel() == 1, reason - assert new_scale.requires_grad is False, reason - self._scale.copy_(new_scale) # type: ignore[union-attr] - else: - # Consume shared inf/nan data collected from optimizers to update the scale. - # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous. - found_infs = [ - found_inf.to(device="cpu", non_blocking=True) - for state in self._per_optimizer_states.values() - for found_inf in state["found_inf_per_device"].values() - ] - - assert len(found_infs) > 0, "No inf checks were recorded prior to update." - - found_inf_combined = found_infs[0] - if len(found_infs) > 1: - for i in range(1, len(found_infs)): - found_inf_combined += found_infs[i] - - to_device = _scale.device - _scale = _scale.to("cpu") - _growth_tracker = _growth_tracker.to("cpu") - - core._amp_update_scale_( - _scale, - _growth_tracker, - found_inf_combined, - self._growth_factor, - self._backoff_factor, - self._growth_interval, - ) - - _scale = _scale.to(to_device) - _growth_tracker = _growth_tracker.to(to_device) - # To prepare for next iteration, clear the data collected from optimizers this iteration. - self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state) - -def gradscaler_init(): - torch.xpu.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler - torch.xpu.amp.GradScaler._unscale_grads_ = _unscale_grads_ - torch.xpu.amp.GradScaler.unscale_ = unscale_ - torch.xpu.amp.GradScaler.update = update - return torch.xpu.amp.GradScaler \ No newline at end of file diff --git a/spaces/Lianglan/Demo_Gpt3.5-turbo_model/app.py b/spaces/Lianglan/Demo_Gpt3.5-turbo_model/app.py deleted file mode 100644 index f7258c58657c90b52cd635eae4645503c206e207..0000000000000000000000000000000000000000 --- a/spaces/Lianglan/Demo_Gpt3.5-turbo_model/app.py +++ /dev/null @@ -1,138 +0,0 @@ -import gradio as gr -import openai -import requests -import csv - - -prompt_templates = {"Default ChatGPT": ""} - -def get_empty_state(): - return {"total_tokens": 0, "messages": []} - -def download_prompt_templates(): - url = "https://raw.githubusercontent.com/f/awesome-chatgpt-prompts/main/prompts.csv" - try: - response = requests.get(url) - reader = csv.reader(response.text.splitlines()) - next(reader) # skip the header row - for row in reader: - if len(row) >= 2: - act = row[0].strip('"') - prompt = row[1].strip('"') - prompt_templates[act] = prompt - - except requests.exceptions.RequestException as e: - print(f"An error occurred while downloading prompt templates: {e}") - return - - choices = list(prompt_templates.keys()) - choices = choices[:1] + sorted(choices[1:]) - return gr.update(value=choices[0], choices=choices) - -def on_token_change(user_token): - openai.api_key = user_token - -def on_prompt_template_change(prompt_template): - if not isinstance(prompt_template, str): return - return prompt_templates[prompt_template] - -def submit_message(user_token, prompt, prompt_template, temperature, max_tokens, context_length, state): - - history = state['messages'] - - if not prompt: - return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state - - prompt_template = prompt_templates[prompt_template] - - system_prompt = [] - if prompt_template: - system_prompt = [{ "role": "system", "content": prompt_template }] - - prompt_msg = { "role": "user", "content": prompt } - - if not user_token: - history.append(prompt_msg) - history.append({ - "role": "system", - "content": "Error: OpenAI API Key is not set." - }) - return '', [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: 0", state - - try: - completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) - - history.append(prompt_msg) - history.append(completion.choices[0].message.to_dict()) - - state['total_tokens'] += completion['usage']['total_tokens'] - - except Exception as e: - history.append(prompt_msg) - history.append({ - "role": "system", - "content": f"Error: {e}" - }) - - total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" - chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] - - return '', chat_messages, total_tokens_used_msg, state - -def clear_conversation(): - return gr.update(value=None, visible=True), None, "", get_empty_state() - - -css = """ - #col-container {max-width: 80%; margin-left: auto; margin-right: auto;} - #chatbox {min-height: 400px;} - #header {text-align: center;} - #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;} - #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;} - #label {font-size: 0.8em; padding: 0.5em; margin: 0;} - .message { font-size: 1.2em; } - """ - -with gr.Blocks(css=css) as demo: - - state = gr.State(get_empty_state()) - - - with gr.Column(elem_id="col-container"): - gr.Markdown("""## OpenAI ChatGPT Demo - Using the ofiicial API (gpt-3.5-turbo model) - Prompt templates from [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts).""", - elem_id="header") - - with gr.Row(): - with gr.Column(): - chatbot = gr.Chatbot(elem_id="chatbox") - input_message = gr.Textbox(show_label=False, placeholder="Enter text and press enter", visible=True).style(container=False) - btn_submit = gr.Button("Submit") - total_tokens_str = gr.Markdown(elem_id="total_tokens_str") - btn_clear_conversation = gr.Button("🔃 Start New Conversation") - with gr.Column(): - gr.Markdown("Enter your OpenAI API Key. You can get one [here](https://platform.openai.com/account/api-keys).", elem_id="label") - user_token = gr.Textbox(value='', placeholder="OpenAI API Key", type="password", show_label=False) - prompt_template = gr.Dropdown(label="Set a custom insruction for the chatbot:", choices=list(prompt_templates.keys())) - prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview") - with gr.Accordion("Advanced parameters", open=False): - temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Temperature", info="Higher = more creative/chaotic") - max_tokens = gr.Slider(minimum=100, maximum=4096, value=1000, step=1, label="Max tokens per response") - context_length = gr.Slider(minimum=1, maximum=10, value=2, step=1, label="Context length", info="Number of previous messages to send to the chatbot. Be careful with high values, it can blow up the token budget quickly.") - - gr.HTML('''


You can duplicate this Space to skip the queue:Duplicate Space
-

visitors

''') - - btn_submit.click(submit_message, [user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) - input_message.submit(submit_message, [user_token, input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) - btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state]) - prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) - user_token.change(on_token_change, inputs=[user_token], outputs=[]) - - - demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False) - - -demo.queue(concurrency_count=10) -demo.launch(height='800px') diff --git a/spaces/Liu-LAB/GPT-academic/docs/test_markdown_format.py b/spaces/Liu-LAB/GPT-academic/docs/test_markdown_format.py deleted file mode 100644 index 896f6f130c69f8a94d6f49feadf7091f0f23c2c9..0000000000000000000000000000000000000000 --- a/spaces/Liu-LAB/GPT-academic/docs/test_markdown_format.py +++ /dev/null @@ -1,130 +0,0 @@ -sample = """ -[1]: https://baike.baidu.com/item/%E8%B4%A8%E8%83%BD%E6%96%B9%E7%A8%8B/1884527 "质能方程（质能方程式）_百度百科" -[2]: https://www.zhihu.com/question/348249281 "如何理解质能方程 E＝mc²？ - 知乎" -[3]: https://zhuanlan.zhihu.com/p/32597385 "质能方程的推导与理解 - 知乎 - 知乎专栏" - -你好，这是必应。质能方程是描述质量与能量之间的当量关系的方程[^1^][1]。用tex格式，质能方程可以写成$$E=mc^2$$，其中$E$是能量，$m$是质量，$c$是光速[^2^][2] [^3^][3]。 -""" -import re - -def preprocess_newbing_out(s): - pattern = r'\^(\d+)\^' # 匹配^数字^ - pattern2 = r'\[(\d+)\]' # 匹配^数字^ - sub = lambda m: '\['+m.group(1)+'\]' # 将匹配到的数字作为替换值 - result = re.sub(pattern, sub, s) # 替换操作 - if '[1]' in result: - result += '

' + "
".join([re.sub(pattern2, sub, r) for r in result.split('\n') if r.startswith('[')]) + '' - return result - - -def close_up_code_segment_during_stream(gpt_reply): - """ - 在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的``` - - Args: - gpt_reply (str): GPT模型返回的回复字符串。 - - Returns: - str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。 - - """ - if '```' not in gpt_reply: - return gpt_reply - if gpt_reply.endswith('```'): - return gpt_reply - - # 排除了以上两个情况，我们 - segments = gpt_reply.split('```') - n_mark = len(segments) - 1 - if n_mark % 2 == 1: - # print('输出代码片段中！') - return gpt_reply+'\n```' - else: - return gpt_reply - -import markdown -from latex2mathml.converter import convert as tex2mathml -from functools import wraps, lru_cache -def markdown_convertion(txt): - """ - 将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。 - """ - pre = '
' - suf = '
' - if txt.startswith(pre) and txt.endswith(suf): - # print('警告，输入了已经经过转化的字符串，二次转化可能出问题') - return txt # 已经被转化过，不需要再次转化 - - markdown_extension_configs = { - 'mdx_math': { - 'enable_dollar_delimiter': True, - 'use_gitlab_delimiters': False, - }, - } - find_equation_pattern = r'\n', '') - return content - - - if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号，且没有代码段```的标识 - # convert everything to html format - split = markdown.markdown(text='---') - convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs) - convert_stage_1 = markdown_bug_hunt(convert_stage_1) - # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s). - # 1. convert to easy-to-copy tex (do not render math) - convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL) - # 2. convert to rendered equation - convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL) - # cat them together - return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf - else: - return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf - - -sample = preprocess_newbing_out(sample) -sample = close_up_code_segment_during_stream(sample) -sample = markdown_convertion(sample) -with open('tmp.html', 'w', encoding='utf8') as f: - f.write(""" - - - My Website - - - - """) - f.write(sample) diff --git a/spaces/LucasCodeBreak/MusicGen/tests/data/test_audio_dataset.py b/spaces/LucasCodeBreak/MusicGen/tests/data/test_audio_dataset.py deleted file mode 100644 index b69c9c397830738b73d6c229009f84b867cda801..0000000000000000000000000000000000000000 --- a/spaces/LucasCodeBreak/MusicGen/tests/data/test_audio_dataset.py +++ /dev/null @@ -1,352 +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. - -from functools import partial -from itertools import product -import json -import math -import os -import random -import typing as tp - -import pytest -import torch -from torch.utils.data import DataLoader - -from audiocraft.data.audio_dataset import ( - AudioDataset, - AudioMeta, - _get_audio_meta, - load_audio_meta, - save_audio_meta -) -from audiocraft.data.zip import PathInZip - -from ..common_utils import TempDirMixin, get_white_noise, save_wav - - -class TestAudioMeta(TempDirMixin): - - def test_get_audio_meta(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(duration * sample_rate) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path('sample.wav') - save_wav(path, wav, sample_rate) - m = _get_audio_meta(path, minimal=True) - assert m.path == path, 'path does not match' - assert m.sample_rate == sample_rate, 'sample rate does not match' - assert m.duration == duration, 'duration does not match' - assert m.amplitude is None - assert m.info_path is None - - def test_save_audio_meta(self): - audio_meta = [ - AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')), - AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json')) - ] - empty_audio_meta = [] - for idx, meta in enumerate([audio_meta, empty_audio_meta]): - path = self.get_temp_path(f'data_{idx}_save.jsonl') - save_audio_meta(path, meta) - with open(path, 'r') as f: - lines = f.readlines() - read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines] - assert len(read_meta) == len(meta) - for m, read_m in zip(meta, read_meta): - assert m == read_m - - def test_load_audio_meta(self): - try: - import dora - except ImportError: - dora = None # type: ignore - - audio_meta = [ - AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')), - AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json')) - ] - empty_meta = [] - for idx, meta in enumerate([audio_meta, empty_meta]): - path = self.get_temp_path(f'data_{idx}_load.jsonl') - with open(path, 'w') as f: - for m in meta: - json_str = json.dumps(m.to_dict()) + '\n' - f.write(json_str) - read_meta = load_audio_meta(path) - assert len(read_meta) == len(meta) - for m, read_m in zip(meta, read_meta): - if dora: - m.path = dora.git_save.to_absolute_path(m.path) - assert m == read_m, f'original={m}, read={read_m}' - - -class TestAudioDataset(TempDirMixin): - - def _create_audio_files(self, - root_name: str, - num_examples: int, - durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.), - sample_rate: int = 16_000, - channels: int = 1): - root_dir = self.get_temp_dir(root_name) - for i in range(num_examples): - if isinstance(durations, float): - duration = durations - elif isinstance(durations, tuple) and len(durations) == 1: - duration = durations[0] - elif isinstance(durations, tuple) and len(durations) == 2: - duration = random.uniform(durations[0], durations[1]) - else: - assert False - n_frames = int(duration * sample_rate) - wav = get_white_noise(channels, n_frames) - path = os.path.join(root_dir, f'example_{i}.wav') - save_wav(path, wav, sample_rate) - return root_dir - - def _create_audio_dataset(self, - root_name: str, - total_num_examples: int, - durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.), - sample_rate: int = 16_000, - channels: int = 1, - segment_duration: tp.Optional[float] = None, - num_examples: int = 10, - shuffle: bool = True, - return_info: bool = False): - root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels) - dataset = AudioDataset.from_path(root_dir, - minimal_meta=True, - segment_duration=segment_duration, - num_samples=num_examples, - sample_rate=sample_rate, - channels=channels, - shuffle=shuffle, - return_info=return_info) - return dataset - - def test_dataset_full(self): - total_examples = 10 - min_duration, max_duration = 1., 4. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), - sample_rate=sample_rate, channels=channels, segment_duration=None) - assert len(dataset) == total_examples - assert dataset.sample_rate == sample_rate - assert dataset.channels == channels - for idx in range(len(dataset)): - sample = dataset[idx] - assert sample.shape[0] == channels - assert sample.shape[1] <= int(max_duration * sample_rate) - assert sample.shape[1] >= int(min_duration * sample_rate) - - def test_dataset_segment(self): - total_examples = 10 - num_samples = 20 - min_duration, max_duration = 1., 4. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples) - assert len(dataset) == num_samples - assert dataset.sample_rate == sample_rate - assert dataset.channels == channels - for idx in range(len(dataset)): - sample = dataset[idx] - assert sample.shape[0] == channels - assert sample.shape[1] == int(segment_duration * sample_rate) - - def test_dataset_equal_audio_and_segment_durations(self): - total_examples = 1 - num_samples = 2 - audio_duration = 1. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples) - assert len(dataset) == num_samples - assert dataset.sample_rate == sample_rate - assert dataset.channels == channels - for idx in range(len(dataset)): - sample = dataset[idx] - assert sample.shape[0] == channels - assert sample.shape[1] == int(segment_duration * sample_rate) - # the random seek_time adds variability on audio read - sample_1 = dataset[0] - sample_2 = dataset[1] - assert not torch.allclose(sample_1, sample_2) - - def test_dataset_samples(self): - total_examples = 1 - num_samples = 2 - audio_duration = 1. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - - create_dataset = partial( - self._create_audio_dataset, - 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples, - ) - - dataset = create_dataset(shuffle=True) - # when shuffle = True, we have different inputs for the same index across epoch - sample_1 = dataset[0] - sample_2 = dataset[0] - assert not torch.allclose(sample_1, sample_2) - - dataset_noshuffle = create_dataset(shuffle=False) - # when shuffle = False, we have same inputs for the same index across epoch - sample_1 = dataset_noshuffle[0] - sample_2 = dataset_noshuffle[0] - assert torch.allclose(sample_1, sample_2) - - def test_dataset_return_info(self): - total_examples = 10 - num_samples = 20 - min_duration, max_duration = 1., 4. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True) - assert len(dataset) == num_samples - assert dataset.sample_rate == sample_rate - assert dataset.channels == channels - for idx in range(len(dataset)): - sample, segment_info = dataset[idx] - assert sample.shape[0] == channels - assert sample.shape[1] == int(segment_duration * sample_rate) - assert segment_info.sample_rate == sample_rate - assert segment_info.total_frames == int(segment_duration * sample_rate) - assert segment_info.n_frames <= int(segment_duration * sample_rate) - assert segment_info.seek_time >= 0 - - def test_dataset_return_info_no_segment_duration(self): - total_examples = 10 - num_samples = 20 - min_duration, max_duration = 1., 4. - segment_duration = None - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True) - assert len(dataset) == total_examples - assert dataset.sample_rate == sample_rate - assert dataset.channels == channels - for idx in range(len(dataset)): - sample, segment_info = dataset[idx] - assert sample.shape[0] == channels - assert sample.shape[1] == segment_info.total_frames - assert segment_info.sample_rate == sample_rate - assert segment_info.n_frames <= segment_info.total_frames - - def test_dataset_collate_fn(self): - total_examples = 10 - num_samples = 20 - min_duration, max_duration = 1., 4. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False) - batch_size = 4 - dataloader = DataLoader( - dataset, - batch_size=batch_size, - num_workers=0 - ) - for idx, batch in enumerate(dataloader): - assert batch.shape[0] == batch_size - - @pytest.mark.parametrize("segment_duration", [1.0, None]) - def test_dataset_with_meta_collate_fn(self, segment_duration): - total_examples = 10 - num_samples = 20 - min_duration, max_duration = 1., 4. - segment_duration = 1. - sample_rate = 16_000 - channels = 1 - dataset = self._create_audio_dataset( - 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate, - channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True) - batch_size = 4 - dataloader = DataLoader( - dataset, - batch_size=batch_size, - collate_fn=dataset.collater, - num_workers=0 - ) - for idx, batch in enumerate(dataloader): - wav, infos = batch - assert wav.shape[0] == batch_size - assert len(infos) == batch_size - - @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [ - [1, True, True, 0.5, 0.5, 0.0], - [1, False, True, 0.25, 0.5, 0.25], - [1, True, False, 0.666, 0.333, 0.0], - [1, False, False, 0.333, 0.333, 0.333], - [None, False, False, 0.333, 0.333, 0.333]]) - def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist): - random.seed(1234) - rng = torch.Generator() - rng.manual_seed(1234) - - def _get_histogram(dataset, repetitions=20_000): - counts = {file_meta.path: 0. for file_meta in meta} - for _ in range(repetitions): - file_meta = dataset.sample_file(rng) - counts[file_meta.path] += 1 - return {name: count / repetitions for name, count in counts.items()} - - meta = [ - AudioMeta(path='a', duration=5, sample_rate=1, weight=2), - AudioMeta(path='b', duration=10, sample_rate=1, weight=None), - AudioMeta(path='c', duration=5, sample_rate=1, weight=0), - ] - dataset = AudioDataset( - meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight, - sample_on_duration=sample_on_duration) - hist = _get_histogram(dataset) - assert math.isclose(hist['a'], a_hist, abs_tol=0.01) - assert math.isclose(hist['b'], b_hist, abs_tol=0.01) - assert math.isclose(hist['c'], c_hist, abs_tol=0.01) - - def test_meta_duration_filter_all(self): - meta = [ - AudioMeta(path='a', duration=5, sample_rate=1, weight=2), - AudioMeta(path='b', duration=10, sample_rate=1, weight=None), - AudioMeta(path='c', duration=5, sample_rate=1, weight=0), - ] - try: - AudioDataset(meta, segment_duration=11, min_segment_ratio=1) - assert False - except AssertionError: - assert True - - def test_meta_duration_filter_long(self): - meta = [ - AudioMeta(path='a', duration=5, sample_rate=1, weight=2), - AudioMeta(path='b', duration=10, sample_rate=1, weight=None), - AudioMeta(path='c', duration=5, sample_rate=1, weight=0), - ] - dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7) - assert len(dataset) == 2 diff --git a/spaces/Luelll/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/Luelll/ChuanhuChatGPT/chatgpt - windows.bat deleted file mode 100644 index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000 --- a/spaces/Luelll/ChuanhuChatGPT/chatgpt - windows.bat +++ /dev/null @@ -1,14 +0,0 @@ -@echo off -echo Opening ChuanhuChatGPT... - -REM Open powershell via bat -start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py" - -REM The web page can be accessed with delayed start http://127.0.0.1:7860/ -ping -n 5 127.0.0.1>nul - -REM access chargpt via your default browser -start "" "http://127.0.0.1:7860/" - - -echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). \ No newline at end of file diff --git a/spaces/MBZ/LoRA-DreamBooth-Training-UI/inference.py b/spaces/MBZ/LoRA-DreamBooth-Training-UI/inference.py deleted file mode 100644 index ce0f2b08df75e6d62f06c4119f1dc859930de032..0000000000000000000000000000000000000000 --- a/spaces/MBZ/LoRA-DreamBooth-Training-UI/inference.py +++ /dev/null @@ -1,94 +0,0 @@ -from __future__ import annotations - -import gc -import pathlib - -import gradio as gr -import PIL.Image -import torch -from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler -from huggingface_hub import ModelCard - - -class InferencePipeline: - def __init__(self, hf_token: str | None = None): - self.hf_token = hf_token - self.pipe = None - self.device = torch.device( - 'cuda:0' if torch.cuda.is_available() else 'cpu') - self.lora_model_id = None - self.base_model_id = None - - def clear(self) -> None: - self.lora_model_id = None - self.base_model_id = None - del self.pipe - self.pipe = None - torch.cuda.empty_cache() - gc.collect() - - @staticmethod - def check_if_model_is_local(lora_model_id: str) -> bool: - return pathlib.Path(lora_model_id).exists() - - @staticmethod - def get_model_card(model_id: str, - hf_token: str | None = None) -> ModelCard: - if InferencePipeline.check_if_model_is_local(model_id): - card_path = (pathlib.Path(model_id) / 'README.md').as_posix() - else: - card_path = model_id - return ModelCard.load(card_path, token=hf_token) - - @staticmethod - def get_base_model_info(lora_model_id: str, - hf_token: str | None = None) -> str: - card = InferencePipeline.get_model_card(lora_model_id, hf_token) - return card.data.base_model - - def load_pipe(self, lora_model_id: str) -> None: - if lora_model_id == self.lora_model_id: - return - base_model_id = self.get_base_model_info(lora_model_id, self.hf_token) - if base_model_id != self.base_model_id: - if self.device.type == 'cpu': - pipe = DiffusionPipeline.from_pretrained( - base_model_id, use_auth_token=self.hf_token) - else: - pipe = DiffusionPipeline.from_pretrained( - base_model_id, - torch_dtype=torch.float16, - use_auth_token=self.hf_token) - pipe = pipe.to(self.device) - pipe.scheduler = DPMSolverMultistepScheduler.from_config( - pipe.scheduler.config) - self.pipe = pipe - self.pipe.unet.load_attn_procs( # type: ignore - lora_model_id, use_auth_token=self.hf_token) - - self.lora_model_id = lora_model_id # type: ignore - self.base_model_id = base_model_id # type: ignore - - def run( - self, - lora_model_id: str, - prompt: str, - lora_scale: float, - seed: int, - n_steps: int, - guidance_scale: float, - ) -> PIL.Image.Image: - if not torch.cuda.is_available(): - raise gr.Error('CUDA is not available.') - - self.load_pipe(lora_model_id) - - generator = torch.Generator(device=self.device).manual_seed(seed) - out = self.pipe( - prompt, - num_inference_steps=n_steps, - guidance_scale=guidance_scale, - generator=generator, - cross_attention_kwargs={'scale': lora_scale}, - ) # type: ignore - return out.images[0] diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/gui_utils.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/gui_utils.py deleted file mode 100644 index daf852b30a84893c836d7c3350b727aeed5d0a6b..0000000000000000000000000000000000000000 --- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/gui_utils.py +++ /dev/null @@ -1,40 +0,0 @@ -from PyQt5.QtCore import Qt -from PyQt5.QtWidgets import (QHBoxLayout, QLabel, QSpinBox, QVBoxLayout, QProgressBar) - - -def create_parameter_box(min_val, max_val, text, step=1, callback=None): - layout = QHBoxLayout() - - dial = QSpinBox() - dial.setMaximumHeight(28) - dial.setMaximumWidth(150) - dial.setMinimum(min_val) - dial.setMaximum(max_val) - dial.setAlignment(Qt.AlignRight) - dial.setSingleStep(step) - dial.valueChanged.connect(callback) - - label = QLabel(text) - label.setAlignment(Qt.AlignRight) - - layout.addWidget(label) - layout.addWidget(dial) - - return dial, layout - - -def create_gauge(text): - layout = QHBoxLayout() - - gauge = QProgressBar() - gauge.setMaximumHeight(28) - gauge.setMaximumWidth(200) - gauge.setAlignment(Qt.AlignCenter) - - label = QLabel(text) - label.setAlignment(Qt.AlignRight) - - layout.addWidget(label) - layout.addWidget(gauge) - - return gauge, layout diff --git a/spaces/MateusA/StoryGenerator/README.md b/spaces/MateusA/StoryGenerator/README.md deleted file mode 100644 index 6b287719ff6ef2eca83b6768e781e0339082d73a..0000000000000000000000000000000000000000 --- a/spaces/MateusA/StoryGenerator/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: StoryGenerator -emoji: 📉 -colorFrom: indigo -colorTo: gray -sdk: gradio -sdk_version: 3.1.1 -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/Miko-opiko/openai-reverse-proxy/Dockerfile b/spaces/Miko-opiko/openai-reverse-proxy/Dockerfile deleted file mode 100644 index 6953fc05439efb70991552cf56f28365b5b6c15b..0000000000000000000000000000000000000000 --- a/spaces/Miko-opiko/openai-reverse-proxy/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM node:18 - -WORKDIR /app - -RUN npm install express express-http-proxy - -COPY . . - -EXPOSE 7860 - -CMD [ "node", "server.js" ] \ No newline at end of file diff --git a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/datasets/transforms.py b/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/datasets/transforms.py deleted file mode 100644 index 91cf9269e4b31008a3ddca34a19b038a9b399991..0000000000000000000000000000000000000000 --- a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/datasets/transforms.py +++ /dev/null @@ -1,311 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Transforms and data augmentation for both image + bbox. -""" -import os -import random - -import PIL -import torch -import torchvision.transforms as T -import torchvision.transforms.functional as F - -from groundingdino.util.box_ops import box_xyxy_to_cxcywh -from groundingdino.util.misc import interpolate - - -def crop(image, target, region): - cropped_image = F.crop(image, *region) - - target = target.copy() - i, j, h, w = region - - # should we do something wrt the original size? - target["size"] = torch.tensor([h, w]) - - fields = ["labels", "area", "iscrowd", "positive_map"] - - if "boxes" in target: - boxes = target["boxes"] - max_size = torch.as_tensor([w, h], dtype=torch.float32) - cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) - cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) - cropped_boxes = cropped_boxes.clamp(min=0) - area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) - target["boxes"] = cropped_boxes.reshape(-1, 4) - target["area"] = area - fields.append("boxes") - - if "masks" in target: - # FIXME should we update the area here if there are no boxes? - target["masks"] = target["masks"][:, i : i + h, j : j + w] - fields.append("masks") - - # remove elements for which the boxes or masks that have zero area - if "boxes" in target or "masks" in target: - # favor boxes selection when defining which elements to keep - # this is compatible with previous implementation - if "boxes" in target: - cropped_boxes = target["boxes"].reshape(-1, 2, 2) - keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) - else: - keep = target["masks"].flatten(1).any(1) - - for field in fields: - if field in target: - target[field] = target[field][keep] - - if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO": - # for debug and visualization only. - if "strings_positive" in target: - target["strings_positive"] = [ - _i for _i, _j in zip(target["strings_positive"], keep) if _j - ] - - return cropped_image, target - - -def hflip(image, target): - flipped_image = F.hflip(image) - - w, h = image.size - - target = target.copy() - if "boxes" in target: - boxes = target["boxes"] - boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor( - [w, 0, w, 0] - ) - target["boxes"] = boxes - - if "masks" in target: - target["masks"] = target["masks"].flip(-1) - - return flipped_image, target - - -def resize(image, target, size, max_size=None): - # size can be min_size (scalar) or (w, h) tuple - - def get_size_with_aspect_ratio(image_size, size, max_size=None): - w, h = image_size - if max_size is not None: - min_original_size = float(min((w, h))) - max_original_size = float(max((w, h))) - if max_original_size / min_original_size * size > max_size: - size = int(round(max_size * min_original_size / max_original_size)) - - if (w <= h and w == size) or (h <= w and h == size): - return (h, w) - - if w < h: - ow = size - oh = int(size * h / w) - else: - oh = size - ow = int(size * w / h) - - return (oh, ow) - - def get_size(image_size, size, max_size=None): - if isinstance(size, (list, tuple)): - return size[::-1] - else: - return get_size_with_aspect_ratio(image_size, size, max_size) - - size = get_size(image.size, size, max_size) - rescaled_image = F.resize(image, size) - - if target is None: - return rescaled_image, None - - ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) - ratio_width, ratio_height = ratios - - target = target.copy() - if "boxes" in target: - boxes = target["boxes"] - scaled_boxes = boxes * torch.as_tensor( - [ratio_width, ratio_height, ratio_width, ratio_height] - ) - target["boxes"] = scaled_boxes - - if "area" in target: - area = target["area"] - scaled_area = area * (ratio_width * ratio_height) - target["area"] = scaled_area - - h, w = size - target["size"] = torch.tensor([h, w]) - - if "masks" in target: - target["masks"] = ( - interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5 - ) - - return rescaled_image, target - - -def pad(image, target, padding): - # assumes that we only pad on the bottom right corners - padded_image = F.pad(image, (0, 0, padding[0], padding[1])) - if target is None: - return padded_image, None - target = target.copy() - # should we do something wrt the original size? - target["size"] = torch.tensor(padded_image.size[::-1]) - if "masks" in target: - target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1])) - return padded_image, target - - -class ResizeDebug(object): - def __init__(self, size): - self.size = size - - def __call__(self, img, target): - return resize(img, target, self.size) - - -class RandomCrop(object): - def __init__(self, size): - self.size = size - - def __call__(self, img, target): - region = T.RandomCrop.get_params(img, self.size) - return crop(img, target, region) - - -class RandomSizeCrop(object): - def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False): - # respect_boxes: True to keep all boxes - # False to tolerence box filter - self.min_size = min_size - self.max_size = max_size - self.respect_boxes = respect_boxes - - def __call__(self, img: PIL.Image.Image, target: dict): - init_boxes = len(target["boxes"]) - max_patience = 10 - for i in range(max_patience): - w = random.randint(self.min_size, min(img.width, self.max_size)) - h = random.randint(self.min_size, min(img.height, self.max_size)) - region = T.RandomCrop.get_params(img, [h, w]) - result_img, result_target = crop(img, target, region) - if ( - not self.respect_boxes - or len(result_target["boxes"]) == init_boxes - or i == max_patience - 1 - ): - return result_img, result_target - return result_img, result_target - - -class CenterCrop(object): - def __init__(self, size): - self.size = size - - def __call__(self, img, target): - image_width, image_height = img.size - crop_height, crop_width = self.size - crop_top = int(round((image_height - crop_height) / 2.0)) - crop_left = int(round((image_width - crop_width) / 2.0)) - return crop(img, target, (crop_top, crop_left, crop_height, crop_width)) - - -class RandomHorizontalFlip(object): - def __init__(self, p=0.5): - self.p = p - - def __call__(self, img, target): - if random.random() < self.p: - return hflip(img, target) - return img, target - - -class RandomResize(object): - def __init__(self, sizes, max_size=None): - assert isinstance(sizes, (list, tuple)) - self.sizes = sizes - self.max_size = max_size - - def __call__(self, img, target=None): - size = random.choice(self.sizes) - return resize(img, target, size, self.max_size) - - -class RandomPad(object): - def __init__(self, max_pad): - self.max_pad = max_pad - - def __call__(self, img, target): - pad_x = random.randint(0, self.max_pad) - pad_y = random.randint(0, self.max_pad) - return pad(img, target, (pad_x, pad_y)) - - -class RandomSelect(object): - """ - Randomly selects between transforms1 and transforms2, - with probability p for transforms1 and (1 - p) for transforms2 - """ - - def __init__(self, transforms1, transforms2, p=0.5): - self.transforms1 = transforms1 - self.transforms2 = transforms2 - self.p = p - - def __call__(self, img, target): - if random.random() < self.p: - return self.transforms1(img, target) - return self.transforms2(img, target) - - -class ToTensor(object): - def __call__(self, img, target): - return F.to_tensor(img), target - - -class RandomErasing(object): - def __init__(self, *args, **kwargs): - self.eraser = T.RandomErasing(*args, **kwargs) - - def __call__(self, img, target): - return self.eraser(img), target - - -class Normalize(object): - def __init__(self, mean, std): - self.mean = mean - self.std = std - - def __call__(self, image, target=None): - image = F.normalize(image, mean=self.mean, std=self.std) - if target is None: - return image, None - target = target.copy() - h, w = image.shape[-2:] - if "boxes" in target: - boxes = target["boxes"] - boxes = box_xyxy_to_cxcywh(boxes) - boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32) - target["boxes"] = boxes - return image, target - - -class Compose(object): - def __init__(self, transforms): - self.transforms = transforms - - def __call__(self, image, target): - for t in self.transforms: - image, target = t(image, target) - return image, target - - def __repr__(self): - format_string = self.__class__.__name__ + "(" - for t in self.transforms: - format_string += "\n" - format_string += " {0}".format(t) - format_string += "\n)" - return format_string diff --git a/spaces/MohammedMaaz/PDF-TEXT-BASED-QA/README.md b/spaces/MohammedMaaz/PDF-TEXT-BASED-QA/README.md deleted file mode 100644 index 71b252ef574b35d3ff89bf1e65d0831b6dfc7060..0000000000000000000000000000000000000000 --- a/spaces/MohammedMaaz/PDF-TEXT-BASED-QA/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: PDF TEXT BASED QA -emoji: 📚 -colorFrom: gray -colorTo: purple -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/misc.py b/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/misc.py deleted file mode 100644 index 3edcc1b51c99e66c568fa5d3d93f131911096489..0000000000000000000000000000000000000000 --- a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/utils/misc.py +++ /dev/null @@ -1,251 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import torch -import torch.nn as nn -import numpy as np -import torch.optim as optim -import os - -import torch.nn.functional as F - -import six -from six.moves import cPickle - -bad_endings = ['with','in','on','of','a','at','to','for','an','this','his','her','that'] -bad_endings += ['the'] - - -def pickle_load(f): - """ Load a pickle. - Parameters - ---------- - f: file-like object - """ - if six.PY3: - return cPickle.load(f, encoding='latin-1') - else: - return cPickle.load(f) - - -def pickle_dump(obj, f): - """ Dump a pickle. - Parameters - ---------- - obj: pickled object - f: file-like object - """ - if six.PY3: - return cPickle.dump(obj, f, protocol=2) - else: - return cPickle.dump(obj, f) - - -# modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/utils/comm.py -def serialize_to_tensor(data): - device = torch.device("cpu") - - buffer = cPickle.dumps(data) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to(device=device) - return tensor - - -def deserialize(tensor): - buffer = tensor.cpu().numpy().tobytes() - return cPickle.loads(buffer) - - -# Input: seq, N*D numpy array, with element 0 .. vocab_size. 0 is END token. -def decode_sequence(ix_to_word, seq): - # N, D = seq.size() - N, D = seq.shape - out = [] - for i in range(N): - txt = '' - for j in range(D): - ix = seq[i,j] - if ix > 0 : - if j >= 1: - txt = txt + ' ' - txt = txt + ix_to_word[str(ix.item())] - else: - break - if int(os.getenv('REMOVE_BAD_ENDINGS', '0')): - flag = 0 - words = txt.split(' ') - for j in range(len(words)): - if words[-j-1] not in bad_endings: - flag = -j - break - txt = ' '.join(words[0:len(words)+flag]) - out.append(txt.replace('@@ ', '')) - return out - - -def save_checkpoint(opt, model, infos, optimizer, histories=None, append=''): - if len(append) > 0: - append = '-' + append - # if checkpoint_path doesn't exist - if not os.path.isdir(opt.checkpoint_path): - os.makedirs(opt.checkpoint_path) - checkpoint_path = os.path.join(opt.checkpoint_path, 'model%s.pth' %(append)) - torch.save(model.state_dict(), checkpoint_path) - print("model saved to {}".format(checkpoint_path)) - optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer%s.pth' %(append)) - torch.save(optimizer.state_dict(), optimizer_path) - with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'%s.pkl' %(append)), 'wb') as f: - pickle_dump(infos, f) - if histories: - with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'%s.pkl' %(append)), 'wb') as f: - pickle_dump(histories, f) - - -def set_lr(optimizer, lr): - for group in optimizer.param_groups: - group['lr'] = lr - -def get_lr(optimizer): - for group in optimizer.param_groups: - return group['lr'] - - -def build_optimizer(params, opt): - if opt.optim == 'rmsprop': - return optim.RMSprop(params, opt.learning_rate, opt.optim_alpha, opt.optim_epsilon, weight_decay=opt.weight_decay) - elif opt.optim == 'adagrad': - return optim.Adagrad(params, opt.learning_rate, weight_decay=opt.weight_decay) - elif opt.optim == 'sgd': - return optim.SGD(params, opt.learning_rate, weight_decay=opt.weight_decay) - elif opt.optim == 'sgdm': - return optim.SGD(params, opt.learning_rate, opt.optim_alpha, weight_decay=opt.weight_decay) - elif opt.optim == 'sgdmom': - return optim.SGD(params, opt.learning_rate, opt.optim_alpha, weight_decay=opt.weight_decay, nesterov=True) - elif opt.optim == 'adam': - return optim.Adam(params, opt.learning_rate, (opt.optim_alpha, opt.optim_beta), opt.optim_epsilon, weight_decay=opt.weight_decay) - elif opt.optim == 'adamw': - return optim.AdamW(params, opt.learning_rate, (opt.optim_alpha, opt.optim_beta), opt.optim_epsilon, weight_decay=opt.weight_decay) - else: - raise Exception("bad option opt.optim: {}".format(opt.optim)) - - -def penalty_builder(penalty_config): - if penalty_config == '': - return lambda x,y: y - pen_type, alpha = penalty_config.split('_') - alpha = float(alpha) - if pen_type == 'wu': - return lambda x,y: length_wu(x,y,alpha) - if pen_type == 'avg': - return lambda x,y: length_average(x,y,alpha) - -def length_wu(length, logprobs, alpha=0.): - """ - NMT length re-ranking score from - "Google's Neural Machine Translation System" :cite:`wu2016google`. - """ - - modifier = (((5 + length) ** alpha) / - ((5 + 1) ** alpha)) - return (logprobs / modifier) - -def length_average(length, logprobs, alpha=0.): - """ - Returns the average probability of tokens in a sequence. - """ - return logprobs / length - - -class NoamOpt(object): - "Optim wrapper that implements rate." - def __init__(self, model_size, factor, warmup, optimizer): - self.optimizer = optimizer - self._step = 0 - self.warmup = warmup - self.factor = factor - self.model_size = model_size - self._rate = 0 - - def step(self): - "Update parameters and rate" - self._step += 1 - rate = self.rate() - for p in self.optimizer.param_groups: - p['lr'] = rate - self._rate = rate - self.optimizer.step() - - def rate(self, step = None): - "Implement `lrate` above" - if step is None: - step = self._step - return self.factor * \ - (self.model_size ** (-0.5) * - min(step ** (-0.5), step * self.warmup ** (-1.5))) - - def __getattr__(self, name): - return getattr(self.optimizer, name) - - def state_dict(self): - state_dict = self.optimizer.state_dict() - state_dict['_step'] = self._step - return state_dict - - def load_state_dict(self, state_dict): - if '_step' in state_dict: - self._step = state_dict['_step'] - del state_dict['_step'] - self.optimizer.load_state_dict(state_dict) - -class ReduceLROnPlateau(object): - "Optim wrapper that implements rate." - def __init__(self, optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08): - self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode, factor, patience, verbose, threshold, threshold_mode, cooldown, min_lr, eps) - self.optimizer = optimizer - self.current_lr = get_lr(optimizer) - - def step(self): - "Update parameters and rate" - self.optimizer.step() - - def scheduler_step(self, val): - self.scheduler.step(val) - self.current_lr = get_lr(self.optimizer) - - def state_dict(self): - return {'current_lr':self.current_lr, - 'scheduler_state_dict': self.scheduler.state_dict(), - 'optimizer_state_dict': self.optimizer.state_dict()} - - def load_state_dict(self, state_dict): - if 'current_lr' not in state_dict: - # it's normal optimizer - self.optimizer.load_state_dict(state_dict) - set_lr(self.optimizer, self.current_lr) # use the lr fromt the option - else: - # it's a schduler - self.current_lr = state_dict['current_lr'] - self.scheduler.load_state_dict(state_dict['scheduler_state_dict']) - self.optimizer.load_state_dict(state_dict['optimizer_state_dict']) - # current_lr is actually useless in this case - - def rate(self, step = None): - "Implement `lrate` above" - if step is None: - step = self._step - return self.factor * \ - (self.model_size ** (-0.5) * - min(step ** (-0.5), step * self.warmup ** (-1.5))) - - def __getattr__(self, name): - return getattr(self.optimizer, name) - -def get_std_opt(model, optim_func='adam', factor=1, warmup=2000): - # return NoamOpt(model.tgt_embed[0].d_model, 2, 4000, - # torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9)) - optim_func = dict(adam=torch.optim.Adam, - adamw=torch.optim.AdamW)[optim_func] - return NoamOpt(model.d_model, factor, warmup, - optim_func(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9)) diff --git a/spaces/NATSpeech/PortaSpeech/inference/tts/ds.py b/spaces/NATSpeech/PortaSpeech/inference/tts/ds.py deleted file mode 100644 index 04b5b4925bfcbfc0e05732054fd3746f1e89bf02..0000000000000000000000000000000000000000 --- a/spaces/NATSpeech/PortaSpeech/inference/tts/ds.py +++ /dev/null @@ -1,30 +0,0 @@ -import torch -# from inference.tts.fs import FastSpeechInfer -# from modules.tts.fs2_orig import FastSpeech2Orig -from inference.tts.base_tts_infer import BaseTTSInfer -from modules.tts.diffspeech.shallow_diffusion_tts import GaussianDiffusion -from utils.commons.ckpt_utils import load_ckpt -from utils.commons.hparams import hparams - - -class DiffSpeechInfer(BaseTTSInfer): - def build_model(self): - dict_size = len(self.ph_encoder) - model = GaussianDiffusion(dict_size, self.hparams) - model.eval() - load_ckpt(model, hparams['work_dir'], 'model') - return model - - def forward_model(self, inp): - sample = self.input_to_batch(inp) - txt_tokens = sample['txt_tokens'] # [B, T_t] - spk_id = sample.get('spk_ids') - with torch.no_grad(): - output = self.model(txt_tokens, spk_id=spk_id, ref_mels=None, infer=True) - mel_out = output['mel_out'] - wav_out = self.run_vocoder(mel_out) - wav_out = wav_out.cpu().numpy() - return wav_out[0] - -if __name__ == '__main__': - DiffSpeechInfer.example_run() diff --git a/spaces/NCTCMumbai/NCTC/README.md b/spaces/NCTCMumbai/NCTC/README.md deleted file mode 100644 index 8290421441be73612d547574abc8bd95e2ce8033..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: NCTC -emoji: 📚 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.42.0 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/NoriZC/vits-models/README.md b/spaces/NoriZC/vits-models/README.md deleted file mode 100644 index 4fd80f50384990fe18e0c74381cace05f346e573..0000000000000000000000000000000000000000 --- a/spaces/NoriZC/vits-models/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Vits Models -emoji: 🏃 -colorFrom: pink -colorTo: indigo -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: zomehwh/vits-models ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OAOA/DifFace/basicsr/data/ffhq_dataset.py b/spaces/OAOA/DifFace/basicsr/data/ffhq_dataset.py deleted file mode 100644 index 23992eb877f6b7b46cf5f40ed3667fc10916269b..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/basicsr/data/ffhq_dataset.py +++ /dev/null @@ -1,80 +0,0 @@ -import random -import time -from os import path as osp -from torch.utils import data as data -from torchvision.transforms.functional import normalize - -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY - - -@DATASET_REGISTRY.register() -class FFHQDataset(data.Dataset): - """FFHQ dataset for StyleGAN. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - io_backend (dict): IO backend type and other kwarg. - mean (list | tuple): Image mean. - std (list | tuple): Image std. - use_hflip (bool): Whether to horizontally flip. - - """ - - def __init__(self, opt): - super(FFHQDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - - self.gt_folder = opt['dataroot_gt'] - self.mean = opt['mean'] - self.std = opt['std'] - - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = self.gt_folder - if not self.gt_folder.endswith('.lmdb'): - raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # FFHQ has 70000 images in total - self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)] - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load gt image - gt_path = self.paths[index] - # avoid errors caused by high latency in reading files - retry = 3 - while retry > 0: - try: - img_bytes = self.file_client.get(gt_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}') - # change another file to read - index = random.randint(0, self.__len__()) - gt_path = self.paths[index] - time.sleep(1) # sleep 1s for occasional server congestion - else: - break - finally: - retry -= 1 - img_gt = imfrombytes(img_bytes, float32=True) - - # random horizontal flip - img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False) - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) - # normalize - normalize(img_gt, self.mean, self.std, inplace=True) - return {'gt': img_gt, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/constrained_decoding/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/constrained_decoding/README.md deleted file mode 100644 index e04b8b6a018214c8233fa87fd91d46a6dd1519d4..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/constrained_decoding/README.md +++ /dev/null @@ -1,123 +0,0 @@ -# (Vectorized) Lexically constrained decoding with dynamic beam allocation - -This page provides instructions for how to use lexically constrained decoding in Fairseq. -Fairseq implements the code described in the following papers: - -* [Fast Lexically Constrained Decoding With Dynamic Beam Allocation](https://www.aclweb.org/anthology/N18-1119/) (Post & Vilar, 2018) -* [Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://www.aclweb.org/anthology/N19-1090/) (Hu et al., 2019) - -## Quick start - -Constrained search is enabled by adding the command-line argument `--constraints` to `fairseq-interactive`. -Constraints are appended to each line of input, separated by tabs. Each constraint (one or more tokens) -is a separate field. - -The following command, using [Fairseq's WMT19 German--English model](https://github.com/pytorch/fairseq/blob/main/examples/wmt19/README.md), -translates the sentence *Die maschinelle Übersetzung ist schwer zu kontrollieren.* with the constraints -"hard" and "to influence". - - echo -e "Die maschinelle Übersetzung ist schwer zu kontrollieren.\thard\ttoinfluence" \ - | normalize.py | tok.py \ - | fairseq-interactive /path/to/model \ - --path /path/to/model/model1.pt \ - --bpe fastbpe \ - --bpe-codes /path/to/model/bpecodes \ - --constraints \ - -s de -t en \ - --beam 10 - -(tok.py and normalize.py can be found in the same directory as this README; they are just shortcuts around Fairseq's WMT19 preprocessing). -This will generate the following output: - - [snip] - S-0 Die masch@@ in@@ elle Über@@ setzung ist schwer zu kontrollieren . - W-0 1.844 seconds - C-0 hard - C-0 influence - H-0 -1.5333266258239746 Mach@@ ine trans@@ lation is hard to influence . - D-0 -1.5333266258239746 Machine translation is hard to influence . - P-0 -0.5434 -0.1423 -0.1930 -0.1415 -0.2346 -1.8031 -0.1701 -11.7727 -0.1815 -0.1511 - -By default, constraints are generated in the order supplied, with any number (zero or more) of tokens generated -between constraints. If you wish for the decoder to order the constraints, then use `--constraints unordered`. -Note that you may want to use a larger beam. - -## Implementation details - -The heart of the implementation is in `fairseq/search.py`, which adds a `LexicallyConstrainedBeamSearch` instance. -This instance of beam search tracks the progress of each hypothesis in the beam through the set of constraints -provided for each input sentence. It does this using one of two classes, both found in `fairseq/token_generation_contstraints.py`: - -* OrderedConstraintState: assumes the `C` input constraints will be generated in the provided order -* UnorderedConstraintState: tries to apply `C` (phrasal) constraints in all `C!` orders - -## Differences from Sockeye - -There are a number of [differences from Sockeye's implementation](https://awslabs.github.io/sockeye/inference.html#lexical-constraints). - -* Generating constraints in the order supplied (the default option here) is not available in Sockeye. -* Due to an improved beam allocation method, there is no need to prune the beam. -* Again due to better allocation, beam sizes as low as 10 or even 5 are often sufficient. -* [The vector extensions described in Hu et al.](https://github.com/edwardjhu/sockeye/tree/trie_constraints) (NAACL 2019) were never merged - into the main Sockeye branch. - -## Citation - -The paper first describing lexical constraints for seq2seq decoding is: - -```bibtex -@inproceedings{hokamp-liu-2017-lexically, - title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search", - author = "Hokamp, Chris and - Liu, Qun", - booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2017", - address = "Vancouver, Canada", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/P17-1141", - doi = "10.18653/v1/P17-1141", - pages = "1535--1546", -} -``` - -The fairseq implementation uses the extensions described in - -```bibtex -@inproceedings{post-vilar-2018-fast, - title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation", - author = "Post, Matt and - Vilar, David", - booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", - month = jun, - year = "2018", - address = "New Orleans, Louisiana", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/N18-1119", - doi = "10.18653/v1/N18-1119", - pages = "1314--1324", -} -``` - -and - -```bibtex -@inproceedings{hu-etal-2019-improved, - title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting", - author = "Hu, J. Edward and - Khayrallah, Huda and - Culkin, Ryan and - Xia, Patrick and - Chen, Tongfei and - Post, Matt and - Van Durme, Benjamin", - booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", - month = jun, - year = "2019", - address = "Minneapolis, Minnesota", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/N19-1090", - doi = "10.18653/v1/N19-1090", - pages = "839--850", -} -``` diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/interactive.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/interactive.py deleted file mode 100644 index cadef2821a74a3b2f051c792d835129bf775714f..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq_cli/interactive.py +++ /dev/null @@ -1,316 +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. -""" -Translate raw text with a trained model. Batches data on-the-fly. -""" - -import ast -import fileinput -import logging -import math -import os -import sys -import time -from argparse import Namespace -from collections import namedtuple - -import numpy as np -import torch -from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.token_generation_constraints import pack_constraints, unpack_constraints -from fairseq_cli.generate import get_symbols_to_strip_from_output - - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.interactive") - - -Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints") -Translation = namedtuple("Translation", "src_str hypos pos_scores alignments") - - -def buffered_read(input, buffer_size): - buffer = [] - with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h: - for src_str in h: - buffer.append(src_str.strip()) - if len(buffer) >= buffer_size: - yield buffer - buffer = [] - - if len(buffer) > 0: - yield buffer - - -def make_batches(lines, cfg, task, max_positions, encode_fn): - def encode_fn_target(x): - return encode_fn(x) - - if cfg.generation.constraints: - # Strip (tab-delimited) contraints, if present, from input lines, - # store them in batch_constraints - batch_constraints = [list() for _ in lines] - for i, line in enumerate(lines): - if "\t" in line: - lines[i], *batch_constraints[i] = line.split("\t") - - # Convert each List[str] to List[Tensor] - for i, constraint_list in enumerate(batch_constraints): - batch_constraints[i] = [ - task.target_dictionary.encode_line( - encode_fn_target(constraint), - append_eos=False, - add_if_not_exist=False, - ) - for constraint in constraint_list - ] - - if cfg.generation.constraints: - constraints_tensor = pack_constraints(batch_constraints) - else: - constraints_tensor = None - - tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn) - - itr = task.get_batch_iterator( - dataset=task.build_dataset_for_inference( - tokens, lengths, constraints=constraints_tensor - ), - max_tokens=cfg.dataset.max_tokens, - max_sentences=cfg.dataset.batch_size, - max_positions=max_positions, - ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, - ).next_epoch_itr(shuffle=False) - for batch in itr: - ids = batch["id"] - src_tokens = batch["net_input"]["src_tokens"] - src_lengths = batch["net_input"]["src_lengths"] - constraints = batch.get("constraints", None) - - yield Batch( - ids=ids, - src_tokens=src_tokens, - src_lengths=src_lengths, - constraints=constraints, - ) - - -def main(cfg: FairseqConfig): - if isinstance(cfg, Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - start_time = time.time() - total_translate_time = 0 - - utils.import_user_module(cfg.common) - - if cfg.interactive.buffer_size < 1: - cfg.interactive.buffer_size = 1 - if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: - cfg.dataset.batch_size = 1 - - assert ( - not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - not cfg.dataset.batch_size - or cfg.dataset.batch_size <= cfg.interactive.buffer_size - ), "--batch-size cannot be larger than --buffer-size" - - logger.info(cfg) - - # Fix seed for stochastic decoding - if cfg.common.seed is not None and not cfg.generation.no_seed_provided: - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - use_cuda = torch.cuda.is_available() and not cfg.common.cpu - - # Setup task, e.g., translation - task = tasks.setup_task(cfg.task) - - # Load ensemble - overrides = ast.literal_eval(cfg.common_eval.model_overrides) - logger.info("loading model(s) from {}".format(cfg.common_eval.path)) - models, _model_args = checkpoint_utils.load_model_ensemble( - utils.split_paths(cfg.common_eval.path), - arg_overrides=overrides, - task=task, - suffix=cfg.checkpoint.checkpoint_suffix, - strict=(cfg.checkpoint.checkpoint_shard_count == 1), - num_shards=cfg.checkpoint.checkpoint_shard_count, - ) - - # Set dictionaries - src_dict = task.source_dictionary - tgt_dict = task.target_dictionary - - # Optimize ensemble for generation - for model in models: - if model is None: - continue - if cfg.common.fp16: - model.half() - if use_cuda and not cfg.distributed_training.pipeline_model_parallel: - model.cuda() - model.prepare_for_inference_(cfg) - - # Initialize generator - generator = task.build_generator(models, cfg.generation) - - # Handle tokenization and BPE - tokenizer = task.build_tokenizer(cfg.tokenizer) - bpe = task.build_bpe(cfg.bpe) - - def encode_fn(x): - if tokenizer is not None: - x = tokenizer.encode(x) - if bpe is not None: - x = bpe.encode(x) - return x - - def decode_fn(x): - if bpe is not None: - x = bpe.decode(x) - if tokenizer is not None: - x = tokenizer.decode(x) - return x - - # Load alignment dictionary for unknown word replacement - # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(cfg.generation.replace_unk) - - max_positions = utils.resolve_max_positions( - task.max_positions(), *[model.max_positions() for model in models] - ) - - if cfg.generation.constraints: - logger.warning( - "NOTE: Constrained decoding currently assumes a shared subword vocabulary." - ) - - if cfg.interactive.buffer_size > 1: - logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size) - logger.info("NOTE: hypothesis and token scores are output in base 2") - logger.info("Type the input sentence and press return:") - start_id = 0 - for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size): - results = [] - for batch in make_batches(inputs, cfg, task, max_positions, encode_fn): - bsz = batch.src_tokens.size(0) - src_tokens = batch.src_tokens - src_lengths = batch.src_lengths - constraints = batch.constraints - if use_cuda: - src_tokens = src_tokens.cuda() - src_lengths = src_lengths.cuda() - if constraints is not None: - constraints = constraints.cuda() - - sample = { - "net_input": { - "src_tokens": src_tokens, - "src_lengths": src_lengths, - }, - } - translate_start_time = time.time() - translations = task.inference_step( - generator, models, sample, constraints=constraints - ) - translate_time = time.time() - translate_start_time - total_translate_time += translate_time - list_constraints = [[] for _ in range(bsz)] - if cfg.generation.constraints: - list_constraints = [unpack_constraints(c) for c in constraints] - for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): - src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) - constraints = list_constraints[i] - results.append( - ( - start_id + id, - src_tokens_i, - hypos, - { - "constraints": constraints, - "time": translate_time / len(translations), - }, - ) - ) - - # sort output to match input order - for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]): - src_str = '' - if src_dict is not None: - src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) - print("S-{}\t{}".format(id_, src_str)) - print("W-{}\t{:.3f}\tseconds".format(id_, info["time"])) - for constraint in info["constraints"]: - print( - "C-{}\t{}".format( - id_, tgt_dict.string(constraint, cfg.common_eval.post_process) - ) - ) - - # Process top predictions - for hypo in hypos[: min(len(hypos), cfg.generation.nbest)]: - hypo_tokens, hypo_str, alignment = utils.post_process_prediction( - hypo_tokens=hypo["tokens"].int().cpu(), - src_str=src_str, - alignment=hypo["alignment"], - align_dict=align_dict, - tgt_dict=tgt_dict, - remove_bpe=cfg.common_eval.post_process, - extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), - ) - detok_hypo_str = decode_fn(hypo_str) - score = hypo["score"] / math.log(2) # convert to base 2 - # original hypothesis (after tokenization and BPE) - print("H-{}\t{}\t{}".format(id_, score, hypo_str)) - # detokenized hypothesis - print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str)) - print( - "P-{}\t{}".format( - id_, - " ".join( - map( - lambda x: "{:.4f}".format(x), - # convert from base e to base 2 - hypo["positional_scores"].div_(math.log(2)).tolist(), - ) - ), - ) - ) - if cfg.generation.print_alignment: - alignment_str = " ".join( - ["{}-{}".format(src, tgt) for src, tgt in alignment] - ) - print("A-{}\t{}".format(id_, alignment_str)) - - # update running id_ counter - start_id += len(inputs) - - logger.info( - "Total time: {:.3f} seconds; translation time: {:.3f}".format( - time.time() - start_time, total_translate_time - ) - ) - - -def cli_main(): - parser = options.get_interactive_generation_parser() - args = options.parse_args_and_arch(parser) - distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/fairseq_task.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/fairseq_task.py deleted file mode 100644 index 64610e45430b664c461163427fe7444661ec0b7d..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/fairseq_task.py +++ /dev/null @@ -1,668 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import warnings -from argparse import Namespace -from typing import Any, Callable, Dict, List - -import torch -from fairseq import metrics, search, tokenizer, utils -from fairseq.data import Dictionary, FairseqDataset, data_utils, encoders, iterators -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim.amp_optimizer import AMPOptimizer -from omegaconf import DictConfig - - -logger = logging.getLogger(__name__) - - -class StatefulContainer(object): - - def __init__(self): - self._state = dict() - self._factories = dict() - - def add_factory(self, name, factory: Callable[[], Any]): - self._factories[name] = factory - - def merge_state_dict(self, state_dict: Dict[str, Any]): - self._state.update(state_dict) - - @property - def state_dict(self) -> Dict[str, Any]: - return self._state - - def __getattr__(self, name): - if name not in self._state and name in self._factories: - self._state[name] = self._factories[name]() - - if name in self._state: - return self._state[name] - - raise AttributeError(f"Task state has no factory for attribute {name}") - - -class FairseqTask(object): - """ - Tasks store dictionaries and provide helpers for loading/iterating over - Datasets, initializing the Model/Criterion and calculating the loss. - - Tasks have limited statefulness. In particular, state that needs to be - saved to/loaded from checkpoints needs to be stored in the `self.state` - :class:`StatefulContainer` object. For example:: - - self.state.add_factory("dictionary", self.load_dictionary) - print(self.state.dictionary) # calls self.load_dictionary() - - This is necessary so that when loading checkpoints, we can properly - recreate the task state after initializing the task instance. - """ - - @classmethod - def add_args(cls, parser): - """Add task-specific arguments to the parser.""" - dc = getattr(cls, "__dataclass", None) - if dc is not None: - gen_parser_from_dataclass(parser, dc()) - - @staticmethod - def logging_outputs_can_be_summed(criterion) -> bool: - """ - Whether the logging outputs returned by `train_step` and `valid_step` can - be summed across workers prior to calling `aggregate_logging_outputs`. - Setting this to True will improves distributed training speed. - """ - return criterion.logging_outputs_can_be_summed() - - def __init__(self, cfg: FairseqDataclass, **kwargs): - self.cfg = cfg - self.datasets = dict() - self.dataset_to_epoch_iter = dict() - self.state = StatefulContainer() - - @classmethod - def load_dictionary(cls, filename): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - return Dictionary.load(filename) - - @classmethod - def build_dictionary( - cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 - ): - """Build the dictionary - - Args: - filenames (list): list of filenames - workers (int): number of concurrent workers - threshold (int): defines the minimum word count - nwords (int): defines the total number of words in the final dictionary, - including special symbols - padding_factor (int): can be used to pad the dictionary size to be a - multiple of 8, which is important on some hardware (e.g., Nvidia - Tensor Cores). - """ - d = Dictionary() - for filename in filenames: - Dictionary.add_file_to_dictionary( - filename, d, tokenizer.tokenize_line, workers - ) - d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) - return d - - @classmethod - def setup_task(cls, cfg: DictConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (omegaconf.DictConfig): parsed command-line arguments - """ - return cls(cfg, **kwargs) - - def has_sharded_data(self, split): - return os.pathsep in getattr(self.cfg, "data", "") - - def load_dataset( - self, - split: str, - combine: bool = False, - task_cfg: FairseqDataclass = None, - **kwargs - ): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - combine (bool): combines a split segmented into pieces into one dataset - task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used - to load datasets - """ - raise NotImplementedError - - def dataset(self, split): - """ - Return a loaded dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - - Returns: - a :class:`~fairseq.data.FairseqDataset` corresponding to *split* - """ - from fairseq.data import FairseqDataset - - if split not in self.datasets: - raise KeyError("Dataset not loaded: " + split) - if not isinstance(self.datasets[split], FairseqDataset): - raise TypeError("Datasets are expected to be of type FairseqDataset") - return self.datasets[split] - - def filter_indices_by_size( - self, indices, dataset, max_positions=None, ignore_invalid_inputs=False - ): - """ - Filter examples that are too large - - Args: - indices (np.array): original array of sample indices - dataset (~fairseq.data.FairseqDataset): dataset to batch - max_positions (optional): max sentence length supported by the - model (default: None). - ignore_invalid_inputs (bool, optional): don't raise Exception for - sentences that are too long (default: False). - Returns: - np.array: array of filtered sample indices - """ - indices, ignored = dataset.filter_indices_by_size(indices, max_positions) - if len(ignored) > 0: - if not ignore_invalid_inputs: - raise Exception( - ( - "Size of sample #{} is invalid (={}) since max_positions={}, " - "skip this example with --skip-invalid-size-inputs-valid-test" - ).format(ignored[0], dataset.size(ignored[0]), max_positions) - ) - logger.warning( - ( - "{:,} samples have invalid sizes and will be skipped, " - "max_positions={}, first few sample ids={}" - ).format(len(ignored), max_positions, ignored[:10]) - ) - return indices - - def can_reuse_epoch_itr(self, dataset): - # We can reuse the epoch iterator across epochs as long as the dataset - # hasn't disabled it. We default to ``False`` here, although in practice - # this will be ``True`` for most datasets that inherit from - # ``FairseqDataset`` due to the base implementation there. - return getattr(dataset, "can_reuse_epoch_itr_across_epochs", False) - - def get_batch_iterator( - self, - dataset, - max_tokens=None, - max_sentences=None, - max_positions=None, - ignore_invalid_inputs=False, - required_batch_size_multiple=1, - seed=1, - num_shards=1, - shard_id=0, - num_workers=0, - epoch=1, - data_buffer_size=0, - disable_iterator_cache=False, - ): - """ - Get an iterator that yields batches of data from the given dataset. - - Args: - dataset (~fairseq.data.FairseqDataset): dataset to batch - max_tokens (int, optional): max number of tokens in each batch - (default: None). - max_sentences (int, optional): max number of sentences in each - batch (default: None). - max_positions (optional): max sentence length supported by the - model (default: None). - ignore_invalid_inputs (bool, optional): don't raise Exception for - sentences that are too long (default: False). - required_batch_size_multiple (int, optional): require batch size to - be a multiple of N (default: 1). - seed (int, optional): seed for random number generator for - reproducibility (default: 1). - num_shards (int, optional): shard the data iterator into N - shards (default: 1). - shard_id (int, optional): which shard of the data iterator to - return (default: 0). - num_workers (int, optional): how many subprocesses to use for data - loading. 0 means the data will be loaded in the main process - (default: 0). - epoch (int, optional): the epoch to start the iterator from - (default: 1). - data_buffer_size (int, optional): number of batches to - preload (default: 0). - disable_iterator_cache (bool, optional): don't cache the - EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) - (default: False). - Returns: - ~fairseq.iterators.EpochBatchIterator: a batched iterator over the - given dataset split - """ - can_reuse_epoch_itr = not disable_iterator_cache and self.can_reuse_epoch_itr( - dataset - ) - if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter: - logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch)) - return self.dataset_to_epoch_iter[dataset] - - assert isinstance(dataset, FairseqDataset) - - # initialize the dataset with the correct starting epoch - dataset.set_epoch(epoch) - - # get indices ordered by example size - with data_utils.numpy_seed(seed): - indices = dataset.ordered_indices() - - # filter examples that are too large - if max_positions is not None: - indices = self.filter_indices_by_size( - indices, dataset, max_positions, ignore_invalid_inputs - ) - - # create mini-batches with given size constraints - batch_sampler = dataset.batch_by_size( - indices, - max_tokens=max_tokens, - max_sentences=max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - ) - - # return a reusable, sharded iterator - epoch_iter = iterators.EpochBatchIterator( - dataset=dataset, - collate_fn=dataset.collater, - batch_sampler=batch_sampler, - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - epoch=epoch, - buffer_size=data_buffer_size, - ) - - if can_reuse_epoch_itr: - self.dataset_to_epoch_iter[dataset] = epoch_iter - - return epoch_iter - - def build_model(self, cfg: FairseqDataclass): - """ - Build the :class:`~fairseq.models.BaseFairseqModel` instance for this - task. - - Args: - cfg (FairseqDataclass): configuration object - - Returns: - a :class:`~fairseq.models.BaseFairseqModel` instance - """ - from fairseq import models, quantization_utils - - model = models.build_model(cfg, self) - model = quantization_utils.quantize_model_scalar(model, cfg) - return model - - def build_criterion(self, cfg: DictConfig): - """ - Build the :class:`~fairseq.criterions.FairseqCriterion` instance for - this task. - - Args: - cfg (omegaconf.DictConfig): configration object - - Returns: - a :class:`~fairseq.criterions.FairseqCriterion` instance - """ - from fairseq import criterions - - return criterions.build_criterion(cfg, self) - - def build_generator( - self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None, - ): - """ - Build a :class:`~fairseq.SequenceGenerator` instance for this - task. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - args (fairseq.dataclass.configs.GenerationConfig): - configuration object (dataclass) for generation - extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass - through to SequenceGenerator - prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]): - If provided, this function constrains the beam search to - allowed tokens only at each step. The provided function - should take 2 arguments: the batch ID (`batch_id: int`) - and a unidimensional tensor of token ids (`inputs_ids: - torch.Tensor`). It has to return a `List[int]` with the - allowed tokens for the next generation step conditioned - on the previously generated tokens (`inputs_ids`) and - the batch ID (`batch_id`). This argument is useful for - constrained generation conditioned on the prefix, as - described in "Autoregressive Entity Retrieval" - (https://arxiv.org/abs/2010.00904) and - https://github.com/facebookresearch/GENRE. - """ - if getattr(args, "score_reference", False): - from fairseq.sequence_scorer import SequenceScorer - - return SequenceScorer( - self.target_dictionary, - compute_alignment=getattr(args, "print_alignment", False), - ) - - from fairseq.sequence_generator import ( - SequenceGenerator, - SequenceGeneratorWithAlignment, - ) - - # Choose search strategy. Defaults to Beam Search. - sampling = getattr(args, "sampling", False) - sampling_topk = getattr(args, "sampling_topk", -1) - sampling_topp = getattr(args, "sampling_topp", -1.0) - diverse_beam_groups = getattr(args, "diverse_beam_groups", -1) - diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5) - match_source_len = getattr(args, "match_source_len", False) - diversity_rate = getattr(args, "diversity_rate", -1) - constrained = getattr(args, "constraints", False) - if prefix_allowed_tokens_fn is None: - prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None) - if ( - sum( - int(cond) - for cond in [ - sampling, - diverse_beam_groups > 0, - match_source_len, - diversity_rate > 0, - ] - ) - > 1 - ): - raise ValueError("Provided Search parameters are mutually exclusive.") - assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling" - assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling" - - if sampling: - search_strategy = search.Sampling( - self.target_dictionary, sampling_topk, sampling_topp - ) - elif diverse_beam_groups > 0: - search_strategy = search.DiverseBeamSearch( - self.target_dictionary, diverse_beam_groups, diverse_beam_strength - ) - elif match_source_len: - # this is useful for tagging applications where the output - # length should match the input length, so we hardcode the - # length constraints for simplicity - search_strategy = search.LengthConstrainedBeamSearch( - self.target_dictionary, - min_len_a=1, - min_len_b=0, - max_len_a=1, - max_len_b=0, - ) - elif diversity_rate > -1: - search_strategy = search.DiverseSiblingsSearch( - self.target_dictionary, diversity_rate - ) - elif constrained: - search_strategy = search.LexicallyConstrainedBeamSearch( - self.target_dictionary, args.constraints - ) - elif prefix_allowed_tokens_fn: - search_strategy = search.PrefixConstrainedBeamSearch( - self.target_dictionary, prefix_allowed_tokens_fn - ) - else: - search_strategy = search.BeamSearch(self.target_dictionary) - - extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} - if seq_gen_cls is None: - if getattr(args, "print_alignment", False): - seq_gen_cls = SequenceGeneratorWithAlignment - extra_gen_cls_kwargs["print_alignment"] = args.print_alignment - else: - seq_gen_cls = SequenceGenerator - - return seq_gen_cls( - models, - self.target_dictionary, - beam_size=getattr(args, "beam", 5), - max_len_a=getattr(args, "max_len_a", 0), - max_len_b=getattr(args, "max_len_b", 200), - min_len=getattr(args, "min_len", 1), - normalize_scores=(not getattr(args, "unnormalized", False)), - len_penalty=getattr(args, "lenpen", 1), - unk_penalty=getattr(args, "unkpen", 0), - temperature=getattr(args, "temperature", 1.0), - match_source_len=getattr(args, "match_source_len", False), - no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), - search_strategy=search_strategy, - **extra_gen_cls_kwargs, - ) - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False, **extra_kwargs - ): - """ - Do forward and backward, and return the loss as computed by *criterion* - for the given *model* and *sample*. - - Args: - sample (dict): the mini-batch. The format is defined by the - :class:`~fairseq.data.FairseqDataset`. - model (~fairseq.models.BaseFairseqModel): the model - criterion (~fairseq.criterions.FairseqCriterion): the criterion - optimizer (~fairseq.optim.FairseqOptimizer): the optimizer - update_num (int): the current update - ignore_grad (bool): multiply loss by 0 if this is set to True - - Returns: - tuple: - - the loss - - the sample size, which is used as the denominator for the - gradient - - logging outputs to display while training - """ - model.train() - model.set_num_updates(update_num) - with torch.autograd.profiler.record_function("forward"): - with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): - loss, sample_size, logging_output = criterion(model, sample) - if ignore_grad: - loss *= 0 - with torch.autograd.profiler.record_function("backward"): - optimizer.backward(loss) - return loss, sample_size, logging_output - - def valid_step(self, sample, model, criterion, **extra_kwargs): - model.eval() - with torch.no_grad(): - loss, sample_size, logging_output = criterion(model, sample) - return loss, sample_size, logging_output - - def optimizer_step(self, optimizer, model, update_num): - optimizer.step() - - def build_dataset_for_inference( - self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs - ) -> torch.utils.data.Dataset: - raise NotImplementedError - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - return generator.generate( - models, sample, prefix_tokens=prefix_tokens, constraints=constraints - ) - - def begin_epoch(self, epoch, model): - """Hook function called before the start of each epoch.""" - pass - - def begin_valid_epoch(self, epoch, model): - """Hook function called before the start of each validation epoch.""" - pass - - def aggregate_logging_outputs(self, logging_outputs, criterion): - """[deprecated] Aggregate logging outputs from data parallel training.""" - utils.deprecation_warning( - "The aggregate_logging_outputs API is deprecated. " - "Please use the reduce_metrics API instead." - ) - with metrics.aggregate() as agg: - self.reduce_metrics(logging_outputs, criterion) - return agg.get_smoothed_values() - - def reduce_metrics(self, logging_outputs, criterion): - """Aggregate logging outputs from data parallel training.""" - # backward compatibility for tasks that override aggregate_logging_outputs - base_func = FairseqTask.aggregate_logging_outputs - self_func = getattr(self, "aggregate_logging_outputs").__func__ - if self_func is not base_func: - utils.deprecation_warning( - "Tasks should implement the reduce_metrics API. " - "Falling back to deprecated aggregate_logging_outputs API." - ) - agg_logging_outputs = self.aggregate_logging_outputs( - logging_outputs, criterion - ) - for k, v in agg_logging_outputs.items(): - metrics.log_scalar(k, v) - return - - if not any("ntokens" in log for log in logging_outputs): - warnings.warn( - "ntokens not found in Criterion logging outputs, cannot log wpb or wps" - ) - else: - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - metrics.log_scalar("wpb", ntokens, priority=180, round=1) - metrics.log_speed("wps", ntokens, priority=90, round=1) - - if not any("nsentences" in log for log in logging_outputs): - warnings.warn( - "nsentences not found in Criterion logging outputs, cannot log bsz" - ) - else: - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - metrics.log_scalar("bsz", nsentences, priority=190, round=1) - - criterion.__class__.reduce_metrics(logging_outputs) - - def state_dict(self): - if self.state is not None: - return self.state.state_dict - return {} - - def load_state_dict(self, state_dict: Dict[str, Any]): - if self.state is not None: - self.state.merge_state_dict(state_dict) - - def max_positions(self): - """Return the max input length allowed by the task.""" - return None - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary` (if applicable - for this task).""" - raise NotImplementedError - - @property - def target_dictionary(self): - """Return the target :class:`~fairseq.data.Dictionary` (if applicable - for this task).""" - raise NotImplementedError - - def build_tokenizer(self, args): - """Build the pre-tokenizer for this task.""" - return encoders.build_tokenizer(args) - - def build_bpe(self, args): - """Build the tokenizer for this task.""" - return encoders.build_bpe(args) - - def get_interactive_tokens_and_lengths(self, lines, encode_fn): - tokens = [ - self.source_dictionary.encode_line( - encode_fn(src_str), add_if_not_exist=False - ).long() - for src_str in lines - ] - lengths = [t.numel() for t in tokens] - return tokens, lengths - - -class LegacyFairseqTask(FairseqTask): - def __init__(self, args: Namespace): - super().__init__(None) - self.args = args - self.datasets = {} - self.dataset_to_epoch_iter = {} - - @classmethod - def setup_task(cls, args: Namespace, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - return cls(args, **kwargs) - - def has_sharded_data(self, split): - return os.pathsep in getattr(self.args, "data", "") - - def build_model(self, args: Namespace): - """ - Build the :class:`~fairseq.models.BaseFairseqModel` instance for this - task. - - Args: - args (argparse.Namespace): parsed command-line arguments - - Returns: - a :class:`~fairseq.models.BaseFairseqModel` instance - """ - from fairseq import models, quantization_utils - - model = models.build_model(args, self) - model = quantization_utils.quantize_model_scalar(model, args) - return model - - def build_criterion(self, args: Namespace): - """ - Build the :class:`~fairseq.criterions.FairseqCriterion` instance for - this task. - - Args: - args (argparse.Namespace): parsed command-line arguments - - Returns: - a :class:`~fairseq.criterions.FairseqCriterion` instance - """ - from fairseq import criterions - - return criterions.build_criterion(args, self) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/feature_transforms/utterance_cmvn.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/feature_transforms/utterance_cmvn.py deleted file mode 100644 index 6bbd0ae821b42ab693f4141e7c161d6d7cb0b15a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/feature_transforms/utterance_cmvn.py +++ /dev/null @@ -1,40 +0,0 @@ -import numpy as np -from fairseq.data.audio.feature_transforms import ( - AudioFeatureTransform, - register_audio_feature_transform, -) - - -@register_audio_feature_transform("utterance_cmvn") -class UtteranceCMVN(AudioFeatureTransform): - """Utterance-level CMVN (cepstral mean and variance normalization)""" - - @classmethod - def from_config_dict(cls, config=None): - _config = {} if config is None else config - return UtteranceCMVN( - _config.get("norm_means", True), - _config.get("norm_vars", True), - ) - - def __init__(self, norm_means=True, norm_vars=True): - self.norm_means, self.norm_vars = norm_means, norm_vars - - def __repr__(self): - return ( - self.__class__.__name__ - + f"(norm_means={self.norm_means}, norm_vars={self.norm_vars})" - ) - - def __call__(self, x): - mean = x.mean(axis=0) - square_sums = (x ** 2).sum(axis=0) - - if self.norm_means: - x = np.subtract(x, mean) - if self.norm_vars: - var = square_sums / x.shape[0] - mean ** 2 - std = np.sqrt(np.maximum(var, 1e-10)) - x = np.divide(x, std) - - return x diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/linearized_convolution.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/linearized_convolution.py deleted file mode 100644 index f7e156cb0c75cb375447859c8b6749311372c35e..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/linearized_convolution.py +++ /dev/null @@ -1,110 +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.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state - -from .conv_tbc import ConvTBC - -from typing import Dict, Optional -from torch import Tensor - -@with_incremental_state -class LinearizedConvolution(ConvTBC): - """An optimized version of nn.Conv1d. - - At training time, this module uses ConvTBC, which is an optimized version - of Conv1d. At inference time, it optimizes incremental generation (i.e., - one time step at a time) by replacing the convolutions with linear layers. - Note that the input order changes from training to inference. - """ - - def __init__(self, in_channels, out_channels, kernel_size, **kwargs): - super().__init__(in_channels, out_channels, kernel_size, **kwargs) - self._linearized_weight = None - self.register_backward_hook(self._clear_linearized_weight) - - def state_dict(self, destination=None, prefix="", keep_vars=False): - state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars) - # don't store redundant _linearized_weight in checkpoints - if prefix + "_linearized_weight" in state: - del state[prefix + "_linearized_weight"] - return state - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - if prefix + "_linearized_weight" in state_dict: - del state_dict[prefix + "_linearized_weight"] - - @torch.jit.export - def forward(self, input, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None): - """ - Args: - incremental_state: Used to buffer signal; if not None, then input is - expected to contain a single frame. If the input order changes - between time steps, call reorder_incremental_state. - Input: - Time x Batch x Channel during training - Batch x Time x Channel during inference - """ - if incremental_state is None: - output = self.conv_tbc(input) - if self.kernel_size[0] > 1 and self.padding[0] > 0: - # remove future timesteps added by padding - output = output[: -self.padding[0], :, :] - return output - - # reshape weight - weight = self._get_linearized_weight() - kw = self.kernel_size[0] - - bsz = input.size(0) # input: bsz x len x dim - if kw > 1: - input = input.data - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = input.new(bsz, kw, input.size(2)).zero_() - self._set_input_buffer(incremental_state, input_buffer) - else: - # shift buffer - input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone() - # append next input - input_buffer[:, -1, :] = input[:, -1, :] - input = input_buffer - with torch.no_grad(): - output = F.linear(input.view(bsz, -1), weight, self.bias) - return output.view(bsz, 1, -1) - - @torch.jit.unused - def reorder_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(0, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - @torch.jit.unused - def _get_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - @torch.jit.unused - def _set_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - @torch.jit.unused - def _get_linearized_weight(self): - if self._linearized_weight is None: - kw = self.kernel_size[0] - weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() - assert weight.size() == (self.out_channels, kw, self.in_channels) - return weight.view(self.out_channels, -1) - return self._linearized_weight - - @torch.jit.unused - def _clear_linearized_weight(self, *args): - self._linearized_weight = None diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/options.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/options.py deleted file mode 100644 index 797b2842db4a68849110a25bb52a47c658966186..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/options.py +++ /dev/null @@ -1,406 +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 argparse -from pathlib import Path -from typing import Callable, List, Optional, Union - -import torch -from fairseq import utils -from fairseq.data.indexed_dataset import get_available_dataset_impl -from fairseq.dataclass.configs import ( - CheckpointConfig, - CommonConfig, - CommonEvalConfig, - DatasetConfig, - DistributedTrainingConfig, - EvalLMConfig, - GenerationConfig, - InteractiveConfig, - OptimizationConfig, - EMAConfig, -) -from fairseq.dataclass.utils import gen_parser_from_dataclass - -# this import is for backward compatibility -from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list # noqa - - -def get_preprocessing_parser(default_task="translation"): - parser = get_parser("Preprocessing", default_task) - add_preprocess_args(parser) - return parser - - -def get_training_parser(default_task="translation"): - parser = get_parser("Trainer", default_task) - add_dataset_args(parser, train=True) - add_distributed_training_args(parser) - add_model_args(parser) - add_optimization_args(parser) - add_checkpoint_args(parser) - add_ema_args(parser) - return parser - - -def get_generation_parser(interactive=False, default_task="translation"): - parser = get_parser("Generation", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser) - add_generation_args(parser) - add_checkpoint_args(parser) - if interactive: - add_interactive_args(parser) - return parser - - -def get_speech_generation_parser(default_task="text_to_speech"): - parser = get_parser("Speech Generation", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser, default_world_size=1) - add_speech_generation_args(parser) - return parser - - -def get_interactive_generation_parser(default_task="translation"): - return get_generation_parser(interactive=True, default_task=default_task) - - -def get_eval_lm_parser(default_task="language_modeling"): - parser = get_parser("Evaluate Language Model", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser, default_world_size=1) - add_eval_lm_args(parser) - return parser - - -def get_validation_parser(default_task=None): - parser = get_parser("Validation", default_task) - add_dataset_args(parser, train=True) - add_distributed_training_args(parser, default_world_size=1) - group = parser.add_argument_group("Evaluation") - gen_parser_from_dataclass(group, CommonEvalConfig()) - return parser - - -def parse_args_and_arch( - parser: argparse.ArgumentParser, - input_args: List[str] = None, - parse_known: bool = False, - suppress_defaults: bool = False, - modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None, -): - """ - Args: - parser (ArgumentParser): the parser - input_args (List[str]): strings to parse, defaults to sys.argv - parse_known (bool): only parse known arguments, similar to - `ArgumentParser.parse_known_args` - suppress_defaults (bool): parse while ignoring all default values - modify_parser (Optional[Callable[[ArgumentParser], None]]): - function to modify the parser, e.g., to set default values - """ - if suppress_defaults: - # Parse args without any default values. This requires us to parse - # twice, once to identify all the necessary task/model args, and a second - # time with all defaults set to None. - args = parse_args_and_arch( - parser, - input_args=input_args, - parse_known=parse_known, - suppress_defaults=False, - ) - suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser]) - suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()}) - args = suppressed_parser.parse_args(input_args) - return argparse.Namespace( - **{k: v for k, v in vars(args).items() if v is not None} - ) - - from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY - - # Before creating the true parser, we need to import optional user module - # in order to eagerly import custom tasks, optimizers, architectures, etc. - usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) - usr_parser.add_argument("--user-dir", default=None) - usr_args, _ = usr_parser.parse_known_args(input_args) - utils.import_user_module(usr_args) - - if modify_parser is not None: - modify_parser(parser) - - # The parser doesn't know about model/criterion/optimizer-specific args, so - # we parse twice. First we parse the model/criterion/optimizer, then we - # parse a second time after adding the *-specific arguments. - # If input_args is given, we will parse those args instead of sys.argv. - args, _ = parser.parse_known_args(input_args) - - # Add model-specific args to parser. - if hasattr(args, "arch"): - model_specific_group = parser.add_argument_group( - "Model-specific configuration", - # Only include attributes which are explicitly given as command-line - # arguments or which have default values. - argument_default=argparse.SUPPRESS, - ) - if args.arch in ARCH_MODEL_REGISTRY: - ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group) - elif args.arch in MODEL_REGISTRY: - MODEL_REGISTRY[args.arch].add_args(model_specific_group) - else: - raise RuntimeError() - - if hasattr(args, "task"): - from fairseq.tasks import TASK_REGISTRY - - TASK_REGISTRY[args.task].add_args(parser) - if getattr(args, "use_bmuf", False): - # hack to support extra args for block distributed data parallelism - from fairseq.optim.bmuf import FairseqBMUF - - FairseqBMUF.add_args(parser) - - # Add *-specific args to parser. - from fairseq.registry import REGISTRIES - - for registry_name, REGISTRY in REGISTRIES.items(): - choice = getattr(args, registry_name, None) - if choice is not None: - cls = REGISTRY["registry"][choice] - if hasattr(cls, "add_args"): - cls.add_args(parser) - elif hasattr(cls, "__dataclass"): - gen_parser_from_dataclass(parser, cls.__dataclass()) - - # Modify the parser a second time, since defaults may have been reset - if modify_parser is not None: - modify_parser(parser) - - # Parse a second time. - if parse_known: - args, extra = parser.parse_known_args(input_args) - else: - args = parser.parse_args(input_args) - extra = None - # Post-process args. - if ( - hasattr(args, "batch_size_valid") and args.batch_size_valid is None - ) or not hasattr(args, "batch_size_valid"): - args.batch_size_valid = args.batch_size - if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None: - args.max_tokens_valid = args.max_tokens - if getattr(args, "memory_efficient_fp16", False): - args.fp16 = True - if getattr(args, "memory_efficient_bf16", False): - args.bf16 = True - args.tpu = getattr(args, "tpu", False) - args.bf16 = getattr(args, "bf16", False) - if args.bf16: - args.tpu = True - if args.tpu and args.fp16: - raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs") - - if getattr(args, "seed", None) is None: - args.seed = 1 # default seed for training - args.no_seed_provided = True - else: - args.no_seed_provided = False - - # Apply architecture configuration. - if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY: - ARCH_CONFIG_REGISTRY[args.arch](args) - - if parse_known: - return args, extra - else: - return args - - -def get_parser(desc, default_task="translation"): - # Before creating the true parser, we need to import optional user module - # in order to eagerly import custom tasks, optimizers, architectures, etc. - usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) - usr_parser.add_argument("--user-dir", default=None) - usr_args, _ = usr_parser.parse_known_args() - utils.import_user_module(usr_args) - - parser = argparse.ArgumentParser(allow_abbrev=False) - gen_parser_from_dataclass(parser, CommonConfig()) - - from fairseq.registry import REGISTRIES - - for registry_name, REGISTRY in REGISTRIES.items(): - parser.add_argument( - "--" + registry_name.replace("_", "-"), - default=REGISTRY["default"], - choices=REGISTRY["registry"].keys(), - ) - - # Task definitions can be found under fairseq/tasks/ - from fairseq.tasks import TASK_REGISTRY - - parser.add_argument( - "--task", - metavar="TASK", - default=default_task, - choices=TASK_REGISTRY.keys(), - help="task", - ) - # fmt: on - return parser - - -def add_preprocess_args(parser): - group = parser.add_argument_group("Preprocessing") - # fmt: off - group.add_argument("-s", "--source-lang", default=None, metavar="SRC", - help="source language") - group.add_argument("-t", "--target-lang", default=None, metavar="TARGET", - help="target language") - group.add_argument("--trainpref", metavar="FP", default=None, - help="train file prefix (also used to build dictionaries)") - group.add_argument("--validpref", metavar="FP", default=None, - help="comma separated, valid file prefixes " - "(words missing from train set are replaced with )") - group.add_argument("--testpref", metavar="FP", default=None, - help="comma separated, test file prefixes " - "(words missing from train set are replaced with )") - group.add_argument("--align-suffix", metavar="FP", default=None, - help="alignment file suffix") - group.add_argument("--destdir", metavar="DIR", default="data-bin", - help="destination dir") - group.add_argument("--thresholdtgt", metavar="N", default=0, type=int, - help="map words appearing less than threshold times to unknown") - group.add_argument("--thresholdsrc", metavar="N", default=0, type=int, - help="map words appearing less than threshold times to unknown") - group.add_argument("--tgtdict", metavar="FP", - help="reuse given target dictionary") - group.add_argument("--srcdict", metavar="FP", - help="reuse given source dictionary") - group.add_argument("--nwordstgt", metavar="N", default=-1, type=int, - help="number of target words to retain") - group.add_argument("--nwordssrc", metavar="N", default=-1, type=int, - help="number of source words to retain") - group.add_argument("--alignfile", metavar="ALIGN", default=None, - help="an alignment file (optional)") - parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap', - choices=get_available_dataset_impl(), - help='output dataset implementation') - group.add_argument("--joined-dictionary", action="store_true", - help="Generate joined dictionary") - group.add_argument("--only-source", action="store_true", - help="Only process the source language") - group.add_argument("--padding-factor", metavar="N", default=8, type=int, - help="Pad dictionary size to be multiple of N") - group.add_argument("--workers", metavar="N", default=1, type=int, - help="number of parallel workers") - group.add_argument("--dict-only", action='store_true', - help="if true, only builds a dictionary and then exits") - # fmt: on - return parser - - -def add_dataset_args(parser, train=False, gen=False): - group = parser.add_argument_group("dataset_data_loading") - gen_parser_from_dataclass(group, DatasetConfig()) - # fmt: on - return group - - -def add_distributed_training_args(parser, default_world_size=None): - group = parser.add_argument_group("distributed_training") - if default_world_size is None: - default_world_size = max(1, torch.cuda.device_count()) - gen_parser_from_dataclass( - group, DistributedTrainingConfig(distributed_world_size=default_world_size) - ) - return group - - -def add_optimization_args(parser): - group = parser.add_argument_group("optimization") - # fmt: off - gen_parser_from_dataclass(group, OptimizationConfig()) - # fmt: on - return group - - -def add_checkpoint_args(parser): - group = parser.add_argument_group("checkpoint") - # fmt: off - gen_parser_from_dataclass(group, CheckpointConfig()) - # fmt: on - return group - - -def add_common_eval_args(group): - gen_parser_from_dataclass(group, CommonEvalConfig()) - - -def add_eval_lm_args(parser): - group = parser.add_argument_group("LM Evaluation") - add_common_eval_args(group) - gen_parser_from_dataclass(group, EvalLMConfig()) - - -def add_generation_args(parser): - group = parser.add_argument_group("Generation") - add_common_eval_args(group) - gen_parser_from_dataclass(group, GenerationConfig()) - return group - - -def add_speech_generation_args(parser): - group = parser.add_argument_group("Speech Generation") - add_common_eval_args(group) # NOTE: remove_bpe is not needed - # fmt: off - group.add_argument('--eos_prob_threshold', default=0.5, type=float, - help='terminate when eos probability exceeds this') - # fmt: on - return group - - -def add_interactive_args(parser): - group = parser.add_argument_group("Interactive") - gen_parser_from_dataclass(group, InteractiveConfig()) - - -def add_model_args(parser): - group = parser.add_argument_group("Model configuration") - # fmt: off - - # Model definitions can be found under fairseq/models/ - # - # The model architecture can be specified in several ways. - # In increasing order of priority: - # 1) model defaults (lowest priority) - # 2) --arch argument - # 3) --encoder/decoder-* arguments (highest priority) - from fairseq.models import ARCH_MODEL_REGISTRY - group.add_argument('--arch', '-a', metavar='ARCH', - choices=ARCH_MODEL_REGISTRY.keys(), - help='model architecture') - # fmt: on - return group - - -def get_args( - data: Union[str, Path], - task: str = "translation", - arch: str = "transformer", - **overrides -): - parser = get_training_parser(task) - args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch]) - - for k, v in overrides.items(): - setattr(args, k, v) - - return args - - -def add_ema_args(parser): - group = parser.add_argument_group("EMA configuration") - gen_parser_from_dataclass(group, EMAConfig()) diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py deleted file mode 100644 index de44b991d7ab0d920ffb769e1402f08e358d37f7..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/transform.py +++ /dev/null @@ -1,351 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -""" -See "Data Augmentation" tutorial for an overview of the system: -https://detectron2.readthedocs.io/tutorials/augmentation.html -""" - -import numpy as np -import torch -import torch.nn.functional as F -from fvcore.transforms.transform import ( - CropTransform, - HFlipTransform, - NoOpTransform, - Transform, - TransformList, -) -from PIL import Image - -try: - import cv2 # noqa -except ImportError: - # OpenCV is an optional dependency at the moment - pass - -__all__ = [ - "ExtentTransform", - "ResizeTransform", - "RotationTransform", - "ColorTransform", - "PILColorTransform", -] - - -class ExtentTransform(Transform): - """ - Extracts a subregion from the source image and scales it to the output size. - - The fill color is used to map pixels from the source rect that fall outside - the source image. - - See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform - """ - - def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0): - """ - Args: - src_rect (x0, y0, x1, y1): src coordinates - output_size (h, w): dst image size - interp: PIL interpolation methods - fill: Fill color used when src_rect extends outside image - """ - super().__init__() - self._set_attributes(locals()) - - def apply_image(self, img, interp=None): - h, w = self.output_size - if len(img.shape) > 2 and img.shape[2] == 1: - pil_image = Image.fromarray(img[:, :, 0], mode="L") - else: - pil_image = Image.fromarray(img) - pil_image = pil_image.transform( - size=(w, h), - method=Image.EXTENT, - data=self.src_rect, - resample=interp if interp else self.interp, - fill=self.fill, - ) - ret = np.asarray(pil_image) - if len(img.shape) > 2 and img.shape[2] == 1: - ret = np.expand_dims(ret, -1) - return ret - - def apply_coords(self, coords): - # Transform image center from source coordinates into output coordinates - # and then map the new origin to the corner of the output image. - h, w = self.output_size - x0, y0, x1, y1 = self.src_rect - new_coords = coords.astype(np.float32) - new_coords[:, 0] -= 0.5 * (x0 + x1) - new_coords[:, 1] -= 0.5 * (y0 + y1) - new_coords[:, 0] *= w / (x1 - x0) - new_coords[:, 1] *= h / (y1 - y0) - new_coords[:, 0] += 0.5 * w - new_coords[:, 1] += 0.5 * h - return new_coords - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=Image.NEAREST) - return segmentation - - -class ResizeTransform(Transform): - """ - Resize the image to a target size. - """ - - def __init__(self, h, w, new_h, new_w, interp=None): - """ - Args: - h, w (int): original image size - new_h, new_w (int): new image size - interp: PIL interpolation methods, defaults to bilinear. - """ - # TODO decide on PIL vs opencv - super().__init__() - if interp is None: - interp = Image.BILINEAR - self._set_attributes(locals()) - - def apply_image(self, img, interp=None): - assert img.shape[:2] == (self.h, self.w) - assert len(img.shape) <= 4 - interp_method = interp if interp is not None else self.interp - - if img.dtype == np.uint8: - if len(img.shape) > 2 and img.shape[2] == 1: - pil_image = Image.fromarray(img[:, :, 0], mode="L") - else: - pil_image = Image.fromarray(img) - pil_image = pil_image.resize((self.new_w, self.new_h), interp_method) - ret = np.asarray(pil_image) - if len(img.shape) > 2 and img.shape[2] == 1: - ret = np.expand_dims(ret, -1) - else: - # PIL only supports uint8 - if any(x < 0 for x in img.strides): - img = np.ascontiguousarray(img) - img = torch.from_numpy(img) - shape = list(img.shape) - shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:] - img = img.view(shape_4d).permute(2, 3, 0, 1) # hw(c) -> nchw - _PIL_RESIZE_TO_INTERPOLATE_MODE = { - Image.NEAREST: "nearest", - Image.BILINEAR: "bilinear", - Image.BICUBIC: "bicubic", - } - mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method] - align_corners = None if mode == "nearest" else False - img = F.interpolate( - img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners - ) - shape[:2] = (self.new_h, self.new_w) - ret = img.permute(2, 3, 0, 1).view(shape).numpy() # nchw -> hw(c) - - return ret - - def apply_coords(self, coords): - coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w) - coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h) - return coords - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=Image.NEAREST) - return segmentation - - def inverse(self): - return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp) - - -class RotationTransform(Transform): - """ - This method returns a copy of this image, rotated the given - number of degrees counter clockwise around its center. - """ - - def __init__(self, h, w, angle, expand=True, center=None, interp=None): - """ - Args: - h, w (int): original image size - angle (float): degrees for rotation - expand (bool): choose if the image should be resized to fit the whole - rotated image (default), or simply cropped - center (tuple (width, height)): coordinates of the rotation center - if left to None, the center will be fit to the center of each image - center has no effect if expand=True because it only affects shifting - interp: cv2 interpolation method, default cv2.INTER_LINEAR - """ - super().__init__() - image_center = np.array((w / 2, h / 2)) - if center is None: - center = image_center - if interp is None: - interp = cv2.INTER_LINEAR - abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle)))) - if expand: - # find the new width and height bounds - bound_w, bound_h = np.rint( - [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin] - ).astype(int) - else: - bound_w, bound_h = w, h - - self._set_attributes(locals()) - self.rm_coords = self.create_rotation_matrix() - # Needed because of this problem https://github.com/opencv/opencv/issues/11784 - self.rm_image = self.create_rotation_matrix(offset=-0.5) - - def apply_image(self, img, interp=None): - """ - img should be a numpy array, formatted as Height * Width * Nchannels - """ - if len(img) == 0 or self.angle % 360 == 0: - return img - assert img.shape[:2] == (self.h, self.w) - interp = interp if interp is not None else self.interp - return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp) - - def apply_coords(self, coords): - """ - coords should be a N * 2 array-like, containing N couples of (x, y) points - """ - coords = np.asarray(coords, dtype=float) - if len(coords) == 0 or self.angle % 360 == 0: - return coords - return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :] - - def apply_segmentation(self, segmentation): - segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST) - return segmentation - - def create_rotation_matrix(self, offset=0): - center = (self.center[0] + offset, self.center[1] + offset) - rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1) - if self.expand: - # Find the coordinates of the center of rotation in the new image - # The only point for which we know the future coordinates is the center of the image - rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :] - new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center - # shift the rotation center to the new coordinates - rm[:, 2] += new_center - return rm - - def inverse(self): - """ - The inverse is to rotate it back with expand, and crop to get the original shape. - """ - if not self.expand: # Not possible to inverse if a part of the image is lost - raise NotImplementedError() - rotation = RotationTransform( - self.bound_h, self.bound_w, -self.angle, True, None, self.interp - ) - crop = CropTransform( - (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h - ) - return TransformList([rotation, crop]) - - -class ColorTransform(Transform): - """ - Generic wrapper for any photometric transforms. - These transformations should only affect the color space and - not the coordinate space of the image (e.g. annotation - coordinates such as bounding boxes should not be changed) - """ - - def __init__(self, op): - """ - Args: - op (Callable): operation to be applied to the image, - which takes in an ndarray and returns an ndarray. - """ - if not callable(op): - raise ValueError("op parameter should be callable") - super().__init__() - self._set_attributes(locals()) - - def apply_image(self, img): - return self.op(img) - - def apply_coords(self, coords): - return coords - - def inverse(self): - return NoOpTransform() - - def apply_segmentation(self, segmentation): - return segmentation - - -class PILColorTransform(ColorTransform): - """ - Generic wrapper for PIL Photometric image transforms, - which affect the color space and not the coordinate - space of the image - """ - - def __init__(self, op): - """ - Args: - op (Callable): operation to be applied to the image, - which takes in a PIL Image and returns a transformed - PIL Image. - For reference on possible operations see: - - https://pillow.readthedocs.io/en/stable/ - """ - if not callable(op): - raise ValueError("op parameter should be callable") - super().__init__(op) - - def apply_image(self, img): - img = Image.fromarray(img) - return np.asarray(super().apply_image(img)) - - -def HFlip_rotated_box(transform, rotated_boxes): - """ - Apply the horizontal flip transform on rotated boxes. - - Args: - rotated_boxes (ndarray): Nx5 floating point array of - (x_center, y_center, width, height, angle_degrees) format - in absolute coordinates. - """ - # Transform x_center - rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0] - # Transform angle - rotated_boxes[:, 4] = -rotated_boxes[:, 4] - return rotated_boxes - - -def Resize_rotated_box(transform, rotated_boxes): - """ - Apply the resizing transform on rotated boxes. For details of how these (approximation) - formulas are derived, please refer to :meth:`RotatedBoxes.scale`. - - Args: - rotated_boxes (ndarray): Nx5 floating point array of - (x_center, y_center, width, height, angle_degrees) format - in absolute coordinates. - """ - scale_factor_x = transform.new_w * 1.0 / transform.w - scale_factor_y = transform.new_h * 1.0 / transform.h - rotated_boxes[:, 0] *= scale_factor_x - rotated_boxes[:, 1] *= scale_factor_y - theta = rotated_boxes[:, 4] * np.pi / 180.0 - c = np.cos(theta) - s = np.sin(theta) - rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s)) - rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c)) - rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi - - return rotated_boxes - - -HFlipTransform.register_type("rotated_box", HFlip_rotated_box) -ResizeTransform.register_type("rotated_box", Resize_rotated_box) - -# not necessary any more with latest fvcore -NoOpTransform.register_type("rotated_box", lambda t, x: x) diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py deleted file mode 100644 index 25ee23009547913733dc528fb8a39ca995fd9e31..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py +++ /dev/null @@ -1,534 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import math -import torch -import torch.nn.functional as F - -from detectron2.layers import cat -from detectron2.layers.roi_align_rotated import ROIAlignRotated -from detectron2.modeling import poolers -from detectron2.modeling.proposal_generator import rpn -from detectron2.modeling.roi_heads.mask_head import mask_rcnn_inference -from detectron2.structures import Boxes, ImageList, Instances, Keypoints - -from .shared import alias, to_device - - -""" -This file contains caffe2-compatible implementation of several detectron2 components. -""" - - -class Caffe2Boxes(Boxes): - """ - Representing a list of detectron2.structures.Boxes from minibatch, each box - is represented by a 5d vector (batch index + 4 coordinates), or a 6d vector - (batch index + 5 coordinates) for RotatedBoxes. - """ - - def __init__(self, tensor): - assert isinstance(tensor, torch.Tensor) - assert tensor.dim() == 2 and tensor.size(-1) in [4, 5, 6], tensor.size() - # TODO: make tensor immutable when dim is Nx5 for Boxes, - # and Nx6 for RotatedBoxes? - self.tensor = tensor - - -# TODO clean up this class, maybe just extend Instances -class InstancesList(object): - """ - Tensor representation of a list of Instances object for a batch of images. - - When dealing with a batch of images with Caffe2 ops, a list of bboxes - (instances) are usually represented by single Tensor with size - (sigma(Ni), 5) or (sigma(Ni), 4) plus a batch split Tensor. This class is - for providing common functions to convert between these two representations. - """ - - def __init__(self, im_info, indices, extra_fields=None): - # [N, 3] -> (H, W, Scale) - self.im_info = im_info - # [N,] -> indice of batch to which the instance belongs - self.indices = indices - # [N, ...] - self.batch_extra_fields = extra_fields or {} - - self.image_size = self.im_info - - def get_fields(self): - """like `get_fields` in the Instances object, - but return each field in tensor representations""" - ret = {} - for k, v in self.batch_extra_fields.items(): - # if isinstance(v, torch.Tensor): - # tensor_rep = v - # elif isinstance(v, (Boxes, Keypoints)): - # tensor_rep = v.tensor - # else: - # raise ValueError("Can't find tensor representation for: {}".format()) - ret[k] = v - return ret - - def has(self, name): - return name in self.batch_extra_fields - - def set(self, name, value): - data_len = len(value) - if len(self.batch_extra_fields): - assert ( - len(self) == data_len - ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self)) - self.batch_extra_fields[name] = value - - def __setattr__(self, name, val): - if name in ["im_info", "indices", "batch_extra_fields", "image_size"]: - super().__setattr__(name, val) - else: - self.set(name, val) - - def __getattr__(self, name): - if name not in self.batch_extra_fields: - raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) - return self.batch_extra_fields[name] - - def __len__(self): - return len(self.indices) - - def flatten(self): - ret = [] - for _, v in self.batch_extra_fields.items(): - if isinstance(v, (Boxes, Keypoints)): - ret.append(v.tensor) - else: - ret.append(v) - return ret - - @staticmethod - def to_d2_instances_list(instances_list): - """ - Convert InstancesList to List[Instances]. The input `instances_list` can - also be a List[Instances], in this case this method is a non-op. - """ - if not isinstance(instances_list, InstancesList): - assert all(isinstance(x, Instances) for x in instances_list) - return instances_list - - ret = [] - for i, info in enumerate(instances_list.im_info): - instances = Instances(torch.Size([int(info[0].item()), int(info[1].item())])) - - ids = instances_list.indices == i - for k, v in instances_list.batch_extra_fields.items(): - if isinstance(v, torch.Tensor): - instances.set(k, v[ids]) - continue - elif isinstance(v, Boxes): - instances.set(k, v[ids, -4:]) - continue - - target_type, tensor_source = v - assert isinstance(tensor_source, torch.Tensor) - assert tensor_source.shape[0] == instances_list.indices.shape[0] - tensor_source = tensor_source[ids] - - if issubclass(target_type, Boxes): - instances.set(k, Boxes(tensor_source[:, -4:])) - elif issubclass(target_type, Keypoints): - instances.set(k, Keypoints(tensor_source)) - elif issubclass(target_type, torch.Tensor): - instances.set(k, tensor_source) - else: - raise ValueError("Can't handle targe type: {}".format(target_type)) - - ret.append(instances) - return ret - - -class Caffe2Compatible(object): - """ - A model can inherit this class to indicate that it can be traced and deployed with caffe2. - """ - - def _get_tensor_mode(self): - return self._tensor_mode - - def _set_tensor_mode(self, v): - self._tensor_mode = v - - tensor_mode = property(_get_tensor_mode, _set_tensor_mode) - """ - If true, the model expects C2-style tensor only inputs/outputs format. - """ - - -class Caffe2RPN(Caffe2Compatible, rpn.RPN): - def _generate_proposals( - self, images, objectness_logits_pred, anchor_deltas_pred, gt_instances=None - ): - assert isinstance(images, ImageList) - if self.tensor_mode: - im_info = images.image_sizes - else: - im_info = torch.tensor([[im_sz[0], im_sz[1], 1.0] for im_sz in images.image_sizes]).to( - images.tensor.device - ) - assert isinstance(im_info, torch.Tensor) - - rpn_rois_list = [] - rpn_roi_probs_list = [] - for scores, bbox_deltas, cell_anchors_tensor, feat_stride in zip( - objectness_logits_pred, - anchor_deltas_pred, - iter(self.anchor_generator.cell_anchors), - self.anchor_generator.strides, - ): - scores = scores.detach() - bbox_deltas = bbox_deltas.detach() - - rpn_rois, rpn_roi_probs = torch.ops._caffe2.GenerateProposals( - scores, - bbox_deltas, - im_info, - cell_anchors_tensor, - spatial_scale=1.0 / feat_stride, - pre_nms_topN=self.pre_nms_topk[self.training], - post_nms_topN=self.post_nms_topk[self.training], - nms_thresh=self.nms_thresh, - min_size=self.min_box_size, - # correct_transform_coords=True, # deprecated argument - angle_bound_on=True, # Default - angle_bound_lo=-180, - angle_bound_hi=180, - clip_angle_thresh=1.0, # Default - legacy_plus_one=False, - ) - rpn_rois_list.append(rpn_rois) - rpn_roi_probs_list.append(rpn_roi_probs) - - # For FPN in D2, in RPN all proposals from different levels are concated - # together, ranked and picked by top post_nms_topk. Then in ROIPooler - # it calculates level_assignments and calls the RoIAlign from - # the corresponding level. - - if len(objectness_logits_pred) == 1: - rpn_rois = rpn_rois_list[0] - rpn_roi_probs = rpn_roi_probs_list[0] - else: - assert len(rpn_rois_list) == len(rpn_roi_probs_list) - rpn_post_nms_topN = self.post_nms_topk[self.training] - - device = rpn_rois_list[0].device - input_list = [to_device(x, "cpu") for x in (rpn_rois_list + rpn_roi_probs_list)] - - # TODO remove this after confirming rpn_max_level/rpn_min_level - # is not needed in CollectRpnProposals. - feature_strides = list(self.anchor_generator.strides) - rpn_min_level = int(math.log2(feature_strides[0])) - rpn_max_level = int(math.log2(feature_strides[-1])) - assert (rpn_max_level - rpn_min_level + 1) == len( - rpn_rois_list - ), "CollectRpnProposals requires continuous levels" - - rpn_rois = torch.ops._caffe2.CollectRpnProposals( - input_list, - # NOTE: in current implementation, rpn_max_level and rpn_min_level - # are not needed, only the subtraction of two matters and it - # can be infer from the number of inputs. Keep them now for - # consistency. - rpn_max_level=2 + len(rpn_rois_list) - 1, - rpn_min_level=2, - rpn_post_nms_topN=rpn_post_nms_topN, - ) - rpn_rois = to_device(rpn_rois, device) - rpn_roi_probs = [] - - proposals = self.c2_postprocess(im_info, rpn_rois, rpn_roi_probs, self.tensor_mode) - return proposals, {} - - def forward(self, images, features, gt_instances=None): - assert not self.training - features = [features[f] for f in self.in_features] - objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features) - return self._generate_proposals( - images, - objectness_logits_pred, - anchor_deltas_pred, - gt_instances, - ) - - @staticmethod - def c2_postprocess(im_info, rpn_rois, rpn_roi_probs, tensor_mode): - proposals = InstancesList( - im_info=im_info, - indices=rpn_rois[:, 0], - extra_fields={ - "proposal_boxes": Caffe2Boxes(rpn_rois), - "objectness_logits": (torch.Tensor, rpn_roi_probs), - }, - ) - if not tensor_mode: - proposals = InstancesList.to_d2_instances_list(proposals) - else: - proposals = [proposals] - return proposals - - -class Caffe2ROIPooler(Caffe2Compatible, poolers.ROIPooler): - @staticmethod - def c2_preprocess(box_lists): - assert all(isinstance(x, Boxes) for x in box_lists) - if all(isinstance(x, Caffe2Boxes) for x in box_lists): - # input is pure-tensor based - assert len(box_lists) == 1 - pooler_fmt_boxes = box_lists[0].tensor - else: - pooler_fmt_boxes = poolers.convert_boxes_to_pooler_format(box_lists) - return pooler_fmt_boxes - - def forward(self, x, box_lists): - assert not self.training - - pooler_fmt_boxes = self.c2_preprocess(box_lists) - num_level_assignments = len(self.level_poolers) - - if num_level_assignments == 1: - if isinstance(self.level_poolers[0], ROIAlignRotated): - c2_roi_align = torch.ops._caffe2.RoIAlignRotated - aligned = True - else: - c2_roi_align = torch.ops._caffe2.RoIAlign - aligned = self.level_poolers[0].aligned - - x0 = x[0] - if x0.is_quantized: - x0 = x0.dequantize() - - out = c2_roi_align( - x0, - pooler_fmt_boxes, - order="NCHW", - spatial_scale=float(self.level_poolers[0].spatial_scale), - pooled_h=int(self.output_size[0]), - pooled_w=int(self.output_size[1]), - sampling_ratio=int(self.level_poolers[0].sampling_ratio), - aligned=aligned, - ) - return out - - device = pooler_fmt_boxes.device - assert ( - self.max_level - self.min_level + 1 == 4 - ), "Currently DistributeFpnProposals only support 4 levels" - fpn_outputs = torch.ops._caffe2.DistributeFpnProposals( - to_device(pooler_fmt_boxes, "cpu"), - roi_canonical_scale=self.canonical_box_size, - roi_canonical_level=self.canonical_level, - roi_max_level=self.max_level, - roi_min_level=self.min_level, - legacy_plus_one=False, - ) - fpn_outputs = [to_device(x, device) for x in fpn_outputs] - - rois_fpn_list = fpn_outputs[:-1] - rois_idx_restore_int32 = fpn_outputs[-1] - - roi_feat_fpn_list = [] - for roi_fpn, x_level, pooler in zip(rois_fpn_list, x, self.level_poolers): - if isinstance(pooler, ROIAlignRotated): - c2_roi_align = torch.ops._caffe2.RoIAlignRotated - aligned = True - else: - c2_roi_align = torch.ops._caffe2.RoIAlign - aligned = bool(pooler.aligned) - - if x_level.is_quantized: - x_level = x_level.dequantize() - - roi_feat_fpn = c2_roi_align( - x_level, - roi_fpn, - order="NCHW", - spatial_scale=float(pooler.spatial_scale), - pooled_h=int(self.output_size[0]), - pooled_w=int(self.output_size[1]), - sampling_ratio=int(pooler.sampling_ratio), - aligned=aligned, - ) - roi_feat_fpn_list.append(roi_feat_fpn) - - roi_feat_shuffled = cat(roi_feat_fpn_list, dim=0) - assert roi_feat_shuffled.numel() > 0 and rois_idx_restore_int32.numel() > 0, ( - "Caffe2 export requires tracing with a model checkpoint + input that can produce valid" - " detections. But no detections were obtained with the given checkpoint and input!" - ) - roi_feat = torch.ops._caffe2.BatchPermutation(roi_feat_shuffled, rois_idx_restore_int32) - return roi_feat - - -class Caffe2FastRCNNOutputsInference: - def __init__(self, tensor_mode): - self.tensor_mode = tensor_mode # whether the output is caffe2 tensor mode - - def __call__(self, box_predictor, predictions, proposals): - """equivalent to FastRCNNOutputLayers.inference""" - num_classes = box_predictor.num_classes - score_thresh = box_predictor.test_score_thresh - nms_thresh = box_predictor.test_nms_thresh - topk_per_image = box_predictor.test_topk_per_image - is_rotated = len(box_predictor.box2box_transform.weights) == 5 - - if is_rotated: - box_dim = 5 - assert box_predictor.box2box_transform.weights[4] == 1, ( - "The weights for Rotated BBoxTransform in C2 have only 4 dimensions," - + " thus enforcing the angle weight to be 1 for now" - ) - box2box_transform_weights = box_predictor.box2box_transform.weights[:4] - else: - box_dim = 4 - box2box_transform_weights = box_predictor.box2box_transform.weights - - class_logits, box_regression = predictions - if num_classes + 1 == class_logits.shape[1]: - class_prob = F.softmax(class_logits, -1) - else: - assert num_classes == class_logits.shape[1] - class_prob = F.sigmoid(class_logits) - # BoxWithNMSLimit will infer num_classes from the shape of the class_prob - # So append a zero column as placeholder for the background class - class_prob = torch.cat((class_prob, torch.zeros(class_prob.shape[0], 1)), dim=1) - - assert box_regression.shape[1] % box_dim == 0 - cls_agnostic_bbox_reg = box_regression.shape[1] // box_dim == 1 - - input_tensor_mode = proposals[0].proposal_boxes.tensor.shape[1] == box_dim + 1 - - rois = type(proposals[0].proposal_boxes).cat([p.proposal_boxes for p in proposals]) - device, dtype = rois.tensor.device, rois.tensor.dtype - if input_tensor_mode: - im_info = proposals[0].image_size - rois = rois.tensor - else: - im_info = torch.tensor( - [[sz[0], sz[1], 1.0] for sz in [x.image_size for x in proposals]] - ) - batch_ids = cat( - [ - torch.full((b, 1), i, dtype=dtype, device=device) - for i, b in enumerate(len(p) for p in proposals) - ], - dim=0, - ) - rois = torch.cat([batch_ids, rois.tensor], dim=1) - - roi_pred_bbox, roi_batch_splits = torch.ops._caffe2.BBoxTransform( - to_device(rois, "cpu"), - to_device(box_regression, "cpu"), - to_device(im_info, "cpu"), - weights=box2box_transform_weights, - apply_scale=True, - rotated=is_rotated, - angle_bound_on=True, - angle_bound_lo=-180, - angle_bound_hi=180, - clip_angle_thresh=1.0, - legacy_plus_one=False, - ) - roi_pred_bbox = to_device(roi_pred_bbox, device) - roi_batch_splits = to_device(roi_batch_splits, device) - - nms_outputs = torch.ops._caffe2.BoxWithNMSLimit( - to_device(class_prob, "cpu"), - to_device(roi_pred_bbox, "cpu"), - to_device(roi_batch_splits, "cpu"), - score_thresh=float(score_thresh), - nms=float(nms_thresh), - detections_per_im=int(topk_per_image), - soft_nms_enabled=False, - soft_nms_method="linear", - soft_nms_sigma=0.5, - soft_nms_min_score_thres=0.001, - rotated=is_rotated, - cls_agnostic_bbox_reg=cls_agnostic_bbox_reg, - input_boxes_include_bg_cls=False, - output_classes_include_bg_cls=False, - legacy_plus_one=False, - ) - roi_score_nms = to_device(nms_outputs[0], device) - roi_bbox_nms = to_device(nms_outputs[1], device) - roi_class_nms = to_device(nms_outputs[2], device) - roi_batch_splits_nms = to_device(nms_outputs[3], device) - roi_keeps_nms = to_device(nms_outputs[4], device) - roi_keeps_size_nms = to_device(nms_outputs[5], device) - if not self.tensor_mode: - roi_class_nms = roi_class_nms.to(torch.int64) - - roi_batch_ids = cat( - [ - torch.full((b, 1), i, dtype=dtype, device=device) - for i, b in enumerate(int(x.item()) for x in roi_batch_splits_nms) - ], - dim=0, - ) - - roi_class_nms = alias(roi_class_nms, "class_nms") - roi_score_nms = alias(roi_score_nms, "score_nms") - roi_bbox_nms = alias(roi_bbox_nms, "bbox_nms") - roi_batch_splits_nms = alias(roi_batch_splits_nms, "batch_splits_nms") - roi_keeps_nms = alias(roi_keeps_nms, "keeps_nms") - roi_keeps_size_nms = alias(roi_keeps_size_nms, "keeps_size_nms") - - results = InstancesList( - im_info=im_info, - indices=roi_batch_ids[:, 0], - extra_fields={ - "pred_boxes": Caffe2Boxes(roi_bbox_nms), - "scores": roi_score_nms, - "pred_classes": roi_class_nms, - }, - ) - - if not self.tensor_mode: - results = InstancesList.to_d2_instances_list(results) - batch_splits = roi_batch_splits_nms.int().tolist() - kept_indices = list(roi_keeps_nms.to(torch.int64).split(batch_splits)) - else: - results = [results] - kept_indices = [roi_keeps_nms] - - return results, kept_indices - - -class Caffe2MaskRCNNInference: - def __call__(self, pred_mask_logits, pred_instances): - """equivalent to mask_head.mask_rcnn_inference""" - if all(isinstance(x, InstancesList) for x in pred_instances): - assert len(pred_instances) == 1 - mask_probs_pred = pred_mask_logits.sigmoid() - mask_probs_pred = alias(mask_probs_pred, "mask_fcn_probs") - pred_instances[0].pred_masks = mask_probs_pred - else: - mask_rcnn_inference(pred_mask_logits, pred_instances) - - -class Caffe2KeypointRCNNInference: - def __init__(self, use_heatmap_max_keypoint): - self.use_heatmap_max_keypoint = use_heatmap_max_keypoint - - def __call__(self, pred_keypoint_logits, pred_instances): - # just return the keypoint heatmap for now, - # there will be option to call HeatmapMaxKeypointOp - output = alias(pred_keypoint_logits, "kps_score") - if all(isinstance(x, InstancesList) for x in pred_instances): - assert len(pred_instances) == 1 - if self.use_heatmap_max_keypoint: - device = output.device - output = torch.ops._caffe2.HeatmapMaxKeypoint( - to_device(output, "cpu"), - pred_instances[0].pred_boxes.tensor, - should_output_softmax=True, # worth make it configerable? - ) - output = to_device(output, device) - output = alias(output, "keypoints_out") - pred_instances[0].pred_keypoints = output - return pred_keypoint_logits diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py deleted file mode 100644 index 13a808e587382216da6fe7ee957603f448172657..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import unittest -import torch - -from detectron2.config import get_cfg -from detectron2.layers import ShapeSpec -from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator - -logger = logging.getLogger(__name__) - - -class TestAnchorGenerator(unittest.TestCase): - def test_default_anchor_generator(self): - cfg = get_cfg() - cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] - cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] - - anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)]) - - # only the last two dimensions of features matter here - num_images = 2 - features = {"stage3": torch.rand(num_images, 96, 1, 2)} - anchors = anchor_generator([features["stage3"]]) - expected_anchor_tensor = torch.tensor( - [ - [-32.0, -8.0, 32.0, 8.0], - [-16.0, -16.0, 16.0, 16.0], - [-8.0, -32.0, 8.0, 32.0], - [-64.0, -16.0, 64.0, 16.0], - [-32.0, -32.0, 32.0, 32.0], - [-16.0, -64.0, 16.0, 64.0], - [-28.0, -8.0, 36.0, 8.0], # -28.0 == -32.0 + STRIDE (4) - [-12.0, -16.0, 20.0, 16.0], - [-4.0, -32.0, 12.0, 32.0], - [-60.0, -16.0, 68.0, 16.0], - [-28.0, -32.0, 36.0, 32.0], - [-12.0, -64.0, 20.0, 64.0], - ] - ) - - self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) - - def test_default_anchor_generator_centered(self): - # test explicit args - anchor_generator = DefaultAnchorGenerator( - sizes=[32, 64], aspect_ratios=[0.25, 1, 4], strides=[4] - ) - - # only the last two dimensions of features matter here - num_images = 2 - features = {"stage3": torch.rand(num_images, 96, 1, 2)} - expected_anchor_tensor = torch.tensor( - [ - [-30.0, -6.0, 34.0, 10.0], - [-14.0, -14.0, 18.0, 18.0], - [-6.0, -30.0, 10.0, 34.0], - [-62.0, -14.0, 66.0, 18.0], - [-30.0, -30.0, 34.0, 34.0], - [-14.0, -62.0, 18.0, 66.0], - [-26.0, -6.0, 38.0, 10.0], - [-10.0, -14.0, 22.0, 18.0], - [-2.0, -30.0, 14.0, 34.0], - [-58.0, -14.0, 70.0, 18.0], - [-26.0, -30.0, 38.0, 34.0], - [-10.0, -62.0, 22.0, 66.0], - ] - ) - - anchors = anchor_generator([features["stage3"]]) - self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) - - anchors = torch.jit.script(anchor_generator)([features["stage3"]]) - self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) - - def test_rrpn_anchor_generator(self): - cfg = get_cfg() - cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]] - cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]] - cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [0, 45] # test single list[float] - anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)]) - - # only the last two dimensions of features matter here - num_images = 2 - features = {"stage3": torch.rand(num_images, 96, 1, 2)} - anchors = anchor_generator([features["stage3"]]) - expected_anchor_tensor = torch.tensor( - [ - [0.0, 0.0, 64.0, 16.0, 0.0], - [0.0, 0.0, 64.0, 16.0, 45.0], - [0.0, 0.0, 32.0, 32.0, 0.0], - [0.0, 0.0, 32.0, 32.0, 45.0], - [0.0, 0.0, 16.0, 64.0, 0.0], - [0.0, 0.0, 16.0, 64.0, 45.0], - [0.0, 0.0, 128.0, 32.0, 0.0], - [0.0, 0.0, 128.0, 32.0, 45.0], - [0.0, 0.0, 64.0, 64.0, 0.0], - [0.0, 0.0, 64.0, 64.0, 45.0], - [0.0, 0.0, 32.0, 128.0, 0.0], - [0.0, 0.0, 32.0, 128.0, 45.0], - [4.0, 0.0, 64.0, 16.0, 0.0], # 4.0 == 0.0 + STRIDE (4) - [4.0, 0.0, 64.0, 16.0, 45.0], - [4.0, 0.0, 32.0, 32.0, 0.0], - [4.0, 0.0, 32.0, 32.0, 45.0], - [4.0, 0.0, 16.0, 64.0, 0.0], - [4.0, 0.0, 16.0, 64.0, 45.0], - [4.0, 0.0, 128.0, 32.0, 0.0], - [4.0, 0.0, 128.0, 32.0, 45.0], - [4.0, 0.0, 64.0, 64.0, 0.0], - [4.0, 0.0, 64.0, 64.0, 45.0], - [4.0, 0.0, 32.0, 128.0, 0.0], - [4.0, 0.0, 32.0, 128.0, 45.0], - ] - ) - - self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor)) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/losses/hyper_laplacian_penalty.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/losses/hyper_laplacian_penalty.py deleted file mode 100644 index 87c42ddffb4a80c31517243c8b66763def65d3eb..0000000000000000000000000000000000000000 --- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/losses/hyper_laplacian_penalty.py +++ /dev/null @@ -1,27 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class HyperLaplacianPenalty(nn.Module): - def __init__(self, num_channels, alpha, eps=1e-6): - super(HyperLaplacianPenalty, self).__init__() - - self.alpha = alpha - self.eps = eps - - self.Kx = torch.Tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).cuda() - self.Kx = self.Kx.expand(1, num_channels, 3, 3) - self.Kx.requires_grad = False - self.Ky = torch.Tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).cuda() - self.Ky = self.Ky.expand(1, num_channels, 3, 3) - self.Ky.requires_grad = False - - def forward(self, x): - gradX = F.conv2d(x, self.Kx, stride=1, padding=1) - gradY = F.conv2d(x, self.Ky, stride=1, padding=1) - grad = torch.sqrt(gradX ** 2 + gradY ** 2 + self.eps) - - loss = (grad ** self.alpha).mean() - - return loss diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/prune-top-level-scopes.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/prune-top-level-scopes.go deleted file mode 100644 index f0fce6196c733a207c1d4a135191c1e77e0c0654..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/prune-top-level-scopes.go and /dev/null differ diff --git a/spaces/Pfs2021Funny/The-CG-Diffusion/app.py b/spaces/Pfs2021Funny/The-CG-Diffusion/app.py deleted file mode 100644 index 31a0b8b8a2f3520fd78d30791feada3bb8cbeaaa..0000000000000000000000000000000000000000 --- a/spaces/Pfs2021Funny/The-CG-Diffusion/app.py +++ /dev/null @@ -1,5 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/wavymulder/Analog-Diffusion").launch() -gr.Interface.load("nitrosocke/classic-anim-diffusion").launch() -gr.Interface.load("nitrosocke/redshift-diffusion").launch() \ No newline at end of file diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/arraymisc/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/arraymisc/__init__.py deleted file mode 100644 index 4b4700d6139ae3d604ff6e542468cce4200c020c..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/arraymisc/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .quantization import dequantize, quantize - -__all__ = ['quantize', 'dequantize'] diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/accuracy.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/accuracy.py deleted file mode 100644 index c0fd2e7e74a0f721c4a814c09d6e453e5956bb38..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/accuracy.py +++ /dev/null @@ -1,78 +0,0 @@ -import torch.nn as nn - - -def accuracy(pred, target, topk=1, thresh=None): - """Calculate accuracy according to the prediction and target. - - Args: - pred (torch.Tensor): The model prediction, shape (N, num_class, ...) - target (torch.Tensor): The target of each prediction, shape (N, , ...) - topk (int | tuple[int], optional): If the predictions in ``topk`` - matches the target, the predictions will be regarded as - correct ones. Defaults to 1. - thresh (float, optional): If not None, predictions with scores under - this threshold are considered incorrect. Default to None. - - Returns: - float | tuple[float]: If the input ``topk`` is a single integer, - the function will return a single float as accuracy. If - ``topk`` is a tuple containing multiple integers, the - function will return a tuple containing accuracies of - each ``topk`` number. - """ - assert isinstance(topk, (int, tuple)) - if isinstance(topk, int): - topk = (topk, ) - return_single = True - else: - return_single = False - - maxk = max(topk) - if pred.size(0) == 0: - accu = [pred.new_tensor(0.) for i in range(len(topk))] - return accu[0] if return_single else accu - assert pred.ndim == target.ndim + 1 - assert pred.size(0) == target.size(0) - assert maxk <= pred.size(1), \ - f'maxk {maxk} exceeds pred dimension {pred.size(1)}' - pred_value, pred_label = pred.topk(maxk, dim=1) - # transpose to shape (maxk, N, ...) - pred_label = pred_label.transpose(0, 1) - correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label)) - if thresh is not None: - # Only prediction values larger than thresh are counted as correct - correct = correct & (pred_value > thresh).t() - res = [] - for k in topk: - correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) - res.append(correct_k.mul_(100.0 / target.numel())) - return res[0] if return_single else res - - -class Accuracy(nn.Module): - """Accuracy calculation module.""" - - def __init__(self, topk=(1, ), thresh=None): - """Module to calculate the accuracy. - - Args: - topk (tuple, optional): The criterion used to calculate the - accuracy. Defaults to (1,). - thresh (float, optional): If not None, predictions with scores - under this threshold are considered incorrect. Default to None. - """ - super().__init__() - self.topk = topk - self.thresh = thresh - - def forward(self, pred, target): - """Forward function to calculate accuracy. - - Args: - pred (torch.Tensor): Prediction of models. - target (torch.Tensor): Target for each prediction. - - Returns: - tuple[float]: The accuracies under different topk criterions. - """ - return accuracy(pred, target, self.topk, self.thresh) diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/index.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/index.py deleted file mode 100644 index b4bf0ac06e14926d193a6ed31f12e3c46329c338..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/commands/index.py +++ /dev/null @@ -1,138 +0,0 @@ -import logging -from optparse import Values -from typing import Any, Iterable, List, Optional, Union - -from pip._vendor.packaging.version import LegacyVersion, Version - -from pip._internal.cli import cmdoptions -from pip._internal.cli.req_command import IndexGroupCommand -from pip._internal.cli.status_codes import ERROR, SUCCESS -from pip._internal.commands.search import print_dist_installation_info -from pip._internal.exceptions import CommandError, DistributionNotFound, PipError -from pip._internal.index.collector import LinkCollector -from pip._internal.index.package_finder import PackageFinder -from pip._internal.models.selection_prefs import SelectionPreferences -from pip._internal.models.target_python import TargetPython -from pip._internal.network.session import PipSession -from pip._internal.utils.misc import write_output - -logger = logging.getLogger(__name__) - - -class IndexCommand(IndexGroupCommand): - """ - Inspect information available from package indexes. - """ - - usage = """ - %prog versions - """ - - def add_options(self) -> None: - cmdoptions.add_target_python_options(self.cmd_opts) - - self.cmd_opts.add_option(cmdoptions.ignore_requires_python()) - self.cmd_opts.add_option(cmdoptions.pre()) - self.cmd_opts.add_option(cmdoptions.no_binary()) - self.cmd_opts.add_option(cmdoptions.only_binary()) - - index_opts = cmdoptions.make_option_group( - cmdoptions.index_group, - self.parser, - ) - - self.parser.insert_option_group(0, index_opts) - self.parser.insert_option_group(0, self.cmd_opts) - - def run(self, options: Values, args: List[str]) -> int: - handlers = { - "versions": self.get_available_package_versions, - } - - logger.warning( - "pip index is currently an experimental command. " - "It may be removed/changed in a future release " - "without prior warning." - ) - - # Determine action - if not args or args[0] not in handlers: - logger.error( - "Need an action (%s) to perform.", - ", ".join(sorted(handlers)), - ) - return ERROR - - action = args[0] - - # Error handling happens here, not in the action-handlers. - try: - handlers[action](options, args[1:]) - except PipError as e: - logger.error(e.args[0]) - return ERROR - - return SUCCESS - - def _build_package_finder( - self, - options: Values, - session: PipSession, - target_python: Optional[TargetPython] = None, - ignore_requires_python: Optional[bool] = None, - ) -> PackageFinder: - """ - Create a package finder appropriate to the index command. - """ - link_collector = LinkCollector.create(session, options=options) - - # Pass allow_yanked=False to ignore yanked versions. - selection_prefs = SelectionPreferences( - allow_yanked=False, - allow_all_prereleases=options.pre, - ignore_requires_python=ignore_requires_python, - ) - - return PackageFinder.create( - link_collector=link_collector, - selection_prefs=selection_prefs, - target_python=target_python, - ) - - def get_available_package_versions(self, options: Values, args: List[Any]) -> None: - if len(args) != 1: - raise CommandError("You need to specify exactly one argument") - - target_python = cmdoptions.make_target_python(options) - query = args[0] - - with self._build_session(options) as session: - finder = self._build_package_finder( - options=options, - session=session, - target_python=target_python, - ignore_requires_python=options.ignore_requires_python, - ) - - versions: Iterable[Union[LegacyVersion, Version]] = ( - candidate.version for candidate in finder.find_all_candidates(query) - ) - - if not options.pre: - # Remove prereleases - versions = ( - version for version in versions if not version.is_prerelease - ) - versions = set(versions) - - if not versions: - raise DistributionNotFound( - "No matching distribution found for {}".format(query) - ) - - formatted_versions = [str(ver) for ver in sorted(versions, reverse=True)] - latest = formatted_versions[0] - - write_output("{} ({})".format(query, latest)) - write_output("Available versions: {}".format(", ".join(formatted_versions))) - print_dist_installation_info(query, latest) diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/direct_url_helpers.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/direct_url_helpers.py deleted file mode 100644 index 0e8e5e1608b911e789a3d346ebe48aa7cc54b79e..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/utils/direct_url_helpers.py +++ /dev/null @@ -1,87 +0,0 @@ -from typing import Optional - -from pip._internal.models.direct_url import ArchiveInfo, DirectUrl, DirInfo, VcsInfo -from pip._internal.models.link import Link -from pip._internal.utils.urls import path_to_url -from pip._internal.vcs import vcs - - -def direct_url_as_pep440_direct_reference(direct_url: DirectUrl, name: str) -> str: - """Convert a DirectUrl to a pip requirement string.""" - direct_url.validate() # if invalid, this is a pip bug - requirement = name + " @ " - fragments = [] - if isinstance(direct_url.info, VcsInfo): - requirement += "{}+{}@{}".format( - direct_url.info.vcs, direct_url.url, direct_url.info.commit_id - ) - elif isinstance(direct_url.info, ArchiveInfo): - requirement += direct_url.url - if direct_url.info.hash: - fragments.append(direct_url.info.hash) - else: - assert isinstance(direct_url.info, DirInfo) - requirement += direct_url.url - if direct_url.subdirectory: - fragments.append("subdirectory=" + direct_url.subdirectory) - if fragments: - requirement += "#" + "&".join(fragments) - return requirement - - -def direct_url_for_editable(source_dir: str) -> DirectUrl: - return DirectUrl( - url=path_to_url(source_dir), - info=DirInfo(editable=True), - ) - - -def direct_url_from_link( - link: Link, source_dir: Optional[str] = None, link_is_in_wheel_cache: bool = False -) -> DirectUrl: - if link.is_vcs: - vcs_backend = vcs.get_backend_for_scheme(link.scheme) - assert vcs_backend - url, requested_revision, _ = vcs_backend.get_url_rev_and_auth( - link.url_without_fragment - ) - # For VCS links, we need to find out and add commit_id. - if link_is_in_wheel_cache: - # If the requested VCS link corresponds to a cached - # wheel, it means the requested revision was an - # immutable commit hash, otherwise it would not have - # been cached. In that case we don't have a source_dir - # with the VCS checkout. - assert requested_revision - commit_id = requested_revision - else: - # If the wheel was not in cache, it means we have - # had to checkout from VCS to build and we have a source_dir - # which we can inspect to find out the commit id. - assert source_dir - commit_id = vcs_backend.get_revision(source_dir) - return DirectUrl( - url=url, - info=VcsInfo( - vcs=vcs_backend.name, - commit_id=commit_id, - requested_revision=requested_revision, - ), - subdirectory=link.subdirectory_fragment, - ) - elif link.is_existing_dir(): - return DirectUrl( - url=link.url_without_fragment, - info=DirInfo(), - subdirectory=link.subdirectory_fragment, - ) - else: - hash = None - hash_name = link.hash_name - if hash_name: - hash = f"{hash_name}={link.hash}" - return DirectUrl( - url=link.url_without_fragment, - info=ArchiveInfo(hash=hash), - subdirectory=link.subdirectory_fragment, - ) diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pyparsing/unicode.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pyparsing/unicode.py deleted file mode 100644 index 06526203911de55da3c2a8c5ae73f48024c3f018..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pyparsing/unicode.py +++ /dev/null @@ -1,352 +0,0 @@ -# unicode.py - -import sys -from itertools import filterfalse -from typing import List, Tuple, Union - - -class _lazyclassproperty: - def __init__(self, fn): - self.fn = fn - self.__doc__ = fn.__doc__ - self.__name__ = fn.__name__ - - def __get__(self, obj, cls): - if cls is None: - cls = type(obj) - if not hasattr(cls, "_intern") or any( - cls._intern is getattr(superclass, "_intern", []) - for superclass in cls.__mro__[1:] - ): - cls._intern = {} - attrname = self.fn.__name__ - if attrname not in cls._intern: - cls._intern[attrname] = self.fn(cls) - return cls._intern[attrname] - - -UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]] - - -class unicode_set: - """ - A set of Unicode characters, for language-specific strings for - ``alphas``, ``nums``, ``alphanums``, and ``printables``. - A unicode_set is defined by a list of ranges in the Unicode character - set, in a class attribute ``_ranges``. Ranges can be specified using - 2-tuples or a 1-tuple, such as:: - - _ranges = [ - (0x0020, 0x007e), - (0x00a0, 0x00ff), - (0x0100,), - ] - - Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x). - - A unicode set can also be defined using multiple inheritance of other unicode sets:: - - class CJK(Chinese, Japanese, Korean): - pass - """ - - _ranges: UnicodeRangeList = [] - - @_lazyclassproperty - def _chars_for_ranges(cls): - ret = [] - for cc in cls.__mro__: - if cc is unicode_set: - break - for rr in getattr(cc, "_ranges", ()): - ret.extend(range(rr[0], rr[-1] + 1)) - return [chr(c) for c in sorted(set(ret))] - - @_lazyclassproperty - def printables(cls): - "all non-whitespace characters in this range" - return "".join(filterfalse(str.isspace, cls._chars_for_ranges)) - - @_lazyclassproperty - def alphas(cls): - "all alphabetic characters in this range" - return "".join(filter(str.isalpha, cls._chars_for_ranges)) - - @_lazyclassproperty - def nums(cls): - "all numeric digit characters in this range" - return "".join(filter(str.isdigit, cls._chars_for_ranges)) - - @_lazyclassproperty - def alphanums(cls): - "all alphanumeric characters in this range" - return cls.alphas + cls.nums - - @_lazyclassproperty - def identchars(cls): - "all characters in this range that are valid identifier characters, plus underscore '_'" - return "".join( - sorted( - set( - "".join(filter(str.isidentifier, cls._chars_for_ranges)) - + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº" - + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ" - + "_" - ) - ) - ) - - @_lazyclassproperty - def identbodychars(cls): - """ - all characters in this range that are valid identifier body characters, - plus the digits 0-9 - """ - return "".join( - sorted( - set( - cls.identchars - + "0123456789" - + "".join( - [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()] - ) - ) - ) - ) - - -class pyparsing_unicode(unicode_set): - """ - A namespace class for defining common language unicode_sets. - """ - - # fmt: off - - # define ranges in language character sets - _ranges: UnicodeRangeList = [ - (0x0020, sys.maxunicode), - ] - - class BasicMultilingualPlane(unicode_set): - "Unicode set for the Basic Multilingual Plane" - _ranges: UnicodeRangeList = [ - (0x0020, 0xFFFF), - ] - - class Latin1(unicode_set): - "Unicode set for Latin-1 Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0020, 0x007E), - (0x00A0, 0x00FF), - ] - - class LatinA(unicode_set): - "Unicode set for Latin-A Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0100, 0x017F), - ] - - class LatinB(unicode_set): - "Unicode set for Latin-B Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0180, 0x024F), - ] - - class Greek(unicode_set): - "Unicode set for Greek Unicode Character Ranges" - _ranges: UnicodeRangeList = [ - (0x0342, 0x0345), - (0x0370, 0x0377), - (0x037A, 0x037F), - (0x0384, 0x038A), - (0x038C,), - (0x038E, 0x03A1), - (0x03A3, 0x03E1), - (0x03F0, 0x03FF), - (0x1D26, 0x1D2A), - (0x1D5E,), - (0x1D60,), - (0x1D66, 0x1D6A), - (0x1F00, 0x1F15), - (0x1F18, 0x1F1D), - (0x1F20, 0x1F45), - (0x1F48, 0x1F4D), - (0x1F50, 0x1F57), - (0x1F59,), - (0x1F5B,), - (0x1F5D,), - (0x1F5F, 0x1F7D), - (0x1F80, 0x1FB4), - (0x1FB6, 0x1FC4), - (0x1FC6, 0x1FD3), - (0x1FD6, 0x1FDB), - (0x1FDD, 0x1FEF), - (0x1FF2, 0x1FF4), - (0x1FF6, 0x1FFE), - (0x2129,), - (0x2719, 0x271A), - (0xAB65,), - (0x10140, 0x1018D), - (0x101A0,), - (0x1D200, 0x1D245), - (0x1F7A1, 0x1F7A7), - ] - - class Cyrillic(unicode_set): - "Unicode set for Cyrillic Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0400, 0x052F), - (0x1C80, 0x1C88), - (0x1D2B,), - (0x1D78,), - (0x2DE0, 0x2DFF), - (0xA640, 0xA672), - (0xA674, 0xA69F), - (0xFE2E, 0xFE2F), - ] - - class Chinese(unicode_set): - "Unicode set for Chinese Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x2E80, 0x2E99), - (0x2E9B, 0x2EF3), - (0x31C0, 0x31E3), - (0x3400, 0x4DB5), - (0x4E00, 0x9FEF), - (0xA700, 0xA707), - (0xF900, 0xFA6D), - (0xFA70, 0xFAD9), - (0x16FE2, 0x16FE3), - (0x1F210, 0x1F212), - (0x1F214, 0x1F23B), - (0x1F240, 0x1F248), - (0x20000, 0x2A6D6), - (0x2A700, 0x2B734), - (0x2B740, 0x2B81D), - (0x2B820, 0x2CEA1), - (0x2CEB0, 0x2EBE0), - (0x2F800, 0x2FA1D), - ] - - class Japanese(unicode_set): - "Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges" - _ranges: UnicodeRangeList = [] - - class Kanji(unicode_set): - "Unicode set for Kanji Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x4E00, 0x9FBF), - (0x3000, 0x303F), - ] - - class Hiragana(unicode_set): - "Unicode set for Hiragana Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x3041, 0x3096), - (0x3099, 0x30A0), - (0x30FC,), - (0xFF70,), - (0x1B001,), - (0x1B150, 0x1B152), - (0x1F200,), - ] - - class Katakana(unicode_set): - "Unicode set for Katakana Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x3099, 0x309C), - (0x30A0, 0x30FF), - (0x31F0, 0x31FF), - (0x32D0, 0x32FE), - (0xFF65, 0xFF9F), - (0x1B000,), - (0x1B164, 0x1B167), - (0x1F201, 0x1F202), - (0x1F213,), - ] - - class Hangul(unicode_set): - "Unicode set for Hangul (Korean) Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x1100, 0x11FF), - (0x302E, 0x302F), - (0x3131, 0x318E), - (0x3200, 0x321C), - (0x3260, 0x327B), - (0x327E,), - (0xA960, 0xA97C), - (0xAC00, 0xD7A3), - (0xD7B0, 0xD7C6), - (0xD7CB, 0xD7FB), - (0xFFA0, 0xFFBE), - (0xFFC2, 0xFFC7), - (0xFFCA, 0xFFCF), - (0xFFD2, 0xFFD7), - (0xFFDA, 0xFFDC), - ] - - Korean = Hangul - - class CJK(Chinese, Japanese, Hangul): - "Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range" - - class Thai(unicode_set): - "Unicode set for Thai Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0E01, 0x0E3A), - (0x0E3F, 0x0E5B) - ] - - class Arabic(unicode_set): - "Unicode set for Arabic Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0600, 0x061B), - (0x061E, 0x06FF), - (0x0700, 0x077F), - ] - - class Hebrew(unicode_set): - "Unicode set for Hebrew Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0591, 0x05C7), - (0x05D0, 0x05EA), - (0x05EF, 0x05F4), - (0xFB1D, 0xFB36), - (0xFB38, 0xFB3C), - (0xFB3E,), - (0xFB40, 0xFB41), - (0xFB43, 0xFB44), - (0xFB46, 0xFB4F), - ] - - class Devanagari(unicode_set): - "Unicode set for Devanagari Unicode Character Range" - _ranges: UnicodeRangeList = [ - (0x0900, 0x097F), - (0xA8E0, 0xA8FF) - ] - - # fmt: on - - -pyparsing_unicode.Japanese._ranges = ( - pyparsing_unicode.Japanese.Kanji._ranges - + pyparsing_unicode.Japanese.Hiragana._ranges - + pyparsing_unicode.Japanese.Katakana._ranges -) - -pyparsing_unicode.BMP = pyparsing_unicode.BasicMultilingualPlane - -# add language identifiers using language Unicode -pyparsing_unicode.العربية = pyparsing_unicode.Arabic -pyparsing_unicode.中文 = pyparsing_unicode.Chinese -pyparsing_unicode.кириллица = pyparsing_unicode.Cyrillic -pyparsing_unicode.Ελληνικά = pyparsing_unicode.Greek -pyparsing_unicode.עִברִית = pyparsing_unicode.Hebrew -pyparsing_unicode.日本語 = pyparsing_unicode.Japanese -pyparsing_unicode.Japanese.漢字 = pyparsing_unicode.Japanese.Kanji -pyparsing_unicode.Japanese.カタカナ = pyparsing_unicode.Japanese.Katakana -pyparsing_unicode.Japanese.ひらがな = pyparsing_unicode.Japanese.Hiragana -pyparsing_unicode.한국어 = pyparsing_unicode.Korean -pyparsing_unicode.ไทย = pyparsing_unicode.Thai -pyparsing_unicode.देवनागरी = pyparsing_unicode.Devanagari diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/backports/makefile.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/backports/makefile.py deleted file mode 100644 index b8fb2154b6d0618b62281578e5e947bca487cee4..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/backports/makefile.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- coding: utf-8 -*- -""" -backports.makefile -~~~~~~~~~~~~~~~~~~ - -Backports the Python 3 ``socket.makefile`` method for use with anything that -wants to create a "fake" socket object. -""" -import io -from socket import SocketIO - - -def backport_makefile( - self, mode="r", buffering=None, encoding=None, errors=None, newline=None -): - """ - Backport of ``socket.makefile`` from Python 3.5. - """ - if not set(mode) <= {"r", "w", "b"}: - raise ValueError("invalid mode %r (only r, w, b allowed)" % (mode,)) - writing = "w" in mode - reading = "r" in mode or not writing - assert reading or writing - binary = "b" in mode - rawmode = "" - if reading: - rawmode += "r" - if writing: - rawmode += "w" - raw = SocketIO(self, rawmode) - self._makefile_refs += 1 - if buffering is None: - buffering = -1 - if buffering < 0: - buffering = io.DEFAULT_BUFFER_SIZE - if buffering == 0: - if not binary: - raise ValueError("unbuffered streams must be binary") - return raw - if reading and writing: - buffer = io.BufferedRWPair(raw, raw, buffering) - elif reading: - buffer = io.BufferedReader(raw, buffering) - else: - assert writing - buffer = io.BufferedWriter(raw, buffering) - if binary: - return buffer - text = io.TextIOWrapper(buffer, encoding, errors, newline) - text.mode = mode - return text diff --git a/spaces/Redgon/bingo/src/lib/bots/bing/types.ts b/spaces/Redgon/bingo/src/lib/bots/bing/types.ts deleted file mode 100644 index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000 --- a/spaces/Redgon/bingo/src/lib/bots/bing/types.ts +++ /dev/null @@ -1,259 +0,0 @@ -export type Author = 'user' | 'system' | 'bot' - -export type BotId = 'bing' - -export enum BingConversationStyle { - Creative = 'Creative', - Balanced = 'Balanced', - Precise = 'Precise' -} - -export enum ErrorCode { - CONVERSATION_LIMIT = 'CONVERSATION_LIMIT', - BING_UNAUTHORIZED = 'BING_UNAUTHORIZED', - BING_FORBIDDEN = 'BING_FORBIDDEN', - BING_CAPTCHA = 'BING_CAPTCHA', - THROTTLE_LIMIT = 'THROTTLE_LIMIT', - NOTFOUND_ERROR = 'NOT_FOUND_ERROR', - UNKOWN_ERROR = 'UNKOWN_ERROR', - NETWORK_ERROR = 'NETWORK_ERROR', -} - -export class ChatError extends Error { - code: ErrorCode - constructor(message: string, code: ErrorCode) { - super(message) - this.code = code - } -} - -export type ChatMessageModel = { - id: string - author: Author - text: string - error?: ChatError - throttling?: Throttling - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] -} - -export interface ConversationModel { - messages: ChatMessageModel[] -} - -export type Event = - | { - type: 'UPDATE_ANSWER' - data: { - text: string - spokenText?: string - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] - throttling?: Throttling - } - } - | { - type: 'DONE' - } - | { - type: 'ERROR' - error: ChatError - } - -export interface SendMessageParams { - prompt: string - imageUrl?: string - options: T - onEvent: (event: Event) => void - signal?: AbortSignal -} - -export interface ConversationResponse { - conversationId: string - clientId: string - conversationSignature: string - result: { - value: string - message?: string - } -} - -export interface Telemetry { - metrics?: null - startTime: string -} - -export interface ChatUpdateArgument { - messages?: ChatResponseMessage[] - throttling?: Throttling - requestId: string - result: null -} - -export type ChatUpdateCompleteResponse = { - type: 2 - invocationId: string - item: ChatResponseItem -} | { - type: 1 - target: string - arguments: ChatUpdateArgument[] -} | { - type: 3 - invocationId: string -} | { - type: 6 | 7 -} - -export interface ChatRequestResult { - value: string - serviceVersion: string - error?: string -} - -export interface ChatResponseItem { - messages: ChatResponseMessage[] - firstNewMessageIndex: number - suggestedResponses: null - conversationId: string - requestId: string - conversationExpiryTime: string - telemetry: Telemetry - result: ChatRequestResult - throttling: Throttling -} -export enum InvocationEventType { - Invocation = 1, - StreamItem = 2, - Completion = 3, - StreamInvocation = 4, - CancelInvocation = 5, - Ping = 6, - Close = 7, -} - -// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts - -export interface ConversationInfo { - conversationId: string - clientId: string - conversationSignature: string - invocationId: number - conversationStyle: BingConversationStyle - prompt: string - imageUrl?: string -} - -export interface BingChatResponse { - conversationSignature: string - conversationId: string - clientId: string - invocationId: number - conversationExpiryTime: Date - response: string - details: ChatResponseMessage -} - -export interface Throttling { - maxNumLongDocSummaryUserMessagesInConversation: number - maxNumUserMessagesInConversation: number - numLongDocSummaryUserMessagesInConversation: number - numUserMessagesInConversation: number -} - -export interface ChatResponseMessage { - text: string - spokenText?: string - author: string - createdAt: Date - timestamp: Date - messageId: string - requestId: string - offense: string - adaptiveCards: AdaptiveCard[] - sourceAttributions: SourceAttribution[] - feedback: Feedback - contentOrigin: string - messageType?: string - contentType?: string - privacy: null - suggestedResponses: SuggestedResponse[] -} - -export interface AdaptiveCard { - type: string - version: string - body: Body[] -} - -export interface Body { - type: string - text: string - wrap: boolean - size?: string -} - -export interface Feedback { - tag: null - updatedOn: null - type: string -} - -export interface SourceAttribution { - providerDisplayName: string - seeMoreUrl: string - searchQuery: string -} - -export interface SuggestedResponse { - text: string - author?: Author - createdAt?: Date - timestamp?: Date - messageId?: string - messageType?: string - offense?: string - feedback?: Feedback - contentOrigin?: string - privacy?: null -} - -export interface KBlobRequest { - knowledgeRequest: KnowledgeRequestContext - imageBase64?: string -} - -export interface KBlobResponse { - blobId: string - processedBlobId?: string -} - -export interface KnowledgeRequestContext { - imageInfo: ImageInfo; - knowledgeRequest: KnowledgeRequest; -} - -export interface ImageInfo { - url?: string; -} - -export interface KnowledgeRequest { - invokedSkills: string[]; - subscriptionId: string; - invokedSkillsRequestData: InvokedSkillsRequestData; - convoData: ConvoData; -} - -export interface ConvoData { - convoid: string; - convotone: BingConversationStyle; -} - -export interface InvokedSkillsRequestData { - enableFaceBlur: boolean; -} - -export interface FileItem { - url: string; - status?: 'loading' | 'error' | 'loaded' -} diff --git a/spaces/RegalHyperus/rvc-anime-game/vc_infer_pipeline.py b/spaces/RegalHyperus/rvc-anime-game/vc_infer_pipeline.py deleted file mode 100644 index 7ff98b2c812f4e74afe92048fb26009fb008479d..0000000000000000000000000000000000000000 --- a/spaces/RegalHyperus/rvc-anime-game/vc_infer_pipeline.py +++ /dev/null @@ -1,320 +0,0 @@ -import numpy as np, parselmouth, torch, pdb -from time import time as ttime -import torch.nn.functional as F -import scipy.signal as signal -import pyworld, os, traceback, faiss -from scipy import signal - -bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) - - -class VC(object): - def __init__(self, tgt_sr, config): - self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( - config.x_pad, - config.x_query, - config.x_center, - config.x_max, - config.is_half, - ) - self.sr = 16000 # hubert输入采样率 - self.window = 160 # 每帧点数 - self.t_pad = self.sr * self.x_pad # 每条前后pad时间 - self.t_pad_tgt = tgt_sr * self.x_pad - self.t_pad2 = self.t_pad * 2 - self.t_query = self.sr * self.x_query # 查询切点前后查询时间 - self.t_center = self.sr * self.x_center # 查询切点位置 - self.t_max = self.sr * self.x_max # 免查询时长阈值 - self.device = config.device - - def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None): - time_step = self.window / self.sr * 1000 - f0_min = 50 - f0_max = 1100 - f0_mel_min = 1127 * np.log(1 + f0_min / 700) - f0_mel_max = 1127 * np.log(1 + f0_max / 700) - if f0_method == "pm": - f0 = ( - parselmouth.Sound(x, self.sr) - .to_pitch_ac( - time_step=time_step / 1000, - voicing_threshold=0.6, - pitch_floor=f0_min, - pitch_ceiling=f0_max, - ) - .selected_array["frequency"] - ) - pad_size = (p_len - len(f0) + 1) // 2 - if pad_size > 0 or p_len - len(f0) - pad_size > 0: - f0 = np.pad( - f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" - ) - elif f0_method == "harvest": - f0, t = pyworld.harvest( - x.astype(np.double), - fs=self.sr, - f0_ceil=f0_max, - f0_floor=f0_min, - frame_period=10, - ) - f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) - f0 = signal.medfilt(f0, 3) - f0 *= pow(2, f0_up_key / 12) - # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - tf0 = self.sr // self.window # 每秒f0点数 - if inp_f0 is not None: - delta_t = np.round( - (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 - ).astype("int16") - replace_f0 = np.interp( - list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] - ) - shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] - f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[ - :shape - ] - # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - f0bak = f0.copy() - f0_mel = 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( - f0_mel_max - f0_mel_min - ) + 1 - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > 255] = 255 - f0_coarse = np.rint(f0_mel).astype(np.int) - return f0_coarse, f0bak # 1-0 - - def vc( - self, - model, - net_g, - sid, - audio0, - pitch, - pitchf, - times, - index, - big_npy, - index_rate, - ): # ,file_index,file_big_npy - feats = torch.from_numpy(audio0) - if self.is_half: - feats = feats.half() - else: - feats = feats.float() - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) - - inputs = { - "source": feats.to(self.device), - "padding_mask": padding_mask, - "output_layer": 9, # layer 9 - } - t0 = ttime() - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = model.final_proj(logits[0]) - - if ( - isinstance(index, type(None)) == False - and isinstance(big_npy, type(None)) == False - and index_rate != 0 - ): - npy = feats[0].cpu().numpy() - if self.is_half: - npy = npy.astype("float32") - - # _, I = index.search(npy, 1) - # npy = big_npy[I.squeeze()] - - score, ix = index.search(npy, k=8) - weight = np.square(1 / score) - weight /= weight.sum(axis=1, keepdims=True) - npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) - - if self.is_half: - npy = npy.astype("float16") - feats = ( - torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate - + (1 - index_rate) * feats - ) - - feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) - t1 = ttime() - p_len = audio0.shape[0] // self.window - if feats.shape[1] < p_len: - p_len = feats.shape[1] - if pitch != None and pitchf != None: - pitch = pitch[:, :p_len] - pitchf = pitchf[:, :p_len] - p_len = torch.tensor([p_len], device=self.device).long() - with torch.no_grad(): - if pitch != None and pitchf != None: - audio1 = ( - (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768) - .data.cpu() - .float() - .numpy() - .astype(np.int16) - ) - else: - audio1 = ( - (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768) - .data.cpu() - .float() - .numpy() - .astype(np.int16) - ) - del feats, p_len, padding_mask - if torch.cuda.is_available(): - torch.cuda.empty_cache() - t2 = ttime() - times[0] += t1 - t0 - times[2] += t2 - t1 - return audio1 - - def pipeline( - self, - model, - net_g, - sid, - audio, - times, - f0_up_key, - f0_method, - file_index, - # file_big_npy, - index_rate, - if_f0, - f0_file=None, - ): - if ( - file_index != "" - # and file_big_npy != "" - # and os.path.exists(file_big_npy) == True - and os.path.exists(file_index) == True - and index_rate != 0 - ): - try: - index = faiss.read_index(file_index) - # big_npy = np.load(file_big_npy) - big_npy = index.reconstruct_n(0, index.ntotal) - except: - traceback.print_exc() - index = big_npy = None - else: - index = big_npy = None - audio = signal.filtfilt(bh, ah, audio) - audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") - opt_ts = [] - if audio_pad.shape[0] > self.t_max: - audio_sum = np.zeros_like(audio) - for i in range(self.window): - audio_sum += audio_pad[i : i - self.window] - for t in range(self.t_center, audio.shape[0], self.t_center): - opt_ts.append( - t - - self.t_query - + np.where( - np.abs(audio_sum[t - self.t_query : t + self.t_query]) - == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min() - )[0][0] - ) - s = 0 - audio_opt = [] - t = None - t1 = ttime() - audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") - p_len = audio_pad.shape[0] // self.window - inp_f0 = None - if hasattr(f0_file, "name") == True: - try: - with open(f0_file.name, "r") as f: - lines = f.read().strip("\n").split("\n") - inp_f0 = [] - for line in lines: - inp_f0.append([float(i) for i in line.split(",")]) - inp_f0 = np.array(inp_f0, dtype="float32") - except: - traceback.print_exc() - sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() - pitch, pitchf = None, None - if if_f0 == 1: - pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0) - pitch = pitch[:p_len] - pitchf = pitchf[:p_len] - pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() - pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() - t2 = ttime() - times[1] += t2 - t1 - for t in opt_ts: - t = t // self.window * self.window - if if_f0 == 1: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[s : t + self.t_pad2 + self.window], - pitch[:, s // self.window : (t + self.t_pad2) // self.window], - pitchf[:, s // self.window : (t + self.t_pad2) // self.window], - times, - index, - big_npy, - index_rate, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - else: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[s : t + self.t_pad2 + self.window], - None, - None, - times, - index, - big_npy, - index_rate, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - s = t - if if_f0 == 1: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[t:], - pitch[:, t // self.window :] if t is not None else pitch, - pitchf[:, t // self.window :] if t is not None else pitchf, - times, - index, - big_npy, - index_rate, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - else: - audio_opt.append( - self.vc( - model, - net_g, - sid, - audio_pad[t:], - None, - None, - times, - index, - big_npy, - index_rate, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - audio_opt = np.concatenate(audio_opt) - del pitch, pitchf, sid - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio_opt diff --git a/spaces/Reself/StableVideo/annotator/midas/midas/midas_net.py b/spaces/Reself/StableVideo/annotator/midas/midas/midas_net.py deleted file mode 100644 index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000 --- a/spaces/Reself/StableVideo/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/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/datasets/voc.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/datasets/voc.py deleted file mode 100644 index abd4cb8947238936faff48fc92c093c8ae06daff..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/datasets/voc.py +++ /dev/null @@ -1,93 +0,0 @@ -from collections import OrderedDict - -from mmcv.utils import print_log - -from mmdet.core import eval_map, eval_recalls -from .builder import DATASETS -from .xml_style import XMLDataset - - -@DATASETS.register_module() -class VOCDataset(XMLDataset): - - CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', - 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', - 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', - 'tvmonitor') - - def __init__(self, **kwargs): - super(VOCDataset, self).__init__(**kwargs) - if 'VOC2007' in self.img_prefix: - self.year = 2007 - elif 'VOC2012' in self.img_prefix: - self.year = 2012 - else: - raise ValueError('Cannot infer dataset year from img_prefix') - - def evaluate(self, - results, - metric='mAP', - logger=None, - proposal_nums=(100, 300, 1000), - iou_thr=0.5, - scale_ranges=None): - """Evaluate in VOC protocol. - - Args: - results (list[list | tuple]): Testing results of the dataset. - metric (str | list[str]): Metrics to be evaluated. Options are - 'mAP', 'recall'. - logger (logging.Logger | str, optional): Logger used for printing - related information during evaluation. Default: None. - proposal_nums (Sequence[int]): Proposal number used for evaluating - recalls, such as recall@100, recall@1000. - Default: (100, 300, 1000). - iou_thr (float | list[float]): IoU threshold. Default: 0.5. - scale_ranges (list[tuple], optional): Scale ranges for evaluating - mAP. If not specified, all bounding boxes would be included in - evaluation. Default: None. - - Returns: - dict[str, float]: AP/recall metrics. - """ - - if not isinstance(metric, str): - assert len(metric) == 1 - metric = metric[0] - allowed_metrics = ['mAP', 'recall'] - if metric not in allowed_metrics: - raise KeyError(f'metric {metric} is not supported') - annotations = [self.get_ann_info(i) for i in range(len(self))] - eval_results = OrderedDict() - iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr - if metric == 'mAP': - assert isinstance(iou_thrs, list) - if self.year == 2007: - ds_name = 'voc07' - else: - ds_name = self.CLASSES - mean_aps = [] - for iou_thr in iou_thrs: - print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') - mean_ap, _ = eval_map( - results, - annotations, - scale_ranges=None, - iou_thr=iou_thr, - dataset=ds_name, - logger=logger) - mean_aps.append(mean_ap) - eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) - eval_results['mAP'] = sum(mean_aps) / len(mean_aps) - elif metric == 'recall': - gt_bboxes = [ann['bboxes'] for ann in annotations] - recalls = eval_recalls( - gt_bboxes, results, proposal_nums, iou_thr, logger=logger) - for i, num in enumerate(proposal_nums): - for j, iou in enumerate(iou_thr): - eval_results[f'recall@{num}@{iou}'] = recalls[i, j] - if recalls.shape[1] > 1: - ar = recalls.mean(axis=1) - for i, num in enumerate(proposal_nums): - eval_results[f'AR@{num}'] = ar[i] - return eval_results diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py deleted file mode 100644 index ab6b3791692a0d1b5da3601875711710b7bd01ba..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/backbones/mobilenet_v2.py +++ /dev/null @@ -1,180 +0,0 @@ -import logging - -import torch.nn as nn -from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init -from annotator.uniformer.mmcv.runner import load_checkpoint -from torch.nn.modules.batchnorm import _BatchNorm - -from ..builder import BACKBONES -from ..utils import InvertedResidual, make_divisible - - -@BACKBONES.register_module() -class MobileNetV2(nn.Module): - """MobileNetV2 backbone. - - Args: - widen_factor (float): Width multiplier, multiply number of - channels in each layer by this amount. Default: 1.0. - strides (Sequence[int], optional): Strides of the first block of each - layer. If not specified, default config in ``arch_setting`` will - be used. - dilations (Sequence[int]): Dilation of each layer. - out_indices (None or Sequence[int]): Output from which stages. - Default: (7, ). - frozen_stages (int): Stages to be frozen (all param fixed). - Default: -1, which means not freezing any parameters. - conv_cfg (dict): Config dict for convolution layer. - Default: None, which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN'). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='ReLU6'). - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. Default: False. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. Default: False. - """ - - # Parameters to build layers. 3 parameters are needed to construct a - # layer, from left to right: expand_ratio, channel, num_blocks. - arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4], - [6, 96, 3], [6, 160, 3], [6, 320, 1]] - - def __init__(self, - widen_factor=1., - strides=(1, 2, 2, 2, 1, 2, 1), - dilations=(1, 1, 1, 1, 1, 1, 1), - out_indices=(1, 2, 4, 6), - frozen_stages=-1, - conv_cfg=None, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU6'), - norm_eval=False, - with_cp=False): - super(MobileNetV2, self).__init__() - self.widen_factor = widen_factor - self.strides = strides - self.dilations = dilations - assert len(strides) == len(dilations) == len(self.arch_settings) - self.out_indices = out_indices - for index in out_indices: - if index not in range(0, 7): - raise ValueError('the item in out_indices must in ' - f'range(0, 8). But received {index}') - - if frozen_stages not in range(-1, 7): - raise ValueError('frozen_stages must be in range(-1, 7). ' - f'But received {frozen_stages}') - self.out_indices = out_indices - self.frozen_stages = frozen_stages - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.norm_eval = norm_eval - self.with_cp = with_cp - - self.in_channels = make_divisible(32 * widen_factor, 8) - - self.conv1 = ConvModule( - in_channels=3, - out_channels=self.in_channels, - kernel_size=3, - stride=2, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg) - - self.layers = [] - - for i, layer_cfg in enumerate(self.arch_settings): - expand_ratio, channel, num_blocks = layer_cfg - stride = self.strides[i] - dilation = self.dilations[i] - out_channels = make_divisible(channel * widen_factor, 8) - inverted_res_layer = self.make_layer( - out_channels=out_channels, - num_blocks=num_blocks, - stride=stride, - dilation=dilation, - expand_ratio=expand_ratio) - layer_name = f'layer{i + 1}' - self.add_module(layer_name, inverted_res_layer) - self.layers.append(layer_name) - - def make_layer(self, out_channels, num_blocks, stride, dilation, - expand_ratio): - """Stack InvertedResidual blocks to build a layer for MobileNetV2. - - Args: - out_channels (int): out_channels of block. - num_blocks (int): Number of blocks. - stride (int): Stride of the first block. - dilation (int): Dilation of the first block. - expand_ratio (int): Expand the number of channels of the - hidden layer in InvertedResidual by this ratio. - """ - layers = [] - for i in range(num_blocks): - layers.append( - InvertedResidual( - self.in_channels, - out_channels, - stride if i == 0 else 1, - expand_ratio=expand_ratio, - dilation=dilation if i == 0 else 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg, - with_cp=self.with_cp)) - self.in_channels = out_channels - - return nn.Sequential(*layers) - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - logger = logging.getLogger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - x = self.conv1(x) - - outs = [] - for i, layer_name in enumerate(self.layers): - layer = getattr(self, layer_name) - x = layer(x) - if i in self.out_indices: - outs.append(x) - - if len(outs) == 1: - return outs[0] - else: - return tuple(outs) - - def _freeze_stages(self): - if self.frozen_stages >= 0: - for param in self.conv1.parameters(): - param.requires_grad = False - for i in range(1, self.frozen_stages + 1): - layer = getattr(self, f'layer{i}') - layer.eval() - for param in layer.parameters(): - param.requires_grad = False - - def train(self, mode=True): - super(MobileNetV2, self).train(mode) - self._freeze_stages() - if mode and self.norm_eval: - for m in self.modules(): - if isinstance(m, _BatchNorm): - m.eval() diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/decode_heads/decode_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/decode_heads/decode_head.py deleted file mode 100644 index 88a661b8f6fec5d4c031d3d85e80777ee63951a6..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/decode_heads/decode_head.py +++ /dev/null @@ -1,234 +0,0 @@ -from abc import ABCMeta, abstractmethod - -import torch -import torch.nn as nn -from annotator.uniformer.mmcv.cnn import normal_init -from annotator.uniformer.mmcv.runner import auto_fp16, force_fp32 - -from annotator.uniformer.mmseg.core import build_pixel_sampler -from annotator.uniformer.mmseg.ops import resize -from ..builder import build_loss -from ..losses import accuracy - - -class BaseDecodeHead(nn.Module, metaclass=ABCMeta): - """Base class for BaseDecodeHead. - - Args: - in_channels (int|Sequence[int]): Input channels. - channels (int): Channels after modules, before conv_seg. - num_classes (int): Number of classes. - dropout_ratio (float): Ratio of dropout layer. Default: 0.1. - conv_cfg (dict|None): Config of conv layers. Default: None. - norm_cfg (dict|None): Config of norm layers. Default: None. - act_cfg (dict): Config of activation layers. - Default: dict(type='ReLU') - in_index (int|Sequence[int]): Input feature index. Default: -1 - input_transform (str|None): Transformation type of input features. - Options: 'resize_concat', 'multiple_select', None. - 'resize_concat': Multiple feature maps will be resize to the - same size as first one and than concat together. - Usually used in FCN head of HRNet. - 'multiple_select': Multiple feature maps will be bundle into - a list and passed into decode head. - None: Only one select feature map is allowed. - Default: None. - loss_decode (dict): Config of decode loss. - Default: dict(type='CrossEntropyLoss'). - ignore_index (int | None): The label index to be ignored. When using - masked BCE loss, ignore_index should be set to None. Default: 255 - sampler (dict|None): The config of segmentation map sampler. - Default: None. - align_corners (bool): align_corners argument of F.interpolate. - Default: False. - """ - - def __init__(self, - in_channels, - channels, - *, - num_classes, - dropout_ratio=0.1, - conv_cfg=None, - norm_cfg=None, - act_cfg=dict(type='ReLU'), - in_index=-1, - input_transform=None, - loss_decode=dict( - type='CrossEntropyLoss', - use_sigmoid=False, - loss_weight=1.0), - ignore_index=255, - sampler=None, - align_corners=False): - super(BaseDecodeHead, self).__init__() - self._init_inputs(in_channels, in_index, input_transform) - self.channels = channels - self.num_classes = num_classes - self.dropout_ratio = dropout_ratio - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.in_index = in_index - self.loss_decode = build_loss(loss_decode) - self.ignore_index = ignore_index - self.align_corners = align_corners - if sampler is not None: - self.sampler = build_pixel_sampler(sampler, context=self) - else: - self.sampler = None - - self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1) - if dropout_ratio > 0: - self.dropout = nn.Dropout2d(dropout_ratio) - else: - self.dropout = None - self.fp16_enabled = False - - def extra_repr(self): - """Extra repr.""" - s = f'input_transform={self.input_transform}, ' \ - f'ignore_index={self.ignore_index}, ' \ - f'align_corners={self.align_corners}' - return s - - def _init_inputs(self, in_channels, in_index, input_transform): - """Check and initialize input transforms. - - The in_channels, in_index and input_transform must match. - Specifically, when input_transform is None, only single feature map - will be selected. So in_channels and in_index must be of type int. - When input_transform - - Args: - in_channels (int|Sequence[int]): Input channels. - in_index (int|Sequence[int]): Input feature index. - input_transform (str|None): Transformation type of input features. - Options: 'resize_concat', 'multiple_select', None. - 'resize_concat': Multiple feature maps will be resize to the - same size as first one and than concat together. - Usually used in FCN head of HRNet. - 'multiple_select': Multiple feature maps will be bundle into - a list and passed into decode head. - None: Only one select feature map is allowed. - """ - - if input_transform is not None: - assert input_transform in ['resize_concat', 'multiple_select'] - self.input_transform = input_transform - self.in_index = in_index - if input_transform is not None: - assert isinstance(in_channels, (list, tuple)) - assert isinstance(in_index, (list, tuple)) - assert len(in_channels) == len(in_index) - if input_transform == 'resize_concat': - self.in_channels = sum(in_channels) - else: - self.in_channels = in_channels - else: - assert isinstance(in_channels, int) - assert isinstance(in_index, int) - self.in_channels = in_channels - - def init_weights(self): - """Initialize weights of classification layer.""" - normal_init(self.conv_seg, mean=0, std=0.01) - - def _transform_inputs(self, inputs): - """Transform inputs for decoder. - - Args: - inputs (list[Tensor]): List of multi-level img features. - - Returns: - Tensor: The transformed inputs - """ - - if self.input_transform == 'resize_concat': - inputs = [inputs[i] for i in self.in_index] - upsampled_inputs = [ - resize( - input=x, - size=inputs[0].shape[2:], - mode='bilinear', - align_corners=self.align_corners) for x in inputs - ] - inputs = torch.cat(upsampled_inputs, dim=1) - elif self.input_transform == 'multiple_select': - inputs = [inputs[i] for i in self.in_index] - else: - inputs = inputs[self.in_index] - - return inputs - - @auto_fp16() - @abstractmethod - def forward(self, inputs): - """Placeholder of forward function.""" - pass - - def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg): - """Forward function for training. - Args: - inputs (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 - `mmseg/datasets/pipelines/formatting.py:Collect`. - gt_semantic_seg (Tensor): Semantic segmentation masks - used if the architecture supports semantic segmentation task. - train_cfg (dict): The training config. - - Returns: - dict[str, Tensor]: a dictionary of loss components - """ - seg_logits = self.forward(inputs) - losses = self.losses(seg_logits, gt_semantic_seg) - return losses - - def forward_test(self, inputs, img_metas, test_cfg): - """Forward function for testing. - - Args: - inputs (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 - `mmseg/datasets/pipelines/formatting.py:Collect`. - test_cfg (dict): The testing config. - - Returns: - Tensor: Output segmentation map. - """ - return self.forward(inputs) - - def cls_seg(self, feat): - """Classify each pixel.""" - if self.dropout is not None: - feat = self.dropout(feat) - output = self.conv_seg(feat) - return output - - @force_fp32(apply_to=('seg_logit', )) - def losses(self, seg_logit, seg_label): - """Compute segmentation loss.""" - loss = dict() - seg_logit = resize( - input=seg_logit, - size=seg_label.shape[2:], - mode='bilinear', - align_corners=self.align_corners) - if self.sampler is not None: - seg_weight = self.sampler.sample(seg_logit, seg_label) - else: - seg_weight = None - seg_label = seg_label.squeeze(1) - loss['loss_seg'] = self.loss_decode( - seg_logit, - seg_label, - weight=seg_weight, - ignore_index=self.ignore_index) - loss['acc_seg'] = accuracy(seg_logit, seg_label) - return loss diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/dist_utils.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/dist_utils.py deleted file mode 100644 index d3a1ef3fda5ceeb31bf15a73779da1b1903ab0fe..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/dist_utils.py +++ /dev/null @@ -1,164 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import functools -import os -import subprocess -from collections import OrderedDict - -import torch -import torch.multiprocessing as mp -from torch import distributed as dist -from torch._utils import (_flatten_dense_tensors, _take_tensors, - _unflatten_dense_tensors) - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'mpi': - _init_dist_mpi(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - # TODO: use local_rank instead of rank % num_gpus - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_mpi(backend, **kwargs): - # TODO: use local_rank instead of rank % num_gpus - rank = int(os.environ['OMPI_COMM_WORLD_RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput( - f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - # use MASTER_ADDR in the environment variable if it already exists - if 'MASTER_ADDR' not in os.environ: - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available() and dist.is_initialized(): - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper - - -def allreduce_params(params, coalesce=True, bucket_size_mb=-1): - """Allreduce parameters. - - Args: - params (list[torch.Parameters]): List of parameters or buffers of a - model. - coalesce (bool, optional): Whether allreduce parameters as a whole. - Defaults to True. - bucket_size_mb (int, optional): Size of bucket, the unit is MB. - Defaults to -1. - """ - _, world_size = get_dist_info() - if world_size == 1: - return - params = [param.data for param in params] - if coalesce: - _allreduce_coalesced(params, world_size, bucket_size_mb) - else: - for tensor in params: - dist.all_reduce(tensor.div_(world_size)) - - -def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): - """Allreduce gradients. - - Args: - params (list[torch.Parameters]): List of parameters of a model - coalesce (bool, optional): Whether allreduce parameters as a whole. - Defaults to True. - bucket_size_mb (int, optional): Size of bucket, the unit is MB. - Defaults to -1. - """ - grads = [ - param.grad.data for param in params - if param.requires_grad and param.grad is not None - ] - _, world_size = get_dist_info() - if world_size == 1: - return - if coalesce: - _allreduce_coalesced(grads, world_size, bucket_size_mb) - else: - for tensor in grads: - dist.all_reduce(tensor.div_(world_size)) - - -def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): - if bucket_size_mb > 0: - bucket_size_bytes = bucket_size_mb * 1024 * 1024 - buckets = _take_tensors(tensors, bucket_size_bytes) - else: - buckets = OrderedDict() - for tensor in tensors: - tp = tensor.type() - if tp not in buckets: - buckets[tp] = [] - buckets[tp].append(tensor) - buckets = buckets.values() - - for bucket in buckets: - flat_tensors = _flatten_dense_tensors(bucket) - dist.all_reduce(flat_tensors) - flat_tensors.div_(world_size) - for tensor, synced in zip( - bucket, _unflatten_dense_tensors(flat_tensors, bucket)): - tensor.copy_(synced) diff --git a/spaces/Ryukijano/fastai_pet_classifier_resnet50/README.md b/spaces/Ryukijano/fastai_pet_classifier_resnet50/README.md deleted file mode 100644 index e060f1246d566e43dfdba9f80cf16e30fc06a8a7..0000000000000000000000000000000000000000 --- a/spaces/Ryukijano/fastai_pet_classifier_resnet50/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Fast Ai Pet Classifier Restnet50 -emoji: 🌍 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/SamiAlghamdi/FirstEver/app.py b/spaces/SamiAlghamdi/FirstEver/app.py deleted file mode 100644 index c28f2ebc67cf45e221443e2668dca90c4b3e8147..0000000000000000000000000000000000000000 --- a/spaces/SamiAlghamdi/FirstEver/app.py +++ /dev/null @@ -1,15 +0,0 @@ - - -import pandas as pd -from transformers import pipeline -import gradio as gr - -# Initialize sentiment analysis pipeline -sentiment_pipeline = pipeline('sentiment-analysis') - -def analyze_text(text): - sentiment = sentiment_pipeline(text)[0] - return sentiment['label'] - -iface = gr.Interface(fn=analyze_text, inputs=gr.inputs.Textbox(lines=13, label="Enter Text"), outputs="text") -iface.launch() diff --git a/spaces/SantiagoTesla/Self_Chatbot/app.py b/spaces/SantiagoTesla/Self_Chatbot/app.py deleted file mode 100644 index 33d70c586d57d4ae738b649515398877542b5efe..0000000000000000000000000000000000000000 --- a/spaces/SantiagoTesla/Self_Chatbot/app.py +++ /dev/null @@ -1,37 +0,0 @@ -import gradio as gr -from transformers import AutoModelForCausalLM, AutoTokenizer -import torch -tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") -model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") - - -def chatbot(input): - - #loop length = number of chats - for step in range(50): - # take user input - #text = input(">> You: ") - # encode the input and add end of string token - input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt") - # concatenate new user input with chat history (if there is) - bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids - # generate a bot response - chat_history_ids = model.generate( - bot_input_ids, - max_length=1000, - do_sample=True, - top_p=0.95, - top_k=0, - temperature=0.75, - pad_token_id=tokenizer.eos_token_id - ) - #print the output - output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) - return output - -inputs = gr.inputs.Textbox(lines=7, label="Chat with AI") -outputs = gr.outputs.Textbox(label="Reply") - -gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Self_Trained_V1", - description="Ask anything you want", - ).launch() \ No newline at end of file diff --git a/spaces/Sapphire-356/Video2MC/model/block/vanilla_transformer_encoder_pretrain.py b/spaces/Sapphire-356/Video2MC/model/block/vanilla_transformer_encoder_pretrain.py deleted file mode 100644 index bb748b16f89c8aae557fa5d35dba0053c3b312c5..0000000000000000000000000000000000000000 --- a/spaces/Sapphire-356/Video2MC/model/block/vanilla_transformer_encoder_pretrain.py +++ /dev/null @@ -1,158 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Variable -import numpy as np -import math -import os -import copy - -def clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) - -class Encoder(nn.Module): - def __init__(self, layer, N): - super(Encoder, self).__init__() - self.layers = clones(layer, N) - self.norm = LayerNorm(layer.size) - - def forward(self, x, mask): - for layer in self.layers: - x = layer(x, mask) - return x - -class LayerNorm(nn.Module): - def __init__(self, features, eps=1e-6): - super(LayerNorm, self).__init__() - self.a_2 = nn.Parameter(torch.ones(features)) - self.b_2 = nn.Parameter(torch.zeros(features)) - self.eps = eps - - def forward(self, x): - mean = x.mean(-1, keepdim=True) - std = x.std(-1, keepdim=True) - return self.a_2 * (x - mean) / (std + self.eps) + self.b_2 - -def attention(query, key, value, mask=None, dropout=None): - d_k = query.size(-1) - scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) - - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e9) - p_attn = F.softmax(scores, dim=-1) - - if dropout is not None: - p_attn = dropout(p_attn) - return torch.matmul(p_attn, value), p_attn - - -class SublayerConnection(nn.Module): - def __init__(self, size, dropout): - super(SublayerConnection, self).__init__() - self.norm = LayerNorm(size) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, sublayer): - return x + self.dropout(sublayer(self.norm(x))) - - -class EncoderLayer(nn.Module): - def __init__(self, size, self_attn, feed_forward, dropout): - super(EncoderLayer, self).__init__() - self.self_attn = self_attn - self.feed_forward = feed_forward - self.sublayer = clones(SublayerConnection(size, dropout), 2) - self.size = size - - def forward(self, x, mask): - x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask)) - return self.sublayer[1](x, self.feed_forward) - - -class MultiHeadedAttention(nn.Module): - def __init__(self, h, d_model, dropout=0.1): - super(MultiHeadedAttention, self).__init__() - assert d_model % h == 0 - self.d_k = d_model // h - self.h = h - self.linears = clones(nn.Linear(d_model, d_model), 4) - self.attn = None - self.dropout = nn.Dropout(p=dropout) - - def forward(self, query, key, value, mask=None): - if mask is not None: - mask = mask.unsqueeze(1) - nbatches = query.size(0) - - query, key, value = \ - [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) - for l, x in zip(self.linears, (query, key, value))] - - x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout) - - x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k) - return self.linears[-1](x) - - -class PositionwiseFeedForward(nn.Module): - def __init__(self, d_model, d_ff, dropout=0.1): - super(PositionwiseFeedForward, self).__init__() - self.w_1 = nn.Linear(d_model, d_ff) - self.w_2 = nn.Linear(d_ff, d_model) - self.gelu = nn.ReLU() - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - return self.w_2(self.dropout(self.gelu(self.w_1(x)))) - -class Transformer(nn.Module): - def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27): - super(Transformer, self).__init__() - - self.pos_embedding = nn.Parameter(torch.randn(1, length, d_model)) - self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout) - - def forward(self, x, mask_MAE=None, mask=None): - x += self.pos_embedding - #print(str(mask_MAE)) - if mask_MAE is not None: - B, _, C = x.shape - x_vis = x[:,~mask_MAE].reshape(B, -1, C) # ~mask means visible - - x = self.model(x_vis, mask) - else: - x = self.model(x, mask) - - return x - - def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1): - c = copy.deepcopy - attn = MultiHeadedAttention(h, d_model) - ff = PositionwiseFeedForward(d_model, d_ff, dropout) - model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N) - return model - - -class Transformer_dec(nn.Module): - def __init__(self, n_layers=3, d_model=256, d_ff=512, h=8, dropout=0.1, length=27): - super(Transformer_dec, self).__init__() - - self.model = self.make_model(N=n_layers, d_model=d_model, d_ff=d_ff, h=h, dropout=dropout) - - - def forward(self, x, return_token_num, mask=None): - - x = self.model(x, mask) - - return x - - def make_model(self, N=3, d_model=256, d_ff=512, h=8, dropout=0.1): - c = copy.deepcopy - attn = MultiHeadedAttention(h, d_model) - ff = PositionwiseFeedForward(d_model, d_ff, dropout) - model = Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N) - return model - - - - diff --git a/spaces/SeViLA/SeViLA/lavis/datasets/builders/base_dataset_builder.py b/spaces/SeViLA/SeViLA/lavis/datasets/builders/base_dataset_builder.py deleted file mode 100644 index 37b7f46ce2f3c99a9a9e5b4facf00811d2107512..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/datasets/builders/base_dataset_builder.py +++ /dev/null @@ -1,233 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -import logging -import os -import shutil -import warnings - -import lavis.common.utils as utils -import torch.distributed as dist -from lavis.common.dist_utils import is_dist_avail_and_initialized, is_main_process -from lavis.common.registry import registry -from lavis.datasets.data_utils import extract_archive -from lavis.processors.base_processor import BaseProcessor -from omegaconf import OmegaConf -from torchvision.datasets.utils import download_url - - -class BaseDatasetBuilder: - train_dataset_cls, eval_dataset_cls = None, None - - def __init__(self, cfg=None): - super().__init__() - - if cfg is None: - # help to create datasets from default config. - self.config = load_dataset_config(self.default_config_path()) - elif isinstance(cfg, str): - self.config = load_dataset_config(cfg) - else: - # when called from task.build_dataset() - self.config = cfg - - self.data_type = self.config.data_type - self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()} - self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()} - - def build_datasets(self): - # download, split, etc... - # only called on 1 GPU/TPU in distributed - - if is_main_process(): - self._download_data() - - if is_dist_avail_and_initialized(): - dist.barrier() - - # at this point, all the annotations and image/videos should be all downloaded to the specified locations. - logging.info("Building datasets...") - datasets = self.build() # dataset['train'/'val'/'test'] - - return datasets - - def build_processors(self): - vis_proc_cfg = self.config.get("vis_processor") - txt_proc_cfg = self.config.get("text_processor") - - if vis_proc_cfg is not None: - vis_train_cfg = vis_proc_cfg.get("train") - vis_eval_cfg = vis_proc_cfg.get("eval") - - self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg) - self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg) - - if txt_proc_cfg is not None: - txt_train_cfg = txt_proc_cfg.get("train") - txt_eval_cfg = txt_proc_cfg.get("eval") - - self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg) - self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg) - - @staticmethod - def _build_proc_from_cfg(cfg): - return ( - registry.get_processor_class(cfg.name).from_config(cfg) - if cfg is not None - else None - ) - - @classmethod - def default_config_path(cls, type="default"): - return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type]) - - def _download_data(self): - self._download_ann() - self._download_vis() - - def _download_ann(self): - """ - Download annotation files if necessary. - All the vision-language datasets should have annotations of unified format. - - storage_path can be: - (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative. - (2) basename/dirname: will be suffixed with base name of URL if dirname is provided. - - Local annotation paths should be relative. - """ - anns = self.config.build_info.annotations - - splits = anns.keys() - - cache_root = registry.get_path("cache_root") - - for split in splits: - info = anns[split] - - urls, storage_paths = info.get("url", None), info.storage - - if isinstance(urls, str): - urls = [urls] - if isinstance(storage_paths, str): - storage_paths = [storage_paths] - - assert len(urls) == len(storage_paths) - - for url_or_filename, storage_path in zip(urls, storage_paths): - # if storage_path is relative, make it full by prefixing with cache_root. - if not os.path.isabs(storage_path): - storage_path = os.path.join(cache_root, storage_path) - - dirname = os.path.dirname(storage_path) - if not os.path.exists(dirname): - os.makedirs(dirname) - - if os.path.isfile(url_or_filename): - src, dst = url_or_filename, storage_path - if not os.path.exists(dst): - shutil.copyfile(src=src, dst=dst) - else: - logging.info("Using existing file {}.".format(dst)) - else: - if os.path.isdir(storage_path): - # if only dirname is provided, suffix with basename of URL. - raise ValueError( - "Expecting storage_path to be a file path, got directory {}".format( - storage_path - ) - ) - else: - filename = os.path.basename(storage_path) - - download_url(url=url_or_filename, root=dirname, filename=filename) - - def _download_vis(self): - - storage_path = self.config.build_info.get(self.data_type).storage - storage_path = utils.get_cache_path(storage_path) - - if not os.path.exists(storage_path): - warnings.warn( - f""" - The specified path {storage_path} for visual inputs does not exist. - Please provide a correct path to the visual inputs or - refer to datasets/download_scripts/README.md for downloading instructions. - """ - ) - - def build(self): - """ - Create by split datasets inheriting torch.utils.data.Datasets. - - # build() can be dataset-specific. Overwrite to customize. - """ - self.build_processors() - - build_info = self.config.build_info - - ann_info = build_info.annotations - vis_info = build_info.get(self.data_type) - - datasets = dict() - for split in ann_info.keys(): - if split not in ["train", "val", "test"]: - continue - - is_train = split == "train" - - # processors - vis_processor = ( - self.vis_processors["train"] - if is_train - else self.vis_processors["eval"] - ) - text_processor = ( - self.text_processors["train"] - if is_train - else self.text_processors["eval"] - ) - - # annotation path - ann_paths = ann_info.get(split).storage - if isinstance(ann_paths, str): - ann_paths = [ann_paths] - - abs_ann_paths = [] - for ann_path in ann_paths: - if not os.path.isabs(ann_path): - ann_path = utils.get_cache_path(ann_path) - abs_ann_paths.append(ann_path) - ann_paths = abs_ann_paths - - # visual data storage path - vis_path = vis_info.storage - #print('vis_path',vis_path) - if not os.path.isabs(vis_path): - # vis_path = os.path.join(utils.get_cache_path(), vis_path) - vis_path = utils.get_cache_path(vis_path) - #print('vis_path2', vis_path) - if not os.path.exists(vis_path): - warnings.warn("storage path {} does not exist.".format(vis_path)) - - # create datasets - dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls - datasets[split] = dataset_cls( - vis_processor=vis_processor, - text_processor=text_processor, - ann_paths=ann_paths, - vis_root=vis_path, - ) - - return datasets - - -def load_dataset_config(cfg_path): - cfg = OmegaConf.load(cfg_path).datasets - cfg = cfg[list(cfg.keys())[0]] - - return cfg diff --git a/spaces/Shakeb100/GroomingGenie_AI/clipseg/datasets/phrasecut.py b/spaces/Shakeb100/GroomingGenie_AI/clipseg/datasets/phrasecut.py deleted file mode 100644 index ef0c5350583c33c64682a35af3d314b02831569c..0000000000000000000000000000000000000000 --- a/spaces/Shakeb100/GroomingGenie_AI/clipseg/datasets/phrasecut.py +++ /dev/null @@ -1,335 +0,0 @@ - -import torch -import numpy as np -import os - -from os.path import join, isdir, isfile, expanduser -from PIL import Image - -from torchvision import transforms -from torchvision.transforms.transforms import Resize - -from torch.nn import functional as nnf -from general_utils import get_from_repository - -from skimage.draw import polygon2mask - - - -def random_crop_slices(origin_size, target_size): - """Gets slices of a random crop. """ - assert origin_size[0] >= target_size[0] and origin_size[1] >= target_size[1], f'actual size: {origin_size}, target size: {target_size}' - - offset_y = torch.randint(0, origin_size[0] - target_size[0] + 1, (1,)).item() # range: 0 <= value < high - offset_x = torch.randint(0, origin_size[1] - target_size[1] + 1, (1,)).item() - - return slice(offset_y, offset_y + target_size[0]), slice(offset_x, offset_x + target_size[1]) - - -def find_crop(seg, image_size, iterations=1000, min_frac=None, best_of=None): - - - best_crops = [] - best_crop_not_ok = float('-inf'), None, None - min_sum = 0 - - seg = seg.astype('bool') - - if min_frac is not None: - #min_sum = seg.sum() * min_frac - min_sum = seg.shape[0] * seg.shape[1] * min_frac - - for iteration in range(iterations): - sl_y, sl_x = random_crop_slices(seg.shape, image_size) - seg_ = seg[sl_y, sl_x] - sum_seg_ = seg_.sum() - - if sum_seg_ > min_sum: - - if best_of is None: - return sl_y, sl_x, False - else: - best_crops += [(sum_seg_, sl_y, sl_x)] - if len(best_crops) >= best_of: - best_crops.sort(key=lambda x:x[0], reverse=True) - sl_y, sl_x = best_crops[0][1:] - - return sl_y, sl_x, False - - else: - if sum_seg_ > best_crop_not_ok[0]: - best_crop_not_ok = sum_seg_, sl_y, sl_x - - else: - # return best segmentation found - return best_crop_not_ok[1:] + (best_crop_not_ok[0] <= min_sum,) - - -class PhraseCut(object): - - def __init__(self, split, image_size=400, negative_prob=0, aug=None, aug_color=False, aug_crop=True, - min_size=0, remove_classes=None, with_visual=False, only_visual=False, mask=None): - super().__init__() - - self.negative_prob = negative_prob - self.image_size = image_size - self.with_visual = with_visual - self.only_visual = only_visual - self.phrase_form = '{}' - self.mask = mask - self.aug_crop = aug_crop - - if aug_color: - self.aug_color = transforms.Compose([ - transforms.ColorJitter(0.5, 0.5, 0.2, 0.05), - ]) - else: - self.aug_color = None - - get_from_repository('PhraseCut', ['PhraseCut.tar'], integrity_check=lambda local_dir: all([ - isdir(join(local_dir, 'VGPhraseCut_v0')), - isdir(join(local_dir, 'VGPhraseCut_v0', 'images')), - isfile(join(local_dir, 'VGPhraseCut_v0', 'refer_train.json')), - len(os.listdir(join(local_dir, 'VGPhraseCut_v0', 'images'))) in {108250, 108249} - ])) - - from third_party.PhraseCutDataset.utils.refvg_loader import RefVGLoader - self.refvg_loader = RefVGLoader(split=split) - - # img_ids where the size in the annotations does not match actual size - invalid_img_ids = set([150417, 285665, 498246, 61564, 285743, 498269, 498010, 150516, 150344, 286093, 61530, - 150333, 286065, 285814, 498187, 285761, 498042]) - - mean = [0.485, 0.456, 0.406] - std = [0.229, 0.224, 0.225] - self.normalize = transforms.Normalize(mean, std) - - self.sample_ids = [(i, j) - for i in self.refvg_loader.img_ids - for j in range(len(self.refvg_loader.get_img_ref_data(i)['phrases'])) - if i not in invalid_img_ids] - - - # self.all_phrases = list(set([p for i in self.refvg_loader.img_ids for p in self.refvg_loader.get_img_ref_data(i)['phrases']])) - - from nltk.stem import WordNetLemmatizer - wnl = WordNetLemmatizer() - - # Filter by class (if remove_classes is set) - if remove_classes is None: - pass - else: - from datasets.generate_lvis_oneshot import PASCAL_SYNSETS, traverse_lemmas, traverse_lemmas_hypo - from nltk.corpus import wordnet - - print('remove pascal classes...') - - get_data = self.refvg_loader.get_img_ref_data # shortcut - keep_sids = None - - if remove_classes[0] == 'pas5i': - subset_id = remove_classes[1] - from datasets.generate_lvis_oneshot import PASCAL_5I_SYNSETS_ORDERED, PASCAL_5I_CLASS_IDS - avoid = [PASCAL_5I_SYNSETS_ORDERED[i] for i in range(20) if i+1 not in PASCAL_5I_CLASS_IDS[subset_id]] - - - elif remove_classes[0] == 'zs': - stop = remove_classes[1] - - from datasets.pascal_zeroshot import PASCAL_VOC_CLASSES_ZS - - avoid = [c for class_set in PASCAL_VOC_CLASSES_ZS[:stop] for c in class_set] - print(avoid) - - elif remove_classes[0] == 'aff': - # avoid = ['drink.v.01', 'sit.v.01', 'ride.v.02'] - # all_lemmas = set(['drink', 'sit', 'ride']) - avoid = ['drink', 'drinks', 'drinking', 'sit', 'sits', 'sitting', - 'ride', 'rides', 'riding', - 'fly', 'flies', 'flying', 'drive', 'drives', 'driving', 'driven', - 'swim', 'swims', 'swimming', - 'wheels', 'wheel', 'legs', 'leg', 'ear', 'ears'] - keep_sids = [(i, j) for i, j in self.sample_ids if - all(x not in avoid for x in get_data(i)['phrases'][j].split(' '))] - - print('avoid classes:', avoid) - - - if keep_sids is None: - all_lemmas = [s for ps in avoid for s in traverse_lemmas_hypo(wordnet.synset(ps), max_depth=None)] - all_lemmas = list(set(all_lemmas)) - all_lemmas = [h.replace('_', ' ').lower() for h in all_lemmas] - all_lemmas = set(all_lemmas) - - # divide into multi word and single word - all_lemmas_s = set(l for l in all_lemmas if ' ' not in l) - all_lemmas_m = set(l for l in all_lemmas if l not in all_lemmas_s) - - # new3 - phrases = [get_data(i)['phrases'][j] for i, j in self.sample_ids] - remove_sids = set((i,j) for (i,j), phrase in zip(self.sample_ids, phrases) - if any(l in phrase for l in all_lemmas_m) or - len(set(wnl.lemmatize(w) for w in phrase.split(' ')).intersection(all_lemmas_s)) > 0 - ) - keep_sids = [(i, j) for i, j in self.sample_ids if (i,j) not in remove_sids] - - print(f'Reduced to {len(keep_sids) / len(self.sample_ids):.3f}') - removed_ids = set(self.sample_ids) - set(keep_sids) - - print('Examples of removed', len(removed_ids)) - for i, j in list(removed_ids)[:20]: - print(i, get_data(i)['phrases'][j]) - - self.sample_ids = keep_sids - - from itertools import groupby - samples_by_phrase = [(self.refvg_loader.get_img_ref_data(i)['phrases'][j], (i, j)) - for i, j in self.sample_ids] - samples_by_phrase = sorted(samples_by_phrase) - samples_by_phrase = groupby(samples_by_phrase, key=lambda x: x[0]) - - self.samples_by_phrase = {prompt: [s[1] for s in prompt_sample_ids] for prompt, prompt_sample_ids in samples_by_phrase} - - self.all_phrases = list(set(self.samples_by_phrase.keys())) - - - if self.only_visual: - assert self.with_visual - self.sample_ids = [(i, j) for i, j in self.sample_ids - if len(self.samples_by_phrase[self.refvg_loader.get_img_ref_data(i)['phrases'][j]]) > 1] - - # Filter by size (if min_size is set) - sizes = [self.refvg_loader.get_img_ref_data(i)['gt_boxes'][j] for i, j in self.sample_ids] - image_sizes = [self.refvg_loader.get_img_ref_data(i)['width'] * self.refvg_loader.get_img_ref_data(i)['height'] for i, j in self.sample_ids] - #self.sizes = [sum([(s[2] - s[0]) * (s[3] - s[1]) for s in size]) for size in sizes] - self.sizes = [sum([s[2] * s[3] for s in size]) / img_size for size, img_size in zip(sizes, image_sizes)] - - if min_size: - print('filter by size') - - self.sample_ids = [self.sample_ids[i] for i in range(len(self.sample_ids)) if self.sizes[i] > min_size] - - self.base_path = join(expanduser('~/datasets/PhraseCut/VGPhraseCut_v0/images/')) - - def __len__(self): - return len(self.sample_ids) - - - def load_sample(self, sample_i, j): - - img_ref_data = self.refvg_loader.get_img_ref_data(sample_i) - - polys_phrase0 = img_ref_data['gt_Polygons'][j] - phrase = img_ref_data['phrases'][j] - phrase = self.phrase_form.format(phrase) - - masks = [] - for polys in polys_phrase0: - for poly in polys: - poly = [p[::-1] for p in poly] # swap x,y - masks += [polygon2mask((img_ref_data['height'], img_ref_data['width']), poly)] - - seg = np.stack(masks).max(0) - img = np.array(Image.open(join(self.base_path, str(img_ref_data['image_id']) + '.jpg'))) - - min_shape = min(img.shape[:2]) - - if self.aug_crop: - sly, slx, exceed = find_crop(seg, (min_shape, min_shape), iterations=50, min_frac=0.05) - else: - sly, slx = slice(0, None), slice(0, None) - - seg = seg[sly, slx] - img = img[sly, slx] - - seg = seg.astype('uint8') - seg = torch.from_numpy(seg).view(1, 1, *seg.shape) - - if img.ndim == 2: - img = np.dstack([img] * 3) - - img = torch.from_numpy(img).permute(2,0,1).unsqueeze(0).float() - - seg = nnf.interpolate(seg, (self.image_size, self.image_size), mode='nearest')[0,0] - img = nnf.interpolate(img, (self.image_size, self.image_size), mode='bilinear', align_corners=True)[0] - - # img = img.permute([2,0, 1]) - img = img / 255.0 - - if self.aug_color is not None: - img = self.aug_color(img) - - img = self.normalize(img) - - - - return img, seg, phrase - - def __getitem__(self, i): - - sample_i, j = self.sample_ids[i] - - img, seg, phrase = self.load_sample(sample_i, j) - - if self.negative_prob > 0: - if torch.rand((1,)).item() < self.negative_prob: - - new_phrase = None - while new_phrase is None or new_phrase == phrase: - idx = torch.randint(0, len(self.all_phrases), (1,)).item() - new_phrase = self.all_phrases[idx] - phrase = new_phrase - seg = torch.zeros_like(seg) - - if self.with_visual: - # find a corresponding visual image - if phrase in self.samples_by_phrase and len(self.samples_by_phrase[phrase]) > 1: - idx = torch.randint(0, len(self.samples_by_phrase[phrase]), (1,)).item() - other_sample = self.samples_by_phrase[phrase][idx] - #print(other_sample) - img_s, seg_s, _ = self.load_sample(*other_sample) - - from datasets.utils import blend_image_segmentation - - if self.mask in {'separate', 'text_and_separate'}: - # assert img.shape[1:] == img_s.shape[1:] == seg_s.shape == seg.shape[1:] - add_phrase = [phrase] if self.mask == 'text_and_separate' else [] - vis_s = add_phrase + [img_s, seg_s, True] - else: - if self.mask.startswith('text_and_'): - mask_mode = self.mask[9:] - label_add = [phrase] - else: - mask_mode = self.mask - label_add = [] - - masked_img_s = torch.from_numpy(blend_image_segmentation(img_s, seg_s, mode=mask_mode, image_size=self.image_size)[0]) - vis_s = label_add + [masked_img_s, True] - - else: - # phrase is unique - vis_s = torch.zeros_like(img) - - if self.mask in {'separate', 'text_and_separate'}: - add_phrase = [phrase] if self.mask == 'text_and_separate' else [] - vis_s = add_phrase + [vis_s, torch.zeros(*vis_s.shape[1:], dtype=torch.uint8), False] - elif self.mask.startswith('text_and_'): - vis_s = [phrase, vis_s, False] - else: - vis_s = [vis_s, False] - else: - assert self.mask == 'text' - vis_s = [phrase] - - seg = seg.unsqueeze(0).float() - - data_x = (img,) + tuple(vis_s) - - return data_x, (seg, torch.zeros(0), i) - - -class PhraseCutPlus(PhraseCut): - - def __init__(self, split, image_size=400, aug=None, aug_color=False, aug_crop=True, min_size=0, remove_classes=None, only_visual=False, mask=None): - super().__init__(split, image_size=image_size, negative_prob=0.2, aug=aug, aug_color=aug_color, aug_crop=aug_crop, min_size=min_size, - remove_classes=remove_classes, with_visual=True, only_visual=only_visual, mask=mask) \ No newline at end of file diff --git a/spaces/SimianLuo/Latent_Consistency_Model/lcm_scheduler.py b/spaces/SimianLuo/Latent_Consistency_Model/lcm_scheduler.py deleted file mode 100644 index 1764beebfc23b6597190b643d7d751e3c69be2c3..0000000000000000000000000000000000000000 --- a/spaces/SimianLuo/Latent_Consistency_Model/lcm_scheduler.py +++ /dev/null @@ -1,479 +0,0 @@ -# Copyright 2023 Stanford University Team and 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. - -# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion -# and https://github.com/hojonathanho/diffusion - -import math -from dataclasses import dataclass -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch - -from diffusers import ConfigMixin, SchedulerMixin -from diffusers.configuration_utils import register_to_config -from diffusers.utils import BaseOutput - - -@dataclass -# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM -class LCMSchedulerOutput(BaseOutput): - """ - Output class for the scheduler's `step` function output. - - Args: - prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): - Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the - denoising loop. - pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): - The predicted denoised sample `(x_{0})` based on the model output from the current timestep. - `pred_original_sample` can be used to preview progress or for guidance. - """ - - prev_sample: torch.FloatTensor - denoised: Optional[torch.FloatTensor] = None - - -# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar -def betas_for_alpha_bar( - num_diffusion_timesteps, - max_beta=0.999, - alpha_transform_type="cosine", -): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of - (1-beta) over time from t = [0,1]. - - Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up - to that part of the diffusion process. - - - Args: - num_diffusion_timesteps (`int`): the number of betas to produce. - max_beta (`float`): the maximum beta to use; use values lower than 1 to - prevent singularities. - alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. - Choose from `cosine` or `exp` - - Returns: - betas (`np.ndarray`): the betas used by the scheduler to step the model outputs - """ - if alpha_transform_type == "cosine": - - def alpha_bar_fn(t): - return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 - - elif alpha_transform_type == "exp": - - def alpha_bar_fn(t): - return math.exp(t * -12.0) - - else: - raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") - - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) - return torch.tensor(betas, dtype=torch.float32) - - -def rescale_zero_terminal_snr(betas): - """ - Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) - - - Args: - betas (`torch.FloatTensor`): - the betas that the scheduler is being initialized with. - - Returns: - `torch.FloatTensor`: rescaled betas with zero terminal SNR - """ - # Convert betas to alphas_bar_sqrt - alphas = 1.0 - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - alphas_bar_sqrt = alphas_cumprod.sqrt() - - # Store old values. - alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() - alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() - - # Shift so the last timestep is zero. - alphas_bar_sqrt -= alphas_bar_sqrt_T - - # Scale so the first timestep is back to the old value. - alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) - - # Convert alphas_bar_sqrt to betas - alphas_bar = alphas_bar_sqrt**2 # Revert sqrt - alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod - alphas = torch.cat([alphas_bar[0:1], alphas]) - betas = 1 - alphas - - return betas - - -class LCMScheduler(SchedulerMixin, ConfigMixin): - """ - `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with - non-Markovian guidance. - - This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic - methods the library implements for all schedulers such as loading and saving. - - Args: - num_train_timesteps (`int`, defaults to 1000): - The number of diffusion steps to train the model. - beta_start (`float`, defaults to 0.0001): - The starting `beta` value of inference. - beta_end (`float`, defaults to 0.02): - The final `beta` value. - beta_schedule (`str`, defaults to `"linear"`): - The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from - `linear`, `scaled_linear`, or `squaredcos_cap_v2`. - trained_betas (`np.ndarray`, *optional*): - Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. - clip_sample (`bool`, defaults to `True`): - Clip the predicted sample for numerical stability. - clip_sample_range (`float`, defaults to 1.0): - The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. - set_alpha_to_one (`bool`, defaults to `True`): - Each diffusion step uses the alphas product value at that step and at the previous one. For the final step - there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, - otherwise it uses the alpha value at step 0. - steps_offset (`int`, defaults to 0): - An offset added to the inference steps. You can use a combination of `offset=1` and - `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable - Diffusion. - prediction_type (`str`, defaults to `epsilon`, *optional*): - Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), - `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen - Video](https://imagen.research.google/video/paper.pdf) paper). - thresholding (`bool`, defaults to `False`): - Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such - as Stable Diffusion. - dynamic_thresholding_ratio (`float`, defaults to 0.995): - The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. - sample_max_value (`float`, defaults to 1.0): - The threshold value for dynamic thresholding. Valid only when `thresholding=True`. - timestep_spacing (`str`, defaults to `"leading"`): - The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. - rescale_betas_zero_snr (`bool`, defaults to `False`): - Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and - dark samples instead of limiting it to samples with medium brightness. Loosely related to - [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). - """ - - # _compatibles = [e.name for e in KarrasDiffusionSchedulers] - order = 1 - - @register_to_config - def __init__( - self, - num_train_timesteps: int = 1000, - beta_start: float = 0.0001, - beta_end: float = 0.02, - beta_schedule: str = "linear", - trained_betas: Optional[Union[np.ndarray, List[float]]] = None, - clip_sample: bool = True, - set_alpha_to_one: bool = True, - steps_offset: int = 0, - prediction_type: str = "epsilon", - thresholding: bool = False, - dynamic_thresholding_ratio: float = 0.995, - clip_sample_range: float = 1.0, - sample_max_value: float = 1.0, - timestep_spacing: str = "leading", - rescale_betas_zero_snr: bool = False, - ): - if trained_betas is not None: - self.betas = torch.tensor(trained_betas, dtype=torch.float32) - elif beta_schedule == "linear": - self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) - elif beta_schedule == "scaled_linear": - # this schedule is very specific to the latent diffusion model. - self.betas = ( - torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 - ) - elif beta_schedule == "squaredcos_cap_v2": - # Glide cosine schedule - self.betas = betas_for_alpha_bar(num_train_timesteps) - else: - raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") - - # Rescale for zero SNR - if rescale_betas_zero_snr: - self.betas = rescale_zero_terminal_snr(self.betas) - - self.alphas = 1.0 - self.betas - self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) - - # At every step in ddim, we are looking into the previous alphas_cumprod - # For the final step, there is no previous alphas_cumprod because we are already at 0 - # `set_alpha_to_one` decides whether we set this parameter simply to one or - # whether we use the final alpha of the "non-previous" one. - self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] - - # standard deviation of the initial noise distribution - self.init_noise_sigma = 1.0 - - # setable values - self.num_inference_steps = None - self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) - - def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: - """ - Ensures interchangeability with schedulers that need to scale the denoising model input depending on the - current timestep. - - Args: - sample (`torch.FloatTensor`): - The input sample. - timestep (`int`, *optional*): - The current timestep in the diffusion chain. - - Returns: - `torch.FloatTensor`: - A scaled input sample. - """ - return sample - - def _get_variance(self, timestep, prev_timestep): - alpha_prod_t = self.alphas_cumprod[timestep] - alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod - beta_prod_t = 1 - alpha_prod_t - beta_prod_t_prev = 1 - alpha_prod_t_prev - - variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) - - return variance - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample - def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: - """ - "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the - prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by - s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing - pixels from saturation at each step. We find that dynamic thresholding results in significantly better - photorealism as well as better image-text alignment, especially when using very large guidance weights." - - https://arxiv.org/abs/2205.11487 - """ - dtype = sample.dtype - batch_size, channels, height, width = sample.shape - - if dtype not in (torch.float32, torch.float64): - sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half - - # Flatten sample for doing quantile calculation along each image - sample = sample.reshape(batch_size, channels * height * width) - - abs_sample = sample.abs() # "a certain percentile absolute pixel value" - - s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) - s = torch.clamp( - s, min=1, max=self.config.sample_max_value - ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] - - s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 - sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" - - sample = sample.reshape(batch_size, channels, height, width) - sample = sample.to(dtype) - - return sample - - def set_timesteps(self, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None): - """ - Sets the discrete timesteps used for the diffusion chain (to be run before inference). - - Args: - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. - """ - - if num_inference_steps > self.config.num_train_timesteps: - raise ValueError( - f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" - f" maximal {self.config.num_train_timesteps} timesteps." - ) - - self.num_inference_steps = num_inference_steps - - # LCM Timesteps Setting: # Linear Spacing - c = self.config.num_train_timesteps // lcm_origin_steps - lcm_origin_timesteps = np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1 # LCM Training Steps Schedule - skipping_step = len(lcm_origin_timesteps) // num_inference_steps - timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps] # LCM Inference Steps Schedule - - self.timesteps = torch.from_numpy(timesteps.copy()).to(device) - - def get_scalings_for_boundary_condition_discrete(self, t): - self.sigma_data = 0.5 # Default: 0.5 - - # By dividing 0.1: This is almost a delta function at t=0. - c_skip = self.sigma_data**2 / ( - (t / 0.1) ** 2 + self.sigma_data**2 - ) - c_out = (( t / 0.1) / ((t / 0.1) **2 + self.sigma_data**2) ** 0.5) - return c_skip, c_out - - - def step( - self, - model_output: torch.FloatTensor, - timeindex: int, - timestep: int, - sample: torch.FloatTensor, - eta: float = 0.0, - use_clipped_model_output: bool = False, - generator=None, - variance_noise: Optional[torch.FloatTensor] = None, - return_dict: bool = True, - ) -> Union[LCMSchedulerOutput, Tuple]: - """ - Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion - process from the learned model outputs (most often the predicted noise). - - Args: - model_output (`torch.FloatTensor`): - The direct output from learned diffusion model. - timestep (`float`): - The current discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - A current instance of a sample created by the diffusion process. - eta (`float`): - The weight of noise for added noise in diffusion step. - use_clipped_model_output (`bool`, defaults to `False`): - If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary - because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no - clipping has happened, "corrected" `model_output` would coincide with the one provided as input and - `use_clipped_model_output` has no effect. - generator (`torch.Generator`, *optional*): - A random number generator. - variance_noise (`torch.FloatTensor`): - Alternative to generating noise with `generator` by directly providing the noise for the variance - itself. Useful for methods such as [`CycleDiffusion`]. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`. - - Returns: - [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`: - If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a - tuple is returned where the first element is the sample tensor. - - """ - if self.num_inference_steps is None: - raise ValueError( - "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" - ) - - # 1. get previous step value - prev_timeindex = timeindex + 1 - if prev_timeindex < len(self.timesteps): - prev_timestep = self.timesteps[prev_timeindex] - else: - prev_timestep = timestep - - # 2. compute alphas, betas - alpha_prod_t = self.alphas_cumprod[timestep] - alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod - - beta_prod_t = 1 - alpha_prod_t - beta_prod_t_prev = 1 - alpha_prod_t_prev - - # 3. Get scalings for boundary conditions - c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep) - - # 4. Different Parameterization: - parameterization = self.config.prediction_type - - if parameterization == "epsilon": # noise-prediction - pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt() - - elif parameterization == "sample": # x-prediction - pred_x0 = model_output - - elif parameterization == "v_prediction": # v-prediction - pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output - - # 4. Denoise model output using boundary conditions - denoised = c_out * pred_x0 + c_skip * sample - - # 5. Sample z ~ N(0, I), For MultiStep Inference - # Noise is not used for one-step sampling. - if len(self.timesteps) > 1: - noise = torch.randn(model_output.shape).to(model_output.device) - prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise - else: - prev_sample = denoised - - if not return_dict: - return (prev_sample, denoised) - - return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised) - - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise - def add_noise( - self, - original_samples: torch.FloatTensor, - noise: torch.FloatTensor, - timesteps: torch.IntTensor, - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as original_samples - alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) - timesteps = timesteps.to(original_samples.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(original_samples.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise - return noisy_samples - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity - def get_velocity( - self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as sample - alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) - timesteps = timesteps.to(sample.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(sample.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample - return velocity - - def __len__(self): - return self.config.num_train_timesteps diff --git a/spaces/SoftChinchilla/Guizmus-SouthParkStyle/README.md b/spaces/SoftChinchilla/Guizmus-SouthParkStyle/README.md deleted file mode 100644 index 2526c8965f2fe8061912b8deb602b8e48a2358b3..0000000000000000000000000000000000000000 --- a/spaces/SoftChinchilla/Guizmus-SouthParkStyle/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Guizmus SouthParkStyle -emoji: ⚡ -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.19.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py deleted file mode 100644 index 6efaea6df7147d59331c5c83022b047e15e6d3b4..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/search.py +++ /dev/null @@ -1,665 +0,0 @@ -#!~/.wine/drive_c/Python25/python.exe -# -*- coding: utf-8 -*- - -# Process memory finder -# Copyright (c) 2009-2014, Mario Vilas -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright -# notice,this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# * Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. - -""" -Process memory search. - -@group Memory search: - Search, - Pattern, - BytePattern, - TextPattern, - RegExpPattern, - HexPattern -""" - -__revision__ = "$Id$" - -__all__ = [ - 'Search', - 'Pattern', - 'BytePattern', - 'TextPattern', - 'RegExpPattern', - 'HexPattern', - ] - -from winappdbg.textio import HexInput -from winappdbg.util import StaticClass, MemoryAddresses -from winappdbg import win32 - -import warnings - -try: - # http://pypi.python.org/pypi/regex - import regex as re -except ImportError: - import re - -#============================================================================== - -class Pattern (object): - """ - Base class for search patterns. - - The following L{Pattern} subclasses are provided by WinAppDbg: - - L{BytePattern} - - L{TextPattern} - - L{RegExpPattern} - - L{HexPattern} - - @see: L{Search.search_process} - """ - - def __init__(self, pattern): - """ - Class constructor. - - The only mandatory argument should be the pattern string. - - This method B{MUST} be reimplemented by subclasses of L{Pattern}. - """ - raise NotImplementedError() - - def __len__(self): - """ - Returns the maximum expected length of the strings matched by this - pattern. Exact behavior is implementation dependent. - - Ideally it should be an exact value, but in some cases it's not - possible to calculate so an upper limit should be returned instead. - - If that's not possible either an exception must be raised. - - This value will be used to calculate the required buffer size when - doing buffered searches. - - This method B{MUST} be reimplemented by subclasses of L{Pattern}. - """ - raise NotImplementedError() - - def read(self, process, address, size): - """ - Reads the requested number of bytes from the process memory at the - given address. - - Subclasses of L{Pattern} tipically don't need to reimplement this - method. - """ - return process.read(address, size) - - def find(self, buffer, pos = None): - """ - Searches for the pattern in the given buffer, optionally starting at - the given position within the buffer. - - This method B{MUST} be reimplemented by subclasses of L{Pattern}. - - @type buffer: str - @param buffer: Buffer to search on. - - @type pos: int - @param pos: - (Optional) Position within the buffer to start searching from. - - @rtype: tuple( int, int ) - @return: Tuple containing the following: - - Position within the buffer where a match is found, or C{-1} if - no match was found. - - Length of the matched data if a match is found, or undefined if - no match was found. - """ - raise NotImplementedError() - - def found(self, address, size, data): - """ - This method gets called when a match is found. - - This allows subclasses of L{Pattern} to filter out unwanted results, - or modify the results before giving them to the caller of - L{Search.search_process}. - - If the return value is C{None} the result is skipped. - - Subclasses of L{Pattern} don't need to reimplement this method unless - filtering is needed. - - @type address: int - @param address: The memory address where the pattern was found. - - @type size: int - @param size: The size of the data that matches the pattern. - - @type data: str - @param data: The data that matches the pattern. - - @rtype: tuple( int, int, str ) - @return: Tuple containing the following: - * The memory address where the pattern was found. - * The size of the data that matches the pattern. - * The data that matches the pattern. - """ - return (address, size, data) - -#------------------------------------------------------------------------------ - -class BytePattern (Pattern): - """ - Fixed byte pattern. - - @type pattern: str - @ivar pattern: Byte string to search for. - - @type length: int - @ivar length: Length of the byte pattern. - """ - - def __init__(self, pattern): - """ - @type pattern: str - @param pattern: Byte string to search for. - """ - self.pattern = str(pattern) - self.length = len(pattern) - - def __len__(self): - """ - Returns the exact length of the pattern. - - @see: L{Pattern.__len__} - """ - return self.length - - def find(self, buffer, pos = None): - return buffer.find(self.pattern, pos), self.length - -#------------------------------------------------------------------------------ - -# FIXME: case insensitive compat.unicode searches are probably buggy! - -class TextPattern (BytePattern): - """ - Text pattern. - - @type isUnicode: bool - @ivar isUnicode: C{True} if the text to search for is a compat.unicode string, - C{False} otherwise. - - @type encoding: str - @ivar encoding: Encoding for the text parameter. - Only used when the text to search for is a Unicode string. - Don't change unless you know what you're doing! - - @type caseSensitive: bool - @ivar caseSensitive: C{True} of the search is case sensitive, - C{False} otherwise. - """ - - def __init__(self, text, encoding = "utf-16le", caseSensitive = False): - """ - @type text: str or compat.unicode - @param text: Text to search for. - - @type encoding: str - @param encoding: (Optional) Encoding for the text parameter. - Only used when the text to search for is a Unicode string. - Don't change unless you know what you're doing! - - @type caseSensitive: bool - @param caseSensitive: C{True} of the search is case sensitive, - C{False} otherwise. - """ - self.isUnicode = isinstance(text, compat.unicode) - self.encoding = encoding - self.caseSensitive = caseSensitive - if not self.caseSensitive: - pattern = text.lower() - if self.isUnicode: - pattern = text.encode(encoding) - super(TextPattern, self).__init__(pattern) - - def read(self, process, address, size): - data = super(TextPattern, self).read(address, size) - if not self.caseSensitive: - if self.isUnicode: - try: - encoding = self.encoding - text = data.decode(encoding, "replace") - text = text.lower() - new_data = text.encode(encoding, "replace") - if len(data) == len(new_data): - data = new_data - else: - data = data.lower() - except Exception: - data = data.lower() - else: - data = data.lower() - return data - - def found(self, address, size, data): - if self.isUnicode: - try: - data = compat.unicode(data, self.encoding) - except Exception: -## traceback.print_exc() # XXX DEBUG - return None - return (address, size, data) - -#------------------------------------------------------------------------------ - -class RegExpPattern (Pattern): - """ - Regular expression pattern. - - @type pattern: str - @ivar pattern: Regular expression in text form. - - @type flags: int - @ivar flags: Regular expression flags. - - @type regexp: re.compile - @ivar regexp: Regular expression in compiled form. - - @type maxLength: int - @ivar maxLength: - Maximum expected length of the strings matched by this regular - expression. - - This value will be used to calculate the required buffer size when - doing buffered searches. - - Ideally it should be an exact value, but in some cases it's not - possible to calculate so an upper limit should be given instead. - - If that's not possible either, C{None} should be used. That will - cause an exception to be raised if this pattern is used in a - buffered search. - """ - - def __init__(self, regexp, flags = 0, maxLength = None): - """ - @type regexp: str - @param regexp: Regular expression string. - - @type flags: int - @param flags: Regular expression flags. - - @type maxLength: int - @param maxLength: Maximum expected length of the strings matched by - this regular expression. - - This value will be used to calculate the required buffer size when - doing buffered searches. - - Ideally it should be an exact value, but in some cases it's not - possible to calculate so an upper limit should be given instead. - - If that's not possible either, C{None} should be used. That will - cause an exception to be raised if this pattern is used in a - buffered search. - """ - self.pattern = regexp - self.flags = flags - self.regexp = re.compile(regexp, flags) - self.maxLength = maxLength - - def __len__(self): - """ - Returns the maximum expected length of the strings matched by this - pattern. This value is taken from the C{maxLength} argument of the - constructor if this class. - - Ideally it should be an exact value, but in some cases it's not - possible to calculate so an upper limit should be returned instead. - - If that's not possible either an exception must be raised. - - This value will be used to calculate the required buffer size when - doing buffered searches. - """ - if self.maxLength is None: - raise NotImplementedError() - return self.maxLength - - def find(self, buffer, pos = None): - if not pos: # make sure pos is an int - pos = 0 - match = self.regexp.search(buffer, pos) - if match: - start, end = match.span() - return start, end - start - return -1, 0 - -#------------------------------------------------------------------------------ - -class HexPattern (RegExpPattern): - """ - Hexadecimal pattern. - - Hex patterns must be in this form:: - "68 65 6c 6c 6f 20 77 6f 72 6c 64" # "hello world" - - Spaces are optional. Capitalization of hex digits doesn't matter. - This is exactly equivalent to the previous example:: - "68656C6C6F20776F726C64" # "hello world" - - Wildcards are allowed, in the form of a C{?} sign in any hex digit:: - "5? 5? c3" # pop register / pop register / ret - "b8 ?? ?? ?? ??" # mov eax, immediate value - - @type pattern: str - @ivar pattern: Hexadecimal pattern. - """ - - def __new__(cls, pattern): - """ - If the pattern is completely static (no wildcards are present) a - L{BytePattern} is created instead. That's because searching for a - fixed byte pattern is faster than searching for a regular expression. - """ - if '?' not in pattern: - return BytePattern( HexInput.hexadecimal(pattern) ) - return object.__new__(cls, pattern) - - def __init__(self, hexa): - """ - Hex patterns must be in this form:: - "68 65 6c 6c 6f 20 77 6f 72 6c 64" # "hello world" - - Spaces are optional. Capitalization of hex digits doesn't matter. - This is exactly equivalent to the previous example:: - "68656C6C6F20776F726C64" # "hello world" - - Wildcards are allowed, in the form of a C{?} sign in any hex digit:: - "5? 5? c3" # pop register / pop register / ret - "b8 ?? ?? ?? ??" # mov eax, immediate value - - @type hexa: str - @param hexa: Pattern to search for. - """ - maxLength = len([x for x in hexa - if x in "?0123456789ABCDEFabcdef"]) / 2 - super(HexPattern, self).__init__(HexInput.pattern(hexa), - maxLength = maxLength) - -#============================================================================== - -class Search (StaticClass): - """ - Static class to group the search functionality. - - Do not instance this class! Use its static methods instead. - """ - - # TODO: aligned searches - # TODO: method to coalesce search results - # TODO: search memory dumps - # TODO: search non-ascii C strings - - @staticmethod - def search_process(process, pattern, minAddr = None, - maxAddr = None, - bufferPages = None, - overlapping = False): - """ - Search for the given pattern within the process memory. - - @type process: L{Process} - @param process: Process to search. - - @type pattern: L{Pattern} - @param pattern: Pattern to search for. - It must be an instance of a subclass of L{Pattern}. - - The following L{Pattern} subclasses are provided by WinAppDbg: - - L{BytePattern} - - L{TextPattern} - - L{RegExpPattern} - - L{HexPattern} - - You can also write your own subclass of L{Pattern} for customized - searches. - - @type minAddr: int - @param minAddr: (Optional) Start the search at this memory address. - - @type maxAddr: int - @param maxAddr: (Optional) Stop the search at this memory address. - - @type bufferPages: int - @param bufferPages: (Optional) Number of memory pages to buffer when - performing the search. Valid values are: - - C{0} or C{None}: - Automatically determine the required buffer size. May not give - complete results for regular expressions that match variable - sized strings. - - C{> 0}: Set the buffer size, in memory pages. - - C{< 0}: Disable buffering entirely. This may give you a little - speed gain at the cost of an increased memory usage. If the - target process has very large contiguous memory regions it may - actually be slower or even fail. It's also the only way to - guarantee complete results for regular expressions that match - variable sized strings. - - @type overlapping: bool - @param overlapping: C{True} to allow overlapping results, C{False} - otherwise. - - Overlapping results yield the maximum possible number of results. - - For example, if searching for "AAAA" within "AAAAAAAA" at address - C{0x10000}, when overlapping is turned off the following matches - are yielded:: - (0x10000, 4, "AAAA") - (0x10004, 4, "AAAA") - - If overlapping is turned on, the following matches are yielded:: - (0x10000, 4, "AAAA") - (0x10001, 4, "AAAA") - (0x10002, 4, "AAAA") - (0x10003, 4, "AAAA") - (0x10004, 4, "AAAA") - - As you can see, the middle results are overlapping the last two. - - @rtype: iterator of tuple( int, int, str ) - @return: An iterator of tuples. Each tuple contains the following: - - The memory address where the pattern was found. - - The size of the data that matches the pattern. - - The data that matches the pattern. - - @raise WindowsError: An error occurred when querying or reading the - process memory. - """ - - # Do some namespace lookups of symbols we'll be using frequently. - MEM_COMMIT = win32.MEM_COMMIT - PAGE_GUARD = win32.PAGE_GUARD - page = MemoryAddresses.pageSize - read = pattern.read - find = pattern.find - - # Calculate the address range. - if minAddr is None: - minAddr = 0 - if maxAddr is None: - maxAddr = win32.LPVOID(-1).value # XXX HACK - - # Calculate the buffer size from the number of pages. - if bufferPages is None: - try: - size = MemoryAddresses.\ - align_address_to_page_end(len(pattern)) + page - except NotImplementedError: - size = None - elif bufferPages > 0: - size = page * (bufferPages + 1) - else: - size = None - - # Get the memory map of the process. - memory_map = process.iter_memory_map(minAddr, maxAddr) - - # Perform search with buffering enabled. - if size: - - # Loop through all memory blocks containing data. - buffer = "" # buffer to hold the memory data - prev_addr = 0 # previous memory block address - last = 0 # position of the last match - delta = 0 # delta of last read address and start of buffer - for mbi in memory_map: - - # Skip blocks with no data to search on. - if not mbi.has_content(): - continue - - # Get the address and size of this block. - address = mbi.BaseAddress # current address to search on - block_size = mbi.RegionSize # total size of the block - if address >= maxAddr: - break - end = address + block_size # end address of the block - - # If the block is contiguous to the previous block, - # coalesce the new data in the buffer. - if delta and address == prev_addr: - buffer += read(process, address, page) - - # If not, clear the buffer and read new data. - else: - buffer = read(process, address, min(size, block_size)) - last = 0 - delta = 0 - - # Search for the pattern in this block. - while 1: - - # Yield each match of the pattern in the buffer. - pos, length = find(buffer, last) - while pos >= last: - match_addr = address + pos - delta - if minAddr <= match_addr < maxAddr: - result = pattern.found( - match_addr, length, - buffer [ pos : pos + length ] ) - if result is not None: - yield result - if overlapping: - last = pos + 1 - else: - last = pos + length - pos, length = find(buffer, last) - - # Advance to the next page. - address = address + page - block_size = block_size - page - prev_addr = address - - # Fix the position of the last match. - last = last - page - if last < 0: - last = 0 - - # Remove the first page in the buffer. - buffer = buffer[ page : ] - delta = page - - # If we haven't reached the end of the block yet, - # read the next page in the block and keep seaching. - if address < end: - buffer = buffer + read(process, address, page) - - # Otherwise, we're done searching this block. - else: - break - - # Perform search with buffering disabled. - else: - - # Loop through all memory blocks containing data. - for mbi in memory_map: - - # Skip blocks with no data to search on. - if not mbi.has_content(): - continue - - # Get the address and size of this block. - address = mbi.BaseAddress - block_size = mbi.RegionSize - if address >= maxAddr: - break; - - # Read the whole memory region. - buffer = process.read(address, block_size) - - # Search for the pattern in this region. - pos, length = find(buffer) - last = 0 - while pos >= last: - match_addr = address + pos - if minAddr <= match_addr < maxAddr: - result = pattern.found( - match_addr, length, - buffer [ pos : pos + length ] ) - if result is not None: - yield result - if overlapping: - last = pos + 1 - else: - last = pos + length - pos, length = find(buffer, last) - - @classmethod - def extract_ascii_strings(cls, process, minSize = 4, maxSize = 1024): - """ - Extract ASCII strings from the process memory. - - @type process: L{Process} - @param process: Process to search. - - @type minSize: int - @param minSize: (Optional) Minimum size of the strings to search for. - - @type maxSize: int - @param maxSize: (Optional) Maximum size of the strings to search for. - - @rtype: iterator of tuple(int, int, str) - @return: Iterator of strings extracted from the process memory. - Each tuple contains the following: - - The memory address where the string was found. - - The size of the string. - - The string. - """ - regexp = r"[\s\w\!\@\#\$\%%\^\&\*\(\)\{\}\[\]\~\`\'\"\:\;\.\,\\\/\-\+\=\_\<\>]{%d,%d}\0" % (minSize, maxSize) - pattern = RegExpPattern(regexp, 0, maxSize) - return cls.search_process(process, pattern, overlapping = False) diff --git a/spaces/Suniilkumaar/SwapMukham/assets/pretrained_models/readme.md b/spaces/Suniilkumaar/SwapMukham/assets/pretrained_models/readme.md deleted file mode 100644 index fd26cd784fbfa3af2cebfb6190b0aa55c92b85e5..0000000000000000000000000000000000000000 --- a/spaces/Suniilkumaar/SwapMukham/assets/pretrained_models/readme.md +++ /dev/null @@ -1,4 +0,0 @@ -## Downolad these models here -- [inswapper_128.onnx](https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx) -- [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth) -- [79999_iter.pth](https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812) diff --git a/spaces/Superlang/remove_background/DIS/IsNetPipeLine.py b/spaces/Superlang/remove_background/DIS/IsNetPipeLine.py deleted file mode 100644 index 91fd2d426c7ed4d884873358b82ba22606fffaa4..0000000000000000000000000000000000000000 --- a/spaces/Superlang/remove_background/DIS/IsNetPipeLine.py +++ /dev/null @@ -1,131 +0,0 @@ -""" - reference: https://github.com/xuebinqin/DIS -""" - -import PIL.Image -import numpy as np -import torch -import torch.nn.functional as F -from PIL import Image -from torch import nn -from torch.autograd import Variable -from torchvision import transforms -from torchvision.transforms.functional import normalize - -from .models import ISNetDIS - -# Helpers -device = 'cuda' if torch.cuda.is_available() else 'cpu' - - -class GOSNormalize(object): - """ - Normalize the Image using torch.transforms - """ - - def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): - self.mean = mean - self.std = std - - def __call__(self, image): - image = normalize(image, self.mean, self.std) - return image - - -def im_preprocess(im, size): - if len(im.shape) < 3: - im = im[:, :, np.newaxis] - if im.shape[2] == 1: - im = np.repeat(im, 3, axis=2) - im_tensor = torch.tensor(im.copy(), dtype=torch.float32) - im_tensor = torch.transpose(torch.transpose(im_tensor, 1, 2), 0, 1) - if len(size) < 2: - return im_tensor, im.shape[0:2] - else: - im_tensor = torch.unsqueeze(im_tensor, 0) - im_tensor = F.upsample(im_tensor, size, mode="bilinear") - im_tensor = torch.squeeze(im_tensor, 0) - - return im_tensor.type(torch.uint8), im.shape[0:2] - - -class IsNetPipeLine: - def __init__(self, model_path=None, model_digit="full"): - self.model_digit = model_digit - self.model = ISNetDIS() - self.cache_size = [1024, 1024] - self.transform = transforms.Compose([ - GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0]) - ]) - - # Build Model - self.build_model(model_path) - - def load_image(self, image: PIL.Image.Image): - im = np.array(image.convert("RGB")) - im, im_shp = im_preprocess(im, self.cache_size) - im = torch.divide(im, 255.0) - shape = torch.from_numpy(np.array(im_shp)) - return self.transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape - - def build_model(self, model_path=None): - if model_path is not None: - self.model.load_state_dict(torch.load(model_path, map_location=device)) - - # convert to half precision - if self.model_digit == "half": - self.model.half() - for layer in self.model.modules(): - if isinstance(layer, nn.BatchNorm2d): - layer.float() - self.model.to(device) - self.model.eval() - - def __call__(self, image: PIL.Image.Image): - image_tensor, orig_size = self.load_image(image) - mask = self.predict(image_tensor, orig_size) - - pil_mask = Image.fromarray(mask).convert('L') - im_rgb = image.convert("RGB") - - im_rgba = im_rgb.copy() - im_rgba.putalpha(pil_mask) - - return [im_rgba, pil_mask] - - def predict(self, inputs_val: torch.Tensor, shapes_val): - """ - Given an Image, predict the mask - """ - - if self.model_digit == "full": - inputs_val = inputs_val.type(torch.FloatTensor) - else: - inputs_val = inputs_val.type(torch.HalfTensor) - - inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable - - ds_val = self.model(inputs_val_v)[0] # list of 6 results - - # B x 1 x H x W # we want the first one which is the most accurate prediction - pred_val = ds_val[0][0, :, :, :] - - # recover the prediction spatial size to the orignal image size - pred_val = torch.squeeze( - F.upsample(torch.unsqueeze(pred_val, 0), (shapes_val[0][0], shapes_val[0][1]), mode='bilinear')) - - ma = torch.max(pred_val) - mi = torch.min(pred_val) - pred_val = (pred_val - mi) / (ma - mi) # max = 1 - - if device == 'cuda': - torch.cuda.empty_cache() - return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8) # it is the mask we need - - -# a = IsNetPipeLine(model_path="save_models/isnet.pth") -# input_image = Image.open("image_0mx.png") -# rgb, mask = a(input_image) -# -# rgb.save("rgb.png") -# mask.save("mask.png") \ No newline at end of file diff --git a/spaces/TEnngal/bingo/src/components/chat-scroll-anchor.tsx b/spaces/TEnngal/bingo/src/components/chat-scroll-anchor.tsx deleted file mode 100644 index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000 --- a/spaces/TEnngal/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/TencentARC/Caption-Anything/caption_anything/captioner/README.md b/spaces/TencentARC/Caption-Anything/caption_anything/captioner/README.md deleted file mode 100644 index e9b387fade5888f6f4330aecfc0d1cdbb1c51703..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/Caption-Anything/caption_anything/captioner/README.md +++ /dev/null @@ -1,13 +0,0 @@ -To run BLIP/BLIP2, you should install transformers from source! -``` -!pip install git+https://github.com/huggingface/transformers.git -``` -To run filter module, you should install CLIP repo as a Python package as follow: -``` -!pip install ftfy regex tqdm -!pip install git+https://github.com/openai/CLIP.git -``` -To accelerate BLIP2 with int8, you should install accelerate -``` -!pip install accelerate bitsandbytes -``` diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md deleted file mode 100644 index b40768913742ca2b2e11c74d5944561931ecb326..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/docs/tutorials/install.md +++ /dev/null @@ -1,261 +0,0 @@ -## Installation - -### Requirements -- Linux or macOS with Python ≥ 3.6 -- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. - Install them together at [pytorch.org](https://pytorch.org) to make sure of this -- OpenCV is optional but needed by demo and visualization - - -### Build Detectron2 from Source - -gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but recommended for faster build. -After having them, run: -``` -python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' -# (add --user if you don't have permission) - -# Or, to install it from a local clone: -git clone https://github.com/facebookresearch/detectron2.git -python -m pip install -e detectron2 - -# On macOS, you may need to prepend the above commands with a few environment variables: -CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ... -``` - -To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the -old build first. You often need to rebuild detectron2 after reinstalling PyTorch. - -### Install Pre-Built Detectron2 (Linux only) - -Choose from this table to install [v0.6 (Oct 2021)](https://github.com/facebookresearch/detectron2/releases): - -
CUDA torch 1.10torch 1.9torch 1.8
11.3
install 
python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
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11.1
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.10/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.8/index.html
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10.1
install 
python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html
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cpu
install 
python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.9/index.html
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python -m pip install detectron2 -f \
-  https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.8/index.html
-
- -Note that: -1. The pre-built packages have to be used with corresponding version of CUDA and the official package of PyTorch. - Otherwise, please build detectron2 from source. -2. New packages are released every few months. Therefore, packages may not contain latest features in the main - branch and may not be compatible with the main branch of a research project that uses detectron2 - (e.g. those in [projects](projects)). - -### Common Installation Issues - -Click each issue for its solutions: - -
- -Undefined symbols that looks like "TH..","at::Tensor...","torch..." - -
- -This usually happens when detectron2 or torchvision is not -compiled with the version of PyTorch you're running. - -If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them -following [pytorch.org](http://pytorch.org). So the versions will match. - -If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases), -uninstall and reinstall the correct pre-built detectron2 that matches pytorch version. - -If the error comes from detectron2 or torchvision that you built manually from source, -remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment. - -If the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue. -
- -
- -Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2. - -This usually happens when detectron2 or torchvision is not -compiled with the version of PyTorch you're running. See the previous common issue for the solution. -
- -
- -Undefined C++ symbols (e.g. "GLIBCXX..") or C++ symbols not found. - -
-Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime. - -This often happens with old anaconda. -It may help to run `conda update libgcc` to upgrade its runtime. - -The fundamental solution is to avoid the mismatch, either by compiling using older version of C++ -compiler, or run the code with proper C++ runtime. -To run the code with a specific C++ runtime, you can use environment variable `LD_PRELOAD=/path/to/libstdc++.so`. - -
- -
- -"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available". - -
-CUDA is not found when building detectron2. -You should make sure - -``` -python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)' -``` - -print `(True, a directory with cuda)` at the time you build detectron2. - -Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config. -
- -
- -"invalid device function" or "no kernel image is available for execution". - -
-Two possibilities: - -* You build detectron2 with one version of CUDA but run it with a different version. - - To check whether it is the case, - use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions. - In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA" - to contain cuda libraries of the same version. - - When they are inconsistent, - you need to either install a different build of PyTorch (or build by yourself) - to match your local CUDA installation, or install a different version of CUDA to match PyTorch. - -* PyTorch/torchvision/Detectron2 is not built for the correct GPU SM architecture (aka. compute capability). - - The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in - `python -m detectron2.utils.collect_env`. It must include - the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus). - - If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already. - If not supported, you need to build them from source. - - When building detectron2/torchvision from source, they detect the GPU device and build for only the device. - This means the compiled code may not work on a different GPU device. - To recompile them for the correct architecture, remove all installed/compiled files, - and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly. - For example, `export TORCH_CUDA_ARCH_LIST="6.0;7.0"` makes it compile for both P100s and V100s. -
- -
- -Undefined CUDA symbols; Cannot open libcudart.so - -
-The version of NVCC you use to build detectron2 or torchvision does -not match the version of CUDA you are running with. -This often happens when using anaconda's CUDA runtime. - -Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions. -In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA" -to contain cuda libraries of the same version. - -When they are inconsistent, -you need to either install a different build of PyTorch (or build by yourself) -to match your local CUDA installation, or install a different version of CUDA to match PyTorch. -
- - -
- -C++ compilation errors from NVCC / NVRTC, or "Unsupported gpu architecture" - -
-A few possibilities: - -1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`. - When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself) - to match your local CUDA installation, or install a different version of CUDA to match PyTorch. - -2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU. - The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus). - The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132). - If your NVCC version is too old, this can be workaround by setting environment variable - `TORCH_CUDA_ARCH_LIST` to a lower, supported capability. - -3. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions. - See [here](https://gist.github.com/ax3l/9489132) for some valid combinations. - Notably, CUDA<=10.1.105 doesn't support GCC>7.3. - - The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`. - -
- - -
- -"ImportError: cannot import name '_C'". - -
-Please build and install detectron2 following the instructions above. - -Or, if you are running code from detectron2's root directory, `cd` to a different one. -Otherwise you may not import the code that you installed. -
- - -
- -Any issue on windows. - -
- -Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=main). -However we do not provide official support for it. -PRs that improves code compatibility on windows are welcome. -
- -
- -ONNX conversion segfault after some "TraceWarning". - -
-The ONNX package is compiled with a too old compiler. - -Please build and install ONNX from its source code using a compiler -whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`). -
- - -
- -"library not found for -lstdc++" on older version of MacOS - -
-See -[this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package). - -
- - -### Installation inside specific environments: - -* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) - which has step-by-step instructions. - -* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands. - diff --git a/spaces/Tihsrah/Credit_Risk_Assessment/README.md b/spaces/Tihsrah/Credit_Risk_Assessment/README.md deleted file mode 100644 index 92399dbc03def89cf6b71b43c73261619462d0f1..0000000000000000000000000000000000000000 --- a/spaces/Tihsrah/Credit_Risk_Assessment/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Credit Risk Assessment -emoji: 🔥 -colorFrom: blue -colorTo: yellow -sdk: streamlit -sdk_version: 1.21.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Together1415/bingo/README.md b/spaces/Together1415/bingo/README.md deleted file mode 100644 index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000 --- a/spaces/Together1415/bingo/README.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: bingo -emoji: 😊 -colorFrom: red -colorTo: red -sdk: docker -license: mit -duplicated_from: hf4all/bingo ---- - -
- -# Bingo - -Bingo，一个让你呼吸顺畅 New Bing。 - -高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。 - -![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars) -![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo) -[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/) -[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/) -[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license) - -问题反馈请前往 https://github.com/weaigc/bingo/issues -
- - diff --git a/spaces/Walterchamy/Kiitec_virtual_assistant/README.md b/spaces/Walterchamy/Kiitec_virtual_assistant/README.md deleted file mode 100644 index 90f1f94c801e2e1b58e6373bc7abfc7fb5542239..0000000000000000000000000000000000000000 --- a/spaces/Walterchamy/Kiitec_virtual_assistant/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Kiitec Virtual Assistant -emoji: 💻 -colorFrom: gray -colorTo: yellow -sdk: streamlit -sdk_version: 1.21.0 -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/Wauplin/gradio-user-history/app.py b/spaces/Wauplin/gradio-user-history/app.py deleted file mode 100644 index 2cd4797777f4131c76287167914cc1e800375a99..0000000000000000000000000000000000000000 --- a/spaces/Wauplin/gradio-user-history/app.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python -import json -import pathlib -import tempfile -from pathlib import Path - -import gradio as gr -import gradio_user_history as gr_user_history -from gradio_client import Client - - -client = Client("runwayml/stable-diffusion-v1-5") - - -def generate(prompt: str, profile: gr.OAuthProfile | None) -> tuple[str, list[str]]: - out_dir = client.predict(prompt, fn_index=1) - - metadata = { - "prompt": prompt, - "negative_prompt": "", - "guidance_scale": 0.9, - } - with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as metadata_file: - json.dump(metadata, metadata_file) - - with (pathlib.Path(out_dir) / "captions.json").open() as f: - paths = list(json.load(f).keys()) - - # Saving user history - for path in paths: - gr_user_history.save_image(label=prompt, image=path, profile=profile, metadata=metadata) - - return paths # type: ignore - - -with gr.Blocks(css="style.css") as demo: - with gr.Group(): - prompt = gr.Text(show_label=False, placeholder="Prompt") - gallery = gr.Gallery( - show_label=False, - columns=2, - rows=2, - height="600px", - object_fit="scale-down", - ) - prompt.submit(fn=generate, inputs=prompt, outputs=gallery) - -with gr.Blocks() as demo_with_history: - with gr.Tab("README"): - gr.Markdown(Path("README.md").read_text().split("---")[-1]) - with gr.Tab("Demo"): - demo.render() - with gr.Tab("Past generations"): - gr_user_history.render() - -if __name__ == "__main__": - demo_with_history.queue().launch() diff --git a/spaces/Widium/Image-Recreation/functions/system/devices.py b/spaces/Widium/Image-Recreation/functions/system/devices.py deleted file mode 100644 index e046ce76d48b77ad82502603c096c893775c9eef..0000000000000000000000000000000000000000 --- a/spaces/Widium/Image-Recreation/functions/system/devices.py +++ /dev/null @@ -1,27 +0,0 @@ -# *************************************************************************** # -# # -# devices.py # -# # -# By: Widium # -# Github : https://github.com/widium # -# # -# Created: 2023/05/05 10:57:02 by Widium # -# Updated: 2023/05/05 10:57:02 by Widium # -# # -# **************************************************************************** # - -import os - -def deactivate_gpu(): - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' - -import tensorflow as tf -from tensorflow.python.client import device_lib - - -def get_available_devices(): - local_device_protos = device_lib.list_local_devices() - devices = [x.name for x in local_device_protos] - print("Available devices:", devices) - -# print("GPU AVAILABLE ?", tf.config.list_physical_devices('GPU')) diff --git a/spaces/WinWut/Lofi-music-style-transfer/model.py b/spaces/WinWut/Lofi-music-style-transfer/model.py deleted file mode 100644 index 216e46d1dd8b3d63b5458e1094de7687031863e9..0000000000000000000000000000000000000000 --- a/spaces/WinWut/Lofi-music-style-transfer/model.py +++ /dev/null @@ -1,657 +0,0 @@ -#Imports - -from __future__ import print_function, division -import tensorflow as tf -from glob import glob -import scipy -import soundfile as sf -import matplotlib.pyplot as plt -from IPython.display import clear_output -from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Concatenate, Conv2D, Conv2DTranspose, GlobalAveragePooling2D, UpSampling2D, LeakyReLU, ReLU, Add, Multiply, Lambda, Dot, BatchNormalization, Activation, ZeroPadding2D, Cropping2D, Cropping1D -from tensorflow.keras.models import Sequential, Model, load_model -from tensorflow.keras.optimizers import Adam -from tensorflow.keras.initializers import TruncatedNormal, he_normal -import tensorflow.keras.backend as K -import datetime -import numpy as np -import random -import matplotlib.pyplot as plt -import collections -from PIL import Image -from skimage.transform import resize -import imageio -import librosa -import librosa.display -from librosa.feature import melspectrogram -import os -import time -import IPython - -#Hyperparameters - -hop=192 #hop size (window size = 6*hop) -sr=16000 #sampling rate -min_level_db=-100 #reference values to normalize data -ref_level_db=20 - -shape=24 #length of time axis of split specrograms to feed to generator -vec_len=128 #length of vector generated by siamese vector -bs = 16 #batch size -delta = 2. #constant for siamese loss - -#There seems to be a problem with Tensorflow STFT, so we'll be using pytorch to handle offline mel-spectrogram generation and waveform reconstruction -#For waveform reconstruction, a gradient-based method is used: - -''' Decorsière, Rémi, Peter L. Søndergaard, Ewen N. MacDonald, and Torsten Dau. -"Inversion of auditory spectrograms, traditional spectrograms, and other envelope representations." -IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 1 (2014): 46-56.''' - -#ORIGINAL CODE FROM https://github.com/yoyololicon/spectrogram-inversion - -import torch -import torch.nn as nn -import torch.nn.functional as F -from tqdm import tqdm -from functools import partial -import math -import heapq -from torchaudio.transforms import MelScale, Spectrogram - - -specobj = Spectrogram(n_fft=6*hop, win_length=6*hop, hop_length=hop, pad=0, power=2, normalized=True) -specfunc = specobj.forward -melobj = MelScale(n_mels=hop, sample_rate=sr, f_min=0.,n_stft=577) -melfunc = melobj.forward - -def melspecfunc(waveform): - specgram = specfunc(waveform) - mel_specgram = melfunc(specgram) - return mel_specgram - -def spectral_convergence(input, target): - return 20 * ((input - target).norm().log10() - target.norm().log10()) - -def GRAD(spec, transform_fn, samples=None, init_x0=None, maxiter=1000, tol=1e-6, verbose=1, evaiter=10, lr=0.003): - - spec = torch.Tensor(spec) - samples = (spec.shape[-1]*hop)-hop - - if init_x0 is None: - init_x0 = spec.new_empty((1,samples)).normal_(std=1e-6) - x = nn.Parameter(init_x0) - T = spec - - criterion = nn.L1Loss() - optimizer = torch.optim.Adam([x], lr=lr) - - bar_dict = {} - metric_func = spectral_convergence - bar_dict['spectral_convergence'] = 0 - metric = 'spectral_convergence' - - init_loss = None - with tqdm(total=maxiter, disable=not verbose) as pbar: - for i in range(maxiter): - optimizer.zero_grad() - V = transform_fn(x) - loss = criterion(V, T) - loss.backward() - optimizer.step() - lr = lr*0.9999 - for param_group in optimizer.param_groups: - param_group['lr'] = lr - - if i % evaiter == evaiter - 1: - with torch.no_grad(): - V = transform_fn(x) - bar_dict[metric] = metric_func(V, spec).item() - l2_loss = criterion(V, spec).item() - pbar.set_postfix(**bar_dict, loss=l2_loss) - pbar.update(evaiter) - - return x.detach().view(-1).cpu() - -def normalize(S): - return np.clip((((S - min_level_db) / -min_level_db)*2.)-1., -1, 1) - -def denormalize(S): - return (((np.clip(S, -1, 1)+1.)/2.) * -min_level_db) + min_level_db - -def prep(wv,hop=192): - S = np.array(torch.squeeze(melspecfunc(torch.Tensor(wv).view(1,-1))).detach().cpu()) - S = librosa.power_to_db(S)-ref_level_db - return normalize(S) - -def deprep(S): - S = denormalize(S)+ref_level_db - S = librosa.db_to_power(S) - wv = GRAD(np.expand_dims(S,0), melspecfunc, maxiter=2000, evaiter=10, tol=1e-8) - return np.array(np.squeeze(wv)) - -#Helper functions - -#Generate spectrograms from waveform array -def tospec(data): - specs=np.empty(data.shape[0], dtype=object) - for i in range(data.shape[0]): - x = data[i] - S=prep(x) - S = np.array(S, dtype=np.float32) - specs[i]=np.expand_dims(S, -1) - print(specs.shape) - return specs - -#Generate multiple spectrograms with a determined length from single wav file -def tospeclong(path, length=4*16000): - x, sr = librosa.load(path,sr=16000) - x,_ = librosa.effects.trim(x) - loudls = librosa.effects.split(x, top_db=50) - xls = np.array([]) - for interv in loudls: - xls = np.concatenate((xls,x[interv[0]:interv[1]])) - x = xls - num = x.shape[0]//length - specs=np.empty(num, dtype=object) - for i in range(num-1): - a = x[i*length:(i+1)*length] - S = prep(a) - S = np.array(S, dtype=np.float32) - try: - sh = S.shape - specs[i]=S - except AttributeError: - print('spectrogram failed') - print(specs.shape) - return specs - -#Waveform array from path of folder containing wav files -def audio_array(path): - ls = glob(f'{path}/*.wav') - adata = [] - for i in range(len(ls)): - try: - x, sr = tf.audio.decode_wav(tf.io.read_file(ls[i]), 1) - except: - print(ls[i],"is broken") - continue - x = np.array(x, dtype=np.float32) - adata.append(x) - return np.array(adata) - -#Concatenate spectrograms in array along the time axis -def testass(a): - but=False - con = np.array([]) - nim = a.shape[0] - for i in range(nim): - im = a[i] - im = np.squeeze(im) - if not but: - con=im - but=True - else: - con = np.concatenate((con,im), axis=1) - return np.squeeze(con) - -#Split spectrograms in chunks with equal size -def splitcut(data): - ls = [] - mini = 0 - minifinal = 10*shape #max spectrogram length - for i in range(data.shape[0]-1): - if data[i].shape[1]<=data[i+1].shape[1]: - mini = data[i].shape[1] - else: - mini = data[i+1].shape[1] - if mini>=3*shape and mini=3*shape: - for n in range(x.shape[1]//minifinal): - ls.append(x[:,n*minifinal:n*minifinal+minifinal,:]) - ls.append(x[:,-minifinal:,:]) - return np.array(ls) - -#Adding Spectral Normalization to convolutional layers - -from tensorflow.python.keras.utils import conv_utils -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import sparse_ops -from tensorflow.python.ops import gen_math_ops -from tensorflow.python.ops import standard_ops -from tensorflow.python.eager import context -from tensorflow.python.framework import tensor_shape - -def l2normalize(v, eps=1e-12): - return v / (tf.norm(v) + eps) - - -class ConvSN2D(tf.keras.layers.Conv2D): - - def __init__(self, filters, kernel_size, power_iterations=1, **kwargs): - super(ConvSN2D, self).__init__(filters, kernel_size, **kwargs) - self.power_iterations = power_iterations - - - def build(self, input_shape): - super(ConvSN2D, self).build(input_shape) - - if self.data_format == 'channels_first': - channel_axis = 1 - else: - channel_axis = -1 - - self.u = self.add_weight(self.name + '_u', - shape=tuple([1, self.kernel.shape.as_list()[-1]]), - initializer=tf.initializers.RandomNormal(0, 1), - trainable=False - ) - - def compute_spectral_norm(self, W, new_u, W_shape): - for _ in range(self.power_iterations): - - new_v = l2normalize(tf.matmul(new_u, tf.transpose(W))) - new_u = l2normalize(tf.matmul(new_v, W)) - - sigma = tf.matmul(tf.matmul(new_v, W), tf.transpose(new_u)) - W_bar = W/sigma - - with tf.control_dependencies([self.u.assign(new_u)]): - W_bar = tf.reshape(W_bar, W_shape) - - return W_bar - - def convolution_op(self, inputs, kernel): - if self.padding == "causal": - tf_padding = "VALID" # Causal padding handled in `call`. - elif isinstance(self.padding, str): - tf_padding = self.padding.upper() - else: - tf_padding = self.padding - - return tf.nn.convolution( - inputs, - kernel, - strides=list(self.strides), - padding=tf_padding, - dilations=list(self.dilation_rate), - ) - def call(self, inputs): - W_shape = self.kernel.shape.as_list() - W_reshaped = tf.reshape(self.kernel, (-1, W_shape[-1])) - new_kernel = self.compute_spectral_norm(W_reshaped, self.u, W_shape) - outputs = self.convolution_op(inputs, new_kernel) - - if self.use_bias: - if self.data_format == 'channels_first': - outputs = tf.nn.bias_add(outputs, self.bias, data_format='NCHW') - else: - outputs = tf.nn.bias_add(outputs, self.bias, data_format='NHWC') - if self.activation is not None: - return self.activation(outputs) - - return outputs - - -class ConvSN2DTranspose(tf.keras.layers.Conv2DTranspose): - - def __init__(self, filters, kernel_size, power_iterations=1, **kwargs): - super(ConvSN2DTranspose, self).__init__(filters, kernel_size, **kwargs) - self.power_iterations = power_iterations - - - def build(self, input_shape): - super(ConvSN2DTranspose, self).build(input_shape) - - if self.data_format == 'channels_first': - channel_axis = 1 - else: - channel_axis = -1 - - self.u = self.add_weight(self.name + '_u', - shape=tuple([1, self.kernel.shape.as_list()[-1]]), - initializer=tf.initializers.RandomNormal(0, 1), - trainable=False - ) - - def compute_spectral_norm(self, W, new_u, W_shape): - for _ in range(self.power_iterations): - - new_v = l2normalize(tf.matmul(new_u, tf.transpose(W))) - new_u = l2normalize(tf.matmul(new_v, W)) - - sigma = tf.matmul(tf.matmul(new_v, W), tf.transpose(new_u)) - W_bar = W/sigma - - with tf.control_dependencies([self.u.assign(new_u)]): - W_bar = tf.reshape(W_bar, W_shape) - - return W_bar - - def call(self, inputs): - W_shape = self.kernel.shape.as_list() - W_reshaped = tf.reshape(self.kernel, (-1, W_shape[-1])) - new_kernel = self.compute_spectral_norm(W_reshaped, self.u, W_shape) - - inputs_shape = array_ops.shape(inputs) - batch_size = inputs_shape[0] - if self.data_format == 'channels_first': - h_axis, w_axis = 2, 3 - else: - h_axis, w_axis = 1, 2 - - height, width = inputs_shape[h_axis], inputs_shape[w_axis] - kernel_h, kernel_w = self.kernel_size - stride_h, stride_w = self.strides - - if self.output_padding is None: - out_pad_h = out_pad_w = None - else: - out_pad_h, out_pad_w = self.output_padding - - out_height = conv_utils.deconv_output_length(height, - kernel_h, - padding=self.padding, - output_padding=out_pad_h, - stride=stride_h, - dilation=self.dilation_rate[0]) - out_width = conv_utils.deconv_output_length(width, - kernel_w, - padding=self.padding, - output_padding=out_pad_w, - stride=stride_w, - dilation=self.dilation_rate[1]) - if self.data_format == 'channels_first': - output_shape = (batch_size, self.filters, out_height, out_width) - else: - output_shape = (batch_size, out_height, out_width, self.filters) - - output_shape_tensor = array_ops.stack(output_shape) - outputs = K.conv2d_transpose( - inputs, - new_kernel, - output_shape_tensor, - strides=self.strides, - padding=self.padding, - data_format=self.data_format, - dilation_rate=self.dilation_rate) - - if not context.executing_eagerly(): - out_shape = self.compute_output_shape(inputs.shape) - outputs.set_shape(out_shape) - - if self.use_bias: - outputs = tf.nn.bias_add( - outputs, - self.bias, - data_format=conv_utils.convert_data_format(self.data_format, ndim=4)) - - if self.activation is not None: - return self.activation(outputs) - return outputs - - -class DenseSN(Dense): - - def build(self, input_shape): - super(DenseSN, self).build(input_shape) - - self.u = self.add_weight(self.name + '_u', - shape=tuple([1, self.kernel.shape.as_list()[-1]]), - initializer=tf.initializers.RandomNormal(0, 1), - trainable=False) - - def compute_spectral_norm(self, W, new_u, W_shape): - new_v = l2normalize(tf.matmul(new_u, tf.transpose(W))) - new_u = l2normalize(tf.matmul(new_v, W)) - sigma = tf.matmul(tf.matmul(new_v, W), tf.transpose(new_u)) - W_bar = W/sigma - with tf.control_dependencies([self.u.assign(new_u)]): - W_bar = tf.reshape(W_bar, W_shape) - return W_bar - - def call(self, inputs): - W_shape = self.kernel.shape.as_list() - W_reshaped = tf.reshape(self.kernel, (-1, W_shape[-1])) - new_kernel = self.compute_spectral_norm(W_reshaped, self.u, W_shape) - rank = len(inputs.shape) - if rank > 2: - outputs = standard_ops.tensordot(inputs, new_kernel, [[rank - 1], [0]]) - if not context.executing_eagerly(): - shape = inputs.shape.as_list() - output_shape = shape[:-1] + [self.units] - outputs.set_shape(output_shape) - else: - inputs = math_ops.cast(inputs, self._compute_dtype) - if K.is_sparse(inputs): - outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, new_kernel) - else: - outputs = gen_math_ops.mat_mul(inputs, new_kernel) - if self.use_bias: - outputs = tf.nn.bias_add(outputs, self.bias) - if self.activation is not None: - return self.activation(outputs) - return outputs - -#Networks Architecture - -init = tf.keras.initializers.he_uniform() - -def conv2d(layer_input, filters, kernel_size=4, strides=2, padding='same', leaky=True, bnorm=True, sn=True): - if leaky: - Activ = LeakyReLU(alpha=0.2) - else: - Activ = ReLU() - if sn: - d = ConvSN2D(filters, kernel_size=kernel_size, strides=strides, padding=padding, kernel_initializer=init, use_bias=False)(layer_input) - else: - d = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding=padding, kernel_initializer=init, use_bias=False)(layer_input) - if bnorm: - d = BatchNormalization()(d) - d = Activ(d) - return d - -def deconv2d(layer_input, layer_res, filters, kernel_size=4, conc=True, scalev=False, bnorm=True, up=True, padding='same', strides=2): - if up: - u = UpSampling2D((1,2))(layer_input) - u = ConvSN2D(filters, kernel_size, strides=(1,1), kernel_initializer=init, use_bias=False, padding=padding)(u) - else: - u = ConvSN2DTranspose(filters, kernel_size, strides=strides, kernel_initializer=init, use_bias=False, padding=padding)(layer_input) - if bnorm: - u = BatchNormalization()(u) - u = LeakyReLU(alpha=0.2)(u) - if conc: - u = Concatenate()([u,layer_res]) - return u - -#Extract function: splitting spectrograms -def extract_image(im): - im1 = Cropping2D(((0,0), (0, 2*(im.shape[2]//3))))(im) - im2 = Cropping2D(((0,0), (im.shape[2]//3,im.shape[2]//3)))(im) - im3 = Cropping2D(((0,0), (2*(im.shape[2]//3), 0)))(im) - return im1,im2,im3 - -#Assemble function: concatenating spectrograms -def assemble_image(lsim): - im1,im2,im3 = lsim - imh = Concatenate(2)([im1,im2,im3]) - return imh - -#U-NET style architecture -def build_generator(input_shape): - h,w,c = input_shape - inp = Input(shape=input_shape) - #downscaling - g0 = tf.keras.layers.ZeroPadding2D((0,1))(inp) - g1 = conv2d(g0, 256, kernel_size=(h,3), strides=1, padding='valid') - g2 = conv2d(g1, 256, kernel_size=(1,9), strides=(1,2)) - g3 = conv2d(g2, 256, kernel_size=(1,7), strides=(1,2)) - #upscaling - g4 = deconv2d(g3,g2, 256, kernel_size=(1,7), strides=(1,2)) - g5 = deconv2d(g4,g1, 256, kernel_size=(1,9), strides=(1,2), bnorm=False) - g6 = ConvSN2DTranspose(1, kernel_size=(h,1), strides=(1,1), kernel_initializer=init, padding='valid', activation='tanh')(g5) - return Model(inp,g6, name='G') - -#Siamese Network -def build_siamese(input_shape): - h,w,c = input_shape - inp = Input(shape=input_shape) - g1 = conv2d(inp, 256, kernel_size=(h,3), strides=1, padding='valid', sn=False) - g2 = conv2d(g1, 256, kernel_size=(1,9), strides=(1,2), sn=False) - g3 = conv2d(g2, 256, kernel_size=(1,7), strides=(1,2), sn=False) - g4 = Flatten()(g3) - g5 = Dense(vec_len)(g4) - return Model(inp, g5, name='S') - -#Discriminator (Critic) Network -def build_critic(input_shape): - h,w,c = input_shape - inp = Input(shape=input_shape) - g1 = conv2d(inp, 512, kernel_size=(h,3), strides=1, padding='valid', bnorm=False) - g2 = conv2d(g1, 512, kernel_size=(1,9), strides=(1,2), bnorm=False) - g3 = conv2d(g2, 512, kernel_size=(1,7), strides=(1,2), bnorm=False) - g4 = Flatten()(g3) - g4 = DenseSN(1, kernel_initializer=init)(g4) - return Model(inp, g4, name='C') - -#Load past models from path to resume training or test -save_model_path = '/content/drive/MyDrive/weights' #@param {type:"string"} -def load(path): - gen = build_generator((hop,shape,1)) - siam = build_siamese((hop,shape,1)) - critic = build_critic((hop,3*shape,1)) - gen.load_weights(path+'/gen.h5') - critic.load_weights(path+'/critic.h5') - siam.load_weights(path+'/siam.h5') - return gen,critic,siam - -#Build models -def build(): - gen = build_generator((hop,shape,1)) - siam = build_siamese((hop,shape,1)) - critic = build_critic((hop,3*shape,1)) #the discriminator accepts as input spectrograms of triple the width of those generated by the generator - return gen,critic,siam - -#Show results mid-training -def save_test_image_full(path): - a = testgena() - print(a.shape) - ab = gen(a, training=False) - ab = testass(ab) - a = testass(a) - abwv = deprep(ab) - awv = deprep(a) - sf.write(path+'/new_file.wav', abwv, sr) - IPython.display.display(IPython.display.Audio(np.squeeze(abwv), rate=sr)) - IPython.display.display(IPython.display.Audio(np.squeeze(awv), rate=sr)) - fig, axs = plt.subplots(ncols=2) - axs[0].imshow(np.flip(a, -2), cmap=None) - axs[0].axis('off') - axs[0].set_title('Source') - axs[1].imshow(np.flip(ab, -2), cmap=None) - axs[1].axis('off') - axs[1].set_title('Generated') - plt.show() - -#Save in training loop -def save_end(epoch,gloss,closs,mloss,n_save=3,save_path=save_model_path): #use custom save_path (i.e. Drive '../content/drive/My Drive/') - if epoch % n_save == 0: - print('Saving...') - path = f'{save_path}/MELGANVC-{str(gloss)[:9]}-{str(closs)[:9]}-{str(mloss)[:9]}' - os.mkdir(path) - gen.save_weights(path+'/gen.h5') - critic.save_weights(path+'/critic.h5') - siam.save_weights(path+'/siam.h5') - save_test_image_full(path) - -#Get models and optimizers -def get_networks(shape, load_model=False, path=None): - if not load_model: - gen,critic,siam = build() - else: - gen,critic,siam = load(path) - print('Built networks') - - opt_gen = Adam(0.0001, 0.5) - opt_disc = Adam(0.0001, 0.5) - - return gen,critic,siam, [opt_gen,opt_disc] - -#Set learning rate -def update_lr(lr): - opt_gen.learning_rate = lr - opt_disc.learning_rate = lr - -#Build models and initialize optimizers -load_model_path='MELGANVC-0.4886211-0.5750153-0-20230612T163214Z-001\MELGANVC-0.4886211-0.5750153-0' #@param {type:"string"} -#If load_model=True, specify the path where the models are saved - -gen,critic,siam, [opt_gen,opt_disc] = get_networks(shape, load_model=True,path="MELGANVC-0.4886211-0.5750153-0") - -#After Training, use these functions to convert data with the generator and save the results - -#Assembling generated Spectrogram chunks into final Spectrogram -def specass(a,spec): - but=False - con = np.array([]) - nim = a.shape[0] - for i in range(nim-1): - im = a[i] - im = np.squeeze(im) - if not but: - con=im - but=True - else: - con = np.concatenate((con,im), axis=1) - diff = spec.shape[1]-(nim*shape) - a = np.squeeze(a) - con = np.concatenate((con,a[-1,:,-diff:]), axis=1) - return np.squeeze(con) - -#Splitting input spectrogram into different chunks to feed to the generator -def chopspec(spec): - dsa=[] - for i in range(spec.shape[1]//shape): - im = spec[:,i*shape:i*shape+shape] - im = np.reshape(im, (im.shape[0],im.shape[1],1)) - dsa.append(im) - imlast = spec[:,-shape:] - imlast = np.reshape(imlast, (imlast.shape[0],imlast.shape[1],1)) - dsa.append(imlast) - return np.array(dsa, dtype=np.float32) - -#Converting from source Spectrogram to target Spectrogram -def towave(spec, name, path='../content/', show=False): - specarr = chopspec(spec) - print(specarr.shape) - a = specarr - print('Generating...') - ab = gen(a, training=False) - print('Assembling and Converting...') - a = specass(a,spec) - ab = specass(ab,spec) - awv = deprep(a) - abwv = deprep(ab) - print('Saving...') - pathfin = f'{path}/{name}' - try: - os.mkdir(pathfin) - except: - pass - sf.write(pathfin+'/AB.wav', abwv, sr) - sf.write(pathfin+'/A.wav', awv, sr) - print('Saved WAV!') - IPython.display.display(IPython.display.Audio(np.squeeze(abwv), rate=sr)) - IPython.display.display(IPython.display.Audio(np.squeeze(awv), rate=sr)) - if show: - fig, axs = plt.subplots(ncols=2) - axs[0].imshow(np.flip(a, -2), cmap=None) - axs[0].axis('off') - axs[0].set_title('Source') - axs[1].imshow(np.flip(ab, -2), cmap=None) - axs[1].axis('off') - axs[1].set_title('Generated') - plt.show() - return abwv \ No newline at end of file diff --git a/spaces/Xule/ChuanhuChatGPT/assets/custom.css b/spaces/Xule/ChuanhuChatGPT/assets/custom.css deleted file mode 100644 index af5e9f2118b843b3bbd7627ed45e970c20b13bef..0000000000000000000000000000000000000000 --- a/spaces/Xule/ChuanhuChatGPT/assets/custom.css +++ /dev/null @@ -1,353 +0,0 @@ -:root { - --chatbot-color-light: #F3F3F3; - --chatbot-color-dark: #121111; -} - -#app_title { - font-weight: var(--prose-header-text-weight); - font-size: var(--text-xxl); - line-height: 1.3; - text-align: left; - margin-top: 6px; - white-space: nowrap; -} -#description { - text-align: center; - margin:16px 0 -} - -/* 覆盖gradio的页脚信息QAQ */ -/* footer { - display: none !important; -} */ -#footer { - text-align: center; -} -#footer div { - display: inline-block; -} -#footer .versions{ - font-size: 85%; - opacity: 0.85; -} - -#float_display { - position: absolute; - max-height: 30px; -} -/* user_info */ -#user_info { - white-space: nowrap; - position: absolute; left: 8em; top: .2em; - z-index: var(--layer-2); - box-shadow: var(--block-shadow); - border: none; border-radius: var(--block-label-radius); - background: var(--color-accent); - padding: var(--block-label-padding); - font-size: var(--block-label-text-size); line-height: var(--line-sm); - width: auto; min-height: 30px!important; - opacity: 1; - transition: opacity 0.3s ease-in-out; -} -#user_info .wrap { - opacity: 0; -} -#user_info p { - color: white; - font-weight: var(--block-label-text-weight); -} -#user_info.hideK { - opacity: 0; - transition: opacity 1s ease-in-out; -} - -/* status_display */ -#status_display { - display: flex; - min-height: 2em; - align-items: flex-end; - justify-content: flex-end; -} -#status_display p { - font-size: .85em; - font-family: monospace; - color: var(--body-text-color-subdued); -} - -#status_display { - transition: all 0.6s; -} -#chuanhu_chatbot { - transition: height 0.3s ease; -} - -/* usage_display */ -.insert_block { - position: relative; - margin: 0; - padding: .5em 1em; - box-shadow: var(--block-shadow); - border-width: var(--block-border-width); - border-color: var(--block-border-color); - border-radius: var(--block-radius); - background: var(--block-background-fill); - width: 100%; - line-height: var(--line-sm); - min-height: 2em; -} -#usage_display p, #usage_display span { - margin: 0; - font-size: .85em; - color: var(--body-text-color-subdued); -} -.progress-bar { - background-color: var(--input-background-fill);; - margin: 0 1em; - height: 20px; - border-radius: 10px; - overflow: hidden; -} -.progress { - background-color: var(--block-title-background-fill); - height: 100%; - border-radius: 10px; - text-align: right; - transition: width 0.5s ease-in-out; -} -.progress-text { - /* color: white; */ - color: var(--color-accent) !important; - font-size: 1em !important; - font-weight: bold; - padding-right: 10px; - line-height: 20px; -} - -.apSwitch { - top: 2px; - display: inline-block; - height: 24px; - position: relative; - width: 48px; - border-radius: 12px; -} -.apSwitch input { - display: none !important; -} -.apSlider { - background-color: var(--block-label-background-fill); - bottom: 0; - cursor: pointer; - left: 0; - position: absolute; - right: 0; - top: 0; - transition: .4s; - font-size: 18px; - border-radius: 12px; -} -.apSlider::before { - bottom: -1.5px; - left: 1px; - position: absolute; - transition: .4s; - content: "🌞"; -} -input:checked + .apSlider { - background-color: var(--block-label-background-fill); -} -input:checked + .apSlider::before { - transform: translateX(23px); - content:"🌚"; -} - -#submit_btn, #cancel_btn { - height: 42px !important; -} -#submit_btn::before { - content: url("data:image/svg+xml, %3Csvg width='21px' height='20px' viewBox='0 0 21 20' version='1.1' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E %3Cg id='page' stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E %3Cg id='send' transform='translate(0.435849, 0.088463)' fill='%23FFFFFF' fill-rule='nonzero'%3E %3Cpath d='M0.579148261,0.0428666046 C0.301105539,-0.0961547561 -0.036517765,0.122307382 0.0032026237,0.420210298 L1.4927172,18.1553639 C1.5125774,18.4334066 1.79062012,18.5922882 2.04880264,18.4929872 L8.24518329,15.8913017 L11.6412765,19.7441794 C11.8597387,19.9825018 12.2370824,19.8832008 12.3165231,19.5852979 L13.9450591,13.4882182 L19.7839562,11.0255541 C20.0619989,10.8865327 20.0818591,10.4694687 19.7839562,10.3105871 L0.579148261,0.0428666046 Z M11.6138902,17.0883151 L9.85385903,14.7195502 L0.718169621,0.618812241 L12.69945,12.9346347 L11.6138902,17.0883151 Z' id='shape'%3E%3C/path%3E %3C/g%3E %3C/g%3E %3C/svg%3E"); - 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height: 21px; -} -/* list */ -ol:not(.options), ul:not(.options) { - padding-inline-start: 2em !important; -} - -/* 亮色（默认） */ -#chuanhu_chatbot { - background-color: var(--chatbot-color-light) !important; - color: #000000 !important; -} -[data-testid = "bot"] { - background-color: #FFFFFF !important; -} -[data-testid = "user"] { - background-color: #95EC69 !important; -} -/* 暗色 */ -.dark #chuanhu_chatbot { - background-color: var(--chatbot-color-dark) !important; - color: #FFFFFF !important; -} -.dark [data-testid = "bot"] { - background-color: #2C2C2C !important; -} -.dark [data-testid = "user"] { - background-color: #26B561 !important; -} - -/* 屏幕宽度大于等于500px的设备 */ -/* update on 2023.4.8: 高度的细致调整已写入JavaScript */ -@media screen and (min-width: 500px) { - #chuanhu_chatbot { - height: calc(100vh - 200px); - } - #chuanhu_chatbot .wrap { - max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } -} -/* 屏幕宽度小于500px的设备 */ -@media screen and (max-width: 499px) { - #chuanhu_chatbot { - height: calc(100vh - 140px); - } - #chuanhu_chatbot .wrap { - max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } - [data-testid = "bot"] { - max-width: 98% !important; - } - #app_title h1{ - letter-spacing: -1px; font-size: 22px; - } -} -/* 对话气泡 */ -[class *= "message"] { - border-radius: var(--radius-xl) !important; - border: none; - padding: var(--spacing-xl) !important; - font-size: var(--text-md) !important; - line-height: var(--line-md) !important; - min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); - min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); -} -[data-testid = "bot"] { - max-width: 85%; - border-bottom-left-radius: 0 !important; -} -[data-testid = "user"] { - max-width: 85%; - width: auto !important; - border-bottom-right-radius: 0 !important; -} -/* 表格 */ -table { - margin: 1em 0; - border-collapse: collapse; - empty-cells: show; -} -td,th { - border: 1.2px solid var(--border-color-primary) !important; - padding: 0.2em; -} -thead { - background-color: rgba(175,184,193,0.2); -} -thead th { - padding: .5em .2em; -} -/* 行内代码 */ -code { - display: inline; - white-space: break-spaces; - border-radius: 6px; - margin: 0 2px 0 2px; - padding: .2em .4em .1em .4em; - background-color: rgba(175,184,193,0.2); -} -/* 代码块 */ -pre code { - display: block; - overflow: auto; - white-space: pre; - background-color: hsla(0, 0%, 0%, 80%)!important; - border-radius: 10px; - padding: 1.4em 1.2em 0em 1.4em; - margin: 1.2em 2em 1.2em 0.5em; - color: #FFF; - box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2); -} -/* 代码高亮样式 */ -.highlight .hll { background-color: #49483e } -.highlight .c { color: #75715e } /* Comment */ -.highlight .err { color: #960050; background-color: #1e0010 } /* Error */ -.highlight .k { color: #66d9ef } /* Keyword */ -.highlight .l { color: #ae81ff } /* Literal */ -.highlight .n { color: #f8f8f2 } /* Name */ -.highlight .o { color: #f92672 } /* Operator */ -.highlight .p { color: #f8f8f2 } /* Punctuation */ -.highlight .ch { color: #75715e } /* Comment.Hashbang */ -.highlight .cm { color: #75715e } /* Comment.Multiline */ -.highlight .cp { color: #75715e } /* Comment.Preproc */ -.highlight .cpf { color: #75715e } /* Comment.PreprocFile */ -.highlight .c1 { color: #75715e } /* Comment.Single */ -.highlight .cs { color: #75715e } /* Comment.Special */ -.highlight .gd { color: #f92672 } /* Generic.Deleted */ -.highlight .ge { font-style: italic } /* Generic.Emph */ -.highlight .gi { color: #a6e22e } /* Generic.Inserted */ -.highlight .gs { font-weight: bold } /* Generic.Strong */ -.highlight .gu { color: #75715e } /* Generic.Subheading */ -.highlight .kc { color: #66d9ef } /* Keyword.Constant */ -.highlight .kd { color: #66d9ef } /* Keyword.Declaration */ -.highlight .kn { color: #f92672 } /* Keyword.Namespace */ -.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */ -.highlight .kr { color: #66d9ef } /* Keyword.Reserved */ -.highlight .kt { color: #66d9ef } /* Keyword.Type */ -.highlight .ld { color: #e6db74 } /* Literal.Date */ -.highlight .m { color: #ae81ff } /* Literal.Number */ -.highlight .s { color: #e6db74 } /* Literal.String */ -.highlight .na { color: #a6e22e } /* Name.Attribute */ -.highlight .nb { color: #f8f8f2 } /* Name.Builtin */ -.highlight .nc { color: #a6e22e } /* Name.Class */ -.highlight .no { color: #66d9ef } /* Name.Constant */ -.highlight .nd { color: #a6e22e } /* Name.Decorator */ -.highlight .ni { color: #f8f8f2 } /* Name.Entity */ -.highlight .ne { color: #a6e22e } /* Name.Exception */ -.highlight .nf { color: #a6e22e } /* Name.Function */ -.highlight .nl { color: #f8f8f2 } /* Name.Label */ -.highlight .nn { color: #f8f8f2 } /* Name.Namespace */ -.highlight .nx { color: #a6e22e } /* Name.Other */ -.highlight .py { color: #f8f8f2 } /* Name.Property */ -.highlight .nt { color: #f92672 } /* Name.Tag */ -.highlight .nv { color: #f8f8f2 } /* Name.Variable */ -.highlight .ow { color: #f92672 } /* Operator.Word */ -.highlight .w { color: #f8f8f2 } /* Text.Whitespace */ -.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */ -.highlight .mf { color: #ae81ff } /* Literal.Number.Float */ -.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */ -.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */ -.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */ -.highlight .sa { color: #e6db74 } /* Literal.String.Affix */ -.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */ -.highlight .sc { color: #e6db74 } /* Literal.String.Char */ -.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */ -.highlight .sd { color: #e6db74 } /* Literal.String.Doc */ -.highlight .s2 { color: #e6db74 } /* Literal.String.Double */ -.highlight .se { color: #ae81ff } /* Literal.String.Escape */ -.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */ -.highlight .si { color: #e6db74 } /* Literal.String.Interpol */ -.highlight .sx { color: #e6db74 } /* Literal.String.Other */ -.highlight .sr { color: #e6db74 } /* Literal.String.Regex */ -.highlight .s1 { color: #e6db74 } /* Literal.String.Single */ -.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */ -.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */ -.highlight .fm { color: #a6e22e } /* Name.Function.Magic */ -.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */ -.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */ -.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */ -.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */ -.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */ diff --git a/spaces/Yntec/photoMovieX/style.css b/spaces/Yntec/photoMovieX/style.css deleted file mode 100644 index 142c4b92e938cc8cd33cde5ab580b5fd6a2aac78..0000000000000000000000000000000000000000 --- a/spaces/Yntec/photoMovieX/style.css +++ /dev/null @@ -1,97 +0,0 @@ -#col-container {color: white; - max-width: 1200px; - margin-left: auto; - margin-right: auto; -} -a { - color: inherit; - text-decoration: underline; -} -.gradio-container { - color: #ffaa66; - background-color: #005566; - font-family: 'IBM Plex Sans', sans-serif; -} -.gr-button { - color: #ffffff !important; - text-shadow: 1px 1px 0 rgba(0, 0, 0, 1) !important; - background-image: linear-gradient(#76635a, #d2a489) !important; - border-radius: 24px !important; - border: solid 1px !important; - border-top-color: #ffc99f !important; - border-right-color: #000000 !important; - border-bottom-color: #000000 !important; - border-left-color: #ffc99f !important; - padding: 6px 30px; -} -input[type='range'] { - accent-color: #9d66e5; -} -.dark input[type='range'] { - accent-color: #dfdfdf; -} -.container { - color: #ffaa66; - max-width: 1200px; - margin: auto; - padding-top: 1.5rem; -} -#gallery { - color: #ffaa66; - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; -} -#gallery>div>.h-full { - color: #ffaa66; - min-height: 20rem; -} -.details:hover { - text-decoration: underline; -} -.gr-button:focus { - border-color: rgb(255 160 0 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(0 0 0 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; -} -#advanced-options { - color: #ffaa66; - margin-bottom: 20px; -} -.footer { - color: #ffaa66; - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; -} -.footer>p { - color: #ffaa66; - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; -} -.dark .logo{ filter: invert(1); } -.dark .footer { - border-color: #303030; -} -.dark .footer>p { - background: #0b0f19; -} -.acknowledgments h4{ - color: #ffaa66; - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; -} - diff --git a/spaces/Yuelili/RealNagrse/scripts/pytorch2onnx.py b/spaces/Yuelili/RealNagrse/scripts/pytorch2onnx.py deleted file mode 100644 index 09d99b2e0171265e70e7507ed8e882b616b449a1..0000000000000000000000000000000000000000 --- a/spaces/Yuelili/RealNagrse/scripts/pytorch2onnx.py +++ /dev/null @@ -1,36 +0,0 @@ -import argparse -import torch -import torch.onnx -from basicsr.archs.rrdbnet_arch import RRDBNet - - -def main(args): - # An instance of the model - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) - if args.params: - keyname = 'params' - else: - keyname = 'params_ema' - model.load_state_dict(torch.load(args.input)[keyname]) - # set the train mode to false since we will only run the forward pass. - model.train(False) - model.cpu().eval() - - # An example input - x = torch.rand(1, 3, 64, 64) - # Export the model - with torch.no_grad(): - torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True) - print(torch_out.shape) - - -if __name__ == '__main__': - """Convert pytorch model to onnx models""" - parser = argparse.ArgumentParser() - parser.add_argument( - '--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path') - parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path') - parser.add_argument('--params', action='store_false', help='Use params instead of params_ema') - args = parser.parse_args() - - main(args) diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/gpt_model.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/gpt_model.py deleted file mode 100644 index 4a64aaf9e56067543a2aab17d9b20f6170b5b75f..0000000000000000000000000000000000000000 --- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/models/gpt_model.py +++ /dev/null @@ -1,213 +0,0 @@ -""" -OpenAI's GPT-2 ported to PyTorch. -""" -import math - -import attr -import torch -from torch import nn -from torch.nn import functional as F -import torch.utils.checkpoint - - -@attr.s(auto_attribs=True, frozen=True) -class HParams: - n_vocab: int - n_ctx: int - n_embed: int - n_hidden: int - n_head: int - n_layer: int - gradient_checkpointing: bool = False - - -class Model(nn.Module): - def __init__(self, hparams: HParams): - super().__init__() - self.hparams = hparams - self.wpe = nn.Embedding(hparams.n_ctx, hparams.n_embed) - nn.init.normal_(self.wpe.weight, std=0.01) - self.wte = nn.Embedding(hparams.n_vocab, hparams.n_embed) - nn.init.normal_(self.wte.weight, std=0.02) - self.blocks = nn.ModuleList( - [Block(hparams) for _ in range(hparams.n_layer)]) - self.ln_f = Norm(self.hparams.n_hidden) - if hparams.n_hidden != hparams.n_embed: - self.in_proj = Conv1D(hparams.n_embed, hparams.n_hidden) - self.out_proj = Conv1D(hparams.n_hidden, hparams.n_embed) - else: - self.in_proj = self.out_proj = None - - def forward(self, x, past=None): - # Embedding - past_length = 0 if past is None else past.shape[-2] - batch_size, n_ctx = x.shape - position = position_for(batch_size, n_ctx, past_length, x.device) - h = self.wte(x) + self.wpe(position) - assert h.shape == (batch_size, n_ctx, self.hparams.n_embed) - if self.in_proj: - h = self.in_proj(h) - # Transformer - presents = [] - for i, block in enumerate(self.blocks): - if self.hparams.gradient_checkpointing: - h, present = torch.utils.checkpoint.checkpoint( - block, h, past[:, i] if past is not None else None) - else: - h, present = block( - h, past=past[:, i] if past is not None else None) - presents.append(present) - h = self.ln_f(h) - if self.out_proj: - h = self.out_proj(h) - # Output logits - h_flat = h.reshape([batch_size * n_ctx, self.hparams.n_embed]) - logits = torch.matmul(h_flat, self.wte.weight.t()) - logits = logits.reshape([batch_size, n_ctx, self.hparams.n_vocab]) - return { - 'presents': torch.stack(tuple(presents), dim=1), - 'logits': logits, - } - - -class Block(nn.Module): - def __init__(self, hparams: HParams): - super().__init__() - self.ln_1 = Norm(hparams.n_hidden) - self.ln_2 = Norm(hparams.n_hidden) - self.mlp = MLP(hparams.n_hidden, hparams.n_hidden * 4) - self.attn = Attention(hparams) - - def forward(self, x, past): - a, present = self.attn(self.ln_1(x), past=past) - x = x + a - m = self.mlp(self.ln_2(x)) - x = x + m - return x, present - - -class Norm(nn.Module): - """ Normalize to mean = 0, std = 1, then do a diagonal affine transform. - """ - def __init__(self, n_features, *, dim=-1, epsilon=1e-5): - super().__init__() - self.n_features = n_features - self.dim = dim - self.epsilon = epsilon - self.g = nn.Parameter(torch.ones(n_features)) - self.b = nn.Parameter(torch.zeros(n_features)) - - def forward(self, x): - assert x.shape[-1] == self.n_features - u = torch.mean(x, dim=self.dim, keepdim=True) - xmu = x - u - s = torch.mean(xmu * xmu, dim=self.dim, keepdim=True) - return xmu * torch.rsqrt(s + self.epsilon) * self.g + self.b - - -class MLP(nn.Module): - def __init__(self, n_features, n_hidden): - super().__init__() - self.c_fc = Conv1D(n_features, n_hidden) - self.c_proj = Conv1D(n_hidden, n_features) - - def forward(self, x): - x = gelu(self.c_fc(x)) - x = self.c_proj(x) - return x - - -class Attention(nn.Module): - def __init__(self, hparams: HParams): - super().__init__() - assert hparams.n_hidden % hparams.n_head == 0 - self.hparams = hparams - self.c_attn = Conv1D(hparams.n_hidden, hparams.n_hidden * 3) - self.c_proj = Conv1D(hparams.n_hidden, hparams.n_hidden) - - def forward(self, x, past): - assert len(x.shape) == 3 # [batch, sequence, features] - assert x.shape[-1] == self.hparams.n_hidden - if past is not None: - # Should be [batch, 2, heads, sequence, features], where 2 is [k, v] - assert len(past.shape) == 5 - assert past.shape[-1] == self.hparams.n_hidden - c = self.c_attn(x) - q, k, v = map(self.split_heads, torch.split(c, x.shape[-1], dim=2)) - present = torch.stack([k, v], dim=1) - if past is not None: - pk, pv = past[:, 0], past[:, 1] - k = torch.cat([pk, k], dim=-2) - v = torch.cat([pv, v], dim=-2) - a = self.multihead_attn(q, k, v) - a = self.merge_heads(a) - a = self.c_proj(a) - return a, present - - def split_heads(self, x): - """ From [batch, sequence, features] to - [batch, heads, sequence, features]. - """ - return self.split_states(x, self.hparams.n_head).permute(0, 2, 1, 3) - - @staticmethod - def split_states(x, n): - """ Reshape the last dimension of x into [n, x.shape[-1]/n]. - """ - *start, m = x.shape - return x.reshape(start + [n, m // n]) - - def merge_heads(self, x): - """ Reverse of split_heads. - """ - return self.merge_states(x.permute(0, 2, 1, 3)) - - @staticmethod - def merge_states(x): - """ Smash the last two dimensions of x into a single dimension. - """ - *start, a, b = x.shape - return x.reshape(start + [a * b]) - - def mask_attn_weights(self, w): - # w has shape [batch, heads, dst_sequence, src_sequence], - # where information flows from src to dst. - _, _, nd, ns = w.shape - b = self.attention_mask(nd, ns, dtype=w.dtype, device=w.device) - b = b.reshape((1, 1, nd, ns)) - w = w * b - 1e4 * (1 - b) - return w - - @staticmethod - def attention_mask(nd, ns, *, dtype, device=None): - """ 1's in the lower triangle, counting from the lower right corner. - Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), - but doesn't produce garbage on TPUs. - """ - i = torch.arange(0, nd).unsqueeze(1) - j = torch.arange(ns) - return (i >= j - ns + nd).to(dtype=dtype, device=device) - - def multihead_attn(self, q, k, v): - # q, k, v have shape [batch, heads, sequence, features] - w = torch.matmul(q, k.permute(0, 1, 3, 2)) - w = w / math.sqrt(v.shape[-1]) - w = self.mask_attn_weights(w) - w = F.softmax(w, dim=-1) - a = torch.matmul(w, v) - return a - - -class Conv1D(nn.Linear): - def reset_parameters(self): - nn.init.normal_(self.weight, std=0.02) - nn.init.zeros_(self.bias) - - -def gelu(x, c=math.sqrt(2 / math.pi)): - return 0.5 * x * (1 + torch.tanh(c * (x + 0.044715 * torch.pow(x, 3)))) - - -def position_for(batch_size, n_steps, past_length, device=None): - return (torch.arange(past_length, n_steps + past_length, device=device) - .unsqueeze(0).repeat(batch_size, 1)) diff --git a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/bpe_toy.py b/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/bpe_toy.py deleted file mode 100644 index 0421b255861cb56eb40bf58a8225807cc396e968..0000000000000000000000000000000000000000 --- a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/bpe_toy.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Author: Rico Sennrich - -"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text. -Unlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary -of a text to a configurable number of symbols, with only a small increase in the number of tokens. -This is an (inefficient) toy implementation that shows the algorithm. For processing large datasets, -indexing and incremental updates can be used to speed up the implementation (see learn_bpe.py). - -Reference: -Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units. -Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany. -""" - - -import re -import sys -import collections - -def get_stats(vocab): - pairs = collections.defaultdict(int) - for word, freq in vocab.items(): - symbols = word.split() - for i in range(len(symbols)-1): - pairs[symbols[i],symbols[i+1]] += freq - return pairs - -def merge_vocab(pair, v_in): - v_out = {} - bigram_pattern = re.escape(' '.join(pair)) - p = re.compile(r'(?' : 5, 'l o w e r' : 2, - 'n e w e s t' : 6, 'w i d e s t' : 3} -num_merges = 15 -for i in range(num_merges): - pairs = get_stats(vocab) - try: - best = max(pairs, key=pairs.get) - except ValueError: - break - if pairs[best] < 2: - sys.stderr.write('no pair has frequency > 1. Stopping\n') - break - vocab = merge_vocab(best, vocab) - print(best) diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/__init__.py deleted file mode 100644 index e54b088acf644d285ecbeb1440c414e722b9db58..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/backbones/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from .darknet import Darknet -from .detectors_resnet import DetectoRS_ResNet -from .detectors_resnext import DetectoRS_ResNeXt -from .hourglass import HourglassNet -from .hrnet import HRNet -from .regnet import RegNet -from .res2net import Res2Net -from .resnest import ResNeSt -from .resnet import ResNet, ResNetV1d -from .resnext import ResNeXt -from .ssd_vgg import SSDVGG -from .trident_resnet import TridentResNet -from .swin_transformer import SwinTransformer -from .uniformer import UniFormer - -__all__ = [ - 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 'Res2Net', - 'HourglassNet', 'DetectoRS_ResNet', 'DetectoRS_ResNeXt', 'Darknet', - 'ResNeSt', 'TridentResNet', 'SwinTransformer', 'UniFormer' -] diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/scnet_roi_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/scnet_roi_head.py deleted file mode 100644 index 85aaa2f0600afbdfc8b0917cb5f341740776a603..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/scnet_roi_head.py +++ /dev/null @@ -1,582 +0,0 @@ -import torch -import torch.nn.functional as F - -from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, - merge_aug_masks, multiclass_nms) -from ..builder import HEADS, build_head, build_roi_extractor -from .cascade_roi_head import CascadeRoIHead - - -@HEADS.register_module() -class SCNetRoIHead(CascadeRoIHead): - """RoIHead for `SCNet `_. - - Args: - num_stages (int): number of cascade stages. - stage_loss_weights (list): loss weight of cascade stages. - semantic_roi_extractor (dict): config to init semantic roi extractor. - semantic_head (dict): config to init semantic head. - feat_relay_head (dict): config to init feature_relay_head. - glbctx_head (dict): config to init global context head. - """ - - def __init__(self, - num_stages, - stage_loss_weights, - semantic_roi_extractor=None, - semantic_head=None, - feat_relay_head=None, - glbctx_head=None, - **kwargs): - super(SCNetRoIHead, self).__init__(num_stages, stage_loss_weights, - **kwargs) - assert self.with_bbox and self.with_mask - assert not self.with_shared_head # shared head is not supported - - if semantic_head is not None: - self.semantic_roi_extractor = build_roi_extractor( - semantic_roi_extractor) - self.semantic_head = build_head(semantic_head) - - if feat_relay_head is not None: - self.feat_relay_head = build_head(feat_relay_head) - - if glbctx_head is not None: - self.glbctx_head = build_head(glbctx_head) - - def init_mask_head(self, mask_roi_extractor, mask_head): - """Initialize ``mask_head``""" - if mask_roi_extractor is not None: - self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor) - self.mask_head = build_head(mask_head) - - def init_weights(self, pretrained): - """Initialize the weights in head. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - for i in range(self.num_stages): - if self.with_bbox: - self.bbox_roi_extractor[i].init_weights() - self.bbox_head[i].init_weights() - if self.with_mask: - self.mask_roi_extractor.init_weights() - self.mask_head.init_weights() - if self.with_semantic: - self.semantic_head.init_weights() - if self.with_glbctx: - self.glbctx_head.init_weights() - if self.with_feat_relay: - self.feat_relay_head.init_weights() - - @property - def with_semantic(self): - """bool: whether the head has semantic head""" - return hasattr(self, - 'semantic_head') and self.semantic_head is not None - - @property - def with_feat_relay(self): - """bool: whether the head has feature relay head""" - return (hasattr(self, 'feat_relay_head') - and self.feat_relay_head is not None) - - @property - def with_glbctx(self): - """bool: whether the head has global context head""" - return hasattr(self, 'glbctx_head') and self.glbctx_head is not None - - def _fuse_glbctx(self, roi_feats, glbctx_feat, rois): - """Fuse global context feats with roi feats.""" - assert roi_feats.size(0) == rois.size(0) - img_inds = torch.unique(rois[:, 0].cpu(), sorted=True).long() - fused_feats = torch.zeros_like(roi_feats) - for img_id in img_inds: - inds = (rois[:, 0] == img_id.item()) - fused_feats[inds] = roi_feats[inds] + glbctx_feat[img_id] - return fused_feats - - def _slice_pos_feats(self, feats, sampling_results): - """Get features from pos rois.""" - num_rois = [res.bboxes.size(0) for res in sampling_results] - num_pos_rois = [res.pos_bboxes.size(0) for res in sampling_results] - inds = torch.zeros(sum(num_rois), dtype=torch.bool) - start = 0 - for i in range(len(num_rois)): - start = 0 if i == 0 else start + num_rois[i - 1] - stop = start + num_pos_rois[i] - inds[start:stop] = 1 - sliced_feats = feats[inds] - return sliced_feats - - def _bbox_forward(self, - stage, - x, - rois, - semantic_feat=None, - glbctx_feat=None): - """Box head forward function used in both training and testing.""" - bbox_roi_extractor = self.bbox_roi_extractor[stage] - bbox_head = self.bbox_head[stage] - bbox_feats = bbox_roi_extractor( - x[:len(bbox_roi_extractor.featmap_strides)], rois) - if self.with_semantic and semantic_feat is not None: - bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], - rois) - if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: - bbox_semantic_feat = F.adaptive_avg_pool2d( - bbox_semantic_feat, bbox_feats.shape[-2:]) - bbox_feats += bbox_semantic_feat - if self.with_glbctx and glbctx_feat is not None: - bbox_feats = self._fuse_glbctx(bbox_feats, glbctx_feat, rois) - cls_score, bbox_pred, relayed_feat = bbox_head( - bbox_feats, return_shared_feat=True) - - bbox_results = dict( - cls_score=cls_score, - bbox_pred=bbox_pred, - relayed_feat=relayed_feat) - return bbox_results - - def _mask_forward(self, - x, - rois, - semantic_feat=None, - glbctx_feat=None, - relayed_feat=None): - """Mask head forward function used in both training and testing.""" - mask_feats = self.mask_roi_extractor( - x[:self.mask_roi_extractor.num_inputs], rois) - if self.with_semantic and semantic_feat is not None: - mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], - rois) - if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: - mask_semantic_feat = F.adaptive_avg_pool2d( - mask_semantic_feat, mask_feats.shape[-2:]) - mask_feats += mask_semantic_feat - if self.with_glbctx and glbctx_feat is not None: - mask_feats = self._fuse_glbctx(mask_feats, glbctx_feat, rois) - if self.with_feat_relay and relayed_feat is not None: - mask_feats = mask_feats + relayed_feat - mask_pred = self.mask_head(mask_feats) - mask_results = dict(mask_pred=mask_pred) - - return mask_results - - def _bbox_forward_train(self, - stage, - x, - sampling_results, - gt_bboxes, - gt_labels, - rcnn_train_cfg, - semantic_feat=None, - glbctx_feat=None): - """Run forward function and calculate loss for box head in training.""" - bbox_head = self.bbox_head[stage] - rois = bbox2roi([res.bboxes for res in sampling_results]) - bbox_results = self._bbox_forward( - stage, - x, - rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat) - - bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, - gt_labels, rcnn_train_cfg) - loss_bbox = bbox_head.loss(bbox_results['cls_score'], - bbox_results['bbox_pred'], rois, - *bbox_targets) - - bbox_results.update( - loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets) - return bbox_results - - def _mask_forward_train(self, - x, - sampling_results, - gt_masks, - rcnn_train_cfg, - semantic_feat=None, - glbctx_feat=None, - relayed_feat=None): - """Run forward function and calculate loss for mask head in - training.""" - pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) - mask_results = self._mask_forward( - x, - pos_rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat, - relayed_feat=relayed_feat) - - mask_targets = self.mask_head.get_targets(sampling_results, gt_masks, - rcnn_train_cfg) - pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) - loss_mask = self.mask_head.loss(mask_results['mask_pred'], - mask_targets, pos_labels) - - mask_results = loss_mask - return mask_results - - def forward_train(self, - x, - img_metas, - proposal_list, - gt_bboxes, - gt_labels, - gt_bboxes_ignore=None, - gt_masks=None, - gt_semantic_seg=None): - """ - 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`. - - proposal_list (list[Tensors]): list of region proposals. - - 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 - - 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. - - gt_semantic_seg (None, list[Tensor]): semantic segmentation masks - used if the architecture supports semantic segmentation task. - - Returns: - dict[str, Tensor]: a dictionary of loss components - """ - losses = dict() - - # semantic segmentation branch - if self.with_semantic: - semantic_pred, semantic_feat = self.semantic_head(x) - loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) - losses['loss_semantic_seg'] = loss_seg - else: - semantic_feat = None - - # global context branch - if self.with_glbctx: - mc_pred, glbctx_feat = self.glbctx_head(x) - loss_glbctx = self.glbctx_head.loss(mc_pred, gt_labels) - losses['loss_glbctx'] = loss_glbctx - else: - glbctx_feat = None - - for i in range(self.num_stages): - self.current_stage = i - rcnn_train_cfg = self.train_cfg[i] - lw = self.stage_loss_weights[i] - - # assign gts and sample proposals - sampling_results = [] - bbox_assigner = self.bbox_assigner[i] - bbox_sampler = self.bbox_sampler[i] - num_imgs = len(img_metas) - if gt_bboxes_ignore is None: - gt_bboxes_ignore = [None for _ in range(num_imgs)] - - for j in range(num_imgs): - assign_result = bbox_assigner.assign(proposal_list[j], - gt_bboxes[j], - gt_bboxes_ignore[j], - gt_labels[j]) - sampling_result = bbox_sampler.sample( - assign_result, - proposal_list[j], - gt_bboxes[j], - gt_labels[j], - feats=[lvl_feat[j][None] for lvl_feat in x]) - sampling_results.append(sampling_result) - - bbox_results = \ - self._bbox_forward_train( - i, x, sampling_results, gt_bboxes, gt_labels, - rcnn_train_cfg, semantic_feat, glbctx_feat) - roi_labels = bbox_results['bbox_targets'][0] - - for name, value in bbox_results['loss_bbox'].items(): - losses[f's{i}.{name}'] = ( - value * lw if 'loss' in name else value) - - # refine boxes - if i < self.num_stages - 1: - pos_is_gts = [res.pos_is_gt for res in sampling_results] - with torch.no_grad(): - proposal_list = self.bbox_head[i].refine_bboxes( - bbox_results['rois'], roi_labels, - bbox_results['bbox_pred'], pos_is_gts, img_metas) - - if self.with_feat_relay: - relayed_feat = self._slice_pos_feats(bbox_results['relayed_feat'], - sampling_results) - relayed_feat = self.feat_relay_head(relayed_feat) - else: - relayed_feat = None - - mask_results = self._mask_forward_train(x, sampling_results, gt_masks, - rcnn_train_cfg, semantic_feat, - glbctx_feat, relayed_feat) - mask_lw = sum(self.stage_loss_weights) - losses['loss_mask'] = mask_lw * mask_results['loss_mask'] - - return losses - - def simple_test(self, x, proposal_list, img_metas, rescale=False): - """Test without augmentation.""" - if self.with_semantic: - _, semantic_feat = self.semantic_head(x) - else: - semantic_feat = None - - if self.with_glbctx: - mc_pred, glbctx_feat = self.glbctx_head(x) - else: - glbctx_feat = None - - num_imgs = len(proposal_list) - img_shapes = tuple(meta['img_shape'] for meta in img_metas) - ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) - scale_factors = tuple(meta['scale_factor'] for meta in img_metas) - - # "ms" in variable names means multi-stage - ms_scores = [] - rcnn_test_cfg = self.test_cfg - - rois = bbox2roi(proposal_list) - for i in range(self.num_stages): - bbox_head = self.bbox_head[i] - bbox_results = self._bbox_forward( - i, - x, - rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat) - # split batch bbox prediction back to each image - cls_score = bbox_results['cls_score'] - bbox_pred = bbox_results['bbox_pred'] - num_proposals_per_img = tuple(len(p) for p in proposal_list) - rois = rois.split(num_proposals_per_img, 0) - cls_score = cls_score.split(num_proposals_per_img, 0) - bbox_pred = bbox_pred.split(num_proposals_per_img, 0) - ms_scores.append(cls_score) - - if i < self.num_stages - 1: - bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] - rois = torch.cat([ - bbox_head.regress_by_class(rois[i], bbox_label[i], - bbox_pred[i], img_metas[i]) - for i in range(num_imgs) - ]) - - # average scores of each image by stages - cls_score = [ - sum([score[i] for score in ms_scores]) / float(len(ms_scores)) - for i in range(num_imgs) - ] - - # apply bbox post-processing to each image individually - det_bboxes = [] - det_labels = [] - for i in range(num_imgs): - det_bbox, det_label = self.bbox_head[-1].get_bboxes( - rois[i], - cls_score[i], - bbox_pred[i], - img_shapes[i], - scale_factors[i], - rescale=rescale, - cfg=rcnn_test_cfg) - det_bboxes.append(det_bbox) - det_labels.append(det_label) - det_bbox_results = [ - bbox2result(det_bboxes[i], det_labels[i], - self.bbox_head[-1].num_classes) - for i in range(num_imgs) - ] - - if self.with_mask: - if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): - mask_classes = self.mask_head.num_classes - det_segm_results = [[[] for _ in range(mask_classes)] - for _ in range(num_imgs)] - else: - if rescale and not isinstance(scale_factors[0], float): - scale_factors = [ - torch.from_numpy(scale_factor).to(det_bboxes[0].device) - for scale_factor in scale_factors - ] - _bboxes = [ - det_bboxes[i][:, :4] * - scale_factors[i] if rescale else det_bboxes[i] - for i in range(num_imgs) - ] - mask_rois = bbox2roi(_bboxes) - - # get relay feature on mask_rois - bbox_results = self._bbox_forward( - -1, - x, - mask_rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat) - relayed_feat = bbox_results['relayed_feat'] - relayed_feat = self.feat_relay_head(relayed_feat) - - mask_results = self._mask_forward( - x, - mask_rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat, - relayed_feat=relayed_feat) - mask_pred = mask_results['mask_pred'] - - # split batch mask prediction back to each image - num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) - mask_preds = mask_pred.split(num_bbox_per_img, 0) - - # apply mask post-processing to each image individually - det_segm_results = [] - for i in range(num_imgs): - if det_bboxes[i].shape[0] == 0: - det_segm_results.append( - [[] for _ in range(self.mask_head.num_classes)]) - else: - segm_result = self.mask_head.get_seg_masks( - mask_preds[i], _bboxes[i], det_labels[i], - self.test_cfg, ori_shapes[i], scale_factors[i], - rescale) - det_segm_results.append(segm_result) - - # return results - if self.with_mask: - return list(zip(det_bbox_results, det_segm_results)) - else: - return det_bbox_results - - def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): - if self.with_semantic: - semantic_feats = [ - self.semantic_head(feat)[1] for feat in img_feats - ] - else: - semantic_feats = [None] * len(img_metas) - - if self.with_glbctx: - glbctx_feats = [self.glbctx_head(feat)[1] for feat in img_feats] - else: - glbctx_feats = [None] * len(img_metas) - - rcnn_test_cfg = self.test_cfg - aug_bboxes = [] - aug_scores = [] - for x, img_meta, semantic_feat, glbctx_feat in zip( - img_feats, img_metas, semantic_feats, glbctx_feats): - # only one image in the batch - img_shape = img_meta[0]['img_shape'] - scale_factor = img_meta[0]['scale_factor'] - flip = img_meta[0]['flip'] - - proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, - scale_factor, flip) - # "ms" in variable names means multi-stage - ms_scores = [] - - rois = bbox2roi([proposals]) - for i in range(self.num_stages): - bbox_head = self.bbox_head[i] - bbox_results = self._bbox_forward( - i, - x, - rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat) - ms_scores.append(bbox_results['cls_score']) - if i < self.num_stages - 1: - bbox_label = bbox_results['cls_score'].argmax(dim=1) - rois = bbox_head.regress_by_class( - rois, bbox_label, bbox_results['bbox_pred'], - img_meta[0]) - - cls_score = sum(ms_scores) / float(len(ms_scores)) - bboxes, scores = self.bbox_head[-1].get_bboxes( - rois, - cls_score, - bbox_results['bbox_pred'], - img_shape, - scale_factor, - rescale=False, - cfg=None) - aug_bboxes.append(bboxes) - aug_scores.append(scores) - - # after merging, bboxes will be rescaled to the original image size - merged_bboxes, merged_scores = merge_aug_bboxes( - aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) - det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, - rcnn_test_cfg.score_thr, - rcnn_test_cfg.nms, - rcnn_test_cfg.max_per_img) - - det_bbox_results = bbox2result(det_bboxes, det_labels, - self.bbox_head[-1].num_classes) - - if self.with_mask: - if det_bboxes.shape[0] == 0: - det_segm_results = [[] - for _ in range(self.mask_head.num_classes)] - else: - aug_masks = [] - for x, img_meta, semantic_feat, glbctx_feat in zip( - img_feats, img_metas, semantic_feats, glbctx_feats): - img_shape = img_meta[0]['img_shape'] - scale_factor = img_meta[0]['scale_factor'] - flip = img_meta[0]['flip'] - _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, - scale_factor, flip) - mask_rois = bbox2roi([_bboxes]) - # get relay feature on mask_rois - bbox_results = self._bbox_forward( - -1, - x, - mask_rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat) - relayed_feat = bbox_results['relayed_feat'] - relayed_feat = self.feat_relay_head(relayed_feat) - mask_results = self._mask_forward( - x, - mask_rois, - semantic_feat=semantic_feat, - glbctx_feat=glbctx_feat, - relayed_feat=relayed_feat) - mask_pred = mask_results['mask_pred'] - aug_masks.append(mask_pred.sigmoid().cpu().numpy()) - merged_masks = merge_aug_masks(aug_masks, img_metas, - self.test_cfg) - ori_shape = img_metas[0][0]['ori_shape'] - det_segm_results = self.mask_head.get_seg_masks( - merged_masks, - det_bboxes, - det_labels, - rcnn_test_cfg, - ori_shape, - scale_factor=1.0, - rescale=False) - return [(det_bbox_results, det_segm_results)] - else: - return [det_bbox_results] diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/apis/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/apis/__init__.py deleted file mode 100644 index 170724be38de42daf2bc1a1910e181d68818f165..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/apis/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .inference import inference_segmentor, init_segmentor, show_result_pyplot -from .test import multi_gpu_test, single_gpu_test -from .train import get_root_logger, set_random_seed, train_segmentor - -__all__ = [ - 'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor', - 'inference_segmentor', 'multi_gpu_test', 'single_gpu_test', - 'show_result_pyplot' -] diff --git a/spaces/abhishek/sketch-to-image/lib/util.py b/spaces/abhishek/sketch-to-image/lib/util.py deleted file mode 100644 index 5471db970580cf9e437c3397190c38b3a7421cda..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/lib/util.py +++ /dev/null @@ -1,280 +0,0 @@ -''' - * Copyright (c) 2023 Salesforce, Inc. - * All rights reserved. - * SPDX-License-Identifier: Apache License 2.0 - * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/ - * By Can Qin - * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet - * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala -''' - -# adopted from -# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py -# and -# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -# and -# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py -# -# thanks! - - -import os -import math -import torch -import torch.nn as nn -import numpy as np -from einops import repeat - -from utils import instantiate_from_config - - -def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if schedule == "linear": - betas = ( - torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 - ) - - elif schedule == "cosine": - timesteps = ( - torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s - ) - alphas = timesteps / (1 + cosine_s) * np.pi / 2 - alphas = torch.cos(alphas).pow(2) - alphas = alphas / alphas[0] - betas = 1 - alphas[1:] / alphas[:-1] - betas = np.clip(betas, a_min=0, a_max=0.999) - - elif schedule == "sqrt_linear": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) - elif schedule == "sqrt": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 - else: - raise ValueError(f"schedule '{schedule}' unknown.") - return betas.numpy() - - -def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): - if ddim_discr_method == 'uniform': - c = num_ddpm_timesteps // num_ddim_timesteps - ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) - elif ddim_discr_method == 'quad': - ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) - else: - raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') - - # assert ddim_timesteps.shape[0] == num_ddim_timesteps - # add one to get the final alpha values right (the ones from first scale to data during sampling) - steps_out = ddim_timesteps + 1 - if verbose: - print(f'Selected timesteps for ddim sampler: {steps_out}') - return steps_out - - -def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): - # select alphas for computing the variance schedule - alphas = alphacums[ddim_timesteps] - alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) - - # according the the formula provided in https://arxiv.org/abs/2010.02502 - sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) - if verbose: - print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') - print(f'For the chosen value of eta, which is {eta}, ' - f'this results in the following sigma_t schedule for ddim sampler {sigmas}') - return sigmas, alphas, alphas_prev - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, - which defines the cumulative product of (1-beta) over time from t = [0,1]. - :param num_diffusion_timesteps: the number of betas to produce. - :param alpha_bar: a lambda that takes an argument t from 0 to 1 and - produces the cumulative product of (1-beta) up to that - part of the diffusion process. - :param max_beta: the maximum beta to use; use values lower than 1 to - prevent singularities. - """ - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) - return np.array(betas) - - -def extract_into_tensor(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - -def checkpoint(func, inputs, params, flag): - """ - Evaluate a function without caching intermediate activations, allowing for - reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. - """ - if flag: - args = tuple(inputs) + tuple(params) - return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) - - -class CheckpointFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, run_function, length, *args): - ctx.run_function = run_function - ctx.input_tensors = list(args[:length]) - ctx.input_params = list(args[length:]) - ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), - "dtype": torch.get_autocast_gpu_dtype(), - "cache_enabled": torch.is_autocast_cache_enabled()} - with torch.no_grad(): - output_tensors = ctx.run_function(*ctx.input_tensors) - return output_tensors - - @staticmethod - def backward(ctx, *output_grads): - ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - input_grads = torch.autograd.grad( - output_tensors, - ctx.input_tensors + ctx.input_params, - output_grads, - allow_unused=True, - ) - del ctx.input_tensors - del ctx.input_params - del output_tensors - return (None, None) + input_grads - - -def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - """ - Create sinusoidal timestep embeddings. - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - else: - embedding = repeat(timesteps, 'b -> b d', d=dim) - return embedding - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def normalization(channels): - """ - Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(32, channels) - - -# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. -class SiLU(nn.Module): - def forward(self, x): - return x * torch.sigmoid(x) - - -class GroupNorm32(nn.GroupNorm): - def forward(self, x): - return super().forward(x.float()).type(x.dtype) - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return nn.Linear(*args, **kwargs) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -class HybridConditioner(nn.Module): - - def __init__(self, c_concat_config, c_crossattn_config): - super().__init__() - self.concat_conditioner = instantiate_from_config(c_concat_config) - self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) - - def forward(self, c_concat, c_crossattn): - c_concat = self.concat_conditioner(c_concat) - c_crossattn = self.crossattn_conditioner(c_crossattn) - return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() diff --git a/spaces/ai4bharat/IndicNLG/README.md b/spaces/ai4bharat/IndicNLG/README.md deleted file mode 100644 index 64f047e727666b3ada45e161188af27d354babf8..0000000000000000000000000000000000000000 --- a/spaces/ai4bharat/IndicNLG/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: IndicNLG -emoji: ⚡ -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.1.7 -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/akhaliq/VQMIVC/ParallelWaveGAN/egs/jnas/voc1/path.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/jnas/voc1/path.sh deleted file mode 100644 index b0ca27c615f70aa29e240222ec370f8ad4e7b45a..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/jnas/voc1/path.sh +++ /dev/null @@ -1,33 +0,0 @@ -# cuda related -export CUDA_HOME=/usr/local/cuda-10.0 -export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}" - -# path related -export PRJ_ROOT="${PWD}/../../.." -if [ -e "${PRJ_ROOT}/tools/venv/bin/activate" ]; then - # shellcheck disable=SC1090 - . "${PRJ_ROOT}/tools/venv/bin/activate" -fi - -# python related -export OMP_NUM_THREADS=1 -export PYTHONIOENCODING=UTF-8 -export MPL_BACKEND=Agg - -# check installation -if ! command -v parallel-wavegan-train > /dev/null; then - echo "Error: It seems setup is not finished." >&2 - echo "Error: Please setup your environment by following README.md" >&2 - return 1 -fi -if ! command -v jq > /dev/null; then - echo "Error: It seems jq is not installed." >&2 - echo "Error: Please install via \`sudo apt-get install jq\`." >&2 - echo "Error: If you do not have sudo, please download from https://stedolan.github.io/jq/download/." >&2 - return 1 -fi -if ! command -v yq > /dev/null; then - echo "Error: It seems yq is not installed." >&2 - echo "Error: Please install via \`pip install yq\`." >&2 - return 1 -fi diff --git a/spaces/akhaliq/deeplab2/CONTRIBUTING.md b/spaces/akhaliq/deeplab2/CONTRIBUTING.md deleted file mode 100644 index 939e5341e74dc2371c8b47f0e27b50581bed5f63..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/deeplab2/CONTRIBUTING.md +++ /dev/null @@ -1,28 +0,0 @@ -# How to Contribute - -We'd love to accept your patches and contributions to this project. There are -just a few small guidelines you need to follow. - -## Contributor License Agreement - -Contributions to this project must be accompanied by a Contributor License -Agreement. You (or your employer) retain the copyright to your contribution; -this simply gives us permission to use and redistribute your contributions as -part of the project. Head over to to see -your current agreements on file or to sign a new one. - -You generally only need to submit a CLA once, so if you've already submitted one -(even if it was for a different project), you probably don't need to do it -again. - -## Code reviews - -All submissions, including submissions by project members, require review. We -use GitHub pull requests for this purpose. Consult -[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more -information on using pull requests. - -## Community Guidelines - -This project follows [Google's Open Source Community -Guidelines](https://opensource.google.com/conduct/). diff --git a/spaces/akhaliq/lama/bin/paper_runfiles/find_best_checkpoint.py b/spaces/akhaliq/lama/bin/paper_runfiles/find_best_checkpoint.py deleted file mode 100644 index 42f5e0f9bb1a2ea25dd9a97a58cf318e6de19532..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/lama/bin/paper_runfiles/find_best_checkpoint.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 - - -import os -from argparse import ArgumentParser - - -def ssim_fid100_f1(metrics, fid_scale=100): - ssim = metrics.loc['total', 'ssim']['mean'] - fid = metrics.loc['total', 'fid']['mean'] - fid_rel = max(0, fid_scale - fid) / fid_scale - f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3) - return f1 - - -def find_best_checkpoint(model_list, models_dir): - with open(model_list) as f: - models = [m.strip() for m in f.readlines()] - with open(f'{model_list}_best', 'w') as f: - for model in models: - print(model) - best_f1 = 0 - best_epoch = 0 - best_step = 0 - with open(os.path.join(models_dir, model, 'train.log')) as fm: - lines = fm.readlines() - for line_index in range(len(lines)): - line = lines[line_index] - if 'Validation metrics after epoch' in line: - sharp_index = line.index('#') - cur_ep = line[sharp_index + 1:] - comma_index = cur_ep.index(',') - cur_ep = int(cur_ep[:comma_index]) - total_index = line.index('total ') - step = int(line[total_index:].split()[1].strip()) - total_line = lines[line_index + 5] - if not total_line.startswith('total'): - continue - words = total_line.strip().split() - f1 = float(words[-1]) - print(f'\tEpoch: {cur_ep}, f1={f1}') - if f1 > best_f1: - best_f1 = f1 - best_epoch = cur_ep - best_step = step - f.write(f'{model}\t{best_epoch}\t{best_step}\t{best_f1}\n') - - -if __name__ == '__main__': - parser = ArgumentParser() - parser.add_argument('model_list') - parser.add_argument('models_dir') - args = parser.parse_args() - find_best_checkpoint(args.model_list, args.models_dir) diff --git a/spaces/akhaliq/yolov7/utils/metrics.py b/spaces/akhaliq/yolov7/utils/metrics.py deleted file mode 100644 index 666b8c7ec1c0a488eab1b4e7f2f0474973589525..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/yolov7/utils/metrics.py +++ /dev/null @@ -1,223 +0,0 @@ -# Model validation metrics - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch - -from . import general - - -def fitness(x): - # Model fitness as a weighted combination of metrics - w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] - return (x[:, :4] * w).sum(1) - - -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): - """ Compute the average precision, given the recall and precision curves. - Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. - # Arguments - tp: True positives (nparray, nx1 or nx10). - conf: Objectness value from 0-1 (nparray). - pred_cls: Predicted object classes (nparray). - target_cls: True object classes (nparray). - plot: Plot precision-recall curve at mAP@0.5 - save_dir: Plot save directory - # Returns - The average precision as computed in py-faster-rcnn. - """ - - # Sort by objectness - i = np.argsort(-conf) - tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] - - # Find unique classes - unique_classes = np.unique(target_cls) - nc = unique_classes.shape[0] # number of classes, number of detections - - # Create Precision-Recall curve and compute AP for each class - px, py = np.linspace(0, 1, 1000), [] # for plotting - ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) - for ci, c in enumerate(unique_classes): - i = pred_cls == c - n_l = (target_cls == c).sum() # number of labels - n_p = i.sum() # number of predictions - - if n_p == 0 or n_l == 0: - continue - else: - # Accumulate FPs and TPs - fpc = (1 - tp[i]).cumsum(0) - tpc = tp[i].cumsum(0) - - # Recall - recall = tpc / (n_l + 1e-16) # recall curve - r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases - - # Precision - precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score - - # AP from recall-precision curve - for j in range(tp.shape[1]): - ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and j == 0: - py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - - # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + 1e-16) - if plot: - plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) - plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') - plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') - plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') - - i = f1.mean(0).argmax() # max F1 index - return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') - - -def compute_ap(recall, precision): - """ Compute the average precision, given the recall and precision curves - # Arguments - recall: The recall curve (list) - precision: The precision curve (list) - # Returns - Average precision, precision curve, recall curve - """ - - # Append sentinel values to beginning and end - mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) - mpre = np.concatenate(([1.], precision, [0.])) - - # Compute the precision envelope - mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) - - # Integrate area under curve - method = 'interp' # methods: 'continuous', 'interp' - if method == 'interp': - x = np.linspace(0, 1, 101) # 101-point interp (COCO) - ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate - else: # 'continuous' - i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve - - return ap, mpre, mrec - - -class ConfusionMatrix: - # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix - def __init__(self, nc, conf=0.25, iou_thres=0.45): - self.matrix = np.zeros((nc + 1, nc + 1)) - self.nc = nc # number of classes - self.conf = conf - self.iou_thres = iou_thres - - def process_batch(self, detections, labels): - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - detections (Array[N, 6]), x1, y1, x2, y2, conf, class - labels (Array[M, 5]), class, x1, y1, x2, y2 - Returns: - None, updates confusion matrix accordingly - """ - detections = detections[detections[:, 4] > self.conf] - gt_classes = labels[:, 0].int() - detection_classes = detections[:, 5].int() - iou = general.box_iou(labels[:, 1:], detections[:, :4]) - - x = torch.where(iou > self.iou_thres) - if x[0].shape[0]: - matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - else: - matches = np.zeros((0, 3)) - - n = matches.shape[0] > 0 - m0, m1, _ = matches.transpose().astype(np.int16) - for i, gc in enumerate(gt_classes): - j = m0 == i - if n and sum(j) == 1: - self.matrix[gc, detection_classes[m1[j]]] += 1 # correct - else: - self.matrix[self.nc, gc] += 1 # background FP - - if n: - for i, dc in enumerate(detection_classes): - if not any(m1 == i): - self.matrix[dc, self.nc] += 1 # background FN - - def matrix(self): - return self.matrix - - def plot(self, save_dir='', names=()): - try: - import seaborn as sn - - array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize - array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) - - fig = plt.figure(figsize=(12, 9), tight_layout=True) - sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size - labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels - sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, - xticklabels=names + ['background FP'] if labels else "auto", - yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) - fig.axes[0].set_xlabel('True') - fig.axes[0].set_ylabel('Predicted') - fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) - except Exception as e: - pass - - def print(self): - for i in range(self.nc + 1): - print(' '.join(map(str, self.matrix[i]))) - - -# Plots ---------------------------------------------------------------------------------------------------------------- - -def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): - # Precision-recall curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - py = np.stack(py, axis=1) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) - else: - ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) - - ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) - ax.set_xlabel('Recall') - ax.set_ylabel('Precision') - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir), dpi=250) - - -def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): - # Metric-confidence curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) - else: - ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) - - y = py.mean(0) - ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir), dpi=250) diff --git a/spaces/alan-chen-intel/dagan-demo/modules/keypoint_detector.py b/spaces/alan-chen-intel/dagan-demo/modules/keypoint_detector.py deleted file mode 100644 index b39069195d8315460546d74d3576d09b03ec8915..0000000000000000000000000000000000000000 --- a/spaces/alan-chen-intel/dagan-demo/modules/keypoint_detector.py +++ /dev/null @@ -1,75 +0,0 @@ -from torch import nn -import torch -import torch.nn.functional as F -from modules.util import Hourglass, make_coordinate_grid, AntiAliasInterpolation2d,Hourglass_2branch -import pdb - -class KPDetector(nn.Module): - """ - Detecting a keypoints. Return keypoint position and jacobian near each keypoint. - """ - - def __init__(self, block_expansion, num_kp, num_channels, max_features, - num_blocks, temperature, estimate_jacobian=False, scale_factor=1, - single_jacobian_map=False, pad=0): - super(KPDetector, self).__init__() - self.predictor = Hourglass(block_expansion, in_features=num_channels, - max_features=max_features, num_blocks=num_blocks) - - self.kp = nn.Conv2d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=(7, 7), - padding=pad) - - if estimate_jacobian: - self.num_jacobian_maps = 1 if single_jacobian_map else num_kp - self.jacobian = nn.Conv2d(in_channels=self.predictor.out_filters, - out_channels=4 * self.num_jacobian_maps, kernel_size=(7, 7), padding=pad) - self.jacobian.weight.data.zero_() - self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) - else: - self.jacobian = None - - self.temperature = temperature - self.scale_factor = scale_factor - if self.scale_factor != 1: - self.down = AntiAliasInterpolation2d(num_channels, self.scale_factor) - - def gaussian2kp(self, heatmap): - """ - Extract the mean and from a heatmap - """ - shape = heatmap.shape - heatmap = heatmap.unsqueeze(-1) - grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) - value = (heatmap * grid).sum(dim=(2, 3)) - kp = {'value': value} - - return kp - - def forward(self, x): - if self.scale_factor != 1: - x = self.down(x) - feature_map = self.predictor(x) #x bz,4,64,64 - prediction = self.kp(feature_map) - - final_shape = prediction.shape - heatmap = prediction.view(final_shape[0], final_shape[1], -1) - heatmap = F.softmax(heatmap / self.temperature, dim=2) - heatmap = heatmap.view(*final_shape) - - out = self.gaussian2kp(heatmap) - - if self.jacobian is not None: - jacobian_map = self.jacobian(feature_map) - # pdb.set_trace() - jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 4, final_shape[2], - final_shape[3]) - heatmap = heatmap.unsqueeze(2) - - jacobian = heatmap * jacobian_map - jacobian = jacobian.view(final_shape[0], final_shape[1], 4, -1) - jacobian = jacobian.sum(dim=-1) - jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 2, 2) - out['jacobian'] = jacobian - - return out - diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/scripts.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/scripts.py deleted file mode 100644 index 913912c7b8e0c2dcbf142f81991dfec0d26f4f41..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/distlib/scripts.py +++ /dev/null @@ -1,429 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Copyright (C) 2013-2015 Vinay Sajip. -# Licensed to the Python Software Foundation under a contributor agreement. -# See LICENSE.txt and CONTRIBUTORS.txt. -# -from io import BytesIO -import logging -import os -import re -import struct -import sys - -from .compat import sysconfig, detect_encoding, ZipFile -from .resources import finder -from .util import (FileOperator, get_export_entry, convert_path, - get_executable, get_platform, in_venv) - -logger = logging.getLogger(__name__) - -_DEFAULT_MANIFEST = ''' - - - - - - - - - - - - -'''.strip() - -# check if Python is called on the first line with this expression -FIRST_LINE_RE = re.compile(b'^#!.*pythonw?[0-9.]*([ \t].*)?$') -SCRIPT_TEMPLATE = r'''# -*- coding: utf-8 -*- -import re -import sys -from %(module)s import %(import_name)s -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(%(func)s()) -''' - - -def enquote_executable(executable): - if ' ' in executable: - # make sure we quote only the executable in case of env - # for example /usr/bin/env "/dir with spaces/bin/jython" - # instead of "/usr/bin/env /dir with spaces/bin/jython" - # otherwise whole - if executable.startswith('/usr/bin/env '): - env, _executable = executable.split(' ', 1) - if ' ' in _executable and not _executable.startswith('"'): - executable = '%s "%s"' % (env, _executable) - else: - if not executable.startswith('"'): - executable = '"%s"' % executable - return executable - -# Keep the old name around (for now), as there is at least one project using it! -_enquote_executable = enquote_executable - -class ScriptMaker(object): - """ - A class to copy or create scripts from source scripts or callable - specifications. - """ - script_template = SCRIPT_TEMPLATE - - executable = None # for shebangs - - def __init__(self, source_dir, target_dir, add_launchers=True, - dry_run=False, fileop=None): - self.source_dir = source_dir - self.target_dir = target_dir - self.add_launchers = add_launchers - self.force = False - self.clobber = False - # It only makes sense to set mode bits on POSIX. - self.set_mode = (os.name == 'posix') or (os.name == 'java' and - os._name == 'posix') - self.variants = set(('', 'X.Y')) - self._fileop = fileop or FileOperator(dry_run) - - self._is_nt = os.name == 'nt' or ( - os.name == 'java' and os._name == 'nt') - self.version_info = sys.version_info - - def _get_alternate_executable(self, executable, options): - if options.get('gui', False) and self._is_nt: # pragma: no cover - dn, fn = os.path.split(executable) - fn = fn.replace('python', 'pythonw') - executable = os.path.join(dn, fn) - return executable - - if sys.platform.startswith('java'): # pragma: no cover - def _is_shell(self, executable): - """ - Determine if the specified executable is a script - (contains a #! line) - """ - try: - with open(executable) as fp: - return fp.read(2) == '#!' - except (OSError, IOError): - logger.warning('Failed to open %s', executable) - return False - - def _fix_jython_executable(self, executable): - if self._is_shell(executable): - # Workaround for Jython is not needed on Linux systems. - import java - - if java.lang.System.getProperty('os.name') == 'Linux': - return executable - elif executable.lower().endswith('jython.exe'): - # Use wrapper exe for Jython on Windows - return executable - return '/usr/bin/env %s' % executable - - def _build_shebang(self, executable, post_interp): - """ - Build a shebang line. In the simple case (on Windows, or a shebang line - which is not too long or contains spaces) use a simple formulation for - the shebang. Otherwise, use /bin/sh as the executable, with a contrived - shebang which allows the script to run either under Python or sh, using - suitable quoting. Thanks to Harald Nordgren for his input. - - See also: http://www.in-ulm.de/~mascheck/various/shebang/#length - https://hg.mozilla.org/mozilla-central/file/tip/mach - """ - if os.name != 'posix': - simple_shebang = True - else: - # Add 3 for '#!' prefix and newline suffix. - shebang_length = len(executable) + len(post_interp) + 3 - if sys.platform == 'darwin': - max_shebang_length = 512 - else: - max_shebang_length = 127 - simple_shebang = ((b' ' not in executable) and - (shebang_length <= max_shebang_length)) - - if simple_shebang: - result = b'#!' + executable + post_interp + b'\n' - else: - result = b'#!/bin/sh\n' - result += b"'''exec' " + executable + post_interp + b' "$0" "$@"\n' - result += b"' '''" - return result - - def _get_shebang(self, encoding, post_interp=b'', options=None): - enquote = True - if self.executable: - executable = self.executable - enquote = False # assume this will be taken care of - elif not sysconfig.is_python_build(): - executable = get_executable() - elif in_venv(): # pragma: no cover - executable = os.path.join(sysconfig.get_path('scripts'), - 'python%s' % sysconfig.get_config_var('EXE')) - else: # pragma: no cover - executable = os.path.join( - sysconfig.get_config_var('BINDIR'), - 'python%s%s' % (sysconfig.get_config_var('VERSION'), - sysconfig.get_config_var('EXE'))) - if not os.path.isfile(executable): - # for Python builds from source on Windows, no Python executables with - # a version suffix are created, so we use python.exe - executable = os.path.join(sysconfig.get_config_var('BINDIR'), - 'python%s' % (sysconfig.get_config_var('EXE'))) - if options: - executable = self._get_alternate_executable(executable, options) - - if sys.platform.startswith('java'): # pragma: no cover - executable = self._fix_jython_executable(executable) - - # Normalise case for Windows - COMMENTED OUT - # executable = os.path.normcase(executable) - # N.B. The normalising operation above has been commented out: See - # issue #124. Although paths in Windows are generally case-insensitive, - # they aren't always. For example, a path containing a ẞ (which is a - # LATIN CAPITAL LETTER SHARP S - U+1E9E) is normcased to ß (which is a - # LATIN SMALL LETTER SHARP S' - U+00DF). The two are not considered by - # Windows as equivalent in path names. - - # If the user didn't specify an executable, it may be necessary to - # cater for executable paths with spaces (not uncommon on Windows) - if enquote: - executable = enquote_executable(executable) - # Issue #51: don't use fsencode, since we later try to - # check that the shebang is decodable using utf-8. - executable = executable.encode('utf-8') - # in case of IronPython, play safe and enable frames support - if (sys.platform == 'cli' and '-X:Frames' not in post_interp - and '-X:FullFrames' not in post_interp): # pragma: no cover - post_interp += b' -X:Frames' - shebang = self._build_shebang(executable, post_interp) - # Python parser starts to read a script using UTF-8 until - # it gets a #coding:xxx cookie. The shebang has to be the - # first line of a file, the #coding:xxx cookie cannot be - # written before. So the shebang has to be decodable from - # UTF-8. - try: - shebang.decode('utf-8') - except UnicodeDecodeError: # pragma: no cover - raise ValueError( - 'The shebang (%r) is not decodable from utf-8' % shebang) - # If the script is encoded to a custom encoding (use a - # #coding:xxx cookie), the shebang has to be decodable from - # the script encoding too. - if encoding != 'utf-8': - try: - shebang.decode(encoding) - except UnicodeDecodeError: # pragma: no cover - raise ValueError( - 'The shebang (%r) is not decodable ' - 'from the script encoding (%r)' % (shebang, encoding)) - return shebang - - def _get_script_text(self, entry): - return self.script_template % dict(module=entry.prefix, - import_name=entry.suffix.split('.')[0], - func=entry.suffix) - - manifest = _DEFAULT_MANIFEST - - def get_manifest(self, exename): - base = os.path.basename(exename) - return self.manifest % base - - def _write_script(self, names, shebang, script_bytes, filenames, ext): - use_launcher = self.add_launchers and self._is_nt - linesep = os.linesep.encode('utf-8') - if not shebang.endswith(linesep): - shebang += linesep - if not use_launcher: - script_bytes = shebang + script_bytes - else: # pragma: no cover - if ext == 'py': - launcher = self._get_launcher('t') - else: - launcher = self._get_launcher('w') - stream = BytesIO() - with ZipFile(stream, 'w') as zf: - zf.writestr('__main__.py', script_bytes) - zip_data = stream.getvalue() - script_bytes = launcher + shebang + zip_data - for name in names: - outname = os.path.join(self.target_dir, name) - if use_launcher: # pragma: no cover - n, e = os.path.splitext(outname) - if e.startswith('.py'): - outname = n - outname = '%s.exe' % outname - try: - self._fileop.write_binary_file(outname, script_bytes) - except Exception: - # Failed writing an executable - it might be in use. - logger.warning('Failed to write executable - trying to ' - 'use .deleteme logic') - dfname = '%s.deleteme' % outname - if os.path.exists(dfname): - os.remove(dfname) # Not allowed to fail here - os.rename(outname, dfname) # nor here - self._fileop.write_binary_file(outname, script_bytes) - logger.debug('Able to replace executable using ' - '.deleteme logic') - try: - os.remove(dfname) - except Exception: - pass # still in use - ignore error - else: - if self._is_nt and not outname.endswith('.' + ext): # pragma: no cover - outname = '%s.%s' % (outname, ext) - if os.path.exists(outname) and not self.clobber: - logger.warning('Skipping existing file %s', outname) - continue - self._fileop.write_binary_file(outname, script_bytes) - if self.set_mode: - self._fileop.set_executable_mode([outname]) - filenames.append(outname) - - variant_separator = '-' - - def get_script_filenames(self, name): - result = set() - if '' in self.variants: - result.add(name) - if 'X' in self.variants: - result.add('%s%s' % (name, self.version_info[0])) - if 'X.Y' in self.variants: - result.add('%s%s%s.%s' % (name, self.variant_separator, - self.version_info[0], self.version_info[1])) - return result - - def _make_script(self, entry, filenames, options=None): - post_interp = b'' - if options: - args = options.get('interpreter_args', []) - if args: - args = ' %s' % ' '.join(args) - post_interp = args.encode('utf-8') - shebang = self._get_shebang('utf-8', post_interp, options=options) - script = self._get_script_text(entry).encode('utf-8') - scriptnames = self.get_script_filenames(entry.name) - if options and options.get('gui', False): - ext = 'pyw' - else: - ext = 'py' - self._write_script(scriptnames, shebang, script, filenames, ext) - - def _copy_script(self, script, filenames): - adjust = False - script = os.path.join(self.source_dir, convert_path(script)) - outname = os.path.join(self.target_dir, os.path.basename(script)) - if not self.force and not self._fileop.newer(script, outname): - logger.debug('not copying %s (up-to-date)', script) - return - - # Always open the file, but ignore failures in dry-run mode -- - # that way, we'll get accurate feedback if we can read the - # script. - try: - f = open(script, 'rb') - except IOError: # pragma: no cover - if not self.dry_run: - raise - f = None - else: - first_line = f.readline() - if not first_line: # pragma: no cover - logger.warning('%s is an empty file (skipping)', script) - return - - match = FIRST_LINE_RE.match(first_line.replace(b'\r\n', b'\n')) - if match: - adjust = True - post_interp = match.group(1) or b'' - - if not adjust: - if f: - f.close() - self._fileop.copy_file(script, outname) - if self.set_mode: - self._fileop.set_executable_mode([outname]) - filenames.append(outname) - else: - logger.info('copying and adjusting %s -> %s', script, - self.target_dir) - if not self._fileop.dry_run: - encoding, lines = detect_encoding(f.readline) - f.seek(0) - shebang = self._get_shebang(encoding, post_interp) - if b'pythonw' in first_line: # pragma: no cover - ext = 'pyw' - else: - ext = 'py' - n = os.path.basename(outname) - self._write_script([n], shebang, f.read(), filenames, ext) - if f: - f.close() - - @property - def dry_run(self): - return self._fileop.dry_run - - @dry_run.setter - def dry_run(self, value): - self._fileop.dry_run = value - - if os.name == 'nt' or (os.name == 'java' and os._name == 'nt'): # pragma: no cover - # Executable launcher support. - # Launchers are from https://bitbucket.org/vinay.sajip/simple_launcher/ - - def _get_launcher(self, kind): - if struct.calcsize('P') == 8: # 64-bit - bits = '64' - else: - bits = '32' - platform_suffix = '-arm' if get_platform() == 'win-arm64' else '' - name = '%s%s%s.exe' % (kind, bits, platform_suffix) - # Issue 31: don't hardcode an absolute package name, but - # determine it relative to the current package - distlib_package = __name__.rsplit('.', 1)[0] - resource = finder(distlib_package).find(name) - if not resource: - msg = ('Unable to find resource %s in package %s' % (name, - distlib_package)) - raise ValueError(msg) - return resource.bytes - - # Public API follows - - def make(self, specification, options=None): - """ - Make a script. - - :param specification: The specification, which is either a valid export - entry specification (to make a script from a - callable) or a filename (to make a script by - copying from a source location). - :param options: A dictionary of options controlling script generation. - :return: A list of all absolute pathnames written to. - """ - filenames = [] - entry = get_export_entry(specification) - if entry is None: - self._copy_script(specification, filenames) - else: - self._make_script(entry, filenames, options=options) - return filenames - - def make_multiple(self, specifications, options=None): - """ - Take a list of specifications and make scripts from them, - :param specifications: A list of specifications. - :return: A list of all absolute pathnames written to, - """ - filenames = [] - for specification in specifications: - filenames.extend(self.make(specification, options)) - return filenames diff --git a/spaces/ali-ghamdan/realesrgan-models/scripts/generate_meta_info.py b/spaces/ali-ghamdan/realesrgan-models/scripts/generate_meta_info.py deleted file mode 100644 index 9c3b7a37e85f534075c50e6c33d7cca999d8b836..0000000000000000000000000000000000000000 --- a/spaces/ali-ghamdan/realesrgan-models/scripts/generate_meta_info.py +++ /dev/null @@ -1,58 +0,0 @@ -import argparse -import cv2 -import glob -import os - - -def main(args): - txt_file = open(args.meta_info, 'w') - for folder, root in zip(args.input, args.root): - img_paths = sorted(glob.glob(os.path.join(folder, '*'))) - for img_path in img_paths: - status = True - if args.check: - # read the image once for check, as some images may have errors - try: - img = cv2.imread(img_path) - except (IOError, OSError) as error: - print(f'Read {img_path} error: {error}') - status = False - if img is None: - status = False - print(f'Img is None: {img_path}') - if status: - # get the relative path - img_name = os.path.relpath(img_path, root) - print(img_name) - txt_file.write(f'{img_name}\n') - - -if __name__ == '__main__': - """Generate meta info (txt file) for only Ground-Truth images. - - It can also generate meta info from several folders into one txt file. - """ - parser = argparse.ArgumentParser() - parser.add_argument( - '--input', - nargs='+', - default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'], - help='Input folder, can be a list') - parser.add_argument( - '--root', - nargs='+', - default=['datasets/DF2K', 'datasets/DF2K'], - help='Folder root, should have the length as input folders') - parser.add_argument( - '--meta_info', - type=str, - default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt', - help='txt path for meta info') - parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok') - args = parser.parse_args() - - assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got ' - f'{len(args.input)} and {len(args.root)}.') - os.makedirs(os.path.dirname(args.meta_info), exist_ok=True) - - main(args) diff --git a/spaces/allknowingroger/Image-Models-Test173/app.py b/spaces/allknowingroger/Image-Models-Test173/app.py deleted file mode 100644 index 4b15b23ca1d159d96b21c758d4404f826be5da84..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test173/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "Yntec/dosmixVAE", - "Shiva1602/my-pet-dog", - "Nikithaa/my-pet-dog", - "Hvijapuram22/my-pet-dog", - "Priyakatta02/my-peacock", - "Jayalakshmi2004/parrot-jlb", - "Aman242526/my-pet-cockteil-bid", - "flobbit/monster-cars-sdxl-lora", - "Yntec/Cetus", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/allknowingroger/Image-Models-Test86/app.py b/spaces/allknowingroger/Image-Models-Test86/app.py deleted file mode 100644 index 787bb46be319041a8db08a2f28be7ef80702f9df..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test86/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -# import os -# import sys -# from pathlib import Path -import time - -models =[ - "stephanebhiri/lora-trained-xl-colab-stp25", - "stephanebhiri/lora-trained-xl-colab-stp23", - "a2a/lora-trained-xl", - "perraju/lora-trained-xl-colab", - "JustAIGuy/lora-trained-xl-colab_2", - "jbilcke-hf/sdxl-starfield", - "goofyai/3d_render_style_xl", - "MirageML/lowpoly-cyberpunk", - "ddPn08/subtly", -] - - -model_functions = {} -model_idx = 1 -for model_path in models: - try: - model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False) - except Exception as error: - def the_fn(txt): - return None - model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"]) - model_idx+=1 - - -def send_it_idx(idx): - def send_it_fn(prompt): - output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt) - return output - return send_it_fn - -def get_prompts(prompt_text): - return prompt_text - -def clear_it(val): - if int(val) != 0: - val = 0 - else: - val = 0 - pass - return val - -def all_task_end(cnt,t_stamp): - to = t_stamp + 60 - et = time.time() - if et > to and t_stamp != 0: - d = gr.update(value=0) - tog = gr.update(value=1) - #print(f'to: {to} et: {et}') - else: - if cnt != 0: - d = gr.update(value=et) - else: - d = gr.update(value=0) - tog = gr.update(value=0) - #print (f'passing: to: {to} et: {et}') - pass - return d, tog - -def all_task_start(): - print("\n\n\n\n\n\n\n") - t = time.gmtime() - t_stamp = time.time() - current_time = time.strftime("%H:%M:%S", t) - return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0) - -def clear_fn(): - nn = len(models) - return tuple([None, *[None for _ in range(nn)]]) - - - -with gr.Blocks(title="SD Models") as my_interface: - with gr.Column(scale=12): - # with gr.Row(): - # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""") - with gr.Row(): - with gr.Row(scale=6): - primary_prompt=gr.Textbox(label="Prompt", value="") - # real_prompt=gr.Textbox(label="Real prompt") - with gr.Row(scale=6): - # improve_prompts_btn=gr.Button("Improve") - with gr.Row(): - run=gr.Button("Run",variant="primary") - clear_btn=gr.Button("Clear") - with gr.Row(): - sd_outputs = {} - model_idx = 1 - for model_path in models: - with gr.Column(scale=3, min_width=320): - with gr.Box(): - sd_outputs[model_idx] = gr.Image(label=model_path) - pass - model_idx += 1 - pass - pass - - with gr.Row(visible=False): - start_box=gr.Number(interactive=False) - end_box=gr.Number(interactive=False) - tog_box=gr.Textbox(value=0,interactive=False) - - start_box.change( - all_task_end, - [start_box, end_box], - [start_box, tog_box], - every=1, - show_progress=False) - - primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box]) - run.click(all_task_start, None, [start_box, end_box, tog_box]) - runs_dict = {} - model_idx = 1 - for model_path in models: - runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]]) - model_idx += 1 - pass - pass - - # improve_prompts_btn_clicked=improve_prompts_btn.click( - # get_prompts, - # inputs=[primary_prompt], - # outputs=[primary_prompt], - # cancels=list(runs_dict.values())) - clear_btn.click( - clear_fn, - None, - [primary_prompt, *list(sd_outputs.values())], - cancels=[*list(runs_dict.values())]) - tog_box.change( - clear_it, - tog_box, - tog_box, - cancels=[*list(runs_dict.values())]) - -my_interface.queue(concurrency_count=600, status_update_rate=1) -my_interface.launch(inline=True, show_api=False) - \ No newline at end of file diff --git a/spaces/amin2809/rvc-models/infer_pack/models.py b/spaces/amin2809/rvc-models/infer_pack/models.py deleted file mode 100644 index 96165f73644e6fb92d0ffedb4a3c9e1a457cb989..0000000000000000000000000000000000000000 --- a/spaces/amin2809/rvc-models/infer_pack/models.py +++ /dev/null @@ -1,982 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from infer_pack import modules -from infer_pack import attentions -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from infer_pack.commons import init_weights -import numpy as np -from infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder256Sim(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - x = self.proj(x) * x_mask - return x, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化 - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMs256NSFsid(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds - ): # 这里ds是id，[bs,1] - # print(1,pitch.shape)#[bs,t] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length) - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - # print(-2,pitchf.shape,z_slice.shape) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_nono(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr=None, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=False, - ) - self.dec = Generator( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, y, y_lengths, ds): # 这里ds是id，[bs,1] - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - z_slice, ids_slice = commons.rand_slice_segments( - z, y_lengths, self.segment_size - ) - o = self.dec(z_slice, g=g) - return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, phone, phone_lengths, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], g=g) - return o, x_mask, (z, z_p, m_p, logs_p) - - -class SynthesizerTrnMs256NSFsid_sim(nn.Module): - """ - Synthesizer for Training - """ - - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - # hop_length, - gin_channels=0, - use_sdp=True, - **kwargs - ): - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256Sim( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - is_half=kwargs["is_half"], - ) - - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward( - self, phone, phone_lengths, pitch, pitchf, y_lengths, ds - ): # y是spec不需要了现在 - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - x, x_mask = self.enc_p(phone, pitch, phone_lengths) - x = self.flow(x, x_mask, g=g, reverse=True) - z_slice, ids_slice = commons.rand_slice_segments( - x, y_lengths, self.segment_size - ) - - pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size) - o = self.dec(z_slice, pitchf, g=g) - return o, ids_slice - - def infer( - self, phone, phone_lengths, pitch, pitchf, ds, max_len=None - ): # y是spec不需要了现在 - g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的 - x, x_mask = self.enc_p(phone, pitch, phone_lengths) - x = self.flow(x, x_mask, g=g, reverse=True) - o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g) - return o, o - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/anasanchezf/cloome/src/clip/clip.py b/spaces/anasanchezf/cloome/src/clip/clip.py deleted file mode 100644 index 6e55c9c588958925f65adcf8b883eb8ece70daa1..0000000000000000000000000000000000000000 --- a/spaces/anasanchezf/cloome/src/clip/clip.py +++ /dev/null @@ -1,258 +0,0 @@ -# Code ported from https://github.com/openai/CLIP - -import hashlib -import os -import urllib -import warnings -from typing import Union, List - -import torch -from PIL import Image -from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, RandomResizedCrop, InterpolationMode, RandomCrop, RandomRotation -from tqdm import tqdm - -from clip.model import build_model -# from clip.tokenizer import SimpleTokenizer as _Tokenizer - -__all__ = ["available_models", "load", "tokenize"] -# _tokenizer = _Tokenizer() - -_MODELS = { - "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", - "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", - "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", - "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", -} - - -class NormalizeByImage(object): - """Normalize an tensor image with mean and standard deviation. - Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform - will normalize each channel of the input ``torch.*Tensor`` i.e. - ``input[channel] = (input[channel] - mean[channel]) / std[channel]`` - Args: - mean (sequence): Sequence of means for each channel. - std (sequence): Sequence of standard deviations for each channel. - """ - - def __call__(self, tensor): - """ - Args: - tensor (Tensor): Tensor image of size (C, H, W) to be normalized. - Returns: - Tensor: Normalized Tensor image. - """ - for t in tensor: - t.sub_(t.mean()).div_(t.std() + 1e-7) - return tensor - - -def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")): - os.makedirs(root, exist_ok=True) - filename = os.path.basename(url) - - expected_sha256 = url.split("/")[-2] - download_target = os.path.join(root, filename) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: - return download_target - else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: - raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") - - return download_target - -def _convert_to_rgb(image): - return image.convert('RGB') - -def _transform(n_px_tr: int, n_px_val: int, is_train: bool, normalize:str = "dataset", preprocess:str = "downsize"): - #normalize = Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - # print(n_px_tr) - # print(n_px_val) - if normalize == "img": - normalize = NormalizeByImage() - elif normalize == "dataset": - normalize = Normalize((47.1314, 40.8138, 53.7692, 46.2656, 28.7243), (47.1314, 40.8138, 53.7692, 46.2656, 28.7243)) # normalize for CellPainting - if normalize == "None": - normalize = None - - if is_train: - if preprocess == "crop": - #resize = RandomResizedCrop(n_px_tr, scale=(0.25,0.3), ratio=(0.95, 1.05), interpolation=InterpolationMode.BICUBIC) - resize = RandomCrop(n_px_tr) - elif preprocess == "downsize": - resize = RandomResizedCrop(n_px_tr, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC) - elif preprocess == "rotate": - resize = Compose([ - RandomRotation((0, 360)), - CenterCrop(n_px_tr) - ]) - - else: - if preprocess == "crop" or "rotate": - resize = Compose([ - #RandomResizedCrop(n_px_tr, scale=(0.25,0.3), ratio=(0.95, 1.05), interpolation=InterpolationMode.BICUBIC) - CenterCrop(n_px_val), - ]) - elif preprocess == "downsize": - resize = Compose([ - Resize(n_px_val, interpolation=InterpolationMode.BICUBIC), - CenterCrop(n_px_val), - ]) - if normalize: - return Compose([ - ToTensor(), - resize, - normalize, - ]) - else: - return Compose([ - ToTensor(), - resize, - ]) - - - -def available_models() -> List[str]: - """Returns the names of available CLIP models""" - return list(_MODELS.keys()) - - -def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=True, is_train=False, pretrained=True): - """Load a CLIP model - Parameters - ---------- - name : str - A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict - device : Union[str, torch.device] - The device to put the loaded model - jit : bool - Whether to load the optimized JIT model (default) or more hackable non-JIT model. - Returns - ------- - model : torch.nn.Module - The CLIP model - preprocess : Callable[[PIL.Image], torch.Tensor] - A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input - """ - if name in _MODELS: - model_path = _download(_MODELS[name]) - elif os.path.isfile(name): - model_path = name - else: - raise RuntimeError(f"Model {name} not found; available models = {available_models()}") - - try: - # loading JIT archive - model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() - state_dict = None - except RuntimeError: - # loading saved state dict - if jit: - warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") - jit = False - state_dict = torch.load(model_path, map_location="cpu") - - if not jit: - try: - model = build_model(state_dict or model.state_dict()).to(device) - except KeyError: - sd = {k[7:]: v for k,v in state_dict["state_dict"].items()} - model = build_model(sd).to(device) - - if str(device) == "cpu": - model.float() - return model, \ - _transform(model.visual.input_resolution, is_train=True), \ - _transform(model.visual.input_resolution, is_train=False) - - # patch the device names - device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) - device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] - - def patch_device(module): - graphs = [module.graph] if hasattr(module, "graph") else [] - if hasattr(module, "forward1"): - graphs.append(module.forward1.graph) - - for graph in graphs: - for node in graph.findAllNodes("prim::Constant"): - if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): - node.copyAttributes(device_node) - - model.apply(patch_device) - patch_device(model.encode_image) - patch_device(model.encode_text) - - # patch dtype to float32 on CPU - if str(device) == "cpu": - float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) - float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] - float_node = float_input.node() - - def patch_float(module): - graphs = [module.graph] if hasattr(module, "graph") else [] - if hasattr(module, "forward1"): - graphs.append(module.forward1.graph) - - for graph in graphs: - for node in graph.findAllNodes("aten::to"): - inputs = list(node.inputs()) - for i in [1, 2]: # dtype can be the second or third argument to aten::to() - if inputs[i].node()["value"] == 5: - inputs[i].node().copyAttributes(float_node) - - model.apply(patch_float) - patch_float(model.encode_image) - patch_float(model.encode_text) - - model.float() - - return model, \ - _transform(model.input_resolution.item(), is_train=True), \ - _transform(model.input_resolution.item(), is_train=False) - - -def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: - """ - Returns the tokenized representation of given input string(s) - Parameters - ---------- - texts : Union[str, List[str]] - An input string or a list of input strings to tokenize - context_length : int - The context length to use; all CLIP models use 77 as the context length - Returns - ------- - A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] - """ - if isinstance(texts, str): - texts = [texts] - - sot_token = _tokenizer.encoder[""] - eot_token = _tokenizer.encoder[""] - all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - if len(tokens) > context_length: # Truncate - tokens = tokens[:context_length] - result[i, :len(tokens)] = torch.tensor(tokens) - - return result diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/theme-toggler.css b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/theme-toggler.css deleted file mode 100644 index b673b5920a24693e7ea15b873e46731b388ec527..0000000000000000000000000000000000000000 --- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/theme-toggler.css +++ /dev/null @@ -1,33 +0,0 @@ -.theme-toggler-container { - margin: 24px 0px 8px 0px; - justify-content: center; -} - -.theme-toggler-container.checkbox input + label, -.theme-toggler-container.checkbox input:checked + label:after { - background: var(--colour-1); -} - -.theme-toggler-container.checkbox input + label:after, -.theme-toggler-container.checkbox input:checked + label { - background: var(--colour-3); -} - -.theme-toggler-container.checkbox span { - font-size: 0.75rem; -} - -.theme-toggler-container.checkbox label { - width: 24px; - height: 16px; -} - -.theme-toggler-container.checkbox label:after { - left: 2px; - width: 10px; - height: 10px; -} - -.theme-toggler-container.checkbox input:checked + label:after { - left: calc(100% - 2px - 10px); -} \ No newline at end of file diff --git a/spaces/aniketingole92/gradiolangchainChatbotopenAI/README.md b/spaces/aniketingole92/gradiolangchainChatbotopenAI/README.md deleted file mode 100644 index f8481f29aee0ca65271f302c08c8f1ebe7579b76..0000000000000000000000000000000000000000 --- a/spaces/aniketingole92/gradiolangchainChatbotopenAI/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: GradiolangchainChatbotopenAI -emoji: 📈 -colorFrom: indigo -colorTo: blue -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/annt/mrc_uit_squadv2/retro_reader/preprocess.py b/spaces/annt/mrc_uit_squadv2/retro_reader/preprocess.py deleted file mode 100644 index fbb334bda950482c30174981305f836d8d512c04..0000000000000000000000000000000000000000 --- a/spaces/annt/mrc_uit_squadv2/retro_reader/preprocess.py +++ /dev/null @@ -1,301 +0,0 @@ -import numpy as np -from .constants import ( - QUESTION_COLUMN_NAME, - CONTEXT_COLUMN_NAME, - ANSWER_COLUMN_NAME, - ANSWERABLE_COLUMN_NAME, - ID_COLUMN_NAME, -) - - -def get_sketch_features(tokenizer, mode, data_args): - - pad_on_right = tokenizer.padding_side == "right" - max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) - - def tokenize_fn(examples): - """Tokenize questions and contexts - Args: - examples (Dict): DatasetDict - Returns: - Dict: Tokenized examples - """ - # truncation과 padding을 통해 tokenization을 진행 - # stride를 이용하여 overflow를 유지 - # 각 example들은 이전의 context와 조금씩 겹침 - # overflow 발생 시 지정한 batch size보다 더 많은 sample이 들어올 수 있음 -> data augmentation - tokenized_examples = tokenizer( - examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME], - examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME], - # 길이가 긴 context가 등장할 경우 truncation을 진행 - truncation="only_second" if pad_on_right else "only_first", - max_length=max_seq_length, - stride=data_args.doc_stride, - # overflow 발생 시 원래 인덱스를 찾을 수 있게 mapping 가능한 값이 필요 - return_overflowing_tokens=True, - return_offsets_mapping=False, - # sentence pair가 입력으로 들어올 때 0과 1로 구분지음 - return_token_type_ids=data_args.return_token_type_ids, - padding="max_length" if data_args.pad_to_max_length else False, - # return_tensors='pt' - ) - return tokenized_examples - - def prepare_train_features(examples): - tokenized_examples = tokenize_fn(examples) - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - - tokenized_examples["labels"] = [] - - for i in range(len(tokenized_examples["input_ids"])): - # 하나의 example이 여러 개의 span을 가질 수 있음 - sample_index = sample_mapping[i] - - # unanswerable label 생성 - # answerable: 0, unanswerable: 1 - is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index] - tokenized_examples["labels"].append(0 if not is_impossible else 1) - - return tokenized_examples - - def prepare_eval_features(examples): - tokenized_examples = tokenize_fn(examples) - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - - tokenized_examples["example_id"] = [] - tokenized_examples["labels"] = [] - - for i in range(len(tokenized_examples["input_ids"])): - # 하나의 example이 여러 개의 span을 가질 수 있음 - sample_index = sample_mapping[i] - - id_col = examples[ID_COLUMN_NAME][sample_index] - tokenized_examples["example_id"].append(id_col) - - # unanswerable label 생성 - # answerable: 0, unanswerable: 1 - is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index] - tokenized_examples["labels"].append(0 if not is_impossible else 1) - - return tokenized_examples - - def prepare_test_features(examples): - tokenized_examples = tokenize_fn(examples) - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - - tokenized_examples["example_id"] = [] - - for i in range(len(tokenized_examples["input_ids"])): - # 하나의 example이 여러 개의 span을 가질 수 있음 - sample_index = sample_mapping[i] - - id_col = examples[ID_COLUMN_NAME][sample_index] - tokenized_examples["example_id"].append(id_col) - - return tokenized_examples - - if mode == "train": - get_features_fn = prepare_train_features - elif mode == "eval": - get_features_fn = prepare_eval_features - elif mode == "test": - get_features_fn = prepare_test_features - - return get_features_fn, True - - -def get_intensive_features(tokenizer, mode, data_args): - - pad_on_right = tokenizer.padding_side == "right" - max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) - beam_based = data_args.intensive_model_type in ["xlnet", "xlm"] - - def tokenize_fn(examples): - """Tokenize questions and contexts - Args: - examples (Dict): DatasetDict - Returns: - Dict: Tokenized examples - """ - # truncation과 padding을 통해 tokenization을 진행 - # stride를 이용하여 overflow를 유지 - # 각 example들은 이전의 context와 조금씩 겹침 - # overflow 발생 시 지정한 batch size보다 더 많은 sample이 들어올 수 있음 - tokenized_examples = tokenizer( - examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME], - examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME], - # 길이가 긴 context가 등장할 경우 truncation을 진행 - truncation="only_second" if pad_on_right else "only_first", - max_length=max_seq_length, - stride=data_args.doc_stride, - # overflow 발생 시 원래 인덱스를 찾을 수 있게 mapping 가능한 값이 필요 - return_overflowing_tokens=True, - # token의 캐릭터 단위 position을 찾을 수 있는 offset을 반환 - # start position과 end position을 찾는데 도움을 줌 - return_offsets_mapping=True, - # sentence pair가 입력으로 들어올 때 0과 1로 구분지음 - return_token_type_ids=data_args.return_token_type_ids, - padding="max_length" if data_args.pad_to_max_length else False, - # return_tensors='pt' - ) - return tokenized_examples - - def prepare_train_features(examples): - tokenized_examples = tokenize_fn(examples) - # Since one example might give us several features if it has a long context, - # we need a map from a feature to its corresponding example. - # This key gives us just that. - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - # The offset mappings will give us a map from token to character position in the original context - # This will help us compute the start_positions and end_positions. - offset_mapping = tokenized_examples.pop("offset_mapping") - - # Let's label those exmaples! - tokenized_examples["start_positions"] = [] - tokenized_examples["end_positions"] = [] - tokenized_examples["is_impossibles"] = [] - if beam_based: - tokenized_examples["cls_index"] = [] - tokenized_examples["p_mask"] = [] - - for i, offsets in enumerate(offset_mapping): - # We will label impossible answers with the index of the CLS token. - input_ids = tokenized_examples["input_ids"][i] - cls_index = input_ids.index(tokenizer.cls_token_id) - - # Grab the sequence corresponding to that example - # (to know what is the context and what is the question.) - sequence_ids = tokenized_examples.sequence_ids(i) - context_index = 1 if pad_on_right else 0 - - # `p_mask` which indicates the tokens that can't be in answers - # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0. - # The cls token gets 0.0 too (for predictions of empty answers). - # iInspired by XLNet. - if beam_based: - tokenized_examples["cls_index"].append(cls_index) - tokenized_examples["p_mask"].append( - [ - 0.0 if s == context_index or k == cls_index else 1.0 - for k, s in enumerate(sequence_ids) - ] - ) - - # One example can give several spans, - # this is the index of the example containing this span of text. - sample_index = sample_mapping[i] - answers = examples[ANSWER_COLUMN_NAME][sample_index] - is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index] - - # If no answers are given, set the cls_index as answer. - if is_impossible or len(answers["answer_start"]) == 0: - tokenized_examples["start_positions"].append(cls_index) - tokenized_examples["end_positions"].append(cls_index) - tokenized_examples["is_impossibles"].append(1.0) # unanswerable - else: - # Start/end character index of the answer in the text. - start_char = answers["answer_start"][0] - end_char = start_char + len(answers["text"][0]) - - # sequence_ids는 0, 1, None의 세 값만 가짐 - # None 0 0 ... 0 None 1 1 ... 1 None - - # Start token index of the current span in the text. - token_start_index = 0 - while sequence_ids[token_start_index] != context_index: - token_start_index += 1 - - # End token index of the current span in the text. - token_end_index = len(input_ids) - 1 - while sequence_ids[token_end_index] != context_index: - token_end_index -= 1 - - # Detect if the answer is out of the span - # (in which case this feature is labeled with the CLS index.) - if not ( - offsets[token_start_index][0] <= start_char and - offsets[token_end_index][1] >= end_char - ): - tokenized_examples["start_positions"].append(cls_index) - tokenized_examples["end_positions"].append(cls_index) - tokenized_examples["is_impossibles"].append(1.0) # unanswerable - else: - # Otherwise move the token_start_index and token_end_index to the two ends of the answer. - # Note: we could go after the last offset if the answer is the last word (edge case). - while ( - token_start_index < len(offsets) and - offsets[token_start_index][0] <= start_char - ): - token_start_index += 1 - tokenized_examples["start_positions"].append(token_start_index - 1) - - while offsets[token_end_index][1] >= end_char: - token_end_index -= 1 - tokenized_examples["end_positions"].append(token_end_index + 1) - - tokenized_examples["is_impossibles"].append(0.0) # answerable - - return tokenized_examples - - def prepare_eval_features(examples): - tokenized_examples = tokenize_fn(examples) - # Since one example might give us several features if it has a long context, - # we need a map from a feature to its corresponding example. - # This key gives us just that. - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - - # For evaluation, we will need to convert our predictions to substrings of the context, - # so we keep the corresponding example_id and we will store the offset mappings. - tokenized_examples["example_id"] = [] - - # We will provide the index of the CLS token ans the p_mask to the model, - # but not the is_impossible label. - if beam_based: - tokenized_examples["cls_index"] = [] - tokenized_examples["p_mask"] = [] - - for i, input_ids in enumerate(tokenized_examples["input_ids"]): - # Find the CLS token in the input ids. - cls_index = input_ids.index(tokenizer.cls_token_id) - - # Grab the sequence corresponding to that example - # (to know what is the context and what is the question.) - sequence_ids = tokenized_examples.sequence_ids(i) - context_index = 1 if pad_on_right else 0 - - # `p_mask` which indicates the tokens that can't be in answers - # Build the p_mask: non special tokens and context gets 0.0, the others get 1.0. - # The cls token gets 0.0 too (for predictions of empty answers). - # iInspired by XLNet. - if beam_based: - tokenized_examples["cls_index"].append(cls_index) - tokenized_examples["p_mask"].append( - [ - 0.0 if s == context_index or k == cls_index else 1.0 - for k, s in enumerate(sequence_ids) - ] - ) - - # One example can give several spans, - # this is the index of the example containing this span of text. - sample_index = sample_mapping[i] - id_col = examples[ID_COLUMN_NAME][sample_index] - tokenized_examples["example_id"].append(id_col) - - # Set to None the offset_mapping that are note part of the context - # so it's easy to determine if a token position is part of the context or not. - tokenized_examples["offset_mapping"][i] = [ - (o if sequence_ids[k] == context_index else None) - for k, o in enumerate(tokenized_examples["offset_mapping"][i]) - ] - - return tokenized_examples - - if mode == "train": - get_features_fn = prepare_train_features - elif mode == "eval": - get_features_fn = prepare_eval_features - elif mode == "test": - get_features_fn = prepare_eval_features - - return get_features_fn, True \ No newline at end of file diff --git a/spaces/ansfarooq7/l4-project/README.md b/spaces/ansfarooq7/l4-project/README.md deleted file mode 100644 index 40caf51a9f75c08dfd47ef2ea22d6e15314c80ed..0000000000000000000000000000000000000000 --- a/spaces/ansfarooq7/l4-project/README.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -title: Limerick Generation -emoji: 🧝 -colorFrom: indigo -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` -`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. \ No newline at end of file diff --git a/spaces/antonovmaxim/text-generation-webui-space/modules/llamacpp_model.py b/spaces/antonovmaxim/text-generation-webui-space/modules/llamacpp_model.py deleted file mode 100644 index 0ed33543dcf5ca61f0dddc6b3c35add9d535df59..0000000000000000000000000000000000000000 --- a/spaces/antonovmaxim/text-generation-webui-space/modules/llamacpp_model.py +++ /dev/null @@ -1,86 +0,0 @@ -''' -Based on -https://github.com/abetlen/llama-cpp-python - -Documentation: -https://abetlen.github.io/llama-cpp-python/ -''' - -import logging -import re - -from llama_cpp import Llama, LlamaCache - -from modules import shared -from modules.callbacks import Iteratorize - - -class LlamaCppModel: - def __init__(self): - self.initialized = False - - def __del__(self): - self.model.__del__() - - @classmethod - def from_pretrained(self, path): - result = self() - - cache_capacity = 0 - if shared.args.cache_capacity is not None: - if 'GiB' in shared.args.cache_capacity: - cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 * 1000 - elif 'MiB' in shared.args.cache_capacity: - cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 - else: - cache_capacity = int(shared.args.cache_capacity) - - logging.info("Cache capacity is " + str(cache_capacity) + " bytes") - - params = { - 'model_path': str(path), - 'n_ctx': 2048, - 'seed': 0, - 'n_threads': shared.args.threads or None, - 'n_batch': shared.args.n_batch, - 'use_mmap': not shared.args.no_mmap, - 'use_mlock': shared.args.mlock, - 'n_gpu_layers': shared.args.n_gpu_layers - } - self.model = Llama(**params) - if cache_capacity > 0: - self.model.set_cache(LlamaCache(capacity_bytes=cache_capacity)) - - # This is ugly, but the model and the tokenizer are the same object in this library. - return result, result - - def encode(self, string): - if type(string) is str: - string = string.encode() - return self.model.tokenize(string) - - def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None): - context = context if type(context) is str else context.decode() - completion_chunks = self.model.create_completion( - prompt=context, - max_tokens=token_count, - temperature=temperature, - top_p=top_p, - top_k=top_k, - repeat_penalty=repetition_penalty, - stream=True - ) - output = "" - for completion_chunk in completion_chunks: - text = completion_chunk['choices'][0]['text'] - output += text - if callback: - callback(text) - return output - - def generate_with_streaming(self, **kwargs): - with Iteratorize(self.generate, kwargs, callback=None) as generator: - reply = '' - for token in generator: - reply += token - yield reply diff --git a/spaces/apratap5/Abhay-ASRLiveSpeechRecognition-ZR/README.md b/spaces/apratap5/Abhay-ASRLiveSpeechRecognition-ZR/README.md deleted file mode 100644 index 3f2f400c2ae7ea2c26e51a15af9693deeeae548c..0000000000000000000000000000000000000000 --- a/spaces/apratap5/Abhay-ASRLiveSpeechRecognition-ZR/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Abhay ASRLiveSpeechRecognition ZR -emoji: ⚡ -colorFrom: pink -colorTo: gray -sdk: gradio -sdk_version: 3.8.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/argilla/argilla-streamlit-customs/my_app/pages/ui-record-creator.py b/spaces/argilla/argilla-streamlit-customs/my_app/pages/ui-record-creator.py deleted file mode 100644 index 1595e6a0cfaa3acb4a16cb9dfe8869757d660e29..0000000000000000000000000000000000000000 --- a/spaces/argilla/argilla-streamlit-customs/my_app/pages/ui-record-creator.py +++ /dev/null @@ -1,117 +0,0 @@ -from ast import literal_eval - -import argilla as rg -import pandas as pd -import spacy -import streamlit as st -from streamlit_tags import st_tags -from text_highlighter import text_highlighter -from utils.commons import ( - ArgillaSingleton, - argilla_login_flow, - get_data_snapshot, - get_dataset_list, -) - -st.set_page_config( - page_title="Argilla - ✍️ - Manual record creator", - page_icon="✍️", - layout="wide", -) - - -api_url, api_key = argilla_login_flow("✍️ Manual record creator") - -st.write( - """ - This page allows you to create and annotate individual records from Argilla without using any code! - In the background it uses `argilla.log()` and `TextClassificationRecord`, `TokenClassificationRecord`, and `Text2TextRecord`. - """ -) - -nlp = spacy.blank("en") -datasets_list = [ - f"{ds['owner']}/{ds['name']}" for ds in get_dataset_list(api_url, api_key) -] -dataset_argilla = st.selectbox( - "Argilla Dataset Name", options=["other"] + datasets_list -) -if dataset_argilla == "other": - ArgillaSingleton.init(api_url, api_key) - dataset_argilla_name = st.text_input("New Dataset Name") - labels = [] - disabled = False - options = ["TextClassification", "TokenClassification", "Text2Text"] -else: - dataset_argilla_name = dataset_argilla.split("/")[-1] - dataset_argilla_workspace = dataset_argilla.split("/")[0] - get_data_snapshot(dataset_argilla_name, dataset_argilla_workspace) - rg.set_workspace(dataset_argilla_workspace) - for dataset in get_dataset_list(api_url, api_key): - if ( - dataset["name"] == dataset_argilla_name - and dataset["owner"] == dataset_argilla_workspace - ): - labels = dataset["labels"] - dataset_type = dataset["task"] - disabled = True - options = [dataset_type] - break - - -if dataset_argilla_name: - dataset_type = st.selectbox("Dataset Type", options, disabled=disabled) - if dataset_type in ["TextClassification", "TokenClassification"]: - labels = st_tags(label="Labels", value=labels, text="Press enter to add more") - - if not any(labels): - st.warning("No labels provided") - - st.stop() - if dataset_type == "TextClassification": - multi_label = st.radio("multi label", [False, True], horizontal=True) - else: - multi_label = False - text = st.text_area("Text") - - if text: - if dataset_type == "TextClassification": - if multi_label: - annotation = st.multiselect("annotation", labels, default=labels) - else: - annotation = st.radio("annotation", labels, horizontal=True) - - record = rg.TextClassificationRecord( - text=text, annotation=annotation, multi_label=multi_label - ) - elif dataset_type == "TokenClassification": - annotation = text_highlighter( - text=text, - labels=labels, - ) - if annotation: - annotation = [(an["tag"], an["start"], an["end"]) for an in annotation] - - tokens = [token.text for token in nlp(text)] - record = rg.TokenClassificationRecord( - text=text, tokens=tokens, annotation=annotation - ) - - elif dataset_type == "Text2Text": - annotation = st.text_area("Annotation") - record = rg.Text2TextRecord(text=text, annotation=annotation) - metadata = st.text_area("Metadata", value="{}") - metadata = literal_eval(metadata) - - record.metadata = metadata - new_record = st.write(pd.DataFrame(record.dict())) - else: - st.warning("Please enter text") - - save = st.button("Save") - if save: - rg.log(record, dataset_argilla_name) - st.success("Saved") -else: - st.warning("Please enter dataset name") - diff --git a/spaces/arseny-chebyshev/vox-diffusion/README.md b/spaces/arseny-chebyshev/vox-diffusion/README.md deleted file mode 100644 index a7e41fca94e51b6297f2f9c3e29aae1b78786423..0000000000000000000000000000000000000000 --- a/spaces/arseny-chebyshev/vox-diffusion/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: vox-diffusion -emoji: 👨‍🔬 -colorFrom: indigo -colorTo: yellow -sdk: gradio -sdk_version: 3.38.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA512.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA512.py deleted file mode 100644 index 20961aca993f588a0d8a7b381d92958af8dba159..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_SHA512.py +++ /dev/null @@ -1,140 +0,0 @@ -# -*- coding: utf-8 -*- -# -# SelfTest/Hash/test_SHA512.py: Self-test for the SHA-512 hash function -# -# Written in 2008 by Dwayne C. Litzenberger -# -# =================================================================== -# The contents of this file are dedicated to the public domain. To -# the extent that dedication to the public domain is not available, -# everyone is granted a worldwide, perpetual, royalty-free, -# non-exclusive license to exercise all rights associated with the -# contents of this file for any purpose whatsoever. -# No rights are reserved. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND -# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS -# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN -# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -# =================================================================== - -"""Self-test suite for Crypto.Hash.SHA512""" - -from binascii import hexlify - -from Crypto.Hash import SHA512 -from .common import make_hash_tests -from Crypto.SelfTest.loader import load_test_vectors - -# Test vectors from various sources -# This is a list of (expected_result, input[, description]) tuples. -test_data_512_other = [ - - # RFC 4634: Section Page 8.4, "Test 1" - ('ddaf35a193617abacc417349ae20413112e6fa4e89a97ea20a9eeee64b55d39a2192992a274fc1a836ba3c23a3feebbd454d4423643ce80e2a9ac94fa54ca49f', 'abc'), - - # RFC 4634: Section Page 8.4, "Test 2.1" - ('8e959b75dae313da8cf4f72814fc143f8f7779c6eb9f7fa17299aeadb6889018501d289e4900f7e4331b99dec4b5433ac7d329eeb6dd26545e96e55b874be909', 'abcdefghbcdefghicdefghijdefghijkefghijklfghijklmghijklmnhijklmnoijklmnopjklmnopqklmnopqrlmnopqrsmnopqrstnopqrstu'), - - # RFC 4634: Section Page 8.4, "Test 3" - ('e718483d0ce769644e2e42c7bc15b4638e1f98b13b2044285632a803afa973ebde0ff244877ea60a4cb0432ce577c31beb009c5c2c49aa2e4eadb217ad8cc09b', 'a' * 10**6, "'a' * 10**6"), - - # Taken from http://de.wikipedia.org/wiki/Secure_Hash_Algorithm - ('cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e', ''), - - ('af9ed2de700433b803240a552b41b5a472a6ef3fe1431a722b2063c75e9f07451f67a28e37d09cde769424c96aea6f8971389db9e1993d6c565c3c71b855723c', 'Franz jagt im komplett verwahrlosten Taxi quer durch Bayern'), -] - - -def get_tests_SHA512(): - - test_vectors = load_test_vectors(("Hash", "SHA2"), - "SHA512ShortMsg.rsp", - "KAT SHA-512", - {"len": lambda x: int(x)}) or [] - - test_data = test_data_512_other[:] - for tv in test_vectors: - try: - if tv.startswith('['): - continue - except AttributeError: - pass - if tv.len == 0: - tv.msg = b"" - test_data.append((hexlify(tv.md), tv.msg, tv.desc)) - - tests = make_hash_tests(SHA512, "SHA512", test_data, - digest_size=64, - oid="2.16.840.1.101.3.4.2.3") - return tests - - -def get_tests_SHA512_224(): - - test_vectors = load_test_vectors(("Hash", "SHA2"), - "SHA512_224ShortMsg.rsp", - "KAT SHA-512/224", - {"len": lambda x: int(x)}) or [] - - test_data = [] - for tv in test_vectors: - try: - if tv.startswith('['): - continue - except AttributeError: - pass - if tv.len == 0: - tv.msg = b"" - test_data.append((hexlify(tv.md), tv.msg, tv.desc)) - - tests = make_hash_tests(SHA512, "SHA512/224", test_data, - digest_size=28, - oid="2.16.840.1.101.3.4.2.5", - extra_params={ "truncate" : "224" }) - return tests - - -def get_tests_SHA512_256(): - - test_vectors = load_test_vectors(("Hash", "SHA2"), - "SHA512_256ShortMsg.rsp", - "KAT SHA-512/256", - {"len": lambda x: int(x)}) or [] - - test_data = [] - for tv in test_vectors: - try: - if tv.startswith('['): - continue - except AttributeError: - pass - if tv.len == 0: - tv.msg = b"" - test_data.append((hexlify(tv.md), tv.msg, tv.desc)) - - tests = make_hash_tests(SHA512, "SHA512/256", test_data, - digest_size=32, - oid="2.16.840.1.101.3.4.2.6", - extra_params={ "truncate" : "256" }) - return tests - - -def get_tests(config={}): - - tests = [] - tests += get_tests_SHA512() - tests += get_tests_SHA512_224() - tests += get_tests_SHA512_256() - return tests - -if __name__ == '__main__': - import unittest - suite = lambda: unittest.TestSuite(get_tests()) - unittest.main(defaultTest='suite') - -# vim:set ts=4 sw=4 sts=4 expandtab: diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/save.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/save.py deleted file mode 100644 index 94ddab6f7b63e469746b43b9874b4ad2079649f5..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/save.py +++ /dev/null @@ -1,134 +0,0 @@ -import json -import pathlib - -from .mimebundle import spec_to_mimebundle - - -def write_file_or_filename(fp, content, mode="w"): - """Write content to fp, whether fp is a string, a pathlib Path or a - file-like object""" - if isinstance(fp, str) or isinstance(fp, pathlib.PurePath): - with open(fp, mode) as f: - f.write(content) - else: - fp.write(content) - - -def save( - chart, - fp, - vega_version, - vegaembed_version, - format=None, - mode=None, - vegalite_version=None, - embed_options=None, - json_kwds=None, - webdriver="chrome", - scale_factor=1, - **kwargs, -): - """Save a chart to file in a variety of formats - - Supported formats are [json, html, png, svg] - - Parameters - ---------- - chart : alt.Chart - the chart instance to save - fp : string filename, pathlib.Path or file-like object - file to which to write the chart. - format : string (optional) - the format to write: one of ['json', 'html', 'png', 'svg']. - If not specified, the format will be determined from the filename. - mode : string (optional) - Either 'vega' or 'vegalite'. If not specified, then infer the mode from - the '$schema' property of the spec, or the ``opt`` dictionary. - If it's not specified in either of those places, then use 'vegalite'. - vega_version : string - For html output, the version of vega.js to use - vegalite_version : string - For html output, the version of vegalite.js to use - vegaembed_version : string - For html output, the version of vegaembed.js to use - embed_options : dict - The vegaEmbed options dictionary. Default is {} - (See https://github.com/vega/vega-embed for details) - json_kwds : dict - Additional keyword arguments are passed to the output method - associated with the specified format. - webdriver : string {'chrome' | 'firefox'} - Webdriver to use for png or svg output - scale_factor : float - scale_factor to use to change size/resolution of png or svg output - **kwargs : - additional kwargs passed to spec_to_mimebundle. - """ - if json_kwds is None: - json_kwds = {} - - if embed_options is None: - embed_options = {} - - if format is None: - if isinstance(fp, str): - format = fp.split(".")[-1] - elif isinstance(fp, pathlib.PurePath): - format = fp.suffix.lstrip(".") - else: - raise ValueError( - "must specify file format: " "['png', 'svg', 'pdf', 'html', 'json']" - ) - - spec = chart.to_dict() - - if mode is None: - if "mode" in embed_options: - mode = embed_options["mode"] - elif "$schema" in spec: - mode = spec["$schema"].split("/")[-2] - else: - mode = "vega-lite" - - if mode not in ["vega", "vega-lite"]: - raise ValueError("mode must be 'vega' or 'vega-lite', " "not '{}'".format(mode)) - - if mode == "vega-lite" and vegalite_version is None: - raise ValueError("must specify vega-lite version") - - if format == "json": - json_spec = json.dumps(spec, **json_kwds) - write_file_or_filename(fp, json_spec, mode="w") - elif format == "html": - mimebundle = spec_to_mimebundle( - spec=spec, - format=format, - mode=mode, - vega_version=vega_version, - vegalite_version=vegalite_version, - vegaembed_version=vegaembed_version, - embed_options=embed_options, - json_kwds=json_kwds, - **kwargs, - ) - write_file_or_filename(fp, mimebundle["text/html"], mode="w") - elif format in ["png", "svg", "pdf"]: - mimebundle = spec_to_mimebundle( - spec=spec, - format=format, - mode=mode, - vega_version=vega_version, - vegalite_version=vegalite_version, - vegaembed_version=vegaembed_version, - webdriver=webdriver, - scale_factor=scale_factor, - **kwargs, - ) - if format == "png": - write_file_or_filename(fp, mimebundle["image/png"], mode="wb") - elif format == "pdf": - write_file_or_filename(fp, mimebundle["application/pdf"], mode="wb") - else: - write_file_or_filename(fp, mimebundle["image/svg+xml"], mode="w") - else: - raise ValueError("unrecognized format: '{}'".format(format)) diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/tests/test_mimebundle.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/tests/test_mimebundle.py deleted file mode 100644 index c893b7ce21d34a050362b3eb1aa3d89376bafbe8..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/utils/tests/test_mimebundle.py +++ /dev/null @@ -1,207 +0,0 @@ -import pytest - -import altair as alt -from ..mimebundle import spec_to_mimebundle - - -@pytest.fixture -def require_altair_saver(): - try: - import altair_saver # noqa: F401 - except ImportError: - pytest.skip("altair_saver not importable; cannot run saver tests") - - -@pytest.fixture -def vegalite_spec(): - return { - "$schema": "https://vega.github.io/schema/vega-lite/v4.json", - "description": "A simple bar chart with embedded data.", - "data": { - "values": [ - {"a": "A", "b": 28}, - {"a": "B", "b": 55}, - {"a": "C", "b": 43}, - {"a": "D", "b": 91}, - {"a": "E", "b": 81}, - {"a": "F", "b": 53}, - {"a": "G", "b": 19}, - {"a": "H", "b": 87}, - {"a": "I", "b": 52}, - ] - }, - "mark": "bar", - "encoding": { - "x": {"field": "a", "type": "ordinal"}, - "y": {"field": "b", "type": "quantitative"}, - }, - } - - -@pytest.fixture -def vega_spec(): - return { - "$schema": "https://vega.github.io/schema/vega/v5.json", - "axes": [ - { - "aria": False, - "domain": False, - "grid": True, - "gridScale": "x", - "labels": False, - "maxExtent": 0, - "minExtent": 0, - "orient": "left", - "scale": "y", - "tickCount": {"signal": "ceil(height/40)"}, - "ticks": False, - "zindex": 0, - }, - { - "grid": False, - "labelAlign": "right", - "labelAngle": 270, - "labelBaseline": "middle", - "orient": "bottom", - "scale": "x", - "title": "a", - "zindex": 0, - }, - { - "grid": False, - "labelOverlap": True, - "orient": "left", - "scale": "y", - "tickCount": {"signal": "ceil(height/40)"}, - "title": "b", - "zindex": 0, - }, - ], - "background": "white", - "data": [ - { - "name": "source_0", - "values": [ - {"a": "A", "b": 28}, - {"a": "B", "b": 55}, - {"a": "C", "b": 43}, - {"a": "D", "b": 91}, - {"a": "E", "b": 81}, - {"a": "F", "b": 53}, - {"a": "G", "b": 19}, - {"a": "H", "b": 87}, - {"a": "I", "b": 52}, - ], - }, - { - "name": "data_0", - "source": "source_0", - "transform": [ - { - "expr": 'isValid(datum["b"]) && isFinite(+datum["b"])', - "type": "filter", - } - ], - }, - ], - "description": "A simple bar chart with embedded data.", - "height": 200, - "marks": [ - { - "encode": { - "update": { - "ariaRoleDescription": {"value": "bar"}, - "description": { - "signal": '"a: " + (isValid(datum["a"]) ? datum["a"] : ""+datum["a"]) + "; b: " + (format(datum["b"], ""))' - }, - "fill": {"value": "#4c78a8"}, - "width": {"band": 1, "scale": "x"}, - "x": {"field": "a", "scale": "x"}, - "y": {"field": "b", "scale": "y"}, - "y2": {"scale": "y", "value": 0}, - } - }, - "from": {"data": "data_0"}, - "name": "marks", - "style": ["bar"], - "type": "rect", - } - ], - "padding": 5, - "scales": [ - { - "domain": {"data": "data_0", "field": "a", "sort": True}, - "name": "x", - "paddingInner": 0.1, - "paddingOuter": 0.05, - "range": {"step": {"signal": "x_step"}}, - "type": "band", - }, - { - "domain": {"data": "data_0", "field": "b"}, - "name": "y", - "nice": True, - "range": [{"signal": "height"}, 0], - "type": "linear", - "zero": True, - }, - ], - "signals": [ - {"name": "x_step", "value": 20}, - { - "name": "width", - "update": "bandspace(domain('x').length, 0.1, 0.05) * x_step", - }, - ], - "style": "cell", - } - - -def test_vegalite_to_vega_mimebundle(require_altair_saver, vegalite_spec, vega_spec): - # temporay fix for https://github.com/vega/vega-lite/issues/7776 - def delete_none(axes): - for axis in axes: - for key, value in list(axis.items()): - if value is None: - del axis[key] - return axes - - bundle = spec_to_mimebundle( - spec=vegalite_spec, - format="vega", - mode="vega-lite", - vega_version=alt.VEGA_VERSION, - vegalite_version=alt.VEGALITE_VERSION, - vegaembed_version=alt.VEGAEMBED_VERSION, - ) - - bundle["application/vnd.vega.v5+json"]["axes"] = delete_none( - bundle["application/vnd.vega.v5+json"]["axes"] - ) - assert bundle == {"application/vnd.vega.v5+json": vega_spec} - - -def test_spec_to_vegalite_mimebundle(vegalite_spec): - bundle = spec_to_mimebundle( - spec=vegalite_spec, - mode="vega-lite", - format="vega-lite", - vegalite_version=alt.VEGALITE_VERSION, - ) - assert bundle == {"application/vnd.vegalite.v4+json": vegalite_spec} - - -def test_spec_to_vega_mimebundle(vega_spec): - bundle = spec_to_mimebundle( - spec=vega_spec, mode="vega", format="vega", vega_version=alt.VEGA_VERSION - ) - assert bundle == {"application/vnd.vega.v5+json": vega_spec} - - -def test_spec_to_json_mimebundle(): - bundle = spec_to_mimebundle( - spec=vegalite_spec, - mode="vega-lite", - format="json", - ) - assert bundle == {"application/json": vegalite_spec} diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/audio/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/audio/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ashercn97/AsherTesting/docs/README.md b/spaces/ashercn97/AsherTesting/docs/README.md deleted file mode 100644 index 06b73b8468ab263a230cb44ba45a6c95f00b2ada..0000000000000000000000000000000000000000 --- a/spaces/ashercn97/AsherTesting/docs/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# text-generation-webui documentation - -## Table of contents - -* [Audio Notification](Audio-Notification.md) -* [Chat mode](Chat-mode.md) -* [DeepSpeed](DeepSpeed.md) -* [Docker](Docker.md) -* [ExLlama](ExLlama.md) -* [Extensions](Extensions.md) -* [FlexGen](FlexGen.md) -* [Generation parameters](Generation-parameters.md) -* [GPTQ models (4 bit mode)](GPTQ-models-(4-bit-mode).md) -* [llama.cpp models](llama.cpp-models.md) -* [LLaMA model](LLaMA-model.md) -* [LoRA](LoRA.md) -* [Low VRAM guide](Low-VRAM-guide.md) -* [RWKV model](RWKV-model.md) -* [Spell book](Spell-book.md) -* [System requirements](System-requirements.md) -* [Training LoRAs](Training-LoRAs.md) -* [Windows installation guide](Windows-installation-guide.md) -* [WSL installation guide](WSL-installation-guide.md) diff --git a/spaces/aubmindlab/Arabic-NLP/backend/utils.py b/spaces/aubmindlab/Arabic-NLP/backend/utils.py deleted file mode 100644 index db38742bd9d65368f533f5e8f9cc84ff2b41bac0..0000000000000000000000000000000000000000 --- a/spaces/aubmindlab/Arabic-NLP/backend/utils.py +++ /dev/null @@ -1,64 +0,0 @@ -import re -import numpy as np -import psutil -import os -from tqdm.auto import tqdm -import logging - -logger = logging.getLogger(__name__) - - -def get_current_ram_usage(): - ram = psutil.virtual_memory() - return ram.available / 1024 / 1024 / 1024, ram.total / 1024 / 1024 / 1024 - - -def download_models(models): - for model in tqdm(models, desc="Downloading models"): - logger.info(f"Downloading {model}") - for i in range(0, 5): - curr_dir = f"{model}/train_{i}/best_model/" - os.makedirs(curr_dir, exist_ok=True) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/config.json -P {curr_dir}" - ) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/pytorch_model.bin -P {curr_dir}" - ) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/special_tokens_map.json -P {curr_dir}" - ) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/tokenizer_config.json -P {curr_dir}" - ) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/training_args.bin -P {curr_dir}" - ) - os.system( - f"wget -q https://huggingface.co/researchaccount/{model}/resolve/main/train_{i}/best_model/vocab.txt -P {curr_dir}" - ) - - -def softmax(x): - return np.exp(x) / sum(np.exp(x)) - - -def ga(file): - code = """ - - - - """ - - a = os.path.dirname(file) + "/static/index.html" - with open(a, "r") as f: - data = f.read() - if len(re.findall("G-", data)) == 0: - with open(a, "w") as ff: - newdata = re.sub("", "" + code, data) - ff.write(newdata) diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.bat b/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.bat deleted file mode 100644 index c8bfe1d5308edb844c68b9dd981a9b59bd03f98c..0000000000000000000000000000000000000000 --- a/spaces/awaawawawa/iurf7irfuyytruyyugb/webui.bat +++ /dev/null @@ -1,62 +0,0 @@ -@echo off - -if not defined PYTHON (set PYTHON=python) -if not defined VENV_DIR (set VENV_DIR=venv) - -set ERROR_REPORTING=FALSE - -mkdir tmp 2>NUL - -%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt -if %ERRORLEVEL% == 0 goto :start_venv -echo Couldn't launch python -goto :show_stdout_stderr - -:start_venv -if [%VENV_DIR%] == [-] goto :skip_venv - -dir %VENV_DIR%\Scripts\Python.exe >tmp/stdout.txt 2>tmp/stderr.txt -if %ERRORLEVEL% == 0 goto :activate_venv - -for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i" -echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME% -%PYTHON_FULLNAME% -m venv %VENV_DIR% >tmp/stdout.txt 2>tmp/stderr.txt -if %ERRORLEVEL% == 0 goto :activate_venv -echo Unable to create venv in directory %VENV_DIR% -goto :show_stdout_stderr - -:activate_venv -set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe" -echo venv %PYTHON% -goto :launch - -:skip_venv - -:launch -%PYTHON% launch.py -pause -exit /b - -:show_stdout_stderr - -echo. -echo exit code: %errorlevel% - -for /f %%i in ("tmp\stdout.txt") do set size=%%~zi -if %size% equ 0 goto :show_stderr -echo. -echo stdout: -type tmp\stdout.txt - -:show_stderr -for /f %%i in ("tmp\stderr.txt") do set size=%%~zi -if %size% equ 0 goto :show_stderr -echo. -echo stderr: -type tmp\stderr.txt - -:endofscript - -echo. -echo Launch unsuccessful. Exiting. -pause diff --git a/spaces/awacke1/Data-Synthesizer-Synthesize-From-Multiple-Sources/README.md b/spaces/awacke1/Data-Synthesizer-Synthesize-From-Multiple-Sources/README.md deleted file mode 100644 index adb334d1f7e115a26f293c4be7d9c547fd6077cd..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Data-Synthesizer-Synthesize-From-Multiple-Sources/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Data Synthesizer Synthesize From Multiple Sources -emoji: ⚡ -colorFrom: gray -colorTo: green -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/awacke1/TTS-STT-Blocks/app.py b/spaces/awacke1/TTS-STT-Blocks/app.py deleted file mode 100644 index 15ed8ec721c4864341852b0c946f4812bb390294..0000000000000000000000000000000000000000 --- a/spaces/awacke1/TTS-STT-Blocks/app.py +++ /dev/null @@ -1,160 +0,0 @@ -import streamlit as st -import datetime -from transformers import pipeline -import gradio as gr - -import tempfile -from typing import Optional -import numpy as np -from TTS.utils.manage import ModelManager -from TTS.utils.synthesizer import Synthesizer - -# PersistDataset ----- -import os -import csv -import gradio as gr -from gradio import inputs, outputs -import huggingface_hub -from huggingface_hub import Repository, hf_hub_download, upload_file -from datetime import datetime - -# created new dataset as awacke1/MindfulStory.csv -DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/MindfulStory.csv" -DATASET_REPO_ID = "awacke1/MindfulStory.csv" -DATA_FILENAME = "MindfulStory.csv" -DATA_FILE = os.path.join("data", DATA_FILENAME) -HF_TOKEN = os.environ.get("HF_TOKEN") - -# Download dataset repo using hub download -try: - hf_hub_download( - repo_id=DATASET_REPO_ID, - filename=DATA_FILENAME, - cache_dir=DATA_DIRNAME, - force_filename=DATA_FILENAME - ) -except: - print("file not found") - -def AIMemory(name: str, message: str): - if name and message: - with open(DATA_FILE, "a") as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"]) - writer.writerow({"name": name, "message": message, "time": str(datetime.now())}) - commit_url = repo.push_to_hub() - return {"name": name, "message": message, "time": str(datetime.now())} - -with open('Mindfulness.txt', 'r') as file: - context = file.read() - -# Set up cloned dataset from repo for operations -repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN) - -# set up ASR -asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") - -# set up TTS -MODEL_NAMES = [ - "en/ljspeech/tacotron2-DDC", - "en/ljspeech/glow-tts", - "en/ljspeech/speedy-speech-wn", - "en/ljspeech/vits", - "en/sam/tacotron-DDC", - "fr/mai/tacotron2-DDC", - "de/thorsten/tacotron2-DCA", -] - -# Use Model Manager to load vocoders -MODELS = {} -manager = ModelManager() -for MODEL_NAME in MODEL_NAMES: - print(f"downloading {MODEL_NAME}") - model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}") - vocoder_name: Optional[str] = model_item["default_vocoder"] - vocoder_path = None - vocoder_config_path = None - if vocoder_name is not None: - vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name) - - synthesizer = Synthesizer( - model_path, config_path, None, vocoder_path, vocoder_config_path, - ) - MODELS[MODEL_NAME] = synthesizer - -# transcribe -def transcribe(audio): - text = asr(audio)["text"] - return text - -#text classifier -classifier = pipeline("text-classification") - - -def speech_to_text(speech): - text = asr(speech)["text"] - #rMem = AIMemory("STT", text) - return text - -def text_to_sentiment(text): - sentiment = classifier(text)[0]["label"] - #rMem = AIMemory(text, sentiment) - return sentiment - -def upsert(text): - date_time =str(datetime.datetime.today()) - doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time) - doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/TTS-STT-Blocks/', u'last': text, u'born': date_time,}) - saved = select('TTS-STT', date_time) - return saved - -def select(collection, document): - doc_ref = db.collection(collection).document(document) - doc = doc_ref.get() - docid = ("The id is: ", doc.id) - contents = ("The contents are: ", doc.to_dict()) - return contents - -def selectall(text): - docs = db.collection('Text2SpeechSentimentSave').stream() - doclist='' - for doc in docs: - r=(f'{doc.id} => {doc.to_dict()}') - doclist += r - return doclist - -def tts(text: str, model_name: str): - print(text, model_name) - synthesizer = MODELS.get(model_name, None) - if synthesizer is None: - raise NameError("model not found") - wavs = synthesizer.tts(text) - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - synthesizer.save_wav(wavs, fp) - - #rMem = AIMemory("TTS", text + model_name) - - return fp.name - -demo = gr.Blocks() -with demo: - audio_file = gr.inputs.Audio(source="microphone", type="filepath") - text = gr.Textbox(label="Speech to Text") - #label = gr.Label() - #saved = gr.Textbox(label="Saved") - #savedAll = gr.Textbox(label="SavedAll") - TTSchoice = gr.inputs.Radio( label="Pick a Text to Speech Model", choices=MODEL_NAMES, ) - audio = gr.Audio(label="Output", interactive=False) - - b1 = gr.Button("Recognize Speech") - #b2 = gr.Button("Classify Sentiment") - #b3 = gr.Button("Save Speech to Text") - #b4 = gr.Button("Retrieve All") - b5 = gr.Button("Read It Back Aloud") - - b1.click(speech_to_text, inputs=audio_file, outputs=text) - #b2.click(text_to_sentiment, inputs=text, outputs=label) - #b3.click(upsert, inputs=text, outputs=saved) - #b4.click(selectall, inputs=text, outputs=savedAll) - b5.click(tts, inputs=[text,TTSchoice], outputs=audio) - -demo.launch(share=True) \ No newline at end of file diff --git a/spaces/awacke1/Text-to-Image-stabilityai-stable-diffusion-2-1/README.md b/spaces/awacke1/Text-to-Image-stabilityai-stable-diffusion-2-1/README.md deleted file mode 100644 index f144fedec8ccd81268adaf0174e4fccbb07f549d..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Text-to-Image-stabilityai-stable-diffusion-2-1/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Text To Image Stabilityai Stable Diffusion 2 1 -emoji: 💩 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.20.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/awacke1/Text-to-Speech-facebook-fastspeech2-en-ljspeech/README.md b/spaces/awacke1/Text-to-Speech-facebook-fastspeech2-en-ljspeech/README.md deleted file mode 100644 index a0d71f74e874568736c0f41dff1bbb8436243beb..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Text-to-Speech-facebook-fastspeech2-en-ljspeech/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Text To Speech Facebook Fastspeech2 En Ljspeech -emoji: 🔥 -colorFrom: purple -colorTo: purple -sdk: gradio -sdk_version: 3.20.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.js b/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.js deleted file mode 100644 index aba02fea00a0ccfb57fd05b7a2a1f134fe072175..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/math/Vector3.js +++ /dev/null @@ -1,727 +0,0 @@ -import { _Math } from './Math.js'; -import { Quaternion } from './Quaternion.js'; - -/** - * @author mrdoob / http://mrdoob.com/ - * @author kile / http://kile.stravaganza.org/ - * @author philogb / http://blog.thejit.org/ - * @author mikael emtinger / http://gomo.se/ - * @author egraether / http://egraether.com/ - * @author WestLangley / http://github.com/WestLangley - */ - -function Vector3( x, y, z ) { - - this.x = x || 0; - this.y = y || 0; - this.z = z || 0; - -} - -Object.assign( Vector3.prototype, { - - isVector3: true, - - set: function ( x, y, z ) { - - this.x = x; - this.y = y; - this.z = z; - - return this; - - }, - - setScalar: function ( scalar ) { - - this.x = scalar; - this.y = scalar; - this.z = scalar; - - return this; - - }, - - setX: function ( x ) { - - this.x = x; - - return this; - - }, - - setY: function ( y ) { - - this.y = y; - - return this; - - }, - - setZ: function ( z ) { - - this.z = z; - - return this; - - }, - - setComponent: function ( index, value ) { - - switch ( index ) { - - case 0: this.x = value; break; - case 1: this.y = value; break; - case 2: this.z = value; break; - default: throw new Error( 'index is out of range: ' + index ); - - } - - return this; - - }, - - getComponent: function ( index ) { - - switch ( index ) { - - case 0: return this.x; - case 1: return this.y; - case 2: return this.z; - default: throw new Error( 'index is out of range: ' + index ); - - } - - }, - - clone: function () { - - return new this.constructor( this.x, this.y, this.z ); - - }, - - copy: function ( v ) { - - this.x = v.x; - this.y = v.y; - this.z = v.z; - - return this; - - }, - - add: function ( v, w ) { - - if ( w !== undefined ) { - - console.warn( 'THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.' ); - return this.addVectors( v, w ); - - } - - this.x += v.x; - this.y += v.y; - this.z += v.z; - - return this; - - }, - - addScalar: function ( s ) { - - this.x += s; - this.y += s; - this.z += s; - - return this; - - }, - - addVectors: function ( a, b ) { - - this.x = a.x + b.x; - this.y = a.y + b.y; - this.z = a.z + b.z; - - return this; - - }, - - addScaledVector: function ( v, s ) { - - this.x += v.x * s; - this.y += v.y * s; - this.z += v.z * s; - - return this; - - }, - - sub: function ( v, w ) { - - if ( w !== undefined ) { - - console.warn( 'THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.' ); - return this.subVectors( v, w ); - - } - - this.x -= v.x; - this.y -= v.y; - this.z -= v.z; - - return this; - - }, - - subScalar: function ( s ) { - - this.x -= s; - this.y -= s; - this.z -= s; - - return this; - - }, - - subVectors: function ( a, b ) { - - this.x = a.x - b.x; - this.y = a.y - b.y; - this.z = a.z - b.z; - - return this; - - }, - - multiply: function ( v, w ) { - - if ( w !== undefined ) { - - console.warn( 'THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.' ); - return this.multiplyVectors( v, w ); - - } - - this.x *= v.x; - this.y *= v.y; - this.z *= v.z; - - return this; - - }, - - multiplyScalar: function ( scalar ) { - - this.x *= scalar; - this.y *= scalar; - this.z *= scalar; - - return this; - - }, - - multiplyVectors: function ( a, b ) { - - this.x = a.x * b.x; - this.y = a.y * b.y; - this.z = a.z * b.z; - - return this; - - }, - - applyEuler: function () { - - var quaternion = new Quaternion(); - - return function applyEuler( euler ) { - - if ( ! ( euler && euler.isEuler ) ) { - - console.error( 'THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.' ); - - } - - return this.applyQuaternion( quaternion.setFromEuler( euler ) ); - - }; - - }(), - - applyAxisAngle: function () { - - var quaternion = new Quaternion(); - - return function applyAxisAngle( axis, angle ) { - - return this.applyQuaternion( quaternion.setFromAxisAngle( axis, angle ) ); - - }; - - }(), - - applyMatrix3: function ( m ) { - - var x = this.x, y = this.y, z = this.z; - var e = m.elements; - - this.x = e[ 0 ] * x + e[ 3 ] * y + e[ 6 ] * z; - this.y = e[ 1 ] * x + e[ 4 ] * y + e[ 7 ] * z; - this.z = e[ 2 ] * x + e[ 5 ] * y + e[ 8 ] * z; - - return this; - - }, - - applyMatrix4: function ( m ) { - - var x = this.x, y = this.y, z = this.z; - var e = m.elements; - - var w = 1 / ( e[ 3 ] * x + e[ 7 ] * y + e[ 11 ] * z + e[ 15 ] ); - - this.x = ( e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z + e[ 12 ] ) * w; - this.y = ( e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z + e[ 13 ] ) * w; - this.z = ( e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z + e[ 14 ] ) * w; - - return this; - - }, - - applyQuaternion: function ( q ) { - - var x = this.x, y = this.y, z = this.z; - var qx = q.x, qy = q.y, qz = q.z, qw = q.w; - - // calculate quat * vector - - var ix = qw * x + qy * z - qz * y; - var iy = qw * y + qz * x - qx * z; - var iz = qw * z + qx * y - qy * x; - var iw = - qx * x - qy * y - qz * z; - - // calculate result * inverse quat - - this.x = ix * qw + iw * - qx + iy * - qz - iz * - qy; - this.y = iy * qw + iw * - qy + iz * - qx - ix * - qz; - this.z = iz * qw + iw * - qz + ix * - qy - iy * - qx; - - return this; - - }, - - project: function ( camera ) { - - return this.applyMatrix4( camera.matrixWorldInverse ).applyMatrix4( camera.projectionMatrix ); - - }, - - unproject: function ( camera ) { - - return this.applyMatrix4( camera.projectionMatrixInverse ).applyMatrix4( camera.matrixWorld ); - - }, - - transformDirection: function ( m ) { - - // input: THREE.Matrix4 affine matrix - // vector interpreted as a direction - - var x = this.x, y = this.y, z = this.z; - var e = m.elements; - - this.x = e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z; - this.y = e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z; - this.z = e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z; - - return this.normalize(); - - }, - - divide: function ( v ) { - - this.x /= v.x; - this.y /= v.y; - this.z /= v.z; - - return this; - - }, - - divideScalar: function ( scalar ) { - - return this.multiplyScalar( 1 / scalar ); - - }, - - min: function ( v ) { - - this.x = Math.min( this.x, v.x ); - this.y = Math.min( this.y, v.y ); - this.z = Math.min( this.z, v.z ); - - return this; - - }, - - max: function ( v ) { - - this.x = Math.max( this.x, v.x ); - this.y = Math.max( this.y, v.y ); - this.z = Math.max( this.z, v.z ); - - return this; - - }, - - clamp: function ( min, max ) { - - // assumes min < max, componentwise - - this.x = Math.max( min.x, Math.min( max.x, this.x ) ); - this.y = Math.max( min.y, Math.min( max.y, this.y ) ); - this.z = Math.max( min.z, Math.min( max.z, this.z ) ); - - return this; - - }, - - clampScalar: function () { - - var min = new Vector3(); - var max = new Vector3(); - - return function clampScalar( minVal, maxVal ) { - - min.set( minVal, minVal, minVal ); - max.set( maxVal, maxVal, maxVal ); - - return this.clamp( min, max ); - - }; - - }(), - - clampLength: function ( min, max ) { - - var length = this.length(); - - return this.divideScalar( length || 1 ).multiplyScalar( Math.max( min, Math.min( max, length ) ) ); - - }, - - floor: function () { - - this.x = Math.floor( this.x ); - this.y = Math.floor( this.y ); - this.z = Math.floor( this.z ); - - return this; - - }, - - ceil: function () { - - this.x = Math.ceil( this.x ); - this.y = Math.ceil( this.y ); - this.z = Math.ceil( this.z ); - - return this; - - }, - - round: function () { - - this.x = Math.round( this.x ); - this.y = Math.round( this.y ); - this.z = Math.round( this.z ); - - return this; - - }, - - roundToZero: function () { - - this.x = ( this.x < 0 ) ? Math.ceil( this.x ) : Math.floor( this.x ); - this.y = ( this.y < 0 ) ? Math.ceil( this.y ) : Math.floor( this.y ); - this.z = ( this.z < 0 ) ? Math.ceil( this.z ) : Math.floor( this.z ); - - return this; - - }, - - negate: function () { - - this.x = - this.x; - this.y = - this.y; - this.z = - this.z; - - return this; - - }, - - dot: function ( v ) { - - return this.x * v.x + this.y * v.y + this.z * v.z; - - }, - - // TODO lengthSquared? - - lengthSq: function () { - - return this.x * this.x + this.y * this.y + this.z * this.z; - - }, - - length: function () { - - return Math.sqrt( this.x * this.x + this.y * this.y + this.z * this.z ); - - }, - - manhattanLength: function () { - - return Math.abs( this.x ) + Math.abs( this.y ) + Math.abs( this.z ); - - }, - - normalize: function () { - - return this.divideScalar( this.length() || 1 ); - - }, - - setLength: function ( length ) { - - return this.normalize().multiplyScalar( length ); - - }, - - lerp: function ( v, alpha ) { - - this.x += ( v.x - this.x ) * alpha; - this.y += ( v.y - this.y ) * alpha; - this.z += ( v.z - this.z ) * alpha; - - return this; - - }, - - lerpVectors: function ( v1, v2, alpha ) { - - return this.subVectors( v2, v1 ).multiplyScalar( alpha ).add( v1 ); - - }, - - cross: function ( v, w ) { - - if ( w !== undefined ) { - - console.warn( 'THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.' ); - return this.crossVectors( v, w ); - - } - - return this.crossVectors( this, v ); - - }, - - crossVectors: function ( a, b ) { - - var ax = a.x, ay = a.y, az = a.z; - var bx = b.x, by = b.y, bz = b.z; - - this.x = ay * bz - az * by; - this.y = az * bx - ax * bz; - this.z = ax * by - ay * bx; - - return this; - - }, - - projectOnVector: function ( vector ) { - - var scalar = vector.dot( this ) / vector.lengthSq(); - - return this.copy( vector ).multiplyScalar( scalar ); - - }, - - projectOnPlane: function () { - - var v1 = new Vector3(); - - return function projectOnPlane( planeNormal ) { - - v1.copy( this ).projectOnVector( planeNormal ); - - return this.sub( v1 ); - - }; - - }(), - - reflect: function () { - - // reflect incident vector off plane orthogonal to normal - // normal is assumed to have unit length - - var v1 = new Vector3(); - - return function reflect( normal ) { - - return this.sub( v1.copy( normal ).multiplyScalar( 2 * this.dot( normal ) ) ); - - }; - - }(), - - angleTo: function ( v ) { - - var theta = this.dot( v ) / ( Math.sqrt( this.lengthSq() * v.lengthSq() ) ); - - // clamp, to handle numerical problems - - return Math.acos( _Math.clamp( theta, - 1, 1 ) ); - - }, - - distanceTo: function ( v ) { - - return Math.sqrt( this.distanceToSquared( v ) ); - - }, - - distanceToSquared: function ( v ) { - - var dx = this.x - v.x, dy = this.y - v.y, dz = this.z - v.z; - - return dx * dx + dy * dy + dz * dz; - - }, - - manhattanDistanceTo: function ( v ) { - - return Math.abs( this.x - v.x ) + Math.abs( this.y - v.y ) + Math.abs( this.z - v.z ); - - }, - - setFromSpherical: function ( s ) { - - return this.setFromSphericalCoords( s.radius, s.phi, s.theta ); - - }, - - setFromSphericalCoords: function ( radius, phi, theta ) { - - var sinPhiRadius = Math.sin( phi ) * radius; - - this.x = sinPhiRadius * Math.sin( theta ); - this.y = Math.cos( phi ) * radius; - this.z = sinPhiRadius * Math.cos( theta ); - - return this; - - }, - - setFromCylindrical: function ( c ) { - - return this.setFromCylindricalCoords( c.radius, c.theta, c.y ); - - }, - - setFromCylindricalCoords: function ( radius, theta, y ) { - - this.x = radius * Math.sin( theta ); - this.y = y; - this.z = radius * Math.cos( theta ); - - return this; - - }, - - setFromMatrixPosition: function ( m ) { - - var e = m.elements; - - this.x = e[ 12 ]; - this.y = e[ 13 ]; - this.z = e[ 14 ]; - - return this; - - }, - - setFromMatrixScale: function ( m ) { - - var sx = this.setFromMatrixColumn( m, 0 ).length(); - var sy = this.setFromMatrixColumn( m, 1 ).length(); - var sz = this.setFromMatrixColumn( m, 2 ).length(); - - this.x = sx; - this.y = sy; - this.z = sz; - - return this; - - }, - - setFromMatrixColumn: function ( m, index ) { - - return this.fromArray( m.elements, index * 4 ); - - }, - - equals: function ( v ) { - - return ( ( v.x === this.x ) && ( v.y === this.y ) && ( v.z === this.z ) ); - - }, - - fromArray: function ( array, offset ) { - - if ( offset === undefined ) offset = 0; - - this.x = array[ offset ]; - this.y = array[ offset + 1 ]; - this.z = array[ offset + 2 ]; - - return this; - - }, - - toArray: function ( array, offset ) { - - if ( array === undefined ) array = []; - if ( offset === undefined ) offset = 0; - - array[ offset ] = this.x; - array[ offset + 1 ] = this.y; - array[ offset + 2 ] = this.z; - - return array; - - }, - - fromBufferAttribute: function ( attribute, index, offset ) { - - if ( offset !== undefined ) { - - console.warn( 'THREE.Vector3: offset has been removed from .fromBufferAttribute().' ); - - } - - this.x = attribute.getX( index ); - this.y = attribute.getY( index ); - this.z = attribute.getZ( index ); - - return this; - - } - -} ); - - -export { Vector3 }; diff --git a/spaces/banana-projects/web3d/node_modules/three/src/scenes/Fog.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/scenes/Fog.d.ts deleted file mode 100644 index ca8b3dbddb4100588f4fe574964e5a6d0856785c..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/scenes/Fog.d.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { Color } from './../math/Color'; - -export interface IFog { - name: string; - color: Color; - clone(): this; - toJSON(): any; -} - -/** - * This class contains the parameters that define linear fog, i.e., that grows linearly denser with the distance. - */ -export class Fog implements IFog { - constructor(hex: number, near?: number, far?: number); - - name: string; - - /** - * Fog color. - */ - color: Color; - - /** - * The minimum distance to start applying fog. Objects that are less than 'near' units from the active camera won't be affected by fog. - */ - near: number; - - /** - * The maximum distance at which fog stops being calculated and applied. Objects that are more than 'far' units away from the active camera won't be affected by fog. - * Default is 1000. - */ - far: number; - - clone(): this; - toJSON(): any; -} diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620151050.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220620151050.py deleted file mode 100644 index c0708c1851e350e44495b182f8b1cf78d3331731..0000000000000000000000000000000000000000 --- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620151050.py +++ /dev/null @@ -1,40 +0,0 @@ -#-*- coding : utf-8-*- -import pandas as pd -import streamlit as st -import os,base64,subprocess -from subprocess import STDOUT #os process manipuation - -@st.cache -def gh(): - """install ghostscript on the linux machine""" - proc = subprocess.Popen('apt-get install -y ghostscript', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash") - proc.wait() - -gh() - -import camelot as cam - -st.title("PDF Table Extractor") - -input_pdf = st.file_uploader(label = "", type = 'pdf') - -page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1) - -if input_pdf is not None: - # byte object into a PDF file - with open("input.pdf", "wb") as f: - base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8') - f.write(base64.b64decode(base64_pdf)) - f.close() - - # read the pdf and parse it using stream - tables = cam.read_pdf("input.pdf", pages=page_number) - result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') - tables[0].to_excel(result,index=False) - # for i in range(0,len(tables)): - # table = tables[i].df - # sheetname = str(i) - # table.to_excel(result, sheetname,index=False) - - with open('result.xlsx','rb') as f: - st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel") \ No newline at end of file diff --git a/spaces/bigPear/digitalWDF/src/finetune.py b/spaces/bigPear/digitalWDF/src/finetune.py deleted file mode 100644 index 08fe9202c3b6d31a9f7250c3689e514dcc7377e3..0000000000000000000000000000000000000000 --- a/spaces/bigPear/digitalWDF/src/finetune.py +++ /dev/null @@ -1,88 +0,0 @@ -# coding=utf-8 -# Implements several parameter-efficient supervised fine-tuning method for ChatGLM. -# This code is inspired by https://github.com/THUDM/ChatGLM-6B/blob/main/ptuning/main.py - - -from utils import ( - load_pretrained, - prepare_args, - prepare_data, - preprocess_data, - plot_loss, - Seq2SeqDataCollatorForChatGLM, - ComputeMetrics, - Seq2SeqTrainerForChatGLM -) - - -def main(): - - # Prepare pretrained model and dataset - model_args, data_args, training_args, finetuning_args = prepare_args() - dataset = prepare_data(model_args, data_args) - model, tokenizer = load_pretrained(model_args, training_args, finetuning_args, training_args.do_train, stage="sft") - dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="sft") - data_collator = Seq2SeqDataCollatorForChatGLM( - tokenizer=tokenizer, - model=model, - ignore_pad_token_for_loss=data_args.ignore_pad_token_for_loss, - inference_mode=(not training_args.do_train) - ) - - # Override the decoding parameters of Seq2SeqTrainer - training_args.generation_max_length = training_args.generation_max_length if \ - training_args.generation_max_length is not None else data_args.max_target_length - training_args.generation_num_beams = data_args.num_beams if \ - data_args.num_beams is not None else training_args.generation_num_beams - - # Initialize our Trainer - trainer = Seq2SeqTrainerForChatGLM( - finetuning_args=finetuning_args, - model=model, - args=training_args, - train_dataset=dataset if training_args.do_train else None, - eval_dataset=dataset if training_args.do_eval else None, - tokenizer=tokenizer, - data_collator=data_collator, - compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None - ) - - # Keyword arguments for `model.generate` - gen_kwargs = { - "do_sample": True, - "top_p": 0.7, - "max_length": 768, - "temperature": 0.95 - } - - # Training - if training_args.do_train: - train_result = trainer.train() - trainer.log_metrics("train", train_result.metrics) - trainer.save_metrics("train", train_result.metrics) - trainer.save_state() - trainer.save_model() - if trainer.is_world_process_zero() and finetuning_args.plot_loss: - plot_loss(training_args) - - # Evaluation - if training_args.do_eval: - metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs) - trainer.log_metrics("eval", metrics) - trainer.save_metrics("eval", metrics) - - # Predict - if training_args.do_predict: - predict_results = trainer.predict(dataset, metric_key_prefix="predict", **gen_kwargs) - trainer.log_metrics("predict", predict_results.metrics) - trainer.save_metrics("predict", predict_results.metrics) - trainer.save_predictions(predict_results, tokenizer) - - -def _mp_fn(index): - # For xla_spawn (TPUs) - main() - - -if __name__ == "__main__": - main() diff --git a/spaces/bingbing520/ChatGPT/modules/llama_func.py b/spaces/bingbing520/ChatGPT/modules/llama_func.py deleted file mode 100644 index e1c513af1bf6d1569b071eb5fc0ce441d0692f83..0000000000000000000000000000000000000000 --- a/spaces/bingbing520/ChatGPT/modules/llama_func.py +++ /dev/null @@ -1,166 +0,0 @@ -import os -import logging - -from llama_index import download_loader -from llama_index import ( - Document, - LLMPredictor, - PromptHelper, - QuestionAnswerPrompt, - RefinePrompt, -) -import colorama -import PyPDF2 -from tqdm import tqdm - -from modules.presets import * -from modules.utils import * -from modules.config import local_embedding - - -def get_index_name(file_src): - file_paths = [x.name for x in file_src] - file_paths.sort(key=lambda x: os.path.basename(x)) - - md5_hash = hashlib.md5() - for file_path in file_paths: - with open(file_path, "rb") as f: - while chunk := f.read(8192): - md5_hash.update(chunk) - - return md5_hash.hexdigest() - - -def block_split(text): - blocks = [] - while len(text) > 0: - blocks.append(Document(text[:1000])) - text = text[1000:] - return blocks - - -def get_documents(file_src): - documents = [] - logging.debug("Loading documents...") - logging.debug(f"file_src: {file_src}") - for file in file_src: - filepath = file.name - filename = os.path.basename(filepath) - file_type = os.path.splitext(filepath)[1] - logging.info(f"loading file: {filename}") - try: - if file_type == ".pdf": - logging.debug("Loading PDF...") - try: - from modules.pdf_func import parse_pdf - from modules.config import advance_docs - - two_column = advance_docs["pdf"].get("two_column", False) - pdftext = parse_pdf(filepath, two_column).text - except: - pdftext = "" - with open(filepath, "rb") as pdfFileObj: - pdfReader = PyPDF2.PdfReader(pdfFileObj) - for page in tqdm(pdfReader.pages): - pdftext += page.extract_text() - text_raw = pdftext - elif file_type == ".docx": - logging.debug("Loading Word...") - DocxReader = download_loader("DocxReader") - loader = DocxReader() - text_raw = loader.load_data(file=filepath)[0].text - elif file_type == ".epub": - logging.debug("Loading EPUB...") - EpubReader = download_loader("EpubReader") - loader = EpubReader() - text_raw = loader.load_data(file=filepath)[0].text - elif file_type == ".xlsx": - logging.debug("Loading Excel...") - text_list = excel_to_string(filepath) - for elem in text_list: - documents.append(Document(elem)) - continue - else: - logging.debug("Loading text file...") - with open(filepath, "r", encoding="utf-8") as f: - text_raw = f.read() - except Exception as e: - logging.error(f"Error loading file: {filename}") - pass - text = add_space(text_raw) - # text = block_split(text) - # documents += text - documents += [Document(text)] - logging.debug("Documents loaded.") - return documents - - -def construct_index( - api_key, - file_src, - max_input_size=4096, - num_outputs=5, - max_chunk_overlap=20, - chunk_size_limit=600, - embedding_limit=None, - separator=" ", -): - from langchain.chat_models import ChatOpenAI - from langchain.embeddings.huggingface import HuggingFaceEmbeddings - from llama_index import GPTSimpleVectorIndex, ServiceContext, LangchainEmbedding, OpenAIEmbedding - - if api_key: - os.environ["OPENAI_API_KEY"] = api_key - else: - # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY - os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx" - chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit - embedding_limit = None if embedding_limit == 0 else embedding_limit - separator = " " if separator == "" else separator - - prompt_helper = PromptHelper( - max_input_size=max_input_size, - num_output=num_outputs, - max_chunk_overlap=max_chunk_overlap, - embedding_limit=embedding_limit, - chunk_size_limit=600, - separator=separator, - ) - index_name = get_index_name(file_src) - if os.path.exists(f"./index/{index_name}.json"): - logging.info("找到了缓存的索引文件，加载中……") - return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json") - else: - try: - documents = get_documents(file_src) - if local_embedding: - embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2")) - else: - embed_model = OpenAIEmbedding() - logging.info("构建索引中……") - with retrieve_proxy(): - service_context = ServiceContext.from_defaults( - prompt_helper=prompt_helper, - chunk_size_limit=chunk_size_limit, - embed_model=embed_model, - ) - index = GPTSimpleVectorIndex.from_documents( - documents, service_context=service_context - ) - logging.debug("索引构建完成！") - os.makedirs("./index", exist_ok=True) - index.save_to_disk(f"./index/{index_name}.json") - logging.debug("索引已保存至本地!") - return index - - except Exception as e: - logging.error("索引构建失败！", e) - print(e) - return None - - -def add_space(text): - punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "} - for cn_punc, en_punc in punctuations.items(): - text = text.replace(cn_punc, en_punc) - return text diff --git a/spaces/bioriAsaeru/text-to-voice/Advance Turbo Flasher Box Crack The Ultimate Tool for Flashing and Repairing.md b/spaces/bioriAsaeru/text-to-voice/Advance Turbo Flasher Box Crack The Ultimate Tool for Flashing and Repairing.md deleted file mode 100644 index 341377bface047a6f775e7bfef6f3e638119a5d0..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Advance Turbo Flasher Box Crack The Ultimate Tool for Flashing and Repairing.md +++ /dev/null @@ -1,7 +0,0 @@ -
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diff --git a/spaces/bofenghuang/whisper-demo-french/run_demo_ct2.py b/spaces/bofenghuang/whisper-demo-french/run_demo_ct2.py deleted file mode 100644 index 7a738b21e4666b2118ab63f683af460f2f4bdd22..0000000000000000000000000000000000000000 --- a/spaces/bofenghuang/whisper-demo-french/run_demo_ct2.py +++ /dev/null @@ -1,324 +0,0 @@ -#! /usr/bin/env python -# coding=utf-8 -# Copyright 2022 Bofeng Huang - -import datetime -import logging -import os -import re -import warnings - -import gradio as gr -import pandas as pd -import psutil -import pytube as pt -import torch -# import whisper -from faster_whisper import WhisperModel -from huggingface_hub import hf_hub_download, snapshot_download -from transformers.utils.logging import disable_progress_bar - -import nltk -nltk.download("punkt") - -from nltk.tokenize import sent_tokenize - -warnings.filterwarnings("ignore") -disable_progress_bar() - -# DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-french" -DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-french-ct2" -# CHECKPOINT_FILENAME = "checkpoint_openai.pt" - -GEN_KWARGS = { - "task": "transcribe", - "language": "fr", - # "without_timestamps": True, - # decode options - # "beam_size": 5, - # "patience": 2, - # disable fallback - # "compression_ratio_threshold": None, - # "logprob_threshold": None, - # vad threshold - # "no_speech_threshold": None, -} - -logging.basicConfig( - format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s", - datefmt="%Y-%m-%dT%H:%M:%SZ", -) -logger = logging.getLogger(__name__) -logger.setLevel(logging.DEBUG) - -# device = 0 if torch.cuda.is_available() else "cpu" -# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -device = "cuda" if torch.cuda.is_available() else "cpu" -logger.info(f"Model will be loaded on device `{device}`") - -cached_models = {} - - -def format_timestamp(seconds): - return str(datetime.timedelta(seconds=round(seconds))) - - -def _return_yt_html_embed(yt_url): - video_id = yt_url.split("?v=")[-1] - HTML_str = ( - f'
' "
" - ) - return HTML_str - - -def download_audio_from_youtube(yt_url, downloaded_filename="audio.wav"): - yt = pt.YouTube(yt_url) - stream = yt.streams.filter(only_audio=True)[0] - # stream.download(filename="audio.mp3") - stream.download(filename=downloaded_filename) - return downloaded_filename - - -def download_video_from_youtube(yt_url, downloaded_filename="video.mp4"): - yt = pt.YouTube(yt_url) - stream = yt.streams.filter(progressive=True, file_extension="mp4").order_by("resolution").desc().first() - stream.download(filename=downloaded_filename) - logger.info(f"Download YouTube video from {yt_url}") - return downloaded_filename - - -def _print_memory_info(): - memory = psutil.virtual_memory() - logger.info( - f"Memory info - Free: {memory.available / (1024 ** 3):.2f} Gb, used: {memory.percent}%, total: {memory.total / (1024 ** 3):.2f} Gb" - ) - - -def _print_cuda_memory_info(): - used_mem, tot_mem = torch.cuda.mem_get_info() - logger.info( - f"CUDA memory info - Free: {used_mem / 1024 ** 3:.2f} Gb, used: {(tot_mem - used_mem) / 1024 ** 3:.2f} Gb, total: {tot_mem / 1024 ** 3:.2f} Gb" - ) - - -def print_memory_info(): - _print_memory_info() - _print_cuda_memory_info() - - -def maybe_load_cached_pipeline(model_name): - model = cached_models.get(model_name) - if model is None: - # downloaded_model_path = hf_hub_download(repo_id=model_name, filename=CHECKPOINT_FILENAME) - downloaded_model_path = snapshot_download(repo_id=model_name) - - # model = whisper.load_model(downloaded_model_path, device=device) - model = WhisperModel(downloaded_model_path, device=device, compute_type="float16") - logger.info(f"`{model_name}` has been loaded on device `{device}`") - - print_memory_info() - - cached_models[model_name] = model - return model - - -def infer(model, filename, with_timestamps, return_df=False): - if with_timestamps: - # model_outputs = model.transcribe(filename, **GEN_KWARGS) - model_outputs, _ = model.transcribe(filename, **GEN_KWARGS) - model_outputs = [segment._asdict() for segment in model_outputs] - if return_df: - # model_outputs_df = pd.DataFrame(model_outputs["segments"]) - model_outputs_df = pd.DataFrame(model_outputs) - # print(model_outputs) - # print(model_outputs_df) - # print(model_outputs_df.info(verbose=True)) - model_outputs_df = model_outputs_df[["start", "end", "text"]] - model_outputs_df["start"] = model_outputs_df["start"].map(format_timestamp) - model_outputs_df["end"] = model_outputs_df["end"].map(format_timestamp) - model_outputs_df["text"] = model_outputs_df["text"].str.strip() - return model_outputs_df - else: - return "\n\n".join( - [ - f'Segment {segment["id"]+1} from {segment["start"]:.2f}s to {segment["end"]:.2f}s:\n{segment["text"].strip()}' - # for segment in model_outputs["segments"] - for segment in model_outputs - ] - ) - else: - # text = model.transcribe(filename, without_timestamps=True, **GEN_KWARGS)["text"] - model_outputs, _ = model.transcribe(filename, without_timestamps=True, **GEN_KWARGS) - text = " ".join([segment.text for segment in model_outputs]) - if return_df: - return pd.DataFrame({"text": sent_tokenize(text)}) - else: - return text - - -def transcribe(microphone, file_upload, with_timestamps, model_name=DEFAULT_MODEL_NAME): - warn_output = "" - if (microphone is not None) and (file_upload is not None): - warn_output = ( - "WARNING: You've uploaded an audio file and used the microphone. " - "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" - ) - - elif (microphone is None) and (file_upload is None): - return "ERROR: You have to either use the microphone or upload an audio file" - - file = microphone if microphone is not None else file_upload - - model = maybe_load_cached_pipeline(model_name) - # text = model.transcribe(file, **GEN_KWARGS)["text"] - # text = infer(model, file, with_timestamps) - text = infer(model, file, with_timestamps, return_df=True) - - logger.info(f'Transcription by `{model_name}`:\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n') - - # return warn_output + text - return text - - -def yt_transcribe(yt_url, with_timestamps, model_name=DEFAULT_MODEL_NAME): - # html_embed_str = _return_yt_html_embed(yt_url) - audio_file_path = download_audio_from_youtube(yt_url) - - model = maybe_load_cached_pipeline(model_name) - # text = model.transcribe("audio.mp3", **GEN_KWARGS)["text"] - # text = infer(model, audio_file_path, with_timestamps) - text = infer(model, audio_file_path, with_timestamps, return_df=True) - - logger.info(f'Transcription by `{model_name}` of "{yt_url}":\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n') - - # return html_embed_str, text - return text - - -def video_transcribe(video_file_path, with_timestamps, model_name=DEFAULT_MODEL_NAME): - if video_file_path is None: - raise ValueError("Failed to transcribe video as no video_file_path has been defined") - - audio_file_path = re.sub(r"\.mp4$", ".wav", video_file_path) - os.system(f'ffmpeg -hide_banner -loglevel error -y -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file_path}"') - - model = maybe_load_cached_pipeline(model_name) - # text = model.transcribe("audio.mp3", **GEN_KWARGS)["text"] - text = infer(model, audio_file_path, with_timestamps, return_df=True) - - logger.info(f'Transcription by `{model_name}`:\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n') - - return text - - -# load default model -maybe_load_cached_pipeline(DEFAULT_MODEL_NAME) - -# default_text_output_df = pd.DataFrame(columns=["start", "end", "text"]) -default_text_output_df = pd.DataFrame(columns=["text"]) - -with gr.Blocks() as demo: - - with gr.Tab("Transcribe Audio"): - gr.Markdown( - f""" -
-

Whisper French Demo: Transcribe Audio

-
- Transcribe long-form microphone or audio inputs! - - Demo uses the fine-tuned checkpoint: {DEFAULT_MODEL_NAME} to transcribe audio files of arbitrary length. - - Efficient inference is supported by [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2). - """ - ) - - microphone_input = gr.inputs.Audio(source="microphone", type="filepath", label="Record", optional=True) - upload_input = gr.inputs.Audio(source="upload", type="filepath", label="Upload File", optional=True) - with_timestamps_input = gr.Checkbox(label="With timestamps?") - - microphone_transcribe_btn = gr.Button("Transcribe Audio") - - # gr.Markdown(''' - # Here you will get generated transcrit. - # ''') - - # microphone_text_output = gr.outputs.Textbox(label="Transcription") - text_output_df2 = gr.DataFrame( - value=default_text_output_df, - label="Transcription", - row_count=(0, "dynamic"), - max_rows=10, - wrap=True, - overflow_row_behaviour="paginate", - ) - - microphone_transcribe_btn.click( - transcribe, inputs=[microphone_input, upload_input, with_timestamps_input], outputs=text_output_df2 - ) - - # with gr.Tab("Transcribe YouTube"): - # gr.Markdown( - # f""" - #
- #

Whisper French Demo: Transcribe YouTube

- #
- # Transcribe long-form YouTube videos! - - # Demo uses the fine-tuned checkpoint: {DEFAULT_MODEL_NAME} to transcribe video files of arbitrary length. - # """ - # ) - - # yt_link_input2 = gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL") - # with_timestamps_input2 = gr.Checkbox(label="With timestamps?", value=True) - - # yt_transcribe_btn = gr.Button("Transcribe YouTube") - - # # yt_text_output = gr.outputs.Textbox(label="Transcription") - # text_output_df3 = gr.DataFrame( - # value=default_text_output_df, - # label="Transcription", - # row_count=(0, "dynamic"), - # max_rows=10, - # wrap=True, - # overflow_row_behaviour="paginate", - # ) - # # yt_html_output = gr.outputs.HTML(label="YouTube Page") - - # yt_transcribe_btn.click(yt_transcribe, inputs=[yt_link_input2, with_timestamps_input2], outputs=[text_output_df3]) - - with gr.Tab("Transcribe Video"): - gr.Markdown( - f""" -
-

Whisper French Demo: Transcribe Video

-
- Transcribe long-form YouTube videos or uploaded video inputs! - - Demo uses the fine-tuned checkpoint: {DEFAULT_MODEL_NAME} to transcribe video files of arbitrary length. - - Efficient inference is supported by [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2). - """ - ) - - yt_link_input = gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL") - download_youtube_btn = gr.Button("Download Youtube video") - downloaded_video_output = gr.Video(label="Video file", mirror_webcam=False) - download_youtube_btn.click(download_video_from_youtube, inputs=[yt_link_input], outputs=[downloaded_video_output]) - - with_timestamps_input3 = gr.Checkbox(label="With timestamps?", value=True) - video_transcribe_btn = gr.Button("Transcribe video") - text_output_df = gr.DataFrame( - value=default_text_output_df, - label="Transcription", - row_count=(0, "dynamic"), - max_rows=10, - wrap=True, - overflow_row_behaviour="paginate", - ) - - video_transcribe_btn.click(video_transcribe, inputs=[downloaded_video_output, with_timestamps_input3], outputs=[text_output_df]) - -# demo.launch(server_name="0.0.0.0", debug=True) -# demo.launch(server_name="0.0.0.0", debug=True, share=True) -demo.launch(enable_queue=True) diff --git a/spaces/brjathu/HMR2.0/upload_logs.py b/spaces/brjathu/HMR2.0/upload_logs.py deleted file mode 100644 index 8ae9460d958aa7d4168eeddc7978355bf07b0d1d..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/upload_logs.py +++ /dev/null @@ -1,7 +0,0 @@ -from huggingface_hub import HfApi -api = HfApi() -api.upload_folder( - folder_path="logs", - repo_id="brjathu/HMR", - repo_type="space", -) \ No newline at end of file diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/dev/run_instant_tests.sh b/spaces/brjathu/HMR2.0/vendor/detectron2/dev/run_instant_tests.sh deleted file mode 100644 index 9fd9ba0c239d3e982c17711c9db872de3730decf..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/dev/run_instant_tests.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -e -# Copyright (c) Facebook, Inc. and its affiliates. - -BIN="python tools/train_net.py" -OUTPUT="instant_test_output" -NUM_GPUS=2 - -CFG_LIST=( "${@:1}" ) -if [ ${#CFG_LIST[@]} -eq 0 ]; then - CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml ) -fi - -echo "========================================================================" -echo "Configs to run:" -echo "${CFG_LIST[@]}" -echo "========================================================================" - -for cfg in "${CFG_LIST[@]}"; do - echo "========================================================================" - echo "Running $cfg ..." - echo "========================================================================" - $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \ - SOLVER.IMS_PER_BATCH $(($NUM_GPUS * 2)) \ - OUTPUT_DIR "$OUTPUT" - rm -rf "$OUTPUT" -done - diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/evaluator.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/evaluator.py deleted file mode 100644 index d5d1d789bbe4b8791aa8529518ba1b964d31daca..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/evaluation/evaluator.py +++ /dev/null @@ -1,421 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -import contextlib -import copy -import io -import itertools -import logging -import numpy as np -import os -from collections import OrderedDict -from typing import Dict, Iterable, List, Optional -import pycocotools.mask as mask_utils -import torch -from pycocotools.coco import COCO -from tabulate import tabulate - -from detectron2.config import CfgNode -from detectron2.data import MetadataCatalog -from detectron2.evaluation import DatasetEvaluator -from detectron2.structures import BoxMode -from detectron2.utils.comm import gather, get_rank, is_main_process, synchronize -from detectron2.utils.file_io import PathManager -from detectron2.utils.logger import create_small_table - -from densepose.converters import ToChartResultConverter, ToMaskConverter -from densepose.data.datasets.coco import maybe_filter_and_map_categories_cocoapi -from densepose.structures import ( - DensePoseChartPredictorOutput, - DensePoseEmbeddingPredictorOutput, - quantize_densepose_chart_result, -) - -from .densepose_coco_evaluation import DensePoseCocoEval, DensePoseEvalMode -from .mesh_alignment_evaluator import MeshAlignmentEvaluator -from .tensor_storage import ( - SingleProcessFileTensorStorage, - SingleProcessRamTensorStorage, - SingleProcessTensorStorage, - SizeData, - storage_gather, -) - - -class DensePoseCOCOEvaluator(DatasetEvaluator): - def __init__( - self, - dataset_name, - distributed, - output_dir=None, - evaluator_type: str = "iuv", - min_iou_threshold: float = 0.5, - storage: Optional[SingleProcessTensorStorage] = None, - embedder=None, - should_evaluate_mesh_alignment: bool = False, - mesh_alignment_mesh_names: Optional[List[str]] = None, - ): - self._embedder = embedder - self._distributed = distributed - self._output_dir = output_dir - self._evaluator_type = evaluator_type - self._storage = storage - self._should_evaluate_mesh_alignment = should_evaluate_mesh_alignment - - assert not ( - should_evaluate_mesh_alignment and embedder is None - ), "Mesh alignment evaluation is activated, but no vertex embedder provided!" - if should_evaluate_mesh_alignment: - self._mesh_alignment_evaluator = MeshAlignmentEvaluator( - embedder, - mesh_alignment_mesh_names, - ) - - self._cpu_device = torch.device("cpu") - self._logger = logging.getLogger(__name__) - - self._metadata = MetadataCatalog.get(dataset_name) - self._min_threshold = min_iou_threshold - json_file = PathManager.get_local_path(self._metadata.json_file) - with contextlib.redirect_stdout(io.StringIO()): - self._coco_api = COCO(json_file) - maybe_filter_and_map_categories_cocoapi(dataset_name, self._coco_api) - - def reset(self): - self._predictions = [] - - def process(self, inputs, outputs): - """ - Args: - inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). - It is a list of dict. Each dict corresponds to an image and - contains keys like "height", "width", "file_name", "image_id". - outputs: the outputs of a COCO model. It is a list of dicts with key - "instances" that contains :class:`Instances`. - The :class:`Instances` object needs to have `densepose` field. - """ - for input, output in zip(inputs, outputs): - instances = output["instances"].to(self._cpu_device) - if not instances.has("pred_densepose"): - continue - prediction_list = prediction_to_dict( - instances, - input["image_id"], - self._embedder, - self._metadata.class_to_mesh_name, - self._storage is not None, - ) - if self._storage is not None: - for prediction_dict in prediction_list: - dict_to_store = {} - for field_name in self._storage.data_schema: - dict_to_store[field_name] = prediction_dict[field_name] - record_id = self._storage.put(dict_to_store) - prediction_dict["record_id"] = record_id - prediction_dict["rank"] = get_rank() - for field_name in self._storage.data_schema: - del prediction_dict[field_name] - self._predictions.extend(prediction_list) - - def evaluate(self, img_ids=None): - if self._distributed: - synchronize() - predictions = gather(self._predictions) - predictions = list(itertools.chain(*predictions)) - else: - predictions = self._predictions - - multi_storage = storage_gather(self._storage) if self._storage is not None else None - - if not is_main_process(): - return - return copy.deepcopy(self._eval_predictions(predictions, multi_storage, img_ids)) - - def _eval_predictions(self, predictions, multi_storage=None, img_ids=None): - """ - Evaluate predictions on densepose. - Return results with the metrics of the tasks. - """ - self._logger.info("Preparing results for COCO format ...") - - if self._output_dir: - PathManager.mkdirs(self._output_dir) - file_path = os.path.join(self._output_dir, "coco_densepose_predictions.pth") - with PathManager.open(file_path, "wb") as f: - torch.save(predictions, f) - - self._logger.info("Evaluating predictions ...") - res = OrderedDict() - results_gps, results_gpsm, results_segm = _evaluate_predictions_on_coco( - self._coco_api, - predictions, - multi_storage, - self._embedder, - class_names=self._metadata.get("thing_classes"), - min_threshold=self._min_threshold, - img_ids=img_ids, - ) - res["densepose_gps"] = results_gps - res["densepose_gpsm"] = results_gpsm - res["densepose_segm"] = results_segm - if self._should_evaluate_mesh_alignment: - res["densepose_mesh_alignment"] = self._evaluate_mesh_alignment() - return res - - def _evaluate_mesh_alignment(self): - self._logger.info("Mesh alignment evaluation ...") - mean_ge, mean_gps, per_mesh_metrics = self._mesh_alignment_evaluator.evaluate() - results = { - "GE": mean_ge * 100, - "GPS": mean_gps * 100, - } - mesh_names = set() - for metric_name in per_mesh_metrics: - for mesh_name, value in per_mesh_metrics[metric_name].items(): - results[f"{metric_name}-{mesh_name}"] = value * 100 - mesh_names.add(mesh_name) - self._print_mesh_alignment_results(results, mesh_names) - return results - - def _print_mesh_alignment_results(self, results: Dict[str, float], mesh_names: Iterable[str]): - self._logger.info("Evaluation results for densepose, mesh alignment:") - self._logger.info(f'| {"Mesh":13s} | {"GErr":7s} | {"GPS":7s} |') - self._logger.info("| :-----------: | :-----: | :-----: |") - for mesh_name in mesh_names: - ge_key = f"GE-{mesh_name}" - ge_str = f"{results[ge_key]:.4f}" if ge_key in results else " " - gps_key = f"GPS-{mesh_name}" - gps_str = f"{results[gps_key]:.4f}" if gps_key in results else " " - self._logger.info(f"| {mesh_name:13s} | {ge_str:7s} | {gps_str:7s} |") - self._logger.info("| :-------------------------------: |") - ge_key = "GE" - ge_str = f"{results[ge_key]:.4f}" if ge_key in results else " " - gps_key = "GPS" - gps_str = f"{results[gps_key]:.4f}" if gps_key in results else " " - self._logger.info(f'| {"MEAN":13s} | {ge_str:7s} | {gps_str:7s} |') - - -def prediction_to_dict(instances, img_id, embedder, class_to_mesh_name, use_storage): - """ - Args: - instances (Instances): the output of the model - img_id (str): the image id in COCO - - Returns: - list[dict]: the results in densepose evaluation format - """ - scores = instances.scores.tolist() - classes = instances.pred_classes.tolist() - raw_boxes_xywh = BoxMode.convert( - instances.pred_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS - ) - - if isinstance(instances.pred_densepose, DensePoseEmbeddingPredictorOutput): - results_densepose = densepose_cse_predictions_to_dict( - instances, embedder, class_to_mesh_name, use_storage - ) - elif isinstance(instances.pred_densepose, DensePoseChartPredictorOutput): - if not use_storage: - results_densepose = densepose_chart_predictions_to_dict(instances) - else: - results_densepose = densepose_chart_predictions_to_storage_dict(instances) - - results = [] - for k in range(len(instances)): - result = { - "image_id": img_id, - "category_id": classes[k], - "bbox": raw_boxes_xywh[k].tolist(), - "score": scores[k], - } - results.append({**result, **results_densepose[k]}) - return results - - -def densepose_chart_predictions_to_dict(instances): - segmentations = ToMaskConverter.convert( - instances.pred_densepose, instances.pred_boxes, instances.image_size - ) - - results = [] - for k in range(len(instances)): - densepose_results_quantized = quantize_densepose_chart_result( - ToChartResultConverter.convert(instances.pred_densepose[k], instances.pred_boxes[k]) - ) - densepose_results_quantized.labels_uv_uint8 = ( - densepose_results_quantized.labels_uv_uint8.cpu() - ) - segmentation = segmentations.tensor[k] - segmentation_encoded = mask_utils.encode( - np.require(segmentation.numpy(), dtype=np.uint8, requirements=["F"]) - ) - segmentation_encoded["counts"] = segmentation_encoded["counts"].decode("utf-8") - result = { - "densepose": densepose_results_quantized, - "segmentation": segmentation_encoded, - } - results.append(result) - return results - - -def densepose_chart_predictions_to_storage_dict(instances): - results = [] - for k in range(len(instances)): - densepose_predictor_output = instances.pred_densepose[k] - result = { - "coarse_segm": densepose_predictor_output.coarse_segm.squeeze(0).cpu(), - "fine_segm": densepose_predictor_output.fine_segm.squeeze(0).cpu(), - "u": densepose_predictor_output.u.squeeze(0).cpu(), - "v": densepose_predictor_output.v.squeeze(0).cpu(), - } - results.append(result) - return results - - -def densepose_cse_predictions_to_dict(instances, embedder, class_to_mesh_name, use_storage): - results = [] - for k in range(len(instances)): - cse = instances.pred_densepose[k] - results.append( - { - "coarse_segm": cse.coarse_segm[0].cpu(), - "embedding": cse.embedding[0].cpu(), - } - ) - return results - - -def _evaluate_predictions_on_coco( - coco_gt, - coco_results, - multi_storage=None, - embedder=None, - class_names=None, - min_threshold: float = 0.5, - img_ids=None, -): - logger = logging.getLogger(__name__) - - densepose_metrics = _get_densepose_metrics(min_threshold) - if len(coco_results) == 0: # cocoapi does not handle empty results very well - logger.warn("No predictions from the model! Set scores to -1") - results_gps = {metric: -1 for metric in densepose_metrics} - results_gpsm = {metric: -1 for metric in densepose_metrics} - results_segm = {metric: -1 for metric in densepose_metrics} - return results_gps, results_gpsm, results_segm - - coco_dt = coco_gt.loadRes(coco_results) - - results = [] - for eval_mode_name in ["GPS", "GPSM", "IOU"]: - eval_mode = getattr(DensePoseEvalMode, eval_mode_name) - coco_eval = DensePoseCocoEval( - coco_gt, coco_dt, "densepose", multi_storage, embedder, dpEvalMode=eval_mode - ) - result = _derive_results_from_coco_eval( - coco_eval, eval_mode_name, densepose_metrics, class_names, min_threshold, img_ids - ) - results.append(result) - return results - - -def _get_densepose_metrics(min_threshold: float = 0.5): - metrics = ["AP"] - if min_threshold <= 0.201: - metrics += ["AP20"] - if min_threshold <= 0.301: - metrics += ["AP30"] - if min_threshold <= 0.401: - metrics += ["AP40"] - metrics.extend(["AP50", "AP75", "APm", "APl", "AR", "AR50", "AR75", "ARm", "ARl"]) - return metrics - - -def _derive_results_from_coco_eval( - coco_eval, eval_mode_name, metrics, class_names, min_threshold: float, img_ids -): - if img_ids is not None: - coco_eval.params.imgIds = img_ids - coco_eval.params.iouThrs = np.linspace( - min_threshold, 0.95, int(np.round((0.95 - min_threshold) / 0.05)) + 1, endpoint=True - ) - coco_eval.evaluate() - coco_eval.accumulate() - coco_eval.summarize() - results = {metric: float(coco_eval.stats[idx] * 100) for idx, metric in enumerate(metrics)} - logger = logging.getLogger(__name__) - logger.info( - f"Evaluation results for densepose, {eval_mode_name} metric: \n" - + create_small_table(results) - ) - if class_names is None or len(class_names) <= 1: - return results - - # Compute per-category AP, the same way as it is done in D2 - # (see detectron2/evaluation/coco_evaluation.py): - precisions = coco_eval.eval["precision"] - # precision has dims (iou, recall, cls, area range, max dets) - assert len(class_names) == precisions.shape[2] - - results_per_category = [] - for idx, name in enumerate(class_names): - # area range index 0: all area ranges - # max dets index -1: typically 100 per image - precision = precisions[:, :, idx, 0, -1] - precision = precision[precision > -1] - ap = np.mean(precision) if precision.size else float("nan") - results_per_category.append((f"{name}", float(ap * 100))) - - # tabulate it - n_cols = min(6, len(results_per_category) * 2) - results_flatten = list(itertools.chain(*results_per_category)) - results_2d = itertools.zip_longest(*[results_flatten[i::n_cols] for i in range(n_cols)]) - table = tabulate( - results_2d, - tablefmt="pipe", - floatfmt=".3f", - headers=["category", "AP"] * (n_cols // 2), - numalign="left", - ) - logger.info(f"Per-category {eval_mode_name} AP: \n" + table) - - results.update({"AP-" + name: ap for name, ap in results_per_category}) - return results - - -def build_densepose_evaluator_storage(cfg: CfgNode, output_folder: str): - storage_spec = cfg.DENSEPOSE_EVALUATION.STORAGE - if storage_spec == "none": - return None - evaluator_type = cfg.DENSEPOSE_EVALUATION.TYPE - # common output tensor sizes - hout = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE - wout = cfg.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE - n_csc = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS - # specific output tensors - if evaluator_type == "iuv": - n_fsc = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1 - schema = { - "coarse_segm": SizeData(dtype="float32", shape=(n_csc, hout, wout)), - "fine_segm": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), - "u": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), - "v": SizeData(dtype="float32", shape=(n_fsc, hout, wout)), - } - elif evaluator_type == "cse": - embed_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE - schema = { - "coarse_segm": SizeData(dtype="float32", shape=(n_csc, hout, wout)), - "embedding": SizeData(dtype="float32", shape=(embed_size, hout, wout)), - } - else: - raise ValueError(f"Unknown evaluator type: {evaluator_type}") - # storage types - if storage_spec == "ram": - storage = SingleProcessRamTensorStorage(schema, io.BytesIO()) - elif storage_spec == "file": - fpath = os.path.join(output_folder, f"DensePoseEvaluatorStorage.{get_rank()}.bin") - PathManager.mkdirs(output_folder) - storage = SingleProcessFileTensorStorage(schema, fpath, "wb") - else: - raise ValueError(f"Unknown storage specification: {storage_spec}") - return storage diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_results.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_results.py deleted file mode 100644 index ce8a7c0e207f5b3b6e755c759a59f5bed9965cef..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_results.py +++ /dev/null @@ -1,355 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import numpy as np -from typing import List, Optional, Tuple -import cv2 -import torch - -from densepose.structures import DensePoseDataRelative - -from ..structures import DensePoseChartResult -from .base import Boxes, Image, MatrixVisualizer - - -class DensePoseResultsVisualizer(object): - def visualize( - self, - image_bgr: Image, - results_and_boxes_xywh: Tuple[Optional[List[DensePoseChartResult]], Optional[Boxes]], - ) -> Image: - densepose_result, boxes_xywh = results_and_boxes_xywh - if densepose_result is None or boxes_xywh is None: - return image_bgr - - boxes_xywh = boxes_xywh.cpu().numpy() - context = self.create_visualization_context(image_bgr) - for i, result in enumerate(densepose_result): - iuv_array = torch.cat( - (result.labels[None].type(torch.float32), result.uv * 255.0) - ).type(torch.uint8) - self.visualize_iuv_arr(context, iuv_array.cpu().numpy(), boxes_xywh[i]) - image_bgr = self.context_to_image_bgr(context) - return image_bgr - - def create_visualization_context(self, image_bgr: Image): - return image_bgr - - def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: - pass - - def context_to_image_bgr(self, context): - return context - - def get_image_bgr_from_context(self, context): - return context - - -class DensePoseMaskedColormapResultsVisualizer(DensePoseResultsVisualizer): - def __init__( - self, - data_extractor, - segm_extractor, - inplace=True, - cmap=cv2.COLORMAP_PARULA, - alpha=0.7, - val_scale=1.0, - **kwargs, - ): - self.mask_visualizer = MatrixVisualizer( - inplace=inplace, cmap=cmap, val_scale=val_scale, alpha=alpha - ) - self.data_extractor = data_extractor - self.segm_extractor = segm_extractor - - def context_to_image_bgr(self, context): - return context - - def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh) -> None: - image_bgr = self.get_image_bgr_from_context(context) - matrix = self.data_extractor(iuv_arr) - segm = self.segm_extractor(iuv_arr) - mask = np.zeros(matrix.shape, dtype=np.uint8) - mask[segm > 0] = 1 - image_bgr = self.mask_visualizer.visualize(image_bgr, mask, matrix, bbox_xywh) - - -def _extract_i_from_iuvarr(iuv_arr): - return iuv_arr[0, :, :] - - -def _extract_u_from_iuvarr(iuv_arr): - return iuv_arr[1, :, :] - - -def _extract_v_from_iuvarr(iuv_arr): - return iuv_arr[2, :, :] - - -class DensePoseResultsMplContourVisualizer(DensePoseResultsVisualizer): - def __init__(self, levels=10, **kwargs): - self.levels = levels - self.plot_args = kwargs - - def create_visualization_context(self, image_bgr: Image): - import matplotlib.pyplot as plt - from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas - - context = {} - context["image_bgr"] = image_bgr - dpi = 100 - height_inches = float(image_bgr.shape[0]) / dpi - width_inches = float(image_bgr.shape[1]) / dpi - fig = plt.figure(figsize=(width_inches, height_inches), dpi=dpi) - plt.axes([0, 0, 1, 1]) - plt.axis("off") - context["fig"] = fig - canvas = FigureCanvas(fig) - context["canvas"] = canvas - extent = (0, image_bgr.shape[1], image_bgr.shape[0], 0) - plt.imshow(image_bgr[:, :, ::-1], extent=extent) - return context - - def context_to_image_bgr(self, context): - fig = context["fig"] - w, h = map(int, fig.get_size_inches() * fig.get_dpi()) - canvas = context["canvas"] - canvas.draw() - image_1d = np.fromstring(canvas.tostring_rgb(), dtype="uint8") - image_rgb = image_1d.reshape(h, w, 3) - image_bgr = image_rgb[:, :, ::-1].copy() - return image_bgr - - def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None: - import matplotlib.pyplot as plt - - u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 - v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 - extent = ( - bbox_xywh[0], - bbox_xywh[0] + bbox_xywh[2], - bbox_xywh[1], - bbox_xywh[1] + bbox_xywh[3], - ) - plt.contour(u, self.levels, extent=extent, **self.plot_args) - plt.contour(v, self.levels, extent=extent, **self.plot_args) - - -class DensePoseResultsCustomContourVisualizer(DensePoseResultsVisualizer): - """ - Contour visualization using marching squares - """ - - def __init__(self, levels=10, **kwargs): - # TODO: colormap is hardcoded - cmap = cv2.COLORMAP_PARULA - if isinstance(levels, int): - self.levels = np.linspace(0, 1, levels) - else: - self.levels = levels - if "linewidths" in kwargs: - self.linewidths = kwargs["linewidths"] - else: - self.linewidths = [1] * len(self.levels) - self.plot_args = kwargs - img_colors_bgr = cv2.applyColorMap((self.levels * 255).astype(np.uint8), cmap) - self.level_colors_bgr = [ - [int(v) for v in img_color_bgr.ravel()] for img_color_bgr in img_colors_bgr - ] - - def visualize_iuv_arr(self, context, iuv_arr: np.ndarray, bbox_xywh: Boxes) -> None: - image_bgr = self.get_image_bgr_from_context(context) - segm = _extract_i_from_iuvarr(iuv_arr) - u = _extract_u_from_iuvarr(iuv_arr).astype(float) / 255.0 - v = _extract_v_from_iuvarr(iuv_arr).astype(float) / 255.0 - self._contours(image_bgr, u, segm, bbox_xywh) - self._contours(image_bgr, v, segm, bbox_xywh) - - def _contours(self, image_bgr, arr, segm, bbox_xywh): - for part_idx in range(1, DensePoseDataRelative.N_PART_LABELS + 1): - mask = segm == part_idx - if not np.any(mask): - continue - arr_min = np.amin(arr[mask]) - arr_max = np.amax(arr[mask]) - I, J = np.nonzero(mask) - i0 = np.amin(I) - i1 = np.amax(I) + 1 - j0 = np.amin(J) - j1 = np.amax(J) + 1 - if (j1 == j0 + 1) or (i1 == i0 + 1): - continue - Nw = arr.shape[1] - 1 - Nh = arr.shape[0] - 1 - for level_idx, level in enumerate(self.levels): - if (level < arr_min) or (level > arr_max): - continue - vp = arr[i0:i1, j0:j1] >= level - bin_codes = vp[:-1, :-1] + vp[1:, :-1] * 2 + vp[1:, 1:] * 4 + vp[:-1, 1:] * 8 - mp = mask[i0:i1, j0:j1] - bin_mask_codes = mp[:-1, :-1] + mp[1:, :-1] * 2 + mp[1:, 1:] * 4 + mp[:-1, 1:] * 8 - it = np.nditer(bin_codes, flags=["multi_index"]) - color_bgr = self.level_colors_bgr[level_idx] - linewidth = self.linewidths[level_idx] - while not it.finished: - if (it[0] != 0) and (it[0] != 15): - i, j = it.multi_index - if bin_mask_codes[i, j] != 0: - self._draw_line( - image_bgr, - arr, - mask, - level, - color_bgr, - linewidth, - it[0], - it.multi_index, - bbox_xywh, - Nw, - Nh, - (i0, j0), - ) - it.iternext() - - def _draw_line( - self, - image_bgr, - arr, - mask, - v, - color_bgr, - linewidth, - bin_code, - multi_idx, - bbox_xywh, - Nw, - Nh, - offset, - ): - lines = self._bin_code_2_lines(arr, v, bin_code, multi_idx, Nw, Nh, offset) - x0, y0, w, h = bbox_xywh - x1 = x0 + w - y1 = y0 + h - for line in lines: - x0r, y0r = line[0] - x1r, y1r = line[1] - pt0 = (int(x0 + x0r * (x1 - x0)), int(y0 + y0r * (y1 - y0))) - pt1 = (int(x0 + x1r * (x1 - x0)), int(y0 + y1r * (y1 - y0))) - cv2.line(image_bgr, pt0, pt1, color_bgr, linewidth) - - def _bin_code_2_lines(self, arr, v, bin_code, multi_idx, Nw, Nh, offset): - i0, j0 = offset - i, j = multi_idx - i += i0 - j += j0 - v0, v1, v2, v3 = arr[i, j], arr[i + 1, j], arr[i + 1, j + 1], arr[i, j + 1] - x0i = float(j) / Nw - y0j = float(i) / Nh - He = 1.0 / Nh - We = 1.0 / Nw - if (bin_code == 1) or (bin_code == 14): - a = (v - v0) / (v1 - v0) - b = (v - v0) / (v3 - v0) - pt1 = (x0i, y0j + a * He) - pt2 = (x0i + b * We, y0j) - return [(pt1, pt2)] - elif (bin_code == 2) or (bin_code == 13): - a = (v - v0) / (v1 - v0) - b = (v - v1) / (v2 - v1) - pt1 = (x0i, y0j + a * He) - pt2 = (x0i + b * We, y0j + He) - return [(pt1, pt2)] - elif (bin_code == 3) or (bin_code == 12): - a = (v - v0) / (v3 - v0) - b = (v - v1) / (v2 - v1) - pt1 = (x0i + a * We, y0j) - pt2 = (x0i + b * We, y0j + He) - return [(pt1, pt2)] - elif (bin_code == 4) or (bin_code == 11): - a = (v - v1) / (v2 - v1) - b = (v - v3) / (v2 - v3) - pt1 = (x0i + a * We, y0j + He) - pt2 = (x0i + We, y0j + b * He) - return [(pt1, pt2)] - elif (bin_code == 6) or (bin_code == 9): - a = (v - v0) / (v1 - v0) - b = (v - v3) / (v2 - v3) - pt1 = (x0i, y0j + a * He) - pt2 = (x0i + We, y0j + b * He) - return [(pt1, pt2)] - elif (bin_code == 7) or (bin_code == 8): - a = (v - v0) / (v3 - v0) - b = (v - v3) / (v2 - v3) - pt1 = (x0i + a * We, y0j) - pt2 = (x0i + We, y0j + b * He) - return [(pt1, pt2)] - elif bin_code == 5: - a1 = (v - v0) / (v1 - v0) - b1 = (v - v1) / (v2 - v1) - pt11 = (x0i, y0j + a1 * He) - pt12 = (x0i + b1 * We, y0j + He) - a2 = (v - v0) / (v3 - v0) - b2 = (v - v3) / (v2 - v3) - pt21 = (x0i + a2 * We, y0j) - pt22 = (x0i + We, y0j + b2 * He) - return [(pt11, pt12), (pt21, pt22)] - elif bin_code == 10: - a1 = (v - v0) / (v3 - v0) - b1 = (v - v0) / (v1 - v0) - pt11 = (x0i + a1 * We, y0j) - pt12 = (x0i, y0j + b1 * He) - a2 = (v - v1) / (v2 - v1) - b2 = (v - v3) / (v2 - v3) - pt21 = (x0i + a2 * We, y0j + He) - pt22 = (x0i + We, y0j + b2 * He) - return [(pt11, pt12), (pt21, pt22)] - return [] - - -try: - import matplotlib - - matplotlib.use("Agg") - DensePoseResultsContourVisualizer = DensePoseResultsMplContourVisualizer -except ModuleNotFoundError: - logger = logging.getLogger(__name__) - logger.warning("Could not import matplotlib, using custom contour visualizer") - DensePoseResultsContourVisualizer = DensePoseResultsCustomContourVisualizer - - -class DensePoseResultsFineSegmentationVisualizer(DensePoseMaskedColormapResultsVisualizer): - def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): - super(DensePoseResultsFineSegmentationVisualizer, self).__init__( - _extract_i_from_iuvarr, - _extract_i_from_iuvarr, - inplace, - cmap, - alpha, - val_scale=255.0 / DensePoseDataRelative.N_PART_LABELS, - **kwargs, - ) - - -class DensePoseResultsUVisualizer(DensePoseMaskedColormapResultsVisualizer): - def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): - super(DensePoseResultsUVisualizer, self).__init__( - _extract_u_from_iuvarr, - _extract_i_from_iuvarr, - inplace, - cmap, - alpha, - val_scale=1.0, - **kwargs, - ) - - -class DensePoseResultsVVisualizer(DensePoseMaskedColormapResultsVisualizer): - def __init__(self, inplace=True, cmap=cv2.COLORMAP_PARULA, alpha=0.7, **kwargs): - super(DensePoseResultsVVisualizer, self).__init__( - _extract_v_from_iuvarr, - _extract_i_from_iuvarr, - inplace, - cmap, - alpha, - val_scale=1.0, - **kwargs, - ) diff --git a/spaces/cchaun/music_tagging/app.py b/spaces/cchaun/music_tagging/app.py deleted file mode 100644 index ab1cab0c9b48e2ea3005cc4a8266f6f1e45809c5..0000000000000000000000000000000000000000 --- a/spaces/cchaun/music_tagging/app.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: UTF-8 -*- -import gradio as gr -import torch, torchaudio -from timeit import default_timer as timer -from torchaudio.transforms import Resample -from models.model import HarmonicCNN - -device = "cuda" if torch.cuda.is_available() else "cpu" - -SAMPLE_RATE = 16000 -AUDIO_LEN = 2.90 - -model = HarmonicCNN() -S = torch.load('models/best_model.pth', map_location=torch.device('cpu')) -model.load_state_dict(S) - -LABELS = [ - "alternative", - "ambient", - "atmospheric", - "chillout", - "classical", - "dance", - "downtempo", - "easylistening", - "electronic", - "experimental", - "folk", - "funk", - "hiphop", - "house", - "indie", - "instrumentalpop", - "jazz", - "lounge", - "metal", - "newage", - "orchestral", - "pop", - "popfolk", - "poprock", - "reggae", - "rock", - "soundtrack", - "techno", - "trance", - "triphop", - "world", - "acousticguitar", - "bass", - "computer", - "drummachine", - "drums", - "electricguitar", - "electricpiano", - "guitar", - "keyboard", - "piano", - "strings", - "synthesizer", - "violin", - "voice", - "emotional", - "energetic", - "film", - "happy", - "relaxing" -] - -example_list = [ - "samples/guitar_acoustic.wav", - "samples/guitar_electric.wav", - "samples/piano.wav", - "samples/violin.wav", - "samples/flute.wav" -] - -def predict(audio_path): - start_time = timer() - wav, sample_rate = torchaudio.load(audio_path) - if sample_rate > SAMPLE_RATE: - resampler = Resample(sample_rate, SAMPLE_RATE) - wav = resampler(wav) - if wav.shape[0] >= 2: - wav = torch.mean(wav, dim=0) - wav = wav.unsqueeze(0) - model.eval() - with torch.inference_mode(): - pred_probs = model(wav) - pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))} - pred_time = round(timer() - start_time, 5) - return pred_labels_and_probs, pred_time - - -title = "Music Tagging" - -demo = gr.Interface(fn=predict, - inputs=gr.Audio(type="filepath"), - outputs=[gr.Label(num_top_classes=10, label="Predictions"), - gr.Number(label="Prediction time (s)")], - examples=example_list, - title=title) - -demo.launch(debug=False) \ No newline at end of file diff --git a/spaces/ceckenrode/Docker-FlanT5-TextGeneratorTranslator/static/style.css b/spaces/ceckenrode/Docker-FlanT5-TextGeneratorTranslator/static/style.css deleted file mode 100644 index 7b50df8f6904c75f560224034d8aadd76656c6f8..0000000000000000000000000000000000000000 --- a/spaces/ceckenrode/Docker-FlanT5-TextGeneratorTranslator/static/style.css +++ /dev/null @@ -1,45 +0,0 @@ -body { - --text: hsl(0 0% 15%); - padding: 2.5rem; - font-family: sans-serif; - color: var(--text); -} - -body.dark-theme { - --text: hsl(0 0% 90%); - background-color: hsl(223 39% 7%); -} - -main { - max-width: 80rem; - text-align: center; -} - -section { - display: flex; - flex-direction: column; - align-items: center; -} - -a { - color: var(--text); -} - -form { - width: 30rem; - margin: 0 auto; -} - -input { - width: 100%; -} - -button { - cursor: pointer; -} - -.text-gen-output { - min-height: 1.2rem; - margin: 1rem; - border: 0.5px solid grey; -} diff --git a/spaces/chendl/compositional_test/transformers/examples/tensorflow/question-answering/run_qa.py b/spaces/chendl/compositional_test/transformers/examples/tensorflow/question-answering/run_qa.py deleted file mode 100644 index ef5f3b3e373a5db31d229e46f5ca9816278a972a..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/transformers/examples/tensorflow/question-answering/run_qa.py +++ /dev/null @@ -1,799 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2020 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. -""" -Fine-tuning the library models for question answering. -""" -# You can also adapt this script on your own question answering task. Pointers for this are left as comments. - -import json -import logging -import os -import sys -from dataclasses import dataclass, field -from pathlib import Path -from typing import Optional - -import evaluate -import tensorflow as tf -from datasets import load_dataset -from utils_qa import postprocess_qa_predictions - -import transformers -from transformers import ( - AutoConfig, - AutoTokenizer, - EvalPrediction, - HfArgumentParser, - PreTrainedTokenizerFast, - PushToHubCallback, - TFAutoModelForQuestionAnswering, - TFTrainingArguments, - create_optimizer, - set_seed, -) -from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry - - -# Will error if the minimal version of Transformers is not installed. Remove at your own risks. -check_min_version("4.28.0") - -logger = logging.getLogger(__name__) - - -# region Arguments -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. - """ - - model_name_or_path: str = field( - metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} - ) - config_name: Optional[str] = field( - default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} - ) - tokenizer_name: Optional[str] = field( - default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} - ) - cache_dir: Optional[str] = field( - default=None, - metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"}, - ) - model_revision: str = field( - default="main", - metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, - ) - use_auth_token: bool = field( - default=False, - metadata={ - "help": ( - "Will use the token generated when running `huggingface-cli login` (necessary to use this script " - "with private models)." - ) - }, - ) - - -@dataclass -class DataTrainingArguments: - """ - Arguments pertaining to what data we are going to input our model for training and eval. - """ - - dataset_name: Optional[str] = field( - default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} - ) - dataset_config_name: Optional[str] = field( - default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} - ) - train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) - validation_file: Optional[str] = field( - default=None, - metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, - ) - test_file: Optional[str] = field( - default=None, - metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."}, - ) - overwrite_cache: bool = field( - default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} - ) - preprocessing_num_workers: Optional[int] = field( - default=None, - metadata={"help": "The number of processes to use for the preprocessing."}, - ) - max_seq_length: int = field( - default=384, - metadata={ - "help": ( - "The maximum total input sequence length after tokenization. Sequences longer " - "than this will be truncated, sequences shorter will be padded." - ) - }, - ) - pad_to_max_length: bool = field( - default=False, - metadata={ - "help": ( - "Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when" - " batching to the maximum length in the batch (which can be faster on GPU but will be slower on TPU)." - ) - }, - ) - max_train_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." - ) - }, - ) - max_eval_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of evaluation examples to this " - "value if set." - ) - }, - ) - max_predict_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of prediction examples to this " - "value if set." - ) - }, - ) - version_2_with_negative: bool = field( - default=False, metadata={"help": "If true, some of the examples do not have an answer."} - ) - null_score_diff_threshold: float = field( - default=0.0, - metadata={ - "help": ( - "The threshold used to select the null answer: if the best answer has a score that is less than " - "the score of the null answer minus this threshold, the null answer is selected for this example. " - "Only useful when `version_2_with_negative=True`." - ) - }, - ) - doc_stride: int = field( - default=128, - metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}, - ) - n_best_size: int = field( - default=20, - metadata={"help": "The total number of n-best predictions to generate when looking for an answer."}, - ) - max_answer_length: int = field( - default=30, - metadata={ - "help": ( - "The maximum length of an answer that can be generated. This is needed because the start " - "and end predictions are not conditioned on one another." - ) - }, - ) - - def __post_init__(self): - if ( - self.dataset_name is None - and self.train_file is None - and self.validation_file is None - and self.test_file is None - ): - raise ValueError("Need either a dataset name or a training/validation file/test_file.") - else: - if self.train_file is not None: - extension = self.train_file.split(".")[-1] - assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." - if self.validation_file is not None: - extension = self.validation_file.split(".")[-1] - assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." - if self.test_file is not None: - extension = self.test_file.split(".")[-1] - assert extension in ["csv", "json"], "`test_file` should be a csv or a json file." - - -# endregion - - -# region Helper classes -class SavePretrainedCallback(tf.keras.callbacks.Callback): - # Hugging Face models have a save_pretrained() method that saves both the weights and the necessary - # metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback - # that saves the model with this method after each epoch. - def __init__(self, output_dir, **kwargs): - super().__init__() - self.output_dir = output_dir - - def on_epoch_end(self, epoch, logs=None): - self.model.save_pretrained(self.output_dir) - - -# endregion - - -def main(): - # region Argument parsing - # See all possible arguments in src/transformers/training_args.py - # or by passing the --help flag to this script. - # We now keep distinct sets of args, for a cleaner separation of concerns. - - parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) - if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): - # If we pass only one argument to the script and it's the path to a json file, - # let's parse it to get our arguments. - model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) - else: - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - - # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The - # information sent is the one passed as arguments along with your Python/PyTorch versions. - send_example_telemetry("run_qa", model_args, data_args, framework="tensorflow") - - output_dir = Path(training_args.output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - # endregion - - # region Checkpoints - checkpoint = None - if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir: - if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file(): - checkpoint = output_dir - logger.info( - f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this" - " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch." - ) - else: - raise ValueError( - f"Output directory ({training_args.output_dir}) already exists and is not empty. " - "Use --overwrite_output_dir to continue regardless." - ) - # endregion - - # region Logging - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - handlers=[logging.StreamHandler(sys.stdout)], - ) - logger.setLevel(logging.INFO if training_args.should_log else logging.WARN) - - # Set the verbosity to info of the Transformers logger (on main process only): - if training_args.should_log: - transformers.utils.logging.set_verbosity_info() - transformers.utils.logging.enable_default_handler() - transformers.utils.logging.enable_explicit_format() - logger.info(f"Training/evaluation parameters {training_args}") - # endregion - - # Set seed before initializing model. - set_seed(training_args.seed) - - # region Load Data - # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) - # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ - # (the dataset will be downloaded automatically from the datasets Hub). - # - # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called - # 'text' is found. You can easily tweak this behavior (see below). - # - # In distributed training, the load_dataset function guarantee that only one local process can concurrently - # download the dataset. - if data_args.dataset_name is not None: - # Downloading and loading a dataset from the hub. - datasets = load_dataset( - data_args.dataset_name, - data_args.dataset_config_name, - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - else: - data_files = {} - if data_args.train_file is not None: - data_files["train"] = data_args.train_file - extension = data_args.train_file.split(".")[-1] - - if data_args.validation_file is not None: - data_files["validation"] = data_args.validation_file - extension = data_args.validation_file.split(".")[-1] - if data_args.test_file is not None: - data_files["test"] = data_args.test_file - extension = data_args.test_file.split(".")[-1] - datasets = load_dataset( - extension, - data_files=data_files, - field="data", - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at - # https://huggingface.co/docs/datasets/loading_datasets.html. - # endregion - - # region Load pretrained model and tokenizer - # - # Distributed training: - # The .from_pretrained methods guarantee that only one local process can concurrently - # download model & vocab. - config = AutoConfig.from_pretrained( - model_args.config_name if model_args.config_name else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) - tokenizer = AutoTokenizer.from_pretrained( - model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - use_fast=True, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) - # endregion - - # region Tokenizer check: this script requires a fast tokenizer. - if not isinstance(tokenizer, PreTrainedTokenizerFast): - raise ValueError( - "This example script only works for models that have a fast tokenizer. Checkout the big table of models at" - " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet" - " this requirement" - ) - # endregion - - # region Preprocessing the datasets - # Preprocessing is slightly different for training and evaluation. - if training_args.do_train: - column_names = datasets["train"].column_names - elif training_args.do_eval: - column_names = datasets["validation"].column_names - else: - column_names = datasets["test"].column_names - question_column_name = "question" if "question" in column_names else column_names[0] - context_column_name = "context" if "context" in column_names else column_names[1] - answer_column_name = "answers" if "answers" in column_names else column_names[2] - - # Padding side determines if we do (question|context) or (context|question). - pad_on_right = tokenizer.padding_side == "right" - - if data_args.max_seq_length > tokenizer.model_max_length: - logger.warning( - f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" - f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." - ) - max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) - - if data_args.pad_to_max_length or isinstance(training_args.strategy, tf.distribute.TPUStrategy): - logger.info("Padding all batches to max length because argument was set or we're on TPU.") - padding = "max_length" - else: - padding = False - - # Training preprocessing - def prepare_train_features(examples): - # Some of the questions have lots of whitespace on the left, which is not useful and will make the - # truncation of the context fail (the tokenized question will take a lots of space). So we remove that - # left whitespace - examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] - - # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results - # in one example possible giving several features when a context is long, each of those features having a - # context that overlaps a bit the context of the previous feature. - tokenized_examples = tokenizer( - examples[question_column_name if pad_on_right else context_column_name], - examples[context_column_name if pad_on_right else question_column_name], - truncation="only_second" if pad_on_right else "only_first", - max_length=max_seq_length, - stride=data_args.doc_stride, - return_overflowing_tokens=True, - return_offsets_mapping=True, - padding=padding, - ) - - # Since one example might give us several features if it has a long context, we need a map from a feature to - # its corresponding example. This key gives us just that. - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - # The offset mappings will give us a map from token to character position in the original context. This will - # help us compute the start_positions and end_positions. - offset_mapping = tokenized_examples.pop("offset_mapping") - - # Let's label those examples! - tokenized_examples["start_positions"] = [] - tokenized_examples["end_positions"] = [] - - for i, offsets in enumerate(offset_mapping): - # We will label impossible answers with the index of the CLS token. - input_ids = tokenized_examples["input_ids"][i] - cls_index = input_ids.index(tokenizer.cls_token_id) - - # Grab the sequence corresponding to that example (to know what is the context and what is the question). - sequence_ids = tokenized_examples.sequence_ids(i) - - # One example can give several spans, this is the index of the example containing this span of text. - sample_index = sample_mapping[i] - answers = examples[answer_column_name][sample_index] - # If no answers are given, set the cls_index as answer. - if len(answers["answer_start"]) == 0: - tokenized_examples["start_positions"].append(cls_index) - tokenized_examples["end_positions"].append(cls_index) - else: - # Start/end character index of the answer in the text. - start_char = answers["answer_start"][0] - end_char = start_char + len(answers["text"][0]) - - # Start token index of the current span in the text. - token_start_index = 0 - while sequence_ids[token_start_index] != (1 if pad_on_right else 0): - token_start_index += 1 - - # End token index of the current span in the text. - token_end_index = len(input_ids) - 1 - while sequence_ids[token_end_index] != (1 if pad_on_right else 0): - token_end_index -= 1 - - # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index). - if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char): - tokenized_examples["start_positions"].append(cls_index) - tokenized_examples["end_positions"].append(cls_index) - else: - # Otherwise move the token_start_index and token_end_index to the two ends of the answer. - # Note: we could go after the last offset if the answer is the last word (edge case). - while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char: - token_start_index += 1 - tokenized_examples["start_positions"].append(token_start_index - 1) - while offsets[token_end_index][1] >= end_char: - token_end_index -= 1 - tokenized_examples["end_positions"].append(token_end_index + 1) - - return tokenized_examples - - processed_datasets = {} - if training_args.do_train: - if "train" not in datasets: - raise ValueError("--do_train requires a train dataset") - train_dataset = datasets["train"] - if data_args.max_train_samples is not None: - # We will select sample from whole data if agument is specified - max_train_samples = min(len(train_dataset), data_args.max_train_samples) - train_dataset = train_dataset.select(range(max_train_samples)) - # Create train feature from dataset - train_dataset = train_dataset.map( - prepare_train_features, - batched=True, - num_proc=data_args.preprocessing_num_workers, - remove_columns=column_names, - load_from_cache_file=not data_args.overwrite_cache, - ) - if data_args.max_train_samples is not None: - # Number of samples might increase during Feature Creation, We select only specified max samples - max_train_samples = min(len(train_dataset), data_args.max_train_samples) - train_dataset = train_dataset.select(range(max_train_samples)) - processed_datasets["train"] = train_dataset - - # Validation preprocessing - def prepare_validation_features(examples): - # Some of the questions have lots of whitespace on the left, which is not useful and will make the - # truncation of the context fail (the tokenized question will take a lots of space). So we remove that - # left whitespace - examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] - - # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results - # in one example possible giving several features when a context is long, each of those features having a - # context that overlaps a bit the context of the previous feature. - tokenized_examples = tokenizer( - examples[question_column_name if pad_on_right else context_column_name], - examples[context_column_name if pad_on_right else question_column_name], - truncation="only_second" if pad_on_right else "only_first", - max_length=max_seq_length, - stride=data_args.doc_stride, - return_overflowing_tokens=True, - return_offsets_mapping=True, - padding=padding, - ) - - # Since one example might give us several features if it has a long context, we need a map from a feature to - # its corresponding example. This key gives us just that. - sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") - - # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the - # corresponding example_id and we will store the offset mappings. - tokenized_examples["example_id"] = [] - - for i in range(len(tokenized_examples["input_ids"])): - # Grab the sequence corresponding to that example (to know what is the context and what is the question). - sequence_ids = tokenized_examples.sequence_ids(i) - context_index = 1 if pad_on_right else 0 - - # One example can give several spans, this is the index of the example containing this span of text. - sample_index = sample_mapping[i] - tokenized_examples["example_id"].append(examples["id"][sample_index]) - - # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token - # position is part of the context or not. - tokenized_examples["offset_mapping"][i] = [ - (o if sequence_ids[k] == context_index else None) - for k, o in enumerate(tokenized_examples["offset_mapping"][i]) - ] - - return tokenized_examples - - if training_args.do_eval: - if "validation" not in datasets: - raise ValueError("--do_eval requires a validation dataset") - eval_examples = datasets["validation"] - if data_args.max_eval_samples is not None: - # We will select sample from whole data - max_eval_samples = min(len(eval_examples), data_args.max_eval_samples) - eval_examples = eval_examples.select(range(max_eval_samples)) - # Validation Feature Creation - eval_dataset = eval_examples.map( - prepare_validation_features, - batched=True, - num_proc=data_args.preprocessing_num_workers, - remove_columns=column_names, - load_from_cache_file=not data_args.overwrite_cache, - ) - if data_args.max_eval_samples is not None: - # During Feature creation dataset samples might increase, we will select required samples again - max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) - eval_dataset = eval_dataset.select(range(max_eval_samples)) - processed_datasets["validation"] = eval_dataset - - if training_args.do_predict: - if "test" not in datasets: - raise ValueError("--do_predict requires a test dataset") - predict_examples = datasets["test"] - if data_args.max_predict_samples is not None: - # We will select sample from whole data - predict_examples = predict_examples.select(range(data_args.max_predict_samples)) - # Predict Feature Creation - predict_dataset = predict_examples.map( - prepare_validation_features, - batched=True, - num_proc=data_args.preprocessing_num_workers, - remove_columns=column_names, - load_from_cache_file=not data_args.overwrite_cache, - ) - if data_args.max_predict_samples is not None: - # During Feature creation dataset samples might increase, we will select required samples again - max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) - predict_dataset = predict_dataset.select(range(max_predict_samples)) - processed_datasets["test"] = predict_dataset - # endregion - - # region Metrics and Post-processing: - def post_processing_function(examples, features, predictions, stage="eval"): - # Post-processing: we match the start logits and end logits to answers in the original context. - predictions = postprocess_qa_predictions( - examples=examples, - features=features, - predictions=predictions, - version_2_with_negative=data_args.version_2_with_negative, - n_best_size=data_args.n_best_size, - max_answer_length=data_args.max_answer_length, - null_score_diff_threshold=data_args.null_score_diff_threshold, - output_dir=training_args.output_dir, - prefix=stage, - ) - # Format the result to the format the metric expects. - if data_args.version_2_with_negative: - formatted_predictions = [ - {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items() - ] - else: - formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()] - - references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples] - return EvalPrediction(predictions=formatted_predictions, label_ids=references) - - metric = evaluate.load("squad_v2" if data_args.version_2_with_negative else "squad") - - def compute_metrics(p: EvalPrediction): - return metric.compute(predictions=p.predictions, references=p.label_ids) - - # endregion - - with training_args.strategy.scope(): - dataset_options = tf.data.Options() - dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF - num_replicas = training_args.strategy.num_replicas_in_sync - - # region Load model and prepare datasets - if checkpoint is None: - model_path = model_args.model_name_or_path - else: - model_path = checkpoint - model = TFAutoModelForQuestionAnswering.from_pretrained( - model_path, - config=config, - cache_dir=model_args.cache_dir, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) - if training_args.do_train: - training_dataset = model.prepare_tf_dataset( - processed_datasets["train"], - shuffle=True, - batch_size=training_args.per_device_train_batch_size * num_replicas, - tokenizer=tokenizer, - ) - - training_dataset = training_dataset.with_options(dataset_options) - - num_train_steps = len(training_dataset) * training_args.num_train_epochs - if training_args.warmup_steps > 0: - num_warmup_steps = training_args.warmup_steps - elif training_args.warmup_ratio > 0: - num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) - else: - num_warmup_steps = 0 - - optimizer, schedule = create_optimizer( - init_lr=training_args.learning_rate, - num_train_steps=len(training_dataset) * training_args.num_train_epochs, - num_warmup_steps=num_warmup_steps, - adam_beta1=training_args.adam_beta1, - adam_beta2=training_args.adam_beta2, - adam_epsilon=training_args.adam_epsilon, - weight_decay_rate=training_args.weight_decay, - adam_global_clipnorm=training_args.max_grad_norm, - ) - - # no user-specified loss = will use the model internal loss - model.compile(optimizer=optimizer, jit_compile=training_args.xla, metrics=["accuracy"]) - - else: - model.compile(optimizer=None, jit_compile=training_args.xla, metrics=["accuracy"]) - training_dataset = None - - if training_args.do_eval: - eval_dataset = model.prepare_tf_dataset( - processed_datasets["validation"], - shuffle=False, - batch_size=training_args.per_device_train_batch_size * num_replicas, - tokenizer=tokenizer, - ) - eval_dataset = eval_dataset.with_options(dataset_options) - else: - eval_dataset = None - - if training_args.do_predict: - predict_dataset = model.prepare_tf_dataset( - processed_datasets["test"], - shuffle=False, - batch_size=training_args.per_device_eval_batch_size * num_replicas, - tokenizer=tokenizer, - ) - predict_dataset = predict_dataset.with_options(dataset_options) - else: - predict_dataset = None - - # endregion - - # region Preparing push_to_hub and model card - push_to_hub_model_id = training_args.push_to_hub_model_id - model_name = model_args.model_name_or_path.split("/")[-1] - if not push_to_hub_model_id: - if data_args.dataset_name is not None: - push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}" - else: - push_to_hub_model_id = f"{model_name}-finetuned-question-answering" - - model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"} - if data_args.dataset_name is not None: - model_card_kwargs["dataset_tags"] = data_args.dataset_name - if data_args.dataset_config_name is not None: - model_card_kwargs["dataset_args"] = data_args.dataset_config_name - model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" - else: - model_card_kwargs["dataset"] = data_args.dataset_name - - if training_args.push_to_hub: - callbacks = [ - PushToHubCallback( - output_dir=training_args.output_dir, - hub_model_id=push_to_hub_model_id, - hub_token=training_args.push_to_hub_token, - tokenizer=tokenizer, - **model_card_kwargs, - ) - ] - else: - callbacks = [] - # endregion - - # region Training and Evaluation - - if training_args.do_train: - # Note that the validation and test datasets have been processed in a different way to the - # training datasets in this example, and so they don't have the same label structure. - # As such, we don't pass them directly to Keras, but instead get model predictions to evaluate - # after training. - model.fit(training_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks) - - if training_args.do_eval: - logger.info("*** Evaluation ***") - - # In this example, we compute advanced metrics at the end of training, but - # if you'd like to compute metrics every epoch that are too complex to be written as - # standard Keras metrics, you can use our KerasMetricCallback. See - # https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks - - eval_predictions = model.predict(eval_dataset) - if isinstance(eval_predictions.start_logits, tf.RaggedTensor): - # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea! - # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even - # the highest probability in a sample. Instead, we use a large negative value, which ensures that the - # padding positions are correctly masked. - eval_start_logits = eval_predictions.start_logits.to_tensor(default_value=-1000).numpy() - eval_end_logits = eval_predictions.end_logits.to_tensor(default_value=-1000).numpy() - else: - eval_start_logits = eval_predictions.start_logits - eval_end_logits = eval_predictions.end_logits - - post_processed_eval = post_processing_function( - datasets["validation"], - processed_datasets["validation"], - (eval_start_logits, eval_end_logits), - ) - metrics = compute_metrics(post_processed_eval) - logging.info("Evaluation metrics:") - for metric, value in metrics.items(): - logging.info(f"{metric}: {value:.3f}") - if training_args.output_dir is not None: - output_eval_file = os.path.join(training_args.output_dir, "all_results.json") - with open(output_eval_file, "w") as writer: - writer.write(json.dumps(metrics)) - # endregion - - # region Prediction - if training_args.do_predict: - logger.info("*** Predict ***") - - test_predictions = model.predict(predict_dataset) - if isinstance(test_predictions.start_logits, tf.RaggedTensor): - # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea! - # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even - # the highest probability in a sample. Instead, we use a large negative value, which ensures that the - # padding positions are correctly masked. - test_start_logits = test_predictions.start_logits.to_tensor(default_value=-1000).numpy() - test_end_logits = test_predictions.end_logits.to_tensor(default_value=-1000).numpy() - else: - test_start_logits = test_predictions.start_logits - test_end_logits = test_predictions.end_logits - post_processed_test = post_processing_function( - datasets["test"], - processed_datasets["test"], - (test_start_logits, test_end_logits), - ) - metrics = compute_metrics(post_processed_test) - - logging.info("Test metrics:") - for metric, value in metrics.items(): - logging.info(f"{metric}: {value:.3f}") - # endregion - - if training_args.output_dir is not None and not training_args.push_to_hub: - # If we're not pushing to hub, at least save a local copy when we're done - model.save_pretrained(training_args.output_dir) - - -if __name__ == "__main__": - main() diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/benchmark/benchmark.py b/spaces/chendl/compositional_test/transformers/src/transformers/benchmark/benchmark.py deleted file mode 100644 index 3c5c877a454e63e9472ad80ea75d155be346a887..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/transformers/src/transformers/benchmark/benchmark.py +++ /dev/null @@ -1,271 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. 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. -""" - Benchmarking the library on inference and training in PyTorch. -""" - - -import timeit -from typing import Callable, Optional - -from ..configuration_utils import PretrainedConfig -from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING -from ..utils import is_py3nvml_available, is_torch_available, logging -from .benchmark_utils import ( - Benchmark, - Memory, - MemorySummary, - measure_peak_memory_cpu, - start_memory_tracing, - stop_memory_tracing, -) - - -if is_torch_available(): - import torch - - from .benchmark_args import PyTorchBenchmarkArguments - - -if is_py3nvml_available(): - import py3nvml.py3nvml as nvml - - -logger = logging.get_logger(__name__) - - -class PyTorchBenchmark(Benchmark): - args: PyTorchBenchmarkArguments - configs: PretrainedConfig - framework: str = "PyTorch" - - @property - def framework_version(self): - return torch.__version__ - - def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float: - _inference = self._prepare_inference_func(model_name, batch_size, sequence_length) - return self._measure_speed(_inference) - - def _inference_memory( - self, model_name: str, batch_size: int, sequence_length: int - ) -> [Memory, Optional[MemorySummary]]: - _inference = self._prepare_inference_func(model_name, batch_size, sequence_length) - return self._measure_memory(_inference) - - def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float: - _train = self._prepare_train_func(model_name, batch_size, sequence_length) - return self._measure_speed(_train) - - def _train_memory( - self, model_name: str, batch_size: int, sequence_length: int - ) -> [Memory, Optional[MemorySummary]]: - _train = self._prepare_train_func(model_name, batch_size, sequence_length) - return self._measure_memory(_train) - - def _prepare_inference_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]: - config = self.config_dict[model_name] - - if self.args.torchscript: - config.torchscript = True - - has_model_class_in_config = ( - hasattr(config, "architectures") - and isinstance(config.architectures, list) - and len(config.architectures) > 0 - ) - if not self.args.only_pretrain_model and has_model_class_in_config: - try: - model_class = config.architectures[0] - transformers_module = __import__("transformers", fromlist=[model_class]) - model_cls = getattr(transformers_module, model_class) - model = model_cls(config) - except ImportError: - raise ImportError( - f"{model_class} does not exist. If you just want to test the pretrained model, you might want to" - " set `--only_pretrain_model` or `args.only_pretrain_model=True`." - ) - else: - model = MODEL_MAPPING[config.__class__](config) - - model.eval() - model.to(self.args.device) - - # encoder-decoder has vocab size saved differently - vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size - input_ids = torch.randint(vocab_size, (batch_size, sequence_length), dtype=torch.long, device=self.args.device) - - if self.args.fp16: - logger.info("Running training in Mixed Precision...") - if not self.args.is_gpu: - raise ValueError("Mixed precision is possible only for GPU.") - # amp seems to have memory leaks so that memory usage - # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439 - model.half() - - if self.args.torchscript: - with torch.no_grad(): - inference_model = torch.jit.trace(model, input_ids) - else: - inference_model = model - - def encoder_decoder_forward(): - with torch.no_grad(): - outputs = inference_model(input_ids, decoder_input_ids=input_ids) - return outputs - - def encoder_forward(): - with torch.no_grad(): - outputs = inference_model(input_ids) - return outputs - - _forward = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward - return _forward - - def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]: - config = self.config_dict[model_name] - - has_model_class_in_config = ( - hasattr(config, "architectures") - and isinstance(config.architectures, list) - and len(config.architectures) > 0 - ) - if not self.args.only_pretrain_model and has_model_class_in_config: - try: - model_class = config.architectures[0] - transformers_module = __import__("transformers", fromlist=[model_class]) - model_cls = getattr(transformers_module, model_class) - model = model_cls(config) - except ImportError: - raise ImportError( - f"{model_class} does not exist. If you just want to test the pretrained model, you might want to" - " set `--only_pretrain_model` or `args.only_pretrain_model=True`." - ) - else: - model = MODEL_WITH_LM_HEAD_MAPPING[config.__class__](config) - - if self.args.torchscript: - raise NotImplementedError("Training for torchscript is currently not implemented") - else: - train_model = model - - model.train() - model.to(self.args.device) - - # encoder-decoder has vocab size saved differently - vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size - input_ids = torch.randint(vocab_size, (batch_size, sequence_length), dtype=torch.long, device=self.args.device) - - if self.args.fp16: - logger.info("Running training in Mixed Precision...") - if not self.args.is_gpu: - raise ValueError("Mixed precision is possible only for GPU.") - - # amp seems to have memory leaks so that memory usage - # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439 - model.half() - - def compute_loss_and_backprob_encoder(): - loss = train_model(input_ids, labels=input_ids)[0] - loss.backward() - return loss - - def compute_loss_and_backprob_encoder_decoder(): - loss = train_model(input_ids, decoder_input_ids=input_ids, labels=input_ids)[0] - loss.backward() - return loss - - _train = ( - compute_loss_and_backprob_encoder_decoder - if config.is_encoder_decoder - else compute_loss_and_backprob_encoder - ) - return _train - - def _measure_speed(self, func) -> float: - try: - if self.args.is_tpu or self.args.torchscript: - # run additional 10 times to stabilize compilation for tpu and torchscript - logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation") - timeit.repeat( - func, - repeat=1, - number=5, - ) - - # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average - runtimes = timeit.repeat( - func, - repeat=self.args.repeat, - number=10, - ) - - if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics: - import torch_xla.debug.metrics as met - - self.print_fn(met.metrics_report()) - - return min(runtimes) / 10.0 - except RuntimeError as e: - self.print_fn(f"Doesn't fit on GPU. {e}") - return "N/A" - - def _measure_memory(self, func: Callable[[], None]) -> [Memory, MemorySummary]: - try: - if self.args.trace_memory_line_by_line: - trace = start_memory_tracing("transformers") - - if self.args.is_tpu: - # tpu - raise NotImplementedError( - "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with" - " `--no-memory` or `args.memory=False`" - ) - elif self.args.is_gpu: - if not is_py3nvml_available(): - logger.warning( - "py3nvml not installed, we won't log GPU memory usage. " - "Install py3nvml (pip install py3nvml) to log information about GPU." - ) - memory = "N/A" - else: - logger.info( - "Measuring total GPU usage on GPU device. Make sure to not have additional processes running" - " on the same GPU." - ) - # init nvml - nvml.nvmlInit() - func() - handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx) - meminfo = nvml.nvmlDeviceGetMemoryInfo(handle) - max_bytes_in_use = meminfo.used - memory = Memory(max_bytes_in_use) - # shutdown nvml - nvml.nvmlShutdown() - else: - # cpu - memory_bytes = measure_peak_memory_cpu(func) - memory = Memory(memory_bytes) if isinstance(memory_bytes, int) else memory_bytes - - if self.args.trace_memory_line_by_line: - summary = stop_memory_tracing(trace) - else: - summary = None - - return memory, summary - except RuntimeError as e: - self.print_fn(f"Doesn't fit on GPU. {e}") - return "N/A", None diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PalmImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PalmImagePlugin.py deleted file mode 100644 index a88a907917dce5dace64fd1e38df86246c8e0305..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/PalmImagePlugin.py +++ /dev/null @@ -1,225 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# - -## -# Image plugin for Palm pixmap images (output only). -## - -from . import Image, ImageFile -from ._binary import o8 -from ._binary import o16be as o16b - -# fmt: off -_Palm8BitColormapValues = ( - (255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255), - (255, 51, 255), (255, 0, 255), (255, 255, 204), (255, 204, 204), - (255, 153, 204), (255, 102, 204), (255, 51, 204), (255, 0, 204), - (255, 255, 153), (255, 204, 153), (255, 153, 153), (255, 102, 153), - (255, 51, 153), (255, 0, 153), (204, 255, 255), (204, 204, 255), - (204, 153, 255), (204, 102, 255), (204, 51, 255), (204, 0, 255), - (204, 255, 204), (204, 204, 204), (204, 153, 204), (204, 102, 204), - (204, 51, 204), (204, 0, 204), (204, 255, 153), (204, 204, 153), - (204, 153, 153), (204, 102, 153), (204, 51, 153), (204, 0, 153), - (153, 255, 255), (153, 204, 255), (153, 153, 255), (153, 102, 255), - (153, 51, 255), (153, 0, 255), (153, 255, 204), (153, 204, 204), - (153, 153, 204), (153, 102, 204), (153, 51, 204), (153, 0, 204), - (153, 255, 153), (153, 204, 153), (153, 153, 153), (153, 102, 153), - (153, 51, 153), (153, 0, 153), (102, 255, 255), (102, 204, 255), - (102, 153, 255), (102, 102, 255), (102, 51, 255), (102, 0, 255), - (102, 255, 204), (102, 204, 204), (102, 153, 204), (102, 102, 204), - (102, 51, 204), (102, 0, 204), (102, 255, 153), (102, 204, 153), - (102, 153, 153), (102, 102, 153), (102, 51, 153), (102, 0, 153), - (51, 255, 255), (51, 204, 255), (51, 153, 255), (51, 102, 255), - (51, 51, 255), (51, 0, 255), (51, 255, 204), (51, 204, 204), - (51, 153, 204), (51, 102, 204), (51, 51, 204), (51, 0, 204), - (51, 255, 153), (51, 204, 153), (51, 153, 153), (51, 102, 153), - (51, 51, 153), (51, 0, 153), (0, 255, 255), (0, 204, 255), - (0, 153, 255), (0, 102, 255), (0, 51, 255), (0, 0, 255), - (0, 255, 204), (0, 204, 204), (0, 153, 204), (0, 102, 204), - (0, 51, 204), (0, 0, 204), (0, 255, 153), (0, 204, 153), - (0, 153, 153), (0, 102, 153), (0, 51, 153), (0, 0, 153), - (255, 255, 102), (255, 204, 102), (255, 153, 102), (255, 102, 102), - (255, 51, 102), (255, 0, 102), (255, 255, 51), (255, 204, 51), - (255, 153, 51), (255, 102, 51), (255, 51, 51), (255, 0, 51), - (255, 255, 0), (255, 204, 0), (255, 153, 0), (255, 102, 0), - (255, 51, 0), (255, 0, 0), (204, 255, 102), (204, 204, 102), - (204, 153, 102), (204, 102, 102), (204, 51, 102), (204, 0, 102), - (204, 255, 51), (204, 204, 51), (204, 153, 51), (204, 102, 51), - (204, 51, 51), (204, 0, 51), (204, 255, 0), (204, 204, 0), - (204, 153, 0), (204, 102, 0), (204, 51, 0), (204, 0, 0), - (153, 255, 102), (153, 204, 102), (153, 153, 102), (153, 102, 102), - (153, 51, 102), (153, 0, 102), (153, 255, 51), (153, 204, 51), - (153, 153, 51), (153, 102, 51), (153, 51, 51), (153, 0, 51), - (153, 255, 0), (153, 204, 0), (153, 153, 0), (153, 102, 0), - (153, 51, 0), (153, 0, 0), (102, 255, 102), (102, 204, 102), - (102, 153, 102), (102, 102, 102), (102, 51, 102), (102, 0, 102), - (102, 255, 51), (102, 204, 51), (102, 153, 51), (102, 102, 51), - (102, 51, 51), (102, 0, 51), (102, 255, 0), (102, 204, 0), - (102, 153, 0), (102, 102, 0), (102, 51, 0), (102, 0, 0), - (51, 255, 102), (51, 204, 102), (51, 153, 102), (51, 102, 102), - (51, 51, 102), (51, 0, 102), (51, 255, 51), (51, 204, 51), - (51, 153, 51), (51, 102, 51), (51, 51, 51), (51, 0, 51), - (51, 255, 0), (51, 204, 0), (51, 153, 0), (51, 102, 0), - (51, 51, 0), (51, 0, 0), (0, 255, 102), (0, 204, 102), - (0, 153, 102), (0, 102, 102), (0, 51, 102), (0, 0, 102), - (0, 255, 51), (0, 204, 51), (0, 153, 51), (0, 102, 51), - (0, 51, 51), (0, 0, 51), (0, 255, 0), (0, 204, 0), - (0, 153, 0), (0, 102, 0), (0, 51, 0), (17, 17, 17), - (34, 34, 34), (68, 68, 68), (85, 85, 85), (119, 119, 119), - (136, 136, 136), (170, 170, 170), (187, 187, 187), (221, 221, 221), - (238, 238, 238), (192, 192, 192), (128, 0, 0), (128, 0, 128), - (0, 128, 0), (0, 128, 128), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), - (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0)) -# fmt: on - - -# so build a prototype image to be used for palette resampling -def build_prototype_image(): - image = Image.new("L", (1, len(_Palm8BitColormapValues))) - image.putdata(list(range(len(_Palm8BitColormapValues)))) - palettedata = () - for colormapValue in _Palm8BitColormapValues: - palettedata += colormapValue - palettedata += (0, 0, 0) * (256 - len(_Palm8BitColormapValues)) - image.putpalette(palettedata) - return image - - -Palm8BitColormapImage = build_prototype_image() - -# OK, we now have in Palm8BitColormapImage, -# a "P"-mode image with the right palette -# -# -------------------------------------------------------------------- - -_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000} - -_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00} - - -# -# -------------------------------------------------------------------- - -## -# (Internal) Image save plugin for the Palm format. - - -def _save(im, fp, filename): - if im.mode == "P": - # we assume this is a color Palm image with the standard colormap, - # unless the "info" dict has a "custom-colormap" field - - rawmode = "P" - bpp = 8 - version = 1 - - elif im.mode == "L": - if im.encoderinfo.get("bpp") in (1, 2, 4): - # this is 8-bit grayscale, so we shift it to get the high-order bits, - # and invert it because - # Palm does greyscale from white (0) to black (1) - bpp = im.encoderinfo["bpp"] - im = im.point( - lambda x, shift=8 - bpp, maxval=(1 << bpp) - 1: maxval - (x >> shift) - ) - elif im.info.get("bpp") in (1, 2, 4): - # here we assume that even though the inherent mode is 8-bit grayscale, - # only the lower bpp bits are significant. - # We invert them to match the Palm. - bpp = im.info["bpp"] - im = im.point(lambda x, maxval=(1 << bpp) - 1: maxval - (x & maxval)) - else: - msg = f"cannot write mode {im.mode} as Palm" - raise OSError(msg) - - # we ignore the palette here - im.mode = "P" - rawmode = "P;" + str(bpp) - version = 1 - - elif im.mode == "1": - # monochrome -- write it inverted, as is the Palm standard - rawmode = "1;I" - bpp = 1 - version = 0 - - else: - msg = f"cannot write mode {im.mode} as Palm" - raise OSError(msg) - - # - # make sure image data is available - im.load() - - # write header - - cols = im.size[0] - rows = im.size[1] - - rowbytes = int((cols + (16 // bpp - 1)) / (16 // bpp)) * 2 - transparent_index = 0 - compression_type = _COMPRESSION_TYPES["none"] - - flags = 0 - if im.mode == "P" and "custom-colormap" in im.info: - flags = flags & _FLAGS["custom-colormap"] - colormapsize = 4 * 256 + 2 - colormapmode = im.palette.mode - colormap = im.getdata().getpalette() - else: - colormapsize = 0 - - if "offset" in im.info: - offset = (rowbytes * rows + 16 + 3 + colormapsize) // 4 - else: - offset = 0 - - fp.write(o16b(cols) + o16b(rows) + o16b(rowbytes) + o16b(flags)) - fp.write(o8(bpp)) - fp.write(o8(version)) - fp.write(o16b(offset)) - fp.write(o8(transparent_index)) - fp.write(o8(compression_type)) - fp.write(o16b(0)) # reserved by Palm - - # now write colormap if necessary - - if colormapsize > 0: - fp.write(o16b(256)) - for i in range(256): - fp.write(o8(i)) - if colormapmode == "RGB": - fp.write( - o8(colormap[3 * i]) - + o8(colormap[3 * i + 1]) - + o8(colormap[3 * i + 2]) - ) - elif colormapmode == "RGBA": - fp.write( - o8(colormap[4 * i]) - + o8(colormap[4 * i + 1]) - + o8(colormap[4 * i + 2]) - ) - - # now convert data to raw form - ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, rowbytes, 1))]) - - if hasattr(fp, "flush"): - fp.flush() - - -# -# -------------------------------------------------------------------- - -Image.register_save("Palm", _save) - -Image.register_extension("Palm", ".palm") - -Image.register_mime("Palm", "image/palm") diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/buffer.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/buffer.py deleted file mode 100644 index b50b9bb678226947a5dbc57b648bb7e99858c2a1..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/buffer.py +++ /dev/null @@ -1,140 +0,0 @@ -import sys -import array -from typing import Any, Iterable - -from clickhouse_connect.driver.exceptions import StreamCompleteException -from clickhouse_connect.driver.types import ByteSource - -must_swap = sys.byteorder == 'big' - - -class ResponseBuffer(ByteSource): - slots = 'slice_sz', 'buf_loc', 'end', 'gen', 'buffer', 'slice' - - def __init__(self, source): - self.slice_sz = 4096 - self.buf_loc = 0 - self.buf_sz = 0 - self.source = source - self.gen = source.gen - self.buffer = bytes() - - def read_bytes(self, sz: int): - if self.buf_loc + sz <= self.buf_sz: - self.buf_loc += sz - return self.buffer[self.buf_loc - sz: self.buf_loc] - # Create a temporary buffer that bridges two or more source chunks - bridge = bytearray(self.buffer[self.buf_loc: self.buf_sz]) - self.buf_loc = 0 - self.buf_sz = 0 - while len(bridge) < sz: - chunk = next(self.gen, None) - if not chunk: - raise StreamCompleteException - x = len(chunk) - if len(bridge) + x <= sz: - bridge.extend(chunk) - else: - tail = sz - len(bridge) - bridge.extend(chunk[:tail]) - self.buffer = chunk - self.buf_sz = x - self.buf_loc = tail - return bridge - - def read_byte(self) -> int: - if self.buf_loc < self.buf_sz: - self.buf_loc += 1 - return self.buffer[self.buf_loc - 1] - self.buf_sz = 0 - self.buf_loc = 0 - chunk = next(self.gen, None) - if not chunk: - raise StreamCompleteException - x = len(chunk) - if x > 1: - self.buffer = chunk - self.buf_loc = 1 - self.buf_sz = x - return chunk[0] - - def read_leb128(self) -> int: - sz = 0 - shift = 0 - while True: - b = self.read_byte() - sz += ((b & 0x7f) << shift) - if (b & 0x80) == 0: - return sz - shift += 7 - - def read_leb128_str(self) -> str: - sz = self.read_leb128() - return self.read_bytes(sz).decode() - - def read_uint64(self) -> int: - return int.from_bytes(self.read_bytes(8), 'little', signed=False) - - def read_str_col(self, - num_rows: int, - encoding: str, - nullable: bool = False, - null_obj: Any = None) -> Iterable[str]: - column = [] - app = column.append - null_map = self.read_bytes(num_rows) if nullable else None - for ix in range(num_rows): - sz = 0 - shift = 0 - while True: - b = self.read_byte() - sz += ((b & 0x7f) << shift) - if (b & 0x80) == 0: - break - shift += 7 - x = self.read_bytes(sz) - if null_map and null_map[ix]: - app(null_obj) - elif encoding: - try: - app(x.decode(encoding)) - except UnicodeDecodeError: - app(x.hex()) - else: - app(x) - return column - - def read_bytes_col(self, sz: int, num_rows: int) -> Iterable[bytes]: - source = self.read_bytes(sz * num_rows) - return [bytes(source[x:x+sz]) for x in range(0, sz * num_rows, sz)] - - def read_fixed_str_col(self, sz: int, num_rows: int, encoding: str) -> Iterable[str]: - source = self.read_bytes(sz * num_rows) - column = [] - app = column.append - for ix in range(0, sz * num_rows, sz): - try: - app(str(source[ix: ix + sz], encoding).rstrip('\x00')) - except UnicodeDecodeError: - app(source[ix: ix + sz].hex()) - return column - - def read_array(self, array_type: str, num_rows: int) -> Iterable[Any]: - column = array.array(array_type) - sz = column.itemsize * num_rows - b = self.read_bytes(sz) - column.frombytes(b) - if must_swap: - column.byteswap() - return column - - @property - def last_message(self): - if len(self.buffer) == 0: - return None - return self.buffer.decode() - - def close(self): - if self.source: - self.source.close() - self.source = None diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/loggingTools.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/loggingTools.py deleted file mode 100644 index 78704f5a9aa4811db98aa3132ed3f12ee0853ee2..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/misc/loggingTools.py +++ /dev/null @@ -1,543 +0,0 @@ -import sys -import logging -import timeit -from functools import wraps -from collections.abc import Mapping, Callable -import warnings -from logging import PercentStyle - - -# default logging level used by Timer class -TIME_LEVEL = logging.DEBUG - -# per-level format strings used by the default formatter -# (the level name is not printed for INFO and DEBUG messages) -DEFAULT_FORMATS = { - "*": "%(levelname)s: %(message)s", - "INFO": "%(message)s", - "DEBUG": "%(message)s", -} - - -class LevelFormatter(logging.Formatter): - """Log formatter with level-specific formatting. - - Formatter class which optionally takes a dict of logging levels to - format strings, allowing to customise the log records appearance for - specific levels. - - - Attributes: - fmt: A dictionary mapping logging levels to format strings. - The ``*`` key identifies the default format string. - datefmt: As per py:class:`logging.Formatter` - style: As per py:class:`logging.Formatter` - - >>> import sys - >>> handler = logging.StreamHandler(sys.stdout) - >>> formatter = LevelFormatter( - ... fmt={ - ... '*': '[%(levelname)s] %(message)s', - ... 'DEBUG': '%(name)s [%(levelname)s] %(message)s', - ... 'INFO': '%(message)s', - ... }) - >>> handler.setFormatter(formatter) - >>> log = logging.getLogger('test') - >>> log.setLevel(logging.DEBUG) - >>> log.addHandler(handler) - >>> log.debug('this uses a custom format string') - test [DEBUG] this uses a custom format string - >>> log.info('this also uses a custom format string') - this also uses a custom format string - >>> log.warning("this one uses the default format string") - [WARNING] this one uses the default format string - """ - - def __init__(self, fmt=None, datefmt=None, style="%"): - if style != "%": - raise ValueError( - "only '%' percent style is supported in both python 2 and 3" - ) - if fmt is None: - fmt = DEFAULT_FORMATS - if isinstance(fmt, str): - default_format = fmt - custom_formats = {} - elif isinstance(fmt, Mapping): - custom_formats = dict(fmt) - default_format = custom_formats.pop("*", None) - else: - raise TypeError("fmt must be a str or a dict of str: %r" % fmt) - super(LevelFormatter, self).__init__(default_format, datefmt) - self.default_format = self._fmt - self.custom_formats = {} - for level, fmt in custom_formats.items(): - level = logging._checkLevel(level) - self.custom_formats[level] = fmt - - def format(self, record): - if self.custom_formats: - fmt = self.custom_formats.get(record.levelno, self.default_format) - if self._fmt != fmt: - self._fmt = fmt - # for python >= 3.2, _style needs to be set if _fmt changes - if PercentStyle: - self._style = PercentStyle(fmt) - return super(LevelFormatter, self).format(record) - - -def configLogger(**kwargs): - """A more sophisticated logging system configuation manager. - - This is more or less the same as :py:func:`logging.basicConfig`, - with some additional options and defaults. - - The default behaviour is to create a ``StreamHandler`` which writes to - sys.stderr, set a formatter using the ``DEFAULT_FORMATS`` strings, and add - the handler to the top-level library logger ("fontTools"). - - A number of optional keyword arguments may be specified, which can alter - the default behaviour. - - Args: - - logger: Specifies the logger name or a Logger instance to be - configured. (Defaults to "fontTools" logger). Unlike ``basicConfig``, - this function can be called multiple times to reconfigure a logger. - If the logger or any of its children already exists before the call is - made, they will be reset before the new configuration is applied. - filename: Specifies that a ``FileHandler`` be created, using the - specified filename, rather than a ``StreamHandler``. - filemode: Specifies the mode to open the file, if filename is - specified. (If filemode is unspecified, it defaults to ``a``). - format: Use the specified format string for the handler. This - argument also accepts a dictionary of format strings keyed by - level name, to allow customising the records appearance for - specific levels. The special ``'*'`` key is for 'any other' level. - datefmt: Use the specified date/time format. - level: Set the logger level to the specified level. - stream: Use the specified stream to initialize the StreamHandler. Note - that this argument is incompatible with ``filename`` - if both - are present, ``stream`` is ignored. - handlers: If specified, this should be an iterable of already created - handlers, which will be added to the logger. Any handler in the - list which does not have a formatter assigned will be assigned the - formatter created in this function. - filters: If specified, this should be an iterable of already created - filters. If the ``handlers`` do not already have filters assigned, - these filters will be added to them. - propagate: All loggers have a ``propagate`` attribute which determines - whether to continue searching for handlers up the logging hierarchy. - If not provided, the "propagate" attribute will be set to ``False``. - """ - # using kwargs to enforce keyword-only arguments in py2. - handlers = kwargs.pop("handlers", None) - if handlers is None: - if "stream" in kwargs and "filename" in kwargs: - raise ValueError( - "'stream' and 'filename' should not be " "specified together" - ) - else: - if "stream" in kwargs or "filename" in kwargs: - raise ValueError( - "'stream' or 'filename' should not be " - "specified together with 'handlers'" - ) - if handlers is None: - filename = kwargs.pop("filename", None) - mode = kwargs.pop("filemode", "a") - if filename: - h = logging.FileHandler(filename, mode) - else: - stream = kwargs.pop("stream", None) - h = logging.StreamHandler(stream) - handlers = [h] - # By default, the top-level library logger is configured. - logger = kwargs.pop("logger", "fontTools") - if not logger or isinstance(logger, str): - # empty "" or None means the 'root' logger - logger = logging.getLogger(logger) - # before (re)configuring, reset named logger and its children (if exist) - _resetExistingLoggers(parent=logger.name) - # use DEFAULT_FORMATS if 'format' is None - fs = kwargs.pop("format", None) - dfs = kwargs.pop("datefmt", None) - # XXX: '%' is the only format style supported on both py2 and 3 - style = kwargs.pop("style", "%") - fmt = LevelFormatter(fs, dfs, style) - filters = kwargs.pop("filters", []) - for h in handlers: - if h.formatter is None: - h.setFormatter(fmt) - if not h.filters: - for f in filters: - h.addFilter(f) - logger.addHandler(h) - if logger.name != "root": - # stop searching up the hierarchy for handlers - logger.propagate = kwargs.pop("propagate", False) - # set a custom severity level - level = kwargs.pop("level", None) - if level is not None: - logger.setLevel(level) - if kwargs: - keys = ", ".join(kwargs.keys()) - raise ValueError("Unrecognised argument(s): %s" % keys) - - -def _resetExistingLoggers(parent="root"): - """Reset the logger named 'parent' and all its children to their initial - state, if they already exist in the current configuration. - """ - root = logging.root - # get sorted list of all existing loggers - existing = sorted(root.manager.loggerDict.keys()) - if parent == "root": - # all the existing loggers are children of 'root' - loggers_to_reset = [parent] + existing - elif parent not in existing: - # nothing to do - return - elif parent in existing: - loggers_to_reset = [parent] - # collect children, starting with the entry after parent name - i = existing.index(parent) + 1 - prefixed = parent + "." - pflen = len(prefixed) - num_existing = len(existing) - while i < num_existing: - if existing[i][:pflen] == prefixed: - loggers_to_reset.append(existing[i]) - i += 1 - for name in loggers_to_reset: - if name == "root": - root.setLevel(logging.WARNING) - for h in root.handlers[:]: - root.removeHandler(h) - for f in root.filters[:]: - root.removeFilters(f) - root.disabled = False - else: - logger = root.manager.loggerDict[name] - logger.level = logging.NOTSET - logger.handlers = [] - logger.filters = [] - logger.propagate = True - logger.disabled = False - - -class Timer(object): - """Keeps track of overall time and split/lap times. - - >>> import time - >>> timer = Timer() - >>> time.sleep(0.01) - >>> print("First lap:", timer.split()) - First lap: ... - >>> time.sleep(0.02) - >>> print("Second lap:", timer.split()) - Second lap: ... - >>> print("Overall time:", timer.time()) - Overall time: ... - - Can be used as a context manager inside with-statements. - - >>> with Timer() as t: - ... time.sleep(0.01) - >>> print("%0.3f seconds" % t.elapsed) - 0... seconds - - If initialised with a logger, it can log the elapsed time automatically - upon exiting the with-statement. - - >>> import logging - >>> log = logging.getLogger("my-fancy-timer-logger") - >>> configLogger(logger=log, level="DEBUG", format="%(message)s", stream=sys.stdout) - >>> with Timer(log, 'do something'): - ... time.sleep(0.01) - Took ... to do something - - The same Timer instance, holding a reference to a logger, can be reused - in multiple with-statements, optionally with different messages or levels. - - >>> timer = Timer(log) - >>> with timer(): - ... time.sleep(0.01) - elapsed time: ...s - >>> with timer('redo it', level=logging.INFO): - ... time.sleep(0.02) - Took ... to redo it - - It can also be used as a function decorator to log the time elapsed to run - the decorated function. - - >>> @timer() - ... def test1(): - ... time.sleep(0.01) - >>> @timer('run test 2', level=logging.INFO) - ... def test2(): - ... time.sleep(0.02) - >>> test1() - Took ... to run 'test1' - >>> test2() - Took ... to run test 2 - """ - - # timeit.default_timer choses the most accurate clock for each platform - _time = timeit.default_timer - default_msg = "elapsed time: %(time).3fs" - default_format = "Took %(time).3fs to %(msg)s" - - def __init__(self, logger=None, msg=None, level=None, start=None): - self.reset(start) - if logger is None: - for arg in ("msg", "level"): - if locals().get(arg) is not None: - raise ValueError("'%s' can't be specified without a 'logger'" % arg) - self.logger = logger - self.level = level if level is not None else TIME_LEVEL - self.msg = msg - - def reset(self, start=None): - """Reset timer to 'start_time' or the current time.""" - if start is None: - self.start = self._time() - else: - self.start = start - self.last = self.start - self.elapsed = 0.0 - - def time(self): - """Return the overall time (in seconds) since the timer started.""" - return self._time() - self.start - - def split(self): - """Split and return the lap time (in seconds) in between splits.""" - current = self._time() - self.elapsed = current - self.last - self.last = current - return self.elapsed - - def formatTime(self, msg, time): - """Format 'time' value in 'msg' and return formatted string. - If 'msg' contains a '%(time)' format string, try to use that. - Otherwise, use the predefined 'default_format'. - If 'msg' is empty or None, fall back to 'default_msg'. - """ - if not msg: - msg = self.default_msg - if msg.find("%(time)") < 0: - msg = self.default_format % {"msg": msg, "time": time} - else: - try: - msg = msg % {"time": time} - except (KeyError, ValueError): - pass # skip if the format string is malformed - return msg - - def __enter__(self): - """Start a new lap""" - self.last = self._time() - self.elapsed = 0.0 - return self - - def __exit__(self, exc_type, exc_value, traceback): - """End the current lap. If timer has a logger, log the time elapsed, - using the format string in self.msg (or the default one). - """ - time = self.split() - if self.logger is None or exc_type: - # if there's no logger attached, or if any exception occurred in - # the with-statement, exit without logging the time - return - message = self.formatTime(self.msg, time) - # Allow log handlers to see the individual parts to facilitate things - # like a server accumulating aggregate stats. - msg_parts = {"msg": self.msg, "time": time} - self.logger.log(self.level, message, msg_parts) - - def __call__(self, func_or_msg=None, **kwargs): - """If the first argument is a function, return a decorator which runs - the wrapped function inside Timer's context manager. - Otherwise, treat the first argument as a 'msg' string and return an updated - Timer instance, referencing the same logger. - A 'level' keyword can also be passed to override self.level. - """ - if isinstance(func_or_msg, Callable): - func = func_or_msg - # use the function name when no explicit 'msg' is provided - if not self.msg: - self.msg = "run '%s'" % func.__name__ - - @wraps(func) - def wrapper(*args, **kwds): - with self: - return func(*args, **kwds) - - return wrapper - else: - msg = func_or_msg or kwargs.get("msg") - level = kwargs.get("level", self.level) - return self.__class__(self.logger, msg, level) - - def __float__(self): - return self.elapsed - - def __int__(self): - return int(self.elapsed) - - def __str__(self): - return "%.3f" % self.elapsed - - -class ChannelsFilter(logging.Filter): - """Provides a hierarchical filter for log entries based on channel names. - - Filters out records emitted from a list of enabled channel names, - including their children. It works the same as the ``logging.Filter`` - class, but allows the user to specify multiple channel names. - - >>> import sys - >>> handler = logging.StreamHandler(sys.stdout) - >>> handler.setFormatter(logging.Formatter("%(message)s")) - >>> filter = ChannelsFilter("A.B", "C.D") - >>> handler.addFilter(filter) - >>> root = logging.getLogger() - >>> root.addHandler(handler) - >>> root.setLevel(level=logging.DEBUG) - >>> logging.getLogger('A.B').debug('this record passes through') - this record passes through - >>> logging.getLogger('A.B.C').debug('records from children also pass') - records from children also pass - >>> logging.getLogger('C.D').debug('this one as well') - this one as well - >>> logging.getLogger('A.B.').debug('also this one') - also this one - >>> logging.getLogger('A.F').debug('but this one does not!') - >>> logging.getLogger('C.DE').debug('neither this one!') - """ - - def __init__(self, *names): - self.names = names - self.num = len(names) - self.lengths = {n: len(n) for n in names} - - def filter(self, record): - if self.num == 0: - return True - for name in self.names: - nlen = self.lengths[name] - if name == record.name: - return True - elif record.name.find(name, 0, nlen) == 0 and record.name[nlen] == ".": - return True - return False - - -class CapturingLogHandler(logging.Handler): - def __init__(self, logger, level): - super(CapturingLogHandler, self).__init__(level=level) - self.records = [] - if isinstance(logger, str): - self.logger = logging.getLogger(logger) - else: - self.logger = logger - - def __enter__(self): - self.original_disabled = self.logger.disabled - self.original_level = self.logger.level - self.original_propagate = self.logger.propagate - - self.logger.addHandler(self) - self.logger.setLevel(self.level) - self.logger.disabled = False - self.logger.propagate = False - - return self - - def __exit__(self, type, value, traceback): - self.logger.removeHandler(self) - self.logger.setLevel(self.original_level) - self.logger.disabled = self.original_disabled - self.logger.propagate = self.original_propagate - - return self - - def emit(self, record): - self.records.append(record) - - def assertRegex(self, regexp, msg=None): - import re - - pattern = re.compile(regexp) - for r in self.records: - if pattern.search(r.getMessage()): - return True - if msg is None: - msg = "Pattern '%s' not found in logger records" % regexp - assert 0, msg - - -class LogMixin(object): - """Mixin class that adds logging functionality to another class. - - You can define a new class that subclasses from ``LogMixin`` as well as - other base classes through multiple inheritance. - All instances of that class will have a ``log`` property that returns - a ``logging.Logger`` named after their respective ``.``. - - For example: - - >>> class BaseClass(object): - ... pass - >>> class MyClass(LogMixin, BaseClass): - ... pass - >>> a = MyClass() - >>> isinstance(a.log, logging.Logger) - True - >>> print(a.log.name) - fontTools.misc.loggingTools.MyClass - >>> class AnotherClass(MyClass): - ... pass - >>> b = AnotherClass() - >>> isinstance(b.log, logging.Logger) - True - >>> print(b.log.name) - fontTools.misc.loggingTools.AnotherClass - """ - - @property - def log(self): - if not hasattr(self, "_log"): - name = ".".join((self.__class__.__module__, self.__class__.__name__)) - self._log = logging.getLogger(name) - return self._log - - -def deprecateArgument(name, msg, category=UserWarning): - """Raise a warning about deprecated function argument 'name'.""" - warnings.warn("%r is deprecated; %s" % (name, msg), category=category, stacklevel=3) - - -def deprecateFunction(msg, category=UserWarning): - """Decorator to raise a warning when a deprecated function is called.""" - - def decorator(func): - @wraps(func) - def wrapper(*args, **kwargs): - warnings.warn( - "%r is deprecated; %s" % (func.__name__, msg), - category=category, - stacklevel=2, - ) - return func(*args, **kwargs) - - return wrapper - - return decorator - - -if __name__ == "__main__": - import doctest - - sys.exit(doctest.testmod(optionflags=doctest.ELLIPSIS).failed) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/mtiLib/__main__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/mtiLib/__main__.py deleted file mode 100644 index 29c802bcc83b3ca35bbd0e6521f47a368b5f9092..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/mtiLib/__main__.py +++ /dev/null @@ -1,5 +0,0 @@ -import sys -from fontTools.mtiLib import main - -if __name__ == "__main__": - sys.exit(main()) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/google/protobuf/util/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/google/protobuf/util/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/cihyFjudo/fairness-paper-search/Astroboy movie free download hd What critics and audiences are saying.md b/spaces/cihyFjudo/fairness-paper-search/Astroboy movie free download hd What critics and audiences are saying.md deleted file mode 100644 index 0db4d15b5d7889d989416486b1d4e9158ed55c8d..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Astroboy movie free download hd What critics and audiences are saying.md +++ /dev/null @@ -1,7 +0,0 @@ -
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Many computer problems are caused by missing or outdated device drivers, especially in Windows 11. If your desktop or laptop is running slow, or keeps crashing or hanging, there is a good chance that updating your drivers will fix the problem.

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Mac OS 10.4 (Tiger) does not included Stuffit Expander. Mac downloads (.bin .hqx .sea .sit .sitx) require Stuffit Expander or other decoding utility. Newer Mac downloads require Stuffit Expander version 5.1.2 or higher. Download the free Aladdin Stuffit Expander for Mac (included with Mac OS X 10.0-10.3, but not with 10.4).

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A download form is required to access some Pro Tools downloads. Completion of the download form is not related to registration of the software, hardware, or any other product. For help with plug-in downloads, please see Download Help FAQ #1.

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sudo apt-get install build-essential linux-headers dkms
git clone git://git.zammit.org/snd-firewire-improve
sudo ln -s $(pwd)/snd-firewire-improve /usr/src/alsa-firewire-3.19
sudo dkms install alsa-firewire/3.19
sudo modprobe snd-digi00x

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I did what you suggested in the previous post and all seemed to be going well. Then I installed your driver for the 003 rack and again everything seemed to be working great.
At the end, however, I got this message:

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Hi Damo, Yes, i Currently have Ubuntu 12.04 running on a 2007 mac mini with a dual boot of osx.
And for the most part the 003 Rack is working great. I did some troubleshooting and have discovered that the problem is playback channel 1. If I send all the audio through Playback channel 2 in Ardour then the sound is crystal clear. Of course then I am only hearing the sound through the right headphone speaker. When I engage channel 1 on the master channel strip as the output in order to get a stereo sound it hisses and crackles with each sound input. This occurs for live monitoring from any channel (1-4) and even with prerecorded sounds. Again, playback channel 2 works great but when playback channel 1 is activated I get hiss and crackle in both ears.
is there some kind of interference occurring? Could it have anything to do with Ubuntu or is it a problem within the internal routing of jack/Ardour/003 driver?
I am still figuring out how to configure a loopback sound device. Do you have any recommendations on a good walkthrough for this available online?
Thank you again!
-Lucas

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TIDE: It sounds like your internal sound card is being selected in JACK instead of the 003, perhaps you need to select the correct hw:X device in the settings. Assuming you have the driver installed correctly, that is all I can suggest. Good luck.

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hi damo,
I just managed successfully to install a digi 002r with your driver. thanks a lot. The only thing I am wondering about, is that Ardour or Jack is crashing after a while. Ardour freezes and tells me, that it is not able to reconnect to server. In qjackctl I disabled the dbus server and I tried to kill and restart jack but it refuses to work. the only thing I can do, is to restart my Laptop. Now I am wondering about deinstalling the Ffado repos, but I am not sure, if I am messing up the whole system. Maybe you can give me some advice.
Greetz Tim

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I hate to beat a dead horse but needing direction to whom who has the knowledge with incorporating both logic and digi 002. I have been doing some massive researching saw many users able to make it work. But the posts that I have been reading has been more than 2-3 years ago and need a little more recent posts.

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I have attempted to do it myself and failing miserably. Long story short, I have switched over to Logic and I am done with pro tools. I can't afford the upgrades and such. But what I would like to see happen is to salvage the digi 002 for as long as I can. So not sure what I am doing wrong.

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Long answer: If AVID's core audio drivers aren't compatible with your system then there's nothing you can do. If they are listed as compatible with your version of macOS but don't appear in Logic's preferences, then they're either not properly installed, or something is wrong with the drivers, and you need to contact AVID about it.

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DOWNLOAD === https://tinurli.com/2uwi96



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diff --git a/spaces/cihyFjudo/fairness-paper-search/Trixie Nonude Model Video.md b/spaces/cihyFjudo/fairness-paper-search/Trixie Nonude Model Video.md deleted file mode 100644 index fd536f816290c897883d9ff1785aaa5b3abb9f1c..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Trixie Nonude Model Video.md +++ /dev/null @@ -1,6 +0,0 @@ -

trixie nonude model video


Download File === https://tinurli.com/2uwjBZ



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diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/texidep.pl b/spaces/colakin/video-generater/public/ffmpeg/doc/texidep.pl deleted file mode 100644 index 099690378e6911de871cbd3ca0c90a67de56154b..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/doc/texidep.pl +++ /dev/null @@ -1,32 +0,0 @@ -#! /usr/bin/env perl - -# This script will print the dependency of a Texinfo file to stdout. -# texidep.pl - -use warnings; -use strict; - -die unless @ARGV == 3; - -my ($src_path, $root, $target) = @ARGV; - -sub print_deps { - my ($file, $deps) = @_; - $deps->{$file} = 1; - - open(my $fh, "<", "$file") or die "Cannot open file '$file': $!"; - while (<$fh>) { - if (my ($i) = /^\@(?:verbatim)?include\s+(\S+)/) { - die "Circular dependency found in file $root\n" if exists $deps->{"doc/$1"}; - print "$target: doc/$1\n"; - - # skip looking for config.texi dependencies, since it has - # none, and is not located in the source tree - if ("$1" ne "config.texi") { - print_deps("$src_path/doc/$1", {%$deps}); - } - } - } -} - -print_deps($root, {}); diff --git a/spaces/colakin/video-generater/public/ffmpeg/ffbuild/pkgconfig_generate.sh b/spaces/colakin/video-generater/public/ffmpeg/ffbuild/pkgconfig_generate.sh deleted file mode 100644 index e5de6716d28b5367bab75cc7efa68566a930755c..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/ffbuild/pkgconfig_generate.sh +++ /dev/null @@ -1,62 +0,0 @@ -#!/bin/sh - -. ffbuild/config.sh - -if test "$shared" = "yes"; then - shared=true -else - shared=false -fi - -shortname=$1 -name=lib${shortname} -fullname=${name}${build_suffix} -comment=$2 -libs=$(eval echo \$extralibs_${shortname}) -deps=$(eval echo \$${shortname}_deps) - -for dep in $deps; do - depname=lib${dep} - fulldepname=${depname}${build_suffix} - . ${depname}/${depname}.version - depversion=$(eval echo \$${depname}_VERSION) - requires="$requires ${fulldepname} >= ${depversion}, " -done -requires=${requires%, } - -version=$(grep ${name}_VERSION= $name/${name}.version | cut -d= -f2) - -cat < $name/$fullname.pc -prefix=$prefix -exec_prefix=\${prefix} -libdir=$libdir -includedir=$incdir - -Name: $fullname -Description: $comment -Version: $version -Requires: $($shared || echo $requires) -Requires.private: $($shared && echo $requires) -Conflicts: -Libs: -L\${libdir} $rpath -l${fullname#lib} $($shared || echo $libs) -Libs.private: $($shared && echo $libs) -Cflags: -I\${includedir} -EOF - -mkdir -p doc/examples/pc-uninstalled -includedir=${source_path} -[ "$includedir" = . ] && includedir="\${pcfiledir}/../../.." - cat < doc/examples/pc-uninstalled/${name}-uninstalled.pc -prefix= -exec_prefix= -libdir=\${pcfiledir}/../../../$name -includedir=${source_path} - -Name: $fullname -Description: $comment -Version: $version -Requires: $requires -Conflicts: -Libs: -L\${libdir} -Wl,-rpath,\${libdir} -l${fullname#lib} $($shared || echo $libs) -Cflags: -I\${includedir} -EOF diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_quantization_misc.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_quantization_misc.h deleted file mode 100644 index c789754f4f1221a4cbb64dab2d433735e52049b9..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aacenc_quantization_misc.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * AAC encoder quantization - * Copyright (C) 2015 Claudio Freire - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * AAC encoder quantization misc reusable function templates - * @author Claudio Freire ( klaussfreire gmail com ) - */ - -#ifndef AVCODEC_AACENC_QUANTIZATION_MISC_H -#define AVCODEC_AACENC_QUANTIZATION_MISC_H - -static inline float quantize_band_cost_cached(struct AACEncContext *s, int w, int g, const float *in, - const float *scaled, int size, int scale_idx, - int cb, const float lambda, const float uplim, - int *bits, float *energy, int rtz) -{ - AACQuantizeBandCostCacheEntry *entry; - av_assert1(scale_idx >= 0 && scale_idx < 256); - entry = &s->quantize_band_cost_cache[scale_idx][w*16+g]; - if (entry->generation != s->quantize_band_cost_cache_generation || entry->cb != cb || entry->rtz != rtz) { - entry->rd = quantize_band_cost(s, in, scaled, size, scale_idx, - cb, lambda, uplim, &entry->bits, &entry->energy); - entry->cb = cb; - entry->rtz = rtz; - entry->generation = s->quantize_band_cost_cache_generation; - } - if (bits) - *bits = entry->bits; - if (energy) - *energy = entry->energy; - return entry->rd; -} - -#endif /* AVCODEC_AACENC_QUANTIZATION_MISC_H */ diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libspeexenc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libspeexenc.c deleted file mode 100644 index 9fdb247863b424a3c0333696e327612fe2c63eff..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libspeexenc.c +++ /dev/null @@ -1,366 +0,0 @@ -/* - * Copyright (C) 2009 Justin Ruggles - * Copyright (c) 2009 Xuggle Incorporated - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * libspeex Speex audio encoder - * - * Usage Guide - * This explains the values that need to be set prior to initialization in - * order to control various encoding parameters. - * - * Channels - * Speex only supports mono or stereo, so avctx->ch_layout.nb_channels must - * be set to 1 or 2. - * - * Sample Rate / Encoding Mode - * Speex has 3 modes, each of which uses a specific sample rate. - * narrowband : 8 kHz - * wideband : 16 kHz - * ultra-wideband : 32 kHz - * avctx->sample_rate must be set to one of these 3 values. This will be - * used to set the encoding mode. - * - * Rate Control - * VBR mode is turned on by setting AV_CODEC_FLAG_QSCALE in avctx->flags. - * avctx->global_quality is used to set the encoding quality. - * For CBR mode, avctx->bit_rate can be used to set the constant bitrate. - * Alternatively, the 'cbr_quality' option can be set from 0 to 10 to set - * a constant bitrate based on quality. - * For ABR mode, set avctx->bit_rate and set the 'abr' option to 1. - * Approx. Bitrate Range: - * narrowband : 2400 - 25600 bps - * wideband : 4000 - 43200 bps - * ultra-wideband : 4400 - 45200 bps - * - * Complexity - * Encoding complexity is controlled by setting avctx->compression_level. - * The valid range is 0 to 10. A higher setting gives generally better - * quality at the expense of encoding speed. This does not affect the - * bit rate. - * - * Frames-per-Packet - * The encoder defaults to using 1 frame-per-packet. However, it is - * sometimes desirable to use multiple frames-per-packet to reduce the - * amount of container overhead. This can be done by setting the - * 'frames_per_packet' option to a value 1 to 8. - * - * - * Optional features - * Speex encoder supports several optional features, which can be useful - * for some conditions. - * - * Voice Activity Detection - * When enabled, voice activity detection detects whether the audio - * being encoded is speech or silence/background noise. VAD is always - * implicitly activated when encoding in VBR, so the option is only useful - * in non-VBR operation. In this case, Speex detects non-speech periods and - * encodes them with just enough bits to reproduce the background noise. - * - * Discontinuous Transmission (DTX) - * DTX is an addition to VAD/VBR operation, that makes it possible to stop transmitting - * completely when the background noise is stationary. - * In file-based operation only 5 bits are used for such frames. - */ - -#include -#include -#include - -#include "libavutil/channel_layout.h" -#include "libavutil/common.h" -#include "libavutil/opt.h" -#include "avcodec.h" -#include "codec_internal.h" -#include "encode.h" -#include "audio_frame_queue.h" - -/* TODO: Think about converting abr, vad, dtx and such flags to a bit field */ -typedef struct LibSpeexEncContext { - AVClass *class; ///< AVClass for private options - SpeexBits bits; ///< libspeex bitwriter context - SpeexHeader header; ///< libspeex header struct - void *enc_state; ///< libspeex encoder state - int frames_per_packet; ///< number of frames to encode in each packet - float vbr_quality; ///< VBR quality 0.0 to 10.0 - int cbr_quality; ///< CBR quality 0 to 10 - int abr; ///< flag to enable ABR - int vad; ///< flag to enable VAD - int dtx; ///< flag to enable DTX - int pkt_frame_count; ///< frame count for the current packet - AudioFrameQueue afq; ///< frame queue -} LibSpeexEncContext; - -static av_cold void print_enc_params(AVCodecContext *avctx, - LibSpeexEncContext *s) -{ - const char *mode_str = "unknown"; - - av_log(avctx, AV_LOG_DEBUG, "channels: %d\n", avctx->ch_layout.nb_channels); - switch (s->header.mode) { - case SPEEX_MODEID_NB: mode_str = "narrowband"; break; - case SPEEX_MODEID_WB: mode_str = "wideband"; break; - case SPEEX_MODEID_UWB: mode_str = "ultra-wideband"; break; - } - av_log(avctx, AV_LOG_DEBUG, "mode: %s\n", mode_str); - if (s->header.vbr) { - av_log(avctx, AV_LOG_DEBUG, "rate control: VBR\n"); - av_log(avctx, AV_LOG_DEBUG, " quality: %f\n", s->vbr_quality); - } else if (s->abr) { - av_log(avctx, AV_LOG_DEBUG, "rate control: ABR\n"); - av_log(avctx, AV_LOG_DEBUG, " bitrate: %"PRId64" bps\n", avctx->bit_rate); - } else { - av_log(avctx, AV_LOG_DEBUG, "rate control: CBR\n"); - av_log(avctx, AV_LOG_DEBUG, " bitrate: %"PRId64" bps\n", avctx->bit_rate); - } - av_log(avctx, AV_LOG_DEBUG, "complexity: %d\n", - avctx->compression_level); - av_log(avctx, AV_LOG_DEBUG, "frame size: %d samples\n", - avctx->frame_size); - av_log(avctx, AV_LOG_DEBUG, "frames per packet: %d\n", - s->frames_per_packet); - av_log(avctx, AV_LOG_DEBUG, "packet size: %d\n", - avctx->frame_size * s->frames_per_packet); - av_log(avctx, AV_LOG_DEBUG, "voice activity detection: %d\n", s->vad); - av_log(avctx, AV_LOG_DEBUG, "discontinuous transmission: %d\n", s->dtx); -} - -static av_cold int encode_init(AVCodecContext *avctx) -{ - LibSpeexEncContext *s = avctx->priv_data; - int channels = avctx->ch_layout.nb_channels; - const SpeexMode *mode; - uint8_t *header_data; - int header_size; - int32_t complexity; - - /* sample rate and encoding mode */ - switch (avctx->sample_rate) { - case 8000: mode = speex_lib_get_mode(SPEEX_MODEID_NB); break; - case 16000: mode = speex_lib_get_mode(SPEEX_MODEID_WB); break; - case 32000: mode = speex_lib_get_mode(SPEEX_MODEID_UWB); break; - default: - av_log(avctx, AV_LOG_ERROR, "Sample rate of %d Hz is not supported. " - "Resample to 8, 16, or 32 kHz.\n", avctx->sample_rate); - return AVERROR(EINVAL); - } - - /* initialize libspeex */ - s->enc_state = speex_encoder_init(mode); - if (!s->enc_state) { - av_log(avctx, AV_LOG_ERROR, "Error initializing libspeex\n"); - return -1; - } - speex_init_header(&s->header, avctx->sample_rate, channels, mode); - - /* rate control method and parameters */ - if (avctx->flags & AV_CODEC_FLAG_QSCALE) { - /* VBR */ - s->header.vbr = 1; - s->vad = 1; /* VAD is always implicitly activated for VBR */ - speex_encoder_ctl(s->enc_state, SPEEX_SET_VBR, &s->header.vbr); - s->vbr_quality = av_clipf(avctx->global_quality / (float)FF_QP2LAMBDA, - 0.0f, 10.0f); - speex_encoder_ctl(s->enc_state, SPEEX_SET_VBR_QUALITY, &s->vbr_quality); - } else { - s->header.bitrate = avctx->bit_rate; - if (avctx->bit_rate > 0) { - /* CBR or ABR by bitrate */ - if (s->abr) { - speex_encoder_ctl(s->enc_state, SPEEX_SET_ABR, - &s->header.bitrate); - speex_encoder_ctl(s->enc_state, SPEEX_GET_ABR, - &s->header.bitrate); - } else { - speex_encoder_ctl(s->enc_state, SPEEX_SET_BITRATE, - &s->header.bitrate); - speex_encoder_ctl(s->enc_state, SPEEX_GET_BITRATE, - &s->header.bitrate); - } - } else { - /* CBR by quality */ - speex_encoder_ctl(s->enc_state, SPEEX_SET_QUALITY, - &s->cbr_quality); - speex_encoder_ctl(s->enc_state, SPEEX_GET_BITRATE, - &s->header.bitrate); - } - /* stereo side information adds about 800 bps to the base bitrate */ - /* TODO: this should be calculated exactly */ - avctx->bit_rate = s->header.bitrate + (channels == 2 ? 800 : 0); - } - - /* VAD is activated with VBR or can be turned on by itself */ - if (s->vad) - speex_encoder_ctl(s->enc_state, SPEEX_SET_VAD, &s->vad); - - /* Activating Discontinuous Transmission */ - if (s->dtx) { - speex_encoder_ctl(s->enc_state, SPEEX_SET_DTX, &s->dtx); - if (!(s->abr || s->vad || s->header.vbr)) - av_log(avctx, AV_LOG_WARNING, "DTX is not much of use without ABR, VAD or VBR\n"); - } - - /* set encoding complexity */ - if (avctx->compression_level > FF_COMPRESSION_DEFAULT) { - complexity = av_clip(avctx->compression_level, 0, 10); - speex_encoder_ctl(s->enc_state, SPEEX_SET_COMPLEXITY, &complexity); - } - speex_encoder_ctl(s->enc_state, SPEEX_GET_COMPLEXITY, &complexity); - avctx->compression_level = complexity; - - /* set packet size */ - avctx->frame_size = s->header.frame_size; - s->header.frames_per_packet = s->frames_per_packet; - - /* set encoding delay */ - speex_encoder_ctl(s->enc_state, SPEEX_GET_LOOKAHEAD, &avctx->initial_padding); - ff_af_queue_init(avctx, &s->afq); - - /* create header packet bytes from header struct */ - /* note: libspeex allocates the memory for header_data, which is freed - below with speex_header_free() */ - header_data = speex_header_to_packet(&s->header, &header_size); - - /* allocate extradata */ - avctx->extradata = av_malloc(header_size + AV_INPUT_BUFFER_PADDING_SIZE); - if (!avctx->extradata) { - speex_header_free(header_data); - speex_encoder_destroy(s->enc_state); - av_log(avctx, AV_LOG_ERROR, "memory allocation error\n"); - return AVERROR(ENOMEM); - } - - /* copy header packet to extradata */ - memcpy(avctx->extradata, header_data, header_size); - avctx->extradata_size = header_size; - speex_header_free(header_data); - - /* init libspeex bitwriter */ - speex_bits_init(&s->bits); - - print_enc_params(avctx, s); - return 0; -} - -static int encode_frame(AVCodecContext *avctx, AVPacket *avpkt, - const AVFrame *frame, int *got_packet_ptr) -{ - LibSpeexEncContext *s = avctx->priv_data; - int16_t *samples = frame ? (int16_t *)frame->data[0] : NULL; - int ret; - - if (samples) { - /* encode Speex frame */ - if (avctx->ch_layout.nb_channels == 2) - speex_encode_stereo_int(samples, s->header.frame_size, &s->bits); - speex_encode_int(s->enc_state, samples, &s->bits); - s->pkt_frame_count++; - if ((ret = ff_af_queue_add(&s->afq, frame)) < 0) - return ret; - } else { - /* handle end-of-stream */ - if (!s->pkt_frame_count) - return 0; - /* add extra terminator codes for unused frames in last packet */ - while (s->pkt_frame_count < s->frames_per_packet) { - speex_bits_pack(&s->bits, 15, 5); - s->pkt_frame_count++; - } - } - - /* write output if all frames for the packet have been encoded */ - if (s->pkt_frame_count == s->frames_per_packet) { - s->pkt_frame_count = 0; - if ((ret = ff_alloc_packet(avctx, avpkt, speex_bits_nbytes(&s->bits))) < 0) - return ret; - ret = speex_bits_write(&s->bits, avpkt->data, avpkt->size); - speex_bits_reset(&s->bits); - - /* Get the next frame pts/duration */ - ff_af_queue_remove(&s->afq, s->frames_per_packet * avctx->frame_size, - &avpkt->pts, &avpkt->duration); - - avpkt->size = ret; - *got_packet_ptr = 1; - return 0; - } - return 0; -} - -static av_cold int encode_close(AVCodecContext *avctx) -{ - LibSpeexEncContext *s = avctx->priv_data; - - speex_bits_destroy(&s->bits); - speex_encoder_destroy(s->enc_state); - - ff_af_queue_close(&s->afq); - - return 0; -} - -#define OFFSET(x) offsetof(LibSpeexEncContext, x) -#define AE AV_OPT_FLAG_AUDIO_PARAM | AV_OPT_FLAG_ENCODING_PARAM -static const AVOption options[] = { - { "abr", "Use average bit rate", OFFSET(abr), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, AE }, - { "cbr_quality", "Set quality value (0 to 10) for CBR", OFFSET(cbr_quality), AV_OPT_TYPE_INT, { .i64 = 8 }, 0, 10, AE }, - { "frames_per_packet", "Number of frames to encode in each packet", OFFSET(frames_per_packet), AV_OPT_TYPE_INT, { .i64 = 1 }, 1, 8, AE }, - { "vad", "Voice Activity Detection", OFFSET(vad), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, AE }, - { "dtx", "Discontinuous Transmission", OFFSET(dtx), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, AE }, - { NULL }, -}; - -static const AVClass speex_class = { - .class_name = "libspeex", - .item_name = av_default_item_name, - .option = options, - .version = LIBAVUTIL_VERSION_INT, -}; - -static const FFCodecDefault defaults[] = { - { "b", "0" }, - { "compression_level", "3" }, - { NULL }, -}; - -const FFCodec ff_libspeex_encoder = { - .p.name = "libspeex", - CODEC_LONG_NAME("libspeex Speex"), - .p.type = AVMEDIA_TYPE_AUDIO, - .p.id = AV_CODEC_ID_SPEEX, - .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_DELAY, - .caps_internal = FF_CODEC_CAP_NOT_INIT_THREADSAFE, - .priv_data_size = sizeof(LibSpeexEncContext), - .init = encode_init, - FF_CODEC_ENCODE_CB(encode_frame), - .close = encode_close, - .p.sample_fmts = (const enum AVSampleFormat[]){ AV_SAMPLE_FMT_S16, - AV_SAMPLE_FMT_NONE }, - CODEC_OLD_CHANNEL_LAYOUTS(AV_CH_LAYOUT_MONO, AV_CH_LAYOUT_STEREO) - .p.ch_layouts = (const AVChannelLayout[]) { AV_CHANNEL_LAYOUT_MONO, - AV_CHANNEL_LAYOUT_STEREO, - { 0 }, - }, - .p.supported_samplerates = (const int[]){ 8000, 16000, 32000, 0 }, - .p.priv_class = &speex_class, - .defaults = defaults, - .p.wrapper_name = "libspeex", -}; diff --git a/spaces/coldlarry/lr_pdf/app.py b/spaces/coldlarry/lr_pdf/app.py deleted file mode 100644 index 3ad4eb057cdfd93c1df0f3a3cfefbe176f2abce4..0000000000000000000000000000000000000000 --- a/spaces/coldlarry/lr_pdf/app.py +++ /dev/null @@ -1,53 +0,0 @@ -import gradio as gr -import openai -# from gpt_reader.pdf_reader import PaperReader -# from gpt_reader.prompt import BASE_POINTS -from Document_QA import QA -from Document_QA import create_embeddings -from Document_QA import Paper -from PyPDF2 import PdfReader - -class GUI: - def __init__(self): - self.api_key = "" - self.session = "" - self.all_embedding =None - self.tokens = 0 - #load pdf and create all embedings - def pdf_init(self, api_key, pdf_path): - openai.api_key = api_key - pdf_reader = PdfReader(pdf_path.name) - paper = Paper(pdf_reader) - all_texts = paper.get_texts() - self.all_embedding, self.tokens = create_embeddings(all_texts) - print("全部文本消耗 {} tokens".format(self.tokens)) - - def get_answer(self, question): - qa = QA(self.all_embedding) - answer,context = qa(question) - return answer.strip() - -with gr.Blocks() as demo: - gr.Markdown( - """ - # CHATGPT-PAPER-READER - [点击此处以支付 $5 成为我们的会员](https://checkout.stripe.com/c/pay/cs_live_a1TwwqhUpsfstnbyiAvbMoXvMzoaII5vskE8tz1cIsMSYUt9hJvoHK2qOK#fidkdWxOYHwnPyd1blppbHNgWjA0TlZXUHNAck9nTWNdXVc1TDRxTXIzQGo9b383N11yfDBhMzBvZ0pAMlNURDBBVWpiMHJObkhkSUZQSktwaWZ9S1dqUzFRRDw0f1dSa0dAQmp%2FYk5TS2tQNTVHa1F1RlVvPCcpJ3VpbGtuQH11anZgYUxhJz8nZEBQZko9MWRMPDxEYUNOZkhIJ3gl) - """) - - with gr.Tab("Upload PDF File"): - pdf_input = gr.File(label="PDF File") - api_input = gr.Textbox(label="OpenAI API Key") - #result = gr.Textbox(label="PDF Summary") - upload_button = gr.Button("Start Analyse") - with gr.Tab("Ask question about your PDF"): - question_input = gr.Textbox(label="Your Question", placeholder="Authors of this paper?") - answer = gr.Textbox(label="Answer") - ask_button = gr.Button("Ask") - - app = GUI() - upload_button.click(fn=app.pdf_init, inputs=[api_input, pdf_input]) - ask_button.click(app.get_answer, inputs=question_input, outputs=answer) - -if __name__ == "__main__": - demo.title = "CHATGPT-PAPER-READER" - demo.launch() # add "share=True" to share CHATGPT-PAPER-READER app on Internet. diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Template Bendera Aqiqah Word Gratis - Desain Cantik dan Menarik.md b/spaces/congsaPfin/Manga-OCR/logs/Download Template Bendera Aqiqah Word Gratis - Desain Cantik dan Menarik.md deleted file mode 100644 index 3c84ace22f1573fed7c63e37414874f94125f735..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Template Bendera Aqiqah Word Gratis - Desain Cantik dan Menarik.md +++ /dev/null @@ -1,196 +0,0 @@ - -

How to Download a Template for Bendera Aqiqah Word

If you are expecting or have recently welcomed a new baby into your Muslim family, you may want to celebrate their birth with a bendera aq

iqah word. Bendera aqiqah is a flag or banner that is used to announce the name of your child and express your gratitude to Allah for this blessing. It is part of the Islamic tradition of aqiqah, which is a welcoming ceremony that involves sacrificing an animal, shaving the baby's head, and giving charity.

-

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In this article, I will show you how to download a template for bendera aqiqah word, which you can use to create your own personalized flag or banner. I will also give you some tips and examples on how to edit and print your template. By the end of this article, you will be able to make a beautiful and unique bendera aqiqah word for your baby.

-

What You Need to Download a Template for Bendera Aqiqah Word

-

Before you start downloading a template for bendera aqiqah word, you will need the following things:

-
    -
  • A computer or laptop with an internet connection.
    • -
  • A software program that can open and edit word documents, such as Microsoft Word, Google Docs, or LibreOffice Writer.
    • -
  • A printer or a printing service that can print your template.
    • -
  • Some paper, scissors, and tape or glue to make your flag or banner.
    • -
-

Once you have these things ready, you can proceed to the next step.

-

Where to Find a Template for Bendera Aqiqah Word

-

There are many sources and websites where you can find and download free or paid templates for bendera aqiqah word. Here are some of them:

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    -
  • Bendera Aqiqah: This website offers various designs and styles of bendera aqiqah word templates that you can download for free. You can also request a custom design for a small fee.
    • -
  • Canva: This website is a popular online graphic design tool that allows you to create and edit your own bendera aqiqah word templates. You can choose from hundreds of templates and customize them with your own text, images, colors, fonts, and more. You can download your template as a PDF or JPG file for free or upgrade to a premium account for more features.
    • -
  • Etsy: This website is an online marketplace where you can buy and sell handmade and vintage goods. You can find many sellers who offer bendera aqiqah word templates that you can download and print. You can also contact them if you want a custom design or a physical product.
    • -
-

These are just some examples of where you can find a template for bendera aqiqah word. You can also search on Google or Pinterest for more options.

-

How to Choose a Template for Bendera Aqiqah Word

-

When choosing a template for bendera aqiqah word, you should consider the following factors:

-
    -
  • The size and shape of your flag or banner: Depending on how much space you have and how you want to display your flag or banner, you should choose a template that fits your needs. For example, if you want to hang it on a wall or a door, you may want a rectangular or triangular shape. If you want to attach it to a pole or a string, you may want a square or circular shape.
    • -
  • The design and style of your flag or banner: Depending on your personal taste and the theme of your baby's name, you should choose a template that matches your preferences. For example, if you want a simple and elegant look, you may want a template that has minimal text and colors. If you want a colorful and festive look, you may want a template that has more text and images.
    • -
  • The quality and resolution of your template: Depending on how clear and sharp you want your flag or banner to look, you should choose a template that has high quality and resolution. For example, if you want to print your template on a large scale, you may want a template that has at least 300 dpi (dots per inch) or higher. If you want to print your template on a small scale, you may want a template that has at least 150 dpi or higher.
    • -
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By considering these factors, you will be able to choose a template that suits your needs and preferences.

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How to Download a Template for Bendera Aqiqah Word

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Once you have chosen your source and template for bendera aqiqah word, you can download it to your computer or laptop by following these steps:

-
    -
  1. Go to the website where you found your template and click on the download button or link.
    2. -
  2. Select the file format that you want to download, such as DOC, DOCX, PDF, or JPG.
    3. -
  3. Choose the location where you want to save your template, such as your desktop or a folder.
    4. -
  4. Wait for the download to finish and check if your template is complete and correct.
    5. -
-

If you encounter any problems or errors during the download process, you can try the following solutions:

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    -
  • Refresh the website or try a different browser.
    • -
  • Check your internet connection and speed.
    • -
  • Clear your cache and cookies.
    • -
  • Contact the website owner or customer service for assistance.
    • -
-

After you have successfully downloaded your template, you can proceed to the next step.

-

How to Edit a Template for Bendera Aqiqah Word

-

After you have downloaded your template for bendera aqiqah word, you can edit it using Microsoft Word or other software that can open and edit word documents. You can customize and personalize your template by changing the text, color, image, size, and shape of your flag or banner. Here are some tips on how to do that:

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How to Change the Text

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To change the text on your template, you can follow these steps:

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    -
  1. Open your template with Microsoft Word or other software.
    2. -
  2. Select the text that you want to change and type in your own text.
    3. -
  3. Adjust the font style, size, alignment, and spacing of your text as needed.
    4. -
  4. Save your changes and preview your template.
    5. -
-

You can change the text on your template to include the following information:

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    -
  • The name of your baby in Arabic and English.
    • -
  • The date of birth of your baby in Hijri and Gregorian calendars.
    • -
  • The names of the parents of your baby.
    • -
  • A short prayer or dua for your baby.
    • -
  • Any other message or greeting that you want to add.
    • -
-

How to Change the Color

-

To change the color scheme of your template, you can follow these steps:

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    -
  1. Open your template with Microsoft Word or other software.
    2. -
  2. Select the element that you want to change the color of, such as the background, font, border, etc.
    3. -
  3. Choose a color from the color palette or use a custom color picker.
    4. -
  4. Save your changes and preview your template.
    5. -
-

You can change the color scheme of your template to match the following factors:

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    -
  • The gender of your baby: You can use pink for a girl, blue for a boy, or neutral colors for either.
    • -
  • The theme of your baby's name: You can use colors that reflect the meaning or origin of your baby's name. For example, if your baby's name is Nur (light), you can use bright colors like yellow or white. If your baby's name is Zara (star), you can use dark colors like black or purple.
    • -
  • Your personal preference: You can use colors that suit your taste and style. For example, if you like warm colors, you can use red, orange, or brown. If you like cool colors, you can use green, blue, or purple.
    • -
-

How to Change the Image

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To change or add an image on your template, you can follow these steps:

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    -
  1. Open your template with Microsoft Word or other software.
    2. -
  2. Select the image that you want to change or insert a new image from your computer or online sources.
    3. -
  3. Resize, crop, rotate, or flip your image as needed.
    4. -
  4. Save your changes and preview your template.
    5. -
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You can change or add an image on your template to include the following types of images:

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    -
  • A photo of your baby: You can use a cute and clear photo of your baby that shows their face and features. You can also use a photo of them with their parents or siblings. Make sure that the photo is appropriate and respectful for an Islamic occasion.
    • -
  • An Islamic symbol: You can use an image that represents Islam or aqiqah, such as a crescent moon and star, a mosque, a Quran, a sheep, etc. You can also use an image that has an Islamic calligraphy or art style. Make sure that the image is authentic and accurate for and shape that is appropriate and proportional. For example, if you want to hang it on a wall or a door, you may want a size and shape that covers the area well. If you want to attach it to a pole or a string, you may want a size and shape that is easy to handle and hang.
    • -
  • The design and style of your flag or banner: Depending on the design and style of your template, you should choose a size and shape that enhances and complements it. For example, if you have a lot of text and images on your template, you may want a size and shape that allows enough space and visibility for them. If you have a simple and minimal template, you may want a size and shape that adds some interest and contrast to it.
    • -
  • Your personal preference: Depending on your personal preference, you should choose a size and shape that suits your taste and style. For example, if you like a traditional and classic look, you may want a size and shape that is rectangular or triangular. If you like a modern and creative look, you may want a size and shape that is square or circular.
    • -
-

How to Print Your Template for Bendera Aqiqah Word

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After you have edited your template for bendera aqiqah word, you can print it using your printer or a printing service. Here are some tips on how to do that:

-

How to Choose the Paper Type and Quality

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To choose the best paper type and quality for your template, you should consider the following factors:

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    -
  • The durability and longevity of your flag or banner: Depending on how long you want to use and keep your flag or banner, you should choose a paper type and quality that is durable and long-lasting. For example, if you want to use your flag or banner for a one-time event, you may choose a paper type and quality that is cheap and disposable. If you want to use your flag or banner for a long time or keep it as a souvenir, you may choose a paper type and quality that is sturdy and resistant.
    • -
  • The appearance and feel of your flag or banner: Depending on how you want your flag or banner to look and feel, you should choose a paper type and quality that is appropriate and attractive. For example, if you want your flag or banner to have a glossy and shiny look, you may choose a paper type and quality that is glossy, such as photo paper or coated paper. If you want your flag or banner to have a matte and smooth look, you may choose a paper type and quality that is matte, such as cardstock or uncoated paper.
    • -
  • The cost and availability of your paper type and quality: Depending on your budget and resources, you should choose a paper type and quality that is affordable and accessible. For example, if you have a low budget and limited resources, you may choose a paper type and quality that is cheap and common, such as copy paper or printer paper. If you have a high budget and ample resources, you may choose a paper type and quality that is expensive and rare, such as specialty paper or fabric paper.
    • -
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How to Choose the Printing Option and Format

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To choose the best printing option and format for your template, you should consider the following factors:

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    -
  • The quality and resolution of your print: Depending on how clear and sharp you want your print to be, you should choose a printing option and format that is high quality and resolution. For example, if you want your print to be very clear and sharp, you should choose a printing option and format that is color or black-and-white, single-sided or double-sided, PDF or JPG, etc.
    • -
  • The size and shape of your print: Depending on the size and shape of your template, you should choose a printing option and format that is appropriate and proportional. For example, if your template is rectangular or triangular, you should choose a printing option and format that is A4 or letter size. If your template is square or circular, you should choose a printing option and format that is A5 or half letter size.
    • -
  • The cost and convenience of your print: Depending on your budget and time, you should choose a printing option and format that is affordable and convenient. For example, if you have a low budget and limited time, you may choose to print your template using your own printer at home. If you have a high budget and ample time, you may choose to print your template using a professional printing service online or offline.
    • -
-

How to Cut and Fold Your Template

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To cut and fold your template into a flag or banner shape, you can follow these steps:

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    -
  1. Print your template on the paper type and quality that you chose.
    2. -
  2. Cut out your template along the edges or the guidelines using scissors or a cutter.
    3. -
  3. Fold your template in half along the middle line or the crease using a ruler or a bone folder.
    4. -
  4. Glue or tape the two sides of your template together along the edges or the margins.
    5. -
  5. Punch holes on the corners or the sides of your template using a hole puncher or a needle.
    6. -
  6. Insert a string or a ribbon through the holes to make a loop or a knot.
    7. -
  7. Hang or attach your flag or banner to the desired location using nails, hooks, clips, etc.
    8. -
-

Examples of Bendera Aqiqah Designs

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To give you some inspiration or reference for your bendera aqiqah word, here are some examples of bendera aqiqah designs that you can use:

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ImageDescription
Pink bendera aqiqah with flowersThis is a pink bendera aqiqah with flowers that is suitable for a girl. It has the name of the baby in Arabic and English, the date of birth in Hijri and Gregorian calendars, the names of the parents, and a short dua. It also has an image of a flower on each corner. It has a rectangular shape and a glossy paper type.
Blue bendera aqiqah with starsThis is a blue bendera aqiqah with stars that is suitable for a boy. It has the name of the baby in Arabic and English, the date of birth in Hijri and Gregorian calendars, the names of the parents, and a short dua. It also has an image of a star on each corner. It has a triangular shape and a matte paper type.
Green bendera aqiqah with mosqueThis is a green bendera aqiqah with mosque that is suitable for either gender. It has the name of the baby in Arabic and English, the date of birth in Hijri and Gregorian calendars, the names of the parents, and a short dua. It also has an image of a mosque on the center. It has a square shape and a cardstock paper type.
-

Conclusion

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In conclusion, downloading a template for bendera aqiqah word is a simple and convenient way to create your own flag or banner for your baby's birth celebration. You can find and download various templates from different sources and websites, and edit them according to your needs and preferences. You can also print them using your printer or a printing service, and cut and fold them into a flag or banner shape. By following the tips and examples in this article, you will be able to make a beautiful and unique bendera aqiqah word for your baby.

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FAQs

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Here are some common questions that people may have about bendera aqiqah word:

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    -
  1. What is the meaning and significance of bendera aqiqah word?
    2. -

    Bendera aqiqah word is a flag or banner that is used to celebrate the birth of a Muslim child and announce their name. It is part of the Islamic tradition of aqiqah, which is a welcoming ceremony that involves sacrificing an animal, shaving the baby's head, and giving charity. Bendera aqiqah word is a way of expressing gratitude to Allah for this blessing and sharing it with others.

    -
  2. What are the benefits of using a template for bendera aqiqah word?
    3. -

    Using a template for bendera aqiqah word has many benefits, such as:

    -
      -
    • It saves you time and effort from designing your own flag or banner from scratch.
      • -
    • It gives you access to various designs and styles that you can choose from.
      • -
    • It allows you to customize and personalize your flag or banner with your own text, images, colors, fonts, etc.
      • -
    • It ensures that your flag or banner is consistent and professional-looking.
      • -
    • It makes your flag or banner more attractive and memorable.
      • -
    -
  3. How can I make my bendera aqiqah word more creative and original?
    4. -

    You can make your bendera aqiqah word more creative and original by:

    -
      -
    • Using your own photos or images that are meaningful and relevant to you and your baby.
      • -
    • Using colors that match your baby's gender, name, or personality.
      • -
    • Using fonts that are easy to read and reflect your style.
      • -
    • Using shapes that are different from the usual ones, such as oval, hexagon, or heart.
      • -
    • Adding some embellishments or decorations to your flag or banner, such as ribbons, beads, stickers, etc.
      • -
    -
  4. How can I display my bendera aqiqah word?
    5. -

    You can display your bendera aqiqah word in various ways, such as:

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      -
    • Hanging it on a wall, a door, a window, or a ceiling.
      • -
    • Attaching it to a pole, a string, or a wire.
      • -
    • Placing it on a table, a shelf, or a frame.
      • -
    • Giving it as a gift, a souvenir, or a keepsake.
      • -
    -
  5. Where can I learn more about bendera aqiqah word?
    6. -

    You can learn more about bendera aqiqah word by:

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      -
    • Reading some articles or books about Islamic traditions and ceremonies.
      • -
    • Watching some videos or tutorials on how to make bendera aqiqah word.
      • -
    • Visiting some websites or blogs that showcase bendera aqiqah word examples and ideas.
      • -
    • Asking some friends or family members who have experience with bendera aqiqah word.
      • -
    -

    I hope you enjoyed this article and learned something new. If you have any questions or comments, please feel free to share them below. Thank you for reading and happy bendera aqiqah word making!

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      diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/psa_mask.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/psa_mask.py deleted file mode 100644 index cdf14e62b50e8d4dd6856c94333c703bcc4c9ab6..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/psa_mask.py +++ /dev/null @@ -1,92 +0,0 @@ -# Modified from https://github.com/hszhao/semseg/blob/master/lib/psa -from torch import nn -from torch.autograd import Function -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['psamask_forward', 'psamask_backward']) - - -class PSAMaskFunction(Function): - - @staticmethod - def symbolic(g, input, psa_type, mask_size): - return g.op( - 'mmcv::MMCVPSAMask', - input, - psa_type_i=psa_type, - mask_size_i=mask_size) - - @staticmethod - def forward(ctx, input, psa_type, mask_size): - ctx.psa_type = psa_type - ctx.mask_size = _pair(mask_size) - ctx.save_for_backward(input) - - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - assert channels == h_mask * w_mask - output = input.new_zeros( - (batch_size, h_feature * w_feature, h_feature, w_feature)) - - ext_module.psamask_forward( - input, - output, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return output - - @staticmethod - def backward(ctx, grad_output): - input = ctx.saved_tensors[0] - psa_type = ctx.psa_type - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - grad_input = grad_output.new_zeros( - (batch_size, channels, h_feature, w_feature)) - ext_module.psamask_backward( - grad_output, - grad_input, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return grad_input, None, None, None - - -psa_mask = PSAMaskFunction.apply - - -class PSAMask(nn.Module): - - def __init__(self, psa_type, mask_size=None): - super(PSAMask, self).__init__() - assert psa_type in ['collect', 'distribute'] - if psa_type == 'collect': - psa_type_enum = 0 - else: - psa_type_enum = 1 - self.psa_type_enum = psa_type_enum - self.mask_size = mask_size - self.psa_type = psa_type - - def forward(self, input): - return psa_mask(input, self.psa_type_enum, self.mask_size) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(psa_type={self.psa_type}, ' - s += f'mask_size={self.mask_size})' - return s diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/samplers/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/samplers/__init__.py deleted file mode 100644 index 85c9f1a9df8a4038fbd4246239b699402e382309..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/samplers/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from .distributed_sampler import ( - InferenceSampler, - RandomSubsetTrainingSampler, - RepeatFactorTrainingSampler, - TrainingSampler, -) - -from .grouped_batch_sampler import GroupedBatchSampler - -__all__ = [ - "GroupedBatchSampler", - "TrainingSampler", - "RandomSubsetTrainingSampler", - "InferenceSampler", - "RepeatFactorTrainingSampler", -] diff --git a/spaces/cymic/Talking_Head_Anime_3/tha3/compute/__init__.py b/spaces/cymic/Talking_Head_Anime_3/tha3/compute/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/cymic/Waifu_Diffusion_Webui/modules/upscaler.py b/spaces/cymic/Waifu_Diffusion_Webui/modules/upscaler.py deleted file mode 100644 index d9d7c5e2a63b7f0fd390b85c57b5a2b0a08421dc..0000000000000000000000000000000000000000 --- a/spaces/cymic/Waifu_Diffusion_Webui/modules/upscaler.py +++ /dev/null @@ -1,121 +0,0 @@ -import os -from abc import abstractmethod - -import PIL -import numpy as np -import torch -from PIL import Image - -import modules.shared -from modules import modelloader, shared - -LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) -from modules.paths import models_path - - -class Upscaler: - name = None - model_path = None - model_name = None - model_url = None - enable = True - filter = None - model = None - user_path = None - scalers: [] - tile = True - - def __init__(self, create_dirs=False): - self.mod_pad_h = None - self.tile_size = modules.shared.opts.ESRGAN_tile - self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap - self.device = modules.shared.device - self.img = None - self.output = None - self.scale = 1 - self.half = not modules.shared.cmd_opts.no_half - self.pre_pad = 0 - self.mod_scale = None - if self.name is not None and create_dirs: - self.model_path = os.path.join(models_path, self.name) - if not os.path.exists(self.model_path): - os.makedirs(self.model_path) - - try: - import cv2 - self.can_tile = True - except: - pass - - @abstractmethod - def do_upscale(self, img: PIL.Image, selected_model: str): - return img - - def upscale(self, img: PIL.Image, scale: int, selected_model: str = None): - self.scale = scale - dest_w = img.width * scale - dest_h = img.height * scale - for i in range(3): - if img.width >= dest_w and img.height >= dest_h: - break - img = self.do_upscale(img, selected_model) - if img.width != dest_w or img.height != dest_h: - img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) - - return img - - @abstractmethod - def load_model(self, path: str): - pass - - def find_models(self, ext_filter=None) -> list: - return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path) - - def update_status(self, prompt): - print(f"\nextras: {prompt}", file=shared.progress_print_out) - - -class UpscalerData: - name = None - data_path = None - scale: int = 4 - scaler: Upscaler = None - model: None - - def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None): - self.name = name - self.data_path = path - self.scaler = upscaler - self.scale = scale - self.model = model - - -class UpscalerNone(Upscaler): - name = "None" - scalers = [] - - def load_model(self, path): - pass - - def do_upscale(self, img, selected_model=None): - return img - - def __init__(self, dirname=None): - super().__init__(False) - self.scalers = [UpscalerData("None", None, self)] - - -class UpscalerLanczos(Upscaler): - scalers = [] - - def do_upscale(self, img, selected_model=None): - return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS) - - def load_model(self, _): - pass - - def __init__(self, dirname=None): - super().__init__(False) - self.name = "Lanczos" - self.scalers = [UpscalerData("Lanczos", None, self)] - diff --git a/spaces/danterivers/music-generation-samples/audiocraft/modules/lstm.py b/spaces/danterivers/music-generation-samples/audiocraft/modules/lstm.py deleted file mode 100644 index c0866175950c1ca4f6cca98649525e6481853bba..0000000000000000000000000000000000000000 --- a/spaces/danterivers/music-generation-samples/audiocraft/modules/lstm.py +++ /dev/null @@ -1,25 +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. - -from torch import nn - - -class StreamableLSTM(nn.Module): - """LSTM without worrying about the hidden state, nor the layout of the data. - Expects input as convolutional layout. - """ - def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True): - super().__init__() - self.skip = skip - self.lstm = nn.LSTM(dimension, dimension, num_layers) - - def forward(self, x): - x = x.permute(2, 0, 1) - y, _ = self.lstm(x) - if self.skip: - y = y + x - y = y.permute(1, 2, 0) - return y diff --git a/spaces/danterivers/music-generation-samples/audiocraft/utils/autocast.py b/spaces/danterivers/music-generation-samples/audiocraft/utils/autocast.py deleted file mode 100644 index ed644843bb37cf8a92a20fbd51d6cebaa43b9a08..0000000000000000000000000000000000000000 --- a/spaces/danterivers/music-generation-samples/audiocraft/utils/autocast.py +++ /dev/null @@ -1,40 +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 - - -class TorchAutocast: - """TorchAutocast utility class. - Allows you to enable and disable autocast. This is specially useful - when dealing with different architectures and clusters with different - levels of support. - - Args: - enabled (bool): Whether to enable torch.autocast or not. - args: Additional args for torch.autocast. - kwargs: Additional kwargs for torch.autocast - """ - def __init__(self, enabled: bool, *args, **kwargs): - self.autocast = torch.autocast(*args, **kwargs) if enabled else None - - def __enter__(self): - if self.autocast is None: - return - try: - self.autocast.__enter__() - except RuntimeError: - device = self.autocast.device - dtype = self.autocast.fast_dtype - raise RuntimeError( - f"There was an error autocasting with dtype={dtype} device={device}\n" - "If you are on the FAIR Cluster, you might need to use autocast_dtype=float16" - ) - - def __exit__(self, *args, **kwargs): - if self.autocast is None: - return - self.autocast.__exit__(*args, **kwargs) diff --git a/spaces/davertor/colorizing_images/README.md b/spaces/davertor/colorizing_images/README.md deleted file mode 100644 index 371c7f02c417686db5c389be0b9c6b2a37acd0d9..0000000000000000000000000000000000000000 --- a/spaces/davertor/colorizing_images/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Colorizing_images -emoji: 📽 -colorFrom: blue -colorTo: red -sdk: streamlit -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/chatbot.css b/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/chatbot.css deleted file mode 100644 index d99584282c052861e5e401add62c3b94eb48ec65..0000000000000000000000000000000000000000 --- a/spaces/dawdqd/ChuanhuChatGPT/web_assets/stylesheet/chatbot.css +++ /dev/null @@ -1,278 +0,0 @@ - -hr.append-display { - margin: 8px 0; - border: none; - height: 1px; - border-top-width: 0; - background-image: linear-gradient(to right, rgba(50,50,50, 0.1), rgba(150, 150, 150, 0.8), rgba(50,50,50, 0.1)); -} -.source-a { - font-size: 0.8em; - max-width: 100%; - margin: 0; - display: flex; - flex-direction: row; - flex-wrap: wrap; - align-items: center; - /* background-color: #dddddd88; */ - border-radius: 1.5rem; - padding: 0.2em; -} -.source-a a { - display: inline-block; - background-color: #aaaaaa50; - border-radius: 1rem; - padding: 0.5em; - text-align: center; - text-overflow: ellipsis; - overflow: hidden; - min-width: 20%; - white-space: nowrap; - margin: 0.2rem 0.1rem; - text-decoration: none !important; - flex: 1; - transition: flex 0.5s; -} -.source-a a:hover { - background-color: #aaaaaa20; - flex: 2; -} - -/* 川虎助理 */ -.agent-prefix { - font-size: smaller; - opacity: 0.6; - padding: 6px 0 4px; -} -.agent-prefix::before { - content: '🐯'; - filter: grayscale(); - padding: 0 4px; -} - -/* 亮色（默认） */ -#chuanhu-chatbot { - background-color: var(--chatbot-background-color-light) !important; - color: var(--chatbot-color-light) !important; -} -[data-testid = "bot"] { - background-color: var(--message-bot-background-color-light) !important; -} -[data-testid = "user"] { - background-color: var(--message-user-background-color-light) !important; -} -/* 暗色 */ -.dark #chuanhu-chatbot { - background-color: var(--chatbot-background-color-dark) !important; - color: var(--chatbot-color-dark) !important; -} -.dark [data-testid = "bot"] { - background-color: var(--message-bot-background-color-dark) !important; -} -.dark [data-testid = "user"] { - background-color: var(--message-user-background-color-dark) !important; -} - -/* 对话气泡 */ -.message { - border-radius: var(--radius-xl) !important; - border: none; - padding: var(--spacing-xl) !important; - font-size: var(--text-md) !important; - line-height: var(--line-md) !important; - min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); - min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl)); -} -[data-testid = "bot"] { - max-width: calc(85% - 38px); - border-top-left-radius: 0 !important; -} -[data-testid = "user"] { - max-width: calc(85% - 38px); - width: auto !important; - border-top-right-radius: 0 !important; -} - -/* 屏幕宽度大于等于500px的设备 */ -/* update on 2023.4.8: 高度的细致调整已写入JavaScript */ -@media screen and (min-width: 500px) { - #chuanhu-chatbot { - height: calc(100vh - 200px); - } - #chuanhu-chatbot>.wrapper>.wrap { - max-height: calc(100vh - 200px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } -} -/* 屏幕宽度小于500px的设备 */ -@media screen and (max-width: 499px) { - #chuanhu-chatbot { - height: calc(100vh - 140px); - } - #chuanhu-chatbot>.wrapper>.wrap { - max-height: calc(100vh - 140px - var(--line-sm)*1rem - 2*var(--block-label-margin) ); - } - [data-testid = "bot"] { - max-width: calc(98% - 20px) !important; - } - .chatbot-avatar { - display: none; - } - #app-title h1{ - letter-spacing: -1px; font-size: 22px; - } -} - -#chuanhu-chatbot>.wrapper>.wrap { - overflow-x: hidden; -} - -.message.user p { - white-space: pre-wrap; -} -.message .user-message { - display: block; - padding: 0 !important; - white-space: pre-wrap; -} - -.message .md-message p { - margin-top: 0.6em !important; - margin-bottom: 0.6em !important; -} -.message .md-message p:first-child { margin-top: 0 !important; } -.message .md-message p:last-of-type { margin-bottom: 0 !important; } - -.message .md-message { - display: block; - padding: 0 !important; -} -.message .raw-message p { - margin:0 !important; -} -.message .raw-message { - display: block; - padding: 0 !important; - white-space: pre-wrap; -} -.message .hideM { - display: none; -} - -/* custom buttons */ -.chuanhu-btn { - border-radius: 5px; - /* background-color: #E6E6E6 !important; */ - color: rgba(120, 120, 120, 0.64) !important; - padding: 4px !important; - position: absolute; - right: -22px; - cursor: pointer !important; - transition: color .2s ease, background-color .2s ease; -} -.chuanhu-btn:hover { - background-color: rgba(167, 167, 167, 0.25) !important; - color: unset !important; -} -.chuanhu-btn:active { - background-color: rgba(167, 167, 167, 0.5) !important; -} -.chuanhu-btn:focus { - outline: none; -} - -.copy-bot-btn { - /* top: 18px; */ - bottom: 0; -} -.toggle-md-btn { - /* top: 0; */ - bottom: 20px; -} - -/* note: this is deprecated */ -.copy-code-btn { - position: relative; - float: right; - font-size: 1em; - cursor: pointer; -} -/* note: the button below disabled in chatbot.py */ -.message div.icon-button > button[title="copy"] { - display: none; -} - - -/* history message */ -.wrapper>.wrap>.history-message { - padding-bottom: 10px !important; -} -.history-message { - /* padding: 0 !important; */ - opacity: 80%; - display: flex; - flex-direction: column; -} -.history-message>.history-message { - padding: 0 !important; -} -.history-message>.message-wrap { - padding: 0 !important; - margin-bottom: 16px; -} -.history-message>.message { - margin-bottom: 16px; -} -.wrapper>.wrap>.history-message::after { - content: ""; - display: block; - height: 2px; - background-color: var(--body-text-color-subdued); - margin-bottom: 10px; - margin-top: -10px; - clear: both; -} -.wrapper>.wrap>.history-message>:last-child::after { - content: "仅供查看"; - display: block; - text-align: center; - color: var(--body-text-color-subdued); - font-size: 0.8em; -} - -/* #chuanhu-chatbot { - transition: height 0.3s ease; - note: find it better without transition animation...; -} */ - - -.message-row { - flex-direction: row; - display: flex; - gap: 8px; - width: 100%; -} -.bot-message-row { - justify-content: flex-start; -} -.user-message-row { - justify-content: flex-end; -} -.chatbot-avatar { - width: 32px; - height: 32px; - background-color: transparent; - background-size: cover; - border-radius: 5px !important; -} -.chatbot-avatar.bot-avatar { - margin-left: 5px; -} -.chatbot-avatar.user-avatar { - margin-right: 10px; -} -.chatbot-avatar img { - border-radius: 5px !important; - object-fit: cover; - width: 100%; - height: 100%; -} \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-4191a31f.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-4191a31f.js deleted file mode 100644 index b13f05aafaad6426d68f7dcaa6de7eff20aa904c..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-4191a31f.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as c,e as m,s as g,a9 as b,m as f,g as r,K as o,Y as d,h as v,j as p,ab as h,ac as w,ad as y,w as j,u as k,k as G}from"./index-9e76ffee.js";function C(n){let s,l,u,i;const _=n[4].default,a=b(_,n,n[3],null);return{c(){s=f("div"),l=f("div"),a&&a.c(),r(l,"class","styler svelte-iyf88w"),o(l,"--block-radius","0px"),o(l,"--block-border-width","0px"),o(l,"--layout-gap","1px"),o(l,"--form-gap-width","1px"),o(l,"--button-border-width","0px"),o(l,"--button-large-radius","0px"),o(l,"--button-small-radius","0px"),r(s,"id",n[0]),r(s,"class",u="gr-group "+n[1].join(" ")+" svelte-iyf88w"),d(s,"hide",!n[2])},m(e,t){v(e,s,t),p(s,l),a&&a.m(l,null),i=!0},p(e,[t]){a&&a.p&&(!i||t&8)&&h(a,_,e,e[3],i?y(_,e[3],t,null):w(e[3]),null),(!i||t&1)&&r(s,"id",e[0]),(!i||t&2&&u!==(u="gr-group "+e[1].join(" ")+" svelte-iyf88w"))&&r(s,"class",u),(!i||t&6)&&d(s,"hide",!e[2])},i(e){i||(j(a,e),i=!0)},o(e){k(a,e),i=!1},d(e){e&&G(s),a&&a.d(e)}}}function S(n,s,l){let{$$slots:u={},$$scope:i}=s,{elem_id:_=""}=s,{elem_classes:a=[]}=s,{visible:e=!0}=s;return n.$$set=t=>{"elem_id"in t&&l(0,_=t.elem_id),"elem_classes"in t&&l(1,a=t.elem_classes),"visible"in t&&l(2,e=t.visible),"$$scope"in t&&l(3,i=t.$$scope)},[_,a,e,i,u]}class q extends c{constructor(s){super(),m(this,s,S,C,g,{elem_id:0,elem_classes:1,visible:2})}}const Y=q,z=["static"];export{Y as Component,z as modes}; -//# sourceMappingURL=index-4191a31f.js.map diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bc19ffad.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bc19ffad.css deleted file mode 100644 index 12b9130ef86ebcd159cf75a369b754295aca6b4d..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-bc19ffad.css +++ /dev/null @@ -1 +0,0 @@ -.preview.svelte-1b19cri.svelte-1b19cri{display:flex;position:absolute;inset:0;flex-direction:column;z-index:var(--layer-2);backdrop-filter:blur(8px);background:var(--background-fill-primary);height:var(--size-full)}.fixed-height.svelte-1b19cri.svelte-1b19cri{min-height:var(--size-80);max-height:55vh}@media (min-width: 1280px){.fixed-height.svelte-1b19cri.svelte-1b19cri{min-height:450px}}.preview.svelte-1b19cri img.svelte-1b19cri{width:var(--size-full);height:calc(var(--size-full) - 60px);object-fit:contain}.preview.svelte-1b19cri img.with-caption.svelte-1b19cri{height:calc(var(--size-full) - 80px)}.caption.svelte-1b19cri.svelte-1b19cri{padding:var(--size-2) var(--size-3);overflow:hidden;color:var(--block-label-text-color);font-weight:var(--weight-semibold);text-align:center;text-overflow:ellipsis;white-space:nowrap}.thumbnails.svelte-1b19cri.svelte-1b19cri{display:flex;position:absolute;bottom:0;justify-content:center;align-items:center;gap:var(--spacing-lg);width:var(--size-full);height:var(--size-14);overflow-x:scroll}.thumbnail-item.svelte-1b19cri.svelte-1b19cri{--ring-color:transparent;position:relative;box-shadow:0 0 0 2px var(--ring-color),var(--shadow-drop);border:1px solid var(--border-color-primary);border-radius:var(--button-small-radius);background:var(--background-fill-secondary);aspect-ratio:var(--ratio-square);width:var(--size-full);height:var(--size-full);overflow:clip}.thumbnail-item.svelte-1b19cri.svelte-1b19cri:hover{--ring-color:var(--color-accent);filter:brightness(1.1)}.thumbnail-item.selected.svelte-1b19cri.svelte-1b19cri{--ring-color:var(--color-accent)}.thumbnail-small.svelte-1b19cri.svelte-1b19cri{flex:none;transform:scale(.9);transition:75ms;width:var(--size-9);height:var(--size-9)}.thumbnail-small.selected.svelte-1b19cri.svelte-1b19cri{--ring-color:var(--color-accent);transform:scale(1);border-color:var(--color-accent)}.thumbnail-small.svelte-1b19cri>img.svelte-1b19cri{width:var(--size-full);height:var(--size-full);overflow:hidden;object-fit:var(--object-fit)}.grid-wrap.svelte-1b19cri.svelte-1b19cri{position:relative;padding:var(--size-2);height:var(--size-full);overflow-y:scroll}.grid-container.svelte-1b19cri.svelte-1b19cri{display:grid;position:relative;grid-template-rows:repeat(var(--grid-rows),minmax(100px,1fr));grid-template-columns:repeat(var(--grid-cols),minmax(100px,1fr));grid-auto-rows:minmax(100px,1fr);gap:var(--spacing-lg)}.thumbnail-lg.svelte-1b19cri>img.svelte-1b19cri{width:var(--size-full);height:var(--size-full);overflow:hidden;object-fit:var(--object-fit)}.thumbnail-lg.svelte-1b19cri:hover .caption-label.svelte-1b19cri{opacity:.5}.caption-label.svelte-1b19cri.svelte-1b19cri{position:absolute;right:var(--block-label-margin);bottom:var(--block-label-margin);z-index:var(--layer-1);border-top:1px solid var(--border-color-primary);border-left:1px solid var(--border-color-primary);border-radius:var(--block-label-radius);background:var(--background-fill-secondary);padding:var(--block-label-padding);max-width:80%;overflow:hidden;font-size:var(--block-label-text-size);text-align:left;text-overflow:ellipsis;white-space:nowrap}.icon-button.svelte-1b19cri.svelte-1b19cri{position:absolute;top:0;right:0;z-index:var(--layer-1)}.icon-buttons.svelte-1b19cri.svelte-1b19cri{display:flex;position:absolute;right:0}.icon-buttons.svelte-1b19cri a.svelte-1b19cri{margin:var(--size-1) 0} diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/app.py b/spaces/deepskyreal/ai-mixer-hotchpotch/app.py deleted file mode 100644 index 79c18b5ed53871799ee893e234b583fc1c1a6215..0000000000000000000000000000000000000000 --- a/spaces/deepskyreal/ai-mixer-hotchpotch/app.py +++ /dev/null @@ -1,99 +0,0 @@ -import os - -import gradio as gr -import numpy as np -import translators as ts -from PIL import Image -from gradio import Blocks, Button, Textbox, Row, Column, Dropdown, Examples, Audio, Markdown -from langchain import Cohere, LLMChain, PromptTemplate -from transformers import BlipProcessor, BlipForConditionalGeneration - -from bark_speaker.txt2audio import gen_tts, AVAILABLE_PROMPTS -from comic_style.comic_style import inference -from sad_talker.src.gradio_demo import SadTalker - -processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") -model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") - - -def translate_into_cn(source): - print(ts.translators_pool) - result = ts.translate_text(query_text=source, translator='alibaba', from_language='en', to_language='zh') - return result - - -def predict_step(cohere_key, img, style): - i_image = Image.fromarray(np.array(img), 'RGB') - - pixel_values = processor(images=i_image, return_tensors="pt", max_length=1024, verbose=True).pixel_values - - output = model.generate(pixel_values) - - preds = processor.batch_decode(output, skip_special_tokens=True) - preds = [pred.strip() for pred in preds] - # 条件：严格按照要求完成任务，输出内容直接为主体内容，输出内容前后不要有其他符号，注意语句保持通顺，输出内容全部是中文，" \ " 不要重复输出内容, 不需要换行，不需要有标题，不需要排版格式。" \ "\n "\n2. Give the - # final output content an evaluation score as required. The score range is 0-100, 0 is the worst, 100 is the best, - # and the score should be objective. The format is [score:xxx]. Add at the end." \ - question = "Requirements: \nYou are a writing master. According to the content: {}, write a 50 words essay in any " \ - "form, by the style of \"{}\" as the final output content. " \ - "\nfinal output content:" \ - .format(preds[0], style) - print("question:{}".format(question)) - template = """{question}""" - prompt = PromptTemplate(template=template, input_variables=["question"]) - llm = Cohere(cohere_api_key=cohere_key, model="command", temperature=0.3, verbose=True) - llm_chain = LLMChain(prompt=prompt, llm=llm) - result = llm_chain.run(question) - print("result:{}".format(result)) - # result = llm.generate([prompt]) - return preds[0], translate_into_cn(result) - - -sad_talker = SadTalker(lazy_load=True) -with Blocks() as demo: - with Row(): - with Column(scale=1): - Markdown("[Cohere](https://dashboard.cohere.ai/)") - cohere_key = gr.Text(label="Cohere Key:") - Markdown("Scene 1:Img2Img(图生图)") - with Row(): - image_upload = gr.Image(type="pil", label="Essay Image") - comic_style_output = gr.Image(type="filepath", label="Comic Style") - Examples( - examples=[os.path.join(os.path.dirname(__file__), "example1.jpeg"), - os.path.join(os.path.dirname(__file__), "example2.jpg")], - fn=inference, - inputs=image_upload, - ) - dropdown = Dropdown( - ["shakespeare", "luxun", "xuzhimo", "moyan", "laoshe"], - value="luxun", - label="Essay Style", - info="选择你需要的文章的风格" - ) - essay_btn = Button("Generate Essay", variant='primary') - with Column(scale=1): - Markdown("Scene 2:ReadImg(识图)") - prediction_output = Textbox(label="Prediction") - Markdown("Scene 3:GenEssay(风格小作文)") - essay_output = Textbox(label="Essay", info="大约50字") - Markdown("Scene 4:Txt2Aud(文字转语音)") - audio_out = Audio(label="Generated Audio", type="filepath").style(height=20) - audio_option = Dropdown(AVAILABLE_PROMPTS, value="Speaker 7 (zh)", label="Acoustic Prompt", - elem_id="speaker_option") - audio_btn = Button("Generate Audio", variant='primary') - with Column(scale=1): - Markdown("Scene 5: Img&Aud2Talker(图片&语音转talker)") - gen_video = gr.Video(label="Generated video", format="mp4") - talker_btn = Button('Generate Talker', elem_id="sadtalker_generate", variant='primary') - - # Step 1 - image_upload.change(fn=inference, inputs=image_upload, outputs=comic_style_output) - # Step 2 - essay_btn.click(fn=predict_step, inputs=[cohere_key, image_upload, dropdown], outputs=[prediction_output, essay_output], - api_name="essay_generate") - # Step 3 - audio_btn.click(fn=gen_tts, inputs=[essay_output, audio_option], outputs=audio_out) - # Step 4 - talker_btn.click(fn=sad_talker.test, inputs=[comic_style_output, audio_out], outputs=[gen_video]) -demo.launch(debug=True) diff --git a/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine_selenium.py b/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine_selenium.py deleted file mode 100644 index b0fcb3fe113a80af20c9dc644cead553238b1ff1..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine_selenium.py +++ /dev/null @@ -1,130 +0,0 @@ -#!/usr/bin/env python -""" -@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation. -""" - -from __future__ import annotations - -import asyncio -import importlib -from concurrent import futures -from copy import deepcopy -from typing import Literal, Dict - -from selenium.webdriver.common.by import By -from selenium.webdriver.support import expected_conditions as EC -from selenium.webdriver.support.wait import WebDriverWait - -from metagpt.config import Config -from metagpt.utils.parse_html import WebPage - - -class SeleniumWrapper: - """Wrapper around Selenium. - - To use this module, you should check the following: - - 1. Run the following command: pip install metagpt[selenium]. - 2. Make sure you have a compatible web browser installed and the appropriate WebDriver set up - for that browser before running. For example, if you have Mozilla Firefox installed on your - computer, you can set the configuration SELENIUM_BROWSER_TYPE to firefox. After that, you - can scrape web pages using the Selenium WebBrowserEngine. - """ - - def __init__( - self, - options: Dict, - browser_type: Literal["chrome", "firefox", "edge", "ie"] | None = None, - launch_kwargs: dict | None = None, - *, - loop: asyncio.AbstractEventLoop | None = None, - executor: futures.Executor | None = None, - ) -> None: - if browser_type is None: - browser_type = options.get("selenium_browser_type") - self.browser_type = browser_type - launch_kwargs = launch_kwargs or {} - if options.get("global_proxy") and "proxy-server" not in launch_kwargs: - launch_kwargs["proxy-server"] = options.get("global_proxy") - - self.executable_path = launch_kwargs.pop("executable_path", None) - self.launch_args = [f"--{k}={v}" for k, v in launch_kwargs.items()] - self._has_run_precheck = False - self._get_driver = None - self.loop = loop - self.executor = executor - - async def run(self, url: str, *urls: str) -> WebPage | list[WebPage]: - await self._run_precheck() - - _scrape = lambda url: self.loop.run_in_executor(self.executor, self._scrape_website, url) - - if urls: - return await asyncio.gather(_scrape(url), *(_scrape(i) for i in urls)) - return await _scrape(url) - - async def _run_precheck(self): - if self._has_run_precheck: - return - self.loop = self.loop or asyncio.get_event_loop() - self._get_driver = await self.loop.run_in_executor( - self.executor, - lambda: _gen_get_driver_func(self.browser_type, *self.launch_args, executable_path=self.executable_path), - ) - self._has_run_precheck = True - - def _scrape_website(self, url): - with self._get_driver() as driver: - try: - driver.get(url) - WebDriverWait(driver, 30).until(EC.presence_of_element_located((By.TAG_NAME, "body"))) - inner_text = driver.execute_script("return document.body.innerText;") - html = driver.page_source - except Exception as e: - inner_text = f"Fail to load page content for {e}" - html = "" - return WebPage(inner_text=inner_text, html=html, url=url) - - -_webdriver_manager_types = { - "chrome": ("webdriver_manager.chrome", "ChromeDriverManager"), - "firefox": ("webdriver_manager.firefox", "GeckoDriverManager"), - "edge": ("webdriver_manager.microsoft", "EdgeChromiumDriverManager"), - "ie": ("webdriver_manager.microsoft", "IEDriverManager"), -} - - -def _gen_get_driver_func(browser_type, *args, executable_path=None): - WebDriver = getattr(importlib.import_module(f"selenium.webdriver.{browser_type}.webdriver"), "WebDriver") - Service = getattr(importlib.import_module(f"selenium.webdriver.{browser_type}.service"), "Service") - Options = getattr(importlib.import_module(f"selenium.webdriver.{browser_type}.options"), "Options") - - if not executable_path: - module_name, type_name = _webdriver_manager_types[browser_type] - DriverManager = getattr(importlib.import_module(module_name), type_name) - driver_manager = DriverManager() - # driver_manager.driver_cache.find_driver(driver_manager.driver)) - executable_path = driver_manager.install() - - def _get_driver(): - options = Options() - options.add_argument("--headless") - options.add_argument("--enable-javascript") - if browser_type == "chrome": - options.add_argument("--no-sandbox") - for i in args: - options.add_argument(i) - return WebDriver(options=deepcopy(options), service=Service(executable_path=executable_path)) - - return _get_driver - - -if __name__ == "__main__": - import fire - - async def main(url: str, *urls: str, browser_type: str = "chrome", **kwargs): - return await SeleniumWrapper(options=Config().runtime_options, - browser_type=browser_type, - **kwargs).run(url, *urls) - - fire.Fire(main) diff --git a/spaces/defengxiang/BIngAI/README.md b/spaces/defengxiang/BIngAI/README.md deleted file mode 100644 index 517aa48de5b4d5f3213fba94fe621cf7762ba9af..0000000000000000000000000000000000000000 --- a/spaces/defengxiang/BIngAI/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: BIngAI -emoji: 🚀 -colorFrom: yellow -colorTo: purple -sdk: docker -pinned: false -license: mit -app_port: 8080 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/deprem-ml/deprem-ocr/utils.py b/spaces/deprem-ml/deprem-ocr/utils.py deleted file mode 100644 index 22da600dc73faca4c356ea3113c7d9a644f2a5a4..0000000000000000000000000000000000000000 --- a/spaces/deprem-ml/deprem-ocr/utils.py +++ /dev/null @@ -1,53 +0,0 @@ -import cv2 -import csv -import json -from deta import Deta -import os -import requests - - -def preprocess_img(inp_image): - gray = cv2.cvtColor(inp_image, cv2.COLOR_BGR2GRAY) - gray_img = cv2.bitwise_not(gray) - return gray_img - - -def save_csv(mahalle, il, sokak, apartman): - adres_full = [mahalle, il, sokak, apartman] - - with open("adress_book.csv", "a", encoding="utf-8") as f: - write = csv.writer(f) - write.writerow(adres_full) - return adres_full - - -def get_json(mahalle, il, sokak, apartman): - adres = {"mahalle": mahalle, "il": il, "sokak": sokak, "apartman": apartman} - dump = json.dumps(adres, indent=4, ensure_ascii=False) - return dump - - -def write_db(data_dict): - # 2) initialize with a project key - deta_key = os.getenv("DETA_KEY") - deta = Deta(deta_key) - - # 3) create and use as many DBs as you want! - users = deta.Base("deprem-ocr") - users.insert(data_dict) - - -def ner_response(ocr_input): - API_URL = "https://api-inference.huggingface.co/models/deprem-ml/deprem-ner" - headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"} - - def query(payload): - response = requests.post(API_URL, headers=headers, json=payload) - return response.json() - - output = query( - { - "inputs": ocr_input, - } - ) - return output diff --git a/spaces/diacanFperku/AutoGPT/Coloring Pixels - RPG Book Download For Pc [hack] !!EXCLUSIVE!!.md b/spaces/diacanFperku/AutoGPT/Coloring Pixels - RPG Book Download For Pc [hack] !!EXCLUSIVE!!.md deleted file mode 100644 index cb48ea8f48c6bb41d8ec1251afba53276d635e43..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Coloring Pixels - RPG Book Download For Pc [hack] !!EXCLUSIVE!!.md +++ /dev/null @@ -1,6 +0,0 @@ -

      Coloring Pixels - RPG Book download for pc [hack]


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      - -Download these worlds or request more on our Pre-order the new book from ... Version Mobile Tutorial Free Download Premium Access Full Hack ... control scheme, higher resolution graphics, and a much smoother framerate. ... We have the ever popular Final Fantasy Sonic series as well as all of the Sonic RPG Episodes. 1fdad05405
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      diff --git a/spaces/diacanFperku/AutoGPT/Hdenvironmentsetup 11.md b/spaces/diacanFperku/AutoGPT/Hdenvironmentsetup 11.md deleted file mode 100644 index 985b5723792dc9f3e202f46b5488fc12fa3734cf..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Hdenvironmentsetup 11.md +++ /dev/null @@ -1,16 +0,0 @@ - -

      How to Set Up Your HD Environment in 11 Easy Steps

      -

      If you want to create stunning high-definition (HD) graphics for your projects, you need to set up your HD environment properly. HD environment is the combination of hardware, software, and settings that allow you to produce and display HD images and videos. In this article, we will show you how to set up your HD environment in 11 easy steps.

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      1. Choose the right monitor. The first step is to choose a monitor that supports HD resolution. HD resolution is typically 1920 x 1080 pixels or higher. You can check the resolution of your monitor by right-clicking on your desktop and selecting Display settings. Look for the option that says Resolution and choose the highest one available. If your monitor does not support HD resolution, you may need to upgrade to a new one.
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      2. Adjust the brightness and contrast. The next step is to adjust the brightness and contrast of your monitor to optimize the quality of your HD images and videos. You can do this by using the buttons or menu on your monitor or by using the Display settings on your computer. You want to make sure that the brightness and contrast are not too high or too low, as this can affect the colors and details of your HD content.
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      3. Calibrate the colors. The third step is to calibrate the colors of your monitor to ensure that they are accurate and consistent. You can do this by using a color calibration tool or software that comes with your monitor or by downloading a free online tool such as Calibrize. You want to make sure that the colors of your monitor match the colors of your HD content and that they are not too warm or too cool.
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      4. Select the right graphics card. The fourth step is to select a graphics card that can handle HD graphics. A graphics card is a device that processes and outputs the images and videos on your monitor. You can check the specifications of your graphics card by right-clicking on your desktop and selecting Device Manager. Look for the option that says Display adapters and click on it. You should see the name and model of your graphics card. If your graphics card does not support HD graphics, you may need to upgrade to a new one.
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      5. Update the drivers. The fifth step is to update the drivers of your graphics card to ensure that they are compatible with your HD content. Drivers are software that allow your graphics card to communicate with your computer and monitor. You can update the drivers of your graphics card by visiting the manufacturer's website and downloading the latest version. You should also check for Windows updates regularly, as they may include driver updates as well.
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      6. Choose the right software. The sixth step is to choose the right software for creating and editing your HD content. There are many software options available for different purposes, such as photo editing, video editing, animation, gaming, etc. You should choose the software that suits your needs and preferences and that supports HD resolution. Some examples of popular software for HD content are Photoshop, Premiere Pro, After Effects, Blender, Unity, etc.
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      7. Adjust the settings. The seventh step is to adjust the settings of your software to optimize the quality of your HD content. You should look for options that allow you to set the resolution, frame rate, bit rate, color depth, compression, etc. of your HD content. You should also look for options that allow you to preview and render your HD content in real time. You want to make sure that the settings are not too high or too low, as this can affect the performance and quality of your HD content.
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      8. Save and export. The eighth step is to save and export your HD content in a suitable format. You should choose a format that preserves the quality of your HD content and that is compatible with your intended platform or device. Some examples of common formats for HD content are JPEG, PNG, MP4, MOV, AVI, etc. You should also choose a file name and location that are easy to remember and access.
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        diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/monotonic_align/core.c b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/monotonic_align/core.c deleted file mode 100644 index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/monotonic_align/core.c +++ /dev/null @@ -1,26530 +0,0 @@ -/* Generated by Cython 3.0.0 */ - -/* BEGIN: Cython Metadata -{ - "distutils": { - "name": "monotonic_align.core", - "sources": [ - "core.pyx" - ] - }, - "module_name": "monotonic_align.core" -} -END: Cython Metadata */ - -#ifndef PY_SSIZE_T_CLEAN -#define PY_SSIZE_T_CLEAN -#endif /* PY_SSIZE_T_CLEAN */ -#if defined(CYTHON_LIMITED_API) && 0 - #ifndef Py_LIMITED_API - #if CYTHON_LIMITED_API+0 > 0x03030000 - #define Py_LIMITED_API CYTHON_LIMITED_API - #else - #define Py_LIMITED_API 0x03030000 - #endif - #endif -#endif - -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.7+ or Python 3.3+. -#else -#define CYTHON_ABI "3_0_0" -#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI -#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." -#define CYTHON_HEX_VERSION 0x030000F0 -#define CYTHON_FUTURE_DIVISION 1 -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#define __PYX_COMMA , -#ifndef HAVE_LONG_LONG - #define HAVE_LONG_LONG -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#if defined(GRAALVM_PYTHON) - /* For very preliminary testing purposes. 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#undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PYPY_VERSION) - #define CYTHON_COMPILING_IN_PYPY 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #if PY_VERSION_HEX < 0x03090000 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(CYTHON_LIMITED_API) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 1 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_CLINE_IN_TRACEBACK - #define CYTHON_CLINE_IN_TRACEBACK 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 1 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #endif - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS 1 - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 1 - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PY_NOGIL) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 1 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #ifndef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 -#else - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 1 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #ifndef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #endif - #ifndef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 1 - #endif - #if PY_MAJOR_VERSION < 3 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #ifndef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 1 - #endif - #ifndef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 1 - #endif - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #elif !defined(CYTHON_USE_UNICODE_WRITER) - #define CYTHON_USE_UNICODE_WRITER 1 - #endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #ifndef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) - #endif - 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#define CYTHON_USE_EXC_INFO_STACK 0 - #elif !defined(CYTHON_USE_EXC_INFO_STACK) - #define CYTHON_USE_EXC_INFO_STACK 1 - #endif - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if !defined(CYTHON_VECTORCALL) -#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) -#endif -#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_MAJOR_VERSION < 3 - #include "longintrepr.h" - #endif - #undef SHIFT - #undef BASE - #undef MASK - #ifdef SIZEOF_VOID_P - enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; - #endif -#endif -#ifndef __has_attribute - #define __has_attribute(x) 0 -#endif -#ifndef __has_cpp_attribute - #define __has_cpp_attribute(x) 0 -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#ifndef CYTHON_UNUSED - #if defined(__cplusplus) - /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(maybe_unused) - #define CYTHON_UNUSED [[maybe_unused]] - #endif - #endif - #endif -#endif -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_UNUSED_VAR -# if defined(__cplusplus) - template void CYTHON_UNUSED_VAR( const T& ) { } -# else -# define CYTHON_UNUSED_VAR(x) (void)(x) -# endif -#endif -#ifndef CYTHON_MAYBE_UNUSED_VAR - #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) -#ifdef _MSC_VER - #ifndef _MSC_STDINT_H_ - #if _MSC_VER < 1300 - typedef unsigned char uint8_t; - typedef unsigned short uint16_t; - typedef unsigned int uint32_t; - #else - typedef unsigned __int8 uint8_t; - typedef unsigned __int16 uint16_t; - typedef unsigned __int32 uint32_t; - #endif - #endif - #if _MSC_VER < 1300 - #ifdef _WIN64 - typedef unsigned long long __pyx_uintptr_t; - #else - typedef unsigned int __pyx_uintptr_t; - #endif - #else - #ifdef _WIN64 - typedef unsigned __int64 __pyx_uintptr_t; - #else - typedef unsigned __int32 __pyx_uintptr_t; - #endif - #endif -#else - #include - typedef uintptr_t __pyx_uintptr_t; -#endif -#ifndef CYTHON_FALLTHROUGH - #if defined(__cplusplus) - /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(fallthrough) - #define CYTHON_FALLTHROUGH [[fallthrough]] - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_cpp_attribute(clang::fallthrough) - #define CYTHON_FALLTHROUGH [[clang::fallthrough]] - #elif __has_cpp_attribute(gnu::fallthrough) - #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] - #endif - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_attribute(fallthrough) - #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) - #else - #define CYTHON_FALLTHROUGH - #endif - #endif - #if defined(__clang__) && defined(__apple_build_version__) - #if __apple_build_version__ < 7000000 - #undef CYTHON_FALLTHROUGH - #define CYTHON_FALLTHROUGH - #endif - #endif -#endif -#ifdef __cplusplus - template - struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; - #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) -#else - #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) -#endif -#if CYTHON_COMPILING_IN_PYPY == 1 - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) -#else - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) -#endif -#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) - -#ifndef CYTHON_INLINE - #if defined(__clang__) - #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) - #elif defined(__GNUC__) - #define CYTHON_INLINE __inline__ - #elif defined(_MSC_VER) - #define CYTHON_INLINE __inline - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_INLINE inline - #else - #define CYTHON_INLINE - #endif -#endif - -#define __PYX_BUILD_PY_SSIZE_T "n" -#define CYTHON_FORMAT_SSIZE_T "z" -#if PY_MAJOR_VERSION < 3 - #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" - #define __Pyx_DefaultClassType PyClass_Type - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#else - #define __Pyx_BUILTIN_MODULE_NAME "builtins" - #define __Pyx_DefaultClassType PyType_Type -#if PY_VERSION_HEX >= 0x030B00A1 - static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, - PyObject *code, PyObject *c, PyObject* n, PyObject *v, - PyObject *fv, PyObject *cell, PyObject* fn, - PyObject *name, int fline, PyObject *lnos) { - PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL; - PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL; - const char *fn_cstr=NULL; - const char *name_cstr=NULL; - PyCodeObject *co=NULL, *result=NULL; - PyObject *type, *value, *traceback; - PyErr_Fetch(&type, &value, &traceback); - if (!(kwds=PyDict_New())) goto end; - if (!(argcount=PyLong_FromLong(a))) goto end; - if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end; - if (!(posonlyargcount=PyLong_FromLong(p))) goto end; - if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end; - if (!(kwonlyargcount=PyLong_FromLong(k))) goto end; - if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end; - if (!(nlocals=PyLong_FromLong(l))) goto end; - if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end; - if (!(stacksize=PyLong_FromLong(s))) goto end; - if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end; - if (!(flags=PyLong_FromLong(f))) goto end; - if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end; - if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end; - if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end; - if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end; - if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end; - if (!(empty = PyTuple_New(0))) goto end; - result = (PyCodeObject*) PyObject_Call(replace, empty, kwds); - end: - Py_XDECREF((PyObject*) co); - Py_XDECREF(kwds); - Py_XDECREF(argcount); - Py_XDECREF(posonlyargcount); - Py_XDECREF(kwonlyargcount); - Py_XDECREF(nlocals); - Py_XDECREF(stacksize); - Py_XDECREF(replace); - Py_XDECREF(empty); - if (type) { - PyErr_Restore(type, value, traceback); - } - return result; - } -#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#else - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#endif -#endif -#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) - #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) -#else - #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) - #define __Pyx_Py_Is(x, y) Py_Is(x, y) -#else - #define __Pyx_Py_Is(x, y) ((x) == (y)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) - #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) -#else - #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) - #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) -#else - #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) - #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) -#else - #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) -#endif -#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) -#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) -#else - #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) -#endif -#ifndef CO_COROUTINE - #define CO_COROUTINE 0x80 -#endif -#ifndef CO_ASYNC_GENERATOR - #define CO_ASYNC_GENERATOR 0x200 -#endif -#ifndef Py_TPFLAGS_CHECKTYPES - #define Py_TPFLAGS_CHECKTYPES 0 -#endif -#ifndef Py_TPFLAGS_HAVE_INDEX - #define Py_TPFLAGS_HAVE_INDEX 0 -#endif -#ifndef Py_TPFLAGS_HAVE_NEWBUFFER - #define Py_TPFLAGS_HAVE_NEWBUFFER 0 -#endif -#ifndef Py_TPFLAGS_HAVE_FINALIZE - #define Py_TPFLAGS_HAVE_FINALIZE 0 -#endif -#ifndef Py_TPFLAGS_SEQUENCE - #define Py_TPFLAGS_SEQUENCE 0 -#endif -#ifndef Py_TPFLAGS_MAPPING - #define Py_TPFLAGS_MAPPING 0 -#endif -#ifndef METH_STACKLESS - #define METH_STACKLESS 0 -#endif -#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) - #ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - #endif - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); - typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast - #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords -#endif -#if CYTHON_METH_FASTCALL - #define __Pyx_METH_FASTCALL METH_FASTCALL - #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast - #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords -#else - #define __Pyx_METH_FASTCALL METH_VARARGS - #define __Pyx_PyCFunction_FastCall PyCFunction - #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords -#endif -#if CYTHON_VECTORCALL - #define __pyx_vectorcallfunc vectorcallfunc - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET - #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) -#elif CYTHON_BACKPORT_VECTORCALL - typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, - size_t nargsf, PyObject *kwnames); - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) -#else - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) -#endif -#if PY_VERSION_HEX < 0x030900B1 - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) - typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); -#else - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) - #define __Pyx_PyCMethod PyCMethod -#endif -#ifndef METH_METHOD - #define METH_METHOD 0x200 -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) - #define PyObject_Malloc(s) PyMem_Malloc(s) - #define PyObject_Free(p) PyMem_Free(p) - #define PyObject_Realloc(p) PyMem_Realloc(p) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) -#else - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyThreadState_Current PyThreadState_Get() -#elif !CYTHON_FAST_THREAD_STATE - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#elif PY_VERSION_HEX >= 0x03060000 - #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() -#elif PY_VERSION_HEX >= 0x03000000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#else - #define __Pyx_PyThreadState_Current _PyThreadState_Current -#endif -#if CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) -{ - void *result; - result = PyModule_GetState(op); - if (!result) - Py_FatalError("Couldn't find the module state"); - return result; -} -#endif -#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) -#else - #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) -#endif -#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) -#include "pythread.h" -#define Py_tss_NEEDS_INIT 0 -typedef int Py_tss_t; -static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { - *key = PyThread_create_key(); - return 0; -} -static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { - Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); - *key = Py_tss_NEEDS_INIT; - return key; -} -static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { - PyObject_Free(key); -} -static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { - return *key != Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { - PyThread_delete_key(*key); - *key = Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { - return PyThread_set_key_value(*key, value); -} -static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { - return PyThread_get_key_value(*key); -} -#endif -#if PY_MAJOR_VERSION < 3 - #if CYTHON_COMPILING_IN_PYPY - #if PYPY_VERSION_NUM < 0x07030600 - #if defined(__cplusplus) && __cplusplus >= 201402L - [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] - #elif defined(__GNUC__) || defined(__clang__) - __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) - #elif defined(_MSC_VER) - __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) - #endif - static CYTHON_INLINE int PyGILState_Check(void) { - return 0; - } - #else // PYPY_VERSION_NUM < 0x07030600 - #endif // PYPY_VERSION_NUM < 0x07030600 - #else - static CYTHON_INLINE int PyGILState_Check(void) { - PyThreadState * tstate = _PyThreadState_Current; - return tstate && (tstate == PyGILState_GetThisThreadState()); - } - #endif -#endif -#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) -#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) -#else -#define __Pyx_PyDict_NewPresized(n) PyDict_New() -#endif -#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS -#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { - PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); - if (res == NULL) PyErr_Clear(); - return res; -} -#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) -#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#else -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { -#if CYTHON_COMPILING_IN_PYPY - return PyDict_GetItem(dict, name); -#else - PyDictEntry *ep; - PyDictObject *mp = (PyDictObject*) dict; - long hash = ((PyStringObject *) name)->ob_shash; - assert(hash != -1); - ep = (mp->ma_lookup)(mp, name, hash); - if (ep == NULL) { - return NULL; - } - return ep->me_value; -#endif -} -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#endif -#if CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) - #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) - #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) -#else - #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) - #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) - #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next -#endif -#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 -#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ - PyTypeObject *type = Py_TYPE(obj);\ - assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ - PyObject_GC_Del(obj);\ - Py_DECREF(type);\ -} -#else -#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) - #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) - #define __Pyx_PyUnicode_DATA(u) ((void*)u) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) -#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) - #define CYTHON_PEP393_ENABLED 1 - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_READY(op) (0) - #else - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) - #endif - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) - #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) - #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) - #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) - #else - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) - #else - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) - #endif - #endif -#else - #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U) - #define __Pyx_PyUnicode_KIND(u) ((int)sizeof(Py_UNICODE)) - #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) -#else - #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ - PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #if !defined(PyUnicode_DecodeUnicodeEscape) - #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) - #endif - #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500) - #undef PyUnicode_Contains - #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) - #endif - #if !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) - #endif - #if !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) - #endif -#endif -#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) -#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) -#else - #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) -#endif -#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) - #define PyObject_ASCII(o) PyObject_Repr(o) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBaseString_Type PyUnicode_Type - #define PyStringObject PyUnicodeObject - #define PyString_Type PyUnicode_Type - #define PyString_Check PyUnicode_Check - #define PyString_CheckExact PyUnicode_CheckExact -#ifndef PyObject_Unicode - #define PyObject_Unicode PyObject_Str -#endif -#endif -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) - #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) -#else - #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) - #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) -#endif -#if CYTHON_COMPILING_IN_CPYTHON - #define __Pyx_PySequence_ListKeepNew(obj)\ - (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) -#else - #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) -#endif -#ifndef PySet_CheckExact - #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) -#endif -#if PY_VERSION_HEX >= 0x030900A4 - #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) - #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) -#else - #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) - #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) -#endif -#if CYTHON_ASSUME_SAFE_MACROS - #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) -#else - #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyIntObject PyLongObject - #define PyInt_Type PyLong_Type - #define PyInt_Check(op) PyLong_Check(op) - #define PyInt_CheckExact(op) PyLong_CheckExact(op) - #define __Pyx_Py3Int_Check(op) PyLong_Check(op) - #define __Pyx_Py3Int_CheckExact(op) PyLong_CheckExact(op) - #define PyInt_FromString PyLong_FromString - #define PyInt_FromUnicode PyLong_FromUnicode - #define PyInt_FromLong PyLong_FromLong - #define PyInt_FromSize_t PyLong_FromSize_t - #define PyInt_FromSsize_t PyLong_FromSsize_t - #define PyInt_AsLong PyLong_AsLong - #define PyInt_AS_LONG PyLong_AS_LONG - #define PyInt_AsSsize_t PyLong_AsSsize_t - #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask - #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask - #define PyNumber_Int PyNumber_Long -#else - #define __Pyx_Py3Int_Check(op) (PyLong_Check(op) || PyInt_Check(op)) - #define __Pyx_Py3Int_CheckExact(op) (PyLong_CheckExact(op) || PyInt_CheckExact(op)) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyBoolObject PyLongObject -#endif -#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY - #ifndef PyUnicode_InternFromString - #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) - #endif -#endif -#if PY_VERSION_HEX < 0x030200A4 - typedef long Py_hash_t; - #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t -#else - #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t -#endif -#if CYTHON_USE_ASYNC_SLOTS - #if PY_VERSION_HEX >= 0x030500B1 - #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods - #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) - #else - #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) - #endif -#else - #define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef __Pyx_PyAsyncMethodsStruct - typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; - } __Pyx_PyAsyncMethodsStruct; -#endif - -#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) - #if !defined(_USE_MATH_DEFINES) - #define _USE_MATH_DEFINES - #endif -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif -#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) -#define __Pyx_truncl trunc -#else -#define __Pyx_truncl truncl -#endif - -#define __PYX_MARK_ERR_POS(f_index, lineno) \ - { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } -#define __PYX_ERR(f_index, lineno, Ln_error) \ - { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } - -#ifdef CYTHON_EXTERN_C - #undef __PYX_EXTERN_C - #define __PYX_EXTERN_C CYTHON_EXTERN_C -#elif defined(__PYX_EXTERN_C) - #ifdef _MSC_VER - #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") - #else - #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. - #endif -#else - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__monotonic_align__core -#define __PYX_HAVE_API__monotonic_align__core -/* Early includes */ -#include "pythread.h" -#include -#include -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { - return (size_t) i < (size_t) limit; -} -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) - #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) -#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) -{ - const wchar_t *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#endif -#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); -#define __Pyx_PySequence_Tuple(obj)\ - (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_VERSION_HEX >= 0x030C00A7 - #ifndef _PyLong_SIGN_MASK - #define _PyLong_SIGN_MASK 3 - #endif - #ifndef _PyLong_NON_SIZE_BITS - #define _PyLong_NON_SIZE_BITS 3 - #endif - #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) - #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) - #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) - #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) - #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_SignedDigitCount(x)\ - ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) - #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) - #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) - #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) - #else - #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) - #endif - typedef Py_ssize_t __Pyx_compact_pylong; - typedef size_t __Pyx_compact_upylong; - #else // Py < 3.12 - #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) - #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) - #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) - #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) - #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) - #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) - #define __Pyx_PyLong_CompactValue(x)\ - ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) - typedef sdigit __Pyx_compact_pylong; - typedef digit __Pyx_compact_upylong; - #endif - #if PY_VERSION_HEX >= 0x030C00A5 - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) - #else - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) - #endif -#endif -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = (char) c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); - if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; - strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - return -1; -} -#endif -#endif - - -/* Test for GCC > 2.95 */ -#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) - #define likely(x) __builtin_expect(!!(x), 1) - #define unlikely(x) __builtin_expect(!!(x), 0) -#else /* !__GNUC__ or GCC < 2.95 */ - #define likely(x) (x) - #define unlikely(x) (x) -#endif /* __GNUC__ */ -static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } - -#if !CYTHON_USE_MODULE_STATE -static PyObject *__pyx_m = NULL; -#endif -static int __pyx_lineno; -static int __pyx_clineno = 0; -static const char * __pyx_cfilenm = __FILE__; -static const char *__pyx_filename; - -/* #### Code section: filename_table ### */ - -static const char *__pyx_f[] = { - "core.pyx", - "", -}; -/* #### Code section: utility_code_proto_before_types ### */ -/* ForceInitThreads.proto */ -#ifndef __PYX_FORCE_INIT_THREADS - #define __PYX_FORCE_INIT_THREADS 0 -#endif - -/* NoFastGil.proto */ -#define __Pyx_PyGILState_Ensure PyGILState_Ensure -#define __Pyx_PyGILState_Release PyGILState_Release -#define __Pyx_FastGIL_Remember() -#define __Pyx_FastGIL_Forget() -#define __Pyx_FastGilFuncInit() - -/* BufferFormatStructs.proto */ -struct __Pyx_StructField_; -#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) -typedef struct { - const char* name; - struct __Pyx_StructField_* fields; - size_t size; - size_t arraysize[8]; - int ndim; - char typegroup; - char is_unsigned; - int flags; -} __Pyx_TypeInfo; -typedef struct __Pyx_StructField_ { - __Pyx_TypeInfo* type; - const char* name; - size_t offset; -} __Pyx_StructField; -typedef struct { - __Pyx_StructField* field; - size_t parent_offset; -} __Pyx_BufFmt_StackElem; -typedef struct { - __Pyx_StructField root; - __Pyx_BufFmt_StackElem* head; - size_t fmt_offset; - size_t new_count, enc_count; - size_t struct_alignment; - int is_complex; - char enc_type; - char new_packmode; - char enc_packmode; - char is_valid_array; -} __Pyx_BufFmt_Context; - -/* Atomics.proto */ -#include -#ifndef CYTHON_ATOMICS - #define CYTHON_ATOMICS 1 -#endif -#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS -#define __pyx_atomic_int_type int -#define __pyx_nonatomic_int_type int -#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ - (__STDC_VERSION__ >= 201112L) &&\ - !defined(__STDC_NO_ATOMICS__)) - #include -#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ - (__cplusplus >= 201103L) ||\ - (defined(_MSC_VER) && _MSC_VER >= 1700))) - #include -#endif -#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ - (__STDC_VERSION__ >= 201112L) &&\ - !defined(__STDC_NO_ATOMICS__) &&\ - ATOMIC_INT_LOCK_FREE == 2) - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type atomic_int - #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) - #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) - #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) - #pragma message ("Using standard C atomics") - #elif defined(__PYX_DEBUG_ATOMICS) - #warning "Using standard C atomics" - #endif -#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ - (__cplusplus >= 201103L) ||\ -\ - (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ - ATOMIC_INT_LOCK_FREE == 2) - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type std::atomic_int - #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) - #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) - #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) - #pragma message ("Using standard C++ atomics") - #elif defined(__PYX_DEBUG_ATOMICS) - #warning "Using standard C++ atomics" - #endif -#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ - (__GNUC_MINOR__ > 1 ||\ - (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) - #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) - #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) - #ifdef __PYX_DEBUG_ATOMICS - #warning "Using GNU atomics" - #endif -#elif CYTHON_ATOMICS && defined(_MSC_VER) - #include - #undef __pyx_atomic_int_type - #define __pyx_atomic_int_type long - #define __pyx_nonatomic_int_type long - #pragma intrinsic (_InterlockedExchangeAdd) - #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) - #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) - #ifdef __PYX_DEBUG_ATOMICS - #pragma message ("Using MSVC atomics") - #endif -#else - #undef CYTHON_ATOMICS - #define CYTHON_ATOMICS 0 - #ifdef __PYX_DEBUG_ATOMICS - #warning "Not using atomics" - #endif -#endif -#if CYTHON_ATOMICS - #define __pyx_add_acquisition_count(memview)\ - __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) - #define __pyx_sub_acquisition_count(memview)\ - __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) -#else - #define __pyx_add_acquisition_count(memview)\ - __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) - #define __pyx_sub_acquisition_count(memview)\ - __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) -#endif - -/* MemviewSliceStruct.proto */ -struct __pyx_memoryview_obj; 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- -/* RaiseDoubleKeywords.proto */ -static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); - -/* ParseKeywords.proto */ -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, - PyObject **argnames[], - PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, - const char* function_name); - -/* ArgTypeTest.proto */ -#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ - ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ - __Pyx__ArgTypeTest(obj, type, name, exact)) -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); - -/* RaiseException.proto */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); - -/* PyFunctionFastCall.proto */ -#if CYTHON_FAST_PYCALL -#if !CYTHON_VECTORCALL -#define __Pyx_PyFunction_FastCall(func, args, nargs)\ - __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); -#endif -#define __Pyx_BUILD_ASSERT_EXPR(cond)\ - (sizeof(char [1 - 2*!(cond)]) - 1) -#ifndef Py_MEMBER_SIZE -#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) -#endif -#if !CYTHON_VECTORCALL -#if PY_VERSION_HEX >= 0x03080000 - #include "frameobject.h" -#if PY_VERSION_HEX >= 0x030b00a6 - #ifndef Py_BUILD_CORE - #define Py_BUILD_CORE 1 - #endif - #include "internal/pycore_frame.h" -#endif - #define __Pxy_PyFrame_Initialize_Offsets() - #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) -#else - static size_t __pyx_pyframe_localsplus_offset = 0; - #include "frameobject.h" - #define __Pxy_PyFrame_Initialize_Offsets()\ - ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ - (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) - #define __Pyx_PyFrame_GetLocalsplus(frame)\ - (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) -#endif -#endif -#endif - -/* PyObjectCall.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); -#else -#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) -#endif - -/* PyObjectCallMethO.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); -#endif - -/* PyObjectFastCall.proto */ -#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); - -/* RaiseUnexpectedTypeError.proto */ -static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); - -/* GCCDiagnostics.proto */ -#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define __Pyx_HAS_GCC_DIAGNOSTIC -#endif - -/* BuildPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); - -/* JoinPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char); - -/* StrEquals.proto */ -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals -#else -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals -#endif - -/* PyObjectFormatSimple.proto */ -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#elif PY_MAJOR_VERSION < 3 - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ - PyObject_Format(s, f)) -#elif CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ - likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ - PyObject_Format(s, f)) -#else - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#endif - -CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ -/* GetAttr.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); - -/* GetItemInt.proto */ -#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ - __Pyx_GetItemInt_Generic(o, to_py_func(i)))) -#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck); - -/* PyObjectCallOneArg.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); - -/* ObjectGetItem.proto */ -#if CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); -#else -#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) -#endif - -/* KeywordStringCheck.proto */ -static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); - -/* DivInt[Py_ssize_t].proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); - -/* UnaryNegOverflows.proto */ -#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ - (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) - -/* GetAttr3.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); - -/* PyDictVersioning.proto */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) -#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ - (version_var) = __PYX_GET_DICT_VERSION(dict);\ - (cache_var) = (value); -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ - (VAR) = __pyx_dict_cached_value;\ - } else {\ - (VAR) = __pyx_dict_cached_value = (LOOKUP);\ - __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ - }\ -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); -#else -#define __PYX_GET_DICT_VERSION(dict) (0) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); -#endif - -/* GetModuleGlobalName.proto */ -#if CYTHON_USE_DICT_VERSIONS -#define __Pyx_GetModuleGlobalName(var, name) do {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ - (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ - __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} while(0) -#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ - PY_UINT64_T __pyx_dict_version;\ - PyObject *__pyx_dict_cached_value;\ - (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} while(0) -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); -#else -#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) -#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); -#endif - -/* AssertionsEnabled.proto */ -#define __Pyx_init_assertions_enabled() -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define __pyx_assertions_enabled() (1) -#elif PY_VERSION_HEX < 0x03080000 || CYTHON_COMPILING_IN_PYPY || defined(Py_LIMITED_API) - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6 - static int __pyx_assertions_enabled_flag; - #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) - #undef __Pyx_init_assertions_enabled - static void __Pyx_init_assertions_enabled(void) { - __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level; - } -#else - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#endif - -/* RaiseTooManyValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); - -/* RaiseNeedMoreValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); - -/* RaiseNoneIterError.proto */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); - -/* ExtTypeTest.proto */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); - -/* GetTopmostException.proto */ -#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE -static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); -#endif - -/* SaveResetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); -#else -#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) -#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) -#endif - -/* GetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* SwapException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* Import.proto */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); - -/* ImportDottedModule.proto */ -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); -#endif - -/* ssize_strlen.proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -/* ListCompAppend.proto */ -#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS -static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { - PyListObject* L = (PyListObject*) list; - Py_ssize_t len = Py_SIZE(list); - if (likely(L->allocated > len)) { - Py_INCREF(x); - PyList_SET_ITEM(list, len, x); - __Pyx_SET_SIZE(list, len + 1); - return 0; - } - return PyList_Append(list, x); -} -#else -#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) -#endif - -/* PySequenceMultiply.proto */ -#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) -static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); - -/* SetItemInt.proto */ -#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ - __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) -static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, - int is_list, int wraparound, int boundscheck); - -/* RaiseUnboundLocalError.proto */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); - -/* DivInt[long].proto */ -static CYTHON_INLINE long __Pyx_div_long(long, long); - -/* PySequenceContains.proto */ -static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { - int result = PySequence_Contains(seq, item); - return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); -} - -/* ImportFrom.proto */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); - -/* HasAttr.proto */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); - -/* ErrOccurredWithGIL.proto */ -static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); - -/* PyObject_GenericGetAttrNoDict.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr -#endif - -/* PyObject_GenericGetAttr.proto */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr -#endif - -/* IncludeStructmemberH.proto */ -#include - -/* FixUpExtensionType.proto */ -#if CYTHON_USE_TYPE_SPECS -static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); -#endif - -/* PyObjectCallNoArg.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); - -/* PyObjectGetMethod.proto */ -static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); - -/* PyObjectCallMethod0.proto */ -static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); - -/* ValidateBasesTuple.proto */ -#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS -static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); -#endif - -/* PyType_Ready.proto */ -CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); - -/* SetVTable.proto */ -static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); - -/* GetVTable.proto */ -static void* __Pyx_GetVtable(PyTypeObject *type); - -/* MergeVTables.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_MergeVtables(PyTypeObject *type); -#endif - -/* SetupReduce.proto */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce(PyObject* type_obj); -#endif - -/* FetchSharedCythonModule.proto */ -static PyObject *__Pyx_FetchSharedCythonABIModule(void); - -/* FetchCommonType.proto */ -#if !CYTHON_USE_TYPE_SPECS -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); 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-#else - PyCMethodObject func; -#endif -#if CYTHON_BACKPORT_VECTORCALL - __pyx_vectorcallfunc func_vectorcall; -#endif -#if PY_VERSION_HEX < 0x030500A0 - PyObject *func_weakreflist; -#endif - PyObject *func_dict; - PyObject *func_name; - PyObject *func_qualname; - PyObject *func_doc; - PyObject *func_globals; - PyObject *func_code; - PyObject *func_closure; -#if PY_VERSION_HEX < 0x030900B1 - PyObject *func_classobj; -#endif - void *defaults; - int defaults_pyobjects; - size_t defaults_size; // used by FusedFunction for copying defaults - int flags; - PyObject *defaults_tuple; - PyObject *defaults_kwdict; - PyObject *(*defaults_getter)(PyObject *); - PyObject *func_annotations; - PyObject *func_is_coroutine; -} __pyx_CyFunctionObject; -#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) -#define __Pyx_IsCyOrPyCFunction(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) -#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) -static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); 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- -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); - -/* MemviewSliceCopyTemplate.proto */ -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -/* MemviewSliceInit.proto */ -#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d -#define __Pyx_MEMVIEW_DIRECT 1 -#define __Pyx_MEMVIEW_PTR 2 -#define __Pyx_MEMVIEW_FULL 4 -#define __Pyx_MEMVIEW_CONTIG 8 -#define __Pyx_MEMVIEW_STRIDED 16 -#define __Pyx_MEMVIEW_FOLLOW 32 -#define __Pyx_IS_C_CONTIG 1 -#define __Pyx_IS_F_CONTIG 2 -static int __Pyx_init_memviewslice( - struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference); 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- -/* CIntFromPy.proto */ -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -/* FormatTypeName.proto */ -#if CYTHON_COMPILING_IN_LIMITED_API -typedef PyObject *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%U" -static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); -#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) -#else -typedef const char *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%.200s" -#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) -#define __Pyx_DECREF_TypeName(obj) -#endif - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - -/* #### Code section: module_declarations ### */ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ -static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ -static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ -static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ - -/* Module declarations from "cython.view" */ - -/* Module declarations from "cython.dataclasses" */ - -/* Module declarations from "cython" */ - -/* Module declarations from "monotonic_align.core" */ -static PyObject *__pyx_collections_abc_Sequence = 0; -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static int __pyx_memoryview_thread_locks_used; -static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/ -static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ -static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ -static int __pyx_memoryview_err_no_memory(void); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ -/* #### Code section: typeinfo ### */ -static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 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__pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; -static const char __pyx_k_MemoryView_of_r_object[] = ""; -static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; -static const char __pyx_k_contiguous_and_indirect[] = ""; -static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; -static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; -static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; -static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; -static const char __pyx_k_strided_and_direct_or_indirect[] = ""; -static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; -static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; -static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; -static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; -static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; -static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; -static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; -static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; -static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; -static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; -static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; -static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -/* #### Code section: decls ### */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void 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-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType -#endif -#ifdef __Pyx_Generator_USED -#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType -#endif -#ifdef __Pyx_IterableCoroutine_USED -#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType -#endif -#ifdef __Pyx_Coroutine_USED -#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType -#endif -#ifdef __Pyx_Coroutine_USED -#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType -#endif -#if CYTHON_USE_MODULE_STATE -#endif -#if CYTHON_USE_MODULE_STATE -#endif -#if CYTHON_USE_MODULE_STATE -#endif -#if CYTHON_USE_MODULE_STATE -#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array -#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum -#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview -#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice -#endif -#define __pyx_array_type __pyx_mstate_global->__pyx_array_type -#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type -#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type -#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type -#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_ -#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII -#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi -#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError -#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri -#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is -#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor -#define __pyx_kp_s_Cannot_create_writable_memory_vi __pyx_mstate_global->__pyx_kp_s_Cannot_create_writable_memory_vi -#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type -#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with -#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct -#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis -#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr -#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0 -#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError -#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d -#define 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__pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d -#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError -#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object -#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError -#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView -#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 -#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23 -#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 -#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 -#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 -#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc -#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer -#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and -#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines -#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base -#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c -#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c -#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class -#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem -#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback -#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections -#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc -#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct -#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect -#define __pyx_kp_s_core_pyx __pyx_mstate_global->__pyx_kp_s_core_pyx -#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count -#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict -#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable -#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object -#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable -#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode -#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate -#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error -#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags -#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format -#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran -#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran -#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc -#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate -#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got -#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi -#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id -#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import -#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index -#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing -#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine -#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled -#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize -#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array -#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main -#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c -#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview -#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode -#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core -#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name -#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 -#define 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-#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range -#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce -#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython -#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex -#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register -#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate -#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython -#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape -#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size -#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec -#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start -#define __pyx_n_s_step __pyx_mstate_global->__pyx_n_s_step -#define __pyx_n_s_stop __pyx_mstate_global->__pyx_n_s_stop -#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct -#define __pyx_kp_s_strided_and_direct_or_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_direct_or_indirect -#define __pyx_kp_s_strided_and_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_indirect -#define __pyx_kp_s_stringsource __pyx_mstate_global->__pyx_kp_s_stringsource -#define __pyx_n_s_struct __pyx_mstate_global->__pyx_n_s_struct -#define __pyx_n_s_sys __pyx_mstate_global->__pyx_n_s_sys -#define __pyx_n_s_t_xs __pyx_mstate_global->__pyx_n_s_t_xs -#define __pyx_n_s_t_ys __pyx_mstate_global->__pyx_n_s_t_ys -#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test -#define __pyx_kp_s_unable_to_allocate_array_data __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_array_data -#define __pyx_kp_s_unable_to_allocate_shape_and_str __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_shape_and_str -#define __pyx_n_s_unpack __pyx_mstate_global->__pyx_n_s_unpack -#define __pyx_n_s_update __pyx_mstate_global->__pyx_n_s_update -#define __pyx_n_s_values __pyx_mstate_global->__pyx_n_s_values -#define __pyx_n_s_version_info __pyx_mstate_global->__pyx_n_s_version_info -#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0 -#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1 -#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3 -#define __pyx_int_112105877 __pyx_mstate_global->__pyx_int_112105877 -#define __pyx_int_136983863 __pyx_mstate_global->__pyx_int_136983863 -#define __pyx_int_184977713 __pyx_mstate_global->__pyx_int_184977713 -#define __pyx_int_neg_1 __pyx_mstate_global->__pyx_int_neg_1 -#define __pyx_k__9 __pyx_mstate_global->__pyx_k__9 -#define __pyx_slice__5 __pyx_mstate_global->__pyx_slice__5 -#define __pyx_tuple__4 __pyx_mstate_global->__pyx_tuple__4 -#define __pyx_tuple__8 __pyx_mstate_global->__pyx_tuple__8 -#define __pyx_tuple__10 __pyx_mstate_global->__pyx_tuple__10 -#define __pyx_tuple__11 __pyx_mstate_global->__pyx_tuple__11 -#define __pyx_tuple__12 __pyx_mstate_global->__pyx_tuple__12 -#define __pyx_tuple__13 __pyx_mstate_global->__pyx_tuple__13 -#define __pyx_tuple__14 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__pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { - Py_ssize_t __pyx_v_new_shape; - int __pyx_v_negative_step; - int __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; - #ifdef WITH_THREAD - PyGILState_STATE __pyx_gilstate_save; - #endif - - /* "View.MemoryView":813 - * cdef bint negative_step - * - * if not is_slice: # <<<<<<<<<<<<<< - * - * if start < 0: - */ - __pyx_t_1 = (!__pyx_v_is_slice); - if (__pyx_t_1) { - - /* "View.MemoryView":815 - * if not is_slice: - * - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if not 0 <= start < shape: - */ - __pyx_t_1 = (__pyx_v_start < 0); - if (__pyx_t_1) { - - /* "View.MemoryView":816 - * - * if start < 0: - * 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- if (n <= 0) { - return PyList_New(0); - } - res = PyList_New(n); - if (unlikely(res == NULL)) return NULL; - __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n); - return res; -} -#endif - -/* BytesEquals */ -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API - return PyObject_RichCompareBool(s1, s2, equals); -#else - if (s1 == s2) { - return (equals == Py_EQ); - } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { - const char *ps1, *ps2; - Py_ssize_t length = PyBytes_GET_SIZE(s1); - if (length != PyBytes_GET_SIZE(s2)) - return (equals == Py_NE); - ps1 = PyBytes_AS_STRING(s1); - ps2 = PyBytes_AS_STRING(s2); - if (ps1[0] != ps2[0]) { - return (equals == Py_NE); - } else if (length == 1) { - return (equals == Py_EQ); - } else { - int result; -#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) - Py_hash_t hash1, hash2; - hash1 = ((PyBytesObject*)s1)->ob_shash; - hash2 = ((PyBytesObject*)s2)->ob_shash; - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - return (equals == Py_NE); - } -#endif - result = memcmp(ps1, ps2, (size_t)length); - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -#endif -} - -/* UnicodeEquals */ -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API - return PyObject_RichCompareBool(s1, s2, equals); -#else -#if PY_MAJOR_VERSION < 3 - PyObject* owned_ref = NULL; -#endif - int s1_is_unicode, s2_is_unicode; - if (s1 == s2) { - goto return_eq; - } - s1_is_unicode = PyUnicode_CheckExact(s1); - s2_is_unicode = PyUnicode_CheckExact(s2); -#if PY_MAJOR_VERSION < 3 - if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { - owned_ref = PyUnicode_FromObject(s2); - if (unlikely(!owned_ref)) - return -1; - s2 = owned_ref; - s2_is_unicode = 1; - } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { - owned_ref = PyUnicode_FromObject(s1); - if (unlikely(!owned_ref)) - return -1; - s1 = owned_ref; - s1_is_unicode = 1; - } else if (((!s2_is_unicode) & (!s1_is_unicode))) { - return __Pyx_PyBytes_Equals(s1, s2, equals); - } -#endif - if (s1_is_unicode & s2_is_unicode) { - Py_ssize_t length; - int kind; - void *data1, *data2; - if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) - return -1; - length = __Pyx_PyUnicode_GET_LENGTH(s1); - if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { - goto return_ne; - } -#if CYTHON_USE_UNICODE_INTERNALS - { - Py_hash_t hash1, hash2; - #if CYTHON_PEP393_ENABLED - hash1 = ((PyASCIIObject*)s1)->hash; - hash2 = ((PyASCIIObject*)s2)->hash; - #else - hash1 = ((PyUnicodeObject*)s1)->hash; - hash2 = ((PyUnicodeObject*)s2)->hash; - #endif - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - goto return_ne; - } - } -#endif - kind = __Pyx_PyUnicode_KIND(s1); - if (kind != __Pyx_PyUnicode_KIND(s2)) { - goto return_ne; - } - data1 = __Pyx_PyUnicode_DATA(s1); - data2 = __Pyx_PyUnicode_DATA(s2); - if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { - goto return_ne; - } else if (length == 1) { - goto return_eq; - } else { - int result = memcmp(data1, data2, (size_t)(length * kind)); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & s2_is_unicode) { - goto return_ne; - } else if ((s2 == Py_None) & s1_is_unicode) { - goto return_ne; - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -return_eq: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ); -return_ne: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_NE); -#endif -} - -/* fastcall */ -#if CYTHON_METH_FASTCALL -static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) -{ - Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames); - for (i = 0; i < n; i++) - { - if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i]; - } - for (i = 0; i < n; i++) - { - int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ); - if (unlikely(eq != 0)) { - if (unlikely(eq < 0)) return NULL; // error - return kwvalues[i]; - } - } - return NULL; // not found (no exception set) -} -#endif - -/* RaiseArgTupleInvalid */ -static void __Pyx_RaiseArgtupleInvalid( - const char* func_name, - int exact, - Py_ssize_t num_min, - Py_ssize_t num_max, - Py_ssize_t num_found) -{ - Py_ssize_t num_expected; - const char *more_or_less; - if (num_found < num_min) { - num_expected = num_min; - more_or_less = "at least"; - } else { - num_expected = num_max; - more_or_less = "at most"; - } - if (exact) { - more_or_less = "exactly"; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); -} - -/* RaiseDoubleKeywords */ -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -/* ParseKeywords */ -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject *const *kwvalues, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds)); - while (1) { - if (kwds_is_tuple) { - if (pos >= PyTuple_GET_SIZE(kwds)) break; - key = PyTuple_GET_ITEM(kwds, pos); - value = kwvalues[pos]; - pos++; - } - else - { - if (!PyDict_Next(kwds, &pos, &key, &value)) break; - } - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = ( - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key) - ); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - #if PY_MAJOR_VERSION < 3 - PyErr_Format(PyExc_TypeError, - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) -{ - __Pyx_TypeName type_name; - __Pyx_TypeName obj_type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - else if (exact) { - #if PY_MAJOR_VERSION == 2 - if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - type_name = __Pyx_PyType_GetName(type); - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME - ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); - __Pyx_DECREF_TypeName(type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - __Pyx_PyThreadState_declare - CYTHON_UNUSED_VAR(cause); - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { - #if PY_VERSION_HEX >= 0x030C00A6 - PyException_SetTraceback(value, tb); - #elif CYTHON_FAST_THREAD_STATE - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#else - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* PyFunctionFastCall */ -#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = __Pyx_PyFrame_GetLocalsplus(f); - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = Py_TYPE(func)->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallMethO */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectFastCall */ -static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { - PyObject *argstuple; - PyObject *result; - size_t i; - argstuple = PyTuple_New((Py_ssize_t)nargs); - if (unlikely(!argstuple)) return NULL; - for (i = 0; i < nargs; i++) { - Py_INCREF(args[i]); - PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]); - } - result = __Pyx_PyObject_Call(func, argstuple, kwargs); - Py_DECREF(argstuple); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { - Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); -#if CYTHON_COMPILING_IN_CPYTHON - if (nargs == 0 && kwargs == NULL) { -#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG) - if (__Pyx_IsCyOrPyCFunction(func)) -#else - if (PyCFunction_Check(func)) -#endif - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - } - else if (nargs == 1 && kwargs == NULL) { - if (PyCFunction_Check(func)) - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, args[0]); - } - } - } -#endif - #if PY_VERSION_HEX < 0x030800B1 - #if CYTHON_FAST_PYCCALL - if (PyCFunction_Check(func)) { - if (kwargs) { - return _PyCFunction_FastCallDict(func, args, nargs, kwargs); - } else { - return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); - } - } - #if PY_VERSION_HEX >= 0x030700A1 - if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { - return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); - } - #endif - #endif - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); - } - #endif - #endif - #if CYTHON_VECTORCALL - vectorcallfunc f = _PyVectorcall_Function(func); - if (f) { - return f(func, args, (size_t)nargs, kwargs); - } - #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL - if (__Pyx_CyFunction_CheckExact(func)) { - __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); - if (f) return f(func, args, (size_t)nargs, kwargs); - } - #endif - if (nargs == 0) { - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); - } - return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); -} - -/* RaiseUnexpectedTypeError */ -static int -__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) -{ - __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, - expected, obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* CIntToDigits */ -static const char DIGIT_PAIRS_10[2*10*10+1] = { - "00010203040506070809" - "10111213141516171819" - "20212223242526272829" - "30313233343536373839" - "40414243444546474849" - "50515253545556575859" - "60616263646566676869" - "70717273747576777879" - "80818283848586878889" - "90919293949596979899" -}; -static const char DIGIT_PAIRS_8[2*8*8+1] = { - "0001020304050607" - "1011121314151617" - "2021222324252627" - "3031323334353637" - "4041424344454647" - "5051525354555657" - "6061626364656667" - "7071727374757677" -}; -static const char DIGITS_HEX[2*16+1] = { - "0123456789abcdef" - "0123456789ABCDEF" -}; - -/* BuildPyUnicode */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char) { - PyObject *uval; - Py_ssize_t uoffset = ulength - clength; -#if CYTHON_USE_UNICODE_INTERNALS - Py_ssize_t i; -#if CYTHON_PEP393_ENABLED - void *udata; - uval = PyUnicode_New(ulength, 127); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_DATA(uval); -#else - Py_UNICODE *udata; - uval = PyUnicode_FromUnicode(NULL, ulength); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_AS_UNICODE(uval); -#endif - if (uoffset > 0) { - i = 0; - if (prepend_sign) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); - i++; - } - for (; i < uoffset; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); - } - } - for (i=0; i < clength; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); - } -#else - { - PyObject *sign = NULL, *padding = NULL; - uval = NULL; - if (uoffset > 0) { - prepend_sign = !!prepend_sign; - if (uoffset > prepend_sign) { - padding = PyUnicode_FromOrdinal(padding_char); - if (likely(padding) && uoffset > prepend_sign + 1) { - PyObject *tmp; - PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); - if (unlikely(!repeat)) goto done_or_error; - tmp = PyNumber_Multiply(padding, repeat); - Py_DECREF(repeat); - Py_DECREF(padding); - padding = tmp; - } - if (unlikely(!padding)) goto done_or_error; - } - if (prepend_sign) { - sign = PyUnicode_FromOrdinal('-'); - if (unlikely(!sign)) goto done_or_error; - } - } - uval = PyUnicode_DecodeASCII(chars, clength, NULL); - if (likely(uval) && padding) { - PyObject *tmp = PyNumber_Add(padding, uval); - Py_DECREF(uval); - uval = tmp; - } - if (likely(uval) && sign) { - PyObject *tmp = PyNumber_Add(sign, uval); - Py_DECREF(uval); - uval = tmp; - } -done_or_error: - Py_XDECREF(padding); - Py_XDECREF(sign); - } -#endif - return uval; -} - -/* CIntToPyUnicode */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(int)*3+2]; - char *dpos, *end = digits + sizeof(int)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - int remaining; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (int) (remaining / (8*8)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (int) (remaining / (10*10)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (int) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* CIntToPyUnicode */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(Py_ssize_t)*3+2]; - char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - Py_ssize_t remaining; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (Py_ssize_t) (remaining / (8*8)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (Py_ssize_t) (remaining / (10*10)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (Py_ssize_t) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* JoinPyUnicode */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char) { -#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - PyObject *result_uval; - int result_ukind, kind_shift; - Py_ssize_t i, char_pos; - void *result_udata; - CYTHON_MAYBE_UNUSED_VAR(max_char); -#if CYTHON_PEP393_ENABLED - result_uval = PyUnicode_New(result_ulength, max_char); - if (unlikely(!result_uval)) return NULL; - result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; - kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; - result_udata = PyUnicode_DATA(result_uval); -#else - result_uval = PyUnicode_FromUnicode(NULL, result_ulength); - if (unlikely(!result_uval)) return NULL; - result_ukind = sizeof(Py_UNICODE); - kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; - result_udata = PyUnicode_AS_UNICODE(result_uval); -#endif - assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); - char_pos = 0; - for (i=0; i < value_count; i++) { - int ukind; - Py_ssize_t ulength; - void *udata; - PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); - if (unlikely(__Pyx_PyUnicode_READY(uval))) - goto bad; - ulength = __Pyx_PyUnicode_GET_LENGTH(uval); - if (unlikely(!ulength)) - continue; - if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) - goto overflow; - ukind = __Pyx_PyUnicode_KIND(uval); - udata = __Pyx_PyUnicode_DATA(uval); - if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { - memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); - } else { - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) - _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); - #else - Py_ssize_t j; - for (j=0; j < ulength; j++) { - Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); - __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); - } - #endif - } - char_pos += ulength; - } - return result_uval; -overflow: - PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); -bad: - Py_DECREF(result_uval); - return NULL; -#else - CYTHON_UNUSED_VAR(max_char); - CYTHON_UNUSED_VAR(result_ulength); - CYTHON_UNUSED_VAR(value_count); - return PyUnicode_Join(__pyx_empty_unicode, value_tuple); -#endif -} - -/* GetAttr */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_USE_TYPE_SLOTS -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -/* GetItemInt */ -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (unlikely(!j)) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_subscript) { - PyObject *r, *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return NULL; - r = mm->mp_subscript(o, key); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return sm->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* PyObjectCallOneArg */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *args[2] = {NULL, arg}; - return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); -} - -/* ObjectGetItem */ -#if CYTHON_USE_TYPE_SLOTS -static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { - PyObject *runerr = NULL; - Py_ssize_t key_value; - key_value = __Pyx_PyIndex_AsSsize_t(index); - if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { - return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); - } - if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { - __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); - PyErr_Clear(); - PyErr_Format(PyExc_IndexError, - "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); - __Pyx_DECREF_TypeName(index_type_name); - } - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { - __Pyx_TypeName obj_type_name; - if (likely(PyType_Check(obj))) { - PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); - if (meth) { - PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); - Py_DECREF(meth); - return result; - } - } - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { - PyTypeObject *tp = Py_TYPE(obj); - PyMappingMethods *mm = tp->tp_as_mapping; - PySequenceMethods *sm = tp->tp_as_sequence; - if (likely(mm && mm->mp_subscript)) { - return mm->mp_subscript(obj, key); - } - if (likely(sm && sm->sq_item)) { - return __Pyx_PyObject_GetIndex(obj, key); - } - return __Pyx_PyObject_GetItem_Slow(obj, key); -} -#endif - -/* KeywordStringCheck */ -static int __Pyx_CheckKeywordStrings( - PyObject *kw, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { - if (unlikely(PyTuple_GET_SIZE(kw) == 0)) - return 1; - if (!kw_allowed) { - key = PyTuple_GET_ITEM(kw, 0); - goto invalid_keyword; - } -#if PY_VERSION_HEX < 0x03090000 - for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) { - key = PyTuple_GET_ITEM(kw, pos); - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } -#endif - return 1; - } - while (PyDict_Next(kw, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if (!kw_allowed && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - #if PY_MAJOR_VERSION < 3 - PyErr_Format(PyExc_TypeError, - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -/* DivInt[Py_ssize_t] */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* GetAttr3 */ -static PyObject *__Pyx_GetAttr3Default(PyObject *d) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - __Pyx_PyErr_Clear(); - Py_INCREF(d); - return d; -} -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r; -#if CYTHON_USE_TYPE_SLOTS - if (likely(PyString_Check(n))) { - r = __Pyx_PyObject_GetAttrStrNoError(o, n); - if (unlikely(!r) && likely(!PyErr_Occurred())) { - r = __Pyx_NewRef(d); - } - return r; - } -#endif - r = PyObject_GetAttr(o, n); - return (likely(r)) ? r : __Pyx_GetAttr3Default(d); -} - -/* PyDictVersioning */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { - PyObject **dictptr = NULL; - Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; - if (offset) { -#if CYTHON_COMPILING_IN_CPYTHON - dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); -#else - dictptr = _PyObject_GetDictPtr(obj); -#endif - } - return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; -} -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) - return 0; - return obj_dict_version == __Pyx_get_object_dict_version(obj); -} -#endif - -/* GetModuleGlobalName */ -#if CYTHON_USE_DICT_VERSIONS -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) -#else -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) -#endif -{ - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 - result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } else if (unlikely(PyErr_Occurred())) { - return NULL; - } -#elif CYTHON_COMPILING_IN_LIMITED_API - if (unlikely(!__pyx_m)) { - return NULL; - } - result = PyObject_GetAttr(__pyx_m, name); - if (likely(result)) { - return result; - } -#else - result = PyDict_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } -#endif -#else - result = PyObject_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } - PyErr_Clear(); -#endif - return __Pyx_GetBuiltinName(name); -} - -/* RaiseTooManyValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* ExtTypeTest */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - __Pyx_TypeName obj_type_name; - __Pyx_TypeName type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - type_name = __Pyx_PyType_GetName(type); - PyErr_Format(PyExc_TypeError, - "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, - obj_type_name, type_name); - __Pyx_DECREF_TypeName(obj_type_name); - __Pyx_DECREF_TypeName(type_name); - return 0; -} - -/* GetTopmostException */ -#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE -static _PyErr_StackItem * -__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) -{ - _PyErr_StackItem *exc_info = tstate->exc_info; - while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && - exc_info->previous_item != NULL) - { - exc_info = exc_info->previous_item; - } - return exc_info; -} -#endif - -/* SaveResetException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - PyObject *exc_value = exc_info->exc_value; - if (exc_value == NULL || exc_value == Py_None) { - *value = NULL; - *type = NULL; - *tb = NULL; - } else { - *value = exc_value; - Py_INCREF(*value); - *type = (PyObject*) Py_TYPE(exc_value); - Py_INCREF(*type); - *tb = PyException_GetTraceback(exc_value); - } - #elif CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - *type = exc_info->exc_type; - *value = exc_info->exc_value; - *tb = exc_info->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); - #endif -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = tstate->exc_info; - PyObject *tmp_value = exc_info->exc_value; - exc_info->exc_value = value; - Py_XDECREF(tmp_value); - Py_XDECREF(type); - Py_XDECREF(tb); - #else - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = type; - exc_info->exc_value = value; - exc_info->exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); - #endif -} -#endif - -/* GetException */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) -#endif -{ - PyObject *local_type = NULL, *local_value, *local_tb = NULL; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if PY_VERSION_HEX >= 0x030C00A6 - local_value = tstate->current_exception; - tstate->current_exception = 0; - if (likely(local_value)) { - local_type = (PyObject*) Py_TYPE(local_value); - Py_INCREF(local_type); - local_tb = PyException_GetTraceback(local_value); - } - #else - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; - #endif -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 - if (unlikely(tstate->current_exception)) -#elif CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if CYTHON_USE_EXC_INFO_STACK - { - _PyErr_StackItem *exc_info = tstate->exc_info; - #if PY_VERSION_HEX >= 0x030B00a4 - tmp_value = exc_info->exc_value; - exc_info->exc_value = local_value; - tmp_type = NULL; - tmp_tb = NULL; - Py_XDECREF(local_type); - Py_XDECREF(local_tb); - #else - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = local_type; - exc_info->exc_value = local_value; - exc_info->exc_traceback = local_tb; - #endif - } - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* SwapException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_value = exc_info->exc_value; - exc_info->exc_value = *value; - if (tmp_value == NULL || tmp_value == Py_None) { - Py_XDECREF(tmp_value); - tmp_value = NULL; - tmp_type = NULL; - tmp_tb = NULL; - } else { - tmp_type = (PyObject*) Py_TYPE(tmp_value); - Py_INCREF(tmp_type); - #if CYTHON_COMPILING_IN_CPYTHON - tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; - Py_XINCREF(tmp_tb); - #else - tmp_tb = PyException_GetTraceback(tmp_value); - #endif - } - #elif CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = *type; - exc_info->exc_value = *value; - exc_info->exc_traceback = *tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - #endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#endif - -/* Import */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *module = 0; - PyObject *empty_dict = 0; - PyObject *empty_list = 0; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (unlikely(!py_import)) - goto bad; - if (!from_list) { - empty_list = PyList_New(0); - if (unlikely(!empty_list)) - goto bad; - from_list = empty_list; - } - #endif - empty_dict = PyDict_New(); - if (unlikely(!empty_dict)) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, 1); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, 1); - #endif - if (unlikely(!module)) { - if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (unlikely(!py_level)) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); - Py_DECREF(py_level); - #else - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, level); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, level); - #endif - #endif - } - } -bad: - Py_XDECREF(empty_dict); - Py_XDECREF(empty_list); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - return module; -} - -/* ImportDottedModule */ -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { - PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; - if (unlikely(PyErr_Occurred())) { - PyErr_Clear(); - } - if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { - partial_name = name; - } else { - slice = PySequence_GetSlice(parts_tuple, 0, count); - if (unlikely(!slice)) - goto bad; - sep = PyUnicode_FromStringAndSize(".", 1); - if (unlikely(!sep)) - goto bad; - partial_name = PyUnicode_Join(sep, slice); - } - PyErr_Format( -#if PY_MAJOR_VERSION < 3 - PyExc_ImportError, - "No module named '%s'", PyString_AS_STRING(partial_name)); -#else -#if PY_VERSION_HEX >= 0x030600B1 - PyExc_ModuleNotFoundError, -#else - PyExc_ImportError, -#endif - "No module named '%U'", partial_name); -#endif -bad: - Py_XDECREF(sep); - Py_XDECREF(slice); - Py_XDECREF(partial_name); - return NULL; -} -#endif -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { - PyObject *imported_module; -#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) - PyObject *modules = PyImport_GetModuleDict(); - if (unlikely(!modules)) - return NULL; - imported_module = __Pyx_PyDict_GetItemStr(modules, name); - Py_XINCREF(imported_module); -#else - imported_module = PyImport_GetModule(name); -#endif - return imported_module; -} -#endif -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { - Py_ssize_t i, nparts; - nparts = PyTuple_GET_SIZE(parts_tuple); - for (i=1; i < nparts && module; i++) { - PyObject *part, *submodule; -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - part = PyTuple_GET_ITEM(parts_tuple, i); -#else - part = PySequence_ITEM(parts_tuple, i); -#endif - submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); -#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) - Py_DECREF(part); -#endif - Py_DECREF(module); - module = submodule; - } - if (unlikely(!module)) { - return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); - } - return module; -} -#endif -static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { -#if PY_MAJOR_VERSION < 3 - PyObject *module, *from_list, *star = __pyx_n_s__3; - CYTHON_UNUSED_VAR(parts_tuple); - from_list = PyList_New(1); - if (unlikely(!from_list)) - return NULL; - Py_INCREF(star); - PyList_SET_ITEM(from_list, 0, star); - module = __Pyx_Import(name, from_list, 0); - Py_DECREF(from_list); - return module; -#else - PyObject *imported_module; - PyObject *module = __Pyx_Import(name, NULL, 0); - if (!parts_tuple || unlikely(!module)) - return module; - imported_module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(imported_module)) { - Py_DECREF(module); - return imported_module; - } - PyErr_Clear(); - return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); -#endif -} -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 - PyObject *module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(module)) { - PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); - if (likely(spec)) { - PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); - if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { - Py_DECREF(spec); - spec = NULL; - } - Py_XDECREF(unsafe); - } - if (likely(!spec)) { - PyErr_Clear(); - return module; - } - Py_DECREF(spec); - Py_DECREF(module); - } else if (PyErr_Occurred()) { - PyErr_Clear(); - } -#endif - return __Pyx__ImportDottedModule(name, parts_tuple); -} - -/* ssize_strlen */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { - size_t len = strlen(s); - if (unlikely(len > PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, "byte string is too long"); - return -1; - } - return (Py_ssize_t) len; -} - -/* FastTypeChecks */ -#if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (cls == a || cls == b) return 1; - mro = cls->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - PyObject *base = PyTuple_GET_ITEM(mro, i); - if (base == (PyObject *)a || base == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - if (exc_type1) { - return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); - } else { - return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } -} -#endif -static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - assert(PyExceptionClass_Check(exc_type)); - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { - return type->tp_as_sequence->sq_repeat(seq, mul); - } else -#endif - { - return __Pyx_PySequence_Multiply_Generic(seq, mul); - } -} - -/* SetItemInt */ -static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { - int r; - if (unlikely(!j)) return -1; - r = PyObject_SetItem(o, j, v); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, - CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { - PyObject* old = PyList_GET_ITEM(o, n); - Py_INCREF(v); - PyList_SET_ITEM(o, n, v); - Py_DECREF(old); - return 1; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_ass_subscript) { - int r; - PyObject *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return -1; - r = mm->mp_ass_subscript(o, key, v); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_ass_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return -1; - PyErr_Clear(); - } - } - return sm->sq_ass_item(o, i, v); - } - } -#else -#if CYTHON_COMPILING_IN_PYPY - if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) -#else - if (is_list || PySequence_Check(o)) -#endif - { - return PySequence_SetItem(o, i, v); - } -#endif - return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); -} - -/* RaiseUnboundLocalError */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - -/* DivInt[long] */ -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* ImportFrom */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - const char* module_name_str = 0; - PyObject* module_name = 0; - PyObject* module_dot = 0; - PyObject* full_name = 0; - PyErr_Clear(); - module_name_str = PyModule_GetName(module); - if (unlikely(!module_name_str)) { goto modbad; } - module_name = PyUnicode_FromString(module_name_str); - if (unlikely(!module_name)) { goto modbad; } - module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); - if (unlikely(!module_dot)) { goto modbad; } - full_name = PyUnicode_Concat(module_dot, name); - if (unlikely(!full_name)) { goto modbad; } - #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) - { - PyObject *modules = PyImport_GetModuleDict(); - if (unlikely(!modules)) - goto modbad; - value = PyObject_GetItem(modules, full_name); - } - #else - value = PyImport_GetModule(full_name); - #endif - modbad: - Py_XDECREF(full_name); - Py_XDECREF(module_dot); - Py_XDECREF(module_name); - } - if (unlikely(!value)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* HasAttr */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { - PyObject *r; - if (unlikely(!__Pyx_PyBaseString_Check(n))) { - PyErr_SetString(PyExc_TypeError, - "hasattr(): attribute name must be string"); - return -1; - } - r = __Pyx_GetAttr(o, n); - if (!r) { - PyErr_Clear(); - return 0; - } else { - Py_DECREF(r); - return 1; - } -} - -/* ErrOccurredWithGIL */ -static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { - int err; - #ifdef WITH_THREAD - PyGILState_STATE _save = PyGILState_Ensure(); - #endif - err = !!PyErr_Occurred(); - #ifdef WITH_THREAD - PyGILState_Release(_save); - #endif - return err; -} - -/* PyObject_GenericGetAttrNoDict */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", - type_name, attr_name); -#else - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", - type_name, PyString_AS_STRING(attr_name)); -#endif - __Pyx_DECREF_TypeName(type_name); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* FixUpExtensionType */ -#if CYTHON_USE_TYPE_SPECS -static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { -#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API - CYTHON_UNUSED_VAR(spec); - CYTHON_UNUSED_VAR(type); -#else - const PyType_Slot *slot = spec->slots; - while (slot && slot->slot && slot->slot != Py_tp_members) - slot++; - if (slot && slot->slot == Py_tp_members) { - int changed = 0; -#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) - const -#endif - PyMemberDef *memb = (PyMemberDef*) slot->pfunc; - while (memb && memb->name) { - if (memb->name[0] == '_' && memb->name[1] == '_') { -#if PY_VERSION_HEX < 0x030900b1 - if (strcmp(memb->name, "__weaklistoffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); - type->tp_weaklistoffset = memb->offset; - changed = 1; - } - else if (strcmp(memb->name, "__dictoffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); - type->tp_dictoffset = memb->offset; - changed = 1; - } -#if CYTHON_METH_FASTCALL - else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); -#if PY_VERSION_HEX >= 0x030800b4 - type->tp_vectorcall_offset = memb->offset; -#else - type->tp_print = (printfunc) memb->offset; -#endif - changed = 1; - } -#endif -#else - if ((0)); -#endif -#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON - else if (strcmp(memb->name, "__module__") == 0) { - PyObject *descr; - assert(memb->type == T_OBJECT); - assert(memb->flags == 0 || memb->flags == READONLY); - descr = PyDescr_NewMember(type, memb); - if (unlikely(!descr)) - return -1; - if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { - Py_DECREF(descr); - return -1; - } - Py_DECREF(descr); - changed = 1; - } -#endif - } - memb++; - } - if (changed) - PyType_Modified(type); - } -#endif - return 0; -} -#endif - -/* PyObjectCallNoArg */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { - PyObject *arg = NULL; - return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); -} - -/* PyObjectGetMethod */ -static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { - PyObject *attr; -#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP - __Pyx_TypeName type_name; - PyTypeObject *tp = Py_TYPE(obj); - PyObject *descr; - descrgetfunc f = NULL; - PyObject **dictptr, *dict; - int meth_found = 0; - assert (*method == NULL); - if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { - attr = __Pyx_PyObject_GetAttrStr(obj, name); - goto try_unpack; - } - if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { - return 0; - } - descr = _PyType_Lookup(tp, name); - if (likely(descr != NULL)) { - Py_INCREF(descr); -#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR - if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) -#elif PY_MAJOR_VERSION >= 3 - #ifdef __Pyx_CyFunction_USED - if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) - #else - if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) - #endif -#else - #ifdef __Pyx_CyFunction_USED - if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) - #else - if (likely(PyFunction_Check(descr))) - #endif -#endif - { - meth_found = 1; - } else { - f = Py_TYPE(descr)->tp_descr_get; - if (f != NULL && PyDescr_IsData(descr)) { - attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); - Py_DECREF(descr); - goto try_unpack; - } - } - } - dictptr = _PyObject_GetDictPtr(obj); - if (dictptr != NULL && (dict = *dictptr) != NULL) { - Py_INCREF(dict); - attr = __Pyx_PyDict_GetItemStr(dict, name); - if (attr != NULL) { - Py_INCREF(attr); - Py_DECREF(dict); - Py_XDECREF(descr); - goto try_unpack; - } - Py_DECREF(dict); - } - if (meth_found) { - *method = descr; - return 1; - } - if (f != NULL) { - attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); - Py_DECREF(descr); - goto try_unpack; - } - if (likely(descr != NULL)) { - *method = descr; - return 0; - } - type_name = __Pyx_PyType_GetName(tp); - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", - type_name, name); -#else - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", - type_name, PyString_AS_STRING(name)); -#endif - __Pyx_DECREF_TypeName(type_name); - return 0; -#else - attr = __Pyx_PyObject_GetAttrStr(obj, name); - goto try_unpack; -#endif -try_unpack: -#if CYTHON_UNPACK_METHODS - if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { - PyObject *function = PyMethod_GET_FUNCTION(attr); - Py_INCREF(function); - Py_DECREF(attr); - *method = function; - return 1; - } -#endif - *method = attr; - return 0; -} - -/* PyObjectCallMethod0 */ -static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { - PyObject *method = NULL, *result = NULL; - int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); - if (likely(is_method)) { - result = __Pyx_PyObject_CallOneArg(method, obj); - Py_DECREF(method); - return result; - } - if (unlikely(!method)) goto bad; - result = __Pyx_PyObject_CallNoArg(method); - Py_DECREF(method); -bad: - return result; -} - -/* ValidateBasesTuple */ -#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS -static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { - Py_ssize_t i, n = PyTuple_GET_SIZE(bases); - for (i = 1; i < n; i++) - { - PyObject *b0 = PyTuple_GET_ITEM(bases, i); - PyTypeObject *b; -#if PY_MAJOR_VERSION < 3 - if (PyClass_Check(b0)) - { - PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", - PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); - return -1; - } -#endif - b = (PyTypeObject*) b0; - if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) - { - __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); - PyErr_Format(PyExc_TypeError, - "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); - __Pyx_DECREF_TypeName(b_name); - return -1; - } - if (dictoffset == 0 && b->tp_dictoffset) - { - __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); - PyErr_Format(PyExc_TypeError, - "extension type '%.200s' has no __dict__ slot, " - "but base type '" __Pyx_FMT_TYPENAME "' has: " - "either add 'cdef dict __dict__' to the extension type " - "or add '__slots__ = [...]' to the base type", - type_name, b_name); - __Pyx_DECREF_TypeName(b_name); - return -1; - } - } - return 0; -} -#endif - -/* PyType_Ready */ -static int __Pyx_PyType_Ready(PyTypeObject *t) { -#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) - (void)__Pyx_PyObject_CallMethod0; -#if CYTHON_USE_TYPE_SPECS - (void)__Pyx_validate_bases_tuple; -#endif - return PyType_Ready(t); -#else - int r; - PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); - if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) - return -1; -#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) - { - int gc_was_enabled; - #if PY_VERSION_HEX >= 0x030A00b1 - gc_was_enabled = PyGC_Disable(); - (void)__Pyx_PyObject_CallMethod0; - #else - PyObject *ret, *py_status; - PyObject *gc = NULL; - #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) - gc = PyImport_GetModule(__pyx_kp_u_gc); - #endif - if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); - if (unlikely(!gc)) return -1; - py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); - if (unlikely(!py_status)) { - Py_DECREF(gc); - return -1; - } - gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); - Py_DECREF(py_status); - if (gc_was_enabled > 0) { - ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); - if (unlikely(!ret)) { - Py_DECREF(gc); - return -1; - } - Py_DECREF(ret); - } else if (unlikely(gc_was_enabled == -1)) { - Py_DECREF(gc); - return -1; - } - #endif - t->tp_flags |= Py_TPFLAGS_HEAPTYPE; -#if PY_VERSION_HEX >= 0x030A0000 - t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; -#endif -#else - (void)__Pyx_PyObject_CallMethod0; -#endif - r = PyType_Ready(t); -#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) - t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; - #if PY_VERSION_HEX >= 0x030A00b1 - if (gc_was_enabled) - PyGC_Enable(); - #else - if (gc_was_enabled) { - PyObject *tp, *v, *tb; - PyErr_Fetch(&tp, &v, &tb); - ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); - if (likely(ret || r == -1)) { - Py_XDECREF(ret); - PyErr_Restore(tp, v, tb); - } else { - Py_XDECREF(tp); - Py_XDECREF(v); - Py_XDECREF(tb); - r = -1; - } - } - Py_DECREF(gc); - #endif - } -#endif - return r; -#endif -} - -/* SetVTable */ -static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { - PyObject *ob = PyCapsule_New(vtable, 0, 0); - if (unlikely(!ob)) - goto bad; -#if CYTHON_COMPILING_IN_LIMITED_API - if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) -#else - if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) -#endif - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -/* GetVTable */ -static void* __Pyx_GetVtable(PyTypeObject *type) { - void* ptr; -#if CYTHON_COMPILING_IN_LIMITED_API - PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); -#else - PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); -#endif - if (!ob) - goto bad; - ptr = PyCapsule_GetPointer(ob, 0); - if (!ptr && !PyErr_Occurred()) - PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); - Py_DECREF(ob); - return ptr; -bad: - Py_XDECREF(ob); - return NULL; -} - -/* MergeVTables */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_MergeVtables(PyTypeObject *type) { - int i; - void** base_vtables; - __Pyx_TypeName tp_base_name; - __Pyx_TypeName base_name; - void* unknown = (void*)-1; - PyObject* bases = type->tp_bases; - int base_depth = 0; - { - PyTypeObject* base = type->tp_base; - while (base) { - base_depth += 1; - base = base->tp_base; - } - } - base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); - base_vtables[0] = unknown; - for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { - void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); - if (base_vtable != NULL) { - int j; - PyTypeObject* base = type->tp_base; - for (j = 0; j < base_depth; j++) { - if (base_vtables[j] == unknown) { - base_vtables[j] = __Pyx_GetVtable(base); - base_vtables[j + 1] = unknown; - } - if (base_vtables[j] == base_vtable) { - break; - } else if (base_vtables[j] == NULL) { - goto bad; - } - base = base->tp_base; - } - } - } - PyErr_Clear(); - free(base_vtables); - return 0; -bad: - tp_base_name = __Pyx_PyType_GetName(type->tp_base); - base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); - PyErr_Format(PyExc_TypeError, - "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); - __Pyx_DECREF_TypeName(tp_base_name); - __Pyx_DECREF_TypeName(base_name); - free(base_vtables); - return -1; -} -#endif - -/* SetupReduce */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_getstate = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; - PyObject *getstate = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); -#else - getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); - if (!getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (getstate) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); -#else - object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); - if (!object_getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (object_getstate != getstate) { - goto __PYX_GOOD; - } - } -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); - if (likely(reduce_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (reduce == object_reduce || PyErr_Occurred()) { - goto __PYX_BAD; - } - setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); - if (likely(setstate_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (!setstate || PyErr_Occurred()) { - goto __PYX_BAD; - } - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto __PYX_GOOD; -__PYX_BAD: - if (!PyErr_Occurred()) { - __Pyx_TypeName type_obj_name = - __Pyx_PyType_GetName((PyTypeObject*)type_obj); - PyErr_Format(PyExc_RuntimeError, - "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); - __Pyx_DECREF_TypeName(type_obj_name); - } - ret = -1; -__PYX_GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); - Py_XDECREF(object_getstate); - Py_XDECREF(getstate); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} -#endif - -/* FetchSharedCythonModule */ -static PyObject *__Pyx_FetchSharedCythonABIModule(void) { - PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME); - if (unlikely(!abi_module)) return NULL; - Py_INCREF(abi_module); - return abi_module; -} - -/* FetchCommonType */ -static int __Pyx_VerifyCachedType(PyObject *cached_type, - const char *name, - Py_ssize_t basicsize, - Py_ssize_t expected_basicsize) { - if (!PyType_Check(cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", name); - return -1; - } - if (basicsize != expected_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - name); - return -1; - } - return 0; -} -#if !CYTHON_USE_TYPE_SPECS -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* abi_module; - const char* object_name; - PyTypeObject *cached_type = NULL; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - object_name = strrchr(type->tp_name, '.'); - object_name = object_name ? object_name+1 : type->tp_name; - cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); - if (cached_type) { - if (__Pyx_VerifyCachedType( - (PyObject *)cached_type, - object_name, - cached_type->tp_basicsize, - type->tp_basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; -done: - Py_DECREF(abi_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#else -static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { - PyObject *abi_module, *cached_type = NULL; - const char* object_name = strrchr(spec->name, '.'); - object_name = object_name ? object_name+1 : spec->name; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - cached_type = PyObject_GetAttrString(abi_module, object_name); - if (cached_type) { - Py_ssize_t basicsize; -#if CYTHON_COMPILING_IN_LIMITED_API - PyObject *py_basicsize; - py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); - if (unlikely(!py_basicsize)) goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; -#else - basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; -#endif - if (__Pyx_VerifyCachedType( - cached_type, - object_name, - basicsize, - spec->basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - CYTHON_UNUSED_VAR(module); - cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); - if (unlikely(!cached_type)) goto bad; - if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; - if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; -done: - Py_DECREF(abi_module); - assert(cached_type == NULL || PyType_Check(cached_type)); - return (PyTypeObject *) cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#endif - -/* PyVectorcallFastCallDict */ -#if CYTHON_METH_FASTCALL -static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) -{ - PyObject *res = NULL; - PyObject *kwnames; - PyObject **newargs; - PyObject **kwvalues; - Py_ssize_t i, pos; - size_t j; - PyObject *key, *value; - unsigned long keys_are_strings; - Py_ssize_t nkw = PyDict_GET_SIZE(kw); - newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); - if (unlikely(newargs == NULL)) { - PyErr_NoMemory(); - return NULL; - } - for (j = 0; j < nargs; j++) newargs[j] = args[j]; - kwnames = PyTuple_New(nkw); - if (unlikely(kwnames == NULL)) { - PyMem_Free(newargs); - return NULL; - } - kwvalues = newargs + nargs; - pos = i = 0; - keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; - while (PyDict_Next(kw, &pos, &key, &value)) { - keys_are_strings &= Py_TYPE(key)->tp_flags; - Py_INCREF(key); - Py_INCREF(value); - PyTuple_SET_ITEM(kwnames, i, key); - kwvalues[i] = value; - i++; - } - if (unlikely(!keys_are_strings)) { - PyErr_SetString(PyExc_TypeError, "keywords must be strings"); - goto cleanup; - } - res = vc(func, newargs, nargs, kwnames); -cleanup: - Py_DECREF(kwnames); - for (i = 0; i < nkw; i++) - Py_DECREF(kwvalues[i]); - PyMem_Free(newargs); - return res; -} -static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) -{ - if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { - return vc(func, args, nargs, NULL); - } - return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); -} -#endif - -/* CythonFunctionShared */ -static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { -#if PY_VERSION_HEX < 0x030900B1 - __Pyx_Py_XDECREF_SET( - __Pyx_CyFunction_GetClassObj(f), - ((classobj) ? __Pyx_NewRef(classobj) : NULL)); -#else - __Pyx_Py_XDECREF_SET( - ((PyCMethodObject *) (f))->mm_class, - (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); -#endif -} -static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) -{ - CYTHON_UNUSED_VAR(closure); - if (unlikely(op->func_doc == NULL)) { - if (((PyCFunctionObject*)op)->m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_doc, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_name, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_qualname, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(value == NULL)) { - PyErr_SetString(PyExc_TypeError, - "function's dictionary may not be deleted"); - return -1; - } - if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "setting function's dictionary to a non-dict"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_dict, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - Py_INCREF(op->func_globals); - return op->func_globals; -} -static PyObject * -__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(op); - CYTHON_UNUSED_VAR(context); - Py_INCREF(Py_None); - return Py_None; -} -static PyObject * -__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) -{ - PyObject* result = (op->func_code) ? op->func_code : Py_None; - CYTHON_UNUSED_VAR(context); - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { - int result = 0; - PyObject *res = op->defaults_getter((PyObject *) op); - if (unlikely(!res)) - return -1; - #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - op->defaults_tuple = PyTuple_GET_ITEM(res, 0); - Py_INCREF(op->defaults_tuple); - op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); - Py_INCREF(op->defaults_kwdict); - #else - op->defaults_tuple = PySequence_ITEM(res, 0); - if (unlikely(!op->defaults_tuple)) result = -1; - else { - op->defaults_kwdict = PySequence_ITEM(res, 1); - if (unlikely(!op->defaults_kwdict)) result = -1; - } - #endif - Py_DECREF(res); - return result; -} -static int -__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { - CYTHON_UNUSED_VAR(context); - if (!value) { - value = Py_None; - } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "__defaults__ must be set to a tuple object"); - return -1; - } - PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " - "currently affect the values used in function calls", 1); - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { - PyObject* result = op->defaults_tuple; - CYTHON_UNUSED_VAR(context); - if (unlikely(!result)) { - if (op->defaults_getter) { - if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; - result = op->defaults_tuple; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { - CYTHON_UNUSED_VAR(context); - if (!value) { - value = Py_None; - } else if (unlikely(value != Py_None && !PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "__kwdefaults__ must be set to a dict object"); - return -1; - } - PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " - "currently affect the values used in function calls", 1); - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { - PyObject* result = op->defaults_kwdict; - CYTHON_UNUSED_VAR(context); - if (unlikely(!result)) { - if (op->defaults_getter) { - if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; - result = op->defaults_kwdict; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { - CYTHON_UNUSED_VAR(context); - if (!value || value == Py_None) { - value = NULL; - } else if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "__annotations__ must be set to a dict object"); - return -1; - } - Py_XINCREF(value); - __Pyx_Py_XDECREF_SET(op->func_annotations, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { - PyObject* result = op->func_annotations; - CYTHON_UNUSED_VAR(context); - if (unlikely(!result)) { - result = PyDict_New(); - if (unlikely(!result)) return NULL; - op->func_annotations = result; - } - Py_INCREF(result); - return result; -} -static PyObject * -__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { - int is_coroutine; - CYTHON_UNUSED_VAR(context); - if (op->func_is_coroutine) { - return __Pyx_NewRef(op->func_is_coroutine); 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- Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - Py_VISIT(m->func_is_coroutine); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); -#else - return PyString_FromFormat("", - PyString_AsString(op->func_qualname), (void *)op); -#endif -} -static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { - PyCFunctionObject* f = (PyCFunctionObject*)func; - PyCFunction meth = f->m_ml->ml_meth; - Py_ssize_t size; - switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { - case METH_VARARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) - return (*meth)(self, arg); - break; - case METH_VARARGS | METH_KEYWORDS: - return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); 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Py_False : Py_True; - Py_DECREF(use_cline_obj); - } else { - PyErr_Clear(); - use_cline = NULL; - } - } - if (!use_cline) { - c_line = 0; - (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); - } - else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { - c_line = 0; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - return c_line; -} -#endif - -/* CodeObjectCache */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} -#endif - -/* AddTraceback */ -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -#if PY_VERSION_HEX >= 0x030b00a6 - #ifndef Py_BUILD_CORE - #define Py_BUILD_CORE 1 - #endif - #include "internal/pycore_frame.h" -#endif -#if CYTHON_COMPILING_IN_LIMITED_API -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - if (c_line) { - (void) __pyx_cfilenm; - (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); - } - _PyTraceback_Add(funcname, filename, py_line); -} -#else -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = NULL; - PyObject *py_funcname = NULL; - #if PY_MAJOR_VERSION < 3 - PyObject *py_srcfile = NULL; - py_srcfile = PyString_FromString(filename); - if (!py_srcfile) goto bad; - #endif - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - funcname = PyUnicode_AsUTF8(py_funcname); - if (!funcname) goto bad; - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - if (!py_funcname) goto bad; - #endif - } - #if PY_MAJOR_VERSION < 3 - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - #else - py_code = PyCode_NewEmpty(filename, funcname, py_line); - #endif - Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline - return py_code; -bad: - Py_XDECREF(py_funcname); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_srcfile); - #endif - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject *ptype, *pvalue, *ptraceback; - if (c_line) { - c_line = __Pyx_CLineForTraceback(tstate, c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) { - /* If the code object creation fails, then we should clear the - fetched exception references and propagate the new exception */ - Py_XDECREF(ptype); - Py_XDECREF(pvalue); - Py_XDECREF(ptraceback); - goto bad; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - tstate, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} -#endif - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - __Pyx_TypeName obj_type_name; - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface", - obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if ((0)) {} - view->obj = NULL; - Py_DECREF(obj); -} -#endif - - -/* MemviewSliceIsContig */ -static int -__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) -{ - int i, index, step, start; - Py_ssize_t itemsize = mvs.memview->view.itemsize; - if (order == 'F') { - step = 1; - start = 0; - } else { - step = -1; - start = ndim - 1; - } - for (i = 0; i < ndim; i++) { - index = start + step * i; - if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) - return 0; - itemsize *= mvs.shape[index]; - } - return 1; -} - -/* OverlappingSlices */ -static void -__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, - void **out_start, void **out_end, - int ndim, size_t itemsize) -{ - char *start, *end; - int i; - start = end = slice->data; - for (i = 0; i < ndim; i++) { - Py_ssize_t stride = slice->strides[i]; - Py_ssize_t extent = slice->shape[i]; - if (extent == 0) { - *out_start = *out_end = start; - return; - } else { - if (stride > 0) - end += stride * (extent - 1); - else - start += stride * (extent - 1); - } - } - *out_start = start; - *out_end = end + itemsize; -} -static int -__pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize) -{ - void *start1, *end1, *start2, *end2; - __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); - __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); - return (start1 < end2) && (start2 < end1); -} - -/* IsLittleEndian */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) -{ - union { - uint32_t u32; - uint8_t u8[4]; - } S; - S.u32 = 0x01020304; - return S.u8[0] == 4; -} - -/* BufferFormatCheck */ -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t <= '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case '?': return "'bool'"; - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparsable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { - CYTHON_UNUSED_VAR(is_complex); - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { - CYTHON_UNUSED_VAR(is_complex); - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number, ndim; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ndim = ctx->head->field->type->ndim; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - CYTHON_FALLTHROUGH; - case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && - (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - CYTHON_FALLTHROUGH; - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* TypeInfoCompare */ - static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -/* MemviewSliceValidateAndInit */ - static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (unlikely(buf->strides[dim] != sizeof(void *))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (unlikely(buf->strides[dim] != buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (unlikely(stride < buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (unlikely(buf->suboffsets)) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) -{ - CYTHON_UNUSED_VAR(ndim); - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (unlikely(buf->ndim != ndim)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; - } - if (unlikely((unsigned) buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->len > 0) { - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) - goto fail; - if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) - goto fail; - } - if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) - goto fail; - } - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_float, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* MemviewSliceCopyTemplate */ - static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (unlikely(from_mvs->suboffsets[i] >= 0)) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -/* MemviewSliceInit */ - static int -__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference) -{ - __Pyx_RefNannyDeclarations - int i, retval=-1; - Py_buffer *buf = &memview->view; - __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (unlikely(memviewslice->memview || memviewslice->data)) { - PyErr_SetString(PyExc_ValueError, - "memviewslice is already initialized!"); - goto fail; - } - if (buf->strides) { - for (i = 0; i < ndim; i++) { - memviewslice->strides[i] = buf->strides[i]; - } - } else { - Py_ssize_t stride = buf->itemsize; - for (i = ndim - 1; i >= 0; i--) { - memviewslice->strides[i] = stride; - stride *= buf->shape[i]; - } - } - for (i = 0; i < ndim; i++) { - memviewslice->shape[i] = buf->shape[i]; - if (buf->suboffsets) { - memviewslice->suboffsets[i] = buf->suboffsets[i]; - } else { - memviewslice->suboffsets[i] = -1; - } - } - memviewslice->memview = memview; - memviewslice->data = (char *)buf->buf; - if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { - Py_INCREF(memview); - } - retval = 0; - goto no_fail; -fail: - memviewslice->memview = 0; - memviewslice->data = 0; - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} -#ifndef Py_NO_RETURN -#define Py_NO_RETURN -#endif -static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { - va_list vargs; - char msg[200]; -#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) - va_start(vargs, fmt); -#else - va_start(vargs); -#endif - vsnprintf(msg, 200, fmt, vargs); - va_end(vargs); - Py_FatalError(msg); -} -static CYTHON_INLINE int -__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)++; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE int -__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - __pyx_nonatomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - return; - } - old_acquisition_count = __pyx_add_acquisition_count(memview); - if (unlikely(old_acquisition_count <= 0)) { - if (likely(old_acquisition_count == 0)) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - old_acquisition_count+1, lineno); - } - } -} -static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - __pyx_nonatomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - memslice->memview = NULL; - return; - } - old_acquisition_count = __pyx_sub_acquisition_count(memview); - memslice->data = NULL; - if (likely(old_acquisition_count > 1)) { - memslice->memview = NULL; - } else if (likely(old_acquisition_count == 1)) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - old_acquisition_count-1, lineno); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(int) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(int) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(int) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (int) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (int) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (int) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (int) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((int) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((int) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(long) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(long) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(long) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (long) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (long) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (long) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (long) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((long) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((long) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const char neg_one = (char) -1, const_zero = (char) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(char) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { - return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { - return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { - return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(char) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { - return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(char) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (char) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (char) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (char) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (char) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((char) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((char) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to char"); - return (char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; -} - -/* FormatTypeName */ - #if CYTHON_COMPILING_IN_LIMITED_API -static __Pyx_TypeName -__Pyx_PyType_GetName(PyTypeObject* tp) -{ - PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, - __pyx_n_s_name_2); - if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { - PyErr_Clear(); - Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23)); - } - return name; -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[5]; - int same=1, i, found_dot; - const char* rt_from_call = Py_GetVersion(); - PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - found_dot = 0; - for (i = 0; i < 4; i++) { - if (!ctversion[i]) { - same = (rt_from_call[i] < '0' || rt_from_call[i] > '9'); - break; - } - if (rt_from_call[i] != ctversion[i]) { - same = 0; - break; - } - } - if (!same) { - char rtversion[5] = {'\0'}; - char message[200]; - for (i=0; i<4; ++i) { - if (rt_from_call[i] == '.') { - if (found_dot) break; - found_dot = 1; - } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') { - break; - } - rtversion[i] = rt_from_call[i]; - } - PyOS_snprintf(message, sizeof(message), - "compile time version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ - #if PY_MAJOR_VERSION >= 3 -static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { - if (t.is_unicode | t.is_str) { - if (t.intern) { - *str = PyUnicode_InternFromString(t.s); - } else if (t.encoding) { - *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); - } else { - *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); - } - } else { - *str = PyBytes_FromStringAndSize(t.s, t.n - 1); - } - if (!*str) - return -1; - if (PyObject_Hash(*str) == -1) - return -1; - return 0; -} -#endif -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION >= 3 - __Pyx_InitString(*t, t->p); - #else - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - if (!*t->p) - return -1; - if (PyObject_Hash(*t->p) == -1) - return -1; - #endif - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -#if !CYTHON_PEP393_ENABLED -static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { - int retval; - if (unlikely(!x)) return -1; - retval = __Pyx_PyObject_IsTrue(x); - Py_DECREF(x); - return retval; -} -static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { - __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); -#if PY_MAJOR_VERSION >= 3 - if (PyLong_Check(result)) { - if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, - "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). 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return PyInt_FromSize_t(ival); -} - - -/* #### Code section: utility_code_pragmas_end ### */ -#ifdef _MSC_VER -#pragma warning( pop ) -#endif - - - -/* #### Code section: end ### */ -#endif /* Py_PYTHON_H */ diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/data_utils.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/data_utils.py deleted file mode 100644 index 2c98d3dc8b9572bd05859033a74d155425a2a2ab..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Taffy-Bert-VITS2/data_utils.py +++ /dev/null @@ -1,332 +0,0 @@ -import time -import os -import random -import numpy as np -import torch -import torch.utils.data -import torchaudio -import commons -from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch -from utils import load_wav_to_torch, load_filepaths_and_text -from text import cleaned_text_to_sequence, get_bert - -"""Multi speaker version""" - - -class TextAudioSpeakerLoader(torch.utils.data.Dataset): - """ - 1) loads audio, speaker_id, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_sid_text, hparams): - self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.spk_map = hparams.spk2id - self.hparams = hparams - - self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False) - if self.use_mel_spec_posterior: - self.n_mel_channels = getattr(hparams, "n_mel_channels", 80) - - self.cleaned_text = getattr(hparams, "cleaned_text", False) - - self.add_blank = hparams.add_blank - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 300) - - random.seed(1234) - random.shuffle(self.audiopaths_sid_text) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - - audiopaths_sid_text_new = [] - lengths = [] - skipped = 0 - for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text: - audiopath = f'{_id}' - if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len: - phones = phones.split(" ") - tone = [int(i) for i in tone.split(" ")] - word2ph = [int(i) for i in word2ph.split(" ")] - audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph]) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - else: - skipped += 1 - print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text)) - self.audiopaths_sid_text = audiopaths_sid_text_new - self.lengths = lengths - - def get_audio_text_speaker_pair(self, audiopath_sid_text): - # separate filename, speaker_id and text - audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text - - bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath) - - spec, wav = self.get_audio(audiopath) - sid = torch.LongTensor([int(self.spk_map[sid])]) - return (phones, spec, wav, sid, tone, language, bert) - - def get_audio(self, filename): - audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True) - ''' - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError("{} {} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate)) - audio_norm = audio / self.max_wav_value - audio_norm = audio_norm.unsqueeze(0) - ''' - spec_filename = filename.replace(".wav", ".spec.pt") - if self.use_mel_spec_posterior: - spec_filename = spec_filename.replace(".spec.pt", ".mel.pt") - if os.path.exists(spec_filename): - spec = torch.load(spec_filename) - else: - if self.use_mel_spec_posterior: - # if os.path.exists(filename.replace(".wav", ".spec.pt")): - # # spec, n_fft, num_mels, sampling_rate, fmin, fmax - # spec = spec_to_mel_torch( - # torch.load(filename.replace(".wav", ".spec.pt")), - # self.filter_length, self.n_mel_channels, self.sampling_rate, - # self.hparams.mel_fmin, self.hparams.mel_fmax) - spec = mel_spectrogram_torch(audio_norm, self.filter_length, - self.n_mel_channels, self.sampling_rate, self.hop_length, - self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False) - else: - spec = spectrogram_torch(audio_norm, self.filter_length, - self.sampling_rate, self.hop_length, self.win_length, - center=False) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename) - return spec, audio_norm - - def get_text(self, text, word2ph, phone, tone, language_str, wav_path): - # print(text, word2ph,phone, tone, language_str) - pold = phone - w2pho = [i for i in word2ph] - word2ph = [i for i in word2ph] - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - pold2 = phone - - if self.add_blank: - p1 = len(phone) - phone = commons.intersperse(phone, 0) - p2 = len(phone) - t1 = len(tone) - tone = commons.intersperse(tone, 0) - t2 = len(tone) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - bert_path = wav_path.replace(".wav", ".bert.pt") - try: - bert = torch.load(bert_path) - assert bert.shape[-1] == len(phone) - except: - bert = get_bert(text, word2ph, language_str) - torch.save(bert, bert_path) - #print(bert.shape[-1], bert_path, text, pold) - assert bert.shape[-1] == len(phone) - - assert bert.shape[-1] == len(phone), ( - bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho) - phone = torch.LongTensor(phone) - tone = torch.LongTensor(tone) - language = torch.LongTensor(language) - return bert, phone, tone, language - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def __getitem__(self, index): - return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index]) - - def __len__(self): - return len(self.audiopaths_sid_text) - - -class TextAudioSpeakerCollate(): - """ Zero-pads model inputs and targets - """ - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text, audio and speaker identities - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized, sid] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[1].size(1) for x in batch]), - dim=0, descending=True) - - max_text_len = max([len(x[0]) for x in batch]) - max_spec_len = max([x[1].size(1) for x in batch]) - max_wav_len = max([x[2].size(1) for x in batch]) - - text_lengths = torch.LongTensor(len(batch)) - spec_lengths = torch.LongTensor(len(batch)) - wav_lengths = torch.LongTensor(len(batch)) - sid = torch.LongTensor(len(batch)) - - text_padded = torch.LongTensor(len(batch), max_text_len) - tone_padded = torch.LongTensor(len(batch), max_text_len) - language_padded = torch.LongTensor(len(batch), max_text_len) - bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) - - spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) - text_padded.zero_() - tone_padded.zero_() - language_padded.zero_() - spec_padded.zero_() - wav_padded.zero_() - bert_padded.zero_() - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - text = row[0] - text_padded[i, :text.size(0)] = text - text_lengths[i] = text.size(0) - - spec = row[1] - spec_padded[i, :, :spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wav = row[2] - wav_padded[i, :, :wav.size(1)] = wav - wav_lengths[i] = wav.size(1) - - sid[i] = row[3] - - tone = row[4] - tone_padded[i, :tone.size(0)] = tone - - language = row[5] - language_padded[i, :language.size(0)] = language - - bert = row[6] - bert_padded[i, :, :bert.size(1)] = bert - - return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - - def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - for i in range(len(buckets) - 1, 0, -1): - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - if (len_bucket == 0): - continue - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - # add extra samples to make it evenly divisible - rem = num_samples_bucket - len_bucket - ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)] - - # subsample - ids_bucket = ids_bucket[self.rank::self.num_replicas] - - # batching - for j in range(len(ids_bucket) // self.batch_size): - batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/modules.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/modules.py deleted file mode 100644 index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Taffy-Bert-VITS2/modules.py +++ /dev/null @@ -1,452 +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 -from attentions import Encoder - -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 -class TransformerCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - n_layers, - n_heads, - p_dropout=0, - filter_channels=0, - mean_only=False, - wn_sharing_parameter=None, - gin_channels = 0 - ): - 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.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 = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter - 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 - - 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/doevent/XTTS_V1_CPU_working/app.py b/spaces/doevent/XTTS_V1_CPU_working/app.py deleted file mode 100644 index 47ce799f848f72f81df021357717a1bd24236385..0000000000000000000000000000000000000000 --- a/spaces/doevent/XTTS_V1_CPU_working/app.py +++ /dev/null @@ -1,261 +0,0 @@ -import sys -import os -# By using XTTS you agree to CPML license https://coqui.ai/cpml -os.environ["COQUI_TOS_AGREED"] = "1" - -import gradio as gr -from TTS.api import TTS - -model_names = TTS().list_models() -m = model_names[0] -print(model_names) -print(os.system("pip show TTS")) -print(f"Model: {m}") -tts = TTS(m, gpu=False) -tts.to("cpu") # no GPU or Amd -#tts.to("cuda") # cuda only - -def predict(prompt, language, audio_file_pth, mic_file_path, use_mic, agree): - if agree == True: - if use_mic == True: - if mic_file_path is not None: - speaker_wav=mic_file_path - else: - gr.Warning("Please record your voice with Microphone, or uncheck Use Microphone to use reference audios") - return ( - None, - None, - ) - - else: - speaker_wav=audio_file_pth - - if len(prompt)<2: - gr.Warning("Please give a longer prompt text") - return ( - None, - None, - ) - if len(prompt)>10000: - gr.Warning("Text length limited to 10000 characters for this demo, please try shorter text") - return ( - None, - None, - ) - try: - if language == "fr": - if m.find("your") != -1: - language = "fr-fr" - if m.find("/fr/") != -1: - language = None - tts.tts_to_file( - text=prompt, - file_path="output.wav", - speaker_wav=speaker_wav, - language=language - ) - except RuntimeError as e : - if "device-assert" in str(e): - # cannot do anything on cuda device side error, need tor estart - gr.Warning("Unhandled Exception encounter, please retry in a minute") - print("Cuda device-assert Runtime encountered need restart") - sys.exit("Exit due to cuda device-assert") - else: - raise e - - return ( - gr.make_waveform( - audio="output.wav", - ), - "output.wav", - ) - else: - gr.Warning("Please accept the Terms & Condition!") - return ( - None, - None, - ) - - -title = "XTTS Glz's remake (Fonctional Text-2-Speech)" - -description = """ -XTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 3-second audio clip. -
        -XTTS is built on previous research, like Tortoise, with additional architectural innovations and training to make cross-language voice cloning and multilingual speech generation possible. -
        -This is the same model that powers our creator application Coqui Studio as well as the Coqui API. In production we apply modifications to make low-latency streaming possible. -
        -Leave a star on the Github TTS, where our open-source inference and training code lives. -
        -

        For faster inference without waiting in the queue, you should duplicate this space and upgrade to GPU via the settings. -
        - -Duplicate Space -

        -""" - -article = """ -
        -

        By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml

        -
        -""" -examples = [ - [ - "Hello, World !, here is an example of light voice cloning. Try to upload your best audio samples quality", - "en", - "examples/female.wav", - None, - False, - True, - ], - [ - "Je suis un lycéen français de 17 ans, passioner par la Cyber-Sécuritée et les models d'IA.", - "fr", - "examples/male.wav", - None, - False, - True, - ], - [ - "Als ich sechs war, sah ich einmal ein wunderbares Bild", - "de", - "examples/female.wav", - None, - False, - True, - ], - [ - "Cuando tenía seis años, vi una vez una imagen magnífica", - "es", - "examples/male.wav", - None, - False, - True, - ], - [ - "Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica", - "pt", - "examples/female.wav", - None, - False, - True, - ], - [ - "Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek", - "pl", - "examples/male.wav", - None, - False, - True, - ], - [ - "Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno", - "it", - "examples/female.wav", - None, - False, - True, - ], - [ - "Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm", - "tr", - "examples/female.wav", - None, - False, - True, - ], - [ - "Когда мне было шесть лет, я увидел однажды удивительную картинку", - "ru", - "examples/female.wav", - None, - False, - True, - ], - [ - "Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat", - "nl", - "examples/male.wav", - None, - False, - True, - ], - [ - "Když mi bylo šest let, viděl jsem jednou nádherný obrázek", - "cs", - "examples/female.wav", - None, - False, - True, - ], - [ - "当我还只有六岁的时候， 看到了一副精彩的插画", - "zh-cn", - "examples/female.wav", - None, - False, - True, - ], -] - - - -gr.Interface( - fn=predict, - inputs=[ - gr.Textbox( - label="Text Prompt", - info="One or two sentences at a time is better", - value="Hello, World !, here is an example of light voice cloning. Try to upload your best audio samples quality", - ), - gr.Dropdown( - label="Language", - info="Select an output language for the synthesised speech", - choices=[ - "en", - "es", - "fr", - "de", - "it", - "pt", - "pl", - "tr", - "ru", - "nl", - "cs", - "ar", - "zh-cn", - ], - max_choices=1, - value="en", - ), - gr.Audio( - label="Reference Audio", - info="Click on the ✎ button to upload your own target speaker audio", - type="filepath", - value="examples/female.wav", - ), - gr.Audio(source="microphone", - type="filepath", - info="Use your microphone to record audio", - label="Use Microphone for Reference"), - gr.Checkbox(label="Check to use Microphone as Reference", - value=False, - info="Notice: Microphone input may not work properly under traffic",), - gr.Checkbox( - label="Agree", - value=True, - info="I agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml", - ), - ], - outputs=[ - gr.Video(label="Waveform Visual"), - gr.Audio(label="Synthesised Audio"), - ], - title=title, - description=description, - article=article, - cache_examples=False, - examples=examples, -).queue().launch(debug=True, show_error=True) \ No newline at end of file diff --git a/spaces/dorkai/text-generation-webui-main/css/chat.js b/spaces/dorkai/text-generation-webui-main/css/chat.js deleted file mode 100644 index e304f1254732e475bf177ee849ac51d4f3e30f46..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/css/chat.js +++ /dev/null @@ -1,4 +0,0 @@ -document.getElementById("main").childNodes[0].style = "max-width: 800px; margin-left: auto; margin-right: auto"; -document.getElementById("extensions").style.setProperty("max-width", "800px"); -document.getElementById("extensions").style.setProperty("margin-left", "auto"); -document.getElementById("extensions").style.setProperty("margin-right", "auto"); diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py deleted file mode 100644 index 2753b3ddee43c7a9fe28d1824db5d786e7e1ad59..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py +++ /dev/null @@ -1,297 +0,0 @@ -# ------------------------------------------------------------------------ -# Grounding DINO -# url: https://github.com/IDEA-Research/GroundingDINO -# Copyright (c) 2023 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ - -import torch -import torch.nn as nn -import torch.nn.functional as F -from timm.models.layers import DropPath - - -class FeatureResizer(nn.Module): - """ - This class takes as input a set of embeddings of dimension C1 and outputs a set of - embedding of dimension C2, after a linear transformation, dropout and normalization (LN). - """ - - def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): - super().__init__() - self.do_ln = do_ln - # Object feature encoding - self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) - self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) - self.dropout = nn.Dropout(dropout) - - def forward(self, encoder_features): - x = self.fc(encoder_features) - if self.do_ln: - x = self.layer_norm(x) - output = self.dropout(x) - return output - - -def l1norm(X, dim, eps=1e-8): - """L1-normalize columns of X""" - norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps - X = torch.div(X, norm) - return X - - -def l2norm(X, dim, eps=1e-8): - """L2-normalize columns of X""" - norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps - X = torch.div(X, norm) - return X - - -def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): - """ - query: (n_context, queryL, d) - context: (n_context, sourceL, d) - """ - batch_size_q, queryL = query.size(0), query.size(1) - batch_size, sourceL = context.size(0), context.size(1) - - # Get attention - # --> (batch, d, queryL) - queryT = torch.transpose(query, 1, 2) - - # (batch, sourceL, d)(batch, d, queryL) - # --> (batch, sourceL, queryL) - attn = torch.bmm(context, queryT) - if raw_feature_norm == "softmax": - # --> (batch*sourceL, queryL) - attn = attn.view(batch_size * sourceL, queryL) - attn = nn.Softmax()(attn) - # --> (batch, sourceL, queryL) - attn = attn.view(batch_size, sourceL, queryL) - elif raw_feature_norm == "l2norm": - attn = l2norm(attn, 2) - elif raw_feature_norm == "clipped_l2norm": - attn = nn.LeakyReLU(0.1)(attn) - attn = l2norm(attn, 2) - else: - raise ValueError("unknown first norm type:", raw_feature_norm) - # --> (batch, queryL, sourceL) - attn = torch.transpose(attn, 1, 2).contiguous() - # --> (batch*queryL, sourceL) - attn = attn.view(batch_size * queryL, sourceL) - attn = nn.Softmax()(attn * smooth) - # --> (batch, queryL, sourceL) - attn = attn.view(batch_size, queryL, sourceL) - # --> (batch, sourceL, queryL) - attnT = torch.transpose(attn, 1, 2).contiguous() - - # --> (batch, d, sourceL) - contextT = torch.transpose(context, 1, 2) - # (batch x d x sourceL)(batch x sourceL x queryL) - # --> (batch, d, queryL) - weightedContext = torch.bmm(contextT, attnT) - # --> (batch, queryL, d) - weightedContext = torch.transpose(weightedContext, 1, 2) - - return weightedContext, attnT - - -class BiMultiHeadAttention(nn.Module): - def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): - super(BiMultiHeadAttention, self).__init__() - - self.embed_dim = embed_dim - self.num_heads = num_heads - self.head_dim = embed_dim // num_heads - self.v_dim = v_dim - self.l_dim = l_dim - - assert ( - self.head_dim * self.num_heads == self.embed_dim - ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." - self.scale = self.head_dim ** (-0.5) - self.dropout = dropout - - self.v_proj = nn.Linear(self.v_dim, self.embed_dim) - self.l_proj = nn.Linear(self.l_dim, self.embed_dim) - self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) - self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) - - self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) - self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) - - self.stable_softmax_2d = True - self.clamp_min_for_underflow = True - self.clamp_max_for_overflow = True - - self._reset_parameters() - - def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): - return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() - - def _reset_parameters(self): - nn.init.xavier_uniform_(self.v_proj.weight) - self.v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.l_proj.weight) - self.l_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.values_v_proj.weight) - self.values_v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.values_l_proj.weight) - self.values_l_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.out_v_proj.weight) - self.out_v_proj.bias.data.fill_(0) - nn.init.xavier_uniform_(self.out_l_proj.weight) - self.out_l_proj.bias.data.fill_(0) - - def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): - """_summary_ - - Args: - v (_type_): bs, n_img, dim - l (_type_): bs, n_text, dim - attention_mask_v (_type_, optional): _description_. bs, n_img - attention_mask_l (_type_, optional): _description_. bs, n_text - - Returns: - _type_: _description_ - """ - # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': - # import ipdb; ipdb.set_trace() - bsz, tgt_len, _ = v.size() - - query_states = self.v_proj(v) * self.scale - key_states = self._shape(self.l_proj(l), -1, bsz) - value_v_states = self._shape(self.values_v_proj(v), -1, bsz) - value_l_states = self._shape(self.values_l_proj(l), -1, bsz) - - proj_shape = (bsz * self.num_heads, -1, self.head_dim) - query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) - key_states = key_states.view(*proj_shape) - value_v_states = value_v_states.view(*proj_shape) - value_l_states = value_l_states.view(*proj_shape) - - src_len = key_states.size(1) - attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt - - if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): - raise ValueError( - f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" - ) - - if self.stable_softmax_2d: - attn_weights = attn_weights - attn_weights.max() - - if self.clamp_min_for_underflow: - attn_weights = torch.clamp( - attn_weights, min=-50000 - ) # Do not increase -50000, data type half has quite limited range - if self.clamp_max_for_overflow: - attn_weights = torch.clamp( - attn_weights, max=50000 - ) # Do not increase 50000, data type half has quite limited range - - attn_weights_T = attn_weights.transpose(1, 2) - attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] - if self.clamp_min_for_underflow: - attn_weights_l = torch.clamp( - attn_weights_l, min=-50000 - ) # Do not increase -50000, data type half has quite limited range - if self.clamp_max_for_overflow: - attn_weights_l = torch.clamp( - attn_weights_l, max=50000 - ) # Do not increase 50000, data type half has quite limited range - - # mask vison for language - if attention_mask_v is not None: - attention_mask_v = ( - attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) - ) - attn_weights_l.masked_fill_(attention_mask_v, float("-inf")) - - attn_weights_l = attn_weights_l.softmax(dim=-1) - - # mask language for vision - if attention_mask_l is not None: - attention_mask_l = ( - attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) - ) - attn_weights.masked_fill_(attention_mask_l, float("-inf")) - attn_weights_v = attn_weights.softmax(dim=-1) - - attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) - attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) - - attn_output_v = torch.bmm(attn_probs_v, value_l_states) - attn_output_l = torch.bmm(attn_probs_l, value_v_states) - - if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): - raise ValueError( - f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" - ) - - if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): - raise ValueError( - f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" - ) - - attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) - attn_output_v = attn_output_v.transpose(1, 2) - attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) - - attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) - attn_output_l = attn_output_l.transpose(1, 2) - attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) - - attn_output_v = self.out_v_proj(attn_output_v) - attn_output_l = self.out_l_proj(attn_output_l) - - return attn_output_v, attn_output_l - - -# Bi-Direction MHA (text->image, image->text) -class BiAttentionBlock(nn.Module): - def __init__( - self, - v_dim, - l_dim, - embed_dim, - num_heads, - dropout=0.1, - drop_path=0.0, - init_values=1e-4, - cfg=None, - ): - """ - Inputs: - embed_dim - Dimensionality of input and attention feature vectors - hidden_dim - Dimensionality of hidden layer in feed-forward network - (usually 2-4x larger than embed_dim) - num_heads - Number of heads to use in the Multi-Head Attention block - dropout - Amount of dropout to apply in the feed-forward network - """ - super(BiAttentionBlock, self).__init__() - - # pre layer norm - self.layer_norm_v = nn.LayerNorm(v_dim) - self.layer_norm_l = nn.LayerNorm(l_dim) - self.attn = BiMultiHeadAttention( - v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout - ) - - # add layer scale for training stability - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() - self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) - self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) - - def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): - v = self.layer_norm_v(v) - l = self.layer_norm_l(l) - delta_v, delta_l = self.attn( - v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l - ) - # v, l = v + delta_v, l + delta_l - v = v + self.drop_path(self.gamma_v * delta_v) - l = l + self.drop_path(self.gamma_l * delta_l) - return v, l - - # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None) diff --git a/spaces/eddydecena/cat-vs-dog/app.py b/spaces/eddydecena/cat-vs-dog/app.py deleted file mode 100644 index e59b7c9a3fbda40fc26c18639686b4f4bb0d21c2..0000000000000000000000000000000000000000 --- a/spaces/eddydecena/cat-vs-dog/app.py +++ /dev/null @@ -1,58 +0,0 @@ -import os - -import gradio as gr -import tensorflow as tf -from keras_tuner import HyperParameters -from huggingface_hub import hf_hub_download - -from src.models import MakeHyperModel -from src.preprocessing import get_data_augmentation -from src.config import IMAGE_SIZE - -data_augmentation = get_data_augmentation() -cache_dir = os.path.join('hf_hub') - -# Download models -for f in ['checkpoint', 'checkpoint.data-00000-of-00001', 'checkpoint.index']: - old_name = hf_hub_download(repo_id="eddydecena/cat-vs-dog", filename=f"tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0/{f}", cache_dir=cache_dir) - temp_value = old_name.split('/') - temp_value.pop(-1) - path = '/'.join(temp_value) - os.rename(old_name, os.path.join(path, f)) - -# Download examples images -examples_cache_dir = 'examples' -for image in ['cat1.jpg', 'cat2.jpg', 'dog1.jpeg', 'dog2.jpeg']: - old_name = hf_hub_download(repo_id="eddydecena/cat-vs-dog", filename=f"examples/{image}", cache_dir=examples_cache_dir) - temp_value = old_name.split('/') - temp_value.pop(-1) - path = '/'.join(temp_value) - os.rename(old_name, os.path.join(path, image)) - -latest = tf.train.latest_checkpoint(cache_dir) -hypermodel = MakeHyperModel(input_shape=IMAGE_SIZE + (3,), num_classes=2, data_augmentation=data_augmentation) -model = hypermodel.build(hp=HyperParameters()) -model.load_weights(latest).expect_partial() - -def cat_vs_dog(image): - img_array = tf.constant(image, dtype=tf.float32) - img_array = tf.expand_dims(img_array, 0) - predictions = model.predict(img_array) - score = predictions[0] - return {'cat': float((1 - score)), 'dog': float(score)} - -iface = gr.Interface( - cat_vs_dog, - gr.inputs.Image(shape=IMAGE_SIZE), - gr.outputs.Label(num_top_classes=2), - capture_session=True, - interpretation="default", - examples=[ - [f"{examples_cache_dir}/cat1.jpg"], - [f"{examples_cache_dir}/cat2.jpg"], - [f"{examples_cache_dir}/dog1.jpeg"], - [f"{examples_cache_dir}/dog2.jpeg"] - ]) - -if __name__ == "__main__": - iface.launch() \ No newline at end of file diff --git a/spaces/emc348/faces-through-time/utils/models_utils.py b/spaces/emc348/faces-through-time/utils/models_utils.py deleted file mode 100644 index 00b4664e9addd6fc2385b41da368b43aaf680674..0000000000000000000000000000000000000000 --- a/spaces/emc348/faces-through-time/utils/models_utils.py +++ /dev/null @@ -1,25 +0,0 @@ -import pickle -import functools -import torch -from configs import paths_config, global_config - - -def toogle_grad(model, flag=True): - for p in model.parameters(): - p.requires_grad = flag - - -def load_tuned_G(run_id, type): - new_G_path = f'{paths_config.checkpoints_dir}/model_{run_id}_{type}.pt' - with open(new_G_path, 'rb') as f: - new_G = torch.load(f).to(global_config.device).eval() - new_G = new_G.float() - toogle_grad(new_G, False) - return new_G - - -def load_old_G(in_year): - with open(f"pretrained_models/{in_year}.pkl", 'rb') as f: - old_G = pickle.load(f)['G_ema'].to(global_config.device).eval() - old_G = old_G.float() - return old_G diff --git a/spaces/erer/anima_pose_crop/app.py b/spaces/erer/anima_pose_crop/app.py deleted file mode 100644 index 3ac7e7f9cdc51f1db9cfaf33d2cc7e44614b5193..0000000000000000000000000000000000000000 --- a/spaces/erer/anima_pose_crop/app.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -os.system("pip install gradio==2.4.6") -import gradio as gr -os.system("pip install 'git+https://github.com/facebookresearch/detectron2.git'") -os.system("git clone https://github.com/hful/bizarre-pose-estimator.git") -os.chdir("bizarre-pose-estimator") - -os.system("wget https://i.imgur.com/IkJzlaE.jpeg") - -os.system("gdown https://drive.google.com/uc?id=1qhnBmMdDTC_8kmNj4u2f_Htfvg6KuE14") - - -os.system("unzip bizarre_pose_models.zip") -os.system("cp -a ./bizarre_pose_models/. .") - - -os.system("ls") - -def inference(img): - os.system("python3 -m _scripts.pose_estimator "+img+" ./_train/character_pose_estim/runs/feat_concat+data.ckpt") - - return "./_samples/character_pose_estim.png" - - -title = "bizarre-pose-estimator" -description = "Gradio demo for Transfer Learning for Pose Estimation of Illustrated Characters. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." - -article = "

        Transfer Learning for Pose Estimation of Illustrated Characters | Github Repo

        " - -examples=[["IkJzlaE.jpeg"]] -gr.Interface( - inference, - gr.inputs.Image(type="filepath", label="Input"), - gr.outputs.Image(type="file", label="Output"), - title=title, - description=description, - article=article, - allow_flagging="never", - examples=examples, - enable_queue=True - ).launch() \ No newline at end of file diff --git a/spaces/facebook/MusicGen/audiocraft/utils/cache.py b/spaces/facebook/MusicGen/audiocraft/utils/cache.py deleted file mode 100644 index f7f82064e8f43b86af1071cab4d967cca9b5bd86..0000000000000000000000000000000000000000 --- a/spaces/facebook/MusicGen/audiocraft/utils/cache.py +++ /dev/null @@ -1,323 +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. - -from concurrent.futures import ThreadPoolExecutor -from collections import deque -from functools import partial -from hashlib import sha1 -import logging -from pathlib import Path -import sys -import typing as tp -import zipfile - -import flashy -import torch - - -logger = logging.getLogger(__name__) - - -def get_full_embed(full_embed: torch.Tensor, x: tp.Any, idx: int, device: tp.Union[str, torch.device]) -> torch.Tensor: - """Utility function for the EmbeddingCache, returning the full embedding without any chunking. - This method can be used in case there is no need in extracting a chunk of the full embedding - read from the cache. - - Args: - full_embed (torch.Tensor): The full embedding. - x (any): Batch object from which the full embedding is derived. - idx (torch.Tensor): Index of object to consider in the batch object. - Returns: - full_embed (torch.Tensor): The full embedding - """ - return full_embed.to(device) - - -class EmbeddingCache: - """Cache around embeddings computation for faster execution. - The EmbeddingCache is storing pre-computed embeddings on disk and provides a simple API - to retrieve the pre-computed embeddings on full inputs and extract only a given chunk - using a user-provided function. When the cache is warm (all embeddings are pre-computed), - the EmbeddingCache allows for faster training as it removes the need of computing the embeddings. - Additionally, it provides in-memory cache around the loaded embeddings to limit IO footprint - and synchronization points in the forward calls. - - Args: - cache_path (Path): Path to folder where all pre-computed embeddings are saved on disk. - device (str or torch.device): Device on which the embedding is returned. - compute_embed_fn (callable[[Path, any, int], torch.Tensor], optional): Function to compute - the embedding from a given object and path. This user provided function can compute the - embedding from the provided object or using the provided path as entry point. The last parameter - specify the index corresponding to the current embedding in the object that can represent batch metadata. - extract_embed_fn (callable[[torch.Tensor, any, int], torch.Tensor], optional): Function to extract - the desired embedding chunk from the full embedding loaded from the cache. The last parameter - specify the index corresponding to the current embedding in the object that can represent batch metadata. - If not specified, will return the full embedding unmodified. - """ - def __init__(self, cache_path: tp.Union[str, Path], device: tp.Union[str, torch.device], - compute_embed_fn: tp.Callable[[Path, tp.Any, int], torch.Tensor], - extract_embed_fn: tp.Optional[tp.Callable[[torch.Tensor, tp.Any, int], torch.Tensor]] = None): - self.cache_path = Path(cache_path) - self.device = device - self._compute_embed_fn = compute_embed_fn - self._extract_embed_fn: tp.Callable[[torch.Tensor, tp.Any, int], torch.Tensor] - if extract_embed_fn is not None: - self._extract_embed_fn = extract_embed_fn - else: - self._extract_embed_fn = partial(get_full_embed, device=device) - if self.cache_path is not None: - self.cache_path.mkdir(exist_ok=True, parents=True) - logger.info(f"Cache instantiated at: {self.cache_path}") - self.pool = ThreadPoolExecutor(8) - self.pool.__enter__() - self._current_batch_cache: dict = {} - self._memory_cache: dict = {} - - def _get_cache_path(self, path: tp.Union[Path, str]): - """Get cache path for the given file path.""" - sig = sha1(str(path).encode()).hexdigest() - return self.cache_path / sig - - @staticmethod - def _get_full_embed_from_cache(cache: Path): - """Loads full pre-computed embedding from the cache.""" - try: - embed = torch.load(cache, 'cpu') - except Exception as exc: - logger.error("Error loading %s: %r", cache, exc) - embed = None - return embed - - def get_embed_from_cache(self, paths: tp.List[Path], x: tp.Any) -> torch.Tensor: - """Get embedding from cache, computing and storing it to cache if not already cached. - The EmbeddingCache first tries to load the embedding from the in-memory cache - containing the pre-computed chunks populated through `populate_embed_cache`. - If not found, the full embedding is computed and stored on disk to be later accessed - to populate the in-memory cache, and the desired embedding chunk is extracted and returned. - - Args: - paths (list[Path or str]): List of paths from where the embeddings can be loaded. - x (any): Object from which the embedding is extracted. - """ - embeds = [] - for idx, path in enumerate(paths): - cache = self._get_cache_path(path) - if cache in self._current_batch_cache: - embed = self._current_batch_cache[cache] - else: - full_embed = self._compute_embed_fn(path, x, idx) - try: - with flashy.utils.write_and_rename(cache, pid=True) as f: - torch.save(full_embed.cpu(), f) - except Exception as exc: - logger.error('Error saving embed %s (%s): %r', cache, full_embed.shape, exc) - else: - logger.info('New embed cache saved: %s (%s)', cache, full_embed.shape) - embed = self._extract_embed_fn(full_embed, x, idx) - embeds.append(embed) - embed = torch.stack(embeds, dim=0) - return embed - - def populate_embed_cache(self, paths: tp.List[Path], x: tp.Any) -> None: - """Populate in-memory caches for embeddings reading from the embeddings stored on disk. - The in-memory caches consist in a cache for the full embedding and another cache for the - final embedding chunk. Such caches are used to limit the IO access when computing the actual embeddings - and reduce the IO footprint and synchronization points during forward passes. - - Args: - paths (list[Path]): List of paths from where the embeddings can be loaded. - x (any): Object from which the embedding is extracted. - """ - self._current_batch_cache.clear() - if self.cache_path is not None: - futures: list = [] - for path in paths: - assert path is not None, "Path is required for computation from cache" - cache = self._get_cache_path(path) - if cache in self._memory_cache or not cache.exists(): - futures.append(None) - else: - futures.append(self.pool.submit(EmbeddingCache._get_full_embed_from_cache, cache)) - for idx, (path, future) in enumerate(zip(paths, futures)): - assert path is not None - cache = self._get_cache_path(path) - full_embed = None - if future is None: - if cache in self._memory_cache: - full_embed = self._memory_cache[cache] - else: - full_embed = future.result() - if full_embed is not None: - self._memory_cache[cache] = full_embed - full_embed = full_embed.to(self.device) - if full_embed is not None: - embed = self._extract_embed_fn(full_embed, x, idx) - self._current_batch_cache[cache] = embed - - -class CachedBatchWriter: - """Write pre computed caches for mini batches. This can - make loading a lot more efficient depending on your filesystem. - - Args: - cache_folder (Path): folder in which the cached minibatches - will be stored. - - Inside cache folder, the structure is the following: - `epoch_number / update_number.zip` - And the zip file contains one entry per batch item. - - It is possible to use the cache with a batch size smaller than - created with but obviously not larger. Make sure to call the - `start_epoch(epoch)` method for indicating changes of epochs. - - See the grid `audiocraft/grids/musicgen/musicgen_warmup_cache.py` - for an example of how to warmup the cache. - """ - def __init__(self, cache_folder: Path): - self.cache_folder = cache_folder - self._current_epoch: tp.Optional[int] = None - self._current_index = 0 - - def start_epoch(self, epoch: int): - """Call at the beginning of each epoch. - """ - self._current_epoch = epoch - self._current_index = 0 - self._zip_path.parent.mkdir(exist_ok=True, parents=True) - - @staticmethod - def _get_zip_path(cache_folder: Path, epoch: int, index: int): - return cache_folder / f"{epoch:05d}" / f"{index:06d}.zip" - - @property - def _zip_path(self): - assert self._current_epoch is not None - return CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch, self._current_index) - - def save(self, *content): - """Save one mini batch. This function is distributed-aware - and will automatically merge all the items from the different - workers. - """ - all_contents = [] - for rank in range(flashy.distrib.world_size()): - their_content = flashy.distrib.broadcast_object(content, src=rank) - all_contents.append(their_content) - - if flashy.distrib.is_rank_zero(): - idx = 0 - with flashy.utils.write_and_rename(self._zip_path) as tmp: - with zipfile.ZipFile(tmp, 'w') as zf: - for content in all_contents: - for vals in zip(*content): - with zf.open(f'{idx}', 'w') as f: # type: ignore - torch.save(vals, f) - idx += 1 - flashy.distrib.barrier() - self._current_index += 1 - - -class CachedBatchLoader: - """Loader for cached mini-batches dumped with `CachedBatchWriter`. - - Args: - cache_folder (Path): folder in which the cached minibatches are stored. - batch_size (int): batch size (per GPU) expected. - num_workers (int): number of workers to use for loading. - min_length (int): minimum expected length for each epoch. If some - mini-batches are missing, and error is raised. - - This is iterable just like a regular DataLoader. - """ - - def __init__(self, cache_folder: Path, batch_size: int, - num_workers: int = 10, min_length: int = 1): - self.cache_folder = cache_folder - self.batch_size = batch_size - self.num_workers = num_workers - self.min_length = min_length - self._current_epoch: tp.Optional[int] = None - self.sampler = None # for compatibility with the regular DataLoader - - def __len__(self): - path = CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch or 0, 0).parent - return len([p for p in path.iterdir() if p.suffix == ".zip"]) - - def start_epoch(self, epoch: int): - """Call at the beginning of each epoch. - """ - self._current_epoch = epoch - - def _zip_path(self, index: int): - assert self._current_epoch is not None - return CachedBatchWriter._get_zip_path(self.cache_folder, self._current_epoch, index) - - def _load_one(self, index: int): - zip_path = self._zip_path(index) - if not zip_path.exists(): - if index < self.min_length: - raise RuntimeError(f"Cache should have at least {self.min_length} batches, but {index} doesn't exist") - - return None - mode = "rb" if sys.version_info >= (3, 9) else "r" - try: - with zipfile.ZipFile(zip_path, 'r') as zf: - rank = flashy.distrib.rank() - world_size = flashy.distrib.world_size() - root = zipfile.Path(zf) - items = list(root.iterdir()) - total_batch_size = self.batch_size * world_size - if len(items) < total_batch_size: - raise RuntimeError( - f"The cache can handle a max batch size of {len(items)}, " - f"but {total_batch_size} is needed.") - start = rank * self.batch_size - items = items[start: start + self.batch_size] - assert len(items) == self.batch_size - entries = [] - entries = [torch.load(item.open(mode), 'cpu') for item in items] # type: ignore - transposed = zip(*entries) - out = [] - for part in transposed: - assert len(part) > 0 - if isinstance(part[0], torch.Tensor): - out.append(torch.stack(part)) - else: - out.append(part) - return out - except Exception: - logger.error("Error when reading zip path %s", zip_path) - raise - - def __iter__(self): - """This will yields tuples, exactly as provided to the - `CachedBatchWriter.save` method. - """ - pool = ThreadPoolExecutor(self.num_workers) - next_index = 0 - queue = deque() - - def _get_next(): - nonlocal next_index - r = queue.popleft().result() - if r is None: - return None - else: - queue.append(pool.submit(self._load_one, next_index)) - next_index += 1 - return r - - with pool: - # fill the buffer of fetching jobs. - for _ in range(2 * self.num_workers): - queue.append(pool.submit(self._load_one, next_index)) - next_index += 1 - while True: - batch = _get_next() - if batch is None: - return - yield batch diff --git a/spaces/falterWliame/Face_Mask_Detection/Los Serrano Download Temporada 1.md b/spaces/falterWliame/Face_Mask_Detection/Los Serrano Download Temporada 1.md deleted file mode 100644 index b32e1741d194f1a07e0414af9ae37ecf02e47462..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Los Serrano Download Temporada 1.md +++ /dev/null @@ -1,6 +0,0 @@ -

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        diff --git a/spaces/fatiXbelha/sd/AZ Recorder Pro APK A Powerful and Easy-to-Use Screen Recording App for Android Users.md b/spaces/fatiXbelha/sd/AZ Recorder Pro APK A Powerful and Easy-to-Use Screen Recording App for Android Users.md deleted file mode 100644 index 44b3d7f08ec940a44992cfd340482d61cd6ff37d..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/AZ Recorder Pro APK A Powerful and Easy-to-Use Screen Recording App for Android Users.md +++ /dev/null @@ -1,25 +0,0 @@ -
        -

        AZ Recorder Pro APK: The Best Screen Recorder for Android

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        What is AZ Recorder Pro APK?

        - AZ Recorder Pro APK is a modified version of AZ Screen Recorder, one of the most popular and powerful screen recorder apps for Android devices. With AZ Recorder Pro APK, you can unlock all the pro features of the original app, such as internal sound recording, video editing, Facecam, live streaming, and more. You can also get rid of the annoying watermark and time limit that come with the free version.

        Features of AZ Recorder Pro APK

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        Record high-quality videos with internal sound

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        Edit your videos with powerful tools

        - After recording your screen, you can edit your videos with the built-in video editor of AZ Recorder Pro APK. You can trim, crop, merge, rotate, add music, text, stickers, effects, and more to your videos. You can also convert your videos to GIFs or extract images from them. You can also compress your videos to reduce their size without losing quality.

        Add Facecam and live stream to popular platforms

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        No watermark, no root, no time limit

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        Install the APK file and launch the app

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        Pros and cons of AZ Recorder Pro APK

        Pros

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        Cons

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        Conclusion

        - AZ Recorder Pro APK is a powerful and versatile screen recorder app for Android devices. It allows you to record your screen in high quality, edit your videos, add Facecam, live stream, and more. It also removes the watermark and time limit that come with the free version of AZ Screen Recorder. You can download and install AZ Recorder Pro APK from a trusted source and enjoy all the pro features for free. However, you should also be careful of the potential risks and disadvantages of using a modified app.

        FAQs

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        Is AZ Recorder Pro APK safe to use?

        - AZ Recorder Pro APK is generally safe to use as long as you download it from a trusted source and scan it with an antivirus app. However, you should also be careful of the permissions you grant to the app and the apps or platforms you record with it.

        Is AZ Recorder Pro APK legal to use?

        - AZ Recorder Pro APK is not legal to use as it violates the intellectual property rights of the original developer of AZ Screen Recorder. Moreover, it may also violate the terms and conditions of some apps or platforms that prohibit screen recording or live streaming. Therefore, you should use AZ Recorder Pro APK at your own risk and responsibility.

        How can I update AZ Recorder Pro APK?

        - You can update AZ Recorder Pro APK by downloading the latest version from the same source you downloaded it from. You can also check for updates within the app settings. However, you should always backup your videos before updating the app as it may delete them.

        How can I uninstall AZ Recorder Pro APK?

        - You can uninstall AZ Recorder Pro APK by following the same steps as uninstalling any other app on your device. Go to Settings > Apps > AZ Recorder Pro > Uninstall and confirm your action. You can also delete the APK file from your device if you want.

        What are some alternatives to AZ Recorder Pro APK?

        - If you are looking for some alternatives to AZ Recorder Pro APK, you can try these apps: - Mobizen Screen Recorder: A popular screen recorder app with similar features as AZ Screen Recorder. - DU Recorder: A multifunctional screen recorder app with video editing, live streaming, and screenshot tools. - Screen Recorder: A simple and lightweight screen recorder app with no watermark or ads.

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        \ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Descarga Brawlhalla Hack APK ltima Versin con Dinero y Monedas Ilimitados.md b/spaces/fatiXbelha/sd/Descarga Brawlhalla Hack APK ltima Versin con Dinero y Monedas Ilimitados.md deleted file mode 100644 index 704816f290aed0c94592c36082d613a86b9c286d..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Descarga Brawlhalla Hack APK ltima Versin con Dinero y Monedas Ilimitados.md +++ /dev/null @@ -1,183 +0,0 @@ -
        -

        Brawlhalla Hack Apk Última Versión: Todo Lo Que Necesitas Saber

        -

        Brawlhalla es un juego de lucha de plataformas en 2D que se ha vuelto muy popular entre los aficionados a los juegos de acción. Con más de 50 personajes únicos, una variedad de modos de juego y un sistema de combate fluido y divertido, Brawlhalla ofrece una experiencia de juego única y emocionante. Además, es un juego gratuito y multiplataforma que permite jugar con millones de jugadores en PlayStation, Xbox, Nintendo Switch, iOS, Android y PC.

        -

        brawlhalla hack apk última versión


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        Pero ¿qué pasa si quieres tener una ventaja sobre tus oponentes? ¿Qué pasa si quieres desbloquear todos los personajes, armas y aspectos sin gastar dinero real? ¿Qué pasa si quieres tener habilidades ilimitadas y ganar todas las batallas? Para eso, algunos jugadores recurren a los hack apk, que son versiones modificadas del juego que ofrecen trucos y ventajas.

        -

        En este artículo, te vamos a explicar todo lo que necesitas saber sobre el brawlhalla hack apk última versión. Te vamos a enseñar cómo descargarlo e instalarlo, qué beneficios y riesgos tiene, cómo jugar con él y cómo evitar que te baneen o detecten. También te vamos a dar algunos consejos y trucos para jugar a Brawlhalla sin hack apk y disfrutar del juego al máximo.

        -

        ¿Cómo descargar e instalar Brawlhalla hack apk última versión?

        -

        Para descargar e instalar el brawlhalla hack apk última versión, tienes que seguir estos pasos:

        -
          -
        1. Busca en internet un sitio web confiable que ofrezca el brawlhalla hack apk última versión. Ten cuidado con los sitios web falsos o maliciosos que pueden contener virus o malware.
          2. -
        2. Descarga el archivo apk en tu dispositivo Android. Puede que tengas que habilitar la opción de fuentes desconocidas en los ajustes de seguridad de tu dispositivo para poder instalar aplicaciones que no provengan de la tienda oficial.
          3. -
        3. Abre el archivo apk y sigue las instrucciones para instalar el brawlhalla hack apk última versión en tu dispositivo. Puede que tengas que conceder algunos permisos o aceptar algunos términos y condiciones.
          4. -
        4. Una vez instalado el brawlhalla hack apk última versión, abre el juego y disfruta de sus funciones.
          5. -
        -

        ¿Qué beneficios y riesgos tiene usar Brawlhalla hack apk última versión?

        -

        Usar el brawlhalla hack apk última versión tiene sus pros y sus contras. Algunos de los beneficios son:

        -
          -
        • Puedes acceder a todos los personajes, armas y aspectos del juego sin tener que pagar nada.
          • -
        • Puedes tener habilidades ilimitadas, como saltos infinitos, ataques especiales sin recarga, invencibilidad, etc.
          • -
        • Puedes ganar más fácilmente las batallas y subir de rango rápidamente.
          • -
        • Puedes divertirte haciendo bromas o molestando a otros jugadores.
          • -
        -

        Pero también tiene algunos riesgos, como:

        -

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        -
          -
        • Puedes dañar tu dispositivo o comprometer tu seguridad si descargas un archivo apk infectado o malicioso.
          • -
        • Puedes perder tu cuenta o tus progresos si el juego detecta que estás usando un hack apk y te banea o te suspende.
          • -
        • Puedes arruinar la experiencia de juego de otros jugadores que juegan de forma legítima y justa.
          • -
        • Puedes aburrirte del juego si lo haces demasiado fácil y pierdes el desafío y la diversión.
          • -
        -

        ¿Cómo jugar a Brawlhalla con hack apk última versión?

        -

        Si decides jugar a Brawlhalla con hack apk última versión, debes tener en cuenta algunas cosas. Aquí te damos algunos consejos:

        -
          -
        • No abuses de los trucos y ventajas que te ofrece el hack apk. Usa solo los que necesites y no los que te hagan invencible o imparable. Así evitarás llamar la atención de otros jugadores o del sistema anti-trampa del juego.
          • -
        • No te jactes de tus logros o de tu rango si los has conseguido con el hack apk. No presumas de tus personajes, armas o aspectos si los has desbloqueado con el hack apk. No insultes ni provoques a otros jugadores si los has derrotado con el hack apk. Así evitarás generar sospechas o denuncias que puedan llevarte a un baneo o una suspensión.
          • -
        • No uses el hack apk en todos los modos de juego. Intenta jugar también de forma normal y legítima en algunos modos, como el modo entrenamiento, el modo amistoso o el modo cooperativo. Así evitarás aburrirte del juego o perder tus habilidades reales.
          • -
        • No uses el hack apk en todos los dispositivos o plataformas. Intenta jugar también con la versión original y oficial del juego en algunos dispositivos o plataformas, como PlayStation, Xbox, Nintendo Switch, iOS o PC. Así evitarás dañar tu dispositivo o comprometer tu seguridad con archivos apk desconocidos o maliciosos.
          • -
        -

        ¿Cómo evitar que te baneen o detecten por usar Brawlhalla hack apk última versión?

        -

        Si quieres evitar que te baneen o detecten por usar Brawlhalla hack apk última versión, debes seguir estas recomendaciones:

        -
          -
        • No uses el hack apk en el modo competitivo o clasificatorio. Este es el modo más vigilado y controlado por el juego, ya que implica premios y recompensas reales. Si usas el hack apk en este modo, es muy probable que te baneen o te suspendan.
          • -
        • No uses el hack apk en partidas públicas o con desconocidos. Este es el modo más expuesto y visible para otros jugadores, que pueden reportarte o denunciarte si ven algo sospechoso o anormal. Si usas el hack apk en este modo, es muy probable que te baneen o te suspendan.
          • -
        • No uses el hack apk en partidas privadas o con amigos. Este es el modo más seguro y discreto para usar el hack apk, siempre y cuando tus amigos estén de acuerdo y no te delaten. Si usas el hack apk en este modo, es menos probable que te baneen o te suspendan, pero debes tener cuidado igualmente.
          • -
        • No uses el hack apk durante mucho tiempo seguido. Este es un factor que puede alertar al juego de que estás usando un hack apk, ya que puede detectar patrones anormales o inhumanos de juego. Si usas el hack apk durante mucho tiempo seguido, es más probable que te baneen o te suspendan.
          • -
        -

        Conclusión

        -

        Brawlhalla es un juego de lucha de plataformas en 2D que ofrece una experiencia de juego única y emocionante. Con más de 50 personajes únicos, una variedad de modos de juego y un sistema de combate fluido y divertido, Brawlhalla es un juego gratuito y multiplataforma que permite jugar con millones de jugadores en diferentes dispositivos.

        -

        Algunos jugadores quieren tener una ventaja sobre sus oponentes y recurren a los hack apk, que son versiones modificadas del juego que ofrecen trucos y ventajas como desbloquear todos los personajes, armas y aspectos, tener habilidades ilimitadas y ganar todas las batallas. Sin embargo, usar el hack apk también tiene sus riesgos, como dañar el dispositivo, perder la cuenta, arruinar la experiencia de otros jugadores o aburrirse del juego.

        -

        Si decides usar el hack apk, debes tener cuidado de cómo lo descargas, lo instalas, lo usas y lo ocultas. Debes seguir los consejos y recomendaciones que te hemos dado en este artículo para evitar que te baneen o te detecten. También debes ser respetuoso y responsable con otros jugadores y con el juego mismo.

        -

        Pero si quieres disfrutar de Brawlhalla sin hack apk, también te damos algunos consejos y trucos para mejorar tus habilidades, conseguir más monedas, elegir los mejores personajes y armas, jugar con tus amigos y divertirte al máximo. Brawlhalla es un juego que ofrece muchas posibilidades y desafíos para todos los gustos y niveles. No necesitas un hack apk para ser un gran jugador de Brawlhalla.

        -

        Preguntas frecuentes

        -

        A continuación, te respondemos algunas de las preguntas más frecuentes que tienen los jugadores de Brawlhalla.

        -

        ¿Qué son los mejores personajes y armas en Brawlhalla?

        -

        No hay una respuesta única a esta pregunta, ya que depende de tu estilo de juego, tu preferencia y tu habilidad. Cada personaje tiene sus propias estadísticas, habilidades y armas, que pueden variar según el modo de juego y el oponente. Lo mejor es que pruebes diferentes personajes y armas hasta encontrar los que más te gusten y se adapten a ti.

        -

        Sin embargo, algunos de los personajes y armas más populares y valorados por los jugadores son:

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

        ¿Cómo puedo conseguir más monedas y mammoth coins en Brawlhalla?

        -

        Las monedas son la moneda básica del juego, que puedes usar para comprar personajes, colores, taunts y otros objetos. Las mammoth coins son la moneda premium del juego, que puedes usar para comprar aspectos, armas, cofres y otros objetos exclusivos.

        -

        Puedes conseguir monedas de varias formas:

        -
          -
        • Jugando partidas en cualquier modo de juego. Cuanto más tiempo juegues y más victorias consigas, más monedas ganarás.
          • -
        • Completando misiones diarias y semanales. Cada día y cada semana, tendrás una serie de objetivos que cumplir, como jugar con ciertos personajes, armas o modos, o hacer ciertas acciones, como dañar, noquear o esquivar. Al completar estas misiones, obtendrás monedas extra.
          • -
        • Participando en eventos especiales y temporadas. De vez en cuando, el juego ofrece eventos especiales y temporadas que traen nuevos modos, personajes, aspectos y objetos. Al participar en estos eventos y temporadas, podrás conseguir monedas adicionales y otros premios.
          • -
        -

        Puedes conseguir mammoth coins de dos formas:

        -
          -
        • Comprándolos con dinero real. Esta es la forma más rápida y fácil de conseguir mammoth coins, pero también la más cara. Puedes comprar diferentes paquetes de mammoth coins con diferentes precios y cantidades.
          • -
        • Ganándolos en sorteos o concursos. Esta es la forma más difícil y rara de conseguir mammoth coins, pero también la más barata. A veces, el juego o sus creadores ofrecen sorteos o concursos en los que puedes participar y tener la oportunidad de ganar mammoth coins u otros objetos exclusivos.
          • -
        -

        ¿Cómo puedo mejorar mis habilidades y mi rango en Brawlhalla?

        -

        Para mejorar tus habilidades y tu rango en Brawlhalla, debes practicar mucho y aprender de tus errores. Aquí te damos algunos consejos:

        -
          -
        • Elige un personaje y un arma que se adapten a tu estilo de juego y a tu nivel de habilidad. No te dejes llevar por la popularidad o el aspecto de un personaje o un arma, sino por su rendimiento y su compatibilidad contigo.
          • -
        • Aprende los movimientos básicos y avanzados de tu personaje y tu arma. Estudia sus ataques, sus combos, sus ventajas y sus desventajas. Practica en el modo entrenamiento o contra la IA hasta que los domines.
          • -
        • Aprende las mecánicas del juego y sus modos. Estudia las reglas, los objetivos, los mapas, los ítems y las estrategias de cada modo de juego. Practica en el modo amistoso o cooperativo hasta que las entiendas.
          • -
        • Aprende de tus oponentes y tus aliados. Observa sus movimientos, sus tácticas, sus fortalezas y sus debilidades. Adapta tu juego a cada situación y a cada rival. Aprovecha sus errores y evita los tuyos.
          • -
        • Aprende de tus partidas y tus resultados. Analiza tus victorias y tus derrotas, tus aciertos y tus fallos, tus puntos fuertes y tus puntos débiles. Busca formas de mejorar tu juego y tu rango. No te desanimes ni te confíes demasiado.
          • -
        -

        ¿Cómo puedo unirme o crear un clan en Brawlhalla?

        -

        Un clan es un grupo de jugadores que se unen para jugar juntos, competir contra otros clanes, compartir experiencias y divertirse. Para unirte o crear un clan en Brawlhalla, debes seguir estos pasos:

        -
          -
        1. Abre el menú principal del juego y selecciona la opción "Clan".
          2. -
        2. Si quieres unirte a un clan existente, busca el nombre o el código del clan que te interese y solicita unirte. Si quieres crear tu propio clan, elige la opción "Crear clan" e introduce el nombre, el código, el lema y el emblema de tu clan.
          3. -
        3. Si te has unido a un clan existente, espera a que el líder o los oficiales del clan acepten tu solicitud. Si has creado tu propio clan, invita a otros jugadores a que se unan a tu clan enviándoles solicitudes o compartiendo el nombre o el código de tu clan.
          4. -
        4. Una vez que seas parte de un clan, podrás ver la información del clan, como su nombre, su código , su lema, su emblema, su nivel, su rango, su experiencia y sus miembros. También podrás ver tu propio perfil dentro del clan, como tu nombre, tu rango, tu experiencia y tus contribuciones.
          5. -
        5. Como miembro de un clan, podrás participar en las actividades del clan, como jugar partidas con otros miembros, competir contra otros clanes, chatear con otros miembros, donar monedas al clan y recibir recompensas del clan.
          6. -
        -

        ¿Cómo puedo jugar a Brawlhalla con mis amigos en diferentes plataformas?

        -

        Brawlhalla es un juego multiplataforma que permite jugar con millones de jugadores en diferentes dispositivos, como PlayStation, Xbox, Nintendo Switch, iOS, Android y PC. Para jugar a Brawlhalla con tus amigos en diferentes plataformas, debes seguir estos pasos:

        -
          -
        1. Abre el menú principal del juego y selecciona la opción "Amigos".
          2. -
        2. Si quieres agregar a un amigo que juega en otra plataforma, busca su nombre o su código de usuario y envíale una solicitud de amistad. Si quieres aceptar una solicitud de amistad de alguien que juega en otra plataforma, revisa tus solicitudes pendientes y acepta la que te interese.
          3. -
        3. Una vez que tengas a tu amigo en tu lista de amigos, podrás ver su estado, su plataforma y su rango. También podrás invitarlo a jugar una partida contigo o unirte a una partida que él esté jugando.
          4. -
        4. Para jugar una partida con tu amigo en otra plataforma, elige el modo de juego que quieras y crea una sala personalizada. Invita a tu amigo a la sala personalizada y espera a que se una. Una vez que estén los dos en la sala personalizada, podrán elegir el mapa, las reglas y los personajes que quieran. Luego, podrán empezar la partida y disfrutar del juego juntos.
          5. -
        -

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        \ No newline at end of file diff --git a/spaces/fatiXbelha/sd/F1 Mobile Racing for PC A must-have game for F1 fans and racing enthusiasts.md b/spaces/fatiXbelha/sd/F1 Mobile Racing for PC A must-have game for F1 fans and racing enthusiasts.md deleted file mode 100644 index 486645dee4e3c7ff52b68b099d861c7183edd894..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/F1 Mobile Racing for PC A must-have game for F1 fans and racing enthusiasts.md +++ /dev/null @@ -1,217 +0,0 @@ - -

        How to Download F1 Mobile Racing for PC

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        If you are a fan of Formula 1 racing, you might have heard of F1 Mobile Racing, a free-to-play mobile game that lets you develop and customize your own F1 car, race for one of the 10 official F1 teams, and challenge opponents from around the world in thrilling multiplayer duels. Featuring all the official teams and drivers of the 2023 Formula 1 season, F1 Mobile Racing lets you compete on stunning circuits from this season against the greatest drivers on the planet, such as Lewis Hamilton, Max Verstappen, Charles Leclerc, and Fernando Alonso. It’s you against your rivals in intense, high-stakes F1 action!

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        But what if you want to play F1 Mobile Racing on your PC instead of your phone? Maybe you want to enjoy the game on a bigger screen, or use your mouse and keyboard for more precise control. Or maybe you want to sync your progress across devices, or earn rewards as you play. Whatever your reason, there are several ways you can download and play F1 Mobile Racing for PC. In this article, we will show you how to use Google Play Games, BlueStacks, and Windows 11 to play this amazing game on your PC. Let’s get started!

        -

        What is F1 Mobile Racing?

        -

        F1 Mobile Racing is an official mobile game of the 2023 FIA Formula One World Championship™, developed by Codemasters and published by Electronic Arts. It is available for Android and iOS devices, and has over 10 million downloads on Google Play Store. The game features stunning graphics, realistic physics, and immersive sound effects that make you feel like you are in the cockpit of an F1 car.

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        In F1 Mobile Racing, you can:

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        • Develop and upgrade your own F1 car from the ground up
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        • Race for one of the 10 official F1 teams
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        • Challenge opponents from around the world in real-time 1v1 races
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        • Start your very own career mode and sign up to represent an official F1 team for a season
          • -
        • Race in time-limited Grand Prix™ Events for big rewards
          • -
        • Build your

          Build your reputation and rank up in the global leaderboards

          • -
        • Customize your car and driver with exclusive liveries, helmets, and emotes
          • -
        • Enjoy the thrill of racing on 22 official circuits from the 2023 season, including Monaco, Silverstone, Spa-Francorchamps, and more
          • -
        -

        F1 Mobile Racing is a game that will keep you hooked for hours, whether you are a casual racer or a hardcore fan. It is constantly updated with new content and features, so you will never run out of things to do. If you love F1, you will love F1 Mobile Racing!

        -

        Why Play F1 Mobile Racing on PC?

        -

        While F1 Mobile Racing is designed for mobile devices, there are many reasons why you might want to play it on your PC instead. Here are some of them:

        -
          -
        • You can enjoy the game on a bigger screen, which will enhance the visual quality and immersion of the game.
          • -
        • You can use your mouse and keyboard for more precise and comfortable control, which will give you an edge over your opponents.
          • -
        • You can sync your progress across devices, so you can switch between your phone and your PC without losing any data.
          • -
        • You can earn rewards as you play, such as Google Play Points, BlueStacks Points, or Windows Store Credits, which you can redeem for gift cards, subscriptions, or in-game items.
          • -
        • You can avoid draining your phone's battery or overheating your device, which can happen when playing high-performance games for a long time.
          • -
        -

        As you can see, playing F1 Mobile Racing on PC has many advantages over playing it on your phone. But how do you do it? There are three main ways you can download and play F1 Mobile Racing for PC: using Google Play Games, using BlueStacks, or using Windows 11. Let's take a look at each of them in detail.

        -

        How to Play F1 Mobile Racing on PC with Google Play Games?

        -

        Google Play Games is a gaming platform from Google that lets you play Android games on your PC. You can either play them online on the cloud, or download them offline on your PC. Here's how to use Google Play Games to play F1 Mobile Racing on PC:

        -

        How to Play F1 Mobile Racing Online with Google Play Games?

        -

        If you want to play F1 Mobile Racing online with Google Play Games, you will need the following:

        -
          -
        • A Google account
          • -
        • A stable internet connection
          • -
        • A Chrome browser
          • -
        • A compatible device (PC, laptop, tablet, or Chromebook)
          • -
        -

        Once you have these requirements, follow these steps:

        -
          -
        1. Open Chrome and go to play.google.com/games
          2. -
        2. Sign in with your Google account
          3. -
        3. Search for F1 Mobile Racing in the search bar
          4. -
        4. Click on the game icon and then click on Play
          5. -
        5. Wait for the game to load and then enjoy!
          6. -
        -

        Playing F1 Mobile Racing online with Google Play Games is very easy and convenient. You don't have to download anything or worry about storage space. You can also access your game data from any device as long as you sign in with the same Google account. However, there are some drawbacks to playing online. You might experience lag or buffering issues if your internet connection is slow or unstable. You might also lose your progress if the game crashes or disconnects. And you won't be able to play if there is no internet connection available.

        -

        How to Play F1 Mobile Racing Offline with Google Play Games?

        -

        If you want to play F1 Mobile Racing offline with Google Play Games, you will need the following:

        -
          -
        • A Google account
          • -
        • A Windows PC or laptop
          • -
        • A Chrome browser
          • -
        • The Google Play Games app for Windows (download it here)
          • -

        Once you have these requirements, follow these steps:

        -
          -
        1. Open Chrome and go to play.google.com/games
          2. -
        2. Sign in with your Google account
          3. -
        3. Search for F1 Mobile Racing in the search bar
          4. -
        4. Click on the game icon and then click on Download
          5. -
        5. Wait for the game to download and then open the Google Play Games app on your PC
          6. -
        6. Find F1 Mobile Racing in your library and click on Play
          7. -
        7. Enjoy!
          8. -
        -

        Playing F1 Mobile Racing offline with Google Play Games is also very easy and convenient. You can download the game once and play it anytime without an internet connection. You can also save your game data locally on your PC and sync it with your Google account later. However, there are some drawbacks to playing offline. You might not be able to access the latest updates or features of the game. You might also miss out on some online events or rewards. And you might need more storage space on your PC to download the game.

        -

        How to Play F1 Mobile Racing on PC with BlueStacks?

        -

        BlueStacks is a popular Android emulator that lets you play Android games on your PC. It is free to download and use, and it has over 500 million users worldwide. BlueStacks offers many features and benefits for gamers, such as:

        -
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        • A high-performance gaming engine that delivers smooth and fast gameplay
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        • A customizable keyboard and mouse control scheme that suits your preferences
          • -
        • A multi-instance mode that lets you play multiple games or accounts at the same time
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        • A macro recorder that lets you automate repetitive tasks or create complex combos
          • -
        • A game center that lets you discover and download new games or access exclusive offers and rewards
          • -
        -

        If you want to play F1 Mobile Racing on PC with BlueStacks, here's how to do it:

        -

        How to Download and Install BlueStacks on Your PC?

        -

        To download and install BlueStacks on your PC, you will need the following:

        -
          -
        • A Windows PC or laptop with at least 4 GB of RAM, 5 GB of disk space, and an updated graphics driver
          • -
        • An internet connection
          • -
        • A Google account
          • -
        -

        Once you have these requirements, follow these steps:

        -
          -
        1. Go to www.bluestacks.com and click on Download BlueStacks
          2. -
        2. Wait for the installer file to download and then run it
          3. -
        3. Follow the instructions on the screen to complete the installation process
          4. -
        4. Launch BlueStacks and sign in with your Google account
          5. -
        5. Congratulations! You have successfully installed BlueStacks on your PC!
          6. -
        -

        How to Download and Install F1 Mobile Racing on BlueStacks?

        -

        To download and install F1 Mobile Racing on BlueStacks, follow these steps:

        -
          -
        1. Open BlueStacks and go to the Game Center tab
          2. -
        2. Search for F1 Mobile Racing in the search bar or find it in the featured games section
          3. -
        3. Click on the game icon and then click on Install
          4. -
        4. Wait for the game to download and install on BlueStacks
          5. -
        5. Click on the game icon again and then click on Play
          6. -
        6. Enjoy!
          7. -
        -

        How to Customize Your Settings and Controls on BlueStacks?

        -

        To customize your settings and controls on BlueStacks, follow these tips:

        -
          -
        • To adjust the graphics quality, sound volume, language, or other settings of the game, click on the gear icon in the top right corner of the game screen and then choose Settings.
          • -
        • To change the keyboard and mouse controls of the game, click on the keyboard icon in the bottom right corner of the game screen and then choose Edit Controls. You can drag and drop different keys to different functions, or create your own custom layout.
          • -
        • To record a macro for the game, click on the macro recorder icon in the right sidebar of BlueStacks and then choose Record Macro. You can name your macro, assign a key to trigger it, and start recording your actions. You can stop recording by pressing Ctrl + Shift + 7. You can edit, delete, or play your macro from the macro manager.
        • To play multiple games or accounts at the same time, click on the multi-instance icon in the right sidebar of BlueStacks and then choose New Instance. You can create a new instance with a fresh Google account, or clone an existing instance with the same Google account. You can switch between different instances by clicking on their icons.
          • -
        -

        BlueStacks is a powerful and versatile Android emulator that lets you play F1 Mobile Racing on PC with ease and comfort. You can customize your settings and controls to suit your preferences, and enjoy many features and benefits that enhance your gaming experience. However, there are some drawbacks to using BlueStacks. You might need a high-end PC to run BlueStacks smoothly, as it consumes a lot of resources. You might also encounter some compatibility issues or bugs with some games or apps. And you might need to update BlueStacks regularly to keep up with the latest versions of Android and Google Play Services.

        -

        How to Play F1 Mobile Racing on PC with Windows 11?

        -

        Windows 11 is the latest operating system from Microsoft, and it comes with a new feature that lets you play Android games on your PC. Windows 11 has a built-in Android subsystem that allows you to run Android apps natively on your PC, without the need for an emulator or a browser. Windows 11 also has a new Microsoft Store that lets you download and install Android apps directly from the Amazon Appstore, which has over 500,000 apps and games available. Here's how to use Windows 11 to play F1 Mobile Racing on PC:

        -

        How to Upgrade to Windows 11?

        -

        To upgrade to Windows 11, you will need the following:

        -
          -
        • A Windows 10 PC or laptop that meets the minimum system requirements for Windows 11 (check them here)
          • -
        • An internet connection
          • -
        • A Microsoft account
          • -
        -

        Once you have these requirements, follow these steps:

        -
          -
        1. Go to www.microsoft.com/en-us/windows/windows-11 and click on Check for Compatibility
          2. -
        2. Download and run the PC Health Check app and see if your PC is eligible for the free upgrade
          3. -
        3. If your PC is eligible, go to Settings > Update & Security > Windows Update and click on Check for Updates
          4. -
        4. Wait for the Windows 11 update to download and install on your PC
          5. -
        5. Restart your PC and follow the instructions on the screen to complete the upgrade process
          6. -
        6. Congratulations! You have successfully upgraded to Windows 11!
          7. -
        -

        How to Download and Install F1 Mobile Racing on Windows 11?

        -

        To download and install F1 Mobile Racing on Windows 11, follow these steps:

        -
          -
        1. Open the Microsoft Store app on your PC and sign in with your Microsoft account
          2. -
        2. Click on the Apps tab and then click on Amazon Appstore
          3. -
        3. Sign in with your Amazon account or create one if you don't have one
          4. -
        4. Search for F1 Mobile Racing in the search bar or find it in the games category
          5. -
        5. Click on the game icon and then click on Get
          6. -
        6. Wait for the game to download and install on your PC
          7. -
        7. Click on the game icon again and then click on Play
          8. -
        8. Enjoy!
          9. -
        -

        How to Customize Your Settings and Controls on Windows 11?

        -

        To customize your settings and controls on Windows 11, follow these tips:

        -
          -
        • To adjust the graphics quality, sound volume, language, or other settings of the game, click on the gear icon in the top right corner of the game screen and then choose Settings.
          • -
        • To change the keyboard and mouse controls of the game, click on the keyboard icon in the bottom right corner of the game screen and then choose Edit Controls. You can drag and drop different keys to different functions, or create your own custom layout.
          • -
        • To resize or reposition the game window, hover over the top edge of the window until you see a menu bar appear. You can then drag the window to any position or size you want.
          • -
        -

        Windows 11 is a new and innovative operating system that lets you play F1 Mobile Racing on PC with ease and convenience. You can download and install Android apps directly from the Microsoft Store, without the need for an emulator or a browser. You can also enjoy a smooth and fast gameplay, as Windows 11 runs Android apps natively on your PC. However, there are some drawbacks to using Windows 11. You might not be able to access all the Android apps or games you want, as some of them might be exclusive to the Google Play Store or other platforms. You might also encounter some compatibility issues or bugs with some apps or games, as Windows 11 is still a new and evolving system. And you might need to upgrade your PC to meet the minimum requirements for Windows 11, which might be costly or inconvenient.

        Conclusion

        -

        F1 Mobile Racing is an amazing game that lets you experience the thrill and excitement of Formula 1 racing on your mobile device. But if you want to take your gaming to the next level, you can also play F1 Mobile Racing on your PC. There are three main ways you can do that: using Google Play Games, using BlueStacks, or using Windows 11. Each of these methods has its own advantages and disadvantages, so you can choose the one that suits your needs and preferences best.

        -

        Whether you play F1 Mobile Racing on your phone or your PC, you will have a blast racing against the best drivers in the world, developing and customizing your own F1 car, and competing in various events and modes. F1 Mobile Racing is a game that will keep you entertained and engaged for hours, so don't hesitate to download it and start your F1 adventure today!

        -

        FAQs

        -

        Here are some frequently asked questions about F1 Mobile Racing and how to play it on PC:

        -
          -
        • Q: Is F1 Mobile Racing free to play?
          • -
        • A: Yes, F1 Mobile Racing is free to download and play on both Android and iOS devices. However, the game also offers in-app purchases that can enhance your gameplay or unlock additional content.
          • -
        • Q: Can I play F1 Mobile Racing with my friends?
          • -
        • A: Yes, F1 Mobile Racing has a multiplayer mode that lets you challenge your friends or other players from around the world in real-time 1v1 races. You can also join a league or a club and cooperate with other players for more rewards and fun.
          • -
        • Q: Can I play F1 Mobile Racing offline?
          • -
        • A: Yes, F1 Mobile Racing has an offline mode that lets you play the game without an internet connection. However, some features and functions might not be available in offline mode, such as multiplayer races, events, or updates.
          • -
        • Q: Which method is the best for playing F1 Mobile Racing on PC?
          • -
        • A: There is no definitive answer to this question, as different methods might suit different players better. You can try out each method and see which one works best for you. However, some factors that might influence your decision are:
          • -
            -
          • Your PC's specifications and performance
            • -
          • Your internet connection speed and stability
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          • Your preferred settings and controls
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          • Your access to different Android apps and games
            • -
          -
        • Q: How can I contact the developers of F1 Mobile Racing?
          • -
        • A: If you have any questions, feedback, or issues regarding F1 Mobile Racing, you can contact the developers through the following channels:
          • - -

        197e85843d
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        \ No newline at end of file diff --git a/spaces/fb700/chat3/README.md b/spaces/fb700/chat3/README.md deleted file mode 100644 index bcac9e5403b49bd0e25736fd59fa2e5b0d65903d..0000000000000000000000000000000000000000 --- a/spaces/fb700/chat3/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chat3.0 -emoji: 🐍 -colorFrom: red -colorTo: yellow -sdk: gradio -sdk_version: 3.27.0 -python_version: 3.11 -app_file: main.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/fclong/summary/fengshen/models/unimc/__init__.py b/spaces/fclong/summary/fengshen/models/unimc/__init__.py deleted file mode 100644 index 26306d9fa6966341d2fa1878e1e13f25b0ab5d94..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/models/unimc/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .modeling_unimc import UniMCPipelines \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/APK Editor Pro Ultra New 5.0 21 The Ultimate Tool for Modifying Android Apps.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/APK Editor Pro Ultra New 5.0 21 The Ultimate Tool for Modifying Android Apps.md deleted file mode 100644 index c2a1d7a8edf381c733cae347278ffdd41b1bc0c0..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/APK Editor Pro Ultra New 5.0 21 The Ultimate Tool for Modifying Android Apps.md +++ /dev/null @@ -1,139 +0,0 @@ -
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        • You need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.
          • -
        • You need to have a stable internet connection to download the app.
          • -
        -

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        -

        Once you have met the requirements, you can follow these steps to download and install APK Editor Pro + Ultra New 5.0 21 on your device:

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        -
          -
        1. Go to the official website of APK Editor Pro + Ultra New 5.0 21 here and click on the Download button.
          2. -
        2. Wait for the download to complete and then locate the downloaded APK file on your device.
          3. -
        3. Tap on the APK file and follow the on-screen instructions to install the app.
          4. -
        4. Grant the necessary permissions and access to the app when prompted.
          5. -
        5. Launch the app and enjoy editing your apps.
          6. -
        -

        How to use APK Editor Pro + Ultra New 5.0 21?

        -

        Using APK Editor Pro + Ultra New 5.0 21 is easy and fun. You can use it to edit any APK file on your device or create your own projects from scratch. Here are the basic steps to use APK Editor Pro + Ultra New 5.0 21:

        -

        Select an APK file to edit

        -

        When you open the app, you will see two options: Select an Apk File and Select Apk from App. You can choose either option depending on whether you want to edit an APK file that is already installed on your device or an APK file that is stored on your device's storage. Tap on the option you want and then browse and select the APK file you want to edit.

        -

        Choose an editing mode

        -

        After selecting an APK file, you will see two editing modes: Full Edit and Simple Edit. You can choose either mode depending on how much control you want over the editing process. Full Edit mode allows you to edit everything in the APK file, including the code, resources, manifest, etc. Simple Edit mode allows you to edit only some basic aspects of the APK file, such as the app name, icon, package name, version, etc. Tap on the mode you want and then proceed to the next step.

        -

        Make the desired changes and save the modified APK file

        -

        In this step, you can make any changes you want to the APK file using the tools and options available in the app. You can use the toolbar at the top to access different functions, such as extracting files, importing files, signing files, aligning files, etc. You can also use the menu at the left to access different sections of the APK file, such as manifest, resources, code, etc. You can edit any section by tapping on it and making changes using the editor or viewer provided by the app. You can also use the search function to find specific files or strings in the APK file.

        -

        Once you are done with making changes, you can save the modified APK file by tapping on the Save button at the top right corner of the screen. You can choose where to save the file and what name to give it. You can also choose whether to sign and align the file or not. After saving the file, you can install it on your device or share it with others.

        -

        Tips and tricks for using APK Editor Pro + Ultra New 5.0 21

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        To make the most out of APK Editor Pro + Ultra New 5.0 21, here are some tips and tricks that you can follow:

        -

        Backup your original APK files before editing

        -

        Before you edit any APK file, it is always a good idea to backup your original APK file in case something goes wrong or you want to restore it later. You can backup your original APK file by using the Backup option in the app or by copying it manually from your device's storage.

        -

        Use the built-in help and tutorials for guidance

        -

        If you are new to APK editing or need some help with using some features or functions of the app, you can use the built-in help and tutorials provided by the app. You can access them by tapping on the Help button at the top left corner of the screen. You can also watch some video tutorials on YouTube or read some blogs or forums online to learn more about APK editing.

        -

        Be careful with modifying system apps or critical files

        -

        While APK Editor Pro + Ultra New 5.0 21 allows you to edit any APK file on your device, you should be careful with modifying system apps or critical files that are essential for the proper functioning of your device. Modifying such files may cause your device to malfunction, crash, or brick. You should only modify such files if you know what you are doing and have a backup of your device's firmware or ROM.

        -

        Conclusion

        -

        APK Editor Pro + Ultra New 5.0 21 is a powerful tool that allows you to edit, modify, and hack any APK file on your device. You can use it to customize your apps, enhance their performance and functionality, remove ads, unlock premium features, create your own modified versions, and more. You can download and install APK Editor Pro + Ultra New 5.0 21 from its official website and use it to edit any APK file on your device. You can also follow some tips and tricks to make the most out of this app and avoid any problems or issues.

        -

        FAQs

        -

        Here are some frequently asked questions about APK Editor Pro + Ultra New 5.0 21:

        -
        PersonajeArmasEstadísticas
        AdaPistolas y lanzaAtaque: 7
        Defensa: 3
        Destreza: 6
        Velocidad: 6
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        - - - - - - - - - - - - - - - - - - - - - - - - -
        QuestionAnswer
        Is APK Editor Pro + Ultra New 5.0 21 safe to use?Yes, APK Editor Pro + Ultra New 5.0 21 is safe to use as long as you download it from its official website and do not modify any malicious or harmful files. However, you should always be careful with what you edit and backup your original files before editing.
        Is APK Editor Pro + Ultra New 5.0 21 legal to use?Yes, APK Editor Pro + Ultra New 5.0 21 is legal to use as long as you do not violate any laws or terms of service of the apps you edit. You should only use it for personal or educational purposes and not for commercial or illegal purposes.
        Does APK Editor Pro + Ultra New 5.0 21 require root access?No, APK Editor Pro + Ultra New 5.0 21 does not require root access to work. However, some features or functions may require root access to work properly, such as editing system apps or files.
        Can I edit online games or apps with APK Editor Pro + Ultra New 5.0 21?No, you cannot edit online games or apps with APK Editor Pro + Ultra New 5.0 21 as they are protected by server-side verification and encryption. If you try to edit them, you may face errors, bans, or account suspensions.
        Can I undo the changes I made with APK Editor Pro + Ultra New 5.0 21?Yes, you can undo the changes you made with APK Editor Pro + Ultra New 5.0 21 by restoring the original APK file that you backed up before editing or by uninstalling and reinstalling the app that you edited.

        401be4b1e0
        -
        -
        \ No newline at end of file diff --git a/spaces/fffffu/bing/src/components/chat-list.tsx b/spaces/fffffu/bing/src/components/chat-list.tsx deleted file mode 100644 index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000 --- a/spaces/fffffu/bing/src/components/chat-list.tsx +++ /dev/null @@ -1,28 +0,0 @@ -import React from 'react' - -import { Separator } from '@/components/ui/separator' -import { ChatMessage } from '@/components/chat-message' -import { ChatMessageModel } from '@/lib/bots/bing/types' - -export interface ChatList { - messages: ChatMessageModel[] -} - -export function ChatList({ messages }: ChatList) { - if (!messages.length) { - return null - } - - return ( -
        - {messages.map((message, index) => ( - - - {index < messages.length - 1 && ( - - )} - - ))} -
        - ) -} diff --git a/spaces/fffiloni/SplitTrack2MusicGen/app.py b/spaces/fffiloni/SplitTrack2MusicGen/app.py deleted file mode 100644 index bddb92546f7ec30e2649ddf68d04e15fd18134b6..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/SplitTrack2MusicGen/app.py +++ /dev/null @@ -1,231 +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. -""" - -from tempfile import NamedTemporaryFile -import torch -import gradio as gr -from scipy.io.wavfile import write - -from audiocraft.models import MusicGen -import tempfile -import os -from audiocraft.data.audio import audio_write - - -MODEL = None - -import yt_dlp as youtube_dl -from moviepy.editor import VideoFileClip - -YT_LENGTH_LIMIT_S = 480 # limit to 1 hour YouTube files - -def download_yt_audio(yt_url, filename): - info_loader = youtube_dl.YoutubeDL() - - try: - info = info_loader.extract_info(yt_url, download=False) - except youtube_dl.utils.DownloadError as err: - raise gr.Error(str(err)) - - file_length = info["duration_string"] - file_h_m_s = file_length.split(":") - file_h_m_s = [int(sub_length) for sub_length in file_h_m_s] - - if len(file_h_m_s) == 1: - file_h_m_s.insert(0, 0) - if len(file_h_m_s) == 2: - file_h_m_s.insert(0, 0) - file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2] - - if file_length_s > YT_LENGTH_LIMIT_S: - yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)) - file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s)) - raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") - - ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"} - - with youtube_dl.YoutubeDL(ydl_opts) as ydl: - try: - ydl.download([yt_url]) - except youtube_dl.utils.ExtractorError as err: - raise gr.Error(str(err)) - - -def convert_to_mp3(input_path, output_path): - try: - video_clip = VideoFileClip(input_path) - audio_clip = video_clip.audio - print("Converting to MP3...") - audio_clip.write_audiofile(output_path) - except Exception as e: - print("Error:", e) - -def load_youtube_audio(yt_link): - - with tempfile.TemporaryDirectory() as tmpdirname: - filepath = os.path.join(tmpdirname, "video.mp4") - download_yt_audio(yt_link, filepath) - - mp3_output_path = "video_sound.mp3" - convert_to_mp3(filepath, mp3_output_path) - print("Conversion complete. MP3 saved at:", mp3_output_path) - - return mp3_output_path - -def split_process(audio, chosen_out_track): - os.makedirs("out", exist_ok=True) - write('test.wav', audio[0], audio[1]) - os.system("python3 -m demucs.separate -n mdx_extra_q -j 4 test.wav -o out") - #return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/bass.wav","./out/mdx_extra_q/test/drums.wav","./out/mdx_extra_q/test/other.wav" - if chosen_out_track == "vocals": - return "./out/mdx_extra_q/test/vocals.wav" - elif chosen_out_track == "bass": - return "./out/mdx_extra_q/test/bass.wav" - elif chosen_out_track == "drums": - return "./out/mdx_extra_q/test/drums.wav" - elif chosen_out_track == "other": - return "./out/mdx_extra_q/test/other.wav" - elif chosen_out_track == "all-in": - return "test.wav" - -def load_model(version): - print("Loading model", version) - return MusicGen.get_pretrained(version) - - -def predict(music_prompt, melody, duration, cfg_coef): - text = music_prompt - global MODEL - topk = int(250) - if MODEL is None or MODEL.name != "melody": - MODEL = load_model("melody") - - if duration > MODEL.lm.cfg.dataset.segment_duration: - raise gr.Error("MusicGen currently supports durations of up to 30 seconds!") - MODEL.set_generation_params( - use_sampling=True, - top_k=250, - top_p=0, - temperature=1.0, - cfg_coef=cfg_coef, - duration=duration, - ) - - if melody: - sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0) - print(melody.shape) - if melody.dim() == 2: - melody = melody[None] - melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)] - output = MODEL.generate_with_chroma( - descriptions=[text], - melody_wavs=melody, - melody_sample_rate=sr, - progress=False - ) - else: - output = MODEL.generate(descriptions=[text], progress=False) - - output = output.detach().cpu().float()[0] - with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file: - audio_write(file.name, output, MODEL.sample_rate, strategy="loudness", add_suffix=False) - #waveform_video = gr.make_waveform(file.name) - return file.name - -css=""" -#col-container {max-width: 910px; margin-left: auto; margin-right: auto;} -a {text-decoration-line: underline; font-weight: 600;} -""" - -with gr.Blocks(css=css) as demo: - with gr.Column(elem_id="col-container"): - gr.Markdown( - """ - # Split Audio Tracks to MusicGen - Upload an audio file, split audio tracks with Demucs, choose a track as conditional sound for MusicGen, get a remix !
        - *** Careful, MusicGen model loaded here can only handle up to 30 second audio, please use the audio component gradio feature to edit your audio before conditioning *** -
        -
        - [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/SplitTrack2MusicGen?duplicate=true) for longer audio, more control and no queue.

        - """ - ) - - with gr.Column(): - uploaded_sound = gr.Audio(type="numpy", label="Input", source="upload") - with gr.Row(): - youtube_link = gr.Textbox(show_label=False, placeholder="TEMPORARILY DISABLED • you can also paste YT link and load it", interactive=False) - yt_load_btn = gr.Button("Load YT song", interactive=False) - - with gr.Row(): - chosen_track = gr.Radio(["vocals", "bass", "drums", "other", "all-in"], label="Track", info="Which track from your audio do you want to mashup ?", value="vocals") - load_sound_btn = gr.Button('Load your chosen track') - #split_vocals = gr.Audio(type="filepath", label="Vocals") - #split_bass = gr.Audio(type="filepath", label="Bass") - #split_drums = gr.Audio(type="filepath", label="Drums") - #split_others = gr.Audio(type="filepath", label="Other") - - with gr.Row(): - music_prompt = gr.Textbox(label="Musical Prompt", info="Describe what kind of music you wish for", interactive=True, placeholder="lofi slow bpm electro chill with organic samples") - melody = gr.Audio(source="upload", type="numpy", label="Track Condition (from previous step)", interactive=False) - with gr.Row(): - #model = gr.Radio(["melody", "medium", "small", "large"], label="MusicGen Model", value="melody", interactive=True) - duration = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Generated Music Duration", interactive=True) - cfg_coef = gr.Slider(label="Classifier Free Guidance", minimum=1.0, maximum=10.0, step=0.1, value=3.0, interactive=True) - with gr.Row(): - submit = gr.Button("Submit") - #with gr.Row(): - # topk = gr.Number(label="Top-k", value=250, interactive=True) - # topp = gr.Number(label="Top-p", value=0, interactive=True) - # temperature = gr.Number(label="Temperature", value=1.0, interactive=True) - # cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True) - - output = gr.Audio(label="Generated Music") - - gr.Examples( - fn=predict, - examples=[ - [ - "An 80s driving pop song with heavy drums and synth pads in the background", - None, - 10, - 3.0 - ], - [ - "A cheerful country song with acoustic guitars", - None, - 10, - 3.0 - ], - [ - "90s rock song with electric guitar and heavy drums", - None, - 10, - 3.0 - ], - [ - "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130", - None, - 10, - 3.0 - ], - [ - "lofi slow bpm electro chill with organic samples", - None, - 10, - 3.0 - ], - ], - inputs=[music_prompt, melody, duration, cfg_coef], - outputs=[output] - ) - yt_load_btn.click(fn=load_youtube_audio, inputs=[youtube_link], outputs=[uploaded_sound], queue=False, api_name=False) - load_sound_btn.click(split_process, inputs=[uploaded_sound, chosen_track], outputs=[melody], api_name="splt_trck") - submit.click(predict, inputs=[music_prompt, melody, duration, cfg_coef], outputs=[output]) - - -demo.queue(max_size=32).launch() diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/perf_hooks.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/perf_hooks.d.ts deleted file mode 100644 index 5c0b228e7d2a75d3d2726f4c4e02681dba341cac..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/perf_hooks.d.ts +++ /dev/null @@ -1,625 +0,0 @@ -/** - * This module provides an implementation of a subset of the W3C [Web Performance APIs](https://w3c.github.io/perf-timing-primer/) as well as additional APIs for - * Node.js-specific performance measurements. - * - * Node.js supports the following [Web Performance APIs](https://w3c.github.io/perf-timing-primer/): - * - * * [High Resolution Time](https://www.w3.org/TR/hr-time-2) - * * [Performance Timeline](https://w3c.github.io/performance-timeline/) - * * [User Timing](https://www.w3.org/TR/user-timing/) - * - * ```js - * const { PerformanceObserver, performance } = require('perf_hooks'); - * - * const obs = new PerformanceObserver((items) => { - * console.log(items.getEntries()[0].duration); - * performance.clearMarks(); - * }); - * obs.observe({ type: 'measure' }); - * performance.measure('Start to Now'); - * - * performance.mark('A'); - * doSomeLongRunningProcess(() => { - * performance.measure('A to Now', 'A'); - * - * performance.mark('B'); - * performance.measure('A to B', 'A', 'B'); - * }); - * ``` - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/perf_hooks.js) - */ -declare module 'perf_hooks' { - import { AsyncResource } from 'node:async_hooks'; - type EntryType = 'node' | 'mark' | 'measure' | 'gc' | 'function' | 'http2' | 'http'; - interface NodeGCPerformanceDetail { - /** - * When `performanceEntry.entryType` is equal to 'gc', `the performance.kind` property identifies - * the type of garbage collection operation that occurred. - * See perf_hooks.constants for valid values. - */ - readonly kind?: number | undefined; - /** - * When `performanceEntry.entryType` is equal to 'gc', the `performance.flags` - * property contains additional information about garbage collection operation. - * See perf_hooks.constants for valid values. - */ - readonly flags?: number | undefined; - } - /** - * @since v8.5.0 - */ - class PerformanceEntry { - protected constructor(); - /** - * The total number of milliseconds elapsed for this entry. This value will not - * be meaningful for all Performance Entry types. - * @since v8.5.0 - */ - readonly duration: number; - /** - * The name of the performance entry. - * @since v8.5.0 - */ - readonly name: string; - /** - * The high resolution millisecond timestamp marking the starting time of the - * Performance Entry. - * @since v8.5.0 - */ - readonly startTime: number; - /** - * The type of the performance entry. It may be one of: - * - * * `'node'` (Node.js only) - * * `'mark'` (available on the Web) - * * `'measure'` (available on the Web) - * * `'gc'` (Node.js only) - * * `'function'` (Node.js only) - * * `'http2'` (Node.js only) - * * `'http'` (Node.js only) - * @since v8.5.0 - */ - readonly entryType: EntryType; - /** - * Additional detail specific to the `entryType`. - * @since v16.0.0 - */ - readonly detail?: NodeGCPerformanceDetail | unknown | undefined; // TODO: Narrow this based on entry type. - toJSON(): any; - } - class PerformanceMark extends PerformanceEntry { - readonly duration: 0; - readonly entryType: 'mark'; - } - class PerformanceMeasure extends PerformanceEntry { - readonly entryType: 'measure'; - } - /** - * _This property is an extension by Node.js. It is not available in Web browsers._ - * - * Provides timing details for Node.js itself. The constructor of this class - * is not exposed to users. - * @since v8.5.0 - */ - class PerformanceNodeTiming extends PerformanceEntry { - /** - * The high resolution millisecond timestamp at which the Node.js process - * completed bootstrapping. If bootstrapping has not yet finished, the property - * has the value of -1. - * @since v8.5.0 - */ - readonly bootstrapComplete: number; - /** - * The high resolution millisecond timestamp at which the Node.js environment was - * initialized. - * @since v8.5.0 - */ - readonly environment: number; - /** - * The high resolution millisecond timestamp of the amount of time the event loop - * has been idle within the event loop's event provider (e.g. `epoll_wait`). This - * does not take CPU usage into consideration. If the event loop has not yet - * started (e.g., in the first tick of the main script), the property has the - * value of 0. - * @since v14.10.0, v12.19.0 - */ - readonly idleTime: number; - /** - * The high resolution millisecond timestamp at which the Node.js event loop - * exited. If the event loop has not yet exited, the property has the value of -1\. - * It can only have a value of not -1 in a handler of the `'exit'` event. - * @since v8.5.0 - */ - readonly loopExit: number; - /** - * The high resolution millisecond timestamp at which the Node.js event loop - * started. If the event loop has not yet started (e.g., in the first tick of the - * main script), the property has the value of -1. - * @since v8.5.0 - */ - readonly loopStart: number; - /** - * The high resolution millisecond timestamp at which the V8 platform was - * initialized. - * @since v8.5.0 - */ - readonly v8Start: number; - } - interface EventLoopUtilization { - idle: number; - active: number; - utilization: number; - } - /** - * @param util1 The result of a previous call to eventLoopUtilization() - * @param util2 The result of a previous call to eventLoopUtilization() prior to util1 - */ - type EventLoopUtilityFunction = (util1?: EventLoopUtilization, util2?: EventLoopUtilization) => EventLoopUtilization; - interface MarkOptions { - /** - * Additional optional detail to include with the mark. - */ - detail?: unknown | undefined; - /** - * An optional timestamp to be used as the mark time. - * @default `performance.now()`. - */ - startTime?: number | undefined; - } - interface MeasureOptions { - /** - * Additional optional detail to include with the mark. - */ - detail?: unknown | undefined; - /** - * Duration between start and end times. - */ - duration?: number | undefined; - /** - * Timestamp to be used as the end time, or a string identifying a previously recorded mark. - */ - end?: number | string | undefined; - /** - * Timestamp to be used as the start time, or a string identifying a previously recorded mark. - */ - start?: number | string | undefined; - } - interface TimerifyOptions { - /** - * A histogram object created using - * `perf_hooks.createHistogram()` that will record runtime durations in - * nanoseconds. - */ - histogram?: RecordableHistogram | undefined; - } - interface Performance { - /** - * If name is not provided, removes all PerformanceMark objects from the Performance Timeline. - * If name is provided, removes only the named mark. - * @param name - */ - clearMarks(name?: string): void; - /** - * If name is not provided, removes all PerformanceMeasure objects from the Performance Timeline. - * If name is provided, removes only the named measure. - * @param name - * @since v16.7.0 - */ - clearMeasures(name?: string): void; - /** - * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime`. - * If you are only interested in performance entries of certain types or that have certain names, see - * `performance.getEntriesByType()` and `performance.getEntriesByName()`. - * @since v16.7.0 - */ - getEntries(): PerformanceEntry[]; - /** - * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime` - * whose `performanceEntry.name` is equal to `name`, and optionally, whose `performanceEntry.entryType` is equal to `type`. - * @param name - * @param type - * @since v16.7.0 - */ - getEntriesByName(name: string, type?: EntryType): PerformanceEntry[]; - /** - * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime` - * whose `performanceEntry.entryType` is equal to `type`. - * @param type - * @since v16.7.0 - */ - getEntriesByType(type: EntryType): PerformanceEntry[]; - /** - * Creates a new PerformanceMark entry in the Performance Timeline. - * A PerformanceMark is a subclass of PerformanceEntry whose performanceEntry.entryType is always 'mark', - * and whose performanceEntry.duration is always 0. - * Performance marks are used to mark specific significant moments in the Performance Timeline. - * @param name - * @return The PerformanceMark entry that was created - */ - mark(name?: string, options?: MarkOptions): PerformanceMark; - /** - * Creates a new PerformanceMeasure entry in the Performance Timeline. - * A PerformanceMeasure is a subclass of PerformanceEntry whose performanceEntry.entryType is always 'measure', - * and whose performanceEntry.duration measures the number of milliseconds elapsed since startMark and endMark. - * - * The startMark argument may identify any existing PerformanceMark in the the Performance Timeline, or may identify - * any of the timestamp properties provided by the PerformanceNodeTiming class. If the named startMark does not exist, - * then startMark is set to timeOrigin by default. - * - * The endMark argument must identify any existing PerformanceMark in the the Performance Timeline or any of the timestamp - * properties provided by the PerformanceNodeTiming class. If the named endMark does not exist, an error will be thrown. - * @param name - * @param startMark - * @param endMark - * @return The PerformanceMeasure entry that was created - */ - measure(name: string, startMark?: string, endMark?: string): PerformanceMeasure; - measure(name: string, options: MeasureOptions): PerformanceMeasure; - /** - * An instance of the PerformanceNodeTiming class that provides performance metrics for specific Node.js operational milestones. - */ - readonly nodeTiming: PerformanceNodeTiming; - /** - * @return the current high resolution millisecond timestamp - */ - now(): number; - /** - * The timeOrigin specifies the high resolution millisecond timestamp from which all performance metric durations are measured. - */ - readonly timeOrigin: number; - /** - * Wraps a function within a new function that measures the running time of the wrapped function. - * A PerformanceObserver must be subscribed to the 'function' event type in order for the timing details to be accessed. - * @param fn - */ - timerify any>(fn: T, options?: TimerifyOptions): T; - /** - * eventLoopUtilization is similar to CPU utilization except that it is calculated using high precision wall-clock time. - * It represents the percentage of time the event loop has spent outside the event loop's event provider (e.g. epoll_wait). - * No other CPU idle time is taken into consideration. - */ - eventLoopUtilization: EventLoopUtilityFunction; - } - interface PerformanceObserverEntryList { - /** - * Returns a list of `PerformanceEntry` objects in chronological order - * with respect to `performanceEntry.startTime`. - * - * ```js - * const { - * performance, - * PerformanceObserver - * } = require('perf_hooks'); - * - * const obs = new PerformanceObserver((perfObserverList, observer) => { - * console.log(perfObserverList.getEntries()); - * - * * [ - * * PerformanceEntry { - * * name: 'test', - * * entryType: 'mark', - * * startTime: 81.465639, - * * duration: 0 - * * }, - * * PerformanceEntry { - * * name: 'meow', - * * entryType: 'mark', - * * startTime: 81.860064, - * * duration: 0 - * * } - * * ] - * - * - * performance.clearMarks(); - * performance.clearMeasures(); - * observer.disconnect(); - * }); - * obs.observe({ type: 'mark' }); - * - * performance.mark('test'); - * performance.mark('meow'); - * ``` - * @since v8.5.0 - */ - getEntries(): PerformanceEntry[]; - /** - * Returns a list of `PerformanceEntry` objects in chronological order - * with respect to `performanceEntry.startTime` whose `performanceEntry.name` is - * equal to `name`, and optionally, whose `performanceEntry.entryType` is equal to`type`. - * - * ```js - * const { - * performance, - * PerformanceObserver - * } = require('perf_hooks'); - * - * const obs = new PerformanceObserver((perfObserverList, observer) => { - * console.log(perfObserverList.getEntriesByName('meow')); - * - * * [ - * * PerformanceEntry { - * * name: 'meow', - * * entryType: 'mark', - * * startTime: 98.545991, - * * duration: 0 - * * } - * * ] - * - * console.log(perfObserverList.getEntriesByName('nope')); // [] - * - * console.log(perfObserverList.getEntriesByName('test', 'mark')); - * - * * [ - * * PerformanceEntry { - * * name: 'test', - * * entryType: 'mark', - * * startTime: 63.518931, - * * duration: 0 - * * } - * * ] - * - * console.log(perfObserverList.getEntriesByName('test', 'measure')); // [] - * - * performance.clearMarks(); - * performance.clearMeasures(); - * observer.disconnect(); - * }); - * obs.observe({ entryTypes: ['mark', 'measure'] }); - * - * performance.mark('test'); - * performance.mark('meow'); - * ``` - * @since v8.5.0 - */ - getEntriesByName(name: string, type?: EntryType): PerformanceEntry[]; - /** - * Returns a list of `PerformanceEntry` objects in chronological order - * with respect to `performanceEntry.startTime` whose `performanceEntry.entryType`is equal to `type`. - * - * ```js - * const { - * performance, - * PerformanceObserver - * } = require('perf_hooks'); - * - * const obs = new PerformanceObserver((perfObserverList, observer) => { - * console.log(perfObserverList.getEntriesByType('mark')); - * - * * [ - * * PerformanceEntry { - * * name: 'test', - * * entryType: 'mark', - * * startTime: 55.897834, - * * duration: 0 - * * }, - * * PerformanceEntry { - * * name: 'meow', - * * entryType: 'mark', - * * startTime: 56.350146, - * * duration: 0 - * * } - * * ] - * - * performance.clearMarks(); - * performance.clearMeasures(); - * observer.disconnect(); - * }); - * obs.observe({ type: 'mark' }); - * - * performance.mark('test'); - * performance.mark('meow'); - * ``` - * @since v8.5.0 - */ - getEntriesByType(type: EntryType): PerformanceEntry[]; - } - type PerformanceObserverCallback = (list: PerformanceObserverEntryList, observer: PerformanceObserver) => void; - class PerformanceObserver extends AsyncResource { - constructor(callback: PerformanceObserverCallback); - /** - * Disconnects the `PerformanceObserver` instance from all notifications. - * @since v8.5.0 - */ - disconnect(): void; - /** - * Subscribes the `PerformanceObserver` instance to notifications of new `PerformanceEntry` instances identified either by `options.entryTypes`or `options.type`: - * - * ```js - * const { - * performance, - * PerformanceObserver - * } = require('perf_hooks'); - * - * const obs = new PerformanceObserver((list, observer) => { - * // Called once asynchronously. `list` contains three items. - * }); - * obs.observe({ type: 'mark' }); - * - * for (let n = 0; n < 3; n++) - * performance.mark(`test${n}`); - * ``` - * @since v8.5.0 - */ - observe( - options: - | { - entryTypes: ReadonlyArray; - buffered?: boolean | undefined; - } - | { - type: EntryType; - buffered?: boolean | undefined; - } - ): void; - } - namespace constants { - const NODE_PERFORMANCE_GC_MAJOR: number; - const NODE_PERFORMANCE_GC_MINOR: number; - const NODE_PERFORMANCE_GC_INCREMENTAL: number; - const NODE_PERFORMANCE_GC_WEAKCB: number; - const NODE_PERFORMANCE_GC_FLAGS_NO: number; - const NODE_PERFORMANCE_GC_FLAGS_CONSTRUCT_RETAINED: number; - const NODE_PERFORMANCE_GC_FLAGS_FORCED: number; - const NODE_PERFORMANCE_GC_FLAGS_SYNCHRONOUS_PHANTOM_PROCESSING: number; - const NODE_PERFORMANCE_GC_FLAGS_ALL_AVAILABLE_GARBAGE: number; - const NODE_PERFORMANCE_GC_FLAGS_ALL_EXTERNAL_MEMORY: number; - const NODE_PERFORMANCE_GC_FLAGS_SCHEDULE_IDLE: number; - } - const performance: Performance; - interface EventLoopMonitorOptions { - /** - * The sampling rate in milliseconds. - * Must be greater than zero. - * @default 10 - */ - resolution?: number | undefined; - } - interface Histogram { - /** - * Returns a `Map` object detailing the accumulated percentile distribution. - * @since v11.10.0 - */ - readonly percentiles: Map; - /** - * The number of times the event loop delay exceeded the maximum 1 hour event - * loop delay threshold. - * @since v11.10.0 - */ - readonly exceeds: number; - /** - * The minimum recorded event loop delay. - * @since v11.10.0 - */ - readonly min: number; - /** - * The maximum recorded event loop delay. - * @since v11.10.0 - */ - readonly max: number; - /** - * The mean of the recorded event loop delays. - * @since v11.10.0 - */ - readonly mean: number; - /** - * The standard deviation of the recorded event loop delays. - * @since v11.10.0 - */ - readonly stddev: number; - /** - * Resets the collected histogram data. - * @since v11.10.0 - */ - reset(): void; - /** - * Returns the value at the given percentile. - * @since v11.10.0 - * @param percentile A percentile value in the range (0, 100]. - */ - percentile(percentile: number): number; - } - interface IntervalHistogram extends Histogram { - /** - * Enables the update interval timer. Returns `true` if the timer was - * started, `false` if it was already started. - * @since v11.10.0 - */ - enable(): boolean; - /** - * Disables the update interval timer. Returns `true` if the timer was - * stopped, `false` if it was already stopped. - * @since v11.10.0 - */ - disable(): boolean; - } - interface RecordableHistogram extends Histogram { - /** - * @since v15.9.0, v14.18.0 - * @param val The amount to record in the histogram. - */ - record(val: number | bigint): void; - /** - * Calculates the amount of time (in nanoseconds) that has passed since the - * previous call to `recordDelta()` and records that amount in the histogram. - * - * ## Examples - * @since v15.9.0, v14.18.0 - */ - recordDelta(): void; - /** - * Adds the values from other to this histogram. - * @since v17.4.0, v16.14.0 - * @param other Recordable Histogram to combine with - */ - add(other: RecordableHistogram): void; - } - /** - * _This property is an extension by Node.js. It is not available in Web browsers._ - * - * Creates an `IntervalHistogram` object that samples and reports the event loop - * delay over time. The delays will be reported in nanoseconds. - * - * Using a timer to detect approximate event loop delay works because the - * execution of timers is tied specifically to the lifecycle of the libuv - * event loop. That is, a delay in the loop will cause a delay in the execution - * of the timer, and those delays are specifically what this API is intended to - * detect. - * - * ```js - * const { monitorEventLoopDelay } = require('perf_hooks'); - * const h = monitorEventLoopDelay({ resolution: 20 }); - * h.enable(); - * // Do something. - * h.disable(); - * console.log(h.min); - * console.log(h.max); - * console.log(h.mean); - * console.log(h.stddev); - * console.log(h.percentiles); - * console.log(h.percentile(50)); - * console.log(h.percentile(99)); - * ``` - * @since v11.10.0 - */ - function monitorEventLoopDelay(options?: EventLoopMonitorOptions): IntervalHistogram; - interface CreateHistogramOptions { - /** - * The minimum recordable value. Must be an integer value greater than 0. - * @default 1 - */ - min?: number | bigint | undefined; - /** - * The maximum recordable value. Must be an integer value greater than min. - * @default Number.MAX_SAFE_INTEGER - */ - max?: number | bigint | undefined; - /** - * The number of accuracy digits. Must be a number between 1 and 5. - * @default 3 - */ - figures?: number | undefined; - } - /** - * Returns a `RecordableHistogram`. - * @since v15.9.0, v14.18.0 - */ - function createHistogram(options?: CreateHistogramOptions): RecordableHistogram; - - import { performance as _performance } from 'perf_hooks'; - global { - /** - * `performance` is a global reference for `require('perf_hooks').performance` - * https://nodejs.org/api/globals.html#performance - * @since v16.0.0 - */ - var performance: typeof globalThis extends { - onmessage: any; - performance: infer T; - } - ? T - : typeof _performance; - } -} -declare module 'node:perf_hooks' { - export * from 'perf_hooks'; -} diff --git a/spaces/glyszt/vt/vtoonify/model/stylegan/op_gpu/upfirdn2d.py b/spaces/glyszt/vt/vtoonify/model/stylegan/op_gpu/upfirdn2d.py deleted file mode 100644 index 3a12f15b3c2347194e3bf0fdfda736415693775f..0000000000000000000000000000000000000000 --- a/spaces/glyszt/vt/vtoonify/model/stylegan/op_gpu/upfirdn2d.py +++ /dev/null @@ -1,209 +0,0 @@ -from collections import abc -import os - -import torch -from torch.nn import functional as F -from torch.autograd import Function -from torch.utils.cpp_extension import load - - -module_path = os.path.dirname(__file__) -upfirdn2d_op = load( - "upfirdn2d", - sources=[ - os.path.join(module_path, "upfirdn2d.cpp"), - os.path.join(module_path, "upfirdn2d_kernel.cu"), - ], -) - - -class UpFirDn2dBackward(Function): - @staticmethod - def forward( - ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size - ): - - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_op.upfirdn2d( - grad_output, - grad_kernel, - down_x, - down_y, - up_x, - up_y, - g_pad_x0, - g_pad_x1, - g_pad_y0, - g_pad_y1, - ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - kernel, = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_op.upfirdn2d( - gradgrad_input, - kernel, - ctx.up_x, - ctx.up_y, - ctx.down_x, - ctx.down_y, - ctx.pad_x0, - ctx.pad_x1, - ctx.pad_y0, - ctx.pad_y1, - ) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view( - ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] - ) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - batch, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_op.upfirdn2d( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 - ) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = None - - if ctx.needs_input_grad[0]: - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - if not isinstance(up, abc.Iterable): - up = (up, up) - - if not isinstance(down, abc.Iterable): - down = (down, down) - - if len(pad) == 2: - pad = (pad[0], pad[1], pad[0], pad[1]) - - if input.device.type == "cpu": - out = upfirdn2d_native(input, kernel, *up, *down, *pad) - - else: - out = UpFirDn2d.apply(input, kernel, up, down, pad) - - return out - - -def upfirdn2d_native( - input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 -): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] - ) - out = out[ - :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), - :, - ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] - ) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x - - return out.view(-1, channel, out_h, out_w) diff --git a/spaces/goarnaiz/Proyecto/README.md b/spaces/goarnaiz/Proyecto/README.md deleted file mode 100644 index c38240f896ae7c98a0aca08a4d6ad9806d120a12..0000000000000000000000000000000000000000 --- a/spaces/goarnaiz/Proyecto/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Proyecto -emoji: 😻 -colorFrom: gray -colorTo: blue -sdk: gradio -sdk_version: 3.0.13 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/gordonchan/h2oo/stopping.py b/spaces/gordonchan/h2oo/stopping.py deleted file mode 100644 index f55de4f79ed17c5bafc18358e611051df0360f77..0000000000000000000000000000000000000000 --- a/spaces/gordonchan/h2oo/stopping.py +++ /dev/null @@ -1,78 +0,0 @@ -import torch -from transformers import StoppingCriteria, StoppingCriteriaList - -from enums import PromptType - - -class StoppingCriteriaSub(StoppingCriteria): - - def __init__(self, stops=[], encounters=[], device="cuda", model_max_length=None): - super().__init__() - assert len(stops) % len(encounters) == 0, "Number of stops and encounters must match" - self.encounters = encounters - self.stops = [stop.to(device) for stop in stops] - self.num_stops = [0] * len(stops) - self.model_max_length = model_max_length - - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: - for stopi, stop in enumerate(self.stops): - if torch.all((stop == input_ids[0][-len(stop):])).item(): - self.num_stops[stopi] += 1 - if self.num_stops[stopi] >= self.encounters[stopi % len(self.encounters)]: - # print("Stopped", flush=True) - return True - if self.model_max_length is not None and input_ids[0].shape[0] >= self.model_max_length: - # critical limit - return True - # print("Tokens: %s" % input_ids[0].cpu().numpy(), flush=True) - # print("Stop Tokens: %s" % [x.cpu().numpy() for x in self.stops], flush=True) - return False - - -def get_stopping(prompt_type, prompt_dict, tokenizer, device, human=':', bot=":", model_max_length=None): - # FIXME: prompt_dict unused currently - if prompt_type in [PromptType.human_bot.name, PromptType.instruct_vicuna.name, PromptType.instruct_with_end.name]: - if prompt_type == PromptType.human_bot.name: - # encounters = [prompt.count(human) + 1, prompt.count(bot) + 1] - # stopping only starts once output is beyond prompt - # 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added - stop_words = [human, bot, '\n' + human, '\n' + bot] - encounters = [1, 2] - elif prompt_type == PromptType.instruct_vicuna.name: - # even below is not enough, generic strings and many ways to encode - stop_words = [ - '### Human:', - """ -### Human:""", - """ -### Human: -""", - '### Assistant:', - """ -### Assistant:""", - """ -### Assistant: -""", - ] - encounters = [1, 2] - else: - # some instruct prompts have this as end, doesn't hurt to stop on it since not common otherwise - stop_words = ['### End'] - encounters = [1] - stop_words_ids = [ - tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words] - # handle single token case - stop_words_ids = [x if len(x.shape) > 0 else torch.tensor([x]) for x in stop_words_ids] - stop_words_ids = [x for x in stop_words_ids if x.shape[0] > 0] - # avoid padding in front of tokens - if tokenizer._pad_token: # use hidden variable to avoid annoying properly logger bug - stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids] - # handle fake \n added - stop_words_ids = [x[1:] if y[0] == '\n' else x for x, y in zip(stop_words_ids, stop_words)] - # build stopper - stopping_criteria = StoppingCriteriaList( - [StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters, device=device, - model_max_length=model_max_length)]) - else: - stopping_criteria = StoppingCriteriaList() - return stopping_criteria diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Maheruh hindi movie in 720p download The plot the songs and the trivia.md b/spaces/gotiQspiryo/whisper-ui/examples/Maheruh hindi movie in 720p download The plot the songs and the trivia.md deleted file mode 100644 index 5f9aaa41a29f8860238859d6e46225f21159e4f9..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Maheruh hindi movie in 720p download The plot the songs and the trivia.md +++ /dev/null @@ -1,6 +0,0 @@ -

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        diff --git a/spaces/gradio/HuBERT/examples/latent_depth/latent_depth_src/modules/__init__.py b/spaces/gradio/HuBERT/examples/latent_depth/latent_depth_src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/download_lotus.sh b/spaces/gradio/HuBERT/examples/multilingual/data_scripts/download_lotus.sh deleted file mode 100644 index c08c701314a8e575637deff78381ab02c2ef6728..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/download_lotus.sh +++ /dev/null @@ -1,46 +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 $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - -SRCDIR=$WORKDIR_ROOT/indic_languages_corpus -DESTDIR=${WORKDIR_ROOT}/ML50/raw/ -mkdir -p $SRCDIR -mkdir -p $DESTDIR - -cd $SRCDIR -wget http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_languages_corpus.tar.gz -tar -xvzf indic_languages_corpus.tar.gz - -SRC_EXTRACT_DIR=$SRCDIR/indic_languages_corpus/bilingual - -cp $SRC_EXTRACT_DIR/ml-en/train.ml $DESTDIR/train.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/train.en $DESTDIR/train.ml_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ml-en/dev.ml $DESTDIR/valid.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/dev.en $DESTDIR/valid.ml_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ml-en/test.ml $DESTDIR/test.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/test.en $DESTDIR/test.ml_IN-en_XX.en_XX - -cp $SRC_EXTRACT_DIR/ur-en/train.ur $DESTDIR/train.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/train.en $DESTDIR/train.ur_PK-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ur-en/dev.ur $DESTDIR/valid.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/dev.en $DESTDIR/valid.ur_PK-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ur-en/test.ur $DESTDIR/test.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/test.en $DESTDIR/test.ur_PK-en_XX.en_XX - -cp $SRC_EXTRACT_DIR/te-en/train.te $DESTDIR/train.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/train.en $DESTDIR/train.te_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/te-en/dev.te $DESTDIR/valid.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/dev.en $DESTDIR/valid.te_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/te-en/test.te $DESTDIR/test.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/test.en $DESTDIR/test.te_IN-en_XX.en_XX diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/new/decoders/decoder_config.py b/spaces/gradio/HuBERT/examples/speech_recognition/new/decoders/decoder_config.py deleted file mode 100644 index 659eb94a9b8187a7c126d7b439ac2742f9d72022..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/speech_recognition/new/decoders/decoder_config.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field -from typing import Optional - -from fairseq.dataclass.configs import FairseqDataclass -from fairseq.dataclass.constants import ChoiceEnum -from omegaconf import MISSING - - -DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"]) - - -@dataclass -class DecoderConfig(FairseqDataclass): - type: DECODER_CHOICES = field( - default="viterbi", - metadata={"help": "The type of decoder to use"}, - ) - - -@dataclass -class FlashlightDecoderConfig(FairseqDataclass): - nbest: int = field( - default=1, - metadata={"help": "Number of decodings to return"}, - ) - unitlm: bool = field( - default=False, - metadata={"help": "If set, use unit language model"}, - ) - lmpath: str = field( - default=MISSING, - metadata={"help": "Language model for KenLM decoder"}, - ) - lexicon: Optional[str] = field( - default=None, - metadata={"help": "Lexicon for Flashlight decoder"}, - ) - beam: int = field( - default=50, - metadata={"help": "Number of beams to use for decoding"}, - ) - beamthreshold: float = field( - default=50.0, - metadata={"help": "Threshold for beam search decoding"}, - ) - beamsizetoken: Optional[int] = field( - default=None, metadata={"help": "Beam size to use"} - ) - wordscore: float = field( - default=-1, - metadata={"help": "Word score for KenLM decoder"}, - ) - unkweight: float = field( - default=-math.inf, - metadata={"help": "Unknown weight for KenLM decoder"}, - ) - silweight: float = field( - default=0, - metadata={"help": "Silence weight for KenLM decoder"}, - ) - lmweight: float = field( - default=2, - metadata={"help": "Weight for LM while interpolating score"}, - ) diff --git a/spaces/gradio/longformer/tvm/_ffi/__init__.py b/spaces/gradio/longformer/tvm/_ffi/__init__.py deleted file mode 100644 index f19851c2407adc145cfcf77812f8bdf6e7f824aa..0000000000000000000000000000000000000000 --- a/spaces/gradio/longformer/tvm/_ffi/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you 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. -"""C interfacing code. - -This namespace contains everything that interacts with C code. -Most TVM C related object are ctypes compatible, which means -they contains a handle field that is ctypes.c_void_p and can -be used via ctypes function calls. - -Some performance critical functions are implemented by cython -and have a ctypes fallback implementation. -""" diff --git a/spaces/gradio/seafoam/app.py b/spaces/gradio/seafoam/app.py deleted file mode 100644 index 2f0243a29d1f59ca5dedc45281a583588a8a4a57..0000000000000000000000000000000000000000 --- a/spaces/gradio/seafoam/app.py +++ /dev/null @@ -1,147 +0,0 @@ -import time - -from theme_dropdown import create_theme_dropdown # noqa: F401 - -import gradio as gr - -dropdown, js = create_theme_dropdown() - -with gr.Blocks(theme='gradio/seafoam') as demo: - with gr.Row().style(equal_height=True): - with gr.Column(scale=10): - gr.Markdown( - """ - # Theme preview: `Seafoam` - To use this theme, set `theme='gradio/seafoam'` in `gr.Blocks()` or `gr.Interface()`. - You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version - of this theme. - """ - ) - with gr.Column(scale=3): - with gr.Box(): - dropdown.render() - toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True) - - dropdown.change(None, dropdown, None, _js=js) - toggle_dark.click( - None, - _js=""" - () => { - document.body.classList.toggle('dark'); - document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)' - } - """, - ) - - name = gr.Textbox( - label="Name", - info="Full name, including middle name. No special characters.", - placeholder="John Doe", - value="John Doe", - interactive=True, - ) - - with gr.Row(): - slider1 = gr.Slider(label="Slider 1") - slider2 = gr.Slider(label="Slider 2") - gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group") - - with gr.Row(): - with gr.Column(variant="panel", scale=1): - gr.Markdown("## Panel 1") - radio = gr.Radio( - ["A", "B", "C"], - label="Radio", - info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.", - ) - drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False) - drop_2 = gr.Dropdown( - ["Option A", "Option B", "Option C"], - multiselect=True, - value=["Option A"], - label="Dropdown", - interactive=True, - ) - check = gr.Checkbox(label="Go") - with gr.Column(variant="panel", scale=2): - img = gr.Image( - "https://gradio.app/assets/img/header-image.jpg", label="Image" - ).style(height=320) - with gr.Row(): - go_btn = gr.Button("Go", label="Primary Button", variant="primary") - clear_btn = gr.Button( - "Clear", label="Secondary Button", variant="secondary" - ) - - def go(*args): - time.sleep(3) - return "https://gradio.app/assets/img/header-image.jpg" - - go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go") - - def clear(): - time.sleep(0.2) - return None - - clear_btn.click(clear, None, img) - - with gr.Row(): - btn1 = gr.Button("Button 1").style(size="sm") - btn2 = gr.UploadButton().style(size="sm") - stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style( - size="sm" - ) - - with gr.Row(): - gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe") - gr.JSON( - value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON" - ) - gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1}) - gr.File() - with gr.Row(): - gr.ColorPicker() - gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4") - gr.Gallery( - [ - ( - "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg", - "lion", - ), - ( - "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png", - "logo", - ), - ( - "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg", - "tower", - ), - ] - ).style(height="200px", grid=2) - - with gr.Row(): - with gr.Column(scale=2): - chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot") - chat_btn = gr.Button("Add messages") - - def chat(history): - time.sleep(2) - yield [["How are you?", "I am good."]] - - chat_btn.click( - lambda history: history - + [["How are you?", "I am good."]] - + (time.sleep(2) or []), - chatbot, - chatbot, - ) - with gr.Column(scale=1): - with gr.Accordion("Advanced Settings"): - gr.Markdown("Hello") - gr.Number(label="Chatbot control 1") - gr.Number(label="Chatbot control 2") - gr.Number(label="Chatbot control 3") - - -if __name__ == "__main__": - demo.queue().launch() diff --git a/spaces/gulabpatel/Real-ESRGAN/inference_realesrgan_video.py b/spaces/gulabpatel/Real-ESRGAN/inference_realesrgan_video.py deleted file mode 100644 index 639b848e6578a2480ee0784e664c7751e325c477..0000000000000000000000000000000000000000 --- a/spaces/gulabpatel/Real-ESRGAN/inference_realesrgan_video.py +++ /dev/null @@ -1,199 +0,0 @@ -import argparse -import glob -import mimetypes -import os -import queue -import shutil -import torch -from basicsr.archs.rrdbnet_arch import RRDBNet -from basicsr.utils.logger import AvgTimer -from tqdm import tqdm - -from realesrgan import IOConsumer, PrefetchReader, RealESRGANer -from realesrgan.archs.srvgg_arch import SRVGGNetCompact - - -def main(): - """Inference demo for Real-ESRGAN. - It mainly for restoring anime videos. - - """ - parser = argparse.ArgumentParser() - parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder') - parser.add_argument( - '-n', - '--model_name', - type=str, - default='RealESRGAN_x4plus', - help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus' - 'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2' - 'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4')) - parser.add_argument('-o', '--output', type=str, default='results', help='Output folder') - parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image') - parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video') - parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing') - parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding') - parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border') - parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face') - parser.add_argument('--half', action='store_true', help='Use half precision during inference') - parser.add_argument('-v', '--video', action='store_true', help='Output a video using ffmpeg') - parser.add_argument('-a', '--audio', action='store_true', help='Keep audio') - parser.add_argument('--fps', type=float, default=None, help='FPS of the output video') - parser.add_argument('--consumer', type=int, default=4, help='Number of IO consumers') - - parser.add_argument( - '--alpha_upsampler', - type=str, - default='realesrgan', - help='The upsampler for the alpha channels. Options: realesrgan | bicubic') - parser.add_argument( - '--ext', - type=str, - default='auto', - help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') - args = parser.parse_args() - - # ---------------------- determine models according to model names ---------------------- # - args.model_name = args.model_name.split('.')[0] - if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) - netscale = 4 - elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) - netscale = 4 - elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) - netscale = 2 - elif args.model_name in [ - 'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2' - ]: # x2 VGG-style model (XS size) - model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu') - netscale = 2 - elif args.model_name in [ - 'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4' - ]: # x4 VGG-style model (XS size) - model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') - netscale = 4 - - # ---------------------- determine model paths ---------------------- # - model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {args.model_name} does not exist.') - - # restorer - upsampler = RealESRGANer( - scale=netscale, - model_path=model_path, - model=model, - tile=args.tile, - tile_pad=args.tile_pad, - pre_pad=args.pre_pad, - half=args.half) - - if args.face_enhance: # Use GFPGAN for face enhancement - from gfpgan import GFPGANer - face_enhancer = GFPGANer( - model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth', - upscale=args.outscale, - arch='clean', - channel_multiplier=2, - bg_upsampler=upsampler) - os.makedirs(args.output, exist_ok=True) - # for saving restored frames - save_frame_folder = os.path.join(args.output, 'frames_tmpout') - os.makedirs(save_frame_folder, exist_ok=True) - - if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file - video_name = os.path.splitext(os.path.basename(args.input))[0] - frame_folder = os.path.join('tmp_frames', video_name) - os.makedirs(frame_folder, exist_ok=True) - # use ffmpeg to extract frames - os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {frame_folder}/frame%08d.png') - # get image path list - paths = sorted(glob.glob(os.path.join(frame_folder, '*'))) - if args.video: - if args.fps is None: - # get input video fps - import ffmpeg - probe = ffmpeg.probe(args.input) - video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video'] - args.fps = eval(video_streams[0]['avg_frame_rate']) - elif mimetypes.guess_type(args.input)[0].startswith('image'): # is an image file - paths = [args.input] - video_name = 'video' - else: - paths = sorted(glob.glob(os.path.join(args.input, '*'))) - video_name = 'video' - - timer = AvgTimer() - timer.start() - pbar = tqdm(total=len(paths), unit='frame', desc='inference') - # set up prefetch reader - reader = PrefetchReader(paths, num_prefetch_queue=4) - reader.start() - - que = queue.Queue() - consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)] - for consumer in consumers: - consumer.start() - - for idx, (path, img) in enumerate(zip(paths, reader)): - imgname, extension = os.path.splitext(os.path.basename(path)) - if len(img.shape) == 3 and img.shape[2] == 4: - img_mode = 'RGBA' - else: - img_mode = None - - try: - if args.face_enhance: - _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) - else: - output, _ = upsampler.enhance(img, outscale=args.outscale) - except RuntimeError as error: - print('Error', error) - print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') - - else: - if args.ext == 'auto': - extension = extension[1:] - else: - extension = args.ext - if img_mode == 'RGBA': # RGBA images should be saved in png format - extension = 'png' - save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}') - - que.put({'output': output, 'save_path': save_path}) - - pbar.update(1) - torch.cuda.synchronize() - timer.record() - avg_fps = 1. / (timer.get_avg_time() + 1e-7) - pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}') - - for _ in range(args.consumer): - que.put('quit') - for consumer in consumers: - consumer.join() - pbar.close() - - # merge frames to video - if args.video: - video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4') - if args.audio: - os.system( - f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}' - f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}') - else: - os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} ' - f'-c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}') - - # delete tmp file - shutil.rmtree(save_frame_folder) - if os.path.isdir(frame_folder): - shutil.rmtree(frame_folder) - - -if __name__ == '__main__': - main() diff --git a/spaces/guney/photo-with-code/photowithcode.py b/spaces/guney/photo-with-code/photowithcode.py deleted file mode 100644 index db08978006204aa5ddeb994bf6194e09af902c06..0000000000000000000000000000000000000000 --- a/spaces/guney/photo-with-code/photowithcode.py +++ /dev/null @@ -1,35 +0,0 @@ -from typing import List, Tuple -import cv2 - -def dim_bg(im: cv2.Mat) -> cv2.Mat: - im[..., 0] = im[..., 0] - im[..., 0].min() - im[..., 1] = im[..., 1] - im[..., 1].min() - im[..., 2] = im[..., 2] - im[..., 2].min() - return im - -def mirror(im: cv2.Mat) -> cv2.Mat: - im = cv2.flip(im, 1) - return im - -def downscale_large_image(im1: cv2.Mat, im2: cv2.Mat) -> Tuple[cv2.Mat, cv2.Mat]: - if im1.shape[0] > im2.shape[0]: - return downscale_large_image(im2, im1) - dim = (im1.shape[1], im1.shape[0]) - im2 = cv2.resize(im2, dim, interpolation = cv2.INTER_AREA) - return (im1, im2) - -def photowithcode_proc(photo: cv2.Mat, code: cv2.Mat, should_dim_bg: bool, should_mirror: bool) -> cv2.Mat: - if should_dim_bg: - print('dimming the background...') - code = dim_bg(code) - - if should_mirror: - print('mirroring code...') - code = mirror(code) - - print('downscaling the larger image...') - im1, im2 = downscale_large_image(photo, code) - - print('composing the image...') - out = cv2.add(im1, im2) - return out diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/batch_mtcnn.py b/spaces/gwang-kim/DATID-3D/pose_estimation/batch_mtcnn.py deleted file mode 100644 index 28c77e289fcc0d6a5f2838d0b08b06d0f184399c..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/batch_mtcnn.py +++ /dev/null @@ -1,80 +0,0 @@ -import argparse -import cv2 -import os -from mtcnn import MTCNN -import random -from tqdm import tqdm -import numpy as np - -detector = MTCNN() -parser = argparse.ArgumentParser() -parser.add_argument('--in_root', type=str, default="", help='process folder') -args = parser.parse_args() -in_root = args.in_root - -out_root = os.path.join(in_root, "debug") -out_detection = os.path.join(in_root, "detections") -if not os.path.exists(out_root): - os.makedirs(out_root) -if not os.path.exists(out_detection): - os.makedirs(out_detection) - -imgs = sorted([x for x in os.listdir(in_root) if x.endswith(".jpg") or x.endswith(".png")]) -random.shuffle(imgs) -for img in tqdm(imgs): - src = os.path.join(in_root, img) - dst = os.path.join(out_detection, img.replace(".jpg", ".txt").replace(".png", ".txt")) - - if not os.path.exists(dst): - image = cv2.cvtColor(cv2.imread(src), cv2.COLOR_BGR2RGB) - print(image.shape) - result = detector.detect_faces(image) - - if len(result)>0: - index = 0 - if len(result)>1: # if multiple faces, take the biggest face - # size = -100000 - lowest_dist = float('Inf') - for r in range(len(result)): - # print(result[r]["box"][0], result[r]["box"][1]) - face_pos = np.array(result[r]["box"][:2]) + np.array(result[r]["box"][2:])/2 - - dist_from_center = np.linalg.norm(face_pos - np.array([1500./2, 1500./2])) - if dist_from_center < lowest_dist: - lowest_dist = dist_from_center - index=r - - - # size_ = result[r]["box"][2] + result[r]["box"][3] - # if size < size_: - # size = size_ - # index = r - - # Result is an array with all the bounding boxes detected. We know that for 'ivan.jpg' there is only one. - bounding_box = result[index]['box'] - keypoints = result[index]['keypoints'] - if result[index]["confidence"] > 0.9: - - cv2.rectangle(image, - (bounding_box[0], bounding_box[1]), - (bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]), - (0,155,255), - 2) - - cv2.circle(image,(keypoints['left_eye']), 2, (0,155,255), 2) - cv2.circle(image,(keypoints['right_eye']), 2, (0,155,255), 2) - cv2.circle(image,(keypoints['nose']), 2, (0,155,255), 2) - cv2.circle(image,(keypoints['mouth_left']), 2, (0,155,255), 2) - cv2.circle(image,(keypoints['mouth_right']), 2, (0,155,255), 2) - - dst = os.path.join(out_root, img) - # cv2.imwrite(dst, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) - - dst = os.path.join(out_detection, img.replace(".jpg", ".txt").replace(".png", ".txt")) - outLand = open(dst, "w") - outLand.write(str(float(keypoints['left_eye'][0])) + " " + str(float(keypoints['left_eye'][1])) + "\n") - outLand.write(str(float(keypoints['right_eye'][0])) + " " + str(float(keypoints['right_eye'][1])) + "\n") - outLand.write(str(float(keypoints['nose'][0])) + " " + str(float(keypoints['nose'][1])) + "\n") - outLand.write(str(float(keypoints['mouth_left'][0])) + " " + str(float(keypoints['mouth_left'][1])) + "\n") - outLand.write(str(float(keypoints['mouth_right'][0])) + " " + str(float(keypoints['mouth_right'][1])) + "\n") - outLand.close() \ No newline at end of file diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/util.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/util.py deleted file mode 100644 index 27bce0ab18a69f142db54084c0be2c014e60c20d..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/util.py +++ /dev/null @@ -1,492 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Miscellaneous utility classes and functions.""" - -import ctypes -import fnmatch -import importlib -import inspect -import numpy as np -import os -import shutil -import sys -import types -import io -import pickle -import re -import requests -import html -import hashlib -import glob -import tempfile -import urllib -import urllib.request -import uuid - -from distutils.util import strtobool -from typing import Any, List, Tuple, Union - - -# Util classes -# ------------------------------------------------------------------------------------------ - - -class EasyDict(dict): - """Convenience class that behaves like a dict but allows access with the attribute syntax.""" - - def __getattr__(self, name: str) -> Any: - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __setattr__(self, name: str, value: Any) -> None: - self[name] = value - - def __delattr__(self, name: str) -> None: - del self[name] - - -class Logger(object): - """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" - - def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): - self.file = None - - if file_name is not None: - self.file = open(file_name, file_mode) - - self.should_flush = should_flush - self.stdout = sys.stdout - self.stderr = sys.stderr - - sys.stdout = self - sys.stderr = self - - def __enter__(self) -> "Logger": - return self - - def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: - self.close() - - def write(self, text: Union[str, bytes]) -> None: - """Write text to stdout (and a file) and optionally flush.""" - if isinstance(text, bytes): - text = text.decode() - if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash - return - - if self.file is not None: - self.file.write(text) - - self.stdout.write(text) - - if self.should_flush: - self.flush() - - def flush(self) -> None: - """Flush written text to both stdout and a file, if open.""" - if self.file is not None: - self.file.flush() - - self.stdout.flush() - - def close(self) -> None: - """Flush, close possible files, and remove stdout/stderr mirroring.""" - self.flush() - - # if using multiple loggers, prevent closing in wrong order - if sys.stdout is self: - sys.stdout = self.stdout - if sys.stderr is self: - sys.stderr = self.stderr - - if self.file is not None: - self.file.close() - self.file = None - - -# Cache directories -# ------------------------------------------------------------------------------------------ - -_dnnlib_cache_dir = None - - -def set_cache_dir(path: str) -> None: - global _dnnlib_cache_dir - _dnnlib_cache_dir = path - - -def make_cache_dir_path(*paths: str) -> str: - if _dnnlib_cache_dir is not None: - return os.path.join(_dnnlib_cache_dir, *paths) - if 'DNNLIB_CACHE_DIR' in os.environ: - return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) - if 'HOME' in os.environ: - return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) - if 'USERPROFILE' in os.environ: - return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) - return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) - -# Small util functions -# ------------------------------------------------------------------------------------------ - - -def format_time(seconds: Union[int, float]) -> str: - """Convert the seconds to human readable string with days, hours, minutes and seconds.""" - s = int(np.rint(seconds)) - - if s < 60: - return "{0}s".format(s) - elif s < 60 * 60: - return "{0}m {1:02}s".format(s // 60, s % 60) - elif s < 24 * 60 * 60: - return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) - else: - return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) - - -def ask_yes_no(question: str) -> bool: - """Ask the user the question until the user inputs a valid answer.""" - while True: - try: - print("{0} [y/n]".format(question)) - return strtobool(input().lower()) - except ValueError: - pass - - -def tuple_product(t: Tuple) -> Any: - """Calculate the product of the tuple elements.""" - result = 1 - - for v in t: - result *= v - - return result - - -_str_to_ctype = { - "uint8": ctypes.c_ubyte, - "uint16": ctypes.c_uint16, - "uint32": ctypes.c_uint32, - "uint64": ctypes.c_uint64, - "int8": ctypes.c_byte, - "int16": ctypes.c_int16, - "int32": ctypes.c_int32, - "int64": ctypes.c_int64, - "float32": ctypes.c_float, - "float64": ctypes.c_double -} - - -def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: - """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" - type_str = None - - if isinstance(type_obj, str): - type_str = type_obj - elif hasattr(type_obj, "__name__"): - type_str = type_obj.__name__ - elif hasattr(type_obj, "name"): - type_str = type_obj.name - else: - raise RuntimeError("Cannot infer type name from input") - - assert type_str in _str_to_ctype.keys() - - my_dtype = np.dtype(type_str) - my_ctype = _str_to_ctype[type_str] - - assert my_dtype.itemsize == ctypes.sizeof(my_ctype) - - return my_dtype, my_ctype - - -def is_pickleable(obj: Any) -> bool: - try: - with io.BytesIO() as stream: - pickle.dump(obj, stream) - return True - except: - return False - - -# Functionality to import modules/objects by name, and call functions by name -# ------------------------------------------------------------------------------------------ - -def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: - """Searches for the underlying module behind the name to some python object. - Returns the module and the object name (original name with module part removed).""" - - # allow convenience shorthands, substitute them by full names - obj_name = re.sub("^np.", "numpy.", obj_name) - obj_name = re.sub("^tf.", "tensorflow.", obj_name) - - # list alternatives for (module_name, local_obj_name) - parts = obj_name.split(".") - name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) - for i in range(len(parts), 0, -1)] - - # try each alternative in turn - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module( - module_name) # may raise ImportError - # may raise AttributeError - get_obj_from_module(module, local_obj_name) - return module, local_obj_name - except: - pass - - # maybe some of the modules themselves contain errors? - for module_name, _local_obj_name in name_pairs: - try: - importlib.import_module(module_name) # may raise ImportError - except ImportError: - if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): - raise - - # maybe the requested attribute is missing? - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module( - module_name) # may raise ImportError - # may raise AttributeError - get_obj_from_module(module, local_obj_name) - except ImportError: - pass - - # we are out of luck, but we have no idea why - raise ImportError(obj_name) - - -def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: - """Traverses the object name and returns the last (rightmost) python object.""" - if obj_name == '': - return module - obj = module - for part in obj_name.split("."): - obj = getattr(obj, part) - return obj - - -def get_obj_by_name(name: str) -> Any: - """Finds the python object with the given name.""" - module, obj_name = get_module_from_obj_name(name) - return get_obj_from_module(module, obj_name) - - -def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: - """Finds the python object with the given name and calls it as a function.""" - assert func_name is not None - # print('func_name: ', func_name) #'training.dataset.ImageFolderDataset' - func_obj = get_obj_by_name(func_name) - assert callable(func_obj) - return func_obj(*args, **kwargs) - - -def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: - """Finds the python class with the given name and constructs it with the given arguments.""" - return call_func_by_name(*args, func_name=class_name, **kwargs) - - -def get_module_dir_by_obj_name(obj_name: str) -> str: - """Get the directory path of the module containing the given object name.""" - module, _ = get_module_from_obj_name(obj_name) - return os.path.dirname(inspect.getfile(module)) - - -def is_top_level_function(obj: Any) -> bool: - """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" - return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ - - -def get_top_level_function_name(obj: Any) -> str: - """Return the fully-qualified name of a top-level function.""" - assert is_top_level_function(obj) - module = obj.__module__ - if module == '__main__': - module = os.path.splitext(os.path.basename( - sys.modules[module].__file__))[0] - return module + "." + obj.__name__ - - -# File system helpers -# ------------------------------------------------------------------------------------------ - -def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: - """List all files recursively in a given directory while ignoring given file and directory names. - Returns list of tuples containing both absolute and relative paths.""" - assert os.path.isdir(dir_path) - base_name = os.path.basename(os.path.normpath(dir_path)) - - if ignores is None: - ignores = [] - - result = [] - - for root, dirs, files in os.walk(dir_path, topdown=True): - for ignore_ in ignores: - dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] - - # dirs need to be edited in-place - for d in dirs_to_remove: - dirs.remove(d) - - files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] - - absolute_paths = [os.path.join(root, f) for f in files] - relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] - - if add_base_to_relative: - relative_paths = [os.path.join(base_name, p) - for p in relative_paths] - - assert len(absolute_paths) == len(relative_paths) - result += zip(absolute_paths, relative_paths) - - return result - - -def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: - """Takes in a list of tuples of (src, dst) paths and copies files. - Will create all necessary directories.""" - for file in files: - target_dir_name = os.path.dirname(file[1]) - - # will create all intermediate-level directories - if not os.path.exists(target_dir_name): - os.makedirs(target_dir_name) - - shutil.copyfile(file[0], file[1]) - - -# URL helpers -# ------------------------------------------------------------------------------------------ - -def is_url(obj: Any, allow_file_urls: bool = False) -> bool: - """Determine whether the given object is a valid URL string.""" - if not isinstance(obj, str) or not "://" in obj: - return False - if allow_file_urls and obj.startswith('file://'): - return True - try: - res = requests.compat.urlparse(obj) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - except: - return False - return True - - -def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: - """Download the given URL and return a binary-mode file object to access the data.""" - assert num_attempts >= 1 - assert not (return_filename and (not cache)) - - # Doesn't look like an URL scheme so interpret it as a local filename. - if not re.match('^[a-z]+://', url): - return url if return_filename else open(url, "rb") - - # Handle file URLs. This code handles unusual file:// patterns that - # arise on Windows: - # - # file:///c:/foo.txt - # - # which would translate to a local '/c:/foo.txt' filename that's - # invalid. Drop the forward slash for such pathnames. - # - # If you touch this code path, you should test it on both Linux and - # Windows. - # - # Some internet resources suggest using urllib.request.url2pathname() but - # but that converts forward slashes to backslashes and this causes - # its own set of problems. - if url.startswith('file://'): - filename = urllib.parse.urlparse(url).path - if re.match(r'^/[a-zA-Z]:', filename): - filename = filename[1:] - return filename if return_filename else open(filename, "rb") - - assert is_url(url) - - # Lookup from cache. - if cache_dir is None: - cache_dir = make_cache_dir_path('downloads') - - url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() - if cache: - cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) - if len(cache_files) == 1: - filename = cache_files[0] - return filename if return_filename else open(filename, "rb") - - # Download. - url_name = None - url_data = None - with requests.Session() as session: - if verbose: - print("Downloading %s ..." % url, end="", flush=True) - for attempts_left in reversed(range(num_attempts)): - try: - with session.get(url) as res: - res.raise_for_status() - if len(res.content) == 0: - raise IOError("No data received") - - if len(res.content) < 8192: - content_str = res.content.decode("utf-8") - if "download_warning" in res.headers.get("Set-Cookie", ""): - links = [html.unescape(link) for link in content_str.split( - '"') if "export=download" in link] - if len(links) == 1: - url = requests.compat.urljoin(url, links[0]) - raise IOError("Google Drive virus checker nag") - if "Google Drive - Quota exceeded" in content_str: - raise IOError( - "Google Drive download quota exceeded -- please try again later") - - match = re.search( - r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) - url_name = match[1] if match else url - url_data = res.content - if verbose: - print(" done") - break - except KeyboardInterrupt: - raise - except: - if not attempts_left: - if verbose: - print(" failed") - raise - if verbose: - print(".", end="", flush=True) - - # Save to cache. - if cache: - safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) - cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) - temp_file = os.path.join( - cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) - os.makedirs(cache_dir, exist_ok=True) - with open(temp_file, "wb") as f: - f.write(url_data) - os.replace(temp_file, cache_file) # atomic - if return_filename: - return cache_file - - # Return data as file object. - assert not return_filename - return io.BytesIO(url_data) diff --git a/spaces/h2oai/wave-tour/examples/ml_h2o_algo.py b/spaces/h2oai/wave-tour/examples/ml_h2o_algo.py deleted file mode 100644 index fbc75f3f9879650f7196100c1c1246d92fcae3ed..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/ml_h2o_algo.py +++ /dev/null @@ -1,68 +0,0 @@ -# WaveML / H2O-3 / Algo -# Configure a specific algo for Wave Models built using H2O-3 AutoML. -# --- -from h2o_wave import main, app, Q, ui, copy_expando -from h2o_wave_ml import build_model, ModelType - -from sklearn.datasets import load_wine -from sklearn.model_selection import train_test_split - - -@app('/demo') -async def serve(q: Q): - if q.args.train: - # train WaveML Model using H2O-3 AutoML - copy_expando(q.args, q.client) - q.client.wave_model = build_model( - train_df=q.client.train_df, - target_column='target', - model_type=ModelType.H2O3, - _h2o3_max_runtime_secs=30, - _h2o3_nfolds=2, - _h2o3_include_algos=[q.client.algo] - ) - model_id = q.client.wave_model.model.model_id - accuracy = round(100 - q.client.wave_model.model.mean_per_class_error() * 100, 2) - - # show training details and prediction option - q.page['example'].algo.value = q.client.algo - q.page['example'].predict.disabled = False - q.page['example'].message.type = 'success' - q.page['example'].message.text = 'Training successfully completed!' - q.page['example'].model_id.content = f'''**H2O AutoML model id:** {model_id}
        - **Accuracy:** {accuracy}%''' - q.page['example'].example_predictions.content = '' - elif q.args.predict: - # predict on test data - preds = q.client.wave_model.predict(test_df=q.client.test_df) - - # show predictions - q.page['example'].message.text = 'Prediction successfully completed!' - q.page['example'].example_predictions.content = f'''**Example predictions:**
        - {preds[0]}
        {preds[1]}
        {preds[2]}''' - else: - # prepare sample train and test dataframes - data = load_wine(as_frame=True)['frame'] - q.client.train_df, q.client.test_df = train_test_split(data, train_size=0.8) - - # algos - algo_choices = [ui.choice(x, x) for x in ['DRF', 'GLM', 'XGBoost', 'GBM', 'DeepLearning']] - - # display ui - q.page['example'] = ui.form_card( - box='1 1 -1 -1', - items=[ - ui.text(content='''The sample dataset used is the - wine dataset.'''), - ui.choice_group(name='algo', label='Select Algo', choices=algo_choices, value='DRF'), - ui.buttons(items=[ - ui.button(name='train', label='Train', primary=True), - ui.button(name='predict', label='Predict', primary=True, disabled=True), - ]), - ui.message_bar(name='message', type='warning', text='Training will take a few seconds'), - ui.text(name='model_id', content=''), - ui.text(name='example_predictions', content='') - ] - ) - - await q.page.save() diff --git a/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/__init__.py b/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/__init__.py deleted file mode 100644 index ece0ea08fe2e939cc260a1dafc0ab5b391b773d9..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/__init__.py +++ /dev/null @@ -1,9 +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. - -# empty diff --git a/spaces/hanstyle/tts/inference.py b/spaces/hanstyle/tts/inference.py deleted file mode 100644 index 04947146bf3ef2c5a72310ffdc7a22a7eb9a2aca..0000000000000000000000000000000000000000 --- a/spaces/hanstyle/tts/inference.py +++ /dev/null @@ -1,283 +0,0 @@ -from os import listdir, path -import numpy as np -import scipy, cv2, os, sys, argparse, audio -import json, subprocess, random, string -from tqdm import tqdm -from glob import glob -import torch, face_detection -from models import Wav2Lip -import platform - -parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models') - -parser.add_argument('--checkpoint_path', type=str, - help='Name of saved checkpoint to load weights from', required=True) - -parser.add_argument('--face', type=str, - help='Filepath of video/image that contains faces to use', required=True) -parser.add_argument('--audio', type=str, - help='Filepath of video/audio file to use as raw audio source', required=True) -parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', - default='results/result_voice.mp4') - -parser.add_argument('--static', type=bool, - help='If True, then use only first video frame for inference', default=False) -parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', - default=25., required=False) - -parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], - help='Padding (top, bottom, left, right). Please adjust to include chin at least') - -parser.add_argument('--face_det_batch_size', type=int, - help='Batch size for face detection', default=16) -parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128) - -parser.add_argument('--resize_factor', default=1, type=int, - help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p') - -parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], - help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' - 'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width') - -parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], - help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.' - 'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).') - -parser.add_argument('--rotate', default=False, action='store_true', - help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.' - 'Use if you get a flipped result, despite feeding a normal looking video') - -parser.add_argument('--nosmooth', default=False, action='store_true', - help='Prevent smoothing face detections over a short temporal window') - -args = parser.parse_args() -args.img_size = 96 - - -temppath = os.path.join(os.path.dirname(__file__), "temp") - -if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: - args.static = True - -def get_smoothened_boxes(boxes, T): - for i in range(len(boxes)): - if i + T > len(boxes): - window = boxes[len(boxes) - T:] - else: - window = boxes[i : i + T] - boxes[i] = np.mean(window, axis=0) - return boxes - -def face_detect(images): - detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, - flip_input=False, device=device) - - batch_size = args.face_det_batch_size - - while 1: - predictions = [] - try: - for i in tqdm(range(0, len(images), batch_size)): - predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size]))) - except RuntimeError: - if batch_size == 1: - raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument') - batch_size //= 2 - print('Recovering from OOM error; New batch size: {}'.format(batch_size)) - continue - break - - results = [] - pady1, pady2, padx1, padx2 = args.pads - for rect, image in zip(predictions, images): - if rect is None: - cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected. - raise ValueError('Face not detected! Ensure the video contains a face in all the frames.') - - y1 = max(0, rect[1] - pady1) - y2 = min(image.shape[0], rect[3] + pady2) - x1 = max(0, rect[0] - padx1) - x2 = min(image.shape[1], rect[2] + padx2) - - results.append([x1, y1, x2, y2]) - - boxes = np.array(results) - if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5) - results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] - - del detector - return results - -def datagen(frames, mels): - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - if args.box[0] == -1: - if not args.static: - face_det_results = face_detect(frames) # BGR2RGB for CNN face detection - else: - face_det_results = face_detect([frames[0]]) - else: - print('Using the specified bounding box instead of face detection...') - y1, y2, x1, x2 = args.box - face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames] - - for i, m in enumerate(mels): - idx = 0 if args.static else i%len(frames) - frame_to_save = frames[idx].copy() - face, coords = face_det_results[idx].copy() - - face = cv2.resize(face, (args.img_size, args.img_size)) - - img_batch.append(face) - mel_batch.append(m) - frame_batch.append(frame_to_save) - coords_batch.append(coords) - - if len(img_batch) >= args.wav2lip_batch_size: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, args.img_size//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - if len(img_batch) > 0: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, args.img_size//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch - -mel_step_size = 16 -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} for inference.'.format(device)) - -def _load(checkpoint_path): - if device == 'cuda': - checkpoint = torch.load(checkpoint_path) - else: - checkpoint = torch.load(checkpoint_path, - map_location=lambda storage, loc: storage) - return checkpoint - -def load_model(path): - model = Wav2Lip() - print("Load checkpoint from: {}".format(path)) - checkpoint = _load(path) - s = checkpoint["state_dict"] - new_s = {} - for k, v in s.items(): - new_s[k.replace('module.', '')] = v - model.load_state_dict(new_s) - - model = model.to(device) - return model.eval() - -def main(): - if not os.path.isfile(args.face): - raise ValueError('--face argument must be a valid path to video/image file') - - elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: - full_frames = [cv2.imread(args.face)] - fps = args.fps - - else: - video_stream = cv2.VideoCapture(args.face) - fps = video_stream.get(cv2.CAP_PROP_FPS) - - print('Reading video frames...') - - full_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading: - video_stream.release() - break - if args.resize_factor > 1: - frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor)) - - if args.rotate: - frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE) - - y1, y2, x1, x2 = args.crop - if x2 == -1: x2 = frame.shape[1] - if y2 == -1: y2 = frame.shape[0] - - frame = frame[y1:y2, x1:x2] - - full_frames.append(frame) - - print ("Number of frames available for inference: "+str(len(full_frames))) - - if not args.audio.endswith('.wav'): - print('Extracting raw audio...') - command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, f'{temppath}/temp.wav') - - subprocess.call(command, shell=True) - args.audio = f'{temppath}/temp.wav' - - wav = audio.load_wav(args.audio, 16000) - mel = audio.melspectrogram(wav) - print(mel.shape) - - if np.isnan(mel.reshape(-1)).sum() > 0: - raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again') - - mel_chunks = [] - mel_idx_multiplier = 80./fps - i = 0 - while 1: - start_idx = int(i * mel_idx_multiplier) - if start_idx + mel_step_size > len(mel[0]): - mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:]) - break - mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size]) - i += 1 - - print("Length of mel chunks: {}".format(len(mel_chunks))) - - full_frames = full_frames[:len(mel_chunks)] - - batch_size = args.wav2lip_batch_size - gen = datagen(full_frames.copy(), mel_chunks) - - for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, - total=int(np.ceil(float(len(mel_chunks))/batch_size)))): - if i == 0: - model = load_model(args.checkpoint_path) - print ("Model loaded") - - frame_h, frame_w = full_frames[0].shape[:-1] - out = cv2.VideoWriter(f'{temppath}/result.avi', - cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h)) - - img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) - mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) - - with torch.no_grad(): - pred = model(mel_batch, img_batch) - - pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. - - for p, f, c in zip(pred, frames, coords): - y1, y2, x1, x2 = c - p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1)) - - f[y1:y2, x1:x2] = p - out.write(f) - - out.release() - - command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, f'{temppath}/result.avi', args.outfile) - subprocess.call(command, shell=platform.system() != 'Windows') - -if __name__ == '__main__': - main() diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/phrasecut.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/phrasecut.py deleted file mode 100644 index 2a68262d2372c69ba9e64535014770ce4be98189..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/phrasecut.py +++ /dev/null @@ -1,8 +0,0 @@ -import torch -import torchvision -import torch.utils.data as data -from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset - - -class PhrasecutDetection(ModulatedDataset): - pass diff --git a/spaces/harmonai/dance-diffusion/app.py b/spaces/harmonai/dance-diffusion/app.py deleted file mode 100644 index 1ae189052071e86fb9289d4a4f1953efb2665bd0..0000000000000000000000000000000000000000 --- a/spaces/harmonai/dance-diffusion/app.py +++ /dev/null @@ -1,60 +0,0 @@ -import gradio as gr -from diffusers import DiffusionPipeline -import scipy.io.wavfile - - -def load_model(model_id): - pipeline = DiffusionPipeline.from_pretrained(model_id) - pipeline = pipeline.to("cuda") - return pipeline - -def denoise(length_sec,model): - pipeline = load_model(model) - audios = pipeline(audio_length_in_s=length_sec).audios - for audio in audios: - scipy.io.wavfile.write("test.wav", pipeline.unet.sample_rate, audio.transpose()) - return "test.wav" - - - - -block = gr.Blocks() - -with block: - gr.HTML( - """ -
        -
        -

        - Dance Diffusion -

        -
        -

        - Dance Diffusion is the first in a suite of generative audio tools for producers and musicians to be released by Harmonai -

        -
        - """ - ) - with gr.Group(): - with gr.Box(): - length = gr.Slider(1.0, 6.0, value=3.0, step=0.5, label="Audio length in seconds") - model = gr.Dropdown(choices=["harmonai/maestro-150k", "harmonai/jmann-small-190k", "harmonai/honk-140k", "harmonai/unlocked-250k","harmonai/jmann-large-580k","harmonai/glitch-440k"], value="harmonai/maestro-150k",type="value", label="Model") - out = gr.Audio(label="Output", type="filepath") - btn = gr.Button("Submit").style(full_width=True) - - btn.click(denoise, inputs=[length,model], outputs=out) - gr.HTML(''' -
        -

        Model by Harmonai -

        -
        - ''') - -block.launch(debug=True) \ No newline at end of file diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/logger.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/logger.py deleted file mode 100644 index b6496d9d6096f557ffa684be80342ec220c6014c..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/logger.py +++ /dev/null @@ -1,221 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import functools -import logging -import os -import sys -import time -from collections import Counter -from fvcore.common.file_io import PathManager -from tabulate import tabulate -from termcolor import colored - - -class _ColorfulFormatter(logging.Formatter): - def __init__(self, *args, **kwargs): - self._root_name = kwargs.pop("root_name") + "." - self._abbrev_name = kwargs.pop("abbrev_name", "") - if len(self._abbrev_name): - self._abbrev_name = self._abbrev_name + "." - super(_ColorfulFormatter, self).__init__(*args, **kwargs) - - def formatMessage(self, record): - record.name = record.name.replace(self._root_name, self._abbrev_name) - log = super(_ColorfulFormatter, self).formatMessage(record) - if record.levelno == logging.WARNING: - prefix = colored("WARNING", "red", attrs=["blink"]) - elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: - prefix = colored("ERROR", "red", attrs=["blink", "underline"]) - else: - return log - return prefix + " " + log - - -@functools.lru_cache() # so that calling setup_logger multiple times won't add many handlers -def setup_logger( - output=None, distributed_rank=0, *, color=True, name="detectron2", abbrev_name=None -): - """ - Initialize the detectron2 logger and set its verbosity level to "DEBUG". - - Args: - output (str): a file name or a directory to save log. If None, will not save log file. - If ends with ".txt" or ".log", assumed to be a file name. - Otherwise, logs will be saved to `output/log.txt`. - name (str): the root module name of this logger - abbrev_name (str): an abbreviation of the module, to avoid long names in logs. - Set to "" to not log the root module in logs. - By default, will abbreviate "detectron2" to "d2" and leave other - modules unchanged. - - Returns: - logging.Logger: a logger - """ - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - logger.propagate = False - - if abbrev_name is None: - abbrev_name = "d2" if name == "detectron2" else name - - plain_formatter = logging.Formatter( - "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S" - ) - # stdout logging: master only - if distributed_rank == 0: - ch = logging.StreamHandler(stream=sys.stdout) - ch.setLevel(logging.DEBUG) - if color: - formatter = _ColorfulFormatter( - colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", - datefmt="%m/%d %H:%M:%S", - root_name=name, - abbrev_name=str(abbrev_name), - ) - else: - formatter = plain_formatter - ch.setFormatter(formatter) - logger.addHandler(ch) - - # file logging: all workers - if output is not None: - if output.endswith(".txt") or output.endswith(".log"): - filename = output - else: - filename = os.path.join(output, "log.txt") - if distributed_rank > 0: - filename = filename + ".rank{}".format(distributed_rank) - PathManager.mkdirs(os.path.dirname(filename)) - - fh = logging.StreamHandler(_cached_log_stream(filename)) - fh.setLevel(logging.DEBUG) - fh.setFormatter(plain_formatter) - logger.addHandler(fh) - - return logger - - -# cache the opened file object, so that different calls to `setup_logger` -# with the same file name can safely write to the same file. -@functools.lru_cache(maxsize=None) -def _cached_log_stream(filename): - return PathManager.open(filename, "a") - - -""" -Below are some other convenient logging methods. -They are mainly adopted from -https://github.com/abseil/abseil-py/blob/master/absl/logging/__init__.py -""" - - -def _find_caller(): - """ - Returns: - str: module name of the caller - tuple: a hashable key to be used to identify different callers - """ - frame = sys._getframe(2) - while frame: - code = frame.f_code - if os.path.join("utils", "logger.") not in code.co_filename: - mod_name = frame.f_globals["__name__"] - if mod_name == "__main__": - mod_name = "detectron2" - return mod_name, (code.co_filename, frame.f_lineno, code.co_name) - frame = frame.f_back - - -_LOG_COUNTER = Counter() -_LOG_TIMER = {} - - -def log_first_n(lvl, msg, n=1, *, name=None, key="caller"): - """ - Log only for the first n times. - - Args: - lvl (int): the logging level - msg (str): - n (int): - name (str): name of the logger to use. Will use the caller's module by default. - key (str or tuple[str]): the string(s) can be one of "caller" or - "message", which defines how to identify duplicated logs. - For example, if called with `n=1, key="caller"`, this function - will only log the first call from the same caller, regardless of - the message content. - If called with `n=1, key="message"`, this function will log the - same content only once, even if they are called from different places. - If called with `n=1, key=("caller", "message")`, this function - will not log only if the same caller has logged the same message before. - """ - if isinstance(key, str): - key = (key,) - assert len(key) > 0 - - caller_module, caller_key = _find_caller() - hash_key = () - if "caller" in key: - hash_key = hash_key + caller_key - if "message" in key: - hash_key = hash_key + (msg,) - - _LOG_COUNTER[hash_key] += 1 - if _LOG_COUNTER[hash_key] <= n: - logging.getLogger(name or caller_module).log(lvl, msg) - - -def log_every_n(lvl, msg, n=1, *, name=None): - """ - Log once per n times. - - Args: - lvl (int): the logging level - msg (str): - n (int): - name (str): name of the logger to use. Will use the caller's module by default. - """ - caller_module, key = _find_caller() - _LOG_COUNTER[key] += 1 - if n == 1 or _LOG_COUNTER[key] % n == 1: - logging.getLogger(name or caller_module).log(lvl, msg) - - -def log_every_n_seconds(lvl, msg, n=1, *, name=None): - """ - Log no more than once per n seconds. - - Args: - lvl (int): the logging level - msg (str): - n (int): - name (str): name of the logger to use. Will use the caller's module by default. - """ - caller_module, key = _find_caller() - last_logged = _LOG_TIMER.get(key, None) - current_time = time.time() - if last_logged is None or current_time - last_logged >= n: - logging.getLogger(name or caller_module).log(lvl, msg) - _LOG_TIMER[key] = current_time - - -def create_small_table(small_dict): - """ - Create a small table using the keys of small_dict as headers. This is only - suitable for small dictionaries. - - Args: - small_dict (dict): a result dictionary of only a few items. - - Returns: - str: the table as a string. - """ - keys, values = tuple(zip(*small_dict.items())) - table = tabulate( - [values], - headers=keys, - tablefmt="pipe", - floatfmt=".3f", - stralign="center", - numalign="center", - ) - return table diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/schp.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/schp.py deleted file mode 100644 index f57470452fac8183dc5c17156439416c15bd3265..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/utils/schp.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- - -""" -@Author : Peike Li -@Contact : peike.li@yahoo.com -@File : schp.py -@Time : 4/8/19 2:11 PM -@Desc : -@License : This source code is licensed under the license found in the - LICENSE file in the root directory of this source tree. -""" - -import os -import torch -import modules - -def moving_average(net1, net2, alpha=1): - for param1, param2 in zip(net1.parameters(), net2.parameters()): - param1.data *= (1.0 - alpha) - param1.data += param2.data * alpha - - -def _check_bn(module, flag): - if issubclass(module.__class__, modules.bn.InPlaceABNSync): - flag[0] = True - - -def check_bn(model): - flag = [False] - model.apply(lambda module: _check_bn(module, flag)) - return flag[0] - - -def reset_bn(module): - if issubclass(module.__class__, modules.bn.InPlaceABNSync): - module.running_mean = torch.zeros_like(module.running_mean) - module.running_var = torch.ones_like(module.running_var) - - -def _get_momenta(module, momenta): - if issubclass(module.__class__, modules.bn.InPlaceABNSync): - momenta[module] = module.momentum - - -def _set_momenta(module, momenta): - if issubclass(module.__class__, modules.bn.InPlaceABNSync): - module.momentum = momenta[module] - - -def bn_re_estimate(loader, model): - if not check_bn(model): - print('No batch norm layer detected') - return - model.train() - momenta = {} - model.apply(reset_bn) - model.apply(lambda module: _get_momenta(module, momenta)) - n = 0 - for i_iter, batch in enumerate(loader): - images, labels, _ = batch - b = images.data.size(0) - momentum = b / (n + b) - for module in momenta.keys(): - module.momentum = momentum - model(images) - n += b - model.apply(lambda module: _set_momenta(module, momenta)) - - -def save_schp_checkpoint(states, is_best_parsing, output_dir, filename='schp_checkpoint.pth.tar'): - save_path = os.path.join(output_dir, filename) - if os.path.exists(save_path): - os.remove(save_path) - torch.save(states, save_path) - if is_best_parsing and 'state_dict' in states: - best_save_path = os.path.join(output_dir, 'model_parsing_best.pth.tar') - if os.path.exists(best_save_path): - os.remove(best_save_path) - torch.save(states, best_save_path) diff --git a/spaces/hasibzunair/fifa-tryon-demo/model/u2net.py b/spaces/hasibzunair/fifa-tryon-demo/model/u2net.py deleted file mode 100644 index 5b85f138f3af4e2ceae1ff07dee514c859a831af..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/model/u2net.py +++ /dev/null @@ -1,525 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class REBNCONV(nn.Module): - def __init__(self,in_ch=3,out_ch=3,dirate=1): - super(REBNCONV,self).__init__() - - self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate) - self.bn_s1 = nn.BatchNorm2d(out_ch) - self.relu_s1 = nn.ReLU(inplace=True) - - def forward(self,x): - - hx = x - xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) - - return xout - -## upsample tensor 'src' to have the same spatial size with tensor 'tar' -def _upsample_like(src,tar): - - src = F.upsample(src,size=tar.shape[2:],mode='bilinear') - - return src - - -### RSU-7 ### -class RSU7(nn.Module):#UNet07DRES(nn.Module): - - def __init__(self, in_ch=3, mid_ch=12, out_ch=3): - super(RSU7,self).__init__() - - self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) - - self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) - self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1) - - self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2) - - self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) - - def forward(self,x): - - hx = x - hxin = self.rebnconvin(hx) - - hx1 = self.rebnconv1(hxin) - hx = self.pool1(hx1) - - hx2 = self.rebnconv2(hx) - hx = self.pool2(hx2) - - hx3 = self.rebnconv3(hx) - hx = self.pool3(hx3) - - hx4 = self.rebnconv4(hx) - hx = self.pool4(hx4) - - hx5 = self.rebnconv5(hx) - hx = self.pool5(hx5) - - hx6 = self.rebnconv6(hx) - - hx7 = self.rebnconv7(hx6) - - hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1)) - hx6dup = _upsample_like(hx6d,hx5) - - hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1)) - hx5dup = _upsample_like(hx5d,hx4) - - hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1)) - hx4dup = _upsample_like(hx4d,hx3) - - hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) - - return hx1d + hxin - -### RSU-6 ### -class RSU6(nn.Module):#UNet06DRES(nn.Module): - - def __init__(self, in_ch=3, mid_ch=12, out_ch=3): - super(RSU6,self).__init__() - - self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) - - self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) - self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1) - - self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2) - - self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) - - def forward(self,x): - - hx = x - - hxin = self.rebnconvin(hx) - - hx1 = self.rebnconv1(hxin) - hx = self.pool1(hx1) - - hx2 = self.rebnconv2(hx) - hx = self.pool2(hx2) - - hx3 = self.rebnconv3(hx) - hx = self.pool3(hx3) - - hx4 = self.rebnconv4(hx) - hx = self.pool4(hx4) - - hx5 = self.rebnconv5(hx) - - hx6 = self.rebnconv6(hx5) - - - hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1)) - hx5dup = _upsample_like(hx5d,hx4) - - hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1)) - hx4dup = _upsample_like(hx4d,hx3) - - hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) - - return hx1d + hxin - -### RSU-5 ### -class RSU5(nn.Module):#UNet05DRES(nn.Module): - - def __init__(self, in_ch=3, mid_ch=12, out_ch=3): - super(RSU5,self).__init__() - - self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) - - self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) - self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1) - - self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2) - - self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) - - def forward(self,x): - - hx = x - - hxin = self.rebnconvin(hx) - - hx1 = self.rebnconv1(hxin) - hx = self.pool1(hx1) - - hx2 = self.rebnconv2(hx) - hx = self.pool2(hx2) - - hx3 = self.rebnconv3(hx) - hx = self.pool3(hx3) - - hx4 = self.rebnconv4(hx) - - hx5 = self.rebnconv5(hx4) - - hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1)) - hx4dup = _upsample_like(hx4d,hx3) - - hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) - - return hx1d + hxin - -### RSU-4 ### -class RSU4(nn.Module):#UNet04DRES(nn.Module): - - def __init__(self, in_ch=3, mid_ch=12, out_ch=3): - super(RSU4,self).__init__() - - self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) - - self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) - self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1) - self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1) - - self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2) - - self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1) - self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) - - def forward(self,x): - - hx = x - - hxin = self.rebnconvin(hx) - - hx1 = self.rebnconv1(hxin) - hx = self.pool1(hx1) - - hx2 = self.rebnconv2(hx) - hx = self.pool2(hx2) - - hx3 = self.rebnconv3(hx) - - hx4 = self.rebnconv4(hx3) - - hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1)) - - return hx1d + hxin - -### RSU-4F ### -class RSU4F(nn.Module):#UNet04FRES(nn.Module): - - def __init__(self, in_ch=3, mid_ch=12, out_ch=3): - super(RSU4F,self).__init__() - - self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) - - self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1) - self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2) - self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4) - - self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8) - - self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4) - self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2) - self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1) - - def forward(self,x): - - hx = x - - hxin = self.rebnconvin(hx) - - hx1 = self.rebnconv1(hxin) - hx2 = self.rebnconv2(hx1) - hx3 = self.rebnconv3(hx2) - - hx4 = self.rebnconv4(hx3) - - hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1)) - hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1)) - hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1)) - - return hx1d + hxin - - -##### U^2-Net #### -class U2NET(nn.Module): - - def __init__(self,in_ch=3,out_ch=1): - super(U2NET,self).__init__() - - self.stage1 = RSU7(in_ch,32,64) - self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage2 = RSU6(64,32,128) - self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage3 = RSU5(128,64,256) - self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage4 = RSU4(256,128,512) - self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage5 = RSU4F(512,256,512) - self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage6 = RSU4F(512,256,512) - - # decoder - self.stage5d = RSU4F(1024,256,512) - self.stage4d = RSU4(1024,128,256) - self.stage3d = RSU5(512,64,128) - self.stage2d = RSU6(256,32,64) - self.stage1d = RSU7(128,16,64) - - self.side1 = nn.Conv2d(64,out_ch,3,padding=1) - self.side2 = nn.Conv2d(64,out_ch,3,padding=1) - self.side3 = nn.Conv2d(128,out_ch,3,padding=1) - self.side4 = nn.Conv2d(256,out_ch,3,padding=1) - self.side5 = nn.Conv2d(512,out_ch,3,padding=1) - self.side6 = nn.Conv2d(512,out_ch,3,padding=1) - - self.outconv = nn.Conv2d(6*out_ch,out_ch,1) - - def forward(self,x): - - hx = x - - #stage 1 - hx1 = self.stage1(hx) - hx = self.pool12(hx1) - - #stage 2 - hx2 = self.stage2(hx) - hx = self.pool23(hx2) - - #stage 3 - hx3 = self.stage3(hx) - hx = self.pool34(hx3) - - #stage 4 - hx4 = self.stage4(hx) - hx = self.pool45(hx4) - - #stage 5 - hx5 = self.stage5(hx) - hx = self.pool56(hx5) - - #stage 6 - hx6 = self.stage6(hx) - hx6up = _upsample_like(hx6,hx5) - - #-------------------- decoder -------------------- - hx5d = self.stage5d(torch.cat((hx6up,hx5),1)) - hx5dup = _upsample_like(hx5d,hx4) - - hx4d = self.stage4d(torch.cat((hx5dup,hx4),1)) - hx4dup = _upsample_like(hx4d,hx3) - - hx3d = self.stage3d(torch.cat((hx4dup,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.stage2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.stage1d(torch.cat((hx2dup,hx1),1)) - - - #side output - d1 = self.side1(hx1d) - - d2 = self.side2(hx2d) - d2 = _upsample_like(d2,d1) - - d3 = self.side3(hx3d) - d3 = _upsample_like(d3,d1) - - d4 = self.side4(hx4d) - d4 = _upsample_like(d4,d1) - - d5 = self.side5(hx5d) - d5 = _upsample_like(d5,d1) - - d6 = self.side6(hx6) - d6 = _upsample_like(d6,d1) - - d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) - - return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6) - -### U^2-Net small ### -class U2NETP(nn.Module): - - def __init__(self,in_ch=3,out_ch=1): - super(U2NETP,self).__init__() - - self.stage1 = RSU7(in_ch,16,64) - self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage2 = RSU6(64,16,64) - self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage3 = RSU5(64,16,64) - self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage4 = RSU4(64,16,64) - self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage5 = RSU4F(64,16,64) - self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True) - - self.stage6 = RSU4F(64,16,64) - - # decoder - self.stage5d = RSU4F(128,16,64) - self.stage4d = RSU4(128,16,64) - self.stage3d = RSU5(128,16,64) - self.stage2d = RSU6(128,16,64) - self.stage1d = RSU7(128,16,64) - - self.side1 = nn.Conv2d(64,out_ch,3,padding=1) - self.side2 = nn.Conv2d(64,out_ch,3,padding=1) - self.side3 = nn.Conv2d(64,out_ch,3,padding=1) - self.side4 = nn.Conv2d(64,out_ch,3,padding=1) - self.side5 = nn.Conv2d(64,out_ch,3,padding=1) - self.side6 = nn.Conv2d(64,out_ch,3,padding=1) - - self.outconv = nn.Conv2d(6*out_ch,out_ch,1) - - def forward(self,x): - - hx = x - - #stage 1 - hx1 = self.stage1(hx) - hx = self.pool12(hx1) - - #stage 2 - hx2 = self.stage2(hx) - hx = self.pool23(hx2) - - #stage 3 - hx3 = self.stage3(hx) - hx = self.pool34(hx3) - - #stage 4 - hx4 = self.stage4(hx) - hx = self.pool45(hx4) - - #stage 5 - hx5 = self.stage5(hx) - hx = self.pool56(hx5) - - #stage 6 - hx6 = self.stage6(hx) - hx6up = _upsample_like(hx6,hx5) - - #decoder - hx5d = self.stage5d(torch.cat((hx6up,hx5),1)) - hx5dup = _upsample_like(hx5d,hx4) - - hx4d = self.stage4d(torch.cat((hx5dup,hx4),1)) - hx4dup = _upsample_like(hx4d,hx3) - - hx3d = self.stage3d(torch.cat((hx4dup,hx3),1)) - hx3dup = _upsample_like(hx3d,hx2) - - hx2d = self.stage2d(torch.cat((hx3dup,hx2),1)) - hx2dup = _upsample_like(hx2d,hx1) - - hx1d = self.stage1d(torch.cat((hx2dup,hx1),1)) - - - #side output - d1 = self.side1(hx1d) - - d2 = self.side2(hx2d) - d2 = _upsample_like(d2,d1) - - d3 = self.side3(hx3d) - d3 = _upsample_like(d3,d1) - - d4 = self.side4(hx4d) - d4 = _upsample_like(d4,d1) - - d5 = self.side5(hx5d) - d5 = _upsample_like(d5,d1) - - d6 = self.side6(hx6) - d6 = _upsample_like(d6,d1) - - d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) - - return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6) diff --git a/spaces/hirol/controlnetOverMask/javascript/lazyload/posex-webui.js b/spaces/hirol/controlnetOverMask/javascript/lazyload/posex-webui.js deleted file mode 100644 index 80d2b1f7a51b7a5cc90c8f9c878cbd910a8ef9e0..0000000000000000000000000000000000000000 --- a/spaces/hirol/controlnetOverMask/javascript/lazyload/posex-webui.js +++ /dev/null @@ -1,415 +0,0 @@ -async function _import() { - if (!globalThis.posex || !globalThis.posex.import) { - return await import('posex'); - } else { - return await globalThis.posex.imports.posex(); - } -} -const { init, init_3d } = await _import(); - -(async function () { - let _r = 0; - function to_gradio(v) { - // force call `change` event on gradio - return [v, _r++]; - } - - function js2py(type, gradio_field, value) { - // set `value` to gradio's field - // (1) Click gradio's button. - // (2) Gradio will fire js callback to retrieve value to be set. - // (3) Gradio will fire another js callback to notify the process has been completed. - return new Promise(resolve => { - const callback_name = `posex-${type}-${gradio_field}`; - - // (2) - globalThis[callback_name] = () => { - - delete globalThis[callback_name]; - - // (3) - const callback_after = callback_name + '_after'; - globalThis[callback_after] = () => { - delete globalThis[callback_after]; - resolve(); - }; - - return to_gradio(JSON.parse(value)); - // return to_gradio(value); - }; - - // (1) - gradioApp().querySelector(`#${callback_name}_set`).click(); - }); - } - - function py2js(type, pyname, ...args) { - // call python's function - // (1) Set args to gradio's field - // (2) Click gradio's button - // (3) JS callback will be kicked with return value from gradio - - // (1) - return (args.length == 0 ? Promise.resolve() : js2py(type, pyname + '_args', JSON.stringify(args))) - .then(() => { - return new Promise(resolve => { - const callback_name = `posex-${type}-${pyname}`; - // (3) - globalThis[callback_name] = value => { - delete globalThis[callback_name]; - resolve(value); - } - // (2) - gradioApp().querySelector(`#${callback_name}_get`).click(); - }); - }); - } - - function reload_poses(json, ui) { - const df = document.createDocumentFragment(); - for (let data of json) { - const fig = document.createElement('figure') - const img = document.createElement('img'); - const cap = document.createElement('figcaption'); - const clo = document.createElement('div'); - const cloimg = document.createElement('img'); - const clo2 = document.createElement('span'); - fig.dataset.poseName = data.name; - cap.textContent = data.name; - clo.classList.add('close'); - cloimg.src = 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAACXBIWXMAAAsSAAALEgHS3X78AAAAG3RFWHRTb2Z0d2FyZQBDZWxzeXMgU3R1ZGlvIFRvb2zBp+F8AAAAxElEQVQ4y82T0REBQRBEX0dAJk4GJwKXgZMBESADIiADJwKXATI4GRBB+1lqKa62+DFV+7E7U6+6e2plm19K/wWQ1AE2QBcobZ9fehVwCb3rWwWShmHwaLsfvW+BAihs71otSJoBc2BuexFBl7anSRlI2gN5OBXQxIpSAB2gCXlcgCzOJGkLkg5ABhyB/B5cqoI1UAb5BbCxPU4CSBqFdda2B1GoE9urVoCkHlCH68N3ZOfzGkNw9dvBZ3Bu+/SHf+GbugG9/4ThhKqF8gAAAABJRU5ErkJggg=='; - clo2.classList.add('close2'); - clo2.textContent = 'delete'; - clo.append(cloimg, clo2); - - img.src = 'data:image/png;base64,' + data.image; - img.title = data.name; - fig.append(clo, img, cap); - - df.appendChild(fig); - } - - ui.saved_poses.innerHTML = ''; - ui.saved_poses.appendChild(df); - } - - function init_ui(type, api) { - const $ = x => document.createElement(x); - - const all_reset = $('button'); - all_reset.innerHTML = '🔄 All Reset'; - all_reset.classList.add('posex_all_reset', 'posex_box'); - - const reset_camera = $('button'); - reset_camera.innerHTML = '🎥 Reset Camera'; - reset_camera.classList.add('posex_reset_camera', 'posex_box'); - - const reset_pose = $('button'); - reset_pose.innerHTML = '🧍 Reset Pose'; - reset_pose.classList.add('posex_reset_pose', 'posex_box'); - - const reset_cont = $('div'); - reset_cont.classList.add('posex_reset_cont'); - reset_cont.append(reset_camera, reset_pose); - - const canvas = $('canvas'); - canvas.width = 512; - canvas.height = 512; - - const camera_marker = $('div'); camera_marker.textContent = '- Camera'; - const fixed_roll_label = $('label'); - const fixed_roll = $('input'); fixed_roll.type = 'checkbox'; fixed_roll.classList.add('posex_fixed_roll', 'posex_camera'); fixed_roll.checked = true; - fixed_roll_label.append(fixed_roll, document.createTextNode('Fixed Roll')); - - const img_marker = $('div'); img_marker.textContent = '- Image'; - const set_img = $('label'); set_img.classList.add('posex_bg'); - const add_img = $('button'); add_img.classList.add('posex_add_body', 'posex_body'); add_img.innerHTML = '🖼 Add';add_img.onclick = () => img_input.click(); - const img_input = $('input'); img_input.type = 'file'; img_input.style.display = 'none'; - set_img.append(add_img, img_input); - const reset_img = $('button'); reset_img.classList.add('posex_bg'); reset_img.innerHTML = '❌ Del'; - const img_cont = $('div'); img_cont.classList.add('posex_bg_cont'); - img_cont.append(set_img, reset_img); - - const body_marker = $('div'); body_marker.textContent = '- Body'; - const add_body = $('button'); add_body.classList.add('posex_add_body', 'posex_body'); add_body.innerHTML = '➕ Add'; - const remove_body = $('button'); remove_body.classList.add('posex_remove_body', 'posex_body'); remove_body.innerHTML = '➖ Remove'; - const canvas_marker = $('div'); canvas_marker.textContent = '- Image Size'; - const canvas_width = $('input'); canvas_width.type = 'number'; canvas_width.value = 512; canvas_width.min = 64; canvas_width.classList.add('posex_canvas_width', 'posex_canvas_size'); - const canvas_height = $('input'); canvas_height.type = 'number'; canvas_height.value = 512; canvas_height.min = 64; canvas_height.classList.add('posex_canvas_height', 'posex_canvas_size'); - const bg_marker = $('div'); bg_marker.textContent = '- Background'; - const set_bg = $('label'); set_bg.classList.add('posex_bg'); - const bg_button = $('button'); bg_button.innerHTML = '🖼 Set'; bg_button.onclick = () => bg_input.click(); - const bg_input = $('input'); bg_input.type = 'file'; bg_input.style.display = 'none'; - set_bg.append(bg_button, bg_input); - const reset_bg = $('button'); reset_bg.classList.add('posex_bg'); reset_bg.innerHTML = '❌ Del'; - const bg_cont = $('div'); bg_cont.classList.add('posex_bg_cont'); - bg_cont.append(set_bg, reset_bg); - const joint_marker = $('div'); joint_marker.textContent = '- Joints and Limbs'; - const limb_width_label = $('label'); - const limb_width = $('input'); limb_width.type = 'range'; limb_width.min = 1; limb_width.max = 16; limb_width.value = 4; limb_width.classList.add('posex_joints', 'posex_limb_width'); - limb_width_label.append(limb_width, document.createTextNode('Limb Width')); - const elliptic_limbs_label = $('label'); - const elliptic_limbs = $('input'); elliptic_limbs.type = 'checkbox'; elliptic_limbs.classList.add('posex_joints', 'posex_elliptic_limbs'); elliptic_limbs.checked = true; - elliptic_limbs_label.append(elliptic_limbs, document.createTextNode('Elliptic Limbs')); - const other_marker = $('div'); other_marker.textContent = '- Others'; - const low_fps_label = $('label'); - const low_fps = $('input'); low_fps.type = 'checkbox'; low_fps.classList.add('posex_low_fps', 'posex_others'); low_fps.checked = false; - low_fps_label.append(low_fps, document.createTextNode('Low fps')); - - const setting_cont = $('div'); - setting_cont.classList.add('posex_setting_cont'); - setting_cont.append( - // camera_marker, - // fixed_roll_label, - // img_marker, - // img_cont, - all_reset, - bg_marker, - bg_cont, - canvas_marker, - canvas_width, - canvas_height, - body_marker, - add_body, - remove_body, - - - // joint_marker, - // limb_width_label, - // elliptic_limbs_label, - // other_marker, - // low_fps_label, - ); - - const canvas_cont = $('div'); - canvas_cont.classList.add('posex_canvas_cont'); - canvas_cont.append( - canvas, - setting_cont, - ); - - const notation = $('p'); - notation.classList.add('posex_notation'); - - const indicator1 = $('div'); - indicator1.classList.add('posex_indicator1'); - - const indicator2 = $('div'); - indicator2.classList.add('posex_indicator2'); - - const copy = $('button'); copy.classList.add('posex_copy', 'posex_misc', 'posex_box'); copy.innerHTML = '📋 Copy to clipboard'; - const save = $('button'); save.classList.add('posex_save', 'posex_misc', 'posex_box'); save.innerHTML = '💾 Download image'; - - const misc_cont = $('div'); - misc_cont.classList.add('posex_misc_cont'); - misc_cont.append( - copy, - save - ); - - const save_pose = $('button'); - save_pose.classList.add('posex_save_pose', 'posex_box'); - save_pose.innerHTML = '💾🧍 Save Pose'; - - const save_pose_callback = async obj => { - await py2js(type, 'savepose', obj); - const json = await py2js(type, 'allposes') - reload_poses(JSON.parse(json), ui); - return { result: '', ok: true }; - }; - - const saved_poses = $('div'); - saved_poses.classList.add('posex_saved_poses'); - - saved_poses.addEventListener('click', async e => { - const get_name = ele => { - while (ele && ele !== document) { - if (ele.dataset && ele.dataset.poseName !== undefined) - return ele.dataset.poseName; - ele = ele.parentNode; - } - return ''; - }; - - let target = e.target; - if (target.tagName === 'IMG') target = target.parentNode; - if (target.classList.contains('close2')) target = target.parentNode; - if (target.tagName === 'FIGURE') { - const name = get_name(target); - if (name.length != 0) { - const json = await py2js(type, 'loadpose', name); - ui.loadPose(JSON.parse(json)); - } - } else if (target.classList.contains('close')) { - const name = get_name(target); - if (name.length != 0) { - await py2js(type, 'delpose', name); - const json = await py2js(type, 'allposes') - reload_poses(JSON.parse(json), ui); - } - } - }, false); - - const get_imgs = $('button'); - get_imgs.classList.add('posex_get_imgs', 'posex_box'); - get_imgs.innerHTML = '💾🧍 get_imgs'; - - const get_imgs_callback = async obj => { - await py2js(type, 'getimgs', obj); - return { result: '', ok: true }; - }; - - const ui = { - canvas, - notation, - indicator1, - indicator2, - all_reset, - reset_camera, - reset_pose, - fixed_roll, - img: img_input, - reset_img, - add_body, - remove_body, - canvas_width, - canvas_height, - bg: bg_input, - reset_bg, - limb_width, - elliptic_limbs, - low_fps, - save, - copy, - save_pose, - save_pose_callback, - saved_poses, - get_imgs, - get_imgs_callback, - }; - - const df = document.createDocumentFragment(); - df.append( - // all_reset, - // reset_cont, - canvas_cont, - indicator2, - indicator1, - notation, - // misc_cont, - // save_pose, - // saved_poses, - // get_imgs, - ); - - return { ui, df }; - }; - - async function init_canvas( - type, - generate_button, - container, - api - ) { - container.classList.add('posex_cont'); - container.innerHTML = ''; - const { ui, df } = init_ui(type, api); - container.appendChild(df); - - ui.container = container; - ui.notify = function (str, type) { if (type === 'error') console.error(str); }; - - // { - // // Send canvas image to ControlNet when button is clicked. - // let force = false; - // gradioApp().addEventListener('click', async e => { - // if (e.target !== generate_button) return; - // - // if (!enabled.checked) return; - // - // if (force) { - // force = false; - // return; - // } - // - // // hook `generate` button to add canvas data - // e.preventDefault(); - // e.stopPropagation(); - // - // const data_url = await ui.getDataURL(); - // await js2py(type, 'base64', data_url); - // force = true; - // generate_button.click(); - // }, true); - // } - - // { - // // Load saved poses. - // const json = await py2js(type, 'allposes') - // reload_poses(JSON.parse(json), ui); - // } - - //界面加载 - init(ui); - - //功能js加载 - const animate = init_3d(ui); - - animate(); - - // onUiTabChange(() => { - // const tabname = get_uiCurrentTabContent().id; - // if (type === 't2i') { - // if (0 <= tabname.indexOf('txt2img')) { - // ui.play(); - // } else { - // ui.stop(); - // } - // } else if (type === 'i2i') { - // if (0 <= tabname.indexOf('img2img')) { - // ui.play(); - // } else { - // ui.stop(); - // } - // } else { - // ui.stop(); - // } - // }); - } - - async function init_t2i() { - const app = gradioApp(); - await init_canvas( - 't2i', - app.querySelector('#txt2img_generate'), - Array.from(app.querySelectorAll('#posex-t2i-html')).at(-1), // ! - { - load_all_poses: app.querySelector('#posex-t2i-api-all_pose'), - delete_pose: app.querySelector('#posex-t2i-api-delete_pose'), - } - ); - } - - // async function init_i2i() { - // const app = gradioApp(); - // await init_canvas( - // 'i2i', - // app.querySelector('#posex-i2i-enabled input[type=checkbox]'), - // app.querySelector('#img2img_generate'), - // Array.from(app.querySelectorAll('#posex-i2i-html')).at(-1), // ! - // { - // load_all_poses: app.querySelector('#posex-i2i-api-all_pose'), - // delete_pose: app.querySelector('#posex-i2i-api-delete_pose'), - // } - // ); - // } - - if (!globalThis.posex) globalThis.posex = {}; - const posex = globalThis.posex; - posex.init_t2i = init_t2i; - // posex.init_i2i = init_i2i; - - posex.script_loaded = true; - document.dispatchEvent(new CustomEvent('posexscriptloaded')); - -})(); diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/network_architecture/__init__.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/network_architecture/__init__.py deleted file mode 100644 index 72b8078b9dddddf22182fec2555d8d118ea72622..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/network_architecture/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from __future__ import absolute_import -from . import * \ No newline at end of file diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/network_trainer.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/network_trainer.py deleted file mode 100644 index 3e60bac0465dfda92edde67617df39c9afe67023..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/network_trainer.py +++ /dev/null @@ -1,770 +0,0 @@ -# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from _warnings import warn -from typing import Tuple - -import matplotlib -from batchgenerators.utilities.file_and_folder_operations import * -from nnunet.network_architecture.neural_network import SegmentationNetwork -from sklearn.model_selection import KFold -from torch import nn -from torch.cuda.amp import GradScaler, autocast -from torch.optim.lr_scheduler import _LRScheduler -import psutil -import SimpleITK as sitk - - -from time import time, sleep -import torch -import numpy as np -from torch.optim import lr_scheduler -import matplotlib.pyplot as plt -import sys -from collections import OrderedDict -import torch.backends.cudnn as cudnn -from abc import abstractmethod -from datetime import datetime -from tqdm import trange -from nnunet.utilities.to_torch import maybe_to_torch, to_cuda -from nnunet.inference.segmentation_export import save_segmentation_nifti_from_softmax, save_segmentation_nifti - - - -class NetworkTrainer(object): - def __init__(self, deterministic=True, fp16=False): - """ - A generic class that can train almost any neural network (RNNs excluded). It provides basic functionality such - as the training loop, tracking of training and validation losses (and the target metric if you implement it) - Training can be terminated early if the validation loss (or the target metric if implemented) do not improve - anymore. This is based on a moving average (MA) of the loss/metric instead of the raw values to get more smooth - results. - - What you need to override: - - __init__ - - initialize - - run_online_evaluation (optional) - - finish_online_evaluation (optional) - - validate - - predict_test_case - """ - self.fp16 = fp16 - self.amp_grad_scaler = None - - if deterministic: - np.random.seed(12345) - torch.manual_seed(12345) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(12345) - cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - else: - cudnn.deterministic = False - torch.backends.cudnn.benchmark = True - - ################# SET THESE IN self.initialize() ################################### - self.network: Tuple[SegmentationNetwork, nn.DataParallel] = None - self.optimizer = None - self.lr_scheduler = None - self.tr_gen = self.val_gen = None - self.was_initialized = False - - ################# SET THESE IN INIT ################################################ - self.output_folder = None - self.fold = None - self.loss = None - self.dataset_directory = None - - ################# SET THESE IN LOAD_DATASET OR DO_SPLIT ############################ - self.dataset = None # these can be None for inference mode - self.dataset_tr = self.dataset_val = None # do not need to be used, they just appear if you are using the suggested load_dataset_and_do_split - - ################# THESE DO NOT NECESSARILY NEED TO BE MODIFIED ##################### - self.patience = 50 - self.val_eval_criterion_alpha = 0.9 # alpha * old + (1-alpha) * new - # if this is too low then the moving average will be too noisy and the training may terminate early. If it is - # too high the training will take forever - self.train_loss_MA_alpha = 0.93 # alpha * old + (1-alpha) * new - self.train_loss_MA_eps = 5e-4 # new MA must be at least this much better (smaller) - self.max_num_epochs = 1000 - self.num_batches_per_epoch = 250 # 250 default - self.num_val_batches_per_epoch = 50 # 50 - self.also_val_in_tr_mode = False - self.lr_threshold = 1e-6 # the network will not terminate training if the lr is still above this threshold - - ################# LEAVE THESE ALONE ################################################ - self.val_eval_criterion_MA = None - self.train_loss_MA = None - self.best_val_eval_criterion_MA = None - self.best_MA_tr_loss_for_patience = None - self.best_epoch_based_on_MA_tr_loss = None - self.all_tr_losses = [] - self.all_val_losses = [] - self.all_val_losses_tr_mode = [] - self.all_val_eval_metrics = [] # does not have to be used - self.epoch = 0 - self.log_file = None - self.deterministic = deterministic - - self.use_progress_bar = True - if 'nnunet_use_progress_bar' in os.environ.keys(): - self.use_progress_bar = bool(int(os.environ['nnunet_use_progress_bar'])) - - ################# Settings for saving checkpoints ################################## - self.save_every = 1 - self.save_latest_only = True # if false it will not store/overwrite _latest but separate files each - # time an intermediate checkpoint is created - self.save_intermediate_checkpoints = True # whether or not to save checkpoint_latest - self.save_best_checkpoint = True # whether or not to save the best checkpoint according to self.best_val_eval_criterion_MA - self.save_final_checkpoint = True # whether or not to save the final checkpoint - - @abstractmethod - def initialize(self, training=True): - """ - create self.output_folder - - modify self.output_folder if you are doing cross-validation (one folder per fold) - - set self.tr_gen and self.val_gen - - call self.initialize_network and self.initialize_optimizer_and_scheduler (important!) - - finally set self.was_initialized to True - :param training: - :return: - """ - - @abstractmethod - def load_dataset(self): - pass - - def do_split(self): - """ - This is a suggestion for if your dataset is a dictionary (my personal standard) - :return: - """ - splits_file = join(self.dataset_directory, "splits_final.pkl") - if not isfile(splits_file): - self.print_to_log_file("Creating new split...") - splits = [] - all_keys_sorted = np.sort(list(self.dataset.keys())) - kfold = KFold(n_splits=5, shuffle=True, random_state=12345) - for i, (train_idx, test_idx) in enumerate(kfold.split(all_keys_sorted)): - train_keys = np.array(all_keys_sorted)[train_idx] - test_keys = np.array(all_keys_sorted)[test_idx] - splits.append(OrderedDict()) - splits[-1]['train'] = train_keys - splits[-1]['val'] = test_keys - save_pickle(splits, splits_file) - - splits = load_pickle(splits_file) - - if self.fold == "all": - tr_keys = val_keys = list(self.dataset.keys()) - else: - tr_keys = splits[self.fold]['train'] - val_keys = splits[self.fold]['val'] - - tr_keys.sort() - val_keys.sort() - - self.dataset_tr = OrderedDict() - for i in tr_keys: - self.dataset_tr[i] = self.dataset[i] - - self.dataset_val = OrderedDict() - for i in val_keys: - self.dataset_val[i] = self.dataset[i] - - def plot_progress(self): - """ - Should probably by improved - :return: - """ - try: - font = {'weight': 'normal', - 'size': 18} - - matplotlib.rc('font', **font) - - fig = plt.figure(figsize=(30, 24)) - ax = fig.add_subplot(111) - ax2 = ax.twinx() - - x_values = list(range(self.epoch + 1)) - - ax.plot(x_values, self.all_tr_losses, color='b', ls='-', label="loss_tr") - - ax.plot(x_values, self.all_val_losses, color='r', ls='-', label="loss_val, train=False") - - if len(self.all_val_losses_tr_mode) > 0: - ax.plot(x_values, self.all_val_losses_tr_mode, color='g', ls='-', label="loss_val, train=True") - if len(self.all_val_eval_metrics) == len(x_values): - ax2.plot(x_values, self.all_val_eval_metrics, color='g', ls='--', label="evaluation metric") - - ax.set_xlabel("epoch") - ax.set_ylabel("loss") - ax2.set_ylabel("evaluation metric") - ax.legend() - ax2.legend(loc=9) - - fig.savefig(join(self.output_folder, "progress.png")) - plt.close() - except IOError: - self.print_to_log_file("failed to plot: ", sys.exc_info()) - - def print_to_log_file(self, *args, also_print_to_console=True, add_timestamp=True): - - timestamp = time() - dt_object = datetime.fromtimestamp(timestamp) - - if add_timestamp: - args = ("%s:" % dt_object, *args) - - if self.log_file is None: - maybe_mkdir_p(self.output_folder) - timestamp = datetime.now() - self.log_file = join(self.output_folder, "training_log_%d_%d_%d_%02.0d_%02.0d_%02.0d.txt" % - (timestamp.year, timestamp.month, timestamp.day, timestamp.hour, timestamp.minute, - timestamp.second)) - with open(self.log_file, 'w') as f: - f.write("Starting... \n") - successful = False - max_attempts = 5 - ctr = 0 - while not successful and ctr < max_attempts: - try: - with open(self.log_file, 'a+') as f: - for a in args: - f.write(str(a)) - f.write(" ") - f.write("\n") - successful = True - except IOError: - print("%s: failed to log: " % datetime.fromtimestamp(timestamp), sys.exc_info()) - sleep(0.5) - ctr += 1 - if also_print_to_console: - print(*args) - - def save_checkpoint(self, fname, save_optimizer=True): - start_time = time() - state_dict = self.network.state_dict() - for key in state_dict.keys(): - state_dict[key] = state_dict[key].cpu() - lr_sched_state_dct = None - if self.lr_scheduler is not None and hasattr(self.lr_scheduler, - 'state_dict'): # not isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau): - lr_sched_state_dct = self.lr_scheduler.state_dict() - # WTF is this!? - # for key in lr_sched_state_dct.keys(): - # lr_sched_state_dct[key] = lr_sched_state_dct[key] - if save_optimizer: - optimizer_state_dict = self.optimizer.state_dict() - else: - optimizer_state_dict = None - - self.print_to_log_file("saving checkpoint...") - save_this = { - 'epoch': self.epoch + 1, - 'state_dict': state_dict, - 'optimizer_state_dict': optimizer_state_dict, - 'lr_scheduler_state_dict': lr_sched_state_dct, - 'plot_stuff': (self.all_tr_losses, self.all_val_losses, self.all_val_losses_tr_mode, - self.all_val_eval_metrics), - 'best_stuff' : (self.best_epoch_based_on_MA_tr_loss, self.best_MA_tr_loss_for_patience, self.best_val_eval_criterion_MA)} - if self.amp_grad_scaler is not None: - save_this['amp_grad_scaler'] = self.amp_grad_scaler.state_dict() - - torch.save(save_this, fname) - self.print_to_log_file("done, saving took %.2f seconds" % (time() - start_time)) - - def load_best_checkpoint(self, train=True): - if self.fold is None: - raise RuntimeError("Cannot load best checkpoint if self.fold is None") - if isfile(join(self.output_folder, "model_best.model")): - self.load_checkpoint(join(self.output_folder, "model_best.model"), train=train) - else: - self.print_to_log_file("WARNING! model_best.model does not exist! Cannot load best checkpoint. Falling " - "back to load_latest_checkpoint") - self.load_latest_checkpoint(train) - - def load_latest_checkpoint(self, train=True): - if isfile(join(self.output_folder, "model_final_checkpoint.model")): - return self.load_checkpoint(join(self.output_folder, "model_final_checkpoint.model"), train=train) - if isfile(join(self.output_folder, "model_latest.model")): - return self.load_checkpoint(join(self.output_folder, "model_latest.model"), train=train) - if isfile(join(self.output_folder, "model_best.model")): - return self.load_best_checkpoint(train) - raise RuntimeError("No checkpoint found") - - def load_final_checkpoint(self, train=False): - filename = join(self.output_folder, "model_final_checkpoint.model") - if not isfile(filename): - raise RuntimeError("Final checkpoint not found. Expected: %s. Please finish the training first." % filename) - return self.load_checkpoint(filename, train=train) - - def load_checkpoint(self, fname, train=True): - self.print_to_log_file("loading checkpoint", fname, "train=", train) - if not self.was_initialized: - self.initialize(train) - # saved_model = torch.load(fname, map_location=torch.device('cuda', torch.cuda.current_device())) - saved_model = torch.load(fname, map_location=torch.device('cpu')) - self.load_checkpoint_ram(saved_model, train) - - @abstractmethod - def initialize_network(self): - """ - initialize self.network here - :return: - """ - pass - - @abstractmethod - def initialize_optimizer_and_scheduler(self): - """ - initialize self.optimizer and self.lr_scheduler (if applicable) here - :return: - """ - pass - - def load_checkpoint_ram(self, checkpoint, train=True): - """ - used for if the checkpoint is already in ram - :param checkpoint: - :param train: - :return: - """ - if not self.was_initialized: - self.initialize(train) - - new_state_dict = OrderedDict() - curr_state_dict_keys = list(self.network.state_dict().keys()) - # if state dict comes form nn.DataParallel but we use non-parallel model here then the state dict keys do not - # match. Use heuristic to make it match - for k, value in checkpoint['state_dict'].items(): - key = k - if key not in curr_state_dict_keys and key.startswith('module.'): - key = key[7:] - new_state_dict[key] = value - - if self.fp16: - self._maybe_init_amp() - if train: - if 'amp_grad_scaler' in checkpoint.keys(): - self.amp_grad_scaler.load_state_dict(checkpoint['amp_grad_scaler']) - # - #new_state_dict['tu_1.0.weight'] = new_state_dict['tu_1.0.weight'].transpose(2, 3) - #new_state_dict['tu_2.0.weight'] = new_state_dict['tu_2.0.weight'].transpose(2, 3) - self.network.load_state_dict(new_state_dict) - self.epoch = checkpoint['epoch'] - if train: - optimizer_state_dict = checkpoint['optimizer_state_dict'] - if optimizer_state_dict is not None: - self.optimizer.load_state_dict(optimizer_state_dict) - - if self.lr_scheduler is not None and hasattr(self.lr_scheduler, 'load_state_dict') and checkpoint[ - 'lr_scheduler_state_dict'] is not None: - self.lr_scheduler.load_state_dict(checkpoint['lr_scheduler_state_dict']) - - if issubclass(self.lr_scheduler.__class__, _LRScheduler): - self.lr_scheduler.step(self.epoch) - - self.all_tr_losses, self.all_val_losses, self.all_val_losses_tr_mode, self.all_val_eval_metrics = checkpoint[ - 'plot_stuff'] - - # load best loss (if present) - if 'best_stuff' in checkpoint.keys(): - self.best_epoch_based_on_MA_tr_loss, self.best_MA_tr_loss_for_patience, self.best_val_eval_criterion_MA = checkpoint[ - 'best_stuff'] - - # after the training is done, the epoch is incremented one more time in my old code. This results in - # self.epoch = 1001 for old trained models when the epoch is actually 1000. This causes issues because - # len(self.all_tr_losses) = 1000 and the plot function will fail. We can easily detect and correct that here - if self.epoch != len(self.all_tr_losses): - self.print_to_log_file("WARNING in loading checkpoint: self.epoch != len(self.all_tr_losses). This is " - "due to an old bug and should only appear when you are loading old models. New " - "models should have this fixed! self.epoch is now set to len(self.all_tr_losses)") - self.epoch = len(self.all_tr_losses) - self.all_tr_losses = self.all_tr_losses[:self.epoch] - self.all_val_losses = self.all_val_losses[:self.epoch] - self.all_val_losses_tr_mode = self.all_val_losses_tr_mode[:self.epoch] - self.all_val_eval_metrics = self.all_val_eval_metrics[:self.epoch] - - self._maybe_init_amp() - - def _maybe_init_amp(self): - if self.fp16 and self.amp_grad_scaler is None: - self.amp_grad_scaler = GradScaler() - - def plot_network_architecture(self): - """ - can be implemented (see nnUNetTrainer) but does not have to. Not implemented here because it imposes stronger - assumptions on the presence of class variables - :return: - """ - pass - - def run_training(self): - if not torch.cuda.is_available(): - self.print_to_log_file("WARNING!!! You are attempting to run training on a CPU (torch.cuda.is_available() is False). This can be VERY slow!") - - _ = self.tr_gen.next() - _ = self.val_gen.next() - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - self._maybe_init_amp() - - maybe_mkdir_p(self.output_folder) - self.plot_network_architecture() - - if cudnn.benchmark and cudnn.deterministic: - warn("torch.backends.cudnn.deterministic is True indicating a deterministic training is desired. " - "But torch.backends.cudnn.benchmark is True as well and this will prevent deterministic training! " - "If you want deterministic then set benchmark=False") - - if not self.was_initialized: - self.initialize(True) - #while False: - while self.epoch < self.max_num_epochs: - # Create GIF from training - #self.store_sample_prediction() - - print(psutil.virtual_memory()) - self.print_to_log_file(psutil.virtual_memory()) - self.print_to_log_file("\nepoch: ", self.epoch) - epoch_start_time = time() - train_losses_epoch = [] - - # train one epoch - self.network.train() - - if self.use_progress_bar: - with trange(self.num_batches_per_epoch) as tbar: - for b in tbar: - tbar.set_description("Epoch {}/{}".format(self.epoch+1, self.max_num_epochs)) - - l = self.run_iteration(self.tr_gen, True) - - tbar.set_postfix(loss=l) - train_losses_epoch.append(l) - else: - for _ in range(self.num_batches_per_epoch): - l = self.run_iteration(self.tr_gen, True) - train_losses_epoch.append(l) - - - self.all_tr_losses.append(np.mean(train_losses_epoch)) - self.print_to_log_file("train loss : %.4f" % self.all_tr_losses[-1]) - - with torch.no_grad(): - # validation with train=False - self.network.eval() - val_losses = [] - for b in range(self.num_val_batches_per_epoch): - l = self.run_iteration(self.val_gen, False, True) - val_losses.append(l) - self.all_val_losses.append(np.mean(val_losses)) - self.print_to_log_file("validation loss: %.4f" % self.all_val_losses[-1]) - - if self.also_val_in_tr_mode: - self.network.train() - # validation with train=True - val_losses = [] - for b in range(self.num_val_batches_per_epoch): - l = self.run_iteration(self.val_gen, False) - val_losses.append(l) - self.all_val_losses_tr_mode.append(np.mean(val_losses)) - self.print_to_log_file("validation loss (train=True): %.4f" % self.all_val_losses_tr_mode[-1]) - - self.update_train_loss_MA() # needed for lr scheduler and stopping of training - - continue_training = self.on_epoch_end() - - - - epoch_end_time = time() - - if not continue_training: - # allows for early stopping - break - - self.epoch += 1 - self.print_to_log_file("This epoch took %f s\n" % (epoch_end_time - epoch_start_time)) - - self.epoch -= 1 # if we don't do this we can get a problem with loading model_final_checkpoint. - - if self.save_final_checkpoint: self.save_checkpoint(join(self.output_folder, "model_final_checkpoint.model")) - # now we can delete latest as it will be identical with final - if isfile(join(self.output_folder, "model_latest.model")): - os.remove(join(self.output_folder, "model_latest.model")) - if isfile(join(self.output_folder, "model_latest.model.pkl")): - os.remove(join(self.output_folder, "model_latest.model.pkl")) - - def store_sample_prediction(self): - self.network.eval() - maybe_mkdir_p(self.output_folder+'/gif/') - output_filename = self.output_folder+'/gif/Crane_2008-11-04_TSX_7_1_034_'+str(self.epoch) +'_.nii.gz' - file_path = self.dataset_directory + '/nnUNetData_plans_mtl_2D_stage0/Crane_2008-11-04_TSX_7_1_034.npy' - properties_path = self.dataset_directory+ '/nnUNetData_plans_mtl_2D_stage0/Crane_2008-11-04_TSX_7_1_034.pkl' - with open(properties_path, 'rb') as f: - properties = pickle.load(f) - img = np.load(file_path)[0][None] - softmax = self.predict_preprocessed_data_return_seg_and_softmax( - img, do_mirroring=True, mirror_axes=self.data_aug_params['mirror_axes'], use_sliding_window=True, - step_size=0.5, use_gaussian=True, all_in_gpu=False, - mixed_precision=True)[1] - - save_segmentation_nifti_from_softmax(softmax, output_filename, properties) - return - - - - def maybe_update_lr(self): - # maybe update learning rate - if self.lr_scheduler is not None: - assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler)) - - if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau): - # lr scheduler is updated with moving average val loss. should be more robust - self.lr_scheduler.step(self.train_loss_MA) - else: - self.lr_scheduler.step(self.epoch + 1) - self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr'])) - - def maybe_save_checkpoint(self): - """ - Saves a checkpoint every save_ever epochs. - :return: - """ - if self.save_intermediate_checkpoints and (self.epoch % self.save_every == (self.save_every - 1)): - self.print_to_log_file("saving scheduled checkpoint file...") - if not self.save_latest_only: - self.save_checkpoint(join(self.output_folder, "model_ep_%03.0d.model" % (self.epoch + 1))) - self.save_checkpoint(join(self.output_folder, "model_latest.model")) - self.print_to_log_file("done") - - def update_eval_criterion_MA(self): - """ - If self.all_val_eval_metrics is unused (len=0) then we fall back to using -self.all_val_losses for the MA to determine early stopping - (not a minimization, but a maximization of a metric and therefore the - in the latter case) - :return: - """ - if self.val_eval_criterion_MA is None: - if len(self.all_val_eval_metrics) == 0: - self.val_eval_criterion_MA = - self.all_val_losses[-1] - else: - self.val_eval_criterion_MA = self.all_val_eval_metrics[-1] - else: - if len(self.all_val_eval_metrics) == 0: - """ - We here use alpha * old - (1 - alpha) * new because new in this case is the vlaidation loss and lower - is better, so we need to negate it. - """ - self.val_eval_criterion_MA = self.val_eval_criterion_alpha * self.val_eval_criterion_MA - ( - 1 - self.val_eval_criterion_alpha) * \ - self.all_val_losses[-1] - else: - self.val_eval_criterion_MA = self.val_eval_criterion_alpha * self.val_eval_criterion_MA + ( - 1 - self.val_eval_criterion_alpha) * \ - self.all_val_eval_metrics[-1] - - def manage_patience(self): - # update patience - continue_training = True - if self.patience is not None: - # if best_MA_tr_loss_for_patience and best_epoch_based_on_MA_tr_loss were not yet initialized, - # initialize them - if self.best_MA_tr_loss_for_patience is None: - self.best_MA_tr_loss_for_patience = self.train_loss_MA - - if self.best_epoch_based_on_MA_tr_loss is None: - self.best_epoch_based_on_MA_tr_loss = self.epoch - - if self.best_val_eval_criterion_MA is None: - self.best_val_eval_criterion_MA = self.val_eval_criterion_MA - - # check if the current epoch is the best one according to moving average of validation criterion. If so - # then save 'best' model - # Do not use this for validation. This is intended for test set prediction only. - #self.print_to_log_file("current best_val_eval_criterion_MA is %.4f0" % self.best_val_eval_criterion_MA) - #self.print_to_log_file("current val_eval_criterion_MA is %.4f" % self.val_eval_criterion_MA) - - if self.val_eval_criterion_MA > self.best_val_eval_criterion_MA: - self.best_val_eval_criterion_MA = self.val_eval_criterion_MA - #self.print_to_log_file("saving best epoch checkpoint...") - if self.save_best_checkpoint: self.save_checkpoint(join(self.output_folder, "model_best.model")) - - # Now see if the moving average of the train loss has improved. If yes then reset patience, else - # increase patience - if self.train_loss_MA + self.train_loss_MA_eps < self.best_MA_tr_loss_for_patience: - self.best_MA_tr_loss_for_patience = self.train_loss_MA - self.best_epoch_based_on_MA_tr_loss = self.epoch - #self.print_to_log_file("New best epoch (train loss MA): %03.4f" % self.best_MA_tr_loss_for_patience) - else: - pass - #self.print_to_log_file("No improvement: current train MA %03.4f, best: %03.4f, eps is %03.4f" % - # (self.train_loss_MA, self.best_MA_tr_loss_for_patience, self.train_loss_MA_eps)) - - # if patience has reached its maximum then finish training (provided lr is low enough) - if self.epoch - self.best_epoch_based_on_MA_tr_loss > self.patience: - if self.optimizer.param_groups[0]['lr'] > self.lr_threshold: - #self.print_to_log_file("My patience ended, but I believe I need more time (lr > 1e-6)") - self.best_epoch_based_on_MA_tr_loss = self.epoch - self.patience // 2 - else: - #self.print_to_log_file("My patience ended") - continue_training = False - else: - pass - #self.print_to_log_file( - # "Patience: %d/%d" % (self.epoch - self.best_epoch_based_on_MA_tr_loss, self.patience)) - - return continue_training - - def on_epoch_end(self): - self.finish_online_evaluation() # does not have to do anything, but can be used to update self.all_val_eval_ - # metrics - - self.plot_progress() - - self.maybe_update_lr() - - self.maybe_save_checkpoint() - - self.update_eval_criterion_MA() - - continue_training = self.manage_patience() - return continue_training - - def update_train_loss_MA(self): - if self.train_loss_MA is None: - self.train_loss_MA = self.all_tr_losses[-1] - else: - self.train_loss_MA = self.train_loss_MA_alpha * self.train_loss_MA + (1 - self.train_loss_MA_alpha) * \ - self.all_tr_losses[-1] - - def run_iteration(self, data_generator, do_backprop=True, run_online_evaluation=False): - data_dict = next(data_generator) - data = data_dict['data'] - target = data_dict['target'] - - data = maybe_to_torch(data) - target = maybe_to_torch(target) - - if torch.cuda.is_available(): - data = to_cuda(data) - target = to_cuda(target) - - self.optimizer.zero_grad() - - if self.fp16: - with autocast(): - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - self.amp_grad_scaler.scale(l).backward() - self.amp_grad_scaler.step(self.optimizer) - self.amp_grad_scaler.update() - else: - output = self.network(data) - del data - l = self.loss(output, target) - - if do_backprop: - l.backward() - self.optimizer.step() - - if run_online_evaluation: - self.run_online_evaluation(output, target) - - del target - - return l.detach().cpu().numpy() - - def run_online_evaluation(self, *args, **kwargs): - """ - Can be implemented, does not have to - :param output_torch: - :param target_npy: - :return: - """ - pass - - def finish_online_evaluation(self): - """ - Can be implemented, does not have to - :return: - """ - pass - - @abstractmethod - def validate(self, *args, **kwargs): - pass - - def find_lr(self, num_iters=1000, init_value=1e-6, final_value=10., beta=0.98): - """ - stolen and adapted from here: https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html - :param num_iters: - :param init_value: - :param final_value: - :param beta: - :return: - """ - import math - self._maybe_init_amp() - mult = (final_value / init_value) ** (1 / num_iters) - lr = init_value - self.optimizer.param_groups[0]['lr'] = lr - avg_loss = 0. - best_loss = 0. - losses = [] - log_lrs = [] - - for batch_num in range(1, num_iters + 1): - # +1 because this one here is not designed to have negative loss... - loss = self.run_iteration(self.tr_gen, do_backprop=True, run_online_evaluation=False).data.item() + 1 - - # Compute the smoothed loss - avg_loss = beta * avg_loss + (1 - beta) * loss - smoothed_loss = avg_loss / (1 - beta ** batch_num) - - # Stop if the loss is exploding - if batch_num > 1 and smoothed_loss > 4 * best_loss: - break - - # Record the best loss - if smoothed_loss < best_loss or batch_num == 1: - best_loss = smoothed_loss - - # Store the values - losses.append(smoothed_loss) - log_lrs.append(math.log10(lr)) - - # Update the lr for the next step - lr *= mult - self.optimizer.param_groups[0]['lr'] = lr - - import matplotlib.pyplot as plt - lrs = [10 ** i for i in log_lrs] - fig = plt.figure() - plt.xscale('log') - plt.plot(lrs[10:-5], losses[10:-5]) - plt.savefig(join(self.output_folder, "lr_finder.png")) - plt.close() - return log_lrs, losses diff --git a/spaces/housexu123/bingo-2.0/src/components/ui/button.tsx b/spaces/housexu123/bingo-2.0/src/components/ui/button.tsx deleted file mode 100644 index 281da005124fa94c89a9a9db7605748a92b60865..0000000000000000000000000000000000000000 --- a/spaces/housexu123/bingo-2.0/src/components/ui/button.tsx +++ /dev/null @@ -1,57 +0,0 @@ -import * as React from 'react' -import { Slot } from '@radix-ui/react-slot' -import { cva, type VariantProps } from 'class-variance-authority' - -import { cn } from '@/lib/utils' - -const buttonVariants = cva( - 'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50', - { - variants: { - variant: { - default: - 'bg-primary text-primary-foreground shadow-md hover:bg-primary/90', - destructive: - 'bg-destructive text-destructive-foreground hover:bg-destructive/90', - outline: - 'border border-input hover:bg-accent hover:text-accent-foreground', - secondary: - 'bg-secondary text-secondary-foreground hover:bg-secondary/80', - ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground', - link: 'text-primary underline-offset-4 shadow-none hover:underline' - }, - size: { - default: 'h-8 px-4 py-2', - sm: 'h-8 rounded-md px-3', - lg: 'h-11 rounded-md px-8', - icon: 'h-8 w-8 p-0' - } - }, - defaultVariants: { - variant: 'default', - size: 'default' - } - } -) - -export interface ButtonProps - extends React.ButtonHTMLAttributes, - VariantProps { - asChild?: boolean -} - -const Button = React.forwardRef( - ({ className, variant, size, asChild = false, ...props }, ref) => { - const Comp = asChild ? Slot : 'button' - return ( - - ) - } -) -Button.displayName = 'Button' - -export { Button, buttonVariants } diff --git a/spaces/huggingface-projects/color-palette-generator-sd/static/_app/immutable/components/pages/_layout.svelte-55da5a4f.js b/spaces/huggingface-projects/color-palette-generator-sd/static/_app/immutable/components/pages/_layout.svelte-55da5a4f.js deleted file mode 100644 index 0ca560a14585bd2b79c6a7219297242351ac5caf..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/color-palette-generator-sd/static/_app/immutable/components/pages/_layout.svelte-55da5a4f.js +++ /dev/null @@ -1 +0,0 @@ -import{S as l,i,s as r,B as u,C as f,D as _,E as c,f as p,t as d}from"../../chunks/index-5559954d.js";function m(n){let s;const o=n[1].default,e=u(o,n,n[0],null);return{c(){e&&e.c()},l(t){e&&e.l(t)},m(t,a){e&&e.m(t,a),s=!0},p(t,[a]){e&&e.p&&(!s||a&1)&&f(e,o,t,t[0],s?c(o,t[0],a,null):_(t[0]),null)},i(t){s||(p(e,t),s=!0)},o(t){d(e,t),s=!1},d(t){e&&e.d(t)}}}function $(n,s,o){let{$$slots:e={},$$scope:t}=s;return n.$$set=a=>{"$$scope"in a&&o(0,t=a.$$scope)},[t,e]}class h extends l{constructor(s){super(),i(this,s,$,m,r,{})}}export{h as default}; diff --git a/spaces/huggingface/Model_Cards_Writing_Tool/README.md b/spaces/huggingface/Model_Cards_Writing_Tool/README.md deleted file mode 100644 index ba140974012b2f8b736a87d7258af3d19b9467bf..0000000000000000000000000000000000000000 --- a/spaces/huggingface/Model_Cards_Writing_Tool/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Modelcard Creator -emoji: ⚡ -colorFrom: red -colorTo: yellow -sdk: streamlit -sdk_version: 1.10.0 -app_file: 1_📝_form.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hussain-shk/IndiSent/indic_nlp_library/indicnlp/langinfo.py b/spaces/hussain-shk/IndiSent/indic_nlp_library/indicnlp/langinfo.py deleted file mode 100644 index efb7e372feeb67d7106eb5c443de2e14053fd204..0000000000000000000000000000000000000000 --- a/spaces/hussain-shk/IndiSent/indic_nlp_library/indicnlp/langinfo.py +++ /dev/null @@ -1,488 +0,0 @@ -# -# Copyright (c) 2013-present, Anoop Kunchukuttan -# All rights reserved. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# - -## language codes -LC_TA='ta' - -SCRIPT_RANGES={ - 'pa':[0x0a00,0x0a7f] , - 'gu':[0x0a80,0x0aff] , - 'or':[0x0b00,0x0b7f] , - 'ta':[0x0b80,0x0bff] , - 'te':[0x0c00,0x0c7f] , - 'kn':[0x0c80,0x0cff] , - 'ml':[0x0d00,0x0d7f] , - 'si':[0x0d80,0x0dff] , - 'hi':[0x0900,0x097f] , - 'mr':[0x0900,0x097f] , - 'kK':[0x0900,0x097f] , - 'sa':[0x0900,0x097f] , - 'ne':[0x0900,0x097f] , - 'sd':[0x0900,0x097f] , - 'bn':[0x0980,0x09ff] , - 'as':[0x0980,0x09ff] , - } - -DRAVIDIAN_LANGUAGES=['ta', 'te', 'kn', 'ml',] -IE_LANGUAGES=['hi', 'mr', 'kK', 'sa', 'ne', 'sd', 'bn', 'as', 'pa', 'gu', 'or', 'si', ] -DANDA_DELIM_LANGUAGES=['as','bn','hi','ne','or','pa','sa','sd'] - -URDU_RANGES=[ - [0x0600,0x06ff], - [0x0750,0x077f], - [0xfb50,0xfdff], - [0xfe70,0xfeff], - ] - -COORDINATED_RANGE_START_INCLUSIVE=0 -COORDINATED_RANGE_END_INCLUSIVE=0x6f - -NUMERIC_OFFSET_START=0x66 -NUMERIC_OFFSET_END=0x6f - -HALANTA_OFFSET=0x4d -AUM_OFFSET=0x50 -NUKTA_OFFSET=0x3c - -RUPEE_SIGN=0x20b9 - -DANDA=0x0964 -DOUBLE_DANDA=0x0965 - -#TODO: add missing fricatives and approximants -VELAR_RANGE=[0x15,0x19] -PALATAL_RANGE=[0x1a,0x1e] -RETROFLEX_RANGE=[0x1f,0x23] -DENTAL_RANGE=[0x24,0x29] -LABIAL_RANGE=[0x2a,0x2e] - -# verify -VOICED_LIST=[0x17,0x18,0x1c,0x1d,0x21,0x22,0x26,0x27,0x2c,0x2d] -UNVOICED_LIST=[0x15,0x16,0x1a,0x1b,0x1f,0x20,0x24,0x25,0x2a,0x2b] #TODO: add sibilants/sonorants -ASPIRATED_LIST=[0x16,0x18,0x1b,0x1d,0x20,0x22,0x25,0x27,0x2b,0x2d] -UNASPIRATED_LIST=[0x15,0x17,0x1a,0x1c,0x1f,0x21,0x24,0x26,0x2a,0x2c] -NASAL_LIST=[0x19,0x1e,0x23,0x28,0x29,0x2d] -FRICATIVE_LIST=[0x36,0x37,0x38] -APPROXIMANT_LIST=[0x2f,0x30,0x31,0x32,0x33,0x34,0x35] - -#TODO: ha has to be properly categorized - -def is_danda_delim(lang): - """ - Returns True if danda/double danda is a possible delimiter for the language - """ - return lang in DANDA_DELIM_LANGUAGES - -def get_offset(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - return ord(c)-SCRIPT_RANGES[lang][0] - -def offset_to_char(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - return chr(c+SCRIPT_RANGES[lang][0]) - -def in_coordinated_range(c_offset): - """ - Applicable to Brahmi derived Indic scripts - """ - return (c_offset>=COORDINATED_RANGE_START_INCLUSIVE and c_offset<=COORDINATED_RANGE_END_INCLUSIVE) - -def is_indiclang_char(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - o=get_offset(c,lang) - return (o>=0 and o<=0x7f) or ord(c)==DANDA or ord(c)==DOUBLE_DANDA - -# def is_vowel(c,lang): -# """ -# Is the character a vowel -# """ -# o=get_offset(c,lang) -# return (o>=0x04 and o<=0x14) - -# def is_vowel_sign(c,lang): -# """ -# Is the character a vowel sign (maatraa) -# """ -# o=get_offset(c,lang) -# return (o>=0x3e and o<=0x4c) - -# def is_halanta(c,lang): -# """ -# Is the character the halanta character -# """ -# o=get_offset(c,lang) -# return (o==HALANTA_OFFSET) - -# def is_nukta(c,lang): -# """ -# Is the character the halanta character -# """ -# o=get_offset(c,lang) -# return (o==NUKTA_OFFSET) - -# def is_aum(c,lang): -# """ -# Is the character a vowel sign (maatraa) -# """ -# o=get_offset(c,lang) -# return (o==AUM_OFFSET) - -# def is_consonant(c,lang): -# """ -# Is the character a consonant -# """ -# o=get_offset(c,lang) -# return (o>=0x15 and o<=0x39) - -# def is_velar(c,lang): -# """ -# Is the character a velar -# """ -# o=get_offset(c,lang) -# return (o>=VELAR_RANGE[0] and o<=VELAR_RANGE[1]) - -# def is_palatal(c,lang): -# """ -# Is the character a palatal -# """ -# o=get_offset(c,lang) -# return (o>=PALATAL_RANGE[0] and o<=PALATAL_RANGE[1]) - -# def is_retroflex(c,lang): -# """ -# Is the character a retroflex -# """ -# o=get_offset(c,lang) -# return (o>=RETROFLEX_RANGE[0] and o<=RETROFLEX_RANGE[1]) - -# def is_dental(c,lang): -# """ -# Is the character a dental -# """ -# o=get_offset(c,lang) -# return (o>=DENTAL_RANGE[0] and o<=DENTAL_RANGE[1]) - -# def is_labial(c,lang): -# """ -# Is the character a labial -# """ -# o=get_offset(c,lang) -# return (o>=LABIAL_RANGE[0] and o<=LABIAL_RANGE[1]) - -# def is_voiced(c,lang): -# """ -# Is the character a voiced consonant -# """ -# o=get_offset(c,lang) -# return o in VOICED_LIST - -# def is_unvoiced(c,lang): -# """ -# Is the character a unvoiced consonant -# """ -# o=get_offset(c,lang) -# return o in UNVOICED_LIST - -# def is_aspirated(c,lang): -# """ -# Is the character a aspirated consonant -# """ -# o=get_offset(c,lang) -# return o in ASPIRATED_LIST - -# def is_unaspirated(c,lang): -# """ -# Is the character a unaspirated consonant -# """ -# o=get_offset(c,lang) -# return o in UNASPIRATED_LIST - -# def is_nasal(c,lang): -# """ -# Is the character a nasal consonant -# """ -# o=get_offset(c,lang) -# return o in NASAL_LIST - -# def is_fricative(c,lang): -# """ -# Is the character a fricative consonant -# """ -# o=get_offset(c,lang) -# return o in FRICATIVE_LIST - -# def is_approximant(c,lang): -# """ -# Is the character an approximant consonant -# """ -# o=get_offset(c,lang) -# return o in APPROXIMANT_LIST - -# def is_number(c,lang): -# """ -# Is the character a number -# """ -# o=get_offset(c,lang) -# return (o>=0x66 and o<=0x6f) - - -def is_vowel(c,lang): - """ - Is the character a vowel - """ - o=get_offset(c,lang) - return (o>=0x04 and o<=0x14) - -def is_vowel_sign(c,lang): - """ - Is the character a vowel sign (maatraa) - """ - o=get_offset(c,lang) - return (o>=0x3e and o<=0x4c) - -def is_halanta(c,lang): - """ - Is the character the halanta character - """ - o=get_offset(c,lang) - return (o==HALANTA_OFFSET) - -def is_nukta(c,lang): - """ - Is the character the halanta character - """ - o=get_offset(c,lang) - return (o==NUKTA_OFFSET) - -def is_aum(c,lang): - """ - Is the character a vowel sign (maatraa) - """ - o=get_offset(c,lang) - return (o==AUM_OFFSET) - -def is_consonant(c,lang): - """ - Is the character a consonant - """ - o=get_offset(c,lang) - return (o>=0x15 and o<=0x39) - -def is_velar(c,lang): - """ - Is the character a velar - """ - o=get_offset(c,lang) - return (o>=VELAR_RANGE[0] and o<=VELAR_RANGE[1]) - -def is_palatal(c,lang): - """ - Is the character a palatal - """ - o=get_offset(c,lang) - return (o>=PALATAL_RANGE[0] and o<=PALATAL_RANGE[1]) - -def is_retroflex(c,lang): - """ - Is the character a retroflex - """ - o=get_offset(c,lang) - return (o>=RETROFLEX_RANGE[0] and o<=RETROFLEX_RANGE[1]) - -def is_dental(c,lang): - """ - Is the character a dental - """ - o=get_offset(c,lang) - return (o>=DENTAL_RANGE[0] and o<=DENTAL_RANGE[1]) - -def is_labial(c,lang): - """ - Is the character a labial - """ - o=get_offset(c,lang) - return (o>=LABIAL_RANGE[0] and o<=LABIAL_RANGE[1]) - -def is_voiced(c,lang): - """ - Is the character a voiced consonant - """ - o=get_offset(c,lang) - return o in VOICED_LIST - -def is_unvoiced(c,lang): - """ - Is the character a unvoiced consonant - """ - o=get_offset(c,lang) - return o in UNVOICED_LIST - -def is_aspirated(c,lang): - """ - Is the character a aspirated consonant - """ - o=get_offset(c,lang) - return o in ASPIRATED_LIST - -def is_unaspirated(c,lang): - """ - Is the character a unaspirated consonant - """ - o=get_offset(c,lang) - return o in UNASPIRATED_LIST - -def is_nasal(c,lang): - """ - Is the character a nasal consonant - """ - o=get_offset(c,lang) - return o in NASAL_LIST - -def is_fricative(c,lang): - """ - Is the character a fricative consonant - """ - o=get_offset(c,lang) - return o in FRICATIVE_LIST - -def is_approximant(c,lang): - """ - Is the character an approximant consonant - """ - o=get_offset(c,lang) - return o in APPROXIMANT_LIST - -def is_number(c,lang): - """ - Is the character a number - """ - o=get_offset(c,lang) - return (o>=0x66 and o<=0x6f) - - -################################################## - -def is_vowel_offset(c_offset): - """ - Is the offset a vowel - """ - return (c_offset>=0x04 and c_offset<=0x14) - -def is_vowel_sign_offset(c_offset): - """ - Is the offset a vowel sign (maatraa) - """ - return (c_offset>=0x3e and c_offset<=0x4c) - -def is_halanta_offset(c_offset): - """ - Is the offset the halanta offset - """ - return (c_offset==HALANTA_OFFSET) - -def is_nukta_offset(c_offset): - """ - Is the offset the halanta offset - """ - return (c_offset==NUKTA_OFFSET) - -def is_aum_offset(c_offset): - """ - Is the offset a vowel sign (maatraa) - """ - return (c_offset==AUM_OFFSET) - -def is_consonant_offset(c_offset): - """ - Is the offset a consonant - """ - return (c_offset>=0x15 and c_offset<=0x39) - -def is_velar_offset(c_offset): - """ - Is the offset a velar - """ - return (c_offset>=VELAR_RANGE[0] and c_offset<=VELAR_RANGE[1]) - -def is_palatal_offset(c_offset): - """ - Is the offset a palatal - """ - return (c_offset>=PALATAL_RANGE[0] and c_offset<=PALATAL_RANGE[1]) - -def is_retroflex_offset(c_offset): - """ - Is the offset a retroflex - """ - return (c_offset>=RETROFLEX_RANGE[0] and c_offset<=RETROFLEX_RANGE[1]) - -def is_dental_offset(c_offset): - """ - Is the offset a dental - """ - return (c_offset>=DENTAL_RANGE[0] and c_offset<=DENTAL_RANGE[1]) - -def is_labial_offset(c_offset): - """ - Is the offset a labial - """ - return (c_offset>=LABIAL_RANGE[0] and c_offset<=LABIAL_RANGE[1]) - -def is_voiced_offset(c_offset): - """ - Is the offset a voiced consonant - """ - return c_offset in VOICED_LIST - -def is_unvoiced_offset(c_offset): - """ - Is the offset a unvoiced consonant - """ - return c_offset in UNVOICED_LIST - -def is_aspirated_offset(c_offset): - """ - Is the offset a aspirated consonant - """ - return c_offset in ASPIRATED_LIST - -def is_unaspirated_offset(c_offset): - """ - Is the offset a unaspirated consonant - """ - return c_offset in UNASPIRATED_LIST - -def is_nasal_offset(c_offset): - """ - Is the offset a nasal consonant - """ - return c_offset in NASAL_LIST - -def is_fricative_offset(c_offset): - """ - Is the offset a fricative consonant - """ - return c_offset in FRICATIVE_LIST - -def is_approximant_offset(c_offset): - """ - Is the offset an approximant consonant - """ - return c_offset in APPROXIMANT_LIST - -def is_number_offset(c_offset): - """ - Is the offset a number - """ - return (c_offset>=0x66 and c_offset<=0x6f) diff --git a/spaces/hylee/finetuned_diffusion/README.md b/spaces/hylee/finetuned_diffusion/README.md deleted file mode 100644 index bca1a00cd251d1c13fc3fe72baad06e256245d3e..0000000000000000000000000000000000000000 --- a/spaces/hylee/finetuned_diffusion/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Finetuned Diffusion -emoji: 🪄🖼️ -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.15.0 -app_file: app.py -pinned: true -license: mit -duplicated_from: anzorq/finetuned_diffusion ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hysts/TADNE/style.css b/spaces/hysts/TADNE/style.css deleted file mode 100644 index 3c8bbe9faf61130e752c100dcf523e3afda611eb..0000000000000000000000000000000000000000 --- a/spaces/hysts/TADNE/style.css +++ /dev/null @@ -1,7 +0,0 @@ -h1 { - text-align: center; -} - -#duplicate-button { - margin: auto; -} diff --git a/spaces/ifey/chatdemo/gradiodemo/button.html b/spaces/ifey/chatdemo/gradiodemo/button.html deleted file mode 100644 index 8f6d2b9bd13972a4cde760816f0e5104b4f888d1..0000000000000000000000000000000000000000 --- a/spaces/ifey/chatdemo/gradiodemo/button.html +++ /dev/null @@ -1,9 +0,0 @@ - - - - 按钮示例 - - - - - diff --git a/spaces/innat/HybridModel-GradCAM/utils/viz_utils.py b/spaces/innat/HybridModel-GradCAM/utils/viz_utils.py deleted file mode 100644 index dc9fb5843029dee8fc8d1ea8f6a47a892a2b253e..0000000000000000000000000000000000000000 --- a/spaces/innat/HybridModel-GradCAM/utils/viz_utils.py +++ /dev/null @@ -1,64 +0,0 @@ -import matplotlib.cm as cm -import numpy as np -import tensorflow as tf -from tensorflow import keras - - -def make_gradcam_heatmap(img_array, grad_model, pred_index=None): - with tf.GradientTape(persistent=True) as tape: - preds, base_top, swin_top = grad_model(img_array) - if pred_index is None: - pred_index = tf.argmax(preds[0]) - class_channel = preds[:, pred_index] - - grads = tape.gradient(class_channel, base_top) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - base_top = base_top[0] - heatmap_a = base_top @ pooled_grads[..., tf.newaxis] - heatmap_a = tf.squeeze(heatmap_a) - heatmap_a = tf.maximum(heatmap_a, 0) / tf.math.reduce_max(heatmap_a) - heatmap_a = heatmap_a.numpy() - - grads = tape.gradient(class_channel, swin_top) - pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)) - swin_top = swin_top[0] - heatmap_b = swin_top @ pooled_grads[..., tf.newaxis] - heatmap_b = tf.squeeze(heatmap_b) - heatmap_b = tf.maximum(heatmap_b, 0) / tf.math.reduce_max(heatmap_b) - heatmap_b = heatmap_b.numpy() - return heatmap_a, heatmap_b, preds - - -def save_and_display_gradcam( - img, - heatmap, - target=None, - pred=None, - cam_path="cam.jpg", - cmap="jet", # inferno, viridis - alpha=0.6, - plot=None, - image_shape=None, -): - # Rescale heatmap to a range 0-255 - heatmap = np.uint8(255 * heatmap) - - # Use jet colormap to colorize heatmap - jet = cm.get_cmap(cmap) - - # Use RGB values of the colormap - jet_colors = jet(np.arange(256))[:, :3] - jet_heatmap = jet_colors[heatmap] - - # Create an image with RGB colorized heatmap - jet_heatmap = keras.utils.array_to_img(jet_heatmap) - jet_heatmap = jet_heatmap.resize((img.shape[0], img.shape[1])) - jet_heatmap = keras.utils.img_to_array(jet_heatmap) - - # Superimpose the heatmap on original image - superimposed_img = img + jet_heatmap * alpha - superimposed_img = keras.utils.array_to_img(superimposed_img) - - size_w, size_h = image_shape[:2] - superimposed_img = superimposed_img.resize((size_h, size_w)) - return superimposed_img diff --git a/spaces/innev/whisper-Base/README.md b/spaces/innev/whisper-Base/README.md deleted file mode 100644 index 8be982e1486afe209350e4b13ebc7fbfed3b035f..0000000000000000000000000000000000000000 --- a/spaces/innev/whisper-Base/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Whisper Base -emoji: 📚 -colorFrom: gray -colorTo: indigo -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/innovatorved/whisper.api/app/main.py b/spaces/innovatorved/whisper.api/app/main.py deleted file mode 100644 index b7d36f161b86592df14c4938b90166911fc8c823..0000000000000000000000000000000000000000 --- a/spaces/innovatorved/whisper.api/app/main.py +++ /dev/null @@ -1,50 +0,0 @@ -from fastapi import FastAPI -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import RedirectResponse - - -from app.api import api_router -from app.core.config import settings -from app.core.errors import error_handler -from app.api.models.ping import PingResponse - -from app.utils import print_routes -from app.utils.checks import run_checks - -if not run_checks(): - raise Exception("Failed to pass all checks") - - -app = FastAPI( - title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json" -) - -# Set all CORS enabled origins -if settings.BACKEND_CORS_ORIGINS: - app.add_middleware( - CORSMiddleware, - allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - ) - - -@app.get("/", include_in_schema=False) -async def redirect_to_docs(): - return RedirectResponse(url="/docs") - - -@app.get("/ping", tags=["ping"], response_model=PingResponse) -async def ping(): - return {"ping": "pong"} - - -# Include routers -app.include_router(api_router, prefix=settings.API_V1_STR) - -# # Error handlers -app.add_exception_handler(500, error_handler) - -# Print all routes -print_routes(app) diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Algebra Lineare E Geometria Schlesinger [Isohunt.to] 27 !!TOP!!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Algebra Lineare E Geometria Schlesinger [Isohunt.to] 27 !!TOP!!.md deleted file mode 100644 index 0edc8f4e6d13251cfa13709959f63b37015e55ac..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Algebra Lineare E Geometria Schlesinger [Isohunt.to] 27 !!TOP!!.md +++ /dev/null @@ -1,14 +0,0 @@ -

        Algebra Lineare e Geometria Schlesinger [Isohunt.to] 27


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        \ No newline at end of file diff --git a/spaces/jb30k/LegalENG/README.md b/spaces/jb30k/LegalENG/README.md deleted file mode 100644 index e4d97f62663fb647ba75ffd1428f893b52879164..0000000000000000000000000000000000000000 --- a/spaces/jb30k/LegalENG/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: LegalHome -emoji: 📚 -colorFrom: blue -colorTo: pink -sdk: gradio -sdk_version: 3.28.3 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/train.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/train.py deleted file mode 100644 index 6dc2f892e1fc134b311e2c9ee42250a2d3713547..0000000000000000000000000000000000000000 --- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/vocoder/train.py +++ /dev/null @@ -1,127 +0,0 @@ -from vocoder.models.fatchord_version import WaveRNN -from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder -from vocoder.distribution import discretized_mix_logistic_loss -from vocoder.display import stream, simple_table -from vocoder.gen_wavernn import gen_testset -from torch.utils.data import DataLoader -from pathlib import Path -from torch import optim -import torch.nn.functional as F -import vocoder.hparams as hp -import numpy as np -import time -import torch -import platform - -def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool, - save_every: int, backup_every: int, force_restart: bool): - # Check to make sure the hop length is correctly factorised - assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length - - # Instantiate the model - print("Initializing the model...") - model = WaveRNN( - rnn_dims=hp.voc_rnn_dims, - fc_dims=hp.voc_fc_dims, - bits=hp.bits, - pad=hp.voc_pad, - upsample_factors=hp.voc_upsample_factors, - feat_dims=hp.num_mels, - compute_dims=hp.voc_compute_dims, - res_out_dims=hp.voc_res_out_dims, - res_blocks=hp.voc_res_blocks, - hop_length=hp.hop_length, - sample_rate=hp.sample_rate, - mode=hp.voc_mode - ) - - if torch.cuda.is_available(): - model = model.cuda() - device = torch.device('cuda') - else: - device = torch.device('cpu') - - # Initialize the optimizer - optimizer = optim.Adam(model.parameters()) - for p in optimizer.param_groups: - p["lr"] = hp.voc_lr - loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss - - # Load the weights - model_dir = models_dir.joinpath(run_id) - model_dir.mkdir(exist_ok=True) - weights_fpath = model_dir.joinpath(run_id + ".pt") - if force_restart or not weights_fpath.exists(): - print("\nStarting the training of WaveRNN from scratch\n") - model.save(weights_fpath, optimizer) - else: - print("\nLoading weights at %s" % weights_fpath) - model.load(weights_fpath, optimizer) - print("WaveRNN weights loaded from step %d" % model.step) - - # Initialize the dataset - metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \ - voc_dir.joinpath("synthesized.txt") - mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath("mels_gta") - wav_dir = syn_dir.joinpath("audio") - dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir) - test_loader = DataLoader(dataset, - batch_size=1, - shuffle=True, - pin_memory=True) - - # Begin the training - simple_table([('Batch size', hp.voc_batch_size), - ('LR', hp.voc_lr), - ('Sequence Len', hp.voc_seq_len)]) - - for epoch in range(1, 350): - data_loader = DataLoader(dataset, - collate_fn=collate_vocoder, - batch_size=hp.voc_batch_size, - num_workers=2 if platform.system() != "Windows" else 0, - shuffle=True, - pin_memory=True) - start = time.time() - running_loss = 0. - - for i, (x, y, m) in enumerate(data_loader, 1): - if torch.cuda.is_available(): - x, m, y = x.cuda(), m.cuda(), y.cuda() - - # Forward pass - y_hat = model(x, m) - if model.mode == 'RAW': - y_hat = y_hat.transpose(1, 2).unsqueeze(-1) - elif model.mode == 'MOL': - y = y.float() - y = y.unsqueeze(-1) - - # Backward pass - loss = loss_func(y_hat, y) - optimizer.zero_grad() - loss.backward() - optimizer.step() - - running_loss += loss.item() - speed = i / (time.time() - start) - avg_loss = running_loss / i - - step = model.get_step() - k = step // 1000 - - if backup_every != 0 and step % backup_every == 0 : - model.checkpoint(model_dir, optimizer) - - if save_every != 0 and step % save_every == 0 : - model.save(weights_fpath, optimizer) - - msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \ - f"Loss: {avg_loss:.4f} | {speed:.1f} " \ - f"steps/s | Step: {k}k | " - stream(msg) - - - gen_testset(model, test_loader, hp.voc_gen_at_checkpoint, hp.voc_gen_batched, - hp.voc_target, hp.voc_overlap, model_dir) - print("") diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/html.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/html.py deleted file mode 100644 index 19199abc6c9f72771e050600bbcf73f57bd496c7..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gradio/components/html.py +++ /dev/null @@ -1,79 +0,0 @@ -"""gr.HTML() component.""" - -from __future__ import annotations - -import warnings -from typing import Any, Callable, Literal - -from gradio_client.documentation import document, set_documentation_group -from gradio_client.serializing import StringSerializable - -from gradio.components.base import IOComponent, _Keywords -from gradio.events import Changeable - -set_documentation_group("component") - - -@document() -class HTML(Changeable, IOComponent, StringSerializable): - """ - Used to display arbitrary HTML output. - Preprocessing: this component does *not* accept input. - Postprocessing: expects a valid HTML {str}. - - Demos: text_analysis - Guides: key-features - """ - - def __init__( - self, - value: str | Callable = "", - *, - label: str | None = None, - every: float | None = None, - show_label: bool | None = None, - visible: bool = True, - elem_id: str | None = None, - elem_classes: list[str] | str | None = None, - **kwargs, - ): - """ - Parameters: - value: Default value. If callable, the function will be called whenever the app loads to set the initial value of the component. - label: component name in interface. - every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. - show_label: if True, will display label. - visible: If False, component will be hidden. - elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. - elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. - """ - IOComponent.__init__( - self, - label=label, - every=every, - show_label=show_label, - visible=visible, - elem_id=elem_id, - elem_classes=elem_classes, - value=value, - **kwargs, - ) - - @staticmethod - def update( - value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE, - label: str | None = None, - show_label: bool | None = None, - visible: bool | None = None, - ): - warnings.warn( - "Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.HTML(...)` instead of `return gr.HTML.update(...)`." - ) - updated_config = { - "label": label, - "show_label": show_label, - "visible": visible, - "value": value, - "__type__": "update", - } - return updated_config diff --git a/spaces/joeddav/zero-shot-demo/app.py b/spaces/joeddav/zero-shot-demo/app.py deleted file mode 100644 index 97e0ad45351268474841d4ac4438f3a3a6ca73cc..0000000000000000000000000000000000000000 --- a/spaces/joeddav/zero-shot-demo/app.py +++ /dev/null @@ -1,148 +0,0 @@ -import streamlit as st -from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline -import torch -import numpy as np -import contextlib -import plotly.express as px -import pandas as pd -from PIL import Image -import datetime -import os -import psutil - -with open("hit_log.txt", mode='a') as file: - file.write(str(datetime.datetime.now()) + '\n') - - -MAX_GRAPH_ROWS = 10 - -MODEL_DESC = { - 'Bart MNLI': """Bart with a classification head trained on MNLI.\n\nSequences are posed as NLI premises and topic labels are turned into premises, i.e. `business` -> `This text is about business.`""", - 'Bart MNLI + Yahoo Answers': """Bart with a classification head trained on MNLI and then further fine-tuned on Yahoo Answers topic classification.\n\nSequences are posed as NLI premises and topic labels are turned into premises, i.e. `business` -> `This text is about business.`""", - 'XLM Roberta XNLI (cross-lingual)': """XLM Roberta, a cross-lingual model, with a classification head trained on XNLI. Supported languages include: _English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili, and Urdu_. - -Note that this model seems to be less reliable than the English-only models when classifying longer sequences. - -Examples were automatically translated and may contain grammatical mistakes. - -Sequences are posed as NLI premises and topic labels are turned into premises, i.e. `business` -> `This text is about business.`""", -} - -ZSL_DESC = """Recently, the NLP science community has begun to pay increasing attention to zero-shot and few-shot applications, such as in the [paper from OpenAI](https://arxiv.org/abs/2005.14165) introducing GPT-3. This demo shows how 🤗 Transformers can be used for zero-shot topic classification, the task of predicting a topic that the model has not been trained on.""" - -CODE_DESC = """```python -from transformers import pipeline -classifier = pipeline('zero-shot-classification', - model='{}') -hypothesis_template = 'This text is about {{}}.' # the template used in this demo - -classifier(sequence, labels, - hypothesis_template=hypothesis_template, - multi_class=multi_class) -# {{'sequence' ..., 'labels': ..., 'scores': ...}} -```""" - -model_ids = { - 'Bart MNLI': 'facebook/bart-large-mnli', - 'Bart MNLI + Yahoo Answers': 'joeddav/bart-large-mnli-yahoo-answers', - 'XLM Roberta XNLI (cross-lingual)': 'joeddav/xlm-roberta-large-xnli' -} - -device = 0 if torch.cuda.is_available() else -1 - -@st.cache(allow_output_mutation=True) -def load_models(): - return {id: AutoModelForSequenceClassification.from_pretrained(id) for id in model_ids.values()} - -models = load_models() - - -@st.cache(allow_output_mutation=True, show_spinner=False) -def load_tokenizer(tok_id): - return AutoTokenizer.from_pretrained(tok_id) - -@st.cache(allow_output_mutation=True, show_spinner=False, hash_funcs={ - torch.nn.Parameter: lambda _: None -}) -def get_most_likely(nli_model_id, sequence, labels, hypothesis_template, multi_class): - classifier = pipeline( - 'zero-shot-classification', - model=models[nli_model_id], - tokenizer=load_tokenizer(nli_model_id), - device=device - ) - outputs = classifier( - sequence, - candidate_labels=labels, - hypothesis_template=hypothesis_template, - multi_label=multi_class - ) - return outputs['labels'], outputs['scores'] - -def load_examples(model_id): - model_id_stripped = model_id.split('/')[-1] - df = pd.read_json(f'texts-{model_id_stripped}.json') - names = df.name.values.tolist() - mapping = {df['name'].iloc[i]: (df['text'].iloc[i], df['labels'].iloc[i]) for i in range(len(names))} - names.append('Custom') - mapping['Custom'] = ('', '') - return names, mapping - -def plot_result(top_topics, scores): - top_topics = np.array(top_topics) - scores = np.array(scores) - scores *= 100 - fig = px.bar(x=scores, y=top_topics, orientation='h', - labels={'x': 'Confidence', 'y': 'Label'}, - text=scores, - range_x=(0,115), - title='Top Predictions', - color=np.linspace(0,1,len(scores)), - color_continuous_scale='GnBu') - fig.update(layout_coloraxis_showscale=False) - fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside') - st.plotly_chart(fig) - - -def main(): - with open("style.css") as f: - st.markdown(''.format(f.read()), unsafe_allow_html=True) - - logo = Image.open('huggingface_logo.png') - st.sidebar.image(logo, width=120) - st.sidebar.markdown(ZSL_DESC) - model_desc = st.sidebar.selectbox('Model', list(MODEL_DESC.keys()), 0) - do_print_code = st.sidebar.checkbox('Show code snippet', False) - st.sidebar.markdown('#### Model Description') - st.sidebar.markdown(MODEL_DESC[model_desc]) - st.sidebar.markdown('Originally proposed by [Yin et al. (2019)](https://arxiv.org/abs/1909.00161). Read more in our [blog post](https://joeddav.github.io/blog/2020/05/29/ZSL.html).') - - model_id = model_ids[model_desc] - ex_names, ex_map = load_examples(model_id) - - st.title('Zero Shot Topic Classification') - example = st.selectbox('Choose an example', ex_names) - height = min((len(ex_map[example][0].split()) + 1) * 2, 200) - sequence = st.text_area('Text', ex_map[example][0], key='sequence', height=height) - labels = st.text_input('Possible topics (separated by `,`)', ex_map[example][1], max_chars=1000) - multi_class = st.checkbox('Allow multiple correct topics', value=True) - - hypothesis_template = "This text is about {}." - - labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0])) - if len(labels) == 0 or len(sequence) == 0: - st.write('Enter some text and at least one possible topic to see predictions.') - return - - if do_print_code: - st.markdown(CODE_DESC.format(model_id)) - - with st.spinner('Classifying...'): - top_topics, scores = get_most_likely(model_id, sequence, labels, hypothesis_template, multi_class) - - plot_result(top_topics[::-1][-MAX_GRAPH_ROWS:], scores[::-1][-MAX_GRAPH_ROWS:]) - - - -if __name__ == '__main__': - main() diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/scripts/__init__.py b/spaces/juancopi81/youtube-music-transcribe/t5x/scripts/__init__.py deleted file mode 100644 index 2ac5693550488d38623ec8e5b56e3fc3de148d40..0000000000000000000000000000000000000000 --- a/spaces/juancopi81/youtube-music-transcribe/t5x/scripts/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright 2022 The T5X Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""This empty file is needed to be recognized as a package by the setuptools.""" diff --git a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/index_func.py b/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/index_func.py deleted file mode 100644 index e3f36bfe0c36bfb2f6083e9ccb81029d1061ccb5..0000000000000000000000000000000000000000 --- a/spaces/juanhuggingface/ChuanhuChatGPT_Beta/modules/index_func.py +++ /dev/null @@ -1,141 +0,0 @@ -import os -import logging - -import colorama -import PyPDF2 -from tqdm import tqdm - -from modules.presets import * -from modules.utils import * -from modules.config import local_embedding - - -def get_index_name(file_src): - file_paths = [x.name for x in file_src] - file_paths.sort(key=lambda x: os.path.basename(x)) - - md5_hash = hashlib.md5() - for file_path in file_paths: - with open(file_path, "rb") as f: - while chunk := f.read(8192): - md5_hash.update(chunk) - - return md5_hash.hexdigest() - - -def get_documents(file_src): - from langchain.schema import Document - from langchain.text_splitter import TokenTextSplitter - text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=30) - - documents = [] - logging.debug("Loading documents...") - logging.debug(f"file_src: {file_src}") - for file in file_src: - filepath = file.name - filename = os.path.basename(filepath) - file_type = os.path.splitext(filename)[1] - logging.info(f"loading file: {filename}") - try: - if file_type == ".pdf": - logging.debug("Loading PDF...") - try: - from modules.pdf_func import parse_pdf - from modules.config import advance_docs - - two_column = advance_docs["pdf"].get("two_column", False) - pdftext = parse_pdf(filepath, two_column).text - except: - pdftext = "" - with open(filepath, "rb") as pdfFileObj: - pdfReader = PyPDF2.PdfReader(pdfFileObj) - for page in tqdm(pdfReader.pages): - pdftext += page.extract_text() - texts = Document(page_content=pdftext, metadata={"source": filepath}) - elif file_type == ".docx": - logging.debug("Loading Word...") - from langchain.document_loaders import UnstructuredWordDocumentLoader - loader = UnstructuredWordDocumentLoader(filepath) - texts = loader.load() - elif file_type == ".pptx": - logging.debug("Loading PowerPoint...") - from langchain.document_loaders import UnstructuredPowerPointLoader - loader = UnstructuredPowerPointLoader(filepath) - texts = loader.load() - elif file_type == ".epub": - logging.debug("Loading EPUB...") - from langchain.document_loaders import UnstructuredEPubLoader - loader = UnstructuredEPubLoader(filepath) - texts = loader.load() - elif file_type == ".xlsx": - logging.debug("Loading Excel...") - text_list = excel_to_string(filepath) - for elem in text_list: - documents.append(Document(page_content=elem, metadata={"source": filepath})) - continue - else: - logging.debug("Loading text file...") - from langchain.document_loaders import TextLoader - loader = TextLoader(filepath, "utf8") - texts = loader.load() - except Exception as e: - import traceback - logging.error(f"Error loading file: {filename}") - traceback.print_exc() - - texts = text_splitter.split_documents(texts) - documents.extend(texts) - logging.debug("Documents loaded.") - return documents - - -def construct_index( - api_key, - file_src, - max_input_size=4096, - num_outputs=5, - max_chunk_overlap=20, - chunk_size_limit=600, - embedding_limit=None, - separator=" ", -): - from langchain.chat_models import ChatOpenAI - from langchain.vectorstores import FAISS - - if api_key: - os.environ["OPENAI_API_KEY"] = api_key - else: - # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY - os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx" - chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit - embedding_limit = None if embedding_limit == 0 else embedding_limit - separator = " " if separator == "" else separator - - index_name = get_index_name(file_src) - index_path = f"./index/{index_name}" - if local_embedding: - from langchain.embeddings.huggingface import HuggingFaceEmbeddings - embeddings = HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2") - else: - from langchain.embeddings import OpenAIEmbeddings - embeddings = OpenAIEmbeddings() - if os.path.exists(index_path): - logging.info("找到了缓存的索引文件，加载中……") - return FAISS.load_local(index_path, embeddings) - else: - try: - documents = get_documents(file_src) - logging.info("构建索引中……") - with retrieve_proxy(): - index = FAISS.from_documents(documents, embeddings) - logging.debug("索引构建完成！") - os.makedirs("./index", exist_ok=True) - index.save_local(index_path) - logging.debug("索引已保存至本地!") - return index - - except Exception as e: - import traceback - logging.error("索引构建失败！", e) - traceback.print_exc() - return None diff --git a/spaces/julien-c/sveltekit-demo/src/routes/todos/[uid].json.ts b/spaces/julien-c/sveltekit-demo/src/routes/todos/[uid].json.ts deleted file mode 100644 index 17891faf59796f1d7309985bba9d6e08ff6ead29..0000000000000000000000000000000000000000 --- a/spaces/julien-c/sveltekit-demo/src/routes/todos/[uid].json.ts +++ /dev/null @@ -1,16 +0,0 @@ -import { api } from './_api'; -import type { RequestHandler } from '@sveltejs/kit'; -import type { Locals } from '$lib/types'; - -// PATCH /todos/:uid.json -export const patch: RequestHandler = async (request) => { - return api(request, `todos/${request.locals.userid}/${request.params.uid}`, { - text: request.body.get('text'), - done: request.body.has('done') ? !!request.body.get('done') : undefined - }); -}; - -// DELETE /todos/:uid.json -export const del: RequestHandler = async (request) => { - return api(request, `todos/${request.locals.userid}/${request.params.uid}`); -}; diff --git a/spaces/justest/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py b/spaces/justest/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py deleted file mode 100644 index 818c605d2d82680f2014ba8f1a3bb66d0ae741b1..0000000000000000000000000000000000000000 --- a/spaces/justest/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py +++ /dev/null @@ -1,139 +0,0 @@ -import requests -import random -import string -import time -import sys -import re -import os - - -def check_email(mail, logging: bool = False): - username = mail.split("@")[0] - domain = mail.split("@")[1] - reqLink = f"https://www.1secmail.com/api/v1/?action=getMessages&login={username}&domain={domain}" - req = requests.get(reqLink) - req.encoding = req.apparent_encoding - req = req.json() - - length = len(req) - - if logging: - os.system("cls" if os.name == "nt" else "clear") - time.sleep(1) - print("Your temporary mail:", mail) - - if logging and length == 0: - print( - "Mailbox is empty. Hold tight. Mailbox is refreshed automatically every 5 seconds.", - ) - else: - messages = [] - id_list = [] - - for i in req: - for k, v in i.items(): - if k == "id": - id_list.append(v) - - x = "mails" if length > 1 else "mail" - - if logging: - print( - f"Mailbox has {length} {x}. (Mailbox is refreshed automatically every 5 seconds.)" - ) - - for i in id_list: - msgRead = f"https://www.1secmail.com/api/v1/?action=readMessage&login={username}&domain={domain}&id={i}" - req = requests.get(msgRead) - req.encoding = req.apparent_encoding - req = req.json() - - for k, v in req.items(): - if k == "from": - sender = v - if k == "subject": - subject = v - if k == "date": - date = v - if k == "textBody": - content = v - - if logging: - print( - "Sender:", - sender, - "\nTo:", - mail, - "\nSubject:", - subject, - "\nDate:", - date, - "\nContent:", - content, - "\n", - ) - messages.append( - { - "sender": sender, - "to": mail, - "subject": subject, - "date": date, - "content": content, - } - ) - - if logging: - os.system("cls" if os.name == "nt" else "clear") - return messages - - -def create_email(custom_domain: bool = False, logging: bool = False): - domainList = ["1secmail.com", "1secmail.net", "1secmail.org"] - domain = random.choice(domainList) - try: - if custom_domain: - custom_domain = input( - "\nIf you enter 'my-test-email' as your domain name, mail address will look like this: 'my-test-email@1secmail.com'" - "\nEnter the name that you wish to use as your domain name: " - ) - - newMail = f"https://www.1secmail.com/api/v1/?login={custom_domain}&domain={domain}" - reqMail = requests.get(newMail) - reqMail.encoding = reqMail.apparent_encoding - - username = re.search(r"login=(.*)&", newMail).group(1) - domain = re.search(r"domain=(.*)", newMail).group(1) - mail = f"{username}@{domain}" - - if logging: - print("\nYour temporary email was created successfully:", mail) - return mail - - else: - name = string.ascii_lowercase + string.digits - random_username = "".join(random.choice(name) for i in range(10)) - newMail = f"https://www.1secmail.com/api/v1/?login={random_username}&domain={domain}" - - reqMail = requests.get(newMail) - reqMail.encoding = reqMail.apparent_encoding - - username = re.search(r"login=(.*)&", newMail).group(1) - domain = re.search(r"domain=(.*)", newMail).group(1) - mail = f"{username}@{domain}" - - if logging: - print("\nYour temporary email was created successfully:", mail) - return mail - - except KeyboardInterrupt: - requests.post( - "https://www.1secmail.com/mailbox", - data={ - "action": "deleteMailbox", - "login": f"{username}", - "domain": f"{domain}", - }, - ) - if logging: - print("\nKeyboard Interrupt Detected! \nTemporary mail was disposed!") - os.system("cls" if os.name == "nt" else "clear") diff --git a/spaces/kadirnar/BioGpt/app.py b/spaces/kadirnar/BioGpt/app.py deleted file mode 100644 index 770ab547dcbde3b0a1b2ce5e13d1c26b2d74fbb3..0000000000000000000000000000000000000000 --- a/spaces/kadirnar/BioGpt/app.py +++ /dev/null @@ -1,143 +0,0 @@ -from transformers import pipeline -from multilingual_translation import text_to_text_generation -from utils import lang_ids -import gradio as gr - -paper_id = "kadirnar/biogpt_paper" - -biogpt_model_list = [ - "microsoft/biogpt", - "microsoft/BioGPT-Large", - "microsoft/BioGPT-Large-PubMedQA" -] - -lang_model_list = [ - "facebook/m2m100_1.2B", - "facebook/m2m100_418M" -] - -whisper_model_list = [ - "openai/whisper-small", - "openai/whisper-medium", - "openai/whisper-tiny", - "openai/whisper-large" -] - -lang_list = list(lang_ids.keys()) - -def whisper_demo(input_audio, model_id): - pipe = pipeline(task="automatic-speech-recognition",model=model_id, device='cuda:0') - pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language='en', task="transcribe") - output_text = pipe(input_audio)['text'] - return output_text - - -def translate_to_english(prompt, lang_model_id, base_lang): - if base_lang == "English": - return prompt - else: - output_text = text_to_text_generation( - prompt=prompt, - model_id=lang_model_id, - device='cuda:0', - target_lang='en' - ) - - return output_text[0] - -def biogpt_text( - prompt: str, - biogpt_model_id: str, - lang_model_id: str, - base_lang: str, -): - - en_prompt = translate_to_english(prompt, lang_model_id, base_lang) - generator = pipeline("text-generation", model=biogpt_model_id, device="cuda:0") - output = generator(en_prompt, max_length=250, num_return_sequences=1, do_sample=True) - output = output[0]['generated_text'] - - if base_lang == "English": - output_text = output - - else: - output_text = text_to_text_generation( - prompt=output, - model_id=lang_model_id, - device='cuda:0', - target_lang=lang_ids[base_lang] - ) - - return en_prompt, output, output_text - -def biogpt_audio( - input_audio: str, - biogpt_model_id: str, - whisper_model_id: str, - base_lang: str, - lang_model_id: str, -): - en_prompt = whisper_demo(input_audio=input_audio, model_id=whisper_model_id) - generator = pipeline("text-generation", model=biogpt_model_id, device="cuda:0") - output = generator(en_prompt, max_length=250, num_return_sequences=1, do_sample=True) - output = output[0]['generated_text'] - if base_lang == "English": - output_text = output - - else: - output_text = text_to_text_generation( - prompt=output, - model_id=lang_model_id, - device='cuda:0', - target_lang=lang_ids[base_lang] - ) - - return en_prompt, output, output_text - -question_example = "Can 'high-risk' human papillomaviruses (HPVs) be detected in human breast milk? context: Using polymerase chain reaction techniques, we evaluated the presence of HPV infection in human breast milk collected from 21 HPV-positive and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no 'high-risk' HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer: This preliminary case-control study indicates the absence of mucosal 'high-risk' HPV types in human breast milk." - -examples = [ - ["COVID-19 is", biogpt_model_list[0], lang_model_list[1], "English"], - [question_example, biogpt_model_list[2], lang_model_list[1], "English"] -] - - -app = gr.Blocks() -with app: - gr.Markdown("# **

        Whisper + M2M100 + BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

        **") - gr.Markdown( - """ -

        - Follow me for more! - Twitter | Github | Linkedin | -
        - """ - ) - with gr.Row(): - with gr.Column(): - with gr.Tab("Text"): - input_text = gr.Textbox(lines=3, value="COVID-19 is", label="Text") - text_biogpt = gr.Dropdown(choices=biogpt_model_list, value=biogpt_model_list[0], label='BioGpt Model') - text_m2m100 = gr.Dropdown(choices=lang_model_list, value=lang_model_list[1], label='Language Model') - text_lang = gr.Dropdown(lang_list, value="English", label="Base Language") - text_button = gr.Button(value="Predict") - - with gr.Tab("Audio"): - input_audio = gr.Audio(source="microphone", type="filepath", label='Audio') - audio_biogpt = gr.Dropdown(choices=biogpt_model_list, value=biogpt_model_list[0], label='BioGpt Model') - audio_whisper = gr.Dropdown(choices=whisper_model_list, value=whisper_model_list[0], label='Audio Model') - audio_lang = gr.Dropdown(lang_list, value="English", label="Base Language") - audio_m2m100 = gr.Dropdown(choices=lang_model_list, value=lang_model_list[1], label='Language Model') - audio_button = gr.Button(value="Predict") - - with gr.Tab("Output"): - with gr.Column(): - prompt_text = gr.Textbox(lines=3, label="Prompt") - output_text = gr.Textbox(lines=3, label="BioGpt Text") - translated_text = gr.Textbox(lines=3,label="Translated Text") - - gr.Examples(examples, inputs=[input_text, text_biogpt, text_m2m100,text_lang], outputs=[prompt_text, output_text, translated_text], fn=biogpt_text, cache_examples=False) - text_button.click(biogpt_text, inputs=[input_text, text_biogpt, text_m2m100 ,text_lang], outputs=[prompt_text, output_text, translated_text]) - audio_button.click(biogpt_audio, inputs=[input_audio, audio_biogpt, audio_whisper, audio_lang, audio_m2m100], outputs=[prompt_text, output_text, translated_text]) - -app.launch() \ No newline at end of file diff --git a/spaces/kaleidoscope-data/data-cleaning-llm/app/__init__.py b/spaces/kaleidoscope-data/data-cleaning-llm/app/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/kargaranamir/LangID-LIME/README.md b/spaces/kargaranamir/LangID-LIME/README.md deleted file mode 100644 index 917332fc1467ef29bed4f79685491c5f016c1647..0000000000000000000000000000000000000000 --- a/spaces/kargaranamir/LangID-LIME/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: LangID-LIME -emoji: 🍋 -colorFrom: blue -colorTo: indigo -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: mit ---- - -This code applies LIME (Local Interpretable Model-Agnostic Explanations) on fasttext language identification. diff --git a/spaces/kavi1025/Youtube-Whisperer/app.py b/spaces/kavi1025/Youtube-Whisperer/app.py deleted file mode 100644 index c3b950d79209e5e4b903442a861cc89227c1448e..0000000000000000000000000000000000000000 --- a/spaces/kavi1025/Youtube-Whisperer/app.py +++ /dev/null @@ -1,93 +0,0 @@ -import gradio as gr -import whisper -from pytube import YouTube - - -class GradioInference(): - def __init__(self): - self.sizes = list(whisper._MODELS.keys()) - self.langs = ["none"] + sorted(list(whisper.tokenizer.LANGUAGES.values())) - self.current_size = "base" - self.loaded_model = whisper.load_model(self.current_size) - self.yt = None - - def __call__(self, link, lang, size, subs): - if self.yt is None: - self.yt = YouTube(link) - path = self.yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4") - - if lang == "none": - lang = None - - if size != self.current_size: - self.loaded_model = whisper.load_model(size) - self.current_size = size - results = self.loaded_model.transcribe(path, language=lang) - - if subs == "None": - return results["text"] - elif subs == ".srt": - return self.srt(results["segments"]) - elif ".csv" == ".csv": - return self.csv(results["segments"]) - - def srt(self, segments): - output = "" - for i, segment in enumerate(segments): - output += f"{i+1}\n" - output += f"{self.format_time(segment['start'])} --> {self.format_time(segment['end'])}\n" - output += f"{segment['text']}\n\n" - return output - - def csv(self, segments): - output = "" - for segment in segments: - output += f"{segment['start']},{segment['end']},{segment['text']}\n" - return output - - def format_time(self, time): - hours = time//3600 - minutes = (time - hours*3600)//60 - seconds = time - hours*3600 - minutes*60 - milliseconds = (time - int(time))*1000 - return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d},{int(milliseconds):03d}" - - def populate_metadata(self, link): - self.yt = YouTube(link) - return self.yt.thumbnail_url, self.yt.title - -gio = GradioInference() -title="Youtube Whisperer" -description="Speech to text transcription of Youtube videos using OpenAI's Whisper" - -block = gr.Blocks() -with block: - gr.HTML( - """ -
        -
        -

        Youtube Whisperer

        -
        -

        - Speech to text transcription of Youtube videos using OpenAI's Whisper -

        -
        - """ - ) - with gr.Group(): - with gr.Box(): - with gr.Row().style(equal_height=True): - sz = gr.Dropdown(label="Model Size", choices=gio.sizes, value='base') - lang = gr.Dropdown(label="Language (Optional)", choices=gio.langs, value="none") - with gr.Row().style(equal_height=True): - wt = gr.Radio(["None", ".srt", ".csv"], label="With Timestamps?") - link = gr.Textbox(label="YouTube Link") - title = gr.Label(label="Video Title") - with gr.Row().style(equal_height=True): - img = gr.Image(label="Thumbnail") - text = gr.Textbox(label="Transcription", placeholder="Transcription Output", lines=10) - with gr.Row().style(equal_height=True): - btn = gr.Button("Transcribe") - btn.click(gio, inputs=[link, lang, sz, wt], outputs=[text]) - link.change(gio.populate_metadata, inputs=[link], outputs=[img, title]) -block.launch() \ No newline at end of file diff --git a/spaces/kazuk/youtube-whisper-05/app.py b/spaces/kazuk/youtube-whisper-05/app.py deleted file mode 100644 index 4a61dc561a016c53ad93a3c556b0ef7bafa964eb..0000000000000000000000000000000000000000 --- a/spaces/kazuk/youtube-whisper-05/app.py +++ /dev/null @@ -1,66 +0,0 @@ -import gradio as gr -import whisper -from pytube import YouTube - -def get_audio(url): - yt = YouTube(url) - return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4") - -def get_transcript(url, model_size, lang, format): - - model = whisper.load_model(model_size) - - if lang == "None": - lang = None - - result = model.transcribe(get_audio(url), fp16=False, language=lang) - - if format == "None": - return result["text"] - elif format == ".srt": - return format_to_srt(result["segments"]) - -def format_to_srt(segments): - output = "" - for i, segment in enumerate(segments): - output += f"{i + 1}\n" - output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" - output += f"{segment['text']}\n\n" - return output - -def format_timestamp(t): - hh = t//3600 - mm = (t - hh*3600)//60 - ss = t - hh*3600 - mm*60 - mi = (t - int(t))*1000 - return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}" - - -langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values())) -model_size = list(whisper._MODELS.keys()) - -with gr.Blocks() as demo: - - with gr.Row(): - - with gr.Column(): - - with gr.Row(): - url = gr.Textbox(placeholder='Youtube video URL', label='URL') - - with gr.Row(): - - model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model") - lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)") - format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)") - - with gr.Row(): - gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.") - transcribe_btn = gr.Button('Transcribe') - - with gr.Column(): - outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription') - - transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs) - -demo.launch(debug=True) diff --git a/spaces/kdrkdrkdr/AzusaTTS/text/cleaners.py b/spaces/kdrkdrkdr/AzusaTTS/text/cleaners.py deleted file mode 100644 index ff8339c46ef55a14f004e94019c686e37729a7df..0000000000000000000000000000000000000000 --- a/spaces/kdrkdrkdr/AzusaTTS/text/cleaners.py +++ /dev/null @@ -1,17 +0,0 @@ -import re - -def japanese_cleaners(text): - from text.japanese import japanese_to_romaji_with_accent - text = japanese_to_romaji_with_accent(text) - if len(text) == 0 or re.match('[A-Za-z]', text[-1]): - text += '.' - return text - - -def japanese_cleaners2(text): - text = text.replace('・・・', '…').replace('・', ' ') - text = japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') \ - .replace('(', '').replace(')', '') \ - .replace('[', '').replace(']', '') \ - .replace('*', ' ').replace('{', '').replace('}', '') - return text \ No newline at end of file diff --git a/spaces/kepajide/keyiwei/text/cleaners.py b/spaces/kepajide/keyiwei/text/cleaners.py deleted file mode 100644 index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000 --- a/spaces/kepajide/keyiwei/text/cleaners.py +++ /dev/null @@ -1,475 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -''' -Cleaners are transformations that run over the input text at both training and eval time. - -Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" -hyperparameter. Some cleaners are English-specific. You'll typically want to use: - 1. "english_cleaners" for English text - 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using - the Unidecode library (https://pypi.python.org/pypi/Unidecode) - 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update - the symbols in symbols.py to match your data). -''' - -import re -from unidecode import unidecode -import pyopenjtalk -from jamo import h2j, j2hcj -from pypinyin import lazy_pinyin, BOPOMOFO -import jieba, cn2an - - -# This is a list of Korean classifiers preceded by pure Korean numerals. -_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통' - -# Regular expression matching whitespace: -_whitespace_re = re.compile(r'\s+') - -# Regular expression matching Japanese without punctuation marks: -_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') - -# Regular expression matching non-Japanese characters or punctuation marks: -_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ - ('mrs', 'misess'), - ('mr', 'mister'), - ('dr', 'doctor'), - ('st', 'saint'), - ('co', 'company'), - ('jr', 'junior'), - ('maj', 'major'), - ('gen', 'general'), - ('drs', 'doctors'), - ('rev', 'reverend'), - ('lt', 'lieutenant'), - ('hon', 'honorable'), - ('sgt', 'sergeant'), - ('capt', 'captain'), - ('esq', 'esquire'), - ('ltd', 'limited'), - ('col', 'colonel'), - ('ft', 'fort'), -]] - -# List of (hangul, hangul divided) pairs: -_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄳ', 'ㄱㅅ'), - ('ㄵ', 'ㄴㅈ'), - ('ㄶ', 'ㄴㅎ'), - ('ㄺ', 'ㄹㄱ'), - ('ㄻ', 'ㄹㅁ'), - ('ㄼ', 'ㄹㅂ'), - ('ㄽ', 'ㄹㅅ'), - ('ㄾ', 'ㄹㅌ'), - ('ㄿ', 'ㄹㅍ'), - ('ㅀ', 'ㄹㅎ'), - ('ㅄ', 'ㅂㅅ'), - ('ㅘ', 'ㅗㅏ'), - ('ㅙ', 'ㅗㅐ'), - ('ㅚ', 'ㅗㅣ'), - ('ㅝ', 'ㅜㅓ'), - ('ㅞ', 'ㅜㅔ'), - ('ㅟ', 'ㅜㅣ'), - ('ㅢ', 'ㅡㅣ'), - ('ㅑ', 'ㅣㅏ'), - ('ㅒ', 'ㅣㅐ'), - ('ㅕ', 'ㅣㅓ'), - ('ㅖ', 'ㅣㅔ'), - ('ㅛ', 'ㅣㅗ'), - ('ㅠ', 'ㅣㅜ') -]] - -# List of (Latin alphabet, hangul) pairs: -_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('a', '에이'), - ('b', '비'), - ('c', '시'), - ('d', '디'), - ('e', '이'), - ('f', '에프'), - ('g', '지'), - ('h', '에이치'), - ('i', '아이'), - ('j', '제이'), - ('k', '케이'), - ('l', '엘'), - ('m', '엠'), - ('n', '엔'), - ('o', '오'), - ('p', '피'), - ('q', '큐'), - ('r', '아르'), - ('s', '에스'), - ('t', '티'), - ('u', '유'), - ('v', '브이'), - ('w', '더블유'), - ('x', '엑스'), - ('y', '와이'), - ('z', '제트') -]] - -# List of (Latin alphabet, bopomofo) pairs: -_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('a', 'ㄟˉ'), - ('b', 'ㄅㄧˋ'), - ('c', 'ㄙㄧˉ'), - ('d', 'ㄉㄧˋ'), - ('e', 'ㄧˋ'), - ('f', 'ㄝˊㄈㄨˋ'), - ('g', 'ㄐㄧˋ'), - ('h', 'ㄝˇㄑㄩˋ'), - ('i', 'ㄞˋ'), - ('j', 'ㄐㄟˋ'), - ('k', 'ㄎㄟˋ'), - ('l', 'ㄝˊㄛˋ'), - ('m', 'ㄝˊㄇㄨˋ'), - ('n', 'ㄣˉ'), - ('o', 'ㄡˉ'), - ('p', 'ㄆㄧˉ'), - ('q', 'ㄎㄧㄡˉ'), - ('r', 'ㄚˋ'), - ('s', 'ㄝˊㄙˋ'), - ('t', 'ㄊㄧˋ'), - ('u', 'ㄧㄡˉ'), - ('v', 'ㄨㄧˉ'), - ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'), - ('x', 'ㄝˉㄎㄨˋㄙˋ'), - ('y', 'ㄨㄞˋ'), - ('z', 'ㄗㄟˋ') -]] - - -# List of (bopomofo, romaji) pairs: -_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('ㄅㄛ', 'p⁼wo'), - ('ㄆㄛ', 'pʰwo'), - ('ㄇㄛ', 'mwo'), - ('ㄈㄛ', 'fwo'), - ('ㄅ', 'p⁼'), - ('ㄆ', 'pʰ'), - ('ㄇ', 'm'), - ('ㄈ', 'f'), - ('ㄉ', 't⁼'), - ('ㄊ', 'tʰ'), - ('ㄋ', 'n'), - ('ㄌ', 'l'), - ('ㄍ', 'k⁼'), - ('ㄎ', 'kʰ'), - ('ㄏ', 'h'), - ('ㄐ', 'ʧ⁼'), - ('ㄑ', 'ʧʰ'), - ('ㄒ', 'ʃ'), - ('ㄓ', 'ʦ`⁼'), - ('ㄔ', 'ʦ`ʰ'), - ('ㄕ', 's`'), - ('ㄖ', 'ɹ`'), - ('ㄗ', 'ʦ⁼'), - ('ㄘ', 'ʦʰ'), - ('ㄙ', 's'), - ('ㄚ', 'a'), - ('ㄛ', 'o'), - ('ㄜ', 'ə'), - ('ㄝ', 'e'), - ('ㄞ', 'ai'), - ('ㄟ', 'ei'), - ('ㄠ', 'au'), - ('ㄡ', 'ou'), - ('ㄧㄢ', 'yeNN'), - ('ㄢ', 'aNN'), - ('ㄧㄣ', 'iNN'), - ('ㄣ', 'əNN'), - ('ㄤ', 'aNg'), - ('ㄧㄥ', 'iNg'), - ('ㄨㄥ', 'uNg'), - ('ㄩㄥ', 'yuNg'), - ('ㄥ', 'əNg'), - ('ㄦ', 'əɻ'), - ('ㄧ', 'i'), - ('ㄨ', 'u'), - ('ㄩ', 'ɥ'), - ('ˉ', '→'), - ('ˊ', '↑'), - ('ˇ', '↓↑'), - ('ˋ', '↓'), - ('˙', ''), - ('，', ','), - ('。', '.'), - ('！', '!'), - ('？', '?'), - ('—', '-') -]] - - -def expand_abbreviations(text): - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def lowercase(text): - return text.lower() - - -def collapse_whitespace(text): - return re.sub(_whitespace_re, ' ', text) - - -def convert_to_ascii(text): - return unidecode(text) - - -def japanese_to_romaji_with_accent(text): - '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html''' - sentences = re.split(_japanese_marks, text) - marks = re.findall(_japanese_marks, text) - text = '' - for i, sentence in enumerate(sentences): - if re.match(_japanese_characters, sentence): - if text!='': - text+=' ' - labels = pyopenjtalk.extract_fullcontext(sentence) - for n, label in enumerate(labels): - phoneme = re.search(r'\-([^\+]*)\+', label).group(1) - if phoneme not in ['sil','pau']: - text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q') - else: - continue - n_moras = int(re.search(r'/F:(\d+)_', label).group(1)) - a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1)) - a2 = int(re.search(r"\+(\d+)\+", label).group(1)) - a3 = int(re.search(r"\+(\d+)/", label).group(1)) - if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']: - a2_next=-1 - else: - a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1)) - # Accent phrase boundary - if a3 == 1 and a2_next == 1: - text += ' ' - # Falling - elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras: - text += '↓' - # Rising - elif a2 == 1 and a2_next == 2: - text += '↑' - if i 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/kingabzpro/real-time-Urdu-ASR/app.py b/spaces/kingabzpro/real-time-Urdu-ASR/app.py deleted file mode 100644 index a4499da8956e148443c61c42a44d3d9bad846daa..0000000000000000000000000000000000000000 --- a/spaces/kingabzpro/real-time-Urdu-ASR/app.py +++ /dev/null @@ -1,45 +0,0 @@ -from transformers import pipeline -import gradio as gr -import time -import unicodedata -p = pipeline("automatic-speech-recognition",model="kingabzpro/wav2vec2-large-xls-r-300m-Urdu") - -def transcribe(audio, state=""): - time.sleep(2) - text = p(audio)["text"] - state += unicodedata.normalize("NFC",text) + " " - return state, state - -################### Gradio Web APP ################################ - -title = "Real-Time Urdu ASR" - -description = """ -

        -

        -This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. -
        -

        -
        -logo -
        -""" - -article = "

        Source Code on DagsHub

        Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers

        visitor badge

        " - - -gr.Interface( - fn=transcribe, - inputs=[ - gr.Audio(source="microphone", type="filepath", streaming=True), - "state" - ], - outputs=[ - "textbox", - "state" - ], - title=title, - description=description, - article=article, - theme='EveryPizza/Cartoony-Gradio-Theme', - live=True).launch() diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/train.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/train.py deleted file mode 100644 index 5a6a06c805109159ff40cad69668f1fc38cf1e9b..0000000000000000000000000000000000000000 --- a/spaces/kira4424/Tacotron-zero-short-voice-clone/train.py +++ /dev/null @@ -1,67 +0,0 @@ -import sys -import torch -import argparse -import numpy as np -from utils.load_yaml import HpsYaml -from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver - -# For reproducibility, comment these may speed up training -torch.backends.cudnn.deterministic = True -torch.backends.cudnn.benchmark = False - -def main(): - # Arguments - parser = argparse.ArgumentParser(description= - 'Training PPG2Mel VC model.') - parser.add_argument('--config', type=str, - help='Path to experiment config, e.g., config/vc.yaml') - parser.add_argument('--name', default=None, type=str, help='Name for logging.') - parser.add_argument('--logdir', default='log/', type=str, - help='Logging path.', required=False) - parser.add_argument('--ckpdir', default='ppg2mel/saved_models/', type=str, - help='Checkpoint path.', required=False) - parser.add_argument('--outdir', default='result/', type=str, - help='Decode output path.', required=False) - parser.add_argument('--load', default=None, type=str, - help='Load pre-trained model (for training only)', required=False) - parser.add_argument('--warm_start', action='store_true', - help='Load model weights only, ignore specified layers.') - parser.add_argument('--seed', default=0, type=int, - help='Random seed for reproducable results.', required=False) - parser.add_argument('--njobs', default=8, type=int, - help='Number of threads for dataloader/decoding.', required=False) - parser.add_argument('--cpu', action='store_true', help='Disable GPU training.') - parser.add_argument('--no-pin', action='store_true', - help='Disable pin-memory for dataloader') - parser.add_argument('--test', action='store_true', help='Test the model.') - parser.add_argument('--no-msg', action='store_true', help='Hide all messages.') - parser.add_argument('--finetune', action='store_true', help='Finetune model') - parser.add_argument('--oneshotvc', action='store_true', help='Oneshot VC model') - parser.add_argument('--bilstm', action='store_true', help='BiLSTM VC model') - parser.add_argument('--lsa', action='store_true', help='Use location-sensitive attention (LSA)') - - ### - - paras = parser.parse_args() - setattr(paras, 'gpu', not paras.cpu) - setattr(paras, 'pin_memory', not paras.no_pin) - setattr(paras, 'verbose', not paras.no_msg) - # Make the config dict dot visitable - config = HpsYaml(paras.config) - - np.random.seed(paras.seed) - torch.manual_seed(paras.seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(paras.seed) - - print(">>> OneShot VC training ...") - mode = "train" - solver = Solver(config, paras, mode) - solver.load_data() - solver.set_model() - solver.exec() - print(">>> Oneshot VC train finished!") - sys.exit(0) - -if __name__ == "__main__": - main() diff --git a/spaces/kira4424/VITS-fast-fine-tuning/mel_processing.py b/spaces/kira4424/VITS-fast-fine-tuning/mel_processing.py deleted file mode 100644 index 3614150259809983e776d3fed83021decca06a9c..0000000000000000000000000000000000000000 --- a/spaces/kira4424/VITS-fast-fine-tuning/mel_processing.py +++ /dev/null @@ -1,112 +0,0 @@ -import math -import os -import random -import torch -from torch import nn -import torch.nn.functional as F -import torch.utils.data -import numpy as np -import librosa -import librosa.util as librosa_util -from librosa.util import normalize, pad_center, tiny -from scipy.signal import get_window -from scipy.io.wavfile import read -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y.float(), 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) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/kiroiineko/rvc-models-tragamundos/app.py b/spaces/kiroiineko/rvc-models-tragamundos/app.py deleted file mode 100644 index 407af7e58dcf042b9db85094eff6937f6a69b9d4..0000000000000000000000000000000000000000 --- a/spaces/kiroiineko/rvc-models-tragamundos/app.py +++ /dev/null @@ -1,180 +0,0 @@ -import os -import json -import argparse -import traceback -import logging -import gradio as gr -import numpy as np -import librosa -import torch -import asyncio -import edge_tts -from datetime import datetime -from fairseq import checkpoint_utils -from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono -from vc_infer_pipeline import VC -from config import ( - is_half, - device -) -logging.getLogger("numba").setLevel(logging.WARNING) -limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces - -def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy): - def vc_fn( - input_audio, - f0_up_key, - f0_method, - index_rate, - tts_mode, - tts_text, - tts_voice - ): - try: - if tts_mode: - if len(tts_text) > 100 and limitation: - return "Text is too long", None - if tts_text is None or tts_voice is None: - return "You need to enter text and select a voice", None - asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3")) - audio, sr = librosa.load("tts.mp3", sr=16000, mono=True) - else: - if input_audio is None: - return "You need to upload an audio", None - sampling_rate, audio = input_audio - duration = audio.shape[0] / sampling_rate - if duration > 20 and limitation: - return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None - audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio.transpose(1, 0)) - if sampling_rate != 16000: - audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) - times = [0, 0, 0] - f0_up_key = int(f0_up_key) - audio_opt = vc.pipeline( - hubert_model, - net_g, - 0, - audio, - times, - f0_up_key, - f0_method, - file_index, - file_big_npy, - index_rate, - if_f0, - ) - print( - f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s" - ) - return "Success", (tgt_sr, audio_opt) - except: - info = traceback.format_exc() - print(info) - return info, (None, None) - return vc_fn - -def load_hubert(): - global hubert_model - models, _, _ = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", - ) - hubert_model = models[0] - hubert_model = hubert_model.to(device) - if is_half: - hubert_model = hubert_model.half() - else: - hubert_model = hubert_model.float() - hubert_model.eval() - -def change_to_tts_mode(tts_mode): - if tts_mode: - return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True) - else: - return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--api', action="store_true", default=False) - parser.add_argument("--colab", action="store_true", default=False, help="share gradio app") - args, unknown = parser.parse_known_args() - load_hubert() - models = [] - tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) - voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] - with open("weights/model_info.json", "r", encoding="utf-8") as f: - models_info = json.load(f) - for name, info in models_info.items(): - if not info['enable']: - continue - title = info['title'] - author = info.get("author", None) - cover = f"weights/{name}/{info['cover']}" - index = f"weights/{name}/{info['feature_retrieval_library']}" - npy = f"weights/{name}/{info['feature_file']}" - cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu") - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩 - net_g.eval().to(device) - if is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, device, is_half) - models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy))) - with gr.Blocks() as app: - gr.Markdown( - "#
        RVC Models (Outdated)\n" - "##
        The input audio should be clean and pure voice without background music.\n" - "###
        Updated Repository: [NEW RVC Models](https://huggingface.co/spaces/ArkanDash/rvc-models-new).\n" - "####
        [Recommended to use google colab for more features](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?usp=sharing)\n" - "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?usp=sharing)\n\n" - "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)" - ) - with gr.Tabs(): - for (name, title, author, cover, vc_fn) in models: - with gr.TabItem(name): - with gr.Row(): - gr.Markdown( - '
        ' - f'
        {title}
        \n'+ - (f'
        Model author: {author}
        ' if author else "")+ - (f'' if cover else "")+ - '
        ' - ) - with gr.Row(): - with gr.Column(): - vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '') - vc_transpose = gr.Number(label="Transpose", value=0) - vc_f0method = gr.Radio( - label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies", - choices=["pm", "harvest"], - value="pm", - interactive=True, - ) - vc_index_ratio = gr.Slider( - minimum=0, - maximum=1, - label="Retrieval feature ratio", - value=0.6, - interactive=True, - ) - tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False) - tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text") - tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") - vc_submit = gr.Button("Generate", variant="primary") - with gr.Column(): - vc_output1 = gr.Textbox(label="Output Message") - vc_output2 = gr.Audio(label="Output Audio") - vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2]) - tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice]) - app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.colab) \ No newline at end of file diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/criss/download_and_preprocess_tatoeba.sh b/spaces/koajoel/PolyFormer/fairseq/examples/criss/download_and_preprocess_tatoeba.sh deleted file mode 100644 index 7ed64f017d5e62695ba73745c840507b994abc0f..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/criss/download_and_preprocess_tatoeba.sh +++ /dev/null @@ -1,46 +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. - -SPM_ENCODE=flores/scripts/spm_encode.py -DATA=data_tmp -SPM_MODEL=criss_checkpoints/sentence.bpe.model -DICT=criss_checkpoints/dict.txt - -if [[ -f flores ]]; then - echo "flores already cloned" -else - git clone https://github.com/facebookresearch/flores -fi -if [[ -f LASER ]]; then - echo "LASER already cloned" -else - git clone https://github.com/facebookresearch/LASER -fi -mkdir -p data_tmp -declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn") -for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do - lang_tatoeba=${lang_tatoeba_map[$lang]} - echo $lang_tatoeba - datadir=$DATA/${lang}-en_XX-tatoeba - rm -rf $datadir - mkdir -p $datadir - TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba - python $SPM_ENCODE \ - --model ${SPM_MODEL} \ - --output_format=piece \ - --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \ - --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX - - # binarize data - fairseq-preprocess \ - --source-lang ${lang} --target-lang en_XX \ - --testpref $datadir/test.bpe.${lang}-en_XX \ - --destdir $datadir \ - --srcdict ${DICT} \ - --joined-dictionary \ - --workers 4 -done diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp b/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp deleted file mode 100644 index 71f612cdbaaca03822eedc002a980d055d2f485c..0000000000000000000000000000000000000000 --- a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/fused_bias_act.cpp +++ /dev/null @@ -1,32 +0,0 @@ - -#include -#include - -torch::Tensor fused_bias_act_op(const torch::Tensor &input, - const torch::Tensor &bias, - const torch::Tensor &refer, int act, int grad, - float alpha, float scale); - -#define CHECK_CUDA(x) \ - TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) \ - TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x) - -torch::Tensor fused_bias_act(const torch::Tensor &input, - const torch::Tensor &bias, - const torch::Tensor &refer, int act, int grad, - float alpha, float scale) { - CHECK_INPUT(input); - CHECK_INPUT(bias); - - at::DeviceGuard guard(input.device()); - - return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); -} \ No newline at end of file diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/schema.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/schema.py deleted file mode 100644 index e94c3d1991e96da81efe13cfe06214166afe80d1..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/altair/vegalite/schema.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Altair schema wrappers""" -# ruff: noqa -from .v5.schema import * diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py deleted file mode 100644 index d6a6421e3b0ff0261079094ea2e2df5de212bce7..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py +++ /dev/null @@ -1,135 +0,0 @@ -# coding=utf-8 -# Copyright 2019-present, the HuggingFace Inc. team. -# -# 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 pathlib import Path -from typing import Union - -from ..constants import HUGGINGFACE_ASSETS_CACHE - - -def cached_assets_path( - library_name: str, - namespace: str = "default", - subfolder: str = "default", - *, - assets_dir: Union[str, Path, None] = None, -): - """Return a folder path to cache arbitrary files. - - `huggingface_hub` provides a canonical folder path to store assets. This is the - recommended way to integrate cache in a downstream library as it will benefit from - the builtins tools to scan and delete the cache properly. - - The distinction is made between files cached from the Hub and assets. Files from the - Hub are cached in a git-aware manner and entirely managed by `huggingface_hub`. See - [related documentation](https://huggingface.co/docs/huggingface_hub/how-to-cache). - All other files that a downstream library caches are considered to be "assets" - (files downloaded from external sources, extracted from a .tar archive, preprocessed - for training,...). - - Once the folder path is generated, it is guaranteed to exist and to be a directory. - The path is based on 3 levels of depth: the library name, a namespace and a - subfolder. Those 3 levels grants flexibility while allowing `huggingface_hub` to - expect folders when scanning/deleting parts of the assets cache. Within a library, - it is expected that all namespaces share the same subset of subfolder names but this - is not a mandatory rule. The downstream library has then full control on which file - structure to adopt within its cache. Namespace and subfolder are optional (would - default to a `"default/"` subfolder) but library name is mandatory as we want every - downstream library to manage its own cache. - - Expected tree: - ```text - assets/ - └── datasets/ - │ ├── SQuAD/ - │ │ ├── downloaded/ - │ │ ├── extracted/ - │ │ └── processed/ - │ ├── Helsinki-NLP--tatoeba_mt/ - │ ├── downloaded/ - │ ├── extracted/ - │ └── processed/ - └── transformers/ - ├── default/ - │ ├── something/ - ├── bert-base-cased/ - │ ├── default/ - │ └── training/ - hub/ - └── models--julien-c--EsperBERTo-small/ - ├── blobs/ - │ ├── (...) - │ ├── (...) - ├── refs/ - │ └── (...) - └── [ 128] snapshots/ - ├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/ - │ ├── (...) - └── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/ - └── (...) - ``` - - - Args: - library_name (`str`): - Name of the library that will manage the cache folder. Example: `"dataset"`. - namespace (`str`, *optional*, defaults to "default"): - Namespace to which the data belongs. Example: `"SQuAD"`. - subfolder (`str`, *optional*, defaults to "default"): - Subfolder in which the data will be stored. Example: `extracted`. - assets_dir (`str`, `Path`, *optional*): - Path to the folder where assets are cached. This must not be the same folder - where Hub files are cached. Defaults to `HF_HOME / "assets"` if not provided. - Can also be set with `HUGGINGFACE_ASSETS_CACHE` environment variable. - - Returns: - Path to the cache folder (`Path`). - - Example: - ```py - >>> from huggingface_hub import cached_assets_path - - >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download") - PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download') - - >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted") - PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted') - - >>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt") - PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default') - - >>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456") - PosixPath('/tmp/tmp123456/datasets/default/default') - ``` - """ - # Resolve assets_dir - if assets_dir is None: - assets_dir = HUGGINGFACE_ASSETS_CACHE - assets_dir = Path(assets_dir).expanduser().resolve() - - # Avoid names that could create path issues - for part in (" ", "/", "\\"): - library_name = library_name.replace(part, "--") - namespace = namespace.replace(part, "--") - subfolder = subfolder.replace(part, "--") - - # Path to subfolder is created - path = assets_dir / library_name / namespace / subfolder - try: - path.mkdir(exist_ok=True, parents=True) - except (FileExistsError, NotADirectoryError): - raise ValueError(f"Corrupted assets folder: cannot create directory because of an existing file ({path}).") - - # Return - return path diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py deleted file mode 100644 index 547a2ae9271fcada607589f2d8593ff2e4ace769..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py +++ /dev/null @@ -1,521 +0,0 @@ -""" -A Cairo backend for Matplotlib -============================== -:Author: Steve Chaplin and others - -This backend depends on cairocffi or pycairo. -""" - -import functools -import gzip -import math - -import numpy as np - -try: - import cairo - if cairo.version_info < (1, 14, 0): # Introduced set_device_scale. - raise ImportError -except ImportError: - try: - import cairocffi as cairo - except ImportError as err: - raise ImportError( - "cairo backend requires that pycairo>=1.14.0 or cairocffi " - "is installed") from err - -import matplotlib as mpl -from .. import _api, cbook, font_manager -from matplotlib.backend_bases import ( - _Backend, FigureCanvasBase, FigureManagerBase, GraphicsContextBase, - RendererBase) -from matplotlib.font_manager import ttfFontProperty -from matplotlib.path import Path -from matplotlib.transforms import Affine2D - - -def _append_path(ctx, path, transform, clip=None): - for points, code in path.iter_segments( - transform, remove_nans=True, clip=clip): - if code == Path.MOVETO: - ctx.move_to(*points) - elif code == Path.CLOSEPOLY: - ctx.close_path() - elif code == Path.LINETO: - ctx.line_to(*points) - elif code == Path.CURVE3: - cur = np.asarray(ctx.get_current_point()) - a = points[:2] - b = points[-2:] - ctx.curve_to(*(cur / 3 + a * 2 / 3), *(a * 2 / 3 + b / 3), *b) - elif code == Path.CURVE4: - ctx.curve_to(*points) - - -def _cairo_font_args_from_font_prop(prop): - """ - Convert a `.FontProperties` or a `.FontEntry` to arguments that can be - passed to `.Context.select_font_face`. - """ - def attr(field): - try: - return getattr(prop, f"get_{field}")() - except AttributeError: - return getattr(prop, field) - - name = attr("name") - slant = getattr(cairo, f"FONT_SLANT_{attr('style').upper()}") - weight = attr("weight") - weight = (cairo.FONT_WEIGHT_NORMAL - if font_manager.weight_dict.get(weight, weight) < 550 - else cairo.FONT_WEIGHT_BOLD) - return name, slant, weight - - -class RendererCairo(RendererBase): - def __init__(self, dpi): - self.dpi = dpi - self.gc = GraphicsContextCairo(renderer=self) - self.width = None - self.height = None - self.text_ctx = cairo.Context( - cairo.ImageSurface(cairo.FORMAT_ARGB32, 1, 1)) - super().__init__() - - def set_context(self, ctx): - surface = ctx.get_target() - if hasattr(surface, "get_width") and hasattr(surface, "get_height"): - size = surface.get_width(), surface.get_height() - elif hasattr(surface, "get_extents"): # GTK4 RecordingSurface. - ext = surface.get_extents() - size = ext.width, ext.height - else: # vector surfaces. - ctx.save() - ctx.reset_clip() - rect, *rest = ctx.copy_clip_rectangle_list() - if rest: - raise TypeError("Cannot infer surface size") - size = rect.width, rect.height - ctx.restore() - self.gc.ctx = ctx - self.width, self.height = size - - @_api.deprecated("3.6", alternative="set_context") - def set_ctx_from_surface(self, surface): - self.gc.ctx = cairo.Context(surface) - - @_api.deprecated("3.6") - def set_width_height(self, width, height): - self.width = width - self.height = height - - def _fill_and_stroke(self, ctx, fill_c, alpha, alpha_overrides): - if fill_c is not None: - ctx.save() - if len(fill_c) == 3 or alpha_overrides: - ctx.set_source_rgba(fill_c[0], fill_c[1], fill_c[2], alpha) - else: - ctx.set_source_rgba(fill_c[0], fill_c[1], fill_c[2], fill_c[3]) - ctx.fill_preserve() - ctx.restore() - ctx.stroke() - - def draw_path(self, gc, path, transform, rgbFace=None): - # docstring inherited - ctx = gc.ctx - # Clip the path to the actual rendering extents if it isn't filled. - clip = (ctx.clip_extents() - if rgbFace is None and gc.get_hatch() is None - else None) - transform = (transform - + Affine2D().scale(1, -1).translate(0, self.height)) - ctx.new_path() - _append_path(ctx, path, transform, clip) - self._fill_and_stroke( - ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha()) - - def draw_markers(self, gc, marker_path, marker_trans, path, transform, - rgbFace=None): - # docstring inherited - - ctx = gc.ctx - ctx.new_path() - # Create the path for the marker; it needs to be flipped here already! - _append_path(ctx, marker_path, marker_trans + Affine2D().scale(1, -1)) - marker_path = ctx.copy_path_flat() - - # Figure out whether the path has a fill - x1, y1, x2, y2 = ctx.fill_extents() - if x1 == 0 and y1 == 0 and x2 == 0 and y2 == 0: - filled = False - # No fill, just unset this (so we don't try to fill it later on) - rgbFace = None - else: - filled = True - - transform = (transform - + Affine2D().scale(1, -1).translate(0, self.height)) - - ctx.new_path() - for i, (vertices, codes) in enumerate( - path.iter_segments(transform, simplify=False)): - if len(vertices): - x, y = vertices[-2:] - ctx.save() - - # Translate and apply path - ctx.translate(x, y) - ctx.append_path(marker_path) - - ctx.restore() - - # Slower code path if there is a fill; we need to draw - # the fill and stroke for each marker at the same time. - # Also flush out the drawing every once in a while to - # prevent the paths from getting way too long. - if filled or i % 1000 == 0: - self._fill_and_stroke( - ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha()) - - # Fast path, if there is no fill, draw everything in one step - if not filled: - self._fill_and_stroke( - ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha()) - - def draw_image(self, gc, x, y, im): - im = cbook._unmultiplied_rgba8888_to_premultiplied_argb32(im[::-1]) - surface = cairo.ImageSurface.create_for_data( - im.ravel().data, cairo.FORMAT_ARGB32, - im.shape[1], im.shape[0], im.shape[1] * 4) - ctx = gc.ctx - y = self.height - y - im.shape[0] - - ctx.save() - ctx.set_source_surface(surface, float(x), float(y)) - ctx.paint() - ctx.restore() - - def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None): - # docstring inherited - - # Note: (x, y) are device/display coords, not user-coords, unlike other - # draw_* methods - if ismath: - self._draw_mathtext(gc, x, y, s, prop, angle) - - else: - ctx = gc.ctx - ctx.new_path() - ctx.move_to(x, y) - - ctx.save() - ctx.select_font_face(*_cairo_font_args_from_font_prop(prop)) - ctx.set_font_size(self.points_to_pixels(prop.get_size_in_points())) - opts = cairo.FontOptions() - opts.set_antialias( - cairo.ANTIALIAS_DEFAULT if mpl.rcParams["text.antialiased"] - else cairo.ANTIALIAS_NONE) - ctx.set_font_options(opts) - if angle: - ctx.rotate(np.deg2rad(-angle)) - ctx.show_text(s) - ctx.restore() - - def _draw_mathtext(self, gc, x, y, s, prop, angle): - ctx = gc.ctx - width, height, descent, glyphs, rects = \ - self._text2path.mathtext_parser.parse(s, self.dpi, prop) - - ctx.save() - ctx.translate(x, y) - if angle: - ctx.rotate(np.deg2rad(-angle)) - - for font, fontsize, idx, ox, oy in glyphs: - ctx.new_path() - ctx.move_to(ox, -oy) - ctx.select_font_face( - *_cairo_font_args_from_font_prop(ttfFontProperty(font))) - ctx.set_font_size(self.points_to_pixels(fontsize)) - ctx.show_text(chr(idx)) - - for ox, oy, w, h in rects: - ctx.new_path() - ctx.rectangle(ox, -oy, w, -h) - ctx.set_source_rgb(0, 0, 0) - ctx.fill_preserve() - - ctx.restore() - - def get_canvas_width_height(self): - # docstring inherited - return self.width, self.height - - def get_text_width_height_descent(self, s, prop, ismath): - # docstring inherited - - if ismath == 'TeX': - return super().get_text_width_height_descent(s, prop, ismath) - - if ismath: - width, height, descent, *_ = \ - self._text2path.mathtext_parser.parse(s, self.dpi, prop) - return width, height, descent - - ctx = self.text_ctx - # problem - scale remembers last setting and font can become - # enormous causing program to crash - # save/restore prevents the problem - ctx.save() - ctx.select_font_face(*_cairo_font_args_from_font_prop(prop)) - ctx.set_font_size(self.points_to_pixels(prop.get_size_in_points())) - - y_bearing, w, h = ctx.text_extents(s)[1:4] - ctx.restore() - - return w, h, h + y_bearing - - def new_gc(self): - # docstring inherited - self.gc.ctx.save() - self.gc._alpha = 1 - self.gc._forced_alpha = False # if True, _alpha overrides A from RGBA - return self.gc - - def points_to_pixels(self, points): - # docstring inherited - return points / 72 * self.dpi - - -class GraphicsContextCairo(GraphicsContextBase): - _joind = { - 'bevel': cairo.LINE_JOIN_BEVEL, - 'miter': cairo.LINE_JOIN_MITER, - 'round': cairo.LINE_JOIN_ROUND, - } - - _capd = { - 'butt': cairo.LINE_CAP_BUTT, - 'projecting': cairo.LINE_CAP_SQUARE, - 'round': cairo.LINE_CAP_ROUND, - } - - def __init__(self, renderer): - super().__init__() - self.renderer = renderer - - def restore(self): - self.ctx.restore() - - def set_alpha(self, alpha): - super().set_alpha(alpha) - _alpha = self.get_alpha() - rgb = self._rgb - if self.get_forced_alpha(): - self.ctx.set_source_rgba(rgb[0], rgb[1], rgb[2], _alpha) - else: - self.ctx.set_source_rgba(rgb[0], rgb[1], rgb[2], rgb[3]) - - def set_antialiased(self, b): - self.ctx.set_antialias( - cairo.ANTIALIAS_DEFAULT if b else cairo.ANTIALIAS_NONE) - - def set_capstyle(self, cs): - self.ctx.set_line_cap(_api.check_getitem(self._capd, capstyle=cs)) - self._capstyle = cs - - def set_clip_rectangle(self, rectangle): - if not rectangle: - return - x, y, w, h = np.round(rectangle.bounds) - ctx = self.ctx - ctx.new_path() - ctx.rectangle(x, self.renderer.height - h - y, w, h) - ctx.clip() - - def set_clip_path(self, path): - if not path: - return - tpath, affine = path.get_transformed_path_and_affine() - ctx = self.ctx - ctx.new_path() - affine = (affine - + Affine2D().scale(1, -1).translate(0, self.renderer.height)) - _append_path(ctx, tpath, affine) - ctx.clip() - - def set_dashes(self, offset, dashes): - self._dashes = offset, dashes - if dashes is None: - self.ctx.set_dash([], 0) # switch dashes off - else: - self.ctx.set_dash( - list(self.renderer.points_to_pixels(np.asarray(dashes))), - offset) - - def set_foreground(self, fg, isRGBA=None): - super().set_foreground(fg, isRGBA) - if len(self._rgb) == 3: - self.ctx.set_source_rgb(*self._rgb) - else: - self.ctx.set_source_rgba(*self._rgb) - - def get_rgb(self): - return self.ctx.get_source().get_rgba()[:3] - - def set_joinstyle(self, js): - self.ctx.set_line_join(_api.check_getitem(self._joind, joinstyle=js)) - self._joinstyle = js - - def set_linewidth(self, w): - self._linewidth = float(w) - self.ctx.set_line_width(self.renderer.points_to_pixels(w)) - - -class _CairoRegion: - def __init__(self, slices, data): - self._slices = slices - self._data = data - - -class FigureCanvasCairo(FigureCanvasBase): - @property - def _renderer(self): - # In theory, _renderer should be set in __init__, but GUI canvas - # subclasses (FigureCanvasFooCairo) don't always interact well with - # multiple inheritance (FigureCanvasFoo inits but doesn't super-init - # FigureCanvasCairo), so initialize it in the getter instead. - if not hasattr(self, "_cached_renderer"): - self._cached_renderer = RendererCairo(self.figure.dpi) - return self._cached_renderer - - def get_renderer(self): - return self._renderer - - def copy_from_bbox(self, bbox): - surface = self._renderer.gc.ctx.get_target() - if not isinstance(surface, cairo.ImageSurface): - raise RuntimeError( - "copy_from_bbox only works when rendering to an ImageSurface") - sw = surface.get_width() - sh = surface.get_height() - x0 = math.ceil(bbox.x0) - x1 = math.floor(bbox.x1) - y0 = math.ceil(sh - bbox.y1) - y1 = math.floor(sh - bbox.y0) - if not (0 <= x0 and x1 <= sw and bbox.x0 <= bbox.x1 - and 0 <= y0 and y1 <= sh and bbox.y0 <= bbox.y1): - raise ValueError("Invalid bbox") - sls = slice(y0, y0 + max(y1 - y0, 0)), slice(x0, x0 + max(x1 - x0, 0)) - data = (np.frombuffer(surface.get_data(), np.uint32) - .reshape((sh, sw))[sls].copy()) - return _CairoRegion(sls, data) - - def restore_region(self, region): - surface = self._renderer.gc.ctx.get_target() - if not isinstance(surface, cairo.ImageSurface): - raise RuntimeError( - "restore_region only works when rendering to an ImageSurface") - surface.flush() - sw = surface.get_width() - sh = surface.get_height() - sly, slx = region._slices - (np.frombuffer(surface.get_data(), np.uint32) - .reshape((sh, sw))[sly, slx]) = region._data - surface.mark_dirty_rectangle( - slx.start, sly.start, slx.stop - slx.start, sly.stop - sly.start) - - def print_png(self, fobj): - self._get_printed_image_surface().write_to_png(fobj) - - def print_rgba(self, fobj): - width, height = self.get_width_height() - buf = self._get_printed_image_surface().get_data() - fobj.write(cbook._premultiplied_argb32_to_unmultiplied_rgba8888( - np.asarray(buf).reshape((width, height, 4)))) - - print_raw = print_rgba - - def _get_printed_image_surface(self): - self._renderer.dpi = self.figure.dpi - width, height = self.get_width_height() - surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height) - self._renderer.set_context(cairo.Context(surface)) - self.figure.draw(self._renderer) - return surface - - def _save(self, fmt, fobj, *, orientation='portrait'): - # save PDF/PS/SVG - - dpi = 72 - self.figure.dpi = dpi - w_in, h_in = self.figure.get_size_inches() - width_in_points, height_in_points = w_in * dpi, h_in * dpi - - if orientation == 'landscape': - width_in_points, height_in_points = ( - height_in_points, width_in_points) - - if fmt == 'ps': - if not hasattr(cairo, 'PSSurface'): - raise RuntimeError('cairo has not been compiled with PS ' - 'support enabled') - surface = cairo.PSSurface(fobj, width_in_points, height_in_points) - elif fmt == 'pdf': - if not hasattr(cairo, 'PDFSurface'): - raise RuntimeError('cairo has not been compiled with PDF ' - 'support enabled') - surface = cairo.PDFSurface(fobj, width_in_points, height_in_points) - elif fmt in ('svg', 'svgz'): - if not hasattr(cairo, 'SVGSurface'): - raise RuntimeError('cairo has not been compiled with SVG ' - 'support enabled') - if fmt == 'svgz': - if isinstance(fobj, str): - fobj = gzip.GzipFile(fobj, 'wb') - else: - fobj = gzip.GzipFile(None, 'wb', fileobj=fobj) - surface = cairo.SVGSurface(fobj, width_in_points, height_in_points) - else: - raise ValueError("Unknown format: {!r}".format(fmt)) - - self._renderer.dpi = self.figure.dpi - self._renderer.set_context(cairo.Context(surface)) - ctx = self._renderer.gc.ctx - - if orientation == 'landscape': - ctx.rotate(np.pi / 2) - ctx.translate(0, -height_in_points) - # Perhaps add an '%%Orientation: Landscape' comment? - - self.figure.draw(self._renderer) - - ctx.show_page() - surface.finish() - if fmt == 'svgz': - fobj.close() - - print_pdf = functools.partialmethod(_save, "pdf") - print_ps = functools.partialmethod(_save, "ps") - print_svg = functools.partialmethod(_save, "svg") - print_svgz = functools.partialmethod(_save, "svgz") - - -@_api.deprecated("3.6") -class _RendererGTKCairo(RendererCairo): - def set_context(self, ctx): - if (cairo.__name__ == "cairocffi" - and not isinstance(ctx, cairo.Context)): - ctx = cairo.Context._from_pointer( - cairo.ffi.cast( - 'cairo_t **', - id(ctx) + object.__basicsize__)[0], - incref=True) - self.gc.ctx = ctx - - -@_Backend.export -class _BackendCairo(_Backend): - backend_version = cairo.version - FigureCanvas = FigureCanvasCairo - FigureManager = FigureManagerBase diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/qt_compat.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/qt_compat.py deleted file mode 100644 index 663671894a74cfa80636707d68594a39ef9ccc0e..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/qt_compat.py +++ /dev/null @@ -1,245 +0,0 @@ -""" -Qt binding and backend selector. - -The selection logic is as follows: -- if any of PyQt6, PySide6, PyQt5, or PySide2 have already been - imported (checked in that order), use it; -- otherwise, if the QT_API environment variable (used by Enthought) is set, use - it to determine which binding to use; -- otherwise, use whatever the rcParams indicate. -""" - -import functools -import operator -import os -import platform -import sys -import signal -import socket -import contextlib - -from packaging.version import parse as parse_version - -import matplotlib as mpl - -from . import _QT_FORCE_QT5_BINDING - -QT_API_PYQT6 = "PyQt6" -QT_API_PYSIDE6 = "PySide6" -QT_API_PYQT5 = "PyQt5" -QT_API_PYSIDE2 = "PySide2" -QT_API_ENV = os.environ.get("QT_API") -if QT_API_ENV is not None: - QT_API_ENV = QT_API_ENV.lower() -_ETS = { # Mapping of QT_API_ENV to requested binding. - "pyqt6": QT_API_PYQT6, "pyside6": QT_API_PYSIDE6, - "pyqt5": QT_API_PYQT5, "pyside2": QT_API_PYSIDE2, -} -# First, check if anything is already imported. -if sys.modules.get("PyQt6.QtCore"): - QT_API = QT_API_PYQT6 -elif sys.modules.get("PySide6.QtCore"): - QT_API = QT_API_PYSIDE6 -elif sys.modules.get("PyQt5.QtCore"): - QT_API = QT_API_PYQT5 -elif sys.modules.get("PySide2.QtCore"): - QT_API = QT_API_PYSIDE2 -# Otherwise, check the QT_API environment variable (from Enthought). This can -# only override the binding, not the backend (in other words, we check that the -# requested backend actually matches). Use _get_backend_or_none to avoid -# triggering backend resolution (which can result in a partially but -# incompletely imported backend_qt5). -elif (mpl.rcParams._get_backend_or_none() or "").lower().startswith("qt5"): - if QT_API_ENV in ["pyqt5", "pyside2"]: - QT_API = _ETS[QT_API_ENV] - else: - _QT_FORCE_QT5_BINDING = True # noqa - QT_API = None -# A non-Qt backend was selected but we still got there (possible, e.g., when -# fully manually embedding Matplotlib in a Qt app without using pyplot). -elif QT_API_ENV is None: - QT_API = None -elif QT_API_ENV in _ETS: - QT_API = _ETS[QT_API_ENV] -else: - raise RuntimeError( - "The environment variable QT_API has the unrecognized value {!r}; " - "valid values are {}".format(QT_API_ENV, ", ".join(_ETS))) - - -def _setup_pyqt5plus(): - global QtCore, QtGui, QtWidgets, __version__ - global _getSaveFileName, _isdeleted, _to_int - - if QT_API == QT_API_PYQT6: - from PyQt6 import QtCore, QtGui, QtWidgets, sip - __version__ = QtCore.PYQT_VERSION_STR - QtCore.Signal = QtCore.pyqtSignal - QtCore.Slot = QtCore.pyqtSlot - QtCore.Property = QtCore.pyqtProperty - _isdeleted = sip.isdeleted - _to_int = operator.attrgetter('value') - elif QT_API == QT_API_PYSIDE6: - from PySide6 import QtCore, QtGui, QtWidgets, __version__ - import shiboken6 - def _isdeleted(obj): return not shiboken6.isValid(obj) - if parse_version(__version__) >= parse_version('6.4'): - _to_int = operator.attrgetter('value') - else: - _to_int = int - elif QT_API == QT_API_PYQT5: - from PyQt5 import QtCore, QtGui, QtWidgets - import sip - __version__ = QtCore.PYQT_VERSION_STR - QtCore.Signal = QtCore.pyqtSignal - QtCore.Slot = QtCore.pyqtSlot - QtCore.Property = QtCore.pyqtProperty - _isdeleted = sip.isdeleted - _to_int = int - elif QT_API == QT_API_PYSIDE2: - from PySide2 import QtCore, QtGui, QtWidgets, __version__ - try: - from PySide2 import shiboken2 - except ImportError: - import shiboken2 - def _isdeleted(obj): - return not shiboken2.isValid(obj) - _to_int = int - else: - raise AssertionError(f"Unexpected QT_API: {QT_API}") - _getSaveFileName = QtWidgets.QFileDialog.getSaveFileName - - -if QT_API in [QT_API_PYQT6, QT_API_PYQT5, QT_API_PYSIDE6, QT_API_PYSIDE2]: - _setup_pyqt5plus() -elif QT_API is None: # See above re: dict.__getitem__. - if _QT_FORCE_QT5_BINDING: - _candidates = [ - (_setup_pyqt5plus, QT_API_PYQT5), - (_setup_pyqt5plus, QT_API_PYSIDE2), - ] - else: - _candidates = [ - (_setup_pyqt5plus, QT_API_PYQT6), - (_setup_pyqt5plus, QT_API_PYSIDE6), - (_setup_pyqt5plus, QT_API_PYQT5), - (_setup_pyqt5plus, QT_API_PYSIDE2), - ] - for _setup, QT_API in _candidates: - try: - _setup() - except ImportError: - continue - break - else: - raise ImportError( - "Failed to import any of the following Qt binding modules: {}" - .format(", ".join(_ETS.values()))) -else: # We should not get there. - raise AssertionError(f"Unexpected QT_API: {QT_API}") -_version_info = tuple(QtCore.QLibraryInfo.version().segments()) - - -if _version_info < (5, 10): - raise ImportError( - f"The Qt version imported is " - f"{QtCore.QLibraryInfo.version().toString()} but Matplotlib requires " - f"Qt>=5.10") - - -# Fixes issues with Big Sur -# https://bugreports.qt.io/browse/QTBUG-87014, fixed in qt 5.15.2 -if (sys.platform == 'darwin' and - parse_version(platform.mac_ver()[0]) >= parse_version("10.16") and - _version_info < (5, 15, 2)): - os.environ.setdefault("QT_MAC_WANTS_LAYER", "1") - - -# PyQt6 enum compat helpers. - - -@functools.lru_cache(None) -def _enum(name): - # foo.bar.Enum.Entry (PyQt6) <=> foo.bar.Entry (non-PyQt6). - return operator.attrgetter( - name if QT_API == 'PyQt6' else name.rpartition(".")[0] - )(sys.modules[QtCore.__package__]) - - -# Backports. - - -def _exec(obj): - # exec on PyQt6, exec_ elsewhere. - obj.exec() if hasattr(obj, "exec") else obj.exec_() - - -@contextlib.contextmanager -def _maybe_allow_interrupt(qapp): - """ - This manager allows to terminate a plot by sending a SIGINT. It is - necessary because the running Qt backend prevents Python interpreter to - run and process signals (i.e., to raise KeyboardInterrupt exception). To - solve this one needs to somehow wake up the interpreter and make it close - the plot window. We do this by using the signal.set_wakeup_fd() function - which organizes a write of the signal number into a socketpair connected - to the QSocketNotifier (since it is part of the Qt backend, it can react - to that write event). Afterwards, the Qt handler empties the socketpair - by a recv() command to re-arm it (we need this if a signal different from - SIGINT was caught by set_wakeup_fd() and we shall continue waiting). If - the SIGINT was caught indeed, after exiting the on_signal() function the - interpreter reacts to the SIGINT according to the handle() function which - had been set up by a signal.signal() call: it causes the qt_object to - exit by calling its quit() method. Finally, we call the old SIGINT - handler with the same arguments that were given to our custom handle() - handler. - - We do this only if the old handler for SIGINT was not None, which means - that a non-python handler was installed, i.e. in Julia, and not SIG_IGN - which means we should ignore the interrupts. - """ - old_sigint_handler = signal.getsignal(signal.SIGINT) - handler_args = None - skip = False - if old_sigint_handler in (None, signal.SIG_IGN, signal.SIG_DFL): - skip = True - else: - wsock, rsock = socket.socketpair() - wsock.setblocking(False) - old_wakeup_fd = signal.set_wakeup_fd(wsock.fileno()) - sn = QtCore.QSocketNotifier( - rsock.fileno(), _enum('QtCore.QSocketNotifier.Type').Read - ) - - # We do not actually care about this value other than running some - # Python code to ensure that the interpreter has a chance to handle the - # signal in Python land. We also need to drain the socket because it - # will be written to as part of the wakeup! There are some cases where - # this may fire too soon / more than once on Windows so we should be - # forgiving about reading an empty socket. - rsock.setblocking(False) - # Clear the socket to re-arm the notifier. - @sn.activated.connect - def _may_clear_sock(*args): - try: - rsock.recv(1) - except BlockingIOError: - pass - - def handle(*args): - nonlocal handler_args - handler_args = args - qapp.quit() - - signal.signal(signal.SIGINT, handle) - try: - yield - finally: - if not skip: - wsock.close() - rsock.close() - sn.setEnabled(False) - signal.set_wakeup_fd(old_wakeup_fd) - signal.signal(signal.SIGINT, old_sigint_handler) - if handler_args is not None: - old_sigint_handler(*handler_args) diff --git a/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5/mask_rcnn_r50_fpn_1x_coco.py b/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5/mask_rcnn_r50_fpn_1x_coco.py deleted file mode 100644 index 65b8c069e3ddb6ac3908c1c93cb261b85366cba1..0000000000000000000000000000000000000000 --- a/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5/mask_rcnn_r50_fpn_1x_coco.py +++ /dev/null @@ -1,259 +0,0 @@ -model = dict( - type='MaskRCNN', - backbone=dict( - type='ResNet', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='SyncBN', requires_grad=True), - norm_eval=True, - style='pytorch', - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), - neck=dict( - type='FPN', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - num_outs=5, - norm_cfg=dict(type='SyncBN', requires_grad=True)), - 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, 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='Shared4Conv1FCBBoxHead', - 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, 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)), - mask_roi_extractor=dict( - type='SingleRoIExtractor', - roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), - out_channels=256, - featmap_strides=[4, 8, 16, 32]), - mask_head=dict( - type='FCNMaskHead', - num_convs=4, - in_channels=256, - conv_out_channels=256, - num_classes=80, - loss_mask=dict( - type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), - 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=True, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=512, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - mask_size=28, - 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, - mask_thr_binary=0.5))) -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, with_mask=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) -] -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', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) -] -data = dict( - samples_per_gpu=2, - workers_per_gpu=2, - train=dict( - type='CocoDataset', - ann_file='data/coco/annotations/instances_train2017.json', - img_prefix='data/coco/train2017/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict( - type='Normalize', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict( - type='Collect', - keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) - ]), - val=dict( - type='CocoDataset', - ann_file='data/coco/annotations/instances_val2017.json', - img_prefix='data/coco/val2017/', - 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', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) - ]), - test=dict( - type='CocoDataset', - ann_file='data/coco/annotations/instances_val2017.json', - img_prefix='data/coco/val2017/', - 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', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - to_rgb=True), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) - ])) -evaluation = dict(metric=['bbox', 'segm'], save_best='auto') -optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05) -optimizer_config = dict(grad_clip=None) -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=500, - warmup_ratio=0.001, - step=[8, 11]) -runner = dict(type='EpochBasedRunner', max_epochs=12) -checkpoint_config = dict(interval=1) -log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) -custom_hooks = [ - dict(type='NumClassCheckHook'), - dict( - type='MMDetWandbHook', - init_kwargs=dict(project='I2B', group='finetune'), - interval=50, - num_eval_images=0, - log_checkpoint=False) -] -dist_params = dict(backend='nccl') -log_level = 'INFO' -load_from = 'work_dirs/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth' -resume_from = None -workflow = [('train', 1)] -opencv_num_threads = 0 -mp_start_method = 'fork' -auto_scale_lr = dict(enable=False, base_batch_size=16) -custom_imports = None -norm_cfg = dict(type='SyncBN', requires_grad=True) -work_dir = 'work_dirs/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5' -auto_resume = False -gpu_ids = range(0, 8) diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ghost Patrick Swayze Film En Entier Francais.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ghost Patrick Swayze Film En Entier Francais.md deleted file mode 100644 index 8061c6d4054dde0662768cd0e4d4e25edf496bd7..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Ghost Patrick Swayze Film En Entier Francais.md +++ /dev/null @@ -1,11 +0,0 @@ - -

        Ghost : le film culte avec Patrick Swayze et Demi Moore

        -

        Ghost est un film américain réalisé par Jerry Zucker en 1990, avec Patrick Swayze, Demi Moore et Whoopi Goldberg dans les rôles principaux. C'est une comédie romantique fantastique qui mêle amour, suspense et humour.

        -

        Le film raconte l'histoire de Sam Wheat (Patrick Swayze), un banquier qui est assassiné par un de ses collègues pour une affaire de détournement de fonds. Son esprit reste sur Terre pour protéger sa compagne Molly Jensen (Demi Moore), une artiste, du danger qui la menace. Il se fait aider par Oda Mae Brown (Whoopi Goldberg), une voyante escroc qui se révèle être la seule à pouvoir le voir et l'entendre.

        -

        Ghost Patrick Swayze Film En Entier Francais


        Download File ✪ https://bytlly.com/2uGy3q



        -

        Ghost a connu un immense succès au box-office mondial, récoltant plus de 500 millions de dollars. Il a également remporté deux Oscars, celui du meilleur scénario original et celui de la meilleure actrice dans un second rôle pour Whoopi Goldberg. Il est considéré comme l'un des films les plus romantiques de tous les temps, notamment grâce à la scène mythique de la poterie sur la chanson Unchained Melody.

        Ghost a reçu un accueil critique très positif, saluant notamment le scénario original de Bruce Joel Rubin, la musique de Maurice Jarre et les performances des acteurs. Le film a été nommé pour cinq Oscars, dont celui du meilleur film, et a remporté ceux du meilleur scénario original et de la meilleure actrice dans un second rôle pour Whoopi Goldberg, qui apporte une touche d'humour au film. Ghost a également été récompensé par deux Golden Globes et deux BAFTA Awards.

        -

        Ghost est devenu le film le plus rentable de l'année 1990, avec plus de 500 millions de dollars de recettes mondiales. Il est également l'un des films les plus populaires auprès du public, qui a été touché par son histoire d'amour émouvante et son message sur la vie après la mort. Le film a marqué toute une génération et a inspiré plusieurs adaptations au théâtre et à la télévision. Il a également acquis une nouvelle dimension tragique après le décès de Patrick Swayze en 2009, des suites d'un cancer du pancréas.

        Ghost a connu plusieurs adaptations au théâtre et à la télévision. En 2010, une comédie musicale basée sur le film a été créée à Londres, puis jouée à Broadway et dans le monde entier. La musique et les paroles sont de Dave Stewart et Glen Ballard, et le livret est de Bruce Joel Rubin lui-même. La comédie musicale reprend les scènes et les chansons les plus célèbres du film, comme Unchained Melody ou I'm Henry VIII, I Am.

        -

        -

        En 2012, une série télévisée japonaise intitulée Gôsuto a été diffusée sur la chaîne Fuji TV. Elle transpose l'histoire du film dans le contexte de la culture japonaise, avec des acteurs locaux. La série comporte neuf épisodes et suit les aventures de Nanami Hoshino (Nanako Matsushima), une femme d'affaires qui est tuée par son collègue et amant Karasawa (Taizo Harada), et qui revient hanter son mari Kimichika (Song Seung-heon), un chef cuisinier, avec l'aide de la médium Unten (Kirin Kiki).

        d5da3c52bf
        -
        -
        \ No newline at end of file diff --git a/spaces/lingbionlp/PhenoTagger-Demo/README.md b/spaces/lingbionlp/PhenoTagger-Demo/README.md deleted file mode 100644 index 55dca8198abd59f0fce567e6f033c7482d52ec4d..0000000000000000000000000000000000000000 --- a/spaces/lingbionlp/PhenoTagger-Demo/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: PhenoTaggger Demo -emoji: ⚡ -colorFrom: indigo -colorTo: yellow -sdk: streamlit -sdk_version: 1.10.0 -python_version: 3.7 -app_file: app.py -pinned: false -license: cc-by-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/linzjian666/vvvtss/README.md b/spaces/linzjian666/vvvtss/README.md deleted file mode 100644 index 9bcceb55478490dd108053e46d42ea53cf02a4e9..0000000000000000000000000000000000000000 --- a/spaces/linzjian666/vvvtss/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Vvvtss -emoji: 🐢 -colorFrom: blue -colorTo: gray -sdk: docker -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/lixq/bingo61/src/components/chat-header.tsx b/spaces/lixq/bingo61/src/components/chat-header.tsx deleted file mode 100644 index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000 --- a/spaces/lixq/bingo61/src/components/chat-header.tsx +++ /dev/null @@ -1,12 +0,0 @@ -import LogoIcon from '@/assets/images/logo.svg' -import Image from 'next/image' - -export function ChatHeader() { - return ( -
        - logo -
        欢迎使用新必应
        -
        由 AI 支持的网页版 Copilot
        -
        - ) -} diff --git a/spaces/ljjggr/bingo/src/components/ui/voice/index.tsx b/spaces/ljjggr/bingo/src/components/ui/voice/index.tsx deleted file mode 100644 index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000 --- a/spaces/ljjggr/bingo/src/components/ui/voice/index.tsx +++ /dev/null @@ -1,28 +0,0 @@ -import './index.scss' - -export interface VoiceProps extends CSSPropertyRule { - num?: number; - duration?: number; -} -export default function Voice({ duration = 400, num = 7, ...others }) { - return ( -
        - {Array.from({ length: num }).map((_, index) => { - const randomDuration = Math.random() * 100 + duration - const initialDelay = Math.random() * 2 * duration - const initialScale = Math.sin((index + 1) * Math.PI / num) - return ( -
        - ) - })} -
        - ) -} diff --git a/spaces/luisoala/glide-test/server.py b/spaces/luisoala/glide-test/server.py deleted file mode 100644 index c5869e1b51d01cf0d8a2f50dc7b7f7b3ac575317..0000000000000000000000000000000000000000 --- a/spaces/luisoala/glide-test/server.py +++ /dev/null @@ -1,175 +0,0 @@ -import base64 -from io import BytesIO -from fastapi import FastAPI - -from PIL import Image -import torch as th - -from glide_text2im.download import load_checkpoint -from glide_text2im.model_creation import ( - create_model_and_diffusion, - model_and_diffusion_defaults, - model_and_diffusion_defaults_upsampler -) - -print("Loading models...") -app = FastAPI() - -# This notebook supports both CPU and GPU. -# On CPU, generating one sample may take on the order of 20 minutes. -# On a GPU, it should be under a minute. - -has_cuda = th.cuda.is_available() -device = th.device('cpu' if not has_cuda else 'cuda') - -# Create base model. -options = model_and_diffusion_defaults() -options['use_fp16'] = has_cuda -options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling -model, diffusion = create_model_and_diffusion(**options) -model.eval() -if has_cuda: - model.convert_to_fp16() -model.to(device) -model.load_state_dict(load_checkpoint('base', device)) -print('total base parameters', sum(x.numel() for x in model.parameters())) - -# Create upsampler model. -options_up = model_and_diffusion_defaults_upsampler() -options_up['use_fp16'] = has_cuda -options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling -model_up, diffusion_up = create_model_and_diffusion(**options_up) -model_up.eval() -if has_cuda: - model_up.convert_to_fp16() -model_up.to(device) -model_up.load_state_dict(load_checkpoint('upsample', device)) -print('total upsampler parameters', sum(x.numel() for x in model_up.parameters())) - - -def get_images(batch: th.Tensor): - """ Display a batch of images inline. """ - scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu() - reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3]) - Image.fromarray(reshaped.numpy()) - - -# Create a classifier-free guidance sampling function -guidance_scale = 3.0 - -def model_fn(x_t, ts, **kwargs): - half = x_t[: len(x_t) // 2] - combined = th.cat([half, half], dim=0) - model_out = model(combined, ts, **kwargs) - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps) - eps = th.cat([half_eps, half_eps], dim=0) - return th.cat([eps, rest], dim=1) - - -@app.get("/") -def read_root(): - return {"glide!"} - -@app.get("/{generate}") -def sample(prompt): - # Sampling parameters - batch_size = 1 - - # Tune this parameter to control the sharpness of 256x256 images. - # A value of 1.0 is sharper, but sometimes results in grainy artifacts. - upsample_temp = 0.997 - - ############################## - # Sample from the base model # - ############################## - - # Create the text tokens to feed to the model. - tokens = model.tokenizer.encode(prompt) - tokens, mask = model.tokenizer.padded_tokens_and_mask( - tokens, options['text_ctx'] - ) - - # Create the classifier-free guidance tokens (empty) - full_batch_size = batch_size * 2 - uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask( - [], options['text_ctx'] - ) - - # Pack the tokens together into model kwargs. - model_kwargs = dict( - tokens=th.tensor( - [tokens] * batch_size + [uncond_tokens] * batch_size, device=device - ), - mask=th.tensor( - [mask] * batch_size + [uncond_mask] * batch_size, - dtype=th.bool, - device=device, - ), - ) - - # Sample from the base model. - model.del_cache() - samples = diffusion.p_sample_loop( - model_fn, - (full_batch_size, 3, options["image_size"], options["image_size"]), - device=device, - clip_denoised=True, - progress=True, - model_kwargs=model_kwargs, - cond_fn=None, - )[:batch_size] - model.del_cache() - - - ############################## - # Upsample the 64x64 samples # - ############################## - - tokens = model_up.tokenizer.encode(prompt) - tokens, mask = model_up.tokenizer.padded_tokens_and_mask( - tokens, options_up['text_ctx'] - ) - - # Create the model conditioning dict. - model_kwargs = dict( - # Low-res image to upsample. - low_res=((samples+1)*127.5).round()/127.5 - 1, - - # Text tokens - tokens=th.tensor( - [tokens] * batch_size, device=device - ), - mask=th.tensor( - [mask] * batch_size, - dtype=th.bool, - device=device, - ), - ) - - # Sample from the base model. - model_up.del_cache() - up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"]) - up_samples = diffusion_up.ddim_sample_loop( - model_up, - up_shape, - noise=th.randn(up_shape, device=device) * upsample_temp, - device=device, - clip_denoised=True, - progress=True, - model_kwargs=model_kwargs, - cond_fn=None, - )[:batch_size] - model_up.del_cache() - - # Show the output - image = get_images(up_samples) - image = to_base64(image) - return {"image": image} - - -def to_base64(pil_image): - buffered = BytesIO() - pil_image.save(buffered, format="JPEG") - return base64.b64encode(buffered.getvalue()) diff --git a/spaces/luisoala/raw2logit/utils/dataset_utils.py b/spaces/luisoala/raw2logit/utils/dataset_utils.py deleted file mode 100644 index 3de684a04776f7f0c48b7846e6b4593e998393f1..0000000000000000000000000000000000000000 --- a/spaces/luisoala/raw2logit/utils/dataset_utils.py +++ /dev/null @@ -1,198 +0,0 @@ -""" -Dataset Import/Download Tools -""" - -import os -import random -import numpy as np -import rawpy -from PIL import Image -from sklearn.model_selection import StratifiedShuffleSplit - -import torch - -from skimage.util.shape import view_as_windows - -IMAGE_FILE_TYPES = ['dng', 'png', 'tif', 'tiff'] - -def load_image(path): - file_type = path.split('.')[-1].lower() - if file_type == 'dng': - img = rawpy.imread(path).raw_image_visible - elif file_type == 'tiff' or file_type == 'tif': - img = np.array(tiff.imread(path), dtype=np.float32) - else: - img = np.array(Image.open(path), dtype=np.float32) - return img - - -def list_images_in_dir(path): - image_list = [os.path.join(path, img_name) - for img_name in sorted(os.listdir(path)) - if img_name.split('.')[-1].lower() in IMAGE_FILE_TYPES] - return image_list - - -def k_fold(dataset, n_splits: int, seed: int, train_size: float): - """Split dataset in subsets for cross-validation - - Args: - dataset (class): dataset to split - n_split (int): Number of re-shuffling & splitting iterations. - seed (int): seed for k_fold splitting - train_size (float): should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. - Returns: - idxs (list): indeces for splitting the dataset. The list contain n_split pair of train/test indeces. - """ - if hasattr(dataset, 'labels'): - x = dataset.images - y = dataset.labels - elif hasattr(dataset, 'masks'): - x = dataset.images - y = dataset.masks - - idxs = [] - - if dataset.task == 'classification': - sss = StratifiedShuffleSplit(n_splits=n_splits, train_size=train_size, random_state=seed) - - for idxs_train, idxs_test in sss.split(x, y): - idxs.append((idxs_train.tolist(), idxs_test.tolist())) - - elif dataset.task == 'segmentation': - for n in range(n_splits): - split_idx = int(len(dataset) * train_size) - indices = np.random.permutation(len(dataset)) - idxs.append((indices[:split_idx].tolist(), indices[split_idx:].tolist())) - - return idxs - - -def split_img(imgs, ROIs=(3, 3), step=(1, 1)): - """Split the imgs in regions of size ROIs. - - Args: - imgs (ndarray): images which you want to split - ROIs (tuple): size of sub-regions splitted (ROIs=region of interests) - step (tuple): step path from one sub-region to the next one (in the x,y axis) - - Returns: - ndarray: splitted subimages. - The size is (x_num_subROIs*y_num_subROIs, **) where: - x_num_subROIs = ( imgs.shape[1]-int(ROIs[1]/2)*2 )/step[1] - y_num_subROIs = ( imgs.shape[0]-int(ROIs[0]/2)*2 )/step[0] - - Example: - >>> from dataset_generator import split - >>> imgs_splitted = split(imgs, ROI_size = (5,5), step=(2,3)) - """ - - if len(ROIs) > 2: - return print("ROIs is a 2 element list") - - if len(step) > 2: - return print("step is a 2 element list") - - if type(imgs) != type(np.array(1)): - return print("imgs should be a ndarray") - - if len(imgs.shape) == 2: # Single image with one channel (HxW) - splitted = view_as_windows(imgs, (ROIs[0], ROIs[1]), (step[0], step[1])) - return splitted.reshape((-1, ROIs[0], ROIs[1])) - - if len(imgs.shape) == 3: - _, _, channels = imgs.shape - if channels <= 3: # Single image more channels (HxWxC) - splitted = view_as_windows(imgs, (ROIs[0], ROIs[1], channels), (step[0], step[1], channels)) - return splitted.reshape((-1, ROIs[0], ROIs[1], channels)) - else: # More images with 1 channel - splitted = view_as_windows(imgs, (1, ROIs[0], ROIs[1]), (1, step[0], step[1])) - return splitted.reshape((-1, ROIs[0], ROIs[1])) - - if len(imgs.shape) == 4: # More images with more channels(BxHxWxC) - _, _, _, channels = imgs.shape - splitted = view_as_windows(imgs, (1, ROIs[0], ROIs[1], channels), (1, step[0], step[1], channels)) - return splitted.reshape((-1, ROIs[0], ROIs[1], channels)) - - -def join_blocks(splitted, final_shape): - """Join blocks to reobtain a splitted image - - Attribute: - splitted (tensor) = image splitted in blocks, size = (N_blocks, Channels, Height, Width) - final_shape (tuple) = size of the final image reconstructed (Height, Width) - Return: - tensor: image restored from blocks. size = (Channels, Height, Width) - - """ - n_blocks, channels, ROI_height, ROI_width = splitted.shape - - rows = final_shape[0] // ROI_height - columns = final_shape[1] // ROI_width - - final_img = torch.empty(rows, channels, ROI_height, ROI_width * columns) - for r in range(rows): - stackblocks = splitted[r * columns] - for c in range(1, columns): - stackblocks = torch.cat((stackblocks, splitted[r * columns + c]), axis=2) - final_img[r] = stackblocks - - joined_img = final_img[0] - - for i in np.arange(1, len(final_img)): - joined_img = torch.cat((joined_img, final_img[i]), axis=1) - - return joined_img - - -def random_ROI(X, Y, ROIs=(512, 512)): - """ Return a random region for each input/target pair images of the dataset - Args: - Y (ndarray): target of your dataset --> size: (BxHxWxC) - X (ndarray): input of your dataset --> size: (BxHxWxC) - ROIs (tuple): size of random region (ROIs=region of interests) - - Returns: - For each pair images (input/target) of the dataset, return respectively random ROIs - Y_cut (ndarray): target of your dataset --> size: (Batch,Channels,ROIs[0],ROIs[1]) - X_cut (ndarray): input of your dataset --> size: (Batch,Channels,ROIs[0],ROIs[1]) - - Example: - >>> from dataset_generator import random_ROI - >>> X,Y = random_ROI(X,Y, ROIs = (10,10)) - """ - - batch, channels, height, width = X.shape - - X_cut = np.empty((batch, ROIs[0], ROIs[1], channels)) - Y_cut = np.empty((batch, ROIs[0], ROIs[1], channels)) - - for i in np.arange(len(X)): - x_size = int(random.random() * (height - (ROIs[0] + 1))) - y_size = int(random.random() * (width - (ROIs[1] + 1))) - X_cut[i] = X[i, x_size:x_size + ROIs[0], y_size:y_size + ROIs[1], :] - Y_cut[i] = Y[i, x_size:x_size + ROIs[0], y_size:y_size + ROIs[1], :] - return X_cut, Y_cut - - -def one2many_random_ROI(X, Y, datasize=1000, ROIs=(512, 512)): - """ Return a dataset of N subimages obtained from random regions of the same image - Args: - Y (ndarray): target of your dataset --> size: (1,H,W,C) - X (ndarray): input of your dataset --> size: (1,H,W,C) - datasize = number of random ROIs to generate - ROIs (tuple): size of random region (ROIs=region of interests) - - Returns: - Y_cut (ndarray): target of your dataset --> size: (Datasize,ROIs[0],ROIs[1],Channels) - X_cut (ndarray): input of your dataset --> size: (Datasize,ROIs[0],ROIs[1],Channels) - """ - - batch, channels, height, width = X.shape - - X_cut = np.empty((datasize, ROIs[0], ROIs[1], channels)) - Y_cut = np.empty((datasize, ROIs[0], ROIs[1], channels)) - - for i in np.arange(datasize): - X_cut[i], Y_cut[i] = random_ROI(X, Y, ROIs) - return X_cut, Y_cut diff --git a/spaces/luost26/DiffAb/diffab/datasets/custom.py b/spaces/luost26/DiffAb/diffab/datasets/custom.py deleted file mode 100644 index 2c2ebff7200fa5225fb341202728c053067cc83c..0000000000000000000000000000000000000000 --- a/spaces/luost26/DiffAb/diffab/datasets/custom.py +++ /dev/null @@ -1,200 +0,0 @@ -import os -import logging -import joblib -import pickle -import lmdb -from Bio import PDB -from Bio.PDB import PDBExceptions -from torch.utils.data import Dataset -from tqdm.auto import tqdm - -from ..utils.protein import parsers -from .sabdab import _label_heavy_chain_cdr, _label_light_chain_cdr -from ._base import register_dataset - - -def preprocess_antibody_structure(task): - pdb_path = task['pdb_path'] - H_id = task.get('heavy_id', 'H') - L_id = task.get('light_id', 'L') - - parser = PDB.PDBParser(QUIET=True) - model = parser.get_structure(id, pdb_path)[0] - - all_chain_ids = [c.id for c in model] - - parsed = { - 'id': task['id'], - 'heavy': None, - 'heavy_seqmap': None, - 'light': None, - 'light_seqmap': None, - 'antigen': None, - 'antigen_seqmap': None, - } - try: - if H_id in all_chain_ids: - ( - parsed['heavy'], - parsed['heavy_seqmap'] - ) = _label_heavy_chain_cdr(*parsers.parse_biopython_structure( - model[H_id], - max_resseq = 113 # Chothia, end of Heavy chain Fv - )) - - if L_id in all_chain_ids: - ( - parsed['light'], - parsed['light_seqmap'] - ) = _label_light_chain_cdr(*parsers.parse_biopython_structure( - model[L_id], - max_resseq = 106 # Chothia, end of Light chain Fv - )) - - if parsed['heavy'] is None and parsed['light'] is None: - raise ValueError( - f'Neither valid antibody H-chain or L-chain is found. ' - f'Please ensure that the chain id of heavy chain is "{H_id}" ' - f'and the id of the light chain is "{L_id}".' - ) - - - ag_chain_ids = [cid for cid in all_chain_ids if cid not in (H_id, L_id)] - if len(ag_chain_ids) > 0: - chains = [model[c] for c in ag_chain_ids] - ( - parsed['antigen'], - parsed['antigen_seqmap'] - ) = parsers.parse_biopython_structure(chains) - - except ( - PDBExceptions.PDBConstructionException, - parsers.ParsingException, - KeyError, - ValueError, - ) as e: - logging.warning('[{}] {}: {}'.format( - task['id'], - e.__class__.__name__, - str(e) - )) - return None - - return parsed - - -@register_dataset('custom') -class CustomDataset(Dataset): - - MAP_SIZE = 32*(1024*1024*1024) # 32GB - - def __init__(self, structure_dir, transform=None, reset=False): - super().__init__() - self.structure_dir = structure_dir - self.transform = transform - - self.db_conn = None - self.db_ids = None - self._load_structures(reset) - - @property - def _cache_db_path(self): - return os.path.join(self.structure_dir, 'structure_cache.lmdb') - - def _connect_db(self): - self._close_db() - self.db_conn = lmdb.open( - self._cache_db_path, - map_size=self.MAP_SIZE, - create=False, - subdir=False, - readonly=True, - lock=False, - readahead=False, - meminit=False, - ) - with self.db_conn.begin() as txn: - keys = [k.decode() for k in txn.cursor().iternext(values=False)] - self.db_ids = keys - - def _close_db(self): - if self.db_conn is not None: - self.db_conn.close() - self.db_conn = None - self.db_ids = None - - def _load_structures(self, reset): - all_pdbs = [] - for fname in os.listdir(self.structure_dir): - if not fname.endswith('.pdb'): continue - all_pdbs.append(fname) - - if reset or not os.path.exists(self._cache_db_path): - todo_pdbs = all_pdbs - else: - self._connect_db() - processed_pdbs = self.db_ids - self._close_db() - todo_pdbs = list(set(all_pdbs) - set(processed_pdbs)) - - if len(todo_pdbs) > 0: - self._preprocess_structures(todo_pdbs) - - def _preprocess_structures(self, pdb_list): - tasks = [] - for pdb_fname in pdb_list: - pdb_path = os.path.join(self.structure_dir, pdb_fname) - tasks.append({ - 'id': pdb_fname, - 'pdb_path': pdb_path, - }) - - data_list = joblib.Parallel( - n_jobs = max(joblib.cpu_count() // 2, 1), - )( - joblib.delayed(preprocess_antibody_structure)(task) - for task in tqdm(tasks, dynamic_ncols=True, desc='Preprocess') - ) - - db_conn = lmdb.open( - self._cache_db_path, - map_size = self.MAP_SIZE, - create=True, - subdir=False, - readonly=False, - ) - ids = [] - with db_conn.begin(write=True, buffers=True) as txn: - for data in tqdm(data_list, dynamic_ncols=True, desc='Write to LMDB'): - if data is None: - continue - ids.append(data['id']) - txn.put(data['id'].encode('utf-8'), pickle.dumps(data)) - - def __len__(self): - return len(self.db_ids) - - def __getitem__(self, index): - self._connect_db() - id = self.db_ids[index] - with self.db_conn.begin() as txn: - data = pickle.loads(txn.get(id.encode())) - if self.transform is not None: - data = self.transform(data) - return data - - -if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--dir', type=str, default='./data/custom') - parser.add_argument('--reset', action='store_true', default=False) - args = parser.parse_args() - - dataset = CustomDataset( - structure_dir = args.dir, - reset = args.reset, - ) - print(dataset[0]) - print(len(dataset)) - \ No newline at end of file diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/allocator/fill_construct_range.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/allocator/fill_construct_range.h deleted file mode 100644 index 9de0f7bcbb86b8ed895ca597d75242578ce125f5..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/detail/allocator/fill_construct_range.h +++ /dev/null @@ -1,36 +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 - -namespace thrust -{ -namespace detail -{ - - -template -__host__ __device__ -inline void fill_construct_range(Allocator &a, Pointer p, Size n, const T &value); - - -} // end detail -} // end thrust - -#include - diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/tabulate.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/tabulate.h deleted file mode 100644 index 70b2720d9a9eb00d8d68f90d2e34fa0623572fb7..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/tabulate.h +++ /dev/null @@ -1,88 +0,0 @@ -/****************************************************************************** - * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of the NVIDIA CORPORATION nor the - * names of its contributors may be used to endorse or promote products - * derived from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY - * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND - * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - ******************************************************************************/ -#pragma once - - -#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC -#include -#include -#include -#include -#include - -namespace thrust -{ -namespace cuda_cub { - -namespace __tabulate { - - template - struct functor - { - Iterator items; - TabulateOp op; - - __host__ __device__ - functor(Iterator items_, TabulateOp op_) - : items(items_), op(op_) {} - - void __device__ operator()(Size idx) - { - items[idx] = op(idx); - } - }; // struct functor - -} // namespace __tabulate - -template -void __host__ __device__ -tabulate(execution_policy& policy, - Iterator first, - Iterator last, - TabulateOp tabulate_op) -{ - typedef typename iterator_traits::difference_type size_type; - - size_type count = thrust::distance(first, last); - - typedef __tabulate::functor functor_t; - - cuda_cub::parallel_for(policy, - functor_t(first, tabulate_op), - count); - - cuda_cub::throw_on_error( - cuda_cub::synchronize(policy) - , "tabulate: failed to synchronize" - ); -} - -} // namespace cuda_cub -} // end namespace thrust -#endif diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/memory_resource.h b/spaces/ma-xu/LIVE/thrust/thrust/system/omp/memory_resource.h deleted file mode 100644 index 6a540d834939b928a4b6049c6a97d2289ab43257..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/memory_resource.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright 2018 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/*! \file omp/memory_resource.h - * \brief Memory resources for the OMP system. - */ - -#pragma once - -#include -#include -#include - -#include - -namespace thrust -{ -namespace system -{ -namespace omp -{ - -//! \cond -namespace detail -{ - typedef thrust::mr::fancy_pointer_resource< - thrust::mr::new_delete_resource, - thrust::omp::pointer - > native_resource; -} -//! \endcond - -/*! \addtogroup memory_resources Memory Resources - * \ingroup memory_management_classes - * \{ - */ - -/*! The memory resource for the OMP system. Uses \p mr::new_delete_resource and tags it with \p omp::pointer. */ -typedef detail::native_resource memory_resource; -/*! An alias for \p omp::memory_resource. */ -typedef detail::native_resource universal_memory_resource; -/*! An alias for \p omp::memory_resource. */ -typedef detail::native_resource universal_host_pinned_memory_resource; - -/*! \} - */ - -} -} -} diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/swinir_model.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/swinir_model.py deleted file mode 100644 index 5ac182f23b4a300aff14b2b45fcdca8c00da90c1..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/models/swinir_model.py +++ /dev/null @@ -1,33 +0,0 @@ -import torch -from torch.nn import functional as F - -from basicsr.utils.registry import MODEL_REGISTRY -from .sr_model import SRModel - - -@MODEL_REGISTRY.register() -class SwinIRModel(SRModel): - - def test(self): - # pad to multiplication of window_size - window_size = self.opt['network_g']['window_size'] - scale = self.opt.get('scale', 1) - mod_pad_h, mod_pad_w = 0, 0 - _, _, h, w = self.lq.size() - if h % window_size != 0: - mod_pad_h = window_size - h % window_size - if w % window_size != 0: - mod_pad_w = window_size - w % window_size - img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(img) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(img) - self.net_g.train() - - _, _, h, w = self.output.size() - self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale] diff --git a/spaces/mandar100/chatbot_dialogpt/app.py b/spaces/mandar100/chatbot_dialogpt/app.py deleted file mode 100644 index 18d2d0ca3e68a8a9ca5107c57e1c4800692e1c20..0000000000000000000000000000000000000000 --- a/spaces/mandar100/chatbot_dialogpt/app.py +++ /dev/null @@ -1,34 +0,0 @@ -from transformers import AutoModelForCausalLM, AutoTokenizer -import torch -import gradio as gr - -tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") -model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") - -def predict(input, history=[]): - # tokenize the new input sentence - new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') - - # append the new user input tokens to the chat history - bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) - - # generate a response - history = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id).tolist() - - # convert the tokens to text, and then split the responses into lines - response = tokenizer.decode(history[0]).split("<|endoftext|>") - #print('decoded_response-->>'+str(response)) - response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list - #print('response-->>'+str(response)) - return response, history - -description = "This is a chatbot application based on the DialoGPT model of Microsoft. Simply type an input to get started with chatting." -title = "Chat with DialoGPT 👾" -examples = [["What is the meaning of life?"]] -gr.Interface(fn=predict, - title=title, - description=description, - examples=examples, - inputs=["text", "state"], - outputs=["chatbot", "state"]).launch() - diff --git a/spaces/maxmax20160403/sovits5.0/vits_decoder/mrd.py b/spaces/maxmax20160403/sovits5.0/vits_decoder/mrd.py deleted file mode 100644 index da6db1a416366603d2e65b400d66c44262e2baef..0000000000000000000000000000000000000000 --- a/spaces/maxmax20160403/sovits5.0/vits_decoder/mrd.py +++ /dev/null @@ -1,62 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm, spectral_norm - -class DiscriminatorR(torch.nn.Module): - def __init__(self, hp, resolution): - super(DiscriminatorR, self).__init__() - - self.resolution = resolution - self.LRELU_SLOPE = hp.mpd.lReLU_slope - - norm_f = weight_norm if hp.mrd.use_spectral_norm == False else spectral_norm - - self.convs = nn.ModuleList([ - norm_f(nn.Conv2d(1, 32, (3, 9), padding=(1, 4))), - norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))), - ]) - self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1))) - - def forward(self, x): - fmap = [] - - x = self.spectrogram(x) - x = x.unsqueeze(1) - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, self.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return fmap, x - - def spectrogram(self, x): - n_fft, hop_length, win_length = self.resolution - x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect') - x = x.squeeze(1) - x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=False) #[B, F, TT, 2] - mag = torch.norm(x, p=2, dim =-1) #[B, F, TT] - - return mag - - -class MultiResolutionDiscriminator(torch.nn.Module): - def __init__(self, hp): - super(MultiResolutionDiscriminator, self).__init__() - self.resolutions = eval(hp.mrd.resolutions) - self.discriminators = nn.ModuleList( - [DiscriminatorR(hp, resolution) for resolution in self.resolutions] - ) - - def forward(self, x): - ret = list() - for disc in self.discriminators: - ret.append(disc(x)) - - return ret # [(feat, score), (feat, score), (feat, score)] diff --git a/spaces/megaaziib/RVC-V2-Huggingface-Version/app.py b/spaces/megaaziib/RVC-V2-Huggingface-Version/app.py deleted file mode 100644 index 9ce7bc25915db5c6a62c57a3b9b8024a730a0595..0000000000000000000000000000000000000000 --- a/spaces/megaaziib/RVC-V2-Huggingface-Version/app.py +++ /dev/null @@ -1,2088 +0,0 @@ -import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np -from mega import Mega -os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" -import threading -from time import sleep -from subprocess import Popen -import faiss -from random import shuffle -import json, datetime, requests -from gtts import gTTS -now_dir = os.getcwd() -sys.path.append(now_dir) -tmp = os.path.join(now_dir, "TEMP") -shutil.rmtree(tmp, ignore_errors=True) -shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) -os.makedirs(tmp, exist_ok=True) -os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) -os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True) -os.environ["TEMP"] = tmp -warnings.filterwarnings("ignore") -torch.manual_seed(114514) -from i18n import I18nAuto - -import signal - -import math - -from utils import load_audio, CSVutil - -global DoFormant, Quefrency, Timbre - -if not os.path.isdir('csvdb/'): - os.makedirs('csvdb') - frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w') - frmnt.close() - stp.close() - -try: - DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') - DoFormant = ( - lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant) - )(DoFormant) -except (ValueError, TypeError, IndexError): - DoFormant, Quefrency, Timbre = False, 1.0, 1.0 - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) - -def download_models(): - # Download hubert base model if not present - if not os.path.isfile('./hubert_base.pt'): - response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt') - - if response.status_code == 200: - with open('./hubert_base.pt', 'wb') as f: - f.write(response.content) - print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.") - else: - raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".") - - # Download rmvpe model if not present - if not os.path.isfile('./rmvpe.pt'): - response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058') - - if response.status_code == 200: - with open('./rmvpe.pt', 'wb') as f: - f.write(response.content) - print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.") - else: - raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".") - -download_models() - -print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n") - -def formant_apply(qfrency, tmbre): - Quefrency = qfrency - Timbre = tmbre - DoFormant = True - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - - return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) - -def get_fshift_presets(): - fshift_presets_list = [] - for dirpath, _, filenames in os.walk("./formantshiftcfg/"): - for filename in filenames: - if filename.endswith(".txt"): - fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/')) - - if len(fshift_presets_list) > 0: - return fshift_presets_list - else: - return '' - - - -def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): - - if (cbox): - - DoFormant = True - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - #print(f"is checked? - {cbox}\ngot {DoFormant}") - - return ( - {"value": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - ) - - - else: - - DoFormant = False - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - - #print(f"is checked? - {cbox}\ngot {DoFormant}") - return ( - {"value": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - ) - - - -def preset_apply(preset, qfer, tmbr): - if str(preset) != '': - with open(str(preset), 'r') as p: - content = p.readlines() - qfer, tmbr = content[0].split('\n')[0], content[1] - - formant_apply(qfer, tmbr) - else: - pass - return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) - -def update_fshift_presets(preset, qfrency, tmbre): - - qfrency, tmbre = preset_apply(preset, qfrency, tmbre) - - if (str(preset) != ''): - with open(str(preset), 'r') as p: - content = p.readlines() - qfrency, tmbre = content[0].split('\n')[0], content[1] - - formant_apply(qfrency, tmbre) - else: - pass - return ( - {"choices": get_fshift_presets(), "__type__": "update"}, - {"value": qfrency, "__type__": "update"}, - {"value": tmbre, "__type__": "update"}, - ) - -i18n = I18nAuto() -#i18n.print() -# 判断是否有能用来训练和加速推理的N卡 -ngpu = torch.cuda.device_count() -gpu_infos = [] -mem = [] -if (not torch.cuda.is_available()) or ngpu == 0: - if_gpu_ok = False -else: - if_gpu_ok = False - for i in range(ngpu): - gpu_name = torch.cuda.get_device_name(i) - if ( - "10" in gpu_name - or "16" in gpu_name - or "20" in gpu_name - or "30" in gpu_name - or "40" in gpu_name - or "A2" in gpu_name.upper() - or "A3" in gpu_name.upper() - or "A4" in gpu_name.upper() - or "P4" in gpu_name.upper() - or "A50" in gpu_name.upper() - or "A60" in gpu_name.upper() - or "70" in gpu_name - or "80" in gpu_name - or "90" in gpu_name - or "M4" in gpu_name.upper() - or "T4" in gpu_name.upper() - or "TITAN" in gpu_name.upper() - ): # A10#A100#V100#A40#P40#M40#K80#A4500 - if_gpu_ok = True # 至少有一张能用的N卡 - gpu_infos.append("%s\t%s" % (i, gpu_name)) - mem.append( - int( - torch.cuda.get_device_properties(i).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - ) -if if_gpu_ok == True and len(gpu_infos) > 0: - gpu_info = "\n".join(gpu_infos) - default_batch_size = min(mem) // 2 -else: - gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") - default_batch_size = 1 -gpus = "-".join([i[0] for i in gpu_infos]) -from lib.infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -import soundfile as sf -from fairseq import checkpoint_utils -import gradio as gr -import logging -from vc_infer_pipeline import VC -from config import Config - -config = Config() -# from trainset_preprocess_pipeline import PreProcess -logging.getLogger("numba").setLevel(logging.WARNING) - -hubert_model = None - -def load_hubert(): - global hubert_model - models, _, _ = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", - ) - hubert_model = models[0] - hubert_model = hubert_model.to(config.device) - if config.is_half: - hubert_model = hubert_model.half() - else: - hubert_model = hubert_model.float() - hubert_model.eval() - - -weight_root = "weights" -index_root = "logs" -names = [] -for name in os.listdir(weight_root): - if name.endswith(".pth"): - names.append(name) -index_paths = [] -for root, dirs, files in os.walk(index_root, topdown=False): - for name in files: - if name.endswith(".index") and "trained" not in name: - index_paths.append("%s/%s" % (root, name)) - - - -def vc_single( - sid, - input_audio_path, - f0_up_key, - f0_file, - f0_method, - file_index, - #file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - crepe_hop_length, -): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 - global tgt_sr, net_g, vc, hubert_model, version - if input_audio_path is None: - return "You need to upload an audio", None - f0_up_key = int(f0_up_key) - try: - audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre) - audio_max = np.abs(audio).max() / 0.95 - if audio_max > 1: - audio /= audio_max - times = [0, 0, 0] - if hubert_model == None: - load_hubert() - if_f0 = cpt.get("f0", 1) - file_index = ( - ( - file_index.strip(" ") - .strip('"') - .strip("\n") - .strip('"') - .strip(" ") - .replace("trained", "added") - ) - ) # 防止小白写错，自动帮他替换掉 - # file_big_npy = ( - # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - # ) - audio_opt = vc.pipeline( - hubert_model, - net_g, - sid, - audio, - input_audio_path, - times, - f0_up_key, - f0_method, - file_index, - # file_big_npy, - index_rate, - if_f0, - filter_radius, - tgt_sr, - resample_sr, - rms_mix_rate, - version, - protect, - crepe_hop_length, - f0_file=f0_file, - ) - if resample_sr >= 16000 and tgt_sr != resample_sr: - tgt_sr = resample_sr - index_info = ( - "Using index:%s." % file_index - if os.path.exists(file_index) - else "Index not used." - ) - return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( - index_info, - times[0], - times[1], - times[2], - ), (tgt_sr, audio_opt) - except: - info = traceback.format_exc() - print(info) - return info, (None, None) - - -def vc_multi( - sid, - dir_path, - opt_root, - paths, - f0_up_key, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - format1, - crepe_hop_length, -): - try: - dir_path = ( - dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - os.makedirs(opt_root, exist_ok=True) - try: - if dir_path != "": - paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] - else: - paths = [path.name for path in paths] - except: - traceback.print_exc() - paths = [path.name for path in paths] - infos = [] - for path in paths: - info, opt = vc_single( - sid, - path, - f0_up_key, - None, - f0_method, - file_index, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - crepe_hop_length - ) - if "Success" in info: - try: - tgt_sr, audio_opt = opt - if format1 in ["wav", "flac"]: - sf.write( - "%s/%s.%s" % (opt_root, os.path.basename(path), format1), - audio_opt, - tgt_sr, - ) - else: - path = "%s/%s.wav" % (opt_root, os.path.basename(path)) - sf.write( - path, - audio_opt, - tgt_sr, - ) - if os.path.exists(path): - os.system( - "ffmpeg -i %s -vn %s -q:a 2 -y" - % (path, path[:-4] + ".%s" % format1) - ) - except: - info += traceback.format_exc() - infos.append("%s->%s" % (os.path.basename(path), info)) - yield "\n".join(infos) - yield "\n".join(infos) - except: - yield traceback.format_exc() - -# 一个选项卡全局只能有一个音色 -def get_vc(sid): - global n_spk, tgt_sr, net_g, vc, cpt, version - if sid == "" or sid == []: - global hubert_model - if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 - print("clean_empty_cache") - del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt - hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() - ###楼下不这么折腾清理不干净 - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g, cpt - if torch.cuda.is_available(): - torch.cuda.empty_cache() - cpt = None - return {"visible": False, "__type__": "update"} - person = "%s/%s" % (weight_root, sid) - print("loading %s" % person) - cpt = torch.load(person, map_location="cpu") - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) - net_g.eval().to(config.device) - if config.is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, config) - n_spk = cpt["config"][-3] - return {"visible": False, "maximum": n_spk, "__type__": "update"} - - -def change_choices(): - names = [] - for name in os.listdir(weight_root): - if name.endswith(".pth"): - names.append(name) - index_paths = [] - for root, dirs, files in os.walk(index_root, topdown=False): - for name in files: - if name.endswith(".index") and "trained" not in name: - index_paths.append("%s/%s" % (root, name)) - return {"choices": sorted(names), "__type__": "update"}, { - "choices": sorted(index_paths), - "__type__": "update", - } - - -def clean(): - return {"value": "", "__type__": "update"} - - -sr_dict = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -def if_done(done, p): - while 1: - if p.poll() == None: - sleep(0.5) - else: - break - done[0] = True - - -def if_done_multi(done, ps): - while 1: - # poll==None代表进程未结束 - # 只要有一个进程未结束都不停 - flag = 1 - for p in ps: - if p.poll() == None: - flag = 0 - sleep(0.5) - break - if flag == 1: - break - done[0] = True - - -def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): - sr = sr_dict[sr] - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") - f.close() - cmd = ( - config.python_cmd - + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " - % (trainset_dir, sr, n_p, now_dir, exp_dir) - + str(config.noparallel) - ) - print(cmd) - p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - -# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) -def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): - gpus = gpus.split("-") - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") - f.close() - if if_f0: - cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( - now_dir, - exp_dir, - n_p, - f0method, - echl, - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open( - "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" - ) as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - ####对不同part分别开多进程 - """ - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - i_gpu=sys.argv[3] - exp_dir=sys.argv[4] - os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) - """ - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = ( - config.python_cmd - + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" - % ( - config.device, - leng, - idx, - n_g, - now_dir, - exp_dir, - version19, - ) - ) - print(cmd) - p = Popen( - cmd, shell=True, cwd=now_dir - ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir - ps.append(p) - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done_multi, - args=( - done, - ps, - ), - ).start() - while 1: - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - - -def change_sr2(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - return ( - ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", - {"visible": True, "__type__": "update"} - ) - -def change_version19(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - return ( - ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", - ) - - -def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 - path_str = "" if version19 == "v1" else "_v2" - if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") - if if_f0_3: - return ( - {"visible": True, "__type__": "update"}, - "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "", - "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "", - ) - return ( - {"visible": False, "__type__": "update"}, - ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "", - ) - - -global log_interval - - -def set_log_interval(exp_dir, batch_size12): - log_interval = 1 - - folder_path = os.path.join(exp_dir, "1_16k_wavs") - - if os.path.exists(folder_path) and os.path.isdir(folder_path): - wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")] - if wav_files: - sample_size = len(wav_files) - log_interval = math.ceil(sample_size / batch_size12) - if log_interval > 1: - log_interval += 1 - return log_interval - -# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) -def click_train( - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, -): - CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) - # 生成filelist - exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) - os.makedirs(exp_dir, exist_ok=True) - gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir) - feature_dir = ( - "%s/3_feature256" % (exp_dir) - if version19 == "v1" - else "%s/3_feature768" % (exp_dir) - ) - - log_interval = set_log_interval(exp_dir, batch_size12) - - if if_f0_3: - f0_dir = "%s/2a_f0" % (exp_dir) - f0nsf_dir = "%s/2b-f0nsf" % (exp_dir) - names = ( - set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) - & set([name.split(".")[0] for name in os.listdir(feature_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) - ) - else: - names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( - [name.split(".")[0] for name in os.listdir(feature_dir)] - ) - opt = [] - for name in names: - if if_f0_3: - opt.append( - "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - f0_dir.replace("\\", "\\\\"), - name, - f0nsf_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - else: - opt.append( - "%s/%s.wav|%s/%s.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - fea_dim = 256 if version19 == "v1" else 768 - if if_f0_3: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) - ) - else: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, spk_id5) - ) - shuffle(opt) - with open("%s/filelist.txt" % exp_dir, "w") as f: - f.write("\n".join(opt)) - print("write filelist done") - # 生成config#无需生成config - # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0" - print("use gpus:", gpus16) - if pretrained_G14 == "": - print("no pretrained Generator") - if pretrained_D15 == "": - print("no pretrained Discriminator") - if gpus16: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - gpus16, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - log_interval, - ) - ) - else: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - log_interval, - ) - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - global PID - PID = p.pid - p.wait() - return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}) - - -# but4.click(train_index, [exp_dir1], info3) -def train_index(exp_dir1, version19): - exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) - os.makedirs(exp_dir, exist_ok=True) - feature_dir = ( - "%s/3_feature256" % (exp_dir) - if version19 == "v1" - else "%s/3_feature768" % (exp_dir) - ) - if os.path.exists(feature_dir) == False: - return "请先进行特征提取!" - listdir_res = list(os.listdir(feature_dir)) - if len(listdir_res) == 0: - return "请先进行特征提取！" - npys = [] - for name in sorted(listdir_res): - phone = np.load("%s/%s" % (feature_dir, name)) - npys.append(phone) - big_npy = np.concatenate(npys, 0) - big_npy_idx = np.arange(big_npy.shape[0]) - np.random.shuffle(big_npy_idx) - big_npy = big_npy[big_npy_idx] - np.save("%s/total_fea.npy" % exp_dir, big_npy) - # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - infos = [] - infos.append("%s,%s" % (big_npy.shape, n_ivf)) - yield "\n".join(infos) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) - infos.append("training") - yield "\n".join(infos) - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) - infos.append("adding") - yield "\n".join(infos) - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - infos.append( - "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (n_ivf, index_ivf.nprobe, exp_dir1, version19) - ) - # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) - # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) - yield "\n".join(infos) - - -# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3) -def train1key( - exp_dir1, - sr2, - if_f0_3, - trainset_dir4, - spk_id5, - np7, - f0method8, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - echl -): - infos = [] - - def get_info_str(strr): - infos.append(strr) - return "\n".join(infos) - - model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1) - preprocess_log_path = "%s/preprocess.log" % model_log_dir - extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir - gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir - feature_dir = ( - "%s/3_feature256" % model_log_dir - if version19 == "v1" - else "%s/3_feature768" % model_log_dir - ) - - os.makedirs(model_log_dir, exist_ok=True) - #########step1:处理数据 - open(preprocess_log_path, "w").close() - cmd = ( - config.python_cmd - + " trainset_preprocess_pipeline_print.py %s %s %s %s " - % (trainset_dir4, sr_dict[sr2], np7, model_log_dir) - + str(config.noparallel) - ) - yield get_info_str(i18n("step1:正在处理数据")) - yield get_info_str(cmd) - p = Popen(cmd, shell=True) - p.wait() - with open(preprocess_log_path, "r") as f: - print(f.read()) - #########step2a:提取音高 - open(extract_f0_feature_log_path, "w") - if if_f0_3: - yield get_info_str("step2a:正在提取音高") - cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % ( - model_log_dir, - np7, - f0method8, - echl - ) - yield get_info_str(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - p.wait() - with open(extract_f0_feature_log_path, "r") as f: - print(f.read()) - else: - yield get_info_str(i18n("step2a:无需提取音高")) - #######step2b:提取特征 - yield get_info_str(i18n("step2b:正在提取特征")) - gpus = gpus16.split("-") - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % ( - config.device, - leng, - idx, - n_g, - model_log_dir, - version19, - ) - yield get_info_str(cmd) - p = Popen( - cmd, shell=True, cwd=now_dir - ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir - ps.append(p) - for p in ps: - p.wait() - with open(extract_f0_feature_log_path, "r") as f: - print(f.read()) - #######step3a:训练模型 - yield get_info_str(i18n("step3a:正在训练模型")) - # 生成filelist - if if_f0_3: - f0_dir = "%s/2a_f0" % model_log_dir - f0nsf_dir = "%s/2b-f0nsf" % model_log_dir - names = ( - set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) - & set([name.split(".")[0] for name in os.listdir(feature_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) - ) - else: - names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( - [name.split(".")[0] for name in os.listdir(feature_dir)] - ) - opt = [] - for name in names: - if if_f0_3: - opt.append( - "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - f0_dir.replace("\\", "\\\\"), - name, - f0nsf_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - else: - opt.append( - "%s/%s.wav|%s/%s.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - fea_dim = 256 if version19 == "v1" else 768 - if if_f0_3: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) - ) - else: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, spk_id5) - ) - shuffle(opt) - with open("%s/filelist.txt" % model_log_dir, "w") as f: - f.write("\n".join(opt)) - yield get_info_str("write filelist done") - if gpus16: - cmd = ( - config.python_cmd - +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - gpus16, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - ) - ) - else: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - ) - ) - yield get_info_str(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - p.wait() - yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")) - #######step3b:训练索引 - npys = [] - listdir_res = list(os.listdir(feature_dir)) - for name in sorted(listdir_res): - phone = np.load("%s/%s" % (feature_dir, name)) - npys.append(phone) - big_npy = np.concatenate(npys, 0) - - big_npy_idx = np.arange(big_npy.shape[0]) - np.random.shuffle(big_npy_idx) - big_npy = big_npy[big_npy_idx] - np.save("%s/total_fea.npy" % model_log_dir, big_npy) - - # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - yield get_info_str("training index") - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str("adding index") - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str( - "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (n_ivf, index_ivf.nprobe, exp_dir1, version19) - ) - yield get_info_str(i18n("全流程结束！")) - - -def whethercrepeornah(radio): - mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False - return ({"visible": mango, "__type__": "update"}) - -# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) -def change_info_(ckpt_path): - if ( - os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")) - == False - ): - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - try: - with open( - ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" - ) as f: - info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) - sr, f0 = info["sample_rate"], info["if_f0"] - version = "v2" if ("version" in info and info["version"] == "v2") else "v1" - return sr, str(f0), version - except: - traceback.print_exc() - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - - -from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM - - -def export_onnx(ModelPath, ExportedPath, MoeVS=True): - cpt = torch.load(ModelPath, map_location="cpu") - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels，为768Vec做准备 - - test_phone = torch.rand(1, 200, hidden_channels) # hidden unit - test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） - test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹） - test_pitchf = torch.rand(1, 200) # nsf基频 - test_ds = torch.LongTensor([0]) # 说话人ID - test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子） - - device = "cpu" # 导出时设备（不影响使用模型） - - - net_g = SynthesizerTrnMsNSFsidM( - *cpt["config"], is_half=False,version=cpt.get("version","v1") - ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16） - net_g.load_state_dict(cpt["weight"], strict=False) - input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] - output_names = [ - "audio", - ] - # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 - torch.onnx.export( - net_g, - ( - test_phone.to(device), - test_phone_lengths.to(device), - test_pitch.to(device), - test_pitchf.to(device), - test_ds.to(device), - test_rnd.to(device), - ), - ExportedPath, - dynamic_axes={ - "phone": [1], - "pitch": [1], - "pitchf": [1], - "rnd": [2], - }, - do_constant_folding=False, - opset_version=16, - verbose=False, - input_names=input_names, - output_names=output_names, - ) - return "Finished" - -#region RVC WebUI App - -def get_presets(): - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - preset_names = [] - for preset in data['presets']: - preset_names.append(preset['name']) - - return preset_names - -def change_choices2(): - audio_files=[] - for filename in os.listdir("./audios"): - if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): - audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) - return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} - -audio_files=[] -for filename in os.listdir("./audios"): - if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): - audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) - -def get_index(): - if check_for_name() != '': - chosen_model=sorted(names)[0].split(".")[0] - logs_path="./logs/"+chosen_model - if os.path.exists(logs_path): - for file in os.listdir(logs_path): - if file.endswith(".index"): - return os.path.join(logs_path, file) - return '' - else: - return '' - -def get_indexes(): - indexes_list=[] - for dirpath, dirnames, filenames in os.walk("./logs/"): - for filename in filenames: - if filename.endswith(".index"): - indexes_list.append(os.path.join(dirpath,filename)) - if len(indexes_list) > 0: - return indexes_list - else: - return '' - -def get_name(): - if len(audio_files) > 0: - return sorted(audio_files)[0] - else: - return '' - -def save_to_wav(record_button): - if record_button is None: - pass - else: - path_to_file=record_button - new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' - new_path='./audios/'+new_name - shutil.move(path_to_file,new_path) - return new_path - -def save_to_wav2(dropbox): - file_path=dropbox.name - shutil.move(file_path,'./audios') - return os.path.join('./audios',os.path.basename(file_path)) - -def match_index(sid0): - folder=sid0.split(".")[0] - parent_dir="./logs/"+folder - if os.path.exists(parent_dir): - for filename in os.listdir(parent_dir): - if filename.endswith(".index"): - index_path=os.path.join(parent_dir,filename) - return index_path - else: - return '' - -def check_for_name(): - if len(names) > 0: - return sorted(names)[0] - else: - return '' - -def download_from_url(url, model): - if url == '': - return "URL cannot be left empty." - if model =='': - return "You need to name your model. For example: My-Model" - url = url.strip() - zip_dirs = ["zips", "unzips"] - for directory in zip_dirs: - if os.path.exists(directory): - shutil.rmtree(directory) - os.makedirs("zips", exist_ok=True) - os.makedirs("unzips", exist_ok=True) - zipfile = model + '.zip' - zipfile_path = './zips/' + zipfile - try: - if "drive.google.com" in url: - subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) - elif "mega.nz" in url: - m = Mega() - m.download_url(url, './zips') - else: - subprocess.run(["wget", url, "-O", zipfile_path]) - for filename in os.listdir("./zips"): - if filename.endswith(".zip"): - zipfile_path = os.path.join("./zips/",filename) - shutil.unpack_archive(zipfile_path, "./unzips", 'zip') - else: - return "No zipfile found." - for root, dirs, files in os.walk('./unzips'): - for file in files: - file_path = os.path.join(root, file) - if file.endswith(".index"): - os.mkdir(f'./logs/{model}') - shutil.copy2(file_path,f'./logs/{model}') - elif "G_" not in file and "D_" not in file and file.endswith(".pth"): - shutil.copy(file_path,f'./weights/{model}.pth') - shutil.rmtree("zips") - shutil.rmtree("unzips") - return "Success." - except: - return "There's been an error." -def success_message(face): - return f'{face.name} has been uploaded.', 'None' -def mouth(size, face, voice, faces): - if size == 'Half': - size = 2 - else: - size = 1 - if faces == 'None': - character = face.name - else: - if faces == 'Ben Shapiro': - character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4' - elif faces == 'Andrew Tate': - character = '/content/wav2lip-HD/inputs/tate-7.mp4' - command = "python inference.py " \ - "--checkpoint_path checkpoints/wav2lip.pth " \ - f"--face {character} " \ - f"--audio {voice} " \ - "--pads 0 20 0 0 " \ - "--outfile /content/wav2lip-HD/outputs/result.mp4 " \ - "--fps 24 " \ - f"--resize_factor {size}" - process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master') - stdout, stderr = process.communicate() - return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.' -eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli'] -eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O'] -chosen_voice = dict(zip(eleven_voices, eleven_voices_ids)) - -def stoptraining(mim): - if int(mim) == 1: - try: - CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True') - os.kill(PID, signal.SIGTERM) - except Exception as e: - print(f"Couldn't click due to {e}") - return ( - {"visible": False, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - ) - - -def elevenTTS(xiapi, text, id, lang): - if xiapi!= '' and id !='': - choice = chosen_voice[id] - CHUNK_SIZE = 1024 - url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}" - headers = { - "Accept": "audio/mpeg", - "Content-Type": "application/json", - "xi-api-key": xiapi - } - if lang == 'en': - data = { - "text": text, - "model_id": "eleven_monolingual_v1", - "voice_settings": { - "stability": 0.5, - "similarity_boost": 0.5 - } - } - else: - data = { - "text": text, - "model_id": "eleven_multilingual_v1", - "voice_settings": { - "stability": 0.5, - "similarity_boost": 0.5 - } - } - - response = requests.post(url, json=data, headers=headers) - with open('./temp_eleven.mp3', 'wb') as f: - for chunk in response.iter_content(chunk_size=CHUNK_SIZE): - if chunk: - f.write(chunk) - aud_path = save_to_wav('./temp_eleven.mp3') - return aud_path, aud_path - else: - tts = gTTS(text, lang=lang) - tts.save('./temp_gTTS.mp3') - aud_path = save_to_wav('./temp_gTTS.mp3') - return aud_path, aud_path - -def upload_to_dataset(files, dir): - if dir == '': - dir = './dataset' - if not os.path.exists(dir): - os.makedirs(dir) - count = 0 - for file in files: - path=file.name - shutil.copy2(path,dir) - count += 1 - return f' {count} files uploaded to {dir}.' - -def zip_downloader(model): - if not os.path.exists(f'./weights/{model}.pth'): - return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth' - index_found = False - for file in os.listdir(f'./logs/{model}'): - if file.endswith('.index') and 'added' in file: - log_file = file - index_found = True - if index_found: - return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" - else: - return f'./weights/{model}.pth', "Could not find Index file." - -with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app: - with gr.Tabs(): - with gr.TabItem("Inference"): - gr.HTML("

        RVC V2 Huggingface Version

        ") - gr.HTML(" Huggingface version made by Clebersla ") - gr.HTML("

        If you want to use this space privately, I recommend you duplicate the space.

        ") - - # Inference Preset Row - # with gr.Row(): - # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) - # mangio_preset_name_save = gr.Textbox( - # label="Your preset name" - # ) - # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") - - # Other RVC stuff - with gr.Row(): - sid0 = gr.Dropdown(label="1.Choose your Model.", choices=sorted(names), value=check_for_name()) - refresh_button = gr.Button("Refresh", variant="primary") - if check_for_name() != '': - get_vc(sorted(names)[0]) - vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0) - #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") - spk_item = gr.Slider( - minimum=0, - maximum=2333, - step=1, - label=i18n("请选择说话人id"), - value=0, - visible=False, - interactive=True, - ) - #clean_button.click(fn=clean, inputs=[], outputs=[sid0]) - sid0.change( - fn=get_vc, - inputs=[sid0], - outputs=[spk_item], - ) - but0 = gr.Button("Convert", variant="primary") - with gr.Row(): - with gr.Column(): - with gr.Row(): - dropbox = gr.File(label="Drop your audio here & hit the Reload button.") - with gr.Row(): - record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath") - with gr.Row(): - input_audio0 = gr.Dropdown( - label="2.Choose your audio.", - value="./audios/someguy.mp3", - choices=audio_files - ) - dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) - dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0]) - refresh_button2 = gr.Button("Refresh", variant="primary", size='sm') - record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) - record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0]) - with gr.Row(): - with gr.Accordion('Text To Speech', open=False): - with gr.Column(): - lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en') - api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='') - elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices) - with gr.Column(): - tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.") - tts_button = gr.Button(value="Speak") - tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0]) - with gr.Row(): - with gr.Accordion('Wav2Lip', open=False): - with gr.Row(): - size = gr.Radio(label='Resolution:',choices=['Half','Full']) - face = gr.UploadButton("Upload A Character",type='file') - faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) - with gr.Row(): - preview = gr.Textbox(label="Status:",interactive=False) - face.upload(fn=success_message,inputs=[face], outputs=[preview, faces]) - with gr.Row(): - animation = gr.Video(type='filepath') - refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation]) - with gr.Row(): - animate_button = gr.Button('Animate') - - with gr.Column(): - with gr.Accordion("Index Settings", open=False): - file_index1 = gr.Dropdown( - label="3. Path to your added.index file (if it didn't automatically find it.)", - choices=get_indexes(), - value=get_index(), - interactive=True, - ) - sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1]) - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index1] - ) - # file_big_npy1 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=0.66, - interactive=True, - ) - vc_output2 = gr.Audio( - label="Output Audio (Click on the Three Dots in the Right Corner to Download)", - type='filepath', - interactive=False, - ) - animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) - with gr.Accordion("Advanced Settings", open=False): - f0method0 = gr.Radio( - label="Optional: Change the Pitch Extraction Algorithm.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.", - choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny - value="rmvpe", - interactive=True, - ) - - crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.", - value=120, - interactive=True, - visible=False, - ) - f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length]) - filter_radius0 = gr.Slider( - minimum=0, - maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), - value=3, - step=1, - interactive=True, - ) - resample_sr0 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, - interactive=True, - visible=False - ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), - value=0.21, - interactive=True, - ) - protect0 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"), - value=0.33, - step=0.01, - interactive=True, - ) - formanting = gr.Checkbox( - value=bool(DoFormant), - label="[EXPERIMENTAL] Formant shift inference audio", - info="Used for male to female and vice-versa conversions", - interactive=True, - visible=True, - ) - - formant_preset = gr.Dropdown( - value='', - choices=get_fshift_presets(), - label="browse presets for formanting", - visible=bool(DoFormant), - ) - formant_refresh_button = gr.Button( - value='\U0001f504', - visible=bool(DoFormant), - variant='primary', - ) - #formant_refresh_button = ToolButton( elem_id='1') - #create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets") - - qfrency = gr.Slider( - value=Quefrency, - info="Default value is 1.0", - label="Quefrency for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - tmbre = gr.Slider( - value=Timbre, - info="Default value is 1.0", - label="Timbre for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - - formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) - frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) - formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) - frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) - formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) - with gr.Row(): - vc_output1 = gr.Textbox("") - f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False) - - but0.click( - vc_single, - [ - spk_item, - input_audio0, - vc_transform0, - f0_file, - f0method0, - file_index1, - # file_index2, - # file_big_npy1, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - protect0, - crepe_hop_length - ], - [vc_output1, vc_output2], - ) - - with gr.Accordion("Batch Conversion",open=False): - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "crepe", "rmvpe"], - value="rmvpe", - interactive=True, - ) - filter_radius1 = gr.Slider( - minimum=0, - maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), - value=3, - step=1, - interactive=True, - ) - with gr.Column(): - file_index3 = gr.Textbox( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), - value="", - interactive=True, - ) - file_index4 = gr.Dropdown( - label=i18n("自动检测index路径,下拉式选择(dropdown)"), - choices=sorted(index_paths), - interactive=True, - ) - refresh_button.click( - fn=lambda: change_choices()[1], - inputs=[], - outputs=file_index4, - ) - # file_big_npy2 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate2 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=1, - interactive=True, - ) - with gr.Column(): - resample_sr1 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, - interactive=True, - ) - rms_mix_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), - value=1, - interactive=True, - ) - protect1 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" - ), - value=0.33, - step=0.01, - interactive=True, - ) - with gr.Column(): - dir_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), - value="E:\codes\py39\\test-20230416b\\todo-songs", - ) - inputs = gr.File( - file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") - ) - with gr.Row(): - format1 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - but1 = gr.Button(i18n("转换"), variant="primary") - vc_output3 = gr.Textbox(label=i18n("输出信息")) - but1.click( - vc_multi, - [ - spk_item, - dir_input, - opt_input, - inputs, - vc_transform1, - f0method1, - file_index3, - file_index4, - # file_big_npy2, - index_rate2, - filter_radius1, - resample_sr1, - rms_mix_rate1, - protect1, - format1, - crepe_hop_length, - ], - [vc_output3], - ) - but1.click(fn=lambda: easy_uploader.clear()) - with gr.TabItem("Download Model"): - with gr.Row(): - url=gr.Textbox(label="Enter the URL to the Model:") - with gr.Row(): - model = gr.Textbox(label="Name your model:") - download_button=gr.Button("Download") - with gr.Row(): - status_bar=gr.Textbox(label="") - download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) - with gr.Row(): - gr.Markdown( - """ - Made with ❤️ by [Alice Oliveira](https://github.com/aliceoq) | Hosted with ❤️ by [Mateus Elias](https://github.com/mateuseap) - """ - ) - - def has_two_files_in_pretrained_folder(): - pretrained_folder = "./pretrained/" - if not os.path.exists(pretrained_folder): - return False - - files_in_folder = os.listdir(pretrained_folder) - num_files = len(files_in_folder) - return num_files >= 2 - - if has_two_files_in_pretrained_folder(): - print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------") - with gr.TabItem("Train", visible=False): - with gr.Row(): - with gr.Column(): - exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") - sr2 = gr.Radio( - label=i18n("目标采样率"), - choices=["40k", "48k"], - value="40k", - interactive=True, - visible=False - ) - if_f0_3 = gr.Radio( - label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), - choices=[True, False], - value=True, - interactive=True, - visible=False - ) - version19 = gr.Radio( - label="RVC version", - choices=["v1", "v2"], - value="v2", - interactive=True, - visible=False, - ) - np7 = gr.Slider( - minimum=0, - maximum=config.n_cpu, - step=1, - label="# of CPUs for data processing (Leave as it is)", - value=config.n_cpu, - interactive=True, - visible=True - ) - trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") - easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) - but1 = gr.Button("1. Process The Dataset", variant="primary") - info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="") - easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1]) - but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] - ) - with gr.Column(): - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("请指定说话人id"), - value=0, - interactive=True, - visible=False - ) - with gr.Accordion('GPU Settings', open=False, visible=False): - gpus6 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - visible=False - ) - gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) - f0method8 = gr.Radio( - label=i18n( - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" - ), - choices=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. - value="rmvpe", - interactive=True, - ) - - extraction_crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("crepe_hop_length"), - value=128, - interactive=True, - visible=False, - ) - f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length]) - but2 = gr.Button("2. Pitch Extraction", variant="primary") - info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8) - but2.click( - extract_f0_feature, - [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], - [info2], - ) - with gr.Row(): - with gr.Column(): - total_epoch11 = gr.Slider( - minimum=1, - maximum=5000, - step=10, - label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", - value=250, - interactive=True, - ) - butstop = gr.Button( - "Stop Training", - variant='primary', - visible=False, - ) - but3 = gr.Button("3. Train Model", variant="primary", visible=True) - - but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) - butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) - - - but4 = gr.Button("4.Train Index", variant="primary") - info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) - with gr.Accordion("Training Preferences (You can leave these as they are)", open=False): - #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) - with gr.Column(): - save_epoch10 = gr.Slider( - minimum=1, - maximum=200, - step=1, - label="Backup every X amount of epochs:", - value=10, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label="Batch Size (LEAVE IT unless you know what you're doing!):", - value=default_batch_size, - interactive=True, - ) - if_save_latest13 = gr.Checkbox( - label="Save only the latest '.ckpt' file to save disk space.", - value=True, - interactive=True, - ) - if_cache_gpu17 = gr.Checkbox( - label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.", - value=False, - interactive=True, - ) - if_save_every_weights18 = gr.Checkbox( - label="Save a small final model to the 'weights' folder at each save point.", - value=True, - interactive=True, - ) - zip_model = gr.Button('5. Download Model') - zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') - zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) - with gr.Group(): - with gr.Accordion("Base Model Locations:", open=False, visible=False): - pretrained_G14 = gr.Textbox( - label=i18n("加载预训练底模G路径"), - value="pretrained_v2/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - label=i18n("加载预训练底模D路径"), - value="pretrained_v2/f0D40k.pth", - interactive=True, - ) - gpus16 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, version19], - ) - version19.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15], - ) - if_f0_3.change( - change_f0, - [if_f0_3, sr2, version19], - [f0method8, pretrained_G14, pretrained_D15], - ) - but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) - but3.click( - click_train, - [ - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - ], - [ - info3, - butstop, - but3, - ], - ) - but4.click(train_index, [exp_dir1, version19], info3) - but5.click( - train1key, - [ - exp_dir1, - sr2, - if_f0_3, - trainset_dir4, - spk_id5, - np7, - f0method8, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - extraction_crepe_hop_length - ], - info3, - ) - - else: - print( - "Pretrained weights not downloaded. Disabling training tab.\n" - "Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n" - "-------------------------------\n" - ) - - app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True) -#endregion \ No newline at end of file diff --git a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_utils.py b/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_utils.py deleted file mode 100644 index b634ce380421571e6e07fb45dd59717b3f63115c..0000000000000000000000000000000000000000 --- a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_utils.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch -import numpy as np -import random -import onnxruntime as ort -def set_random_seed(seed=0): - ort.set_seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.backends.cudnn.deterministic = True - random.seed(seed) - np.random.seed(seed) - -def runonnx(model_path, **kwargs): - ort_session = ort.InferenceSession(model_path) - outputs = ort_session.run( - None, - kwargs - ) - return outputs \ No newline at end of file diff --git a/spaces/merve/data-leak/source/hidden-bias/style.css b/spaces/merve/data-leak/source/hidden-bias/style.css deleted file mode 100644 index 4b0d163f9dc4af367dc0b84036c5e177b8f4db0b..0000000000000000000000000000000000000000 --- a/spaces/merve/data-leak/source/hidden-bias/style.css +++ /dev/null @@ -1,275 +0,0 @@ -/* Copyright 2020 Google LLC. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -.tooltip { - top: -1000px; - position: fixed; - padding: 10px; - background: rgba(255, 255, 255, .90); - border: 1px solid lightgray; - pointer-events: none; - font-family: monospace; - font-size: 14px; - width: 170px; -} -.tooltip-hidden{ - opacity: 0; - transition: all .3s; - transition-delay: .1s; -} - -@media (max-width: 590px){ - div.tooltip{ - bottom: -1px; - width: calc(100%); - left: -1px !important; - right: -1px !important; - top: auto !important; - width: auto !important; - } -} - -/* Ensure the last panel can be activated on tall screens */ -@media (min-height: 1700px){ - #container{ - margin-bottom: 900px; - } -} - -.tooltip span{ - padding: 2px; -} - -svg{ - overflow: visible; -} - -.domain{ - display: none; -} - -text{ - /*pointer-events: none;*/ - text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff; -} - - - - - - -#container{ - position: relative; - width: auto; -} - -#container h3{ - font-weight: 500; -} - -#sections{ - width: 340px; -} - -#sections > div{ - background: white; - opacity: .2; - margin-bottom: 200px; - line-height: 1.4em; -} -#sections > div:last-child{ - padding-bottom: 80vh; -} -#sections > div.graph-scroll-active{ - opacity: 1; -} - -#graph{ - margin-left: 40px; - width: 500px; - position: -webkit-sticky; - position: sticky; - top: 0px; - float: right; -} - -@media (max-width: 925px) { - #graph{ - width: 100%; - margin-left: 0px; - float: none; - } - - #sections{ - width: auto; - position: relative; - margin: 0px auto; - } - - #sections > div{ - background: rgba(255,255,255,.5); - padding: 10px; - border-top: 1px solid; - border-bottom: 1px solid; - margin-bottom: 80vh; - } -} - - -.mono{ - font-family: monospace; -} - - -svg{ - overflow: visible; -} - - - - -.axis{ - font-size: 12px; -} -.axis{ - color: #999; -} -.axis text{ - fill: #999; -} -.axis line{ - stroke: #ccc; -} - -div.axis b{ - margin-bottom: 100px; - display: block; -} - -.axis .blink{ - color: orange; -} - - - - - - -.highlight{ - color: #fff; - padding-left: 3px; - padding-right: 3px; - padding-top: 1px; - padding-bottom: 1px; - border-radius: 3px; -} - -/*.highlight.blue{ background: blue; }*/ -/*.highlight.orange{ background: orange; }*/ -.highlight.yellow{ background: #ff0; color: #000; } -.highlight.blue{ background: #8effff; color: #000; } -.highlight.male{ background: #7DDAD3; color: #000; } -.highlight.female{ background: #9B86EF; color: #000; } - -.annotation .highlight{ - padding: 0px; - padding-left: 2px; - padding-right: 2px; - margin-left: -2px; - margin-right: -2px; - border-radius: 3px; - /*height: 12px;*/ - display: inline-block; -} - - -#graph .highlight.yellow, #graph .highlight.blue{ - padding-left: 0px; - padding: 0px; -} - - -.circle{ - background: #eee; - border: 1px solid #ccc; - font-family: monospace; - padding-left: 4.2px; - padding-right: 4.2px; - padding-top: 0px; - padding-bottom: 0px; - - border-radius: 1000px; - width: 20px; - height: 20px; -} - - -.strikethrough{ - text-decoration: line-through; - color: #000; -} - - -.annotation div{ - font-size: 12px; - line-height: 13px; - font-family: 'Google Sans', sans-serif; -} - - -.annotations path{ - fill: none; - stroke: black; - stroke-width: .5px; -} - - -.img-slide img{ - width: 30px; - transform: rotate(-90deg); - margin-left: -10px; - margin-right: -4px; - position: relative; - top: 5px; -} - -.img-slide img:nth-of-type(1){ - transform: rotate(90deg); - margin-left: -10px; - margin-right: -4px; - top: 0px; -} - - - - - -div.axis b{ - margin-bottom: 0px; -} - -div.axis{ - line-height: 14px; -} - - -circle:hover{ - stroke: #000; - stroke-width: 2; -} - - - - diff --git a/spaces/merve/uncertainty-calibration/source/third_party/topojson-server.js b/spaces/merve/uncertainty-calibration/source/third_party/topojson-server.js deleted file mode 100644 index 1dd21b5598fb337243b0e2be15d44d95e32ae03d..0000000000000000000000000000000000000000 --- a/spaces/merve/uncertainty-calibration/source/third_party/topojson-server.js +++ /dev/null @@ -1,2 +0,0 @@ -// https://github.com/topojson/topojson-server v3.0.1 Copyright 2019 Mike Bostock -!function(r,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((r=r||self).topojson=r.topojson||{})}(this,function(r){"use strict";var n=Object.prototype.hasOwnProperty;function t(r,n,t,e,o,i){3===arguments.length&&(e=i=Array,o=null);for(var a=new e(r=1<=r)throw new Error("full hashmap");l=a[c=c+1&f]}return a[c]=e,u[c]=i,i},maybeSet:function(e,i){for(var c=n(e)&f,l=a[c],s=0;l!=o;){if(t(l,e))return u[c];if(++s>=r)throw new Error("full hashmap");l=a[c=c+1&f]}return a[c]=e,u[c]=i,i},get:function(e,i){for(var c=n(e)&f,l=a[c],s=0;l!=o;){if(t(l,e))return u[c];if(++s>=r)break;l=a[c=c+1&f]}return i},keys:function(){for(var r=[],n=0,t=a.length;n>7^a[2]^a[3])}function f(r){var n,o,i,a,f=r.coordinates,c=r.lines,l=r.rings,s=function(){for(var r=t(1.4*f.length,A,E,Int32Array,-1,Int32Array),n=new Int32Array(f.length),e=0,o=f.length;e=0){var i=v[t];o===n&&i===e||o===e&&i===n||(++y,p[t]=1)}else g[t]=n,v[t]=e}}function A(r){return u(f[r])}function E(r,n){return e(f[r],f[n])}h=g=v=null;var L,S=function(r,n,t,e,o){3===arguments.length&&(e=Array,o=null);for(var i=new e(r=1<=r)throw new Error("full hashset");f=i[u=u+1&a]}return i[u]=e,!0},has:function(e){for(var u=n(e)&a,f=i[u],c=0;f!=o;){if(t(f,e))return!0;if(++c>=r)break;f=i[u=u+1&a]}return!1},values:function(){for(var r=[],n=0,t=i.length;n>1);no&&(o=n),ai&&(i=a)}function c(r){r.forEach(f)}function l(r){r.forEach(c)}for(var s in r)a(r[s]);return o>=t&&i>=e?[t,e,o,i]:void 0}(r=function(r){var n,t,e={};for(n in r)e[n]=null==(t=r[n])?{type:null}:("FeatureCollection"===t.type?function(r){var n={type:"GeometryCollection",geometries:r.features.map(l)};return null!=r.bbox&&(n.bbox=r.bbox),n}:"Feature"===t.type?l:s)(t);return e}(r)),a=o>0&&i&&function(r,t,e){var o=t[0],i=t[1],a=t[2],u=t[3],f=a-o?(e-1)/(a-o):1,c=u-i?(e-1)/(u-i):1;function l(r){return[Math.round((r[0]-o)*f),Math.round((r[1]-i)*c)]}function s(r,n){for(var t,e,a,u,l,s=-1,h=0,g=r.length,v=new Array(g);++s 2: - dim += list(range(2, grad_input.ndim)) - - grad_bias = grad_input.sum(dim).detach() - - return grad_input, grad_bias - - @staticmethod - def backward(ctx, gradgrad_input, gradgrad_bias): - out, = ctx.saved_tensors - gradgrad_out = fused.fused_bias_act( - gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale - ) - - return gradgrad_out, None, None, None - - -class FusedLeakyReLUFunction(Function): - @staticmethod - def forward(ctx, input, bias, negative_slope, scale): - empty = input.new_empty(0) - out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) - ctx.save_for_backward(out) - ctx.negative_slope = negative_slope - ctx.scale = scale - - return out - - @staticmethod - def backward(ctx, grad_output): - out, = ctx.saved_tensors - - grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( - grad_output, out, ctx.negative_slope, ctx.scale - ) - - return grad_input, grad_bias, None, None - - -class FusedLeakyReLU(nn.Module): - def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): - super().__init__() - - self.bias = nn.Parameter(torch.zeros(channel)) - self.negative_slope = negative_slope - self.scale = scale - - def forward(self, input): - return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) - - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): - if use_fallback or input.device.type == 'cpu': - return scale * F.leaky_relu( - input + bias.view((1, -1)+(1,)*(input.ndim-2)), negative_slope=negative_slope - ) - else: - return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/spaces/miku-hutao/vits-uma-genshin-honkai/commons.py b/spaces/miku-hutao/vits-uma-genshin-honkai/commons.py deleted file mode 100644 index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000 --- a/spaces/miku-hutao/vits-uma-genshin-honkai/commons.py +++ /dev/null @@ -1,172 +0,0 @@ -import math -import torch -from torch.nn import functional as F -import torch.jit - - -def script_method(fn, _rcb=None): - return fn - - -def script(obj, optimize=True, _frames_up=0, _rcb=None): - return obj - - -torch.jit.script_method = script_method -torch.jit.script = script - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/mipbkhn/BreastCancer/README.md b/spaces/mipbkhn/BreastCancer/README.md deleted file mode 100644 index 9785f80dd31ab918528e06c7fa9b6d88ced3f6da..0000000000000000000000000000000000000000 --- a/spaces/mipbkhn/BreastCancer/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Breast Cancer Detection -emoji: 😻 -colorFrom: red -colorTo: red -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/mkManishKumar/Bank-Customer-Churn/app.py b/spaces/mkManishKumar/Bank-Customer-Churn/app.py deleted file mode 100644 index e2adc6845c3573686571683a17ff11416849f4a8..0000000000000000000000000000000000000000 --- a/spaces/mkManishKumar/Bank-Customer-Churn/app.py +++ /dev/null @@ -1,67 +0,0 @@ -import joblib -import streamlit as st -import pandas as pd - -def main(): - st.title("Bank Customer Churn") - - credit_score = st.number_input("Credit Score") - country = st.selectbox("Country", options=['France', 'Spain', 'Germany']) - - if country: - st.success(country) - - gender = st.radio("Select Gender: ", ('Male', 'Female')) - if (gender == 'Male'): - st.success("Male") - else: - st.success("Female") - - age = st.number_input("Age") - tenure = st.number_input("Tenure") - balance = st.number_input("Balance") - products_number = st.number_input("Products Number") - credit_card = st.number_input("Credit Card") - active_member = st.number_input("Active Member") - estimated_salary = st.number_input("Estimated Salary") - submit_button = st.button("Submit") - - if submit_button: - # Process the form data - process_form_data(credit_score, country, gender, age, tenure, - balance, products_number, credit_card, active_member, estimated_salary) - -def process_form_data(credit_score, country, gender, age, tenure, - balance, products_number, credit_card, active_member, estimated_salary): - - - encoders = joblib.load('encoders .joblib') - model = joblib.load('rf_model.joblib') - scaler = joblib.load('StandardScaler.joblib') - - - dataDict = {'credit_score': credit_score, 'country': country, 'gender': gender, 'age': age, 'tenure':tenure, - 'balance':balance, 'products_number': products_number, 'credit_card': credit_card, - 'active_member': active_member, 'estimated_salary': estimated_salary} - - df = pd.DataFrame([dataDict]) - - decodedData = df.copy() - for col in ['country', 'gender']: - encoder = encoders[col] - decodedData[col] = encoder.transform(decodedData[col]) - # - decodedData = scaler.transform(decodedData) - result = model.predict(decodedData) - - st.write(df) - - if result == 1: - st.warning("Churn") - if result == 0: - st.success("Stay") - - - -if __name__ == "__main__": - main() diff --git a/spaces/mlkorra/competitive-analysis/app.py b/spaces/mlkorra/competitive-analysis/app.py deleted file mode 100644 index adcbacc637deeb548a7fe11770b7c392a76704c4..0000000000000000000000000000000000000000 --- a/spaces/mlkorra/competitive-analysis/app.py +++ /dev/null @@ -1,156 +0,0 @@ -from json import load -import streamlit as st -import pandas as pd -import numpy as np -import re -import string - -from nltk.stem import WordNetLemmatizer -import umap - -import plotly.graph_objects as go -from plotly import tools -import plotly.offline as py -import plotly.express as px - -from nltk.corpus import stopwords -import nltk -nltk.download('stopwords') -nltk.download('wordnet') -from bertopic import BERTopic -import pickle -import os - -def read_markdown(path,parent='about/'): - with open(os.path.join(parent,path)) as f: - return f.read() - -def visualizer(prob_req, embed, df, index, company_name): - - with st.spinner("Visualizing the results !!!"): - - fname = 'topicmodel/saving_example.sav' - reducer= pickle.load((open(fname, 'rb'))) #load the umap dimensionality reduction model trained on rest of probablities - embed_req= reducer.transform(prob_req) - - #add scatter plot for all embeddings from our dataset - fig1 = px.scatter( - embed, x=0, y=1, - color=df.iloc[index]['headquarters'], labels={'color': 'states'}, hover_name= df.iloc[index]['company_name'] + " with industry group: "+ df.iloc[index]['industry_groups']) - #add the data for users request and display - fig1.add_trace( - go.Scatter( - x=embed_req[:,0], - y=embed_req[:,1], - mode='markers', - marker_symbol="hexagon2", marker_size=15, - showlegend=True, name= company_name, hovertext= company_name)) - st.plotly_chart(fig1) - -def clean_text(text): - - """util function to clean the text""" - - text = str(text).lower() - text = re.sub('https?://\S+|www\.\S+', '', text) - text = re.sub('<.,*?>+', '', text) - text = re.sub('[%s]' % re.escape(string.punctuation), '', text) - - return text - -def preprocess(name, group, state, states_used, desc): - desc = desc.replace(name,'') - cat = "".join(cat for cat in group.split(",")) - cleaned= desc + " " + cat - - stop_words = stopwords.words('english') - lemmatizer = WordNetLemmatizer() - text = clean_text(cleaned) - text = ' '.join(w for w in text.split(' ') if w not in stop_words) - text = ' '.join(lemmatizer.lemmatize(w) for w in text.split(' ')) - return text - -@st.cache(persist=True,suppress_st_warning=True,show_spinner=False) -def load_topic_model(model_path, name, group, state, states_used, desc): - - with st.spinner("Creating Topic Models ....."): - - #load Bertopic - model=BERTopic.load(model_path) - #load dataset (used for creating scatter plot) - - data_path = 'topicmodel/data.csv' - df = pd.read_csv(data_path) - #load embeddings reduced by UMAP for the points to be displayed by scatter plot - - embeddings_path = 'topicmodel/embed.npy' - embeddings = np.load(embeddings_path) - #preprocess user inputs - request= preprocess(name, group, state, states_used, desc) - index=[] - #only select states that user wants to compare - for state_used in states_used: - index.extend(df.index[df['headquarters'].str.contains(state_used)].tolist()) - select=embeddings[index] - - #use bert topic to get probabilities - topic, prob_req= model.transform([request]) - #st.text("Modelling done! plotting results now...") - - return topic, prob_req, select, df, index - -def app(): - - st.title("Competitive Analysis of Companies ") - - check_examples = st.sidebar.checkbox("Try Examples!") - - st.markdown(read_markdown("userguide.md")) - - states= ['Georgia', 'California', 'Texas', 'Tennessee', 'Massachusetts', - 'New York', 'Ohio', 'Delaware', 'Florida', 'Washington', - 'Connecticut', 'Colorado', 'South Carolina', 'New Jersey', - 'Michigan', 'Maryland', 'Pennsylvania', 'Virginia', 'Vermont', - 'Minnesota', 'Illinois', 'North Carolina', 'Montana', 'Kentucky', - 'Oregon', 'Iowa', 'District of Columbia', 'Arizona', 'Wisconsin', - 'Louisiana', 'Idaho', 'Utah', 'Nevada', 'Nebraska', 'New Mexico', - 'Missouri', 'Kansas', 'New Hampshire', 'Wyoming', 'Arkansas', - 'Indiana', 'North Dakota', 'Hawaii', 'Alabama', 'Maine', - 'Rhode Island', 'Mississippi', 'Alaska', 'Oklahoma', - 'Washington DC', 'Giorgia'] - #state= st.selectbox('Select state the company is based in', states) - #states_used = st.multiselect('Select states you want to analyse', states) - - examples = [['Coursera','Education','California',['California','New York','Ohio'],'We are a social entrepreneurship company that partners with the top universities in the world to offer courses online for anyone to take, for free. We envision a future where the top universities are educating not only thousands of students, but millions. Our technology enables the best professors to teach tens or hundreds of thousands of students']] - - if check_examples: - example = examples[0] - companyname = st.text_input('Input company name here:', example[0]) - companygrp = st.text_input('Input industry group here:', example[1]) - companydesc = st.text_input("Input company description: (can be found in the company's linkedin page)", example[4]) - state = st.selectbox('Select state the company is based in',states,index = 1) - states_used = st.multiselect('Select states you want to analyse', states,example[3]) - #model_path = 'topicmodel/my_model.pkl' - #topic,prob_req,embed,df,index = load_topic_model(model_path,example[0],example[1],example[2],example[3],example[4]) - #visualizer(prob_req,embed,df,index,company_name) - - else: - - companyname = st.text_input('Input company name here:', value="") - companygrp = st.text_input('Input industry group here:', value="") - companydesc = st.text_input("Input company description: (can be found in the company's linkedin page)", value="") - state= st.selectbox('Select state the company is based in', states) - states_used = st.multiselect('Select states you want to analyse', states) - - if(st.button("Analyse Competition")): - - if companyname=="" or companydesc=="" or companygrp=="" or states_used==[]: - st.error("Some fields are empty!") - else: - model_path = 'topicmodel/my_model.pkl' - topic,prob_req,embed,df,index = load_topic_model(model_path, companyname, companygrp, state, states_used, companydesc) - visualizer(prob_req, embed, df, index, companyname) - - -if __name__ == "__main__": - app() diff --git a/spaces/mmkuznecov/faceblur/app.py b/spaces/mmkuznecov/faceblur/app.py deleted file mode 100644 index e68dd69586c7b3944a01a20e0b1752b4f7435c0d..0000000000000000000000000000000000000000 --- a/spaces/mmkuznecov/faceblur/app.py +++ /dev/null @@ -1,37 +0,0 @@ -import gradio as gr -import cv2 -import numpy as np -from PIL import Image -from yolov8 import YOLOv8Face - -model = YOLOv8Face('weights/yolov8n-face.onnx') - - -def detect_and_blur_faces(image, blur_style): - boxes, scores, classids, landmarks = model.detect(image) - - output_image = image.copy() - - for i, box in enumerate(boxes): - x1, y1, w, h = [int(val) for val in box] - x2, y2 = x1 + w, y1 + h - - face = output_image[y1:y2, x1:x2] - blurred_face = cv2.GaussianBlur(face, (99, 99), 30) - - if blur_style == 'Oval': - mask = np.zeros((y2-y1, x2-x1, 3), dtype=np.uint8) - ellipse_mask = cv2.ellipse(mask, (w//2, h//2), (w//2, h//2), 0, 0, 360, (255, 255, 255), -1) - blurred_face = np.where(ellipse_mask==np.array([255, 255, 255]), blurred_face, face) - - output_image[y1:y2, x1:x2] = blurred_face - - return output_image - - -# Set up the Gradio interface. -image_input = gr.inputs.Image(shape=(None, None)) -blur_style = gr.inputs.Radio(['Rectangle', 'Oval'], label="Blur Style") -image_output = gr.outputs.Image(type='numpy') - -gr.Interface(fn=detect_and_blur_faces, inputs=[image_input, blur_style], outputs=image_output, title="Face Detection and Blurring").launch() \ No newline at end of file diff --git a/spaces/mrdbourke/foodvision_mini/app.py b/spaces/mrdbourke/foodvision_mini/app.py deleted file mode 100644 index 790c523922aec5041e97ecd9de99f0961fa5f0c5..0000000000000000000000000000000000000000 --- a/spaces/mrdbourke/foodvision_mini/app.py +++ /dev/null @@ -1,77 +0,0 @@ -### 1. Imports and class names setup ### -import gradio as gr -import os -import torch - -from model import create_effnetb2_model -from timeit import default_timer as timer -from typing import Tuple, Dict - -# Setup class names -class_names = ["pizza", "steak", "sushi"] - -### 2. Model and transforms preparation ### - -# Create EffNetB2 model -effnetb2, effnetb2_transforms = create_effnetb2_model( - num_classes=3, # len(class_names) would also work -) - -# Load saved weights -effnetb2.load_state_dict( - torch.load( - f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", - map_location=torch.device("cpu"), # load to CPU - ) -) - -### 3. Predict function ### - -# Create predict function -def predict(img) -> Tuple[Dict, float]: - """Transforms and performs a prediction on img and returns prediction and time taken. - """ - # Start the timer - start_time = timer() - - # Transform the target image and add a batch dimension - img = effnetb2_transforms(img).unsqueeze(0) - - # Put model into evaluation mode and turn on inference mode - effnetb2.eval() - with torch.inference_mode(): - # Pass the transformed image through the model and turn the prediction logits into prediction probabilities - pred_probs = torch.softmax(effnetb2(img), dim=1) - - # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter) - pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} - - # Calculate the prediction time - pred_time = round(timer() - start_time, 5) - - # Return the prediction dictionary and prediction time - return pred_labels_and_probs, pred_time - -### 4. Gradio app ### - -# Create title, description and article strings -title = "FoodVision Mini 🍕🥩🍣" -description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi." -article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." - -# Create examples list from "examples/" directory -example_list = [["examples/" + example] for example in os.listdir("examples")] - -# Create the Gradio demo -demo = gr.Interface(fn=predict, # mapping function from input to output - inputs=gr.Image(type="pil"), # what are the inputs? - outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs? - gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs - # Create examples list from "examples/" directory - examples=example_list, - title=title, - description=description, - article=article) - -# Launch the demo! -demo.launch() diff --git a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/__init__.py b/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/__init__.py deleted file mode 100644 index 8b3c9cdc35a03a4e4585bd6bbc9c793331eb1723..0000000000000000000000000000000000000000 --- a/spaces/mshkdm/VToonify/vtoonify/model/stylegan/lpips/__init__.py +++ /dev/null @@ -1,161 +0,0 @@ - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -#from skimage.measure import compare_ssim -from skimage.metrics import structural_similarity as compare_ssim -import torch -from torch.autograd import Variable - -from model.stylegan.lpips import dist_model - -class PerceptualLoss(torch.nn.Module): - def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric) - # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss - super(PerceptualLoss, self).__init__() - print('Setting up Perceptual loss...') - self.use_gpu = use_gpu - self.spatial = spatial - self.gpu_ids = gpu_ids - self.model = dist_model.DistModel() - self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids) - print('...[%s] initialized'%self.model.name()) - print('...Done') - - def forward(self, pred, target, normalize=False): - """ - Pred and target are Variables. - If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] - If normalize is False, assumes the images are already between [-1,+1] - - Inputs pred and target are Nx3xHxW - Output pytorch Variable N long - """ - - if normalize: - target = 2 * target - 1 - pred = 2 * pred - 1 - - return self.model.forward(target, pred) - -def normalize_tensor(in_feat,eps=1e-10): - norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True)) - return in_feat/(norm_factor+eps) - -def l2(p0, p1, range=255.): - return .5*np.mean((p0 / range - p1 / range)**2) - -def psnr(p0, p1, peak=255.): - return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2)) - -def dssim(p0, p1, range=255.): - return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. - -def rgb2lab(in_img,mean_cent=False): - from skimage import color - img_lab = color.rgb2lab(in_img) - if(mean_cent): - img_lab[:,:,0] = img_lab[:,:,0]-50 - return img_lab - -def tensor2np(tensor_obj): - # change dimension of a tensor object into a numpy array - return tensor_obj[0].cpu().float().numpy().transpose((1,2,0)) - -def np2tensor(np_obj): - # change dimenion of np array into tensor array - return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) - -def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False): - # image tensor to lab tensor - from skimage import color - - img = tensor2im(image_tensor) - img_lab = color.rgb2lab(img) - if(mc_only): - img_lab[:,:,0] = img_lab[:,:,0]-50 - if(to_norm and not mc_only): - img_lab[:,:,0] = img_lab[:,:,0]-50 - img_lab = img_lab/100. - - return np2tensor(img_lab) - -def tensorlab2tensor(lab_tensor,return_inbnd=False): - from skimage import color - import warnings - warnings.filterwarnings("ignore") - - lab = tensor2np(lab_tensor)*100. - lab[:,:,0] = lab[:,:,0]+50 - - rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1) - if(return_inbnd): - # convert back to lab, see if we match - lab_back = color.rgb2lab(rgb_back.astype('uint8')) - mask = 1.*np.isclose(lab_back,lab,atol=2.) - mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis]) - return (im2tensor(rgb_back),mask) - else: - return im2tensor(rgb_back) - -def rgb2lab(input): - from skimage import color - return color.rgb2lab(input / 255.) - -def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): - image_numpy = image_tensor[0].cpu().float().numpy() - image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor - return image_numpy.astype(imtype) - -def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): - return torch.Tensor((image / factor - cent) - [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) - -def tensor2vec(vector_tensor): - return vector_tensor.data.cpu().numpy()[:, :, 0, 0] - -def voc_ap(rec, prec, use_07_metric=False): - """ ap = voc_ap(rec, prec, [use_07_metric]) - Compute VOC AP given precision and recall. - If use_07_metric is true, uses the - VOC 07 11 point method (default:False). - """ - if use_07_metric: - # 11 point metric - ap = 0. - for t in np.arange(0., 1.1, 0.1): - if np.sum(rec >= t) == 0: - p = 0 - else: - p = np.max(prec[rec >= t]) - ap = ap + p / 11. - else: - # correct AP calculation - # first append sentinel values at the end - mrec = np.concatenate(([0.], rec, [1.])) - mpre = np.concatenate(([0.], prec, [0.])) - - # compute the precision envelope - for i in range(mpre.size - 1, 0, -1): - mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) - - # to calculate area under PR curve, look for points - # where X axis (recall) changes value - i = np.where(mrec[1:] != mrec[:-1])[0] - - # and sum (\Delta recall) * prec - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) - return ap - -def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): -# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): - image_numpy = image_tensor[0].cpu().float().numpy() - image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor - return image_numpy.astype(imtype) - -def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): -# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): - return torch.Tensor((image / factor - cent) - [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/adam.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/adam.py deleted file mode 100644 index d3ae9e64a74774310adcd9968d2eae23368890f9..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/adam.py +++ /dev/null @@ -1,239 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import math -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import Any, List - -import torch -import torch.distributed as dist -import torch.optim -from fairseq.dataclass import FairseqDataclass -from fairseq.optim import FairseqOptimizer, register_optimizer -from fairseq.optim.fused_adam import get_fused_adam_class -from omegaconf import II, OmegaConf - - -logger = logging.getLogger(__name__) - - -@dataclass -class FairseqAdamConfig(FairseqDataclass): - adam_betas: Any = field( - default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"} - ) - adam_eps: float = field( - default=1e-8, metadata={"help": "epsilon for Adam optimizer"} - ) - weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) - use_old_adam: bool = field( - default=False, metadata={"help": "Use fairseq.optim.adam.Adam"} - ) - fp16_adam_stats: bool = field( - default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} - ) - # TODO common vars below in parent - tpu: bool = II("common.tpu") - lr: List[float] = II("optimization.lr") - - -@register_optimizer("adam", dataclass=FairseqAdamConfig) -class FairseqAdam(FairseqOptimizer): - """Adam optimizer for fairseq. - - Important note: this optimizer corresponds to the "AdamW" variant of - Adam in its weight decay behavior. As such, it is most closely - analogous to torch.optim.AdamW from PyTorch. - """ - - def __init__(self, cfg: FairseqAdamConfig, params): - super().__init__(cfg) - fused_adam_cls = get_fused_adam_class() - use_fused_adam = ( - not getattr(cfg, "use_old_adam", False) - and fused_adam_cls is not None - and torch.cuda.is_available() - ) - if getattr(cfg, "tpu", False): - if self.cfg.fp16_adam_stats: - raise NotImplementedError("--fp16-adam-stats is only supported on GPU") - # on TPUs we use the Adam defined here, since it - # automatically casts gradients to FP32 - self._optimizer = Adam(params, **self.optimizer_config) - elif use_fused_adam: - logger.info("using FusedAdam") - self._optimizer = fused_adam_cls( - params, - use_fp16_stats=self.cfg.fp16_adam_stats, - **self.optimizer_config - ) - else: - if self.cfg.fp16_adam_stats: - raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1") - self._optimizer = Adam(params, **self.optimizer_config) - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.cfg.lr[0] - if isinstance(self.cfg.lr, Collection) - else self.cfg.lr, - "betas": eval(self.cfg.adam_betas) - if isinstance(self.cfg.adam_betas, str) - else OmegaConf.to_container(self.cfg.adam_betas), - "eps": self.cfg.adam_eps, - "weight_decay": self.cfg.weight_decay, - } - - def average_params(self): - """Reduce Params is only used during BMUF distributed training.""" - state_dict = self.optimizer.state_dict() - total_gpus = float(dist.get_world_size()) - - for _, value in state_dict["state"].items(): - value["exp_avg"] /= total_gpus - value["exp_avg_sq"] /= total_gpus - dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM) - dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM) - - -class Adam(torch.optim.Optimizer): - r"""Implements Adam algorithm. - - This implementation is modified from torch.optim.Adam based on: - `Fixed Weight Decay Regularization in Adam` - (see https://arxiv.org/abs/1711.05101) - - It has been proposed in `Adam: A Method for Stochastic Optimization`_. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - - .. _Adam\: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__( - self, - params, - lr=1e-3, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=0, - amsgrad=False, - ): - defaults = dict( - lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad - ) - super(Adam, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data - if grad.dtype in {torch.float16, torch.bfloat16}: - grad = grad.float() - if grad.is_sparse: - raise RuntimeError( - "Adam does not support sparse gradients, please consider SparseAdam instead" - ) - amsgrad = group.get("amsgrad", False) - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state = self.state[p] - - # State initialization - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p_data_fp32) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) - else: - state["exp_avg"] = state["exp_avg"].to(p_data_fp32) - state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) - if amsgrad: - state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( - p_data_fp32 - ) - - exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] - if amsgrad: - max_exp_avg_sq = state["max_exp_avg_sq"] - beta1, beta2 = group["betas"] - - state["step"] += 1 - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - if amsgrad: - # Maintains the maximum of all 2nd moment running avg. till now - torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) - # Use the max. for normalizing running avg. of gradient - denom = max_exp_avg_sq.sqrt().add_(group["eps"]) - else: - denom = exp_avg_sq.sqrt().add_(group["eps"]) - - bias_correction1 = 1 - beta1 ** state["step"] - bias_correction2 = 1 - beta2 ** state["step"] - step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 - - if group["weight_decay"] != 0: - p_data_fp32.add_( - p_data_fp32, alpha=-group["weight_decay"] * group["lr"] - ) - - p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/frm_text_to_speech.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/frm_text_to_speech.py deleted file mode 100644 index 1fa9b0f83e742aefce764e2858a81f99db911afd..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/frm_text_to_speech.py +++ /dev/null @@ -1,56 +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 fairseq.data.audio.frm_text_to_speech_dataset import FrmTextToSpeechDatasetCreator -from fairseq.tasks import register_task -from fairseq.tasks.text_to_speech import TextToSpeechTask - - -logging.basicConfig( - format='%(asctime)s | %(levelname)s | %(name)s | %(message)s', - datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO -) -logger = logging.getLogger(__name__) - - -@register_task('frm_text_to_speech') -class FrmTextToSpeechTask(TextToSpeechTask): - @staticmethod - def add_args(parser): - TextToSpeechTask.add_args(parser) - parser.add_argument( - "--do_chunk", action="store_true", help="train on chunks" - ) - parser.add_argument("--chunk_bound", default=-1, type=int) - parser.add_argument("--chunk_init", default=50, type=int) - parser.add_argument("--chunk_incr", default=5, type=int) - parser.add_argument("--add_eos", action="store_true") - parser.add_argument("--dedup", action="store_true") - parser.add_argument("--ref_fpu", default=-1, type=float) - - def load_dataset(self, split, **unused_kwargs): - is_train_split = split.startswith("train") - pre_tokenizer = self.build_tokenizer(self.args) - bpe_tokenizer = self.build_bpe(self.args) - self.datasets[split] = FrmTextToSpeechDatasetCreator.from_tsv( - self.args.data, - self.data_cfg, - split, - self.src_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split=is_train_split, - n_frames_per_step=self.args.n_frames_per_step, - speaker_to_id=self.speaker_to_id, - do_chunk=self.args.do_chunk, - chunk_bound=self.args.chunk_bound, - chunk_init=self.args.chunk_init, - chunk_incr=self.args.chunk_incr, - add_eos=self.args.add_eos, - dedup=self.args.dedup, - ref_fpu=self.args.ref_fpu - ) diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Risale I Hamidiye Pdf Free.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Risale I Hamidiye Pdf Free.md deleted file mode 100644 index 429551ebf3dbea2558e48c7ba39d23cda74cc489..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Risale I Hamidiye Pdf Free.md +++ /dev/null @@ -1,24 +0,0 @@ - -

        Risale-i Hamidiye: A Classic Work on the Life and Prophethood of Muhammad

        -

        Risale-i Hamidiye is a book written by Huseyin Cisri, a prominent scholar and jurist from Syria in the late 19th century. The book is a comprehensive biography of Prophet Muhammad (peace be upon him) and a defense of his prophethood against the claims of Jews, Christians, and atheists. The book also explains the wisdom and benefits of the Islamic teachings and practices, such as prayer, fasting, charity, pilgrimage, modesty, marriage, justice, and penal laws.

        -

        risale i hamidiye pdf free


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        The book was dedicated to Sultan Abdulhamid II, the Ottoman caliph at the time, and was named after him. The book was well received by the sultan and the public, and was translated into Turkish by Ismail Hakki of Manastir, a famous scholar and writer from Macedonia. The book contains 114 proofs of Muhammad's prophethood, derived from the Quran, the Sunnah (the sayings and actions of Muhammad), and the previous scriptures such as the Torah, the Psalms, and the Gospel. The book also refutes some of the common misconceptions and objections raised by the opponents of Islam, such as Islam being spread by the sword, evolution theory, bad scholars, and Islam being outdated.

        -

        Risale-i Hamidiye is a valuable source of information and inspiration for anyone who wants to learn more about Prophet Muhammad (peace be upon him) and his message. The book is available online in PDF format for free download from various websites. You can also find printed copies of the book in some libraries and bookstores.

        - -

        Some of the topics covered in Risale-i Hamidiye are:

        -

        -
          -
        • The proofs of God's existence and unity from the signs of creation and the testimony of reason.
          • -
        • The necessity of revelation and prophethood for the guidance of humanity.
          • -
        • The miracles and prophecies of Prophet Muhammad (peace be upon him) as evidence of his truthfulness.
          • -
        • The excellence and superiority of Prophet Muhammad (peace be upon him) over all other prophets and messengers.
          • -
        • The authenticity and preservation of the Quran as the final and perfect word of God.
          • -
        • The harmony and consistency of the Quran with the previous scriptures and natural sciences.
          • -
        • The beauty and eloquence of the Quran as a linguistic miracle.
          • -
        • The benefits and wisdoms of the Islamic laws and morals for individual and social welfare.
          • -
        • The refutation of some false religions and ideologies that contradict Islam.
          • -
        • The invitation to embrace Islam as the only true and universal religion.
          • -
        -

        Risale-i Hamidiye is a masterpiece of Islamic literature that combines rational arguments, scriptural evidences, historical facts, and spiritual insights. It is a must-read for anyone who wants to increase their knowledge and faith in Islam. It is also a useful tool for da'wah (inviting others to Islam) and apologetics (defending Islam against criticism). The book is written in a clear and eloquent style that appeals to both scholars and laymen. It is divided into 114 chapters, corresponding to the number of surahs (chapters) in the Quran. Each chapter deals with a specific topic or issue related to Islam and Prophet Muhammad (peace be upon him).

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        -
        -
        \ No newline at end of file diff --git a/spaces/nguyennghia0902/SentimentAnalysis_usingBERT/streamlit_app.py/Homepage.py b/spaces/nguyennghia0902/SentimentAnalysis_usingBERT/streamlit_app.py/Homepage.py deleted file mode 100644 index 6a75ca955c6435bd6aafb68618f872e7aaaebf45..0000000000000000000000000000000000000000 --- a/spaces/nguyennghia0902/SentimentAnalysis_usingBERT/streamlit_app.py/Homepage.py +++ /dev/null @@ -1,43 +0,0 @@ -import streamlit as st -from st_pages import Page, show_pages - -st.set_page_config(page_title="Sentiment Analysis", page_icon="🏠") - -show_pages( - [ - Page("streamlit_app.py/Homepage.py", "Home", "🏠"), - Page( - "streamlit_app.py/pages/Sentiment_Analysis.py", "Sentiment Analysis", "📝" - ), - ] -) - -st.title("Seminar Công nghệ Tri thức - Transformer trong NLP") -st.markdown( - """ - **Team members:** - | Student ID | Full Name | - | ---------- | ------------------------ | - | 19120600 | Bùi Nguyên Nghĩa | - | 19120607 | Phạm Thị Nguyệt | - """ -) - -st.header("The Need for Sentiment Analysis") -st.markdown( - """ - Sentiment analysis algorithms are used to detect sentiment in a comment or a review. - It is said that around 90% of consumers read online reviews before visiting a business or buying a product. - These reviews can be positive or negative or neutral, and it is important to know what the customers are saying about your business. - """ -) - -st.header("Technology used") -st.markdown( - """ - In this demo, we used BERT as the model for sentiment analysis. BERT is a transformer-based model that was proposed in 2018 by Google. - It is a pre-trained model that can be used for various NLP tasks such as sentiment analysis, question answering, etc. - """ -) - - diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/colormap.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/colormap.py deleted file mode 100644 index 14ded1659b40b161358c4aaf9cc84ffe0ffafe64..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/utils/colormap.py +++ /dev/null @@ -1,158 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -""" -An awesome colormap for really neat visualizations. -Copied from Detectron, and removed gray colors. -""" - -import numpy as np -import random - -__all__ = ["colormap", "random_color", "random_colors"] - -# fmt: off -# RGB: -_COLORS = np.array( - [ - 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, - 0.333, 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, - 0.000, 0.000, 0.000, - 0.143, 0.143, 0.143, - 0.857, 0.857, 0.857, - 1.000, 1.000, 1.000 - ] -).astype(np.float32).reshape(-1, 3) -# fmt: on - - -def colormap(rgb=False, maximum=255): - """ - Args: - rgb (bool): whether to return RGB colors or BGR colors. - maximum (int): either 255 or 1 - - Returns: - ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] - """ - assert maximum in [255, 1], maximum - c = _COLORS * maximum - if not rgb: - c = c[:, ::-1] - return c - - -def random_color(rgb=False, maximum=255): - """ - Args: - rgb (bool): whether to return RGB colors or BGR colors. - maximum (int): either 255 or 1 - - Returns: - ndarray: a vector of 3 numbers - """ - idx = np.random.randint(0, len(_COLORS)) - ret = _COLORS[idx] * maximum - if not rgb: - ret = ret[::-1] - return ret - - -def random_colors(N, rgb=False, maximum=255): - """ - Args: - N (int): number of unique colors needed - rgb (bool): whether to return RGB colors or BGR colors. - maximum (int): either 255 or 1 - - Returns: - ndarray: a list of random_color - """ - indices = random.sample(range(len(_COLORS)), N) - ret = [_COLORS[i] * maximum for i in indices] - if not rgb: - ret = [x[::-1] for x in ret] - return ret - - -if __name__ == "__main__": - import cv2 - - size = 100 - H, W = 10, 10 - canvas = np.random.rand(H * size, W * size, 3).astype("float32") - for h in range(H): - for w in range(W): - idx = h * W + w - if idx >= len(_COLORS): - break - canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] - cv2.imshow("a", canvas) - cv2.waitKey(0) diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/builtin_datasets.md b/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/builtin_datasets.md deleted file mode 100644 index 0ba82423ad498bdd86274ada56a201134a590d94..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/builtin_datasets.md +++ /dev/null @@ -1 +0,0 @@ -../../datasets/README.md \ No newline at end of file diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/dev/run_instant_tests.sh b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/dev/run_instant_tests.sh deleted file mode 100644 index 23a9c67cefe3cfca790181c90b27f2471d8a7771..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/dev/run_instant_tests.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -e -# Copyright (c) Facebook, Inc. and its affiliates. - -BIN="python train_net.py" -OUTPUT="instant_test_output" -NUM_GPUS=2 -SOLVER_IMS_PER_BATCH=$((NUM_GPUS * 2)) - -CFG_LIST=( "${@:1}" ) -if [ ${#CFG_LIST[@]} -eq 0 ]; then - CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml ) -fi - -echo "========================================================================" -echo "Configs to run:" -echo "${CFG_LIST[@]}" -echo "========================================================================" - -for cfg in "${CFG_LIST[@]}"; do - echo "========================================================================" - echo "Running $cfg ..." - echo "========================================================================" - $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \ - SOLVER.IMS_PER_BATCH $SOLVER_IMS_PER_BATCH \ - OUTPUT_DIR "$OUTPUT" - rm -rf "$OUTPUT" -done - diff --git a/spaces/nilaymodi/dandelin-vilt-b32-finetuned-vqa/app.py b/spaces/nilaymodi/dandelin-vilt-b32-finetuned-vqa/app.py deleted file mode 100644 index 07f4abab652bc7ebf13f61160be67be837cae28d..0000000000000000000000000000000000000000 --- a/spaces/nilaymodi/dandelin-vilt-b32-finetuned-vqa/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/dandelin/vilt-b32-finetuned-vqa").launch() \ No newline at end of file diff --git a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cache_aligned_vector.h b/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cache_aligned_vector.h deleted file mode 100644 index 871298d25b9293fa8b3c1acf97f109e007f5fd9e..0000000000000000000000000000000000000000 --- a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cache_aligned_vector.h +++ /dev/null @@ -1,1117 +0,0 @@ -/* - * Copyright 2021 Google LLC - * - * 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. - */ - -#ifndef LYRA_CODEC_SPARSE_MATMUL_VECTOR_CACHE_ALIGNED_VECTOR_H_ -#define LYRA_CODEC_SPARSE_MATMUL_VECTOR_CACHE_ALIGNED_VECTOR_H_ - -#if defined __aarch64__ -#include -#endif -#if defined __AVX__ || defined __AVX2__ -#include -#endif - -#include -#include -#include -#include -#include -#include -#include - -#include "absl/strings/str_format.h" -#include "sparse_matmul/numerics/fast_transcendentals.h" -#include "sparse_matmul/numerics/fixed_types.h" -#include "sparse_matmul/numerics/type_utils.h" -#include "sparse_matmul/os/coop_threads.h" -#include "sparse_matmul/vector/aligned_malloc.h" - -namespace csrblocksparse { - -template -class MutableVectorView; -template -class VectorView; - -// CacheAlignedVector is a simple vector-like class that makes sure its -// underlying buffer is aligned to a |kCacheLineSize| boundary. It is meant -// for numeric computation and cannot be used to store objects that are -// not POD as it will neither call their constructors nor destructors. -// -// It is meant to be used with the CSRBlockSparseMatrix class for -// implenting basic neural network layers composed of SpMV. -// -// This class is thread compatible. -template -class CacheAlignedVector { - static_assert(std::is_pod::value, - "CacheAlignedVector can only be" - " used with POD"); - - public: - using value_type = DataType; - - explicit CacheAlignedVector(std::size_t size) : size_(size), data_(nullptr) { - gen_ = absl::make_unique(0); - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - } - - explicit CacheAlignedVector(const std::vector& input) - : size_(input.size()), data_(nullptr) { - gen_ = absl::make_unique(0); - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - memcpy(data_, input.data(), size_ * sizeof(DataType)); - } - - template - explicit CacheAlignedVector(const std::vector& input) - : size_(input.size()), data_(nullptr) { - gen_ = absl::make_unique(0); - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - for (int i = 0; i < size_; ++i) - data_[i] = static_cast(input.data()[i]); - } - - CacheAlignedVector(const DataType* input, int size) - : size_(size), data_(nullptr) { - gen_ = absl::make_unique(0); - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - memcpy(data_, input, size_ * sizeof(DataType)); - } - - template - explicit CacheAlignedVector(const InputType* input, int size) - : size_(size), data_(nullptr) { - gen_ = absl::make_unique(0); - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - for (int i = 0; i < size_; ++i) data_[i] = static_cast(input[i]); - } - - CacheAlignedVector() : size_(0), data_(nullptr) {} - - ~CacheAlignedVector() { - aligned_free(data_); - data_ = nullptr; - size_ = 0; - } - - // Copies are _deep_ copies - CacheAlignedVector(CacheAlignedVector const& other) - : size_(0), data_(nullptr), gen_(nullptr) { - if (other.gen_) - gen_ = absl::make_unique(std::minstd_rand(*other.gen_)); - this->resize(other.size()); - memcpy(data_, other.data(), size_ * sizeof(DataType)); - } - // Copies a slice of the input. - CacheAlignedVector(CacheAlignedVector const& other, int start, int end) - : size_(0), data_(nullptr), gen_(nullptr) { - if (other.gen_) - gen_ = absl::make_unique(std::minstd_rand(*other.gen_)); - this->resize(end - start); - memcpy(data_, other.data() + start, size_ * sizeof(DataType)); - } - - void operator=(CacheAlignedVector const& other) { - if (other.gen_) - gen_ = absl::make_unique(std::minstd_rand(*other.gen_)); - else - gen_.reset(nullptr); - this->resize(other.size()); - memcpy(data_, other.data(), size_ * sizeof(DataType)); - } - - CacheAlignedVector(CacheAlignedVector&& other) - : size_(0), data_(nullptr), gen_(std::move(other.gen_)) { - size_ = other.size_; - data_ = other.data_; - other.size_ = 0; - other.data_ = nullptr; - } - - CacheAlignedVector& operator=( - CacheAlignedVector&& other) { - aligned_free(data_); - if (other.gen_) - gen_ = absl::make_unique(std::move(*other.gen_)); - else - gen_.reset(nullptr); - size_ = other.size_; - data_ = other.data_; - other.size_ = 0; - other.data_ = nullptr; - return *this; - } - - VectorView AsView() const { - return VectorView(this->data(), this->size(), 1); - } - - MutableVectorView AsMutableView() { - return MutableVectorView(this->data(), this->size(), 1); - } - - // Copies the |split_points| to use in ReducingSample. - void PrepareForThreads(const std::vector& split_points, - int block_height) { - maxes_.resize(split_points.size() - 1); - thread_starts_ = split_points; - for (int t = 0; t < thread_starts_.size(); ++t) { - thread_starts_[t] *= block_height; - } - } - - void FillRandom(float min = -10.f, float max = 10.f) { - // 10 is smaller than any nonzero bound of the range of any data type. - std::uniform_real_distribution dist(min, max); - for (std::size_t i = 0; i < size_; i++) { - data_[i] = DataType(dist(*gen_)); - } - } - - void FillZero() { - for (std::size_t i = 0; i < size_; i++) { - data_[i] = DataType(0.f); - } - } - - void FillOnes() { - for (std::size_t i = 0; i < size_; i++) { - data_[i] = DataType(1.f); - } - } - - void FillWith(const DataType& value) { - for (std::size_t i = 0; i < size_; i++) { - data_[i] = value; - } - } - - // Interprets |data_| as logits and samples from the distribution, this - // version operates IN PLACE and uses an internal random source. - template - typename std::enable_if::value, int>::type Sample( - float temperature = 1.f) { - return Sample(temperature, gen_.get(), this); - } - - // Interprets |data_| as logits and samples. This version requires the random - // source and temporary memory to be passed in. It is thread safe assuming - // no other threads are using the generator and temporary memory. -#if defined __aarch64__ - template - typename std::enable_if::value, int>::type Sample( - float temperature, std::minstd_rand* gen, - CacheAlignedVector* scratch) const { - DCHECK(scratch->size() >= size_); - // Round down to nearest multiple of 8. - int SIMD_iterations = 8 * (size_ / 8); - float* scratch_ptr = scratch->data(); - std::uniform_real_distribution dist; - float random_number = dist(*gen); - - float32x4_t sum = vdupq_n_f32(0.f); - float32x4_t sum1 = vdupq_n_f32(0.f); - float32x4_t max_value = vdupq_n_f32(std::numeric_limits::lowest()); - float32x4_t max_value1 = vdupq_n_f32(std::numeric_limits::lowest()); - float32x4_t inv_temp = vdupq_n_f32(1.f / temperature); - // Compute sum of exp(x) for the denominator. - // Hand unroll by 2, gives speed improvement. - constexpr int kUnrollFactor = 2; - constexpr int kElementsPerIter = kUnrollFactor * kSIMDWidth; - for (std::size_t i = 0; i < SIMD_iterations; i += kElementsPerIter) { - max_value = vmaxq_f32(vld1q_f32(data_ + i), max_value); - max_value1 = vmaxq_f32(vld1q_f32(data_ + i + 4), max_value1); - } - - // Pairwise reduction. - max_value = vpmaxq_f32(max_value, max_value1); - // Duplicate (dupq) maximum across vector (maxnmvq). - float scalar_max_value = vmaxvq_f32(max_value); - - for (int i = SIMD_iterations; i < size_; ++i) { - scalar_max_value = std::max(data_[i], scalar_max_value); - } - - max_value = vdupq_n_f32(scalar_max_value); - - for (std::size_t i = 0; i < SIMD_iterations; i += kElementsPerIter) { - // Load and multiply by temperature. - float32x4_t x = - vmulq_f32(vsubq_f32(vld1q_f32(data_ + i), max_value), inv_temp); - float32x4_t x1 = - vmulq_f32(vsubq_f32(vld1q_f32(data_ + i + 4), max_value), inv_temp); - - float32x4_t exponent = fast_exp(x); - float32x4_t exponent1 = fast_exp(x1); - - sum = vaddq_f32(sum, exponent); - sum1 = vaddq_f32(sum1, exponent1); - - vst1q_f32(scratch_ptr + i, exponent); - vst1q_f32(scratch_ptr + i + 4, exponent1); - } - - // Horizontally reduce the two sums. - sum = vpaddq_f32(sum, sum1); - sum = vpaddq_f32(sum, sum); - float denom = vgetq_lane_f32(sum, 0) + vgetq_lane_f32(sum, 1); - - for (int i = SIMD_iterations; i < size_; ++i) { - float x = (data_[i] - scalar_max_value) / temperature; - float x_exp = expf(x); - denom += x_exp; - scratch_ptr[i] = x_exp; - } - - // Note: rather than normalize all the probabilities, we can just - // apply the inverse normalization to the random number. - random_number *= denom; - - // Now do the scan in serial, return as soon as possible. - // TODO(b/188821456): This could be made into a parallel SIMD scan - // followed by a binary search, for a small speedup. - float cumsum = 0.f; - for (std::size_t i = 0; i < size_; i++) { - cumsum += scratch_ptr[i]; - if (cumsum >= random_number) return i; - } - return size_ - 1; - } - - template - static inline int32x4_t vmul_temp_fixed(int32x4_t x, int32x2_t inv_temp) { - int32x2_t xh = vget_high_s32(x); - int32x2_t xl = vget_low_s32(x); - int32x2_t ph = vqrshrn_n_s64(vmull_s32(xh, inv_temp), Q::kMantissaBits); - int32x2_t pl = vqrshrn_n_s64(vmull_s32(xl, inv_temp), Q::kMantissaBits); - return vcombine_s32(pl, ph); - } - - template - static inline int float_to_fixed(float x) { - return static_cast(x * (1 << Q::kMantissaBits)); - } - - template - static inline float fixed_to_float(int x) { - const float inv_denom = 1.f / (1 << Q::kMantissaBits); - return static_cast(x) * inv_denom; - } - - template - typename std::enable_if::value, int>::type Sample( - float temperature, std::minstd_rand* gen, - CacheAlignedVector* scratch) const { - DCHECK(scratch->size() >= size_); - // Round down to nearest multiple of 8. - int SIMD_iterations = 8 * (size_ / 8); - int* scratch_ptr = scratch->data(); - float scalar_inv_temp = 1.f / temperature; - - int32x4_t sum = vdupq_n_s32(0); - int32x4_t sum1 = vdupq_n_s32(0); - int32x4_t max_value = vdupq_n_s32(std::numeric_limits::lowest()); - int32x4_t max_value1 = vdupq_n_s32(std::numeric_limits::lowest()); - int32x2_t inv_temp = vdup_n_s32(float_to_fixed(scalar_inv_temp)); - // Compute sum of exp(x) for the denominator. - // Hand unroll by 2, gives speed improvement. - - const int* data_ptr = reinterpret_cast(data_); - constexpr int kUnrollFactor = 2; - constexpr int kElementsPerIter = kUnrollFactor * kSIMDWidth; - for (std::size_t i = 0; i < SIMD_iterations; i += kElementsPerIter) { - max_value = vmaxq_s32(vld1q_s32(data_ptr + i), max_value); - max_value1 = vmaxq_s32(vld1q_s32(data_ptr + i + kSIMDWidth), max_value1); - } - - // Pairwise reduction. - max_value = vpmaxq_s32(max_value, max_value1); - int scalar_max_value = vmaxvq_s32(max_value); - - for (int i = SIMD_iterations; i < size_; ++i) { - scalar_max_value = std::max(data_[i].raw_val(), scalar_max_value); - } - max_value = vdupq_n_s32(scalar_max_value); - // We clip all loaded values to a lower bound of the lowest possible arg to - // exp + the max value that we are going to subtract, to prevent underflow - // in exp and also to avoid wrap-around with values that are already minint. - int32x4_t clip_min = - vdupq_n_s32(scalar_max_value - (80 << MantissaBitsOf::value)); - - for (std::size_t i = 0; i < SIMD_iterations; i += kElementsPerIter) { - // Load and multiply by temperature. - int32x4_t loaded = vmaxq_s32(vld1q_s32(data_ptr + i), clip_min); - int32x4_t x = vmul_temp_fixed(vsubq_s32(loaded, max_value), inv_temp); - loaded = vmaxq_s32(vld1q_s32(data_ptr + i + kSIMDWidth), clip_min); - int32x4_t x1 = vmul_temp_fixed(vsubq_s32(loaded, max_value), inv_temp); - - int32x4_t exponent = vcvtq_n_s32_f32(fast_exp_fixed(x), - Q::kMantissaBits); - int32x4_t exponent1 = vcvtq_n_s32_f32( - fast_exp_fixed(x1), Q::kMantissaBits); - - sum = vaddq_s32(sum, exponent); - sum1 = vaddq_s32(sum1, exponent1); - - vst1q_s32(scratch_ptr + i, exponent); - vst1q_s32(scratch_ptr + i + kSIMDWidth, exponent1); - } - - // Horizontally reduce the two sums. - sum = vpaddq_s32(sum, sum1); - sum = vpaddq_s32(sum, sum); - float denom = - fixed_to_float(vgetq_lane_s32(sum, 0) + vgetq_lane_s32(sum, 1)); - for (int i = SIMD_iterations; i < size_; ++i) { - float x_exp = fast_exp_fixed( - DataType((data_[i].raw_val() - scalar_max_value) * scalar_inv_temp)); - - denom += x_exp; - scratch_ptr[i] = float_to_fixed(x_exp); - } - - // Note: rather than normalize all the probabilities, we can just - // apply the inverse normalization to the random number. - std::uniform_real_distribution dist; - int random_number = float_to_fixed(dist(*gen) * denom); - - // Now do the scan in serial, return as soon as possible. - // TODO(b/188821456): This could be made into a parallel SIMD scan - // followed by a binary search, for a small speedup. - int cumsum = 0; - for (std::size_t i = 0; i < size_; i += kSIMDWidth) { - int32x4_t next_vals = vld1q_s32(&scratch_ptr[i]); - cumsum += vaddvq_s32(next_vals); - if (cumsum >= random_number) { - int high_sum = vaddv_s32(vget_high_s32(next_vals)); - if (cumsum - high_sum > random_number) { - // One of the lower ones. - return (cumsum - high_sum - scratch_ptr[i + 1] > random_number) - ? i - : i + 1; - } else { - // One of the upper ones. - return (cumsum - scratch_ptr[i + 3] > random_number) ? i + 2 : i + 3; - } - } - } - return size_ - 1; - } -#endif // defined __aarch64__ - - template -#if defined __aarch64__ - typename std::enable_if< - !std::is_same::value && !IsFixed32Type::value, int>::type -#else - int -#endif - Sample(float temperature, std::minstd_rand* gen, - CacheAlignedVector* scratch, int tid = 0, - SpinBarrier* barrier = nullptr) const { - return ScalarSample(temperature, gen, scratch, tid, 0, -1, barrier); - } - - int ScalarSample(float temperature, std::minstd_rand* gen, - CacheAlignedVector* scratch, int tid = 0, - const int mindex = 0, const int maxdex = -1, - SpinBarrier* barrier = nullptr) const { - // TODO(b/188821456) Don't ignore |tid| and |barrier|. Currently all threads - // duplicate the same work and ignore |tid| and |barrier|, but they could - // be used to execute a reducing max over the data before the exp operation. - DCHECK_EQ(barrier, nullptr); - DCHECK_EQ(tid, 0); - DCHECK(scratch->size() >= size_); - DCHECK(size_ % 8 == 0) << "CacheAlignedVector size must be a multiple of " - "8 to allow for maximum SIMD and loop unroll, " - "got " - << size_ % 8; - DCHECK(size_ > mindex >= 0); - DCHECK((maxdex == -1) || (0 <= mindex < maxdex < size_)); - int maxindex = maxdex > 0 ? maxdex : size_; - - float* scratch_ptr = scratch->data(); - std::uniform_real_distribution dist; - float random_number = dist(*gen); - - float sum = 0.f; - float max_value = std::numeric_limits::lowest(); - for (int i = mindex; i < maxindex; ++i) { - max_value = std::max(max_value, static_cast(data_[i])); - } - float inv_temperature = 1.f / temperature; - for (int i = mindex; i < maxindex; ++i) { - float exponent = fast_exp((static_cast(data_[i]) - max_value) * - inv_temperature); - scratch_ptr[i] = exponent; - sum += exponent; - } - - // Note: rather than normalize all the probabilities, we can just - // apply the inverse normalization to the random number. - random_number *= sum; - - float cumsum = 0.f; - for (std::size_t i = mindex; i < maxindex; i++) { - cumsum += scratch_ptr[i]; - if (cumsum >= random_number) return i; - } - return maxindex - 1; - } - -#if defined __AVX2__ - // Some AVX2-only code. - // Returns the max of |data_| in the range [|t_start|, |t_end|). - inline int ThreadMax(int t_start, int t_end) const { - // Note: The AVX2 code requires that the number of threads and the output - // size be a power of 2. For efficiency purposes, these should be checked - // when preparing for threads in an architecture class. - // The output size must be a power of 2 so the binary search for the sample - // point works correctly. - // The number of threads must be a power of 2 so that it nicely divides the - // output size, which has to be a power of 2. - __m256i maxes = - _mm256_load_si256(reinterpret_cast<__m256i const*>(data_ + t_start)); - for (int i = t_start + kSIMDWidth; i < t_end; i += kSIMDWidth) { - __m256i data = - _mm256_load_si256(reinterpret_cast<__m256i const*>(data_ + i)); - maxes = _mm256_max_epi32(maxes, data); - } - // Max within the register. - // Bring the top lane down to the bottom. - __m256i other = _mm256_permute4x64_epi64(maxes, 0xe); - maxes = _mm256_max_epi32(maxes, other); - // Bring the 2nd 64 bits to the bottom. - other = _mm256_shuffle_epi32(maxes, 0xe); - maxes = _mm256_max_epi32(maxes, other); - // Bring the 2nd 32 bits to the bottom. - other = _mm256_shuffle_epi32(maxes, 1); - maxes = _mm256_max_epi32(maxes, other); - return _mm256_extract_epi32(maxes, 0); - } - - // Applies exp (approximately) to the difference between |data_| and - // |max_value|, storing the result in scratch, and returns the sum. - template - inline float ApplyExpAndSum(int max_value, float* scratch_ptr) { - // Rough approximation for exp(x). See fast_exp_fixed. - // Constant clipping limit on exp arg. Since its value is never positive, - // we only need to clip on the negative side. - constexpr int kClipLimit = -(80 << kMantissaBits); - __m256i clip_val = _mm256_set1_epi32(kClipLimit); - // Multiplication factor to convert x from log base e to log base 2, shifted - // by an amount that lines up the binary point with the float32 - // representation, after the multiplication - static const int kLogFactor = (1 << (23 - kMantissaBits)) / logf(2.f); - __m256i log_factor = _mm256_set1_epi32(kLogFactor); - // Fix the exponent bias and add the additive fudge factor for the mantissa - // to finish the approximate conversion. - constexpr int kAddConstant = (127 << 23) - 366000; - __m256i constant = _mm256_set1_epi32(kAddConstant); - // Broadcast the max_value. - __m256i max_val = _mm256_set1_epi32(max_value); - // Add the max to the |clip_val|, so it can be used before the subtraction. - clip_val = _mm256_add_epi32(clip_val, max_val); - // The sum of the exps. - __m256 sum1 = _mm256_setzero_ps(); - for (int i = 0; i < size_; i += kSIMDWidth) { - // |data_| - |max_value|. - __m256i data = - _mm256_load_si256(reinterpret_cast<__m256i const*>(data_ + i)); - // Clip to negative limit before the subtraction of |max_val| to avoid - // wrap-around with min-int values. - data = _mm256_max_epi32(data, clip_val); - __m256i difference = _mm256_sub_epi32(data, max_val); - // Exponent trick exp. - // Multiply by |log_factor|, keeping only the lower 32 bits. - difference = _mm256_mullo_epi32(difference, log_factor); - // Add the constant. - difference = _mm256_add_epi32(difference, constant); - // Reinterpret the results as float32. - __m256 float_exp = _mm256_castsi256_ps(difference); - // Sum the results and save to scratch space. - _mm256_store_ps(scratch_ptr + i, float_exp); - sum1 = _mm256_add_ps(sum1, float_exp); - } - // Horizontally add the 8 values in sum. - // Get the top lane down to the bottom. - __m256 sum2 = _mm256_permute2f128_ps(sum1, sum1, 1); - sum1 = _mm256_add_ps(sum1, sum2); - sum1 = _mm256_hadd_ps(sum1, sum1); - sum1 = _mm256_hadd_ps(sum1, sum1); - return _mm256_cvtss_f32(sum1); - } - - // Binary search for the index where the cumulative sum meets random_target. - inline void FindSamplePoint(const float* scratch_ptr, float* random_target, - int* start, int* end) { - int halfsize = (*end - *start) / 2; - do { - // Sum the first half. - // We sum the section in two independent parts, so we can step down 2 - // levels if we get a hit in this half. - int quartersize = halfsize / (2 * kSIMDWidth); - quartersize *= kSIMDWidth; - halfsize = quartersize * 2; - // The sums of the quarters. - __m256 sum1 = _mm256_setzero_ps(); - __m256 sum2 = _mm256_setzero_ps(); - const float* ptr1 = scratch_ptr + *start; - const float* ptr2 = ptr1 + quartersize; - for (int i = 0; i < quartersize; i += kSIMDWidth) { - __m256 data1 = _mm256_load_ps(ptr1 + i); - __m256 data2 = _mm256_load_ps(ptr2 + i); - sum1 = _mm256_add_ps(sum1, data1); - sum2 = _mm256_add_ps(sum2, data2); - } - // Horizontally add the two sums, keeping the results separate. - // Numbering |sum1|=[0-7] and |sum2|=[8-15]... - sum1 = _mm256_hadd_ps(sum1, sum2); - // |sum1| now has [0+1, 2+3, 8+9, 10+11, 4+5, 6+7, 12+13, 14+15]. - // Bring the top lane down to the bottom. - sum2 = _mm256_permute2f128_ps(sum1, sum1, 1); - sum1 = _mm256_hadd_ps(sum1, sum2); - // Now |sum1| has [0-3, 8-11, 4-7, 12-15], so swap the middle two - // elements. - sum1 = _mm256_shuffle_ps(sum1, sum1, 0xd8); - sum1 = _mm256_hadd_ps(sum1, sum1); - // Now |sum1| has [0-7, 8-15, ....]. - float bottom_quarter = _mm256_cvtss_f32(sum1); - if (bottom_quarter >= *random_target) { - *end = *start + quartersize; - } else { - float bottom_half = _mm256_cvtss_f32(_mm256_hadd_ps(sum1, sum1)); - if (bottom_half >= *random_target) { - *start += quartersize; - *end = *start + quartersize; - *random_target -= bottom_quarter; - } else { - *start += halfsize; - *random_target -= bottom_half; - } - } - halfsize = (*end - *start) / 2; - } while (halfsize >= kSIMDWidth * 2); - } -#endif // __AVX2__ code - - // Fixed32 version. - template - typename std::enable_if::value, int>::type ThreadMax( - int tid) const { - int t_start = thread_starts_[tid]; - int t_end = thread_starts_[tid + 1]; -#if defined __AVX2__ - return ThreadMax(t_start, t_end); -#else - // With operator<, could use std::max_element. - int max_value = data_[t_start].raw_val(); - for (int i = t_start + 1; i < t_end; ++i) { - max_value = std::max(max_value, data_[i].raw_val()); - } - return max_value; -#endif - } - - // As Sample above, except that if |tid| and |barrier| are provided, it will - // save some time by running a local max in each thread before combining them - // and doing the rest of the work duplicated across all threads. - // Fixed32 version. - template - typename std::enable_if::value, int>::type ReducingSample( - std::minstd_rand* gen, CacheAlignedVector* scratch, int tid = 0, - float temperature = 1.0f, SpinBarrier* barrier = nullptr) { - if (barrier != nullptr) barrier->barrier(); - // Sample only accepts tid of 0, as it would ignore it anyway. - // All threads duplicate the same work in this path. - return Sample(temperature, gen, scratch, /*tid=*/0); - } - - template - typename std::enable_if::value, int>::type ReducingSample( - std::minstd_rand* gen, CacheAlignedVector* scratch, int tid = 0, - float temperature = 1.0f, SpinBarrier* barrier = nullptr) { - int max_value; - if (barrier == nullptr) { - // There is only one thread. - max_value = ThreadMax(tid); - } else { - // Reduce max using the threads to do some of the work. - maxes_[tid] = ThreadMax(tid); - barrier->barrier(); - // The rest of the work is duplicated by all threads. - max_value = *std::max_element(maxes_.begin(), maxes_.end()); - } - float* scratch_ptr = scratch->data(); - std::uniform_real_distribution dist; - float sum = 0.0f; -#if defined __AVX2__ - sum = ApplyExpAndSum::value>(max_value, scratch_ptr); -#else - int clip_limit = max_value - (80 << MantissaBitsOf::value); - for (int i = 0; i < size_; ++i) { - int difference = std::max(data_[i].raw_val(), clip_limit) - max_value; - float exponent = expf(static_cast(DataType(difference))); - scratch_ptr[i] = exponent; - sum += exponent; - } -#endif // __AVX2__ - - float random_target = dist(*gen) * sum; - int start = 0; - int end = size_; - -#if defined __AVX2__ - FindSamplePoint(scratch_ptr, &random_target, &start, &end); - // The scalar code finishes the job from here... -#endif // __AVX2__ - float cumsum = 0.f; - for (std::size_t i = start; i < end; i++) { - cumsum += scratch_ptr[i]; - if (cumsum >= random_target) return i; - } - return end - 1; - } - - template - typename std::enable_if::value, void>::type Exp() { -#if defined __aarch64__ - DCHECK(size_ % 16 == 0) << "CacheAlignedVector size must be a multiple of " - "16 to allow for maximum SIMD and loop unroll " - "got " - << size_ % 16; - constexpr int kUnrollFactor = 4; - constexpr int kElementsPerIter = kUnrollFactor * kSIMDWidth; - for (std::size_t i = 0; i < size_; i += kElementsPerIter) { - float32x4_t x = vld1q_f32(data_ + i); - float32x4_t x1 = vld1q_f32(data_ + i + 4); - float32x4_t x2 = vld1q_f32(data_ + i + 8); - float32x4_t x3 = vld1q_f32(data_ + i + 12); - - vst1q_f32(data_ + i, fast_exp(x)); - vst1q_f32(data_ + i + 4, fast_exp(x1)); - vst1q_f32(data_ + i + 8, fast_exp(x2)); - vst1q_f32(data_ + i + 12, fast_exp(x3)); - } -#else - for (int i = 0; i < size_; ++i) { - data_[i] = expf(data_[i]); - } -#endif // defined __aarch64__ - } - - template - typename std::enable_if::value, void>::type Sigmoid() { -#if defined __aarch64__ - DCHECK(size_ % 8 == 0) << "CacheAlignedVector size must be a multiple of " - "8 to allow for maximum SIMD and loop unroll " - "got " - << size_ % 8; - constexpr int kUnrollFactor = 2; - constexpr int kElementsPerIter = kUnrollFactor * kSIMDWidth; - for (std::size_t i = 0; i < size_; i += kElementsPerIter) { - float32x4_t x = vld1q_f32(data_ + i); - float32x4_t x1 = vld1q_f32(data_ + i + 4); - - vst1q_f32(data_ + i, fast_sigmoid(x)); - vst1q_f32(data_ + i + 4, fast_sigmoid(x1)); - } -#else - for (int i = 0; i < size_; ++i) { - data_[i] = 1.f / (1.f + expf(-data_[i])); - } -#endif // defined __aarch64__ - } - - template - typename std::enable_if< - IsFixed32Type::value && IsFixed32Type::value, void>::type - // For benchmarking only. - Sigmoid(const int32_t* sigmoid_table, CacheAlignedVector* result) { -#if defined __AVX2__ - for (int i = 0; i < size_; i += kSIMDWidth) { - __m256i x_in = _mm256_loadu_si256(reinterpret_cast<__m256i*>(data_ + i)); - __m256i output = fixed32_sigmoid_fixed16::value, - MantissaBitsOf::value>( - sigmoid_table, x_in); - _mm256_store_si256(reinterpret_cast<__m256i*>(result->data() + i), - output); - } -#else - for (int i = 0; i < size_; ++i) { - result->data()[i] = 1.f / (1.f + expf(-data_[i])); - } -#endif // defined __AVX2__ - } - - template - typename std::enable_if::value, void>::type Tanh() { -#if defined __aarch64__ - DCHECK(size_ % 8 == 0) << "CacheAlignedVector size must be a multiple of " - "8 to allow for maximum SIMD and loop unroll " - "got " - << size_ % 8; - constexpr int kUnrollFactor = 2; - constexpr int kElementsPerIter = kUnrollFactor * kSIMDWidth; - for (std::size_t i = 0; i < size_; i += kElementsPerIter) { - float32x4_t x = vld1q_f32(data_ + i); - float32x4_t x1 = vld1q_f32(data_ + i + 4); - - vst1q_f32(data_ + i, fast_tanh(x)); - vst1q_f32(data_ + i + 4, fast_tanh(x1)); - } -#else - for (int i = 0; i < size_; ++i) { - data_[i] = tanhf(data_[i]); - } -#endif // defined __aarch64__ - } - - template - typename std::enable_if< - IsFixed32Type::value && IsFixed32Type::value, void>::type - // For benchmarking only - Tanh(const int32_t* tanh_table, CacheAlignedVector* result) { -#if defined __AVX2__ - for (int i = 0; i < size_; i += kSIMDWidth) { - __m256i x_in = _mm256_loadu_si256(reinterpret_cast<__m256i*>(data_ + i)); - __m256i output = - fixed32_tanh_fixed16::value, - MantissaBitsOf::value>(tanh_table, x_in); - _mm256_store_si256(reinterpret_cast<__m256i*>(result->data() + i), - output); - } -#else - for (int i = 0; i < size_; ++i) { - result->data()[i] = tanhf(data_[i]); - } -#endif // defined __AVX2__ - } - - // Returns |data_| cast to the correct integer type if fixed point. - template - typename std::enable_if::value, const int32_t*>::type - cast_data() const { - return reinterpret_cast(data_); - } - template - typename std::enable_if::value, const int16_t*>::type - cast_data() const { - return reinterpret_cast(data_); - } - template - typename std::enable_if::value || IsFixed16Type::value), - const Q*>::type - cast_data() const { - return data_; - } - const DataType* begin() const { return data_; } - const DataType* end() const { return data_ + size_; } - const DataType* data() const { return data_; } - DataType* data() { return data_; } - - const DataType& operator[](int pos) const { return data_[pos]; } - DataType& operator[](int pos) { return data_[pos]; } - - std::size_t size() const { return size_; } - bool empty() const { return size_ == 0; } - std::size_t bytes() const { return size_ * sizeof(DataType); } - - int rows() const { return size_; } - int cols() const { return 1; } - - // Stride to get to move over by one column (which is the number of rows). - int col_stride() const { return size_; } - - void Print() const { - for (int i = 0; i < size(); ++i) - absl::PrintF("[%d]=%g\n", i, static_cast(data_[i])); - } - - float maximum() const { - float max_val = std::numeric_limits::lowest(); - for (int i = 0; i < size_; ++i) { - max_val = std::max(max_val, std::abs(static_cast(data_[i]))); - } - - return max_val; - } - - private: - void resize(std::size_t size) { - aligned_free(data_); - size_ = size; - data_ = reinterpret_cast( - aligned_malloc(size_ * sizeof(DataType), kCacheLineSize)); - } - - std::size_t size_; - DataType* data_; - // Data used by the threaded version for sampling only. - std::vector maxes_; // Max value of logits. - std::vector thread_starts_; // First index for this thread. -#if defined __AVX__ || defined __AVX2__ - static constexpr int kCacheLineSize = 64; - static constexpr int kSIMDWidth = 8; -#else - static constexpr int kCacheLineSize = 128; - static constexpr int kSIMDWidth = 4; -#endif // __AVX__ - std::unique_ptr gen_; -}; - -// Used for doing Sparse Matrix * Dense Matrix multiplication. This class is -// not intended to be a general Matrix class, just for the RHS of a SpMM, hence -// the name fat vector rather than Matrix. The data layout is COLUMN MAJOR. -template -class FatCacheAlignedVector { - public: - using value_type = T; - - FatCacheAlignedVector() : rows_(0), cols_(0) {} - - // Creates a new vector that is (rows, cols), doesn't init memory. - FatCacheAlignedVector(int rows, int cols) - : vector_(rows * cols), rows_(rows), cols_(cols) {} - - // Copies and reshapes vector from (1, size) to (|rows|, size / |rows|). - FatCacheAlignedVector(const CacheAlignedVector& vector, int rows) - : vector_(vector), rows_(rows) { - CHECK_EQ(vector_.size() % rows_, 0); - cols_ = vector_.size() / rows_; - } - - template - explicit FatCacheAlignedVector(const FatCacheAlignedVector& vector) - : vector_(vector.size()), rows_(vector.rows()), cols_(vector.cols()) { - for (int i = 0; i < vector.size(); ++i) { - vector_[i] = static_cast(vector[i]); - } - } - - // Moves and reshapes vector from (1, size) to (|rows|, size / |rows|) - FatCacheAlignedVector(CacheAlignedVector&& vector, int rows) - : vector_(vector), rows_(rows) { - CHECK_EQ(vector_.size() % rows_, 0); - cols_ = vector_.size() / rows_; - } - - VectorView slice(const int col) const { - return VectorView(this->data() + rows() * col, rows(), 1); - } - MutableVectorView slice(const int col) { - return MutableVectorView(this->data() + rows() * col, rows(), 1); - } - - const T* data() const { return vector_.data(); } - T* data() { return vector_.data(); } - // Returns |data_| cast to the correct integer type if fixed point. - template - typename std::enable_if::value, const int32_t*>::type - cast_data() const { - return vector_.cast_data(); - } - template - typename std::enable_if::value, const int16_t*>::type - cast_data() const { - return vector_.cast_data(); - } - template - typename std::enable_if::value || IsFixed16Type::value), - const Q*>::type - cast_data() const { - return vector_.cast_data(); - } - - int rows() const { return rows_; } - int cols() const { return cols_; } - int size() const { return rows_ * cols_; } - bool empty() const { return rows_ == 0 || cols_ == 0; } - std::size_t bytes() const { return vector_.bytes(); } - - void reshape(int rows, int cols) { - CHECK_EQ(rows * cols, rows_ * cols_); - rows_ = rows; - cols_ = cols; - } - - float maximum() const { return vector_.maximum(); } - - // Stride to get to move over by one column (which is the number of rows). - int col_stride() const { return rows_; } - - void FillOnes() { vector_.FillOnes(); } - void FillZero() { vector_.FillZero(); } - void FillRandom(float min = -10.f, float max = 10.f) { - vector_.FillRandom(min, max); - } - - const T& operator[](int pos) const { return vector_[pos]; } - T& operator[](int pos) { return vector_[pos]; } - - private: - CacheAlignedVector vector_; - int rows_; - int cols_; -}; - -// View into a 2D Matrix. Currently only supports partitions by row. This is -// expected to be used with underlying data that is COLUMN MAJOR. -template -class MutableVectorView { - public: - using value_type = T; - - // Construct from a raw pointer, |rows|, |cols| and |col_stride|. - // |col_stride| will default to |rows| if not specified. - explicit MutableVectorView(T* data = nullptr, int rows = 0, int cols = 0, - int col_stride = 0) - : data_(data), - rows_(rows), - cols_(cols), - col_stride_(col_stride > 0 ? col_stride : rows) {} - - // Construct from a CacheAlignedVector, must have one column, can optionally - // specify an offset and row count. - explicit MutableVectorView(CacheAlignedVector* vector) - : MutableVectorView(vector->data(), vector->rows(), 1) {} - - explicit MutableVectorView(CacheAlignedVector* vector, int pos = 0, - int rows = 0) - : MutableVectorView(vector->data() + pos, - rows == 0 ? vector->rows() - pos : rows, 1, - vector->rows()) {} - - // Construct from a FatCacheAlignedVector, can optionally specify an offset, - // and row count. Views that have fewer columns than the original are not - // supported. - explicit MutableVectorView(FatCacheAlignedVector* vector) - : MutableVectorView(vector->data(), vector->rows(), vector->cols()) {} - - MutableVectorView(FatCacheAlignedVector* vector, int pos, int rows) - : MutableVectorView(vector->data() + pos, rows, vector->cols(), - vector->rows()) {} - - T* data() { return data_; } - const T* data() const { return data_; } - - // Returns |data_| cast to the correct integer type if fixed point. - template - typename std::enable_if::value, const int32_t*>::type - cast_data() const { - return reinterpret_cast(data_); - } - template - typename std::enable_if::value, const int16_t*>::type - cast_data() const { - return reinterpret_cast(data_); - } - template - typename std::enable_if::value || IsFixed16Type::value), - const Q*>::type - cast_data() const { - return data_; - } - - // Number of columns in the underlying (Fat)CacheAlignedVector. - int cols() const { return cols_; } - - // Number of rows in this view. - int rows() const { return rows_; } - - // Returns true if there's nothing in the MutableVectorView. - bool empty() const { return rows_ == 0 || cols_ == 0; } - - // Stride to get to the next column (usually the number of rows in the - // underlying data structure). - int col_stride() const { return col_stride_; } - - // Returns the total number of bytes that are "owned" by this view. Uses - // cols and not col_stride. - std::size_t bytes() const { return rows_ * cols_ * sizeof(T); } - - void reshape(int rows, int cols) { - CHECK_EQ(rows * cols, rows_ * cols_); - rows_ = rows; - cols_ = cols; - col_stride_ = rows_; - } - - const T& operator[](int pos) const { return data_[pos]; } - T& operator[](int pos) { return data_[pos]; } - - protected: - T* data_; - int rows_; - int cols_; - int col_stride_; -}; - -// Specialization of MutableVectorView which is read-only. -template -class VectorView : public MutableVectorView { - public: - using value_type = T; - - explicit VectorView(const MutableVectorView& other) - : MutableVectorView(other.data(), other.rows(), other.cols(), - other.col_stride()) {} - - // Construct from a raw pointer, |rows|, |cols| and |col_stride|. - // |col_stride| will default to |rows| if not specified. - explicit VectorView(const T* data = nullptr, int rows = 0, int cols = 0, - int col_stride = 0) - : MutableVectorView(data, rows, cols, col_stride) {} - - // Construct from a CacheAlignedVector, must have one column, can optionally - // specify an offset and row count - explicit VectorView(const CacheAlignedVector& vector) - : MutableVectorView(vector.data(), vector.rows(), 1) {} - - explicit VectorView(const CacheAlignedVector& vector, int pos = 0, - int rows = 0) - : MutableVectorView(vector.data() + pos, - rows == 0 ? vector.rows() - pos : rows, 1, - vector.rows()) {} - - // Construct from a FatCacheAlignedVector, can optionally specify an offset, - // and row count. Views that have fewer columns than the original are not - // supported. - explicit VectorView(const FatCacheAlignedVector& vector) - : MutableVectorView(vector.data(), vector.rows(), - vector.cols()) {} - - VectorView(const FatCacheAlignedVector& vector, int pos, int rows) - : MutableVectorView(vector.data() + pos, rows, vector.cols(), - vector.rows()) {} - - VectorView& operator=(const MutableVectorView& other) { - this->data_ = other.data(); - this->rows_ = other.rows(); - this->cols_ = other.cols(); - this->col_stride_ = other.col_stride(); - return *this; - } -}; - -} // namespace csrblocksparse -#endif // LYRA_CODEC_SPARSE_MATMUL_VECTOR_CACHE_ALIGNED_VECTOR_H_ diff --git a/spaces/ogawa0071/cyberagent-open-calm-small/app.py b/spaces/ogawa0071/cyberagent-open-calm-small/app.py deleted file mode 100644 index 3bb5b5452d8f3871e354cc55eb11dc08cf788cbc..0000000000000000000000000000000000000000 --- a/spaces/ogawa0071/cyberagent-open-calm-small/app.py +++ /dev/null @@ -1,28 +0,0 @@ -import gradio as gr -from transformers import pipeline - -generator = pipeline( - "text-generation", - model="cyberagent/open-calm-small", - tokenizer="cyberagent/open-calm-small", -) - - -def generate(text): - result = generator(text) - return result[0]["generated_text"] - - -examples = [ - ["AIによって私達の暮らしは、"], -] - -demo = gr.Interface( - fn=generate, - inputs=gr.Textbox(lines=5, label="Input Text"), - outputs=gr.Textbox(lines=5, label="Generated Text"), - examples=examples, - description="# [CyberAgent OpenCALM-Small](https://huggingface.co/cyberagent/open-calm-small)", -) - -demo.launch() diff --git a/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/utils/region.c b/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/utils/region.c deleted file mode 100644 index 61a7cc720e2f63e41c117fec4c2b90aacf30a54f..0000000000000000000000000000000000000000 --- a/spaces/oguzakif/video-object-remover/SiamMask/utils/pysot/utils/region.c +++ /dev/null @@ -1,13085 +0,0 @@ -/* Generated by Cython 0.29.34 */ - -/* BEGIN: Cython Metadata -{ - "distutils": { - "depends": [ - "src/region.h" - ], - "include_dirs": [ - "src/", - "." - ], - "name": "region", - "sources": [ - "region.pyx", - "src/region.c" - ] - }, - "module_name": "region" -} -END: Cython Metadata */ - -#ifndef PY_SSIZE_T_CLEAN -#define PY_SSIZE_T_CLEAN -#endif /* PY_SSIZE_T_CLEAN */ -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.6+ or Python 3.3+. -#else -#define CYTHON_ABI "0_29_34" -#define CYTHON_HEX_VERSION 0x001D22F0 -#define CYTHON_FUTURE_DIVISION 0 -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#define __PYX_COMMA , -#ifndef HAVE_LONG_LONG - #if PY_VERSION_HEX >= 0x02070000 - #define HAVE_LONG_LONG - #endif -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#ifdef PYPY_VERSION - #define CYTHON_COMPILING_IN_PYPY 1 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PYSTON_VERSION) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PY_NOGIL) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_NOGIL 1 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #ifndef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 -#else - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 1 - #define CYTHON_COMPILING_IN_NOGIL 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #if PY_VERSION_HEX < 0x02070000 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) - #define CYTHON_USE_PYTYPE_LOOKUP 1 - #endif - #if PY_MAJOR_VERSION < 3 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #if PY_VERSION_HEX < 0x02070000 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #elif !defined(CYTHON_USE_PYLONG_INTERNALS) - #define CYTHON_USE_PYLONG_INTERNALS (PY_VERSION_HEX < 0x030C00A5) - #endif - #ifndef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 1 - #endif - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #elif !defined(CYTHON_USE_UNICODE_WRITER) - #define CYTHON_USE_UNICODE_WRITER 1 - #endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #if PY_VERSION_HEX >= 0x030B00A4 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #elif !defined(CYTHON_FAST_THREAD_STATE) - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000) - #endif - #ifndef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) - #endif - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) - #endif - #ifndef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS ((PY_VERSION_HEX >= 0x030600B1) && (PY_VERSION_HEX < 0x030C00A5)) - #endif - #if PY_VERSION_HEX >= 0x030B00A4 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #elif !defined(CYTHON_USE_EXC_INFO_STACK) - #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) - #endif - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_MAJOR_VERSION < 3 - #include "longintrepr.h" - #endif - #undef SHIFT - #undef BASE - #undef MASK - #ifdef SIZEOF_VOID_P - enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; - #endif -#endif -#ifndef __has_attribute - #define __has_attribute(x) 0 -#endif -#ifndef __has_cpp_attribute - #define __has_cpp_attribute(x) 0 -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_MAYBE_UNUSED_VAR -# if defined(__cplusplus) - template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } -# else -# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) -# endif -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) -#ifdef _MSC_VER - #ifndef _MSC_STDINT_H_ - #if _MSC_VER < 1300 - typedef unsigned char uint8_t; - typedef unsigned int uint32_t; - #else - typedef unsigned __int8 uint8_t; - typedef unsigned __int32 uint32_t; - #endif - #endif -#else - #include -#endif -#ifndef CYTHON_FALLTHROUGH - #if defined(__cplusplus) && __cplusplus >= 201103L - #if __has_cpp_attribute(fallthrough) - #define CYTHON_FALLTHROUGH [[fallthrough]] - #elif __has_cpp_attribute(clang::fallthrough) - #define CYTHON_FALLTHROUGH [[clang::fallthrough]] - #elif __has_cpp_attribute(gnu::fallthrough) - #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_attribute(fallthrough) - #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) - #else - #define CYTHON_FALLTHROUGH - #endif - #endif - #if defined(__clang__ ) && defined(__apple_build_version__) - #if __apple_build_version__ < 7000000 - #undef CYTHON_FALLTHROUGH - #define CYTHON_FALLTHROUGH - #endif - #endif -#endif - -#ifndef CYTHON_INLINE - #if defined(__clang__) - #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) - #elif defined(__GNUC__) - #define CYTHON_INLINE __inline__ - #elif defined(_MSC_VER) - #define CYTHON_INLINE __inline - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_INLINE inline - #else - #define CYTHON_INLINE - #endif -#endif - -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define Py_OptimizeFlag 0 -#endif -#define __PYX_BUILD_PY_SSIZE_T "n" -#define CYTHON_FORMAT_SSIZE_T "z" -#if PY_MAJOR_VERSION < 3 - #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" - #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) - #define __Pyx_DefaultClassType PyClass_Type -#else - #define __Pyx_BUILTIN_MODULE_NAME "builtins" - #define __Pyx_DefaultClassType PyType_Type -#if PY_VERSION_HEX >= 0x030B00A1 - static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f, - PyObject *code, PyObject *c, PyObject* n, PyObject *v, - PyObject *fv, PyObject *cell, PyObject* fn, - PyObject *name, int fline, PyObject *lnos) { - PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL; - PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL; - const char *fn_cstr=NULL; - const char *name_cstr=NULL; - PyCodeObject* co=NULL; - PyObject *type, *value, *traceback; - PyErr_Fetch(&type, &value, &traceback); - if (!(kwds=PyDict_New())) goto end; - if (!(argcount=PyLong_FromLong(a))) goto end; - if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end; - if (!(posonlyargcount=PyLong_FromLong(0))) goto end; - if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end; - if (!(kwonlyargcount=PyLong_FromLong(k))) goto end; - if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end; - if (!(nlocals=PyLong_FromLong(l))) goto end; - if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end; - if (!(stacksize=PyLong_FromLong(s))) goto end; - if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end; - if (!(flags=PyLong_FromLong(f))) goto end; - if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end; - if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end; - if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end; - if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end; - if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too; - if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here - if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too; - Py_XDECREF((PyObject*)co); - co = (PyCodeObject*)call_result; - call_result = NULL; - if (0) { - cleanup_code_too: - Py_XDECREF((PyObject*)co); - co = NULL; - } - end: - Py_XDECREF(kwds); - Py_XDECREF(argcount); - Py_XDECREF(posonlyargcount); - Py_XDECREF(kwonlyargcount); - Py_XDECREF(nlocals); - Py_XDECREF(stacksize); - Py_XDECREF(replace); - Py_XDECREF(call_result); - Py_XDECREF(empty); - if (type) { - PyErr_Restore(type, value, traceback); - } - return co; - } -#else - #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#endif - #define __Pyx_DefaultClassType PyType_Type -#endif -#ifndef Py_TPFLAGS_CHECKTYPES - #define Py_TPFLAGS_CHECKTYPES 0 -#endif -#ifndef Py_TPFLAGS_HAVE_INDEX - #define Py_TPFLAGS_HAVE_INDEX 0 -#endif -#ifndef Py_TPFLAGS_HAVE_NEWBUFFER - #define Py_TPFLAGS_HAVE_NEWBUFFER 0 -#endif -#ifndef Py_TPFLAGS_HAVE_FINALIZE - #define Py_TPFLAGS_HAVE_FINALIZE 0 -#endif -#ifndef METH_STACKLESS - #define METH_STACKLESS 0 -#endif -#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) - #ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - #endif - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); - typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast - #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords -#endif -#if CYTHON_FAST_PYCCALL -#define __Pyx_PyFastCFunction_Check(func)\ - ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) -#else -#define __Pyx_PyFastCFunction_Check(func) 0 -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) - #define PyObject_Malloc(s) PyMem_Malloc(s) - #define PyObject_Free(p) PyMem_Free(p) - #define PyObject_Realloc(p) PyMem_Realloc(p) -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 - #define PyMem_RawMalloc(n) PyMem_Malloc(n) - #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) - #define PyMem_RawFree(p) PyMem_Free(p) -#endif -#if CYTHON_COMPILING_IN_PYSTON - #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) -#else - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) -#endif -#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#elif PY_VERSION_HEX >= 0x03060000 - #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() -#elif PY_VERSION_HEX >= 0x03000000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#else - #define __Pyx_PyThreadState_Current _PyThreadState_Current -#endif -#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) -#include "pythread.h" -#define Py_tss_NEEDS_INIT 0 -typedef int Py_tss_t; -static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { - *key = PyThread_create_key(); - return 0; -} -static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { - Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); - *key = Py_tss_NEEDS_INIT; - return key; -} -static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { - PyObject_Free(key); -} -static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { - return *key != Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { - PyThread_delete_key(*key); - *key = Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { - return PyThread_set_key_value(*key, value); -} -static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { - return PyThread_get_key_value(*key); -} -#endif -#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) -#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) -#else -#define __Pyx_PyDict_NewPresized(n) PyDict_New() -#endif -#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS -#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) -#else -#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) -#endif -#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) - #define CYTHON_PEP393_ENABLED 1 - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_READY(op) (0) - #else - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) - #endif - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) - #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) - #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) - #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) - #else - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) - #else - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) - #endif - #endif -#else - #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) - #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) - #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) -#endif -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) -#else - #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) - #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ - PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) - #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) -#endif -#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) -#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? 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__Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, - PyObject *dict); -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, - PyObject *dict); -static int __pyx_CyFunction_init(void); - -/* CythonFunction.proto */ -static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, - int flags, PyObject* qualname, - PyObject *closure, - PyObject *module, PyObject *globals, - PyObject* code); - -/* Py3ClassCreate.proto */ -static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname, - PyObject *mkw, PyObject *modname, PyObject *doc); -static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict, - PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass); - -/* Globals.proto */ -static PyObject* __Pyx_Globals(void); - -/* CLineInTraceback.proto */ -#ifdef CYTHON_CLINE_IN_TRACEBACK -#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) -#else -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); -#endif - -/* CodeObjectCache.proto */ -typedef struct { - PyCodeObject* code_object; - int code_line; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); -static PyCodeObject *__pyx_find_code_object(int code_line); -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); - -/* AddTraceback.proto */ -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); - -/* GCCDiagnostics.proto */ -#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define __Pyx_HAS_GCC_DIAGNOSTIC -#endif - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum____pyx_t_6region_RegionType(enum __pyx_t_6region_RegionType value); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdlib' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from 'c_region' */ - -/* Module declarations from 'region' */ -static PyTypeObject *__pyx_ptype_6region_RegionBounds = 0; -static PyTypeObject *__pyx_ptype_6region_Rectangle = 0; -static PyTypeObject *__pyx_ptype_6region_Polygon = 0; -static PyTypeObject *__pyx_ptype___Pyx_EnumMeta = 0; -static PyObject *__Pyx_OrderedDict = 0; -static PyObject *__Pyx_EnumBase = 0; -static PyObject *__Pyx_globals = 0; -static PyObject *__pyx_unpickle___Pyx_EnumMeta__set_state(struct __pyx_obj___Pyx_EnumMeta *, PyObject *); /*proto*/ -#define __Pyx_MODULE_NAME "region" -extern int __pyx_module_is_main_region; -int __pyx_module_is_main_region = 0; - -/* Implementation of 'region' */ -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_ValueError; -static const char __pyx_k_i[] = "i"; -static const char __pyx_k_v[] = "v"; -static const char __pyx_k_x[] = "x"; -static const char __pyx_k_y[] = "y"; -static const char __pyx_k__5[] = ""; -static const char __pyx_k_cls[] = "cls"; -static const char __pyx_k_dct[] = "dct"; -static const char __pyx_k_doc[] = "__doc__"; -static const char __pyx_k_inf[] = "inf"; -static const char __pyx_k_nan[] = "nan"; -static const char __pyx_k_new[] = "__new__"; -static const char __pyx_k_res[] = "res"; -static const char __pyx_k_ret[] = "ret"; -static const char __pyx_k_s_s[] = "%s.%s"; -static const char __pyx_k_set[] = "set"; -static const char __pyx_k_str[] = "__str__"; -static const char __pyx_k_top[] = "top"; -static const char __pyx_k_MASK[] = "MASK"; -static const char __pyx_k_dict[] = "__dict__"; -static const char __pyx_k_enum[] = "enum"; -static const char __pyx_k_init[] = "__init__"; -static const char __pyx_k_left[] = "left"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_name[] = "name"; -static const char __pyx_k_repr[] = "__repr__"; -static const char __pyx_k_self[] = "self"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_3f_3f[] = "({:.3f} {:.3f}) "; -static const char __pyx_k_EMTPY[] = "EMTPY"; -static const char __pyx_k_class[] = "__class__"; -static const char __pyx_k_only1[] = "only1"; -static const char __pyx_k_only2[] = "only2"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_right[] = "right"; -static const char __pyx_k_s_s_d[] = 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-static const char __pyx_k_Polygon[] = "Polygon"; -static const char __pyx_k_SPECIAL[] = "SPECIAL"; -static const char __pyx_k_members[] = "__members__"; -static const char __pyx_k_overlap[] = "overlap"; -static const char __pyx_k_parents[] = "parents"; -static const char __pyx_k_prepare[] = "__prepare__"; -static const char __pyx_k_EnumBase[] = "EnumBase"; -static const char __pyx_k_EnumType[] = "EnumType"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_overlaps[] = "overlaps"; -static const char __pyx_k_polygon1[] = "polygon1"; -static const char __pyx_k_polygon2[] = "polygon2"; -static const char __pyx_k_pyx_type[] = "__pyx_type"; -static const char __pyx_k_qualname[] = "__qualname__"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_template[] = "template"; -static const char __pyx_k_RECTANGEL[] = "RECTANGEL"; -static const char __pyx_k_Rectangle[] = "Rectangle"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_ctemplate[] = "ctemplate"; -static const char __pyx_k_metaclass[] = "__metaclass__"; -static const char __pyx_k_no_bounds[] = "no_bounds"; -static const char __pyx_k_polygons1[] = "polygons1"; -static const char __pyx_k_polygons2[] = "polygons2"; -static const char __pyx_k_ptemplate[] = "ptemplate"; -static const char __pyx_k_pyx_state[] = "__pyx_state"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_RegionType[] = "RegionType"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_c_polygon1[] = "c_polygon1"; -static const char __pyx_k_c_polygon2[] = "c_polygon2"; -static const char __pyx_k_pno_bounds[] = "pno_bounds"; -static const char __pyx_k_polygon1_2[] = "polygon1_"; -static const char __pyx_k_polygon2_2[] = "polygon2_"; -static const char __pyx_k_pyx_result[] = "__pyx_result"; -static const char __pyx_k_region_pyx[] = "region.pyx"; -static const char __pyx_k_MemoryError[] = "MemoryError"; -static const char __pyx_k_OrderedDict[] = "OrderedDict"; -static const char __pyx_k_PickleError[] = "PickleError"; -static const char __pyx_k_collections[] = "collections"; -static const char __pyx_k_vot_overlap[] = "vot_overlap"; -static const char __pyx_k_Pyx_EnumBase[] = "__Pyx_EnumBase"; -static const char __pyx_k_RegionBounds[] = "RegionBounds"; -static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; -static const char __pyx_k_stringsource[] = "stringsource"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_vot_float2str[] = "vot_float2str"; -static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_vot_overlap_traj[] = "vot_overlap_traj"; -static const char __pyx_k_Pyx_EnumBase___new[] = "__Pyx_EnumBase.__new__"; -static const char __pyx_k_Pyx_EnumBase___str[] = "__Pyx_EnumBase.__str__"; -static const char 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PyObject *__pyx_kp_s_no_default___reduce___due_to_non; -static PyObject *__pyx_n_s_only1; -static PyObject *__pyx_n_s_only2; -static PyObject *__pyx_n_s_output; -static PyObject *__pyx_n_s_overlap; -static PyObject *__pyx_n_s_overlaps; -static PyObject *__pyx_n_s_parents; -static PyObject *__pyx_n_s_pickle; -static PyObject *__pyx_n_s_pno_bounds; -static PyObject *__pyx_n_s_points; -static PyObject *__pyx_n_s_polygon1; -static PyObject *__pyx_n_s_polygon1_2; -static PyObject *__pyx_n_s_polygon2; -static PyObject *__pyx_n_s_polygon2_2; -static PyObject *__pyx_n_s_polygons1; -static PyObject *__pyx_n_s_polygons2; -static PyObject *__pyx_n_s_prepare; -static PyObject *__pyx_n_s_ptemplate; -static PyObject *__pyx_n_s_pyx_PickleError; -static PyObject *__pyx_n_s_pyx_checksum; -static PyObject *__pyx_n_s_pyx_result; -static PyObject *__pyx_n_s_pyx_state; -static PyObject *__pyx_n_s_pyx_type; -static PyObject *__pyx_n_s_pyx_unpickle___Pyx_EnumMeta; -static PyObject *__pyx_n_s_qualname; -static PyObject *__pyx_n_s_range; -static PyObject *__pyx_n_s_reduce; -static PyObject *__pyx_n_s_reduce_cython; -static PyObject *__pyx_n_s_reduce_ex; -static PyObject *__pyx_n_s_region; -static PyObject *__pyx_kp_s_region_pyx; -static PyObject *__pyx_n_s_repr; -static PyObject *__pyx_n_s_res; -static PyObject *__pyx_n_s_ret; -static PyObject *__pyx_n_s_right; -static PyObject *__pyx_kp_s_s_s; -static PyObject *__pyx_kp_s_s_s_d; -static PyObject *__pyx_n_s_self; -static PyObject *__pyx_n_s_set; -static PyObject *__pyx_n_s_setstate; -static PyObject *__pyx_n_s_setstate_cython; -static PyObject *__pyx_n_s_str; -static PyObject *__pyx_kp_s_stringsource; -static PyObject *__pyx_n_s_template; -static PyObject *__pyx_n_s_test; -static PyObject *__pyx_n_s_top; -static PyObject *__pyx_kp_s_top_3f_bottom_3f_left_3f_reight; -static PyObject *__pyx_n_s_update; -static PyObject *__pyx_n_s_v; -static PyObject *__pyx_n_s_value; -static PyObject *__pyx_n_s_values; -static PyObject *__pyx_n_s_vot_float2str; -static PyObject *__pyx_n_s_vot_overlap; -static PyObject *__pyx_n_s_vot_overlap_traj; -static PyObject *__pyx_n_s_width; -static PyObject *__pyx_n_s_x; -static PyObject *__pyx_kp_s_x_3f_y_3f_width_3f_height_3f; -static PyObject *__pyx_n_s_y; -static int __pyx_pf_6region_12RegionBounds___cinit__(struct __pyx_obj_6region_RegionBounds *__pyx_v_self); /* proto */ -static int __pyx_pf_6region_12RegionBounds_2__init__(struct __pyx_obj_6region_RegionBounds *__pyx_v_self, PyObject *__pyx_v_top, PyObject *__pyx_v_bottom, PyObject *__pyx_v_left, PyObject *__pyx_v_right); /* proto */ -static void __pyx_pf_6region_12RegionBounds_4__dealloc__(struct __pyx_obj_6region_RegionBounds *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6region_12RegionBounds_6__str__(struct __pyx_obj_6region_RegionBounds *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6region_12RegionBounds_8get(struct __pyx_obj_6region_RegionBounds *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6region_12RegionBounds_10set(struct __pyx_obj_6region_RegionBounds *__pyx_v_self, PyObject *__pyx_v_top, PyObject *__pyx_v_bottom, PyObject *__pyx_v_left, PyObject *__pyx_v_right); /* proto */ -static PyObject *__pyx_pf_6region_12RegionBounds_12__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_6region_RegionBounds *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6region_12RegionBounds_14__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_6region_RegionBounds *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_pf_6region_9Rectangle___cinit__(struct __pyx_obj_6region_Rectangle *__pyx_v_self); /* proto */ -static int __pyx_pf_6region_9Rectangle_2__init__(struct __pyx_obj_6region_Rectangle *__pyx_v_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y, PyObject *__pyx_v_width, PyObject *__pyx_v_height); /* proto */ -static void __pyx_pf_6region_9Rectangle_4__dealloc__(struct __pyx_obj_6region_Rectangle *__pyx_v_self); /* proto */ -static 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- if (unlikely(!result)) { - PyErr_Format(PyExc_NameError, -#if PY_MAJOR_VERSION >= 3 - "name '%U' is not defined", name); -#else - "name '%.200s' is not defined", PyString_AS_STRING(name)); -#endif - } - return result; -} - -/* RaiseArgTupleInvalid */ -static void __Pyx_RaiseArgtupleInvalid( - const char* func_name, - int exact, - Py_ssize_t num_min, - Py_ssize_t num_max, - Py_ssize_t num_found) -{ - Py_ssize_t num_expected; - const char *more_or_less; - if (num_found < num_min) { - num_expected = num_min; - more_or_less = "at least"; - } else { - num_expected = num_max; - more_or_less = "at most"; - } - if (exact) { - more_or_less = "exactly"; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); -} - -/* KeywordStringCheck */ -static int __Pyx_CheckKeywordStrings( - PyObject *kwdict, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - while (PyDict_Next(kwdict, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if ((!kw_allowed) && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -/* RaiseDoubleKeywords */ -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -/* ParseKeywords */ -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* PyFunctionFastCall */ -#if CYTHON_FAST_PYCALL -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = __Pyx_PyFrame_GetLocalsplus(f); - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif -#endif - -/* PyCFunctionFastCall */ -#if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - int flags = PyCFunction_GET_FLAGS(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { - return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); - } else { - return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); - } -} -#endif - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = Py_TYPE(func)->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyErrFetchRestore */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_FAST_THREAD_STATE - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#else - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* DivInt[Py_ssize_t] */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* PyObjectCallMethO */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ -#if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif - if (likely(PyCFunction_Check(func))) { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (__Pyx_PyFastCFunction_Check(func)) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* GetItemInt */ -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* ObjectGetItem */ -#if CYTHON_USE_TYPE_SLOTS -static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { - PyObject *runerr = NULL; - Py_ssize_t key_value; - PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; - if (unlikely(!(m && m->sq_item))) { - PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); - return NULL; - } - key_value = __Pyx_PyIndex_AsSsize_t(index); - if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { - return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); - } - if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { - PyErr_Clear(); - PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); - } - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { - PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; - if (likely(m && m->mp_subscript)) { - return m->mp_subscript(obj, key); - } - return __Pyx_PyObject_GetIndex(obj, key); -} -#endif - -/* PyIntBinop */ -#if !CYTHON_COMPILING_IN_PYPY -static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) { - (void)inplace; - (void)zerodivision_check; - #if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(op1))) { - const long b = intval; - long x; - long a = PyInt_AS_LONG(op1); - x = (long)((unsigned long)a + b); - if (likely((x^a) >= 0 || (x^b) >= 0)) - return PyInt_FromLong(x); - return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - #endif - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(op1))) { - const long b = intval; - long a, x; -#ifdef HAVE_LONG_LONG - const PY_LONG_LONG llb = intval; - PY_LONG_LONG lla, llx; -#endif - const digit* digits = ((PyLongObject*)op1)->ob_digit; - const Py_ssize_t size = Py_SIZE(op1); - if (likely(__Pyx_sst_abs(size) <= 1)) { - a = likely(size) ? digits[0] : 0; - if (size == -1) a = -a; - } else { - switch (size) { - case -2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 2: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case -3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 3: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case -4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - case 4: - if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); - break; -#ifdef HAVE_LONG_LONG - } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { - lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); - goto long_long; -#endif - } - CYTHON_FALLTHROUGH; - default: return PyLong_Type.tp_as_number->nb_add(op1, op2); - } - } - x = a + b; - return PyLong_FromLong(x); -#ifdef HAVE_LONG_LONG - long_long: - llx = lla + llb; - return PyLong_FromLongLong(llx); -#endif - - - } - #endif - if (PyFloat_CheckExact(op1)) { - const long b = intval; - double a = PyFloat_AS_DOUBLE(op1); - double result; - PyFPE_START_PROTECT("add", return NULL) - result = ((double)a) + (double)b; - PyFPE_END_PROTECT(result) - return PyFloat_FromDouble(result); - } - return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); -} -#endif - -/* pyobject_as_double */ -static double __Pyx__PyObject_AsDouble(PyObject* obj) { - PyObject* float_value; -#if !CYTHON_USE_TYPE_SLOTS - float_value = PyNumber_Float(obj); if ((0)) goto bad; -#else - PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number; - if (likely(nb) && likely(nb->nb_float)) { - float_value = nb->nb_float(obj); - if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) { - PyErr_Format(PyExc_TypeError, - "__float__ returned non-float (type %.200s)", - Py_TYPE(float_value)->tp_name); - Py_DECREF(float_value); - goto bad; - } - } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) { -#if PY_MAJOR_VERSION >= 3 - float_value = PyFloat_FromString(obj); -#else - float_value = PyFloat_FromString(obj, 0); -#endif - } else { - PyObject* args = PyTuple_New(1); - if (unlikely(!args)) goto bad; - PyTuple_SET_ITEM(args, 0, obj); - float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0); - PyTuple_SET_ITEM(args, 0, 0); - Py_DECREF(args); - } -#endif - if (likely(float_value)) { - double value = PyFloat_AS_DOUBLE(float_value); - Py_DECREF(float_value); - return value; - } -bad: - return (double)-1; -} - -/* PyDictVersioning */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { - PyObject **dictptr = NULL; - Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; - if (offset) { -#if CYTHON_COMPILING_IN_CPYTHON - dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); -#else - dictptr = _PyObject_GetDictPtr(obj); -#endif - } - return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; -} -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) - return 0; - return obj_dict_version == __Pyx_get_object_dict_version(obj); -} -#endif - -/* GetModuleGlobalName */ -#if CYTHON_USE_DICT_VERSIONS -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) -#else -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) -#endif -{ - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 - result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } else if (unlikely(PyErr_Occurred())) { - return NULL; - } -#else - result = PyDict_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } -#endif -#else - result = PyObject_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } - PyErr_Clear(); -#endif - return __Pyx_GetBuiltinName(name); -} - -/* PyObjectCallNoArg */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, NULL, 0); - } -#endif -#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG) - if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func))) -#else - if (likely(PyCFunction_Check(func))) -#endif - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); -} -#endif - -/* decode_c_string */ -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - Py_ssize_t length; - if (unlikely((start < 0) | (stop < 0))) { - size_t slen = strlen(cstring); - if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, - "c-string too long to convert to Python"); - return NULL; - } - length = (Py_ssize_t) slen; - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - if (unlikely(stop <= start)) - return __Pyx_NewRef(__pyx_empty_unicode); - length = stop - start; - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -/* GetException */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) -#endif -{ - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if CYTHON_USE_EXC_INFO_STACK - { - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = local_type; - exc_info->exc_value = local_value; - exc_info->exc_traceback = local_tb; - } - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* SwapException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = *type; - exc_info->exc_value = *value; - exc_info->exc_traceback = *tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - #endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#endif - -/* GetTopmostException */ -#if CYTHON_USE_EXC_INFO_STACK -static _PyErr_StackItem * -__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) -{ - _PyErr_StackItem *exc_info = tstate->exc_info; - while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && - exc_info->previous_item != NULL) - { - exc_info = exc_info->previous_item; - } - return exc_info; -} -#endif - -/* SaveResetException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - *type = exc_info->exc_type; - *value = exc_info->exc_value; - *tb = exc_info->exc_traceback; - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - #endif - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = type; - exc_info->exc_value = value; - exc_info->exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyObjectSetAttrStr */ -#if CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) { - PyTypeObject* tp = Py_TYPE(obj); - if (likely(tp->tp_setattro)) - return tp->tp_setattro(obj, attr_name, value); -#if PY_MAJOR_VERSION < 3 - if (likely(tp->tp_setattr)) - return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value); -#endif - return PyObject_SetAttr(obj, attr_name, value); -} -#endif - -/* PyErrExceptionMatches */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; icurexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - if (unlikely(PyTuple_Check(err))) - return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); - return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetAttr */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_USE_TYPE_SLOTS -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -/* GetAttr3 */ -static PyObject *__Pyx_GetAttr3Default(PyObject *d) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - __Pyx_PyErr_Clear(); - Py_INCREF(d); - return d; -} -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r = __Pyx_GetAttr(o, n); - return (likely(r)) ? r : __Pyx_GetAttr3Default(d); -} - -/* Import */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* ImportFrom */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* PyObjectCall2Args */ -static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { - PyObject *args, *result = NULL; - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(function)) { - PyObject *args[2] = {arg1, arg2}; - return __Pyx_PyFunction_FastCall(function, args, 2); - } - #endif - #if CYTHON_FAST_PYCCALL - if (__Pyx_PyFastCFunction_Check(function)) { - PyObject *args[2] = {arg1, arg2}; - return __Pyx_PyCFunction_FastCall(function, args, 2); - } - #endif - args = PyTuple_New(2); - if (unlikely(!args)) goto done; - Py_INCREF(arg1); - PyTuple_SET_ITEM(args, 0, arg1); - Py_INCREF(arg2); - PyTuple_SET_ITEM(args, 1, arg2); - Py_INCREF(function); - result = __Pyx_PyObject_Call(function, args, NULL); - Py_DECREF(args); - Py_DECREF(function); -done: - return result; -} - -/* HasAttr */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { - PyObject *r; - if (unlikely(!__Pyx_PyBaseString_Check(n))) { - PyErr_SetString(PyExc_TypeError, - "hasattr(): attribute name must be string"); - return -1; - } - r = __Pyx_GetAttr(o, n); - if (unlikely(!r)) { - PyErr_Clear(); - return 0; - } else { - Py_DECREF(r); - return 1; - } -} - -/* PyObject_GenericGetAttrNoDict */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'%.50s' object has no attribute '%U'", - tp->tp_name, attr_name); -#else - "'%.50s' object has no attribute '%.400s'", - tp->tp_name, PyString_AS_STRING(attr_name)); -#endif - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* PyObjectGetAttrStrNoError */ -static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - __Pyx_PyErr_Clear(); -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { - PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 - PyTypeObject* tp = Py_TYPE(obj); - if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { - return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); - } -#endif - result = __Pyx_PyObject_GetAttrStr(obj, attr_name); - if (unlikely(!result)) { - __Pyx_PyObject_GetAttrStr_ClearAttributeError(); - } - return result; -} - -/* SetupReduce */ -static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_getstate = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; - PyObject *getstate = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); -#else - getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); - if (!getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (getstate) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); -#else - object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); - if (!object_getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (object_getstate != getstate) { - goto __PYX_GOOD; - } - } -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); - if (likely(reduce_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (reduce == object_reduce || PyErr_Occurred()) { - goto __PYX_BAD; - } - setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); - if (likely(setstate_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (!setstate || PyErr_Occurred()) { - goto __PYX_BAD; - } - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto __PYX_GOOD; -__PYX_BAD: - if (!PyErr_Occurred()) - PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); - ret = -1; -__PYX_GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); - Py_XDECREF(object_getstate); - Py_XDECREF(getstate); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} - -/* CalculateMetaclass */ -static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) { - Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases); - for (i=0; i < nbases; i++) { - PyTypeObject *tmptype; - PyObject *tmp = PyTuple_GET_ITEM(bases, i); - tmptype = Py_TYPE(tmp); -#if PY_MAJOR_VERSION < 3 - if (tmptype == &PyClass_Type) - continue; -#endif - if (!metaclass) { - metaclass = tmptype; - continue; - } - if (PyType_IsSubtype(metaclass, tmptype)) - continue; - if (PyType_IsSubtype(tmptype, metaclass)) { - metaclass = tmptype; - continue; - } - PyErr_SetString(PyExc_TypeError, - "metaclass conflict: " - "the metaclass of a derived class " - "must be a (non-strict) subclass " - "of the metaclasses of all its bases"); - return NULL; - } - if (!metaclass) { -#if PY_MAJOR_VERSION < 3 - metaclass = &PyClass_Type; -#else - metaclass = &PyType_Type; -#endif - } - Py_INCREF((PyObject*) metaclass); - return (PyObject*) metaclass; -} - -/* FetchCommonType */ -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* fake_module; - PyTypeObject* cached_type = NULL; - fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); - if (!fake_module) return NULL; - Py_INCREF(fake_module); - cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); - if (cached_type) { - if (!PyType_Check((PyObject*)cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", - type->tp_name); - goto bad; - } - if (cached_type->tp_basicsize != type->tp_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - type->tp_name); - goto bad; - } - } else { - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; - } -done: - Py_DECREF(fake_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} - -/* CythonFunctionShared */ -#include -static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) -{ - if (unlikely(op->func_doc == NULL)) { - if (op->func.m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - PyObject *tmp = op->func_doc; - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - op->func_doc = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - tmp = op->func_name; - Py_INCREF(value); - op->func_name = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - PyObject *tmp; -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - tmp = op->func_qualname; - Py_INCREF(value); - op->func_qualname = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) -{ - PyObject *self; - self = m->func_closure; - if (self == NULL) - self = Py_None; - Py_INCREF(self); - return self; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, CYTHON_UNUSED void *context) -{ - PyObject *tmp; - if (unlikely(value == NULL)) { - PyErr_SetString(PyExc_TypeError, - "function's dictionary may not be deleted"); - return -1; - } - if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "setting function's dictionary to a non-dict"); - return -1; - } - tmp = op->func_dict; - Py_INCREF(value); - op->func_dict = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(op->func_globals); - return op->func_globals; -} -static PyObject * -__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - Py_INCREF(Py_None); - return Py_None; -} -static PyObject * -__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) -{ - PyObject* result = (op->func_code) ? op->func_code : Py_None; - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { - int result = 0; - PyObject *res = op->defaults_getter((PyObject *) op); - if (unlikely(!res)) - return -1; - #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - op->defaults_tuple = PyTuple_GET_ITEM(res, 0); - Py_INCREF(op->defaults_tuple); - op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); - Py_INCREF(op->defaults_kwdict); - #else - op->defaults_tuple = PySequence_ITEM(res, 0); - if (unlikely(!op->defaults_tuple)) result = -1; - else { - op->defaults_kwdict = PySequence_ITEM(res, 1); - if (unlikely(!op->defaults_kwdict)) result = -1; - } - #endif - Py_DECREF(res); - return result; -} -static int -__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { - PyObject* tmp; - if (!value) { - value = Py_None; - } else if (value != Py_None && !PyTuple_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__defaults__ must be set to a tuple object"); - return -1; - } - Py_INCREF(value); - tmp = op->defaults_tuple; - op->defaults_tuple = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { - PyObject* result = op->defaults_tuple; - if (unlikely(!result)) { - if (op->defaults_getter) { - if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; - result = op->defaults_tuple; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { - PyObject* tmp; - if (!value) { - value = Py_None; - } else if (value != Py_None && !PyDict_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__kwdefaults__ must be set to a dict object"); - return -1; - } - Py_INCREF(value); - tmp = op->defaults_kwdict; - op->defaults_kwdict = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { - PyObject* result = op->defaults_kwdict; - if (unlikely(!result)) { - if (op->defaults_getter) { - if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; - result = op->defaults_kwdict; - } else { - result = Py_None; - } - } - Py_INCREF(result); - return result; -} -static int -__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, CYTHON_UNUSED void *context) { - PyObject* tmp; - if (!value || value == Py_None) { - value = NULL; - } else if (!PyDict_Check(value)) { - PyErr_SetString(PyExc_TypeError, - "__annotations__ must be set to a dict object"); - return -1; - } - Py_XINCREF(value); - tmp = op->func_annotations; - op->func_annotations = value; - Py_XDECREF(tmp); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *context) { - PyObject* result = op->func_annotations; - if (unlikely(!result)) { - result = PyDict_New(); - if (unlikely(!result)) return NULL; - op->func_annotations = result; - } - Py_INCREF(result); - return result; -} -static PyGetSetDef __pyx_CyFunction_getsets[] = { - {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, - {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, - {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, - {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, - {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, - {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, - {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, - {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, - {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, - {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, - {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, - {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, - {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, - {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, - {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, - {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, - {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, - {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, - {0, 0, 0, 0, 0} -}; -static PyMemberDef __pyx_CyFunction_members[] = { - {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0}, - {0, 0, 0, 0, 0} -}; -static PyObject * -__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) -{ -#if PY_MAJOR_VERSION >= 3 - Py_INCREF(m->func_qualname); - return m->func_qualname; -#else - return PyString_FromString(m->func.m_ml->ml_name); -#endif -} -static PyMethodDef __pyx_CyFunction_methods[] = { - {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, - {0, 0, 0, 0} -}; -#if PY_VERSION_HEX < 0x030500A0 -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) -#else -#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) -#endif -static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - if (unlikely(op == NULL)) - return NULL; - op->flags = flags; - __Pyx_CyFunction_weakreflist(op) = NULL; - op->func.m_ml = ml; - op->func.m_self = (PyObject *) op; - Py_XINCREF(closure); - op->func_closure = closure; - Py_XINCREF(module); - op->func.m_module = module; - op->func_dict = NULL; - op->func_name = NULL; - Py_INCREF(qualname); - op->func_qualname = qualname; - op->func_doc = NULL; - op->func_classobj = NULL; - op->func_globals = globals; - Py_INCREF(op->func_globals); - Py_XINCREF(code); - op->func_code = code; - op->defaults_pyobjects = 0; - op->defaults_size = 0; - op->defaults = NULL; - op->defaults_tuple = NULL; - op->defaults_kwdict = NULL; - op->defaults_getter = NULL; - op->func_annotations = NULL; - return (PyObject *) op; -} -static int -__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) -{ - Py_CLEAR(m->func_closure); - Py_CLEAR(m->func.m_module); - Py_CLEAR(m->func_dict); - Py_CLEAR(m->func_name); - Py_CLEAR(m->func_qualname); - Py_CLEAR(m->func_doc); - Py_CLEAR(m->func_globals); - Py_CLEAR(m->func_code); - Py_CLEAR(m->func_classobj); - Py_CLEAR(m->defaults_tuple); - Py_CLEAR(m->defaults_kwdict); - Py_CLEAR(m->func_annotations); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_XDECREF(pydefaults[i]); - PyObject_Free(m->defaults); - m->defaults = NULL; - } - return 0; -} -static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - if (__Pyx_CyFunction_weakreflist(m) != NULL) - PyObject_ClearWeakRefs((PyObject *) m); - __Pyx_CyFunction_clear(m); - PyObject_GC_Del(m); -} -static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) -{ - PyObject_GC_UnTrack(m); - __Pyx__CyFunction_dealloc(m); -} -static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) -{ - Py_VISIT(m->func_closure); - Py_VISIT(m->func.m_module); - Py_VISIT(m->func_dict); - Py_VISIT(m->func_name); - Py_VISIT(m->func_qualname); - Py_VISIT(m->func_doc); - Py_VISIT(m->func_globals); - Py_VISIT(m->func_code); - Py_VISIT(m->func_classobj); - Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) -{ -#if PY_MAJOR_VERSION < 3 - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { - Py_INCREF(func); - return func; - } - if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { - if (type == NULL) - type = (PyObject *)(Py_TYPE(obj)); - return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); - } - if (obj == Py_None) - obj = NULL; -#endif - return __Pyx_PyMethod_New(func, obj, type); -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); 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-static int __pyx_CyFunction_init(void) { - __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); - if (unlikely(__pyx_CyFunctionType == NULL)) { - return -1; - } - return 0; -} -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults = PyObject_Malloc(size); - if (unlikely(!m->defaults)) - return PyErr_NoMemory(); - memset(m->defaults, 0, size); - m->defaults_pyobjects = pyobjects; - m->defaults_size = size; - return m->defaults; -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_tuple = tuple; - Py_INCREF(tuple); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_kwdict = dict; - Py_INCREF(dict); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->func_annotations = dict; - Py_INCREF(dict); -} - -/* CythonFunction */ -static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - PyObject *op = __Pyx_CyFunction_Init( - PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), - ml, flags, qualname, closure, module, globals, code - ); - if (likely(op)) { - PyObject_GC_Track(op); - } - return op; -} - -/* Py3ClassCreate */ -static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, - PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) { - PyObject *ns; - if (metaclass) { - PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare); - if (prep) { - PyObject *pargs = PyTuple_Pack(2, name, bases); - if (unlikely(!pargs)) { - Py_DECREF(prep); - return NULL; - } - ns = PyObject_Call(prep, pargs, mkw); - Py_DECREF(prep); - Py_DECREF(pargs); - } else { - if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - PyErr_Clear(); - ns = PyDict_New(); - } - } else { - ns = PyDict_New(); - } - if (unlikely(!ns)) - return NULL; - if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad; - if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad; - if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad; - return ns; -bad: - Py_DECREF(ns); - return NULL; -} -static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, - PyObject *dict, PyObject *mkw, - int calculate_metaclass, int allow_py2_metaclass) { - PyObject *result, *margs; - PyObject *owned_metaclass = NULL; - if (allow_py2_metaclass) { - owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass); - if (owned_metaclass) { - metaclass = owned_metaclass; 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- int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -#if PY_VERSION_HEX >= 0x030b00a6 - #ifndef Py_BUILD_CORE - #define Py_BUILD_CORE 1 - #endif - #include "internal/pycore_frame.h" -#endif -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = NULL; - PyObject *py_funcname = NULL; - #if PY_MAJOR_VERSION < 3 - PyObject *py_srcfile = NULL; - py_srcfile = PyString_FromString(filename); - if (!py_srcfile) goto bad; - #endif - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - funcname = PyUnicode_AsUTF8(py_funcname); - if (!funcname) goto bad; - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - if (!py_funcname) goto bad; - #endif - } - #if PY_MAJOR_VERSION < 3 - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - #else - py_code = PyCode_NewEmpty(filename, funcname, py_line); - #endif - Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline - return py_code; -bad: - Py_XDECREF(py_funcname); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_srcfile); - #endif - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject *ptype, *pvalue, *ptraceback; - if (c_line) { - c_line = __Pyx_CLineForTraceback(tstate, c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) { - /* If the code object creation fails, then we should clear the - fetched exception references and propagate the new exception */ - Py_XDECREF(ptype); - Py_XDECREF(pvalue); - Py_XDECREF(ptraceback); - goto bad; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - tstate, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -/* CIntFromPyVerify */ -#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntFromPy */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntFromPy */ -static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const size_t neg_one = (size_t) -1, const_zero = (size_t) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(size_t) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (size_t) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (size_t) 0; - case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0]) - case 2: - if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) { - return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) { - return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) { - return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (size_t) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(size_t) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (size_t) 0; - case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, +digits[0]) - case -2: - if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { - return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { - return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); - } - } - break; - } -#endif - if (sizeof(size_t) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - size_t val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (size_t) -1; - } - } else { - size_t val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (size_t) -1; - val = __Pyx_PyInt_As_size_t(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to size_t"); - return (size_t) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to size_t"); - return (size_t) -1; -} - -/* CIntToPy */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntToPy */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum____pyx_t_6region_RegionType(enum __pyx_t_6region_RegionType value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const enum __pyx_t_6region_RegionType neg_one = (enum __pyx_t_6region_RegionType) -1, const_zero = (enum __pyx_t_6region_RegionType) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum __pyx_t_6region_RegionType) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum __pyx_t_6region_RegionType) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum __pyx_t_6region_RegionType) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum __pyx_t_6region_RegionType) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum __pyx_t_6region_RegionType) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum __pyx_t_6region_RegionType), - little, !is_unsigned); - } -} - -/* FastTypeChecks */ -#if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; - if (!res) { - res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } - return res; -} -#endif -static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - assert(PyExceptionClass_Check(exc_type)); - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; i '9'); - break; - } - if (rt_from_call[i] != ctversion[i]) { - same = 0; - break; - } - } - if (!same) { - char rtversion[5] = {'\0'}; - char message[200]; - for (i=0; i<4; ++i) { - if (rt_from_call[i] == '.') { - if (found_dot) break; - found_dot = 1; - } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') { - break; - } - rtversion[i] = rt_from_call[i]; - } - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - if (PyObject_Hash(*t->p) == -1) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -#if !CYTHON_PEP393_ENABLED -static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { - int retval; - if (unlikely(!x)) return -1; - retval = __Pyx_PyObject_IsTrue(x); - Py_DECREF(x); - return retval; -} -static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { -#if PY_MAJOR_VERSION >= 3 - if (PyLong_Check(result)) { - if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, - "__int__ returned non-int (type %.200s). " - "The ability to return an instance of a strict subclass of int " - "is deprecated, and may be removed in a future version of Python.", - Py_TYPE(result)->tp_name)) { - Py_DECREF(result); - return NULL; - } - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - type_name, type_name, Py_TYPE(result)->tp_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(b); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { - if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { - return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); -#if PY_MAJOR_VERSION < 3 - } else if (likely(PyInt_CheckExact(o))) { - return PyInt_AS_LONG(o); -#endif - } else { - Py_ssize_t ival; - PyObject *x; - x = PyNumber_Index(o); - if (!x) return -1; - ival = PyInt_AsLong(x); - Py_DECREF(x); - return ival; - } -} -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { - return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/spaces/onursavas/Document-Layout-Analysis-via-Segmentation/README.md b/spaces/onursavas/Document-Layout-Analysis-via-Segmentation/README.md deleted file mode 100644 index e5691d9dd63d1588d58043aea189ff14aab44c0e..0000000000000000000000000000000000000000 --- a/spaces/onursavas/Document-Layout-Analysis-via-Segmentation/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Document Layout Analysis -emoji: 🐠 -colorFrom: gray -colorTo: purple -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: qoobeeshy/yolo-document-layout-analysis ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/paj/dubharv/app.py b/spaces/paj/dubharv/app.py deleted file mode 100644 index 21e167f31c392614491ad23e00f5466d32decc68..0000000000000000000000000000000000000000 --- a/spaces/paj/dubharv/app.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import sys -import gradio as gr -import time -import subprocess - - -os.system('git clone https://github.com/Rudrabha/Wav2Lip.git') -os.system('curl -o ./Wav2Lip/face_detection/detection/sfd/s3fd.pth https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth') -os.system('mv ./Wav2Lip/* .') - -title = "Wav2Lip Huggingface Interface Test" -description = "A simple demo for Wav2Lip Official Repo" -article = "Official Repo: https://github.com/Rudrabha/Wav2Lip" - -def inference(face, audio): - os.system("python inference.py --checkpoint_path ./wav2lip.pth --face {} --audio {}".format(face, audio)) - - # p = subprocess.Popen(('python inference.py --checkpoint_path ./wav2lip.pth --face {} --audio {}', str(i)) - # p.wait() - - fpath = "./results/result_voice.mp4" - - while not os.path.exists(fpath): - time.sleep(1) - - - if os.path.isfile(fpath): - return fpath - - return "./results/result_voice.mp4" - - -iface = gr.Interface(inference, inputs=[gr.inputs.Video(type="mp4", source="upload", label="Talking Face Video (in mp4 format)", optional=False), gr.inputs.Audio(source="upload", type="filepath", label="Audio", optional=False)], outputs=["video"], title=title, description=description, article=article, examples=[], enable_queue=False) -iface.launch() diff --git a/spaces/perilli/tortoise-tts-v2/tortoise/is_this_from_tortoise.py b/spaces/perilli/tortoise-tts-v2/tortoise/is_this_from_tortoise.py deleted file mode 100644 index 289844f499fb45694bfb61f395867b81155daf8b..0000000000000000000000000000000000000000 --- a/spaces/perilli/tortoise-tts-v2/tortoise/is_this_from_tortoise.py +++ /dev/null @@ -1,14 +0,0 @@ -import argparse - -from api import classify_audio_clip -from tortoise.utils.audio import load_audio - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="../examples/favorite_riding_hood.mp3") - args = parser.parse_args() - - clip = load_audio(args.clip, 24000) - clip = clip[:, :220000] - prob = classify_audio_clip(clip) - print(f"This classifier thinks there is a {prob*100}% chance that this clip was generated from Tortoise.") \ No newline at end of file diff --git a/spaces/perilli/tortoise-tts-v2/utils/typical_sampling.py b/spaces/perilli/tortoise-tts-v2/utils/typical_sampling.py deleted file mode 100644 index ff6bf487947e88a55fa45f2ffec1b9540df1d4fd..0000000000000000000000000000000000000000 --- a/spaces/perilli/tortoise-tts-v2/utils/typical_sampling.py +++ /dev/null @@ -1,33 +0,0 @@ -import torch -from transformers import LogitsWarper - - -class TypicalLogitsWarper(LogitsWarper): - def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1): - self.filter_value = filter_value - self.mass = mass - self.min_tokens_to_keep = min_tokens_to_keep - - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: - # calculate entropy - normalized = torch.nn.functional.log_softmax(scores, dim=-1) - p = torch.exp(normalized) - ent = -(normalized * p).nansum(-1, keepdim=True) - - # shift and sort - shifted_scores = torch.abs((-normalized) - ent) - sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False) - sorted_logits = scores.gather(-1, sorted_indices) - cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1) - - # Remove tokens with cumulative mass above the threshold - last_ind = (cumulative_probs < self.mass).sum(dim=1) - last_ind[last_ind < 0] = 0 - sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1)) - if self.min_tokens_to_keep > 1: - # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below) - sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0 - indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) - - scores = scores.masked_fill(indices_to_remove, self.filter_value) - return scores \ No newline at end of file diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/utils_GBIF.py b/spaces/phyloforfun/VoucherVision/vouchervision/utils_GBIF.py deleted file mode 100644 index 2ddb101b177c8a82b81b9457bb131545db3a9f9c..0000000000000000000000000000000000000000 --- a/spaces/phyloforfun/VoucherVision/vouchervision/utils_GBIF.py +++ /dev/null @@ -1,944 +0,0 @@ -import os, time, requests, yaml, re, csv, sys, inspect -from dataclasses import dataclass, field -# from difflib import diff_bytes -import pandas as pd -import numpy as np -from PIL import Image -import matplotlib.pyplot as plt -from urllib.parse import urlparse -from requests.adapters import HTTPAdapter -from urllib3.util import Retry -from torch import ge -from re import S -from threading import Lock -from random import shuffle -from collections import defaultdict - -currentdir = os.path.dirname(os.path.dirname(inspect.getfile(inspect.currentframe()))) -parentdir = os.path.dirname(currentdir) -sys.path.append(parentdir) -sys.path.append(currentdir) -from concurrent.futures import ThreadPoolExecutor as th - - -from vouchervision.general_utils import bcolors, validate_dir - -''' -For download parallelization, I followed this guide https://rednafi.github.io/digressions/python/2020/04/21/python-concurrent-futures.html -''' - -''' -#################################################################################################### -Read config files -#################################################################################################### -''' -def get_cfg_from_full_path(path_cfg): - with open(path_cfg, "r") as ymlfile: - cfg = yaml.full_load(ymlfile) - return cfg - -''' -Classes -''' -@dataclass -class ImageCandidate: - cfg: str = '' - herb_code: str = '' - specimen_id: str = '' - family: str = '' - genus: str = '' - species: str = '' - fullname: str = '' - - filename_image: str = '' - filename_image_jpg: str = '' - - url: str = '' - headers_occ: str = '' - headers_img: str = '' - - occ_row: list = field(init=False,default_factory=None) - image_row: list = field(init=False,default_factory=None) - - - def __init__(self, cfg, image_row, occ_row, url, lock): - # self.headers_occ = list(occ_row.columns.values) - # self.headers_img = list(image_row.columns.values) - self.headers_occ = occ_row - self.headers_img = image_row - self.occ_row = occ_row # pd.DataFrame(data=occ_row,columns=self.headers_occ) - self.image_row = image_row # pd.DataFrame(data=image_row,columns=self.headers_img) - self.url = url - self.cfg = cfg - - self.filename_image, self.filename_image_jpg, self.herb_code, self.specimen_id, self.family, self.genus, self.species, self.fullname = generate_image_filename(occ_row) - self.download_image(lock) - - def download_image(self, lock) -> None: - dir_destination = self.cfg['dir_destination_images'] - MP_low = self.cfg['MP_low'] - MP_high = self.cfg['MP_high'] - # Define URL get parameters - sep = '_' - session = requests.Session() - retry = Retry(connect=1) #2, backoff_factor=0.5) - adapter = HTTPAdapter(max_retries=retry) - session.mount('http://', adapter) - session.mount('https://', adapter) - - print(f"{bcolors.BOLD} {self.fullname}{bcolors.ENDC}") - print(f"{bcolors.BOLD} URL: {self.url}{bcolors.ENDC}") - try: - response = session.get(self.url, stream=True, timeout=1.0) - img = Image.open(response.raw) - self._save_matching_image(img, MP_low, MP_high, dir_destination, lock) - print(f"{bcolors.OKGREEN} SUCCESS{bcolors.ENDC}") - except Exception as e: - print(f"{bcolors.FAIL} SKIP No Connection or ERROR --> {e}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Status Code --> {response.status_code}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Reasone --> {response.reason}{bcolors.ENDC}") - - def _save_matching_image(self, img, MP_low, MP_high, dir_destination, lock) -> None: - img_mp, img_w, img_h = check_image_size(img) - if img_mp < MP_low: - print(f"{bcolors.WARNING} SKIP < {MP_low}MP: {img_mp}{bcolors.ENDC}") - - elif MP_low <= img_mp <= MP_high: - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - #imgSaveName = pd.DataFrame({"image_path": [image_path]}) - self._add_occ_and_img_data(lock) - - print(f"{bcolors.OKGREEN} Regular MP: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - - elif img_mp > MP_high: - if self.cfg['do_resize']: - [img_w, img_h] = calc_resize(img_w, img_h) - newsize = (img_w, img_h) - img = img.resize(newsize) - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - #imgSaveName = pd.DataFrame({"imgSaveName": [imgSaveName]}) - self._add_occ_and_img_data(lock) - - print(f"{bcolors.OKGREEN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - else: - print(f"{bcolors.OKCYAN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKCYAN} SKIP: {image_path}{bcolors.ENDC}") - - def _add_occ_and_img_data(self, lock) -> None: - self.image_row = self.image_row.to_frame().transpose().rename(columns={"identifier": "url"}) - self.image_row = self.image_row.rename(columns={"gbifID": "gbifID_images"}) - - new_data = {'fullname': [self.fullname], 'filename_image': [self.filename_image], 'filename_image_jpg': [self.filename_image_jpg]} - new_data = pd.DataFrame(data=new_data) - - all_data = [new_data.reset_index(), self.image_row.reset_index(), self.occ_row.reset_index()] - combined = pd.concat(all_data,ignore_index=False, axis=1) - - w_1 = new_data.shape[1] + 1 - w_2 = self.image_row.shape[1] + 1 - w_3 = self.occ_row.shape[1] - - combined.drop([combined.columns[0], combined.columns[w_1], combined.columns[w_1 + w_2]], axis=1, inplace=True) - headers = np.hstack((new_data.columns.values, self.image_row.columns.values, self.occ_row.columns.values)) - combined.columns = headers - self._append_combined_occ_image(self.cfg, combined, lock) - - def _append_combined_occ_image(self, cfg, combined, lock) -> None: - path_csv_combined = os.path.join(cfg['dir_destination_csv'], cfg['filename_combined']) - with lock: - try: - # Add row once the file exists - csv_combined = pd.read_csv(path_csv_combined,dtype=str) - combined.to_csv(path_csv_combined, mode='a', header=False, index=False) - print(f'{bcolors.OKGREEN} Added 1 row to combined CSV: {path_csv_combined}{bcolors.ENDC}') - - except Exception as e: - print(f"{bcolors.WARNING} Initializing new combined .csv file: [occ,images]: {path_csv_combined}{bcolors.ENDC}") - combined.to_csv(path_csv_combined, mode='w', header=True, index=False) - - - -@dataclass -class ImageCandidateMulti: - cfg: str = '' - herb_code: str = '' - specimen_id: str = '' - family: str = '' - genus: str = '' - species: str = '' - fullname: str = '' - - filename_image: str = '' - filename_image_jpg: str = '' - - url: str = '' - headers_occ: str = '' - headers_img: str = '' - - occ_row: list = field(init=False,default_factory=None) - image_row: list = field(init=False,default_factory=None) - - download_success: bool = False - - - def __init__(self, cfg, image_row, occ_row, url, dir_destination, lock): - # Convert the Series to a DataFrame with one row - try: - # Now, you can access columns and data as you would in a DataFrame - self.headers_occ = occ_row - self.headers_img = image_row - except Exception as e: - print(f"Exception occurred: {e}") - - - self.occ_row = occ_row # pd.DataFrame(data=occ_row,columns=self.headers_occ) - self.image_row = image_row # pd.DataFrame(data=image_row,columns=self.headers_img) - self.url = url - self.cfg = cfg - - self.filename_image, self.filename_image_jpg, self.herb_code, self.specimen_id, self.family, self.genus, self.species, self.fullname = generate_image_filename(occ_row) - - self.download_success = self.download_image(dir_destination, lock) - - - - def download_image(self, dir_destination, lock) -> None: - # dir_destination = self.cfg['dir_destination_images'] - MP_low = self.cfg['MP_low'] - MP_high = self.cfg['MP_high'] - # Define URL get parameters - sep = '_' - session = requests.Session() - retry = Retry(connect=1) #2, backoff_factor=0.5) - adapter = HTTPAdapter(max_retries=retry) - session.mount('http://', adapter) - session.mount('https://', adapter) - - print(f"{bcolors.BOLD} {self.fullname}{bcolors.ENDC}") - print(f"{bcolors.BOLD} URL: {self.url}{bcolors.ENDC}") - try: - response = session.get(self.url, stream=True, timeout=1.0) - img = Image.open(response.raw) - self._save_matching_image(img, MP_low, MP_high, dir_destination, lock) - print(f"{bcolors.OKGREEN} SUCCESS{bcolors.ENDC}") - return True - except Exception as e: - print(f"{bcolors.FAIL} SKIP No Connection or ERROR --> {e}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Status Code --> {response.status_code}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Reasone --> {response.reason}{bcolors.ENDC}") - return False - - def _save_matching_image(self, img, MP_low, MP_high, dir_destination, lock) -> None: - img_mp, img_w, img_h = check_image_size(img) - if img_mp < MP_low: - print(f"{bcolors.WARNING} SKIP < {MP_low}MP: {img_mp}{bcolors.ENDC}") - - elif MP_low <= img_mp <= MP_high: - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - #imgSaveName = pd.DataFrame({"image_path": [image_path]}) - self._add_occ_and_img_data(lock) - - print(f"{bcolors.OKGREEN} Regular MP: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - - elif img_mp > MP_high: - if self.cfg['do_resize']: - [img_w, img_h] = calc_resize(img_w, img_h) - newsize = (img_w, img_h) - img = img.resize(newsize) - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - #imgSaveName = pd.DataFrame({"imgSaveName": [imgSaveName]}) - self._add_occ_and_img_data(lock) - - print(f"{bcolors.OKGREEN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - else: - print(f"{bcolors.OKCYAN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKCYAN} SKIP: {image_path}{bcolors.ENDC}") - - def _add_occ_and_img_data(self, lock) -> None: - self.image_row = self.image_row.to_frame().transpose().rename(columns={"identifier": "url"}) - self.image_row = self.image_row.rename(columns={"gbifID": "gbifID_images"}) - - new_data = {'fullname': [self.fullname], 'filename_image': [self.filename_image], 'filename_image_jpg': [self.filename_image_jpg]} - new_data = pd.DataFrame(data=new_data) - - all_data = [new_data.reset_index(), self.image_row.reset_index(), self.occ_row.reset_index()] - combined = pd.concat(all_data,ignore_index=False, axis=1) - - w_1 = new_data.shape[1] + 1 - w_2 = self.image_row.shape[1] + 1 - w_3 = self.occ_row.shape[1] - - combined.drop([combined.columns[0], combined.columns[w_1], combined.columns[w_1 + w_2]], axis=1, inplace=True) - headers = np.hstack((new_data.columns.values, self.image_row.columns.values, self.occ_row.columns.values)) - combined.columns = headers - self._append_combined_occ_image(self.cfg, combined, lock) - - def _append_combined_occ_image(self, cfg, combined, lock) -> None: - path_csv_combined = os.path.join(cfg['dir_destination_csv'], cfg['filename_combined']) - with lock: - try: - # Add row once the file exists - csv_combined = pd.read_csv(path_csv_combined,dtype=str) - combined.to_csv(path_csv_combined, mode='a', header=False, index=False) - print(f'{bcolors.OKGREEN} Added 1 row to combined CSV: {path_csv_combined}{bcolors.ENDC}') - - except Exception as e: - print(f"{bcolors.WARNING} Initializing new combined .csv file: [occ,images]: {path_csv_combined}{bcolors.ENDC}") - combined.to_csv(path_csv_combined, mode='w', header=True, index=False) - -class SharedCounter: - def __init__(self): - self.img_count_dict = {} - self.lock = Lock() - - def increment(self, key, value=1): - with self.lock: - self.img_count_dict[key] = self.img_count_dict.get(key, 0) + value - - def get_count(self, key): - with self.lock: - return self.img_count_dict.get(key, 0) - - - -@dataclass -class ImageCandidateCustom: - cfg: str = '' - # herb_code: str = '' - # specimen_id: str = '' - # family: str = '' - # genus: str = '' - # species: str = '' - fullname: str = '' - - filename_image: str = '' - filename_image_jpg: str = '' - - url: str = '' - # headers_occ: str = '' - headers_img: str = '' - - # occ_row: list = field(init=False,default_factory=None) - image_row: list = field(init=False,default_factory=None) - - - def __init__(self, cfg, image_row, url, col_name, lock): - # self.headers_occ = list(occ_row.columns.values) - # self.headers_img = list(image_row.columns.values) - self.image_row = image_row # pd.DataFrame(data=image_row,columns=self.headers_img) - - self.url = url - self.cfg = cfg - self.col_name = col_name - - self.fullname = image_row[col_name] - self.filename_image = image_row[col_name] - self.filename_image_jpg = ''.join([image_row[col_name], '.jpg']) - - self.download_image(lock) - - def download_image(self, lock) -> None: - dir_destination = self.cfg['dir_destination_images'] - MP_low = self.cfg['MP_low'] - MP_high = self.cfg['MP_high'] - # Define URL get parameters - sep = '_' - session = requests.Session() - retry = Retry(connect=1) #2, backoff_factor=0.5) - adapter = HTTPAdapter(max_retries=retry) - session.mount('http://', adapter) - session.mount('https://', adapter) - - print(f"{bcolors.BOLD} {self.fullname}{bcolors.ENDC}") - print(f"{bcolors.BOLD} URL: {self.url}{bcolors.ENDC}") - try: - response = session.get(self.url, stream=True, timeout=1.0) - img = Image.open(response.raw) - self._save_matching_image(img, MP_low, MP_high, dir_destination, lock) - print(f"{bcolors.OKGREEN} SUCCESS{bcolors.ENDC}") - except Exception as e: - print(f"{bcolors.FAIL} SKIP No Connection or ERROR --> {e}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Status Code --> {response.status_code}{bcolors.ENDC}") - print(f"{bcolors.WARNING} Reasone --> {response.reason}{bcolors.ENDC}") - - def _save_matching_image(self, img, MP_low, MP_high, dir_destination, lock) -> None: - img_mp, img_w, img_h = check_image_size(img) - if img_mp < MP_low: - print(f"{bcolors.WARNING} SKIP < {MP_low}MP: {img_mp}{bcolors.ENDC}") - - elif MP_low <= img_mp <= MP_high: - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - print(f"{bcolors.OKGREEN} Regular MP: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - - elif img_mp > MP_high: - if self.cfg['do_resize']: - [img_w, img_h] = calc_resize(img_w, img_h) - newsize = (img_w, img_h) - img = img.resize(newsize) - image_path = os.path.join(dir_destination,self.filename_image_jpg) - img.save(image_path) - - print(f"{bcolors.OKGREEN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKGREEN} Image Saved: {image_path}{bcolors.ENDC}") - else: - print(f"{bcolors.OKCYAN} {MP_high}MP+ Resize: {img_mp}{bcolors.ENDC}") - print(f"{bcolors.OKCYAN} SKIP: {image_path}{bcolors.ENDC}") - - -''' -#################################################################################################### -General Functions -#################################################################################################### -''' -# If image is larger than MP max, downsample to have long side = 5000 -def calc_resize(w,h): - if h > w: - ratio = h/w - new_h = 5000 - new_w = round(5000/ratio) - elif w >= h: - ratio = w/h - new_w = 5000 - new_h = round(5000/ratio) - return new_w, new_h - -def check_image_size(img): - [img_w, img_h] = img.size - img_mp = round(img_w * img_h / 1000000,1) - return img_mp, img_w, img_h - -def check_n_images_in_group(detailedOcc,N): - fam = detailedOcc['fullname'].unique() - for f in fam: - ct = len(detailedOcc[detailedOcc['fullname'].str.match(f)]) - if ct == N: - print(f"{bcolors.OKGREEN}{f}: {ct}{bcolors.ENDC}") - else: - print(f"{bcolors.FAIL}{f}: {ct}{bcolors.ENDC}") - - - -''' -#################################################################################################### -Functions for --> download_GBIF_from_user_file.py -#################################################################################################### -''' - -# def download_subset_images_user_file(dir_home,dir_destination,n_already_downloaded,MP_low,MP_high,wishlist,filename_occ,filename_img): -# # (dirWishlists,dirNewImg,alreadyDownloaded,MP_Low,MP_High,wishlist,aggOcc_filename,aggImg_filename): -# sep = '_' -# aggOcc = pd.DataFrame() -# aggImg = pd.DataFrame() - -# # Define URL get parameters -# session = requests.Session() -# retry = Retry(connect=1) #2, backoff_factor=0.5) -# adapter = HTTPAdapter(max_retries=retry) -# session.mount('http://', adapter) -# session.mount('https://', adapter) - -# listMax = wishlist.shape[0] -# for index, spp in wishlist.iterrows(): -# imageFound = False -# currentFamily = spp['family'] -# # currentSpecies = spp['genus'] + ' ' + spp['species'] -# currentFullname = spp['fullname'] -# currentURL = spp['url'] -# currentBarcode = spp['barcode'] -# currentHerb = spp['herbCode'] -# print(f"{bcolors.BOLD}Family: {currentFamily}{bcolors.ENDC}") -# print(f"{bcolors.BOLD} {currentFullname}{bcolors.ENDC}") -# print(f"{bcolors.BOLD} In Download List: {index} / {listMax}{bcolors.ENDC}") - -# imgFilename = [currentHerb, currentBarcode, currentFullname] -# imgFilename = sep.join(imgFilename) -# imgFilenameJPG = imgFilename + ".jpg" -# print(f"{bcolors.BOLD} URL: {currentURL}{bcolors.ENDC}") -# try: -# img = Image.open(session.get(currentURL, stream=True, timeout=1.0).raw) -# imageFound, alreadyDownloaded, aggOcc, aggImg = save_matching_image_user_file(alreadyDownloaded,img,MP_Low,MP_High,dirNewImg,imgFilenameJPG) -# print(f"{bcolors.OKGREEN} SUCCESS{bcolors.ENDC}") -# except Exception as e: -# print(f"{bcolors.WARNING} SKIP No Connection or ERROR{bcolors.ENDC}") - - -# aggOcc.to_csv(os.path.join(dir_home,aggOcc_filename),index=False) -# aggImg.to_csv(os.path.join(dir_home,aggImg_filename),index=False) - -# return alreadyDownloaded, aggOcc, aggImg - - -# Return entire row of file_to_search that matches the gbif_id, else return [] -def find_gbifID(gbif_id,file_to_search): - row_found = file_to_search.loc[file_to_search['gbifID'].astype(str).str.match(str(gbif_id)),:] - if row_found.empty: - print(f"{bcolors.WARNING} gbif_id: {gbif_id} not found in occurrences file{bcolors.ENDC}") - row_found = None - else: - print(f"{bcolors.OKGREEN} gbif_id: {gbif_id} successfully found in occurrences file{bcolors.ENDC}") - return row_found - -def validate_herb_code(occ_row): - # print(occ_row) - # Herbarium codes are not always in the correct column, we need to find the right one - try: - opts = [occ_row['institutionCode'], - occ_row['institutionID'], - occ_row['ownerInstitutionCode'], - occ_row['collectionCode'], - occ_row['publisher'], - occ_row['occurrenceID']] - opts = [item for item in opts if not(pd.isnull(item.values)) == True] - except: - opts = [str(occ_row['institutionCode']), - str(occ_row['institutionID']), - str(occ_row['ownerInstitutionCode']), - str(occ_row['collectionCode']), - str(occ_row['publisher']), - str(occ_row['occurrenceID'])] - opts = pd.DataFrame(opts) - opts = opts.dropna() - opts = opts.apply(lambda x: x[0]).tolist() - - opts_short = [] - - for word in opts: - #print(word) - if len(word) <= 8: - if word is not None: - opts_short = opts_short + [word] - - if len(opts_short) == 0: - try: - herb_code = occ_row['publisher'].values[0].replace(" ","-") - except: - try: - herb_code = occ_row['publisher'].replace(" ","-") - except: - herb_code = "ERROR" - try: - inst_ID = occ_row['institutionID'].values[0] - occ_ID = occ_row['occurrenceID'].values[0] - except: - inst_ID = occ_row['institutionID'] - occ_ID = occ_row['occurrenceID'] - if inst_ID == "UBC Herbarium": - herb_code = "UBC" - elif inst_ID == "Naturalis Biodiversity Center": - herb_code = "L" - elif inst_ID == "Forest Herbarium Ibadan (FHI)": - herb_code = "FHI" - elif 'id.luomus.fi' in occ_ID: - herb_code = "FinBIF" - else: - if len(opts_short) > 0: - herb_code = opts_short[0] - - try: - herb_code = herb_code.values[0] - except: - herb_code = herb_code - - # Specific cases that require manual overrides - # If you see an herbarium DWC file with a similar error, add them here - if herb_code == "Qarshi-Botanical-Garden,-Qarshi-Industries-Pvt.-Ltd,-Pakistan": - herb_code = "Qarshi-Botanical-Garden" - elif herb_code == "12650": - herb_code = "SDSU" - elif herb_code == "322": - herb_code = "SDSU" - elif herb_code == "GC-University,-Lahore": - herb_code = "GC-University-Lahore" - elif herb_code == "Institute-of-Biology-of-Komi-Scientific-Centre-of-the-Ural-Branch-of-the-Russian-Academy-of-Sciences": - herb_code = "Komi-Scientific-Centre" - - return herb_code - -def remove_illegal_chars(text): - cleaned = re.sub(r"[^a-zA-Z0-9_-]","",text) - return cleaned - -def keep_first_word(text): - if (' ' in text) == True: - cleaned = text.split(' ')[0] - else: - cleaned = text - return cleaned - -# Create a filename for the downloaded image -# In the case sensitive format: -# HERBARIUM_barcode_Family_Genus_species.jpg -def generate_image_filename(occ_row): - herb_code = remove_illegal_chars(validate_herb_code(occ_row)) - try: - specimen_id = str(occ_row['gbifID'].values[0]) - family = remove_illegal_chars(occ_row['family'].values[0]) - genus = remove_illegal_chars(occ_row['genus'].values[0]) - species = remove_illegal_chars(keep_first_word(occ_row['specificEpithet'].values[0])) - except: - specimen_id = str(occ_row['gbifID']) - family = remove_illegal_chars(occ_row['family']) - genus = remove_illegal_chars(occ_row['genus']) - species = remove_illegal_chars(keep_first_word(occ_row['specificEpithet'])) - fullname = '_'.join([family, genus, species]) - - filename_image = '_'.join([herb_code, specimen_id, fullname]) - filename_image_jpg = '.'.join([filename_image, 'jpg']) - - return filename_image, filename_image_jpg, herb_code, specimen_id, family, genus, species, fullname - -def read_DWC_file(cfg): - dir_home = cfg['dir_home'] - filename_occ = cfg['filename_occ'] - filename_img = cfg['filename_img'] - # read the images.csv or occurences.csv file. can be txt ro csv - occ_df = ingest_DWC(filename_occ,dir_home) - images_df = ingest_DWC(filename_img,dir_home) - return occ_df, images_df - -def read_DWC_file_multiDirs(cfg, dir_sub): - filename_occ = cfg['filename_occ'] - filename_img = cfg['filename_img'] - # read the images.csv or occurences.csv file. can be txt ro csv - occ_df = ingest_DWC(filename_occ,dir_sub) - images_df = ingest_DWC(filename_img,dir_sub) - return occ_df, images_df - -def ingest_DWC(DWC_csv_or_txt_file,dir_home): - if DWC_csv_or_txt_file.split('.')[1] == 'txt': - df = pd.read_csv(os.path.join(dir_home,DWC_csv_or_txt_file), sep="\t",header=0, low_memory=False, dtype=str) - elif DWC_csv_or_txt_file.split('.')[1] == 'csv': - df = pd.read_csv(os.path.join(dir_home,DWC_csv_or_txt_file), sep=",",header=0, low_memory=False, dtype=str) - else: - print(f"{bcolors.FAIL}DWC file {DWC_csv_or_txt_file} is not '.txt' or '.csv' and was not opened{bcolors.ENDC}") - return df - -''' -####################################################################### -Main function for the config_download_from_GBIF_all_images_in_file.yml -see yml for details -####################################################################### -''' -def download_all_images_in_images_csv_multiDirs(cfg): - dir_destination_parent = cfg['dir_destination_images'] - dir_destination_csv = cfg['dir_destination_csv'] - n_already_downloaded = cfg['n_already_downloaded'] - n_max_to_download = cfg['n_max_to_download'] - n_imgs_per_species = cfg['n_imgs_per_species'] - MP_low = cfg['MP_low'] - MP_high = cfg['MP_high'] - do_shuffle_occurrences = cfg['do_shuffle_occurrences'] - - shared_counter = SharedCounter() - - # (dirWishlists,dirNewImg,alreadyDownloaded,MP_Low,MP_High,aggOcc_filename,aggImg_filename): - - - # Get DWC files - for dir_DWC, dirs_sub, __ in os.walk(cfg['dir_home']): - for dir_sub in dirs_sub: - dir_home = os.path.join(dir_DWC, dir_sub) - dir_destination = os.path.join(dir_destination_parent, dir_sub) - - validate_dir(dir_destination) - validate_dir(dir_destination_csv) - - occ_df, images_df = read_DWC_file_multiDirs(cfg, dir_home) - - # Shuffle the order of the occurrences DataFrame if the flag is set - if do_shuffle_occurrences: - occ_df = occ_df.sample(frac=1).reset_index(drop=True) - - # Report summary - print(f"{bcolors.BOLD}Beginning of images file:{bcolors.ENDC}") - print(images_df.head()) - print(f"{bcolors.BOLD}Beginning of occurrence file:{bcolors.ENDC}") - print(occ_df.head()) - - # Ignore problematic Herbaria - if cfg['ignore_banned_herb']: - for banned_url in cfg['banned_url_stems']: - images_df = images_df[~images_df['identifier'].str.contains(banned_url, na=False)] - - # Report summary - n_imgs = images_df.shape[0] - n_occ = occ_df.shape[0] - print(f"{bcolors.BOLD}Number of images in images file: {n_imgs}{bcolors.ENDC}") - print(f"{bcolors.BOLD}Number of occurrence to search through: {n_occ}{bcolors.ENDC}") - - results = process_image_batch_multiDirs(cfg, images_df, occ_df, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences) - - -def download_all_images_in_images_csv(cfg): - dir_destination = cfg['dir_destination_images'] - dir_destination_csv = cfg['dir_destination_csv'] - - # (dirWishlists,dirNewImg,alreadyDownloaded,MP_Low,MP_High,aggOcc_filename,aggImg_filename): - validate_dir(dir_destination) - validate_dir(dir_destination_csv) - - if cfg['is_custom_file']: - download_from_custom_file(cfg) - else: - # Get DWC files - occ_df, images_df = read_DWC_file(cfg) - - # Report summary - print(f"{bcolors.BOLD}Beginning of images file:{bcolors.ENDC}") - print(images_df.head()) - print(f"{bcolors.BOLD}Beginning of occurrence file:{bcolors.ENDC}") - print(occ_df.head()) - - # Ignore problematic Herbaria - if cfg['ignore_banned_herb']: - for banned_url in cfg['banned_url_stems']: - images_df = images_df[~images_df['identifier'].str.contains(banned_url, na=False)] - - # Report summary - n_imgs = images_df.shape[0] - n_occ = occ_df.shape[0] - print(f"{bcolors.BOLD}Number of images in images file: {n_imgs}{bcolors.ENDC}") - print(f"{bcolors.BOLD}Number of occurrence to search through: {n_occ}{bcolors.ENDC}") - - results = process_image_batch(cfg, images_df, occ_df) - -def process_image_batch(cfg, images_df, occ_df): - futures_list = [] - results = [] - - # single threaded, useful for debugging - # for index, image_row in images_df.iterrows(): - # futures = process_each_image_row( cfg, image_row, occ_df) - # futures_list.append(futures) - # for future in futures_list: - # try: - # result = future.result(timeout=60) - # results.append(result) - # except Exception: - # results.append(None) - lock = Lock() - - with th(max_workers=13) as executor: - for index, image_row in images_df.iterrows(): - futures = executor.submit(process_each_image_row, cfg, image_row, occ_df, lock) - futures_list.append(futures) - - for future in futures_list: - try: - result = future.result(timeout=60) - results.append(result) - except Exception: - results.append(None) - return results - - -def process_image_batch_multiDirs(cfg, images_df, occ_df, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences): - futures_list = [] - results = [] - - lock = Lock() - - if do_shuffle_occurrences: - images_df = images_df.sample(frac=1).reset_index(drop=True) - - # Partition occ_df based on the first word of the 'specificEpithet' column - partition_dict = defaultdict(list) - for index, row in occ_df.iterrows(): - first_word = row['specificEpithet'] # Assuming keep_first_word is defined - partition_dict[first_word].append(row) - - # Convert lists to DataFrames - for key in partition_dict.keys(): - partition_dict[key] = pd.DataFrame(partition_dict[key]) - - num_workers = 13 - - with th(max_workers=num_workers) as executor: - for specific_epithet, partition in partition_dict.items(): - future = executor.submit(process_occ_chunk_multiDirs, cfg, images_df, partition, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences, lock) - futures_list.append(future) - - for future in futures_list: - try: - result = future.result(timeout=60) - results.append(result) - except Exception: - results.append(None) - return results - -def process_occ_chunk_multiDirs(cfg, images_df, occ_chunk, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences, lock): - results = [] - for index, occ_row in occ_chunk.iterrows(): - result = process_each_occ_row_multiDirs(cfg, images_df, occ_row, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences, lock) - results.append(result) - return results - -def process_each_occ_row_multiDirs(cfg, images_df, occ_row, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences, lock): - print(f"{bcolors.BOLD}Working on occurrence: {occ_row['gbifID']}{bcolors.ENDC}") - gbif_id = occ_row['gbifID'] - - image_row = find_gbifID_in_images(gbif_id, images_df) # New function to find the image_row - - if image_row is not None: - filename_image, filename_image_jpg, herb_code, specimen_id, family, genus, species, fullname = generate_image_filename(occ_row) - - current_count = shared_counter.get_count(fullname) - - # If the fullname is not in the counter yet, increment it - if current_count == 0: - shared_counter.increment(fullname) - - print(shared_counter.get_count(fullname)) - if shared_counter.get_count(fullname) > n_imgs_per_species: - print(f"Reached image limit for {fullname}. Skipping.") - return - else: - - gbif_url = image_row['identifier'] - - image_candidate = ImageCandidateMulti(cfg, image_row, occ_row, gbif_url, dir_destination, lock) - if image_candidate.download_success: - shared_counter.increment(fullname) - else: - pass - -def find_gbifID_in_images(gbif_id, images_df): - image_row = images_df[images_df['gbifID'] == gbif_id] - if image_row.empty: - return None - return image_row.iloc[0] - - -def process_each_image_row_multiDirs(cfg, image_row, occ_df, dir_destination, shared_counter, n_imgs_per_species, do_shuffle_occurrences, lock): - print(f"{bcolors.BOLD}Working on image: {image_row['gbifID']}{bcolors.ENDC}") - gbif_id = image_row['gbifID'] - gbif_url = image_row['identifier'] - - occ_row = find_gbifID(gbif_id,occ_df) - - if occ_row is not None: - filename_image, filename_image_jpg, herb_code, specimen_id, family, genus, species, fullname = generate_image_filename(occ_row) - - current_count = shared_counter.get_count(fullname) - - # If the fullname is not in the counter yet, increment it - if current_count == 0: - shared_counter.increment(fullname) - - print(shared_counter.get_count(fullname)) - if shared_counter.get_count(fullname) > n_imgs_per_species: - print(f"Reached image limit for {fullname}. Skipping.") - return - - image_candidate = ImageCandidateMulti(cfg, image_row, occ_row, gbif_url, dir_destination, lock) - if image_candidate.download_success: - shared_counter.increment(fullname) - else: - pass - - -def process_each_image_row(cfg, image_row, occ_df, lock): - print(f"{bcolors.BOLD}Working on image: {image_row['gbifID']}{bcolors.ENDC}") - gbif_id = image_row['gbifID'] - gbif_url = image_row['identifier'] - - occ_row = find_gbifID(gbif_id,occ_df) - - if occ_row is not None: - ImageInfo = ImageCandidate(cfg, image_row, occ_row, gbif_url, lock) - # ImageInfo.download_image(cfg, occ_row, image_row) - else: - pass - -def download_from_custom_file(cfg): - # Get DWC files - images_df = read_custom_file(cfg) - - col_url = cfg['col_url'] - col_name = cfg['col_name'] - if col_url == None: - col_url = 'identifier' - else: - col_url = col_url - - # Report summary - print(f"{bcolors.BOLD}Beginning of images file:{bcolors.ENDC}") - print(images_df.head()) - - # Ignore problematic Herbaria - if cfg['ignore_banned_herb']: - for banned_url in cfg['banned_url_stems']: - images_df = images_df[~images_df[col_url].str.contains(banned_url, na=False)] - - # Report summary - n_imgs = images_df.shape[0] - print(f"{bcolors.BOLD}Number of images in images file: {n_imgs}{bcolors.ENDC}") - - results = process_custom_image_batch(cfg, images_df) - -def read_custom_file(cfg): - dir_home = cfg['dir_home'] - filename_img = cfg['filename_img'] - # read the images.csv or occurences.csv file. can be txt ro csv - images_df = ingest_DWC(filename_img,dir_home) - return images_df - -# def ingest_DWC(DWC_csv_or_txt_file,dir_home): -# if DWC_csv_or_txt_file.split('.')[1] == 'txt': -# df = pd.read_csv(os.path.join(dir_home,DWC_csv_or_txt_file), sep="\t",header=0, low_memory=False, dtype=str) -# elif DWC_csv_or_txt_file.split('.')[1] == 'csv': -# df = pd.read_csv(os.path.join(dir_home,DWC_csv_or_txt_file), sep=",",header=0, low_memory=False, dtype=str) -# else: -# print(f"{bcolors.FAIL}DWC file {DWC_csv_or_txt_file} is not '.txt' or '.csv' and was not opened{bcolors.ENDC}") -# return df - -def process_custom_image_batch(cfg, images_df): - futures_list = [] - results = [] - - lock = Lock() - - with th(max_workers=13) as executor: - for index, image_row in images_df.iterrows(): - futures = executor.submit(process_each_custom_image_row, cfg, image_row, lock) - futures_list.append(futures) - - for future in futures_list: - try: - result = future.result(timeout=60) - results.append(result) - except Exception: - results.append(None) - return results - -def process_each_custom_image_row(cfg, image_row, lock): - col_url = cfg['col_url'] - col_name = cfg['col_name'] - - if col_url == None: - col_url = 'identifier' - else: - col_url = col_url - - gbif_url = image_row[col_url] - - print(f"{bcolors.BOLD}Working on image: {image_row[col_name]}{bcolors.ENDC}") - if image_row is not None: - ImageInfo = ImageCandidateCustom(cfg, image_row, gbif_url, col_name, lock) - else: - pass \ No newline at end of file diff --git a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py b/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py deleted file mode 100644 index 63080c674900a181f66380bcfe6c185b7469cebd..0000000000000000000000000000000000000000 --- a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Mishalsgpt.py +++ /dev/null @@ -1,23 +0,0 @@ -import os, requests, uuid -from ...typing import sha256, Dict, get_type_hints - -url = 'https://mishalsgpt.vercel.app' -model = ['gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - headers = { - 'Content-Type': 'application/json', - } - data = { - 'model': model, - 'temperature': 0.7, - 'messages': messages - } - response = requests.post(url + '/api/openai/v1/chat/completions', - headers=headers, json=data, stream=True) - yield response.json()['choices'][0]['message']['content'] - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) diff --git a/spaces/pinkq/Newbing/src/components/ui/badge.tsx b/spaces/pinkq/Newbing/src/components/ui/badge.tsx deleted file mode 100644 index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000 --- a/spaces/pinkq/Newbing/src/components/ui/badge.tsx +++ /dev/null @@ -1,36 +0,0 @@ -import * as React from 'react' -import { cva, type VariantProps } from 'class-variance-authority' - -import { cn } from '@/lib/utils' - -const badgeVariants = cva( - 'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2', - { - variants: { - variant: { - default: - 'border-transparent bg-primary text-primary-foreground hover:bg-primary/80', - secondary: - 'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80', - destructive: - 'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80', - outline: 'text-foreground' - } - }, - defaultVariants: { - variant: 'default' - } - } -) - -export interface BadgeProps - extends React.HTMLAttributes, - VariantProps {} - -function Badge({ className, variant, ...props }: BadgeProps) { - return ( -
        - ) -} - -export { Badge, badgeVariants } diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py deleted file mode 100644 index f1ddb2ebdf9eb702718fd31e09ff92b592da519f..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py +++ /dev/null @@ -1,188 +0,0 @@ -# SPDX-FileCopyrightText: 2015 Eric Larson -# -# SPDX-License-Identifier: Apache-2.0 - -import hashlib -import os -from textwrap import dedent - -from ..cache import BaseCache, SeparateBodyBaseCache -from ..controller import CacheController - -try: - FileNotFoundError -except NameError: - # py2.X - FileNotFoundError = (IOError, OSError) - - -def _secure_open_write(filename, fmode): - # We only want to write to this file, so open it in write only mode - flags = os.O_WRONLY - - # os.O_CREAT | os.O_EXCL will fail if the file already exists, so we only - # will open *new* files. - # We specify this because we want to ensure that the mode we pass is the - # mode of the file. - flags |= os.O_CREAT | os.O_EXCL - - # Do not follow symlinks to prevent someone from making a symlink that - # we follow and insecurely open a cache file. - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - - # On Windows we'll mark this file as binary - if hasattr(os, "O_BINARY"): - flags |= os.O_BINARY - - # Before we open our file, we want to delete any existing file that is - # there - try: - os.remove(filename) - except (IOError, OSError): - # The file must not exist already, so we can just skip ahead to opening - pass - - # Open our file, the use of os.O_CREAT | os.O_EXCL will ensure that if a - # race condition happens between the os.remove and this line, that an - # error will be raised. Because we utilize a lockfile this should only - # happen if someone is attempting to attack us. - fd = os.open(filename, flags, fmode) - try: - return os.fdopen(fd, "wb") - - except: - # An error occurred wrapping our FD in a file object - os.close(fd) - raise - - -class _FileCacheMixin: - """Shared implementation for both FileCache variants.""" - - def __init__( - self, - directory, - forever=False, - filemode=0o0600, - dirmode=0o0700, - use_dir_lock=None, - lock_class=None, - ): - - if use_dir_lock is not None and lock_class is not None: - raise ValueError("Cannot use use_dir_lock and lock_class together") - - try: - from lockfile import LockFile - from lockfile.mkdirlockfile import MkdirLockFile - except ImportError: - notice = dedent( - """ - NOTE: In order to use the FileCache you must have - lockfile installed. You can install it via pip: - pip install lockfile - """ - ) - raise ImportError(notice) - - else: - if use_dir_lock: - lock_class = MkdirLockFile - - elif lock_class is None: - lock_class = LockFile - - self.directory = directory - self.forever = forever - self.filemode = filemode - self.dirmode = dirmode - self.lock_class = lock_class - - @staticmethod - def encode(x): - return hashlib.sha224(x.encode()).hexdigest() - - def _fn(self, name): - # NOTE: This method should not change as some may depend on it. - # See: https://github.com/ionrock/cachecontrol/issues/63 - hashed = self.encode(name) - parts = list(hashed[:5]) + [hashed] - return os.path.join(self.directory, *parts) - - def get(self, key): - name = self._fn(key) - try: - with open(name, "rb") as fh: - return fh.read() - - except FileNotFoundError: - return None - - def set(self, key, value, expires=None): - name = self._fn(key) - self._write(name, value) - - def _write(self, path, data: bytes): - """ - Safely write the data to the given path. - """ - # Make sure the directory exists - try: - os.makedirs(os.path.dirname(path), self.dirmode) - except (IOError, OSError): - pass - - with self.lock_class(path) as lock: - # Write our actual file - with _secure_open_write(lock.path, self.filemode) as fh: - fh.write(data) - - def _delete(self, key, suffix): - name = self._fn(key) + suffix - if not self.forever: - try: - os.remove(name) - except FileNotFoundError: - pass - - -class FileCache(_FileCacheMixin, BaseCache): - """ - Traditional FileCache: body is stored in memory, so not suitable for large - downloads. - """ - - def delete(self, key): - self._delete(key, "") - - -class SeparateBodyFileCache(_FileCacheMixin, SeparateBodyBaseCache): - """ - Memory-efficient FileCache: body is stored in a separate file, reducing - peak memory usage. - """ - - def get_body(self, key): - name = self._fn(key) + ".body" - try: - return open(name, "rb") - except FileNotFoundError: - return None - - def set_body(self, key, body): - name = self._fn(key) + ".body" - self._write(name, body) - - def delete(self, key): - self._delete(key, "") - self._delete(key, ".body") - - -def url_to_file_path(url, filecache): - """Return the file cache path based on the URL. - - This does not ensure the file exists! - """ - key = CacheController.cache_url(url) - return filecache._fn(key) diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress_bar.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress_bar.py deleted file mode 100644 index 67361df2e49d48dd56c91e291ba92553e9afe344..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress_bar.py +++ /dev/null @@ -1,224 +0,0 @@ -import math -from functools import lru_cache -from time import monotonic -from typing import Iterable, List, Optional - -from .color import Color, blend_rgb -from .color_triplet import ColorTriplet -from .console import Console, ConsoleOptions, RenderResult -from .jupyter import JupyterMixin -from .measure import Measurement -from .segment import Segment -from .style import Style, StyleType - -# Number of characters before 'pulse' animation repeats -PULSE_SIZE = 20 - - -class ProgressBar(JupyterMixin): - """Renders a (progress) bar. Used by rich.progress. - - Args: - total (float, optional): Number of steps in the bar. Defaults to 100. Set to None to render a pulsing animation. - completed (float, optional): Number of steps completed. Defaults to 0. - width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None. - pulse (bool, optional): Enable pulse effect. Defaults to False. Will pulse if a None total was passed. - style (StyleType, optional): Style for the bar background. Defaults to "bar.back". - complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete". - finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished". - pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse". - animation_time (Optional[float], optional): Time in seconds to use for animation, or None to use system time. - """ - - def __init__( - self, - total: Optional[float] = 100.0, - completed: float = 0, - width: Optional[int] = None, - pulse: bool = False, - style: StyleType = "bar.back", - complete_style: StyleType = "bar.complete", - finished_style: StyleType = "bar.finished", - pulse_style: StyleType = "bar.pulse", - animation_time: Optional[float] = None, - ): - self.total = total - self.completed = completed - self.width = width - self.pulse = pulse - self.style = style - self.complete_style = complete_style - self.finished_style = finished_style - self.pulse_style = pulse_style - self.animation_time = animation_time - - self._pulse_segments: Optional[List[Segment]] = None - - def __repr__(self) -> str: - return f"" - - @property - def percentage_completed(self) -> Optional[float]: - """Calculate percentage complete.""" - if self.total is None: - return None - completed = (self.completed / self.total) * 100.0 - completed = min(100, max(0.0, completed)) - return completed - - @lru_cache(maxsize=16) - def _get_pulse_segments( - self, - fore_style: Style, - back_style: Style, - color_system: str, - no_color: bool, - ascii: bool = False, - ) -> List[Segment]: - """Get a list of segments to render a pulse animation. - - Returns: - List[Segment]: A list of segments, one segment per character. - """ - bar = "-" if ascii else "━" - segments: List[Segment] = [] - if color_system not in ("standard", "eight_bit", "truecolor") or no_color: - segments += [Segment(bar, fore_style)] * (PULSE_SIZE // 2) - segments += [Segment(" " if no_color else bar, back_style)] * ( - PULSE_SIZE - (PULSE_SIZE // 2) - ) - return segments - - append = segments.append - fore_color = ( - fore_style.color.get_truecolor() - if fore_style.color - else ColorTriplet(255, 0, 255) - ) - back_color = ( - back_style.color.get_truecolor() - if back_style.color - else ColorTriplet(0, 0, 0) - ) - cos = math.cos - pi = math.pi - _Segment = Segment - _Style = Style - from_triplet = Color.from_triplet - - for index in range(PULSE_SIZE): - position = index / PULSE_SIZE - fade = 0.5 + cos((position * pi * 2)) / 2.0 - color = blend_rgb(fore_color, back_color, cross_fade=fade) - append(_Segment(bar, _Style(color=from_triplet(color)))) - return segments - - def update(self, completed: float, total: Optional[float] = None) -> None: - """Update progress with new values. - - Args: - completed (float): Number of steps completed. - total (float, optional): Total number of steps, or ``None`` to not change. Defaults to None. - """ - self.completed = completed - self.total = total if total is not None else self.total - - def _render_pulse( - self, console: Console, width: int, ascii: bool = False - ) -> Iterable[Segment]: - """Renders the pulse animation. - - Args: - console (Console): Console instance. - width (int): Width in characters of pulse animation. - - Returns: - RenderResult: [description] - - Yields: - Iterator[Segment]: Segments to render pulse - """ - fore_style = console.get_style(self.pulse_style, default="white") - back_style = console.get_style(self.style, default="black") - - pulse_segments = self._get_pulse_segments( - fore_style, back_style, console.color_system, console.no_color, ascii=ascii - ) - segment_count = len(pulse_segments) - current_time = ( - monotonic() if self.animation_time is None else self.animation_time - ) - segments = pulse_segments * (int(width / segment_count) + 2) - offset = int(-current_time * 15) % segment_count - segments = segments[offset : offset + width] - yield from segments - - def __rich_console__( - self, console: Console, options: ConsoleOptions - ) -> RenderResult: - - width = min(self.width or options.max_width, options.max_width) - ascii = options.legacy_windows or options.ascii_only - should_pulse = self.pulse or self.total is None - if should_pulse: - yield from self._render_pulse(console, width, ascii=ascii) - return - - completed: Optional[float] = ( - min(self.total, max(0, self.completed)) if self.total is not None else None - ) - - bar = "-" if ascii else "━" - half_bar_right = " " if ascii else "╸" - half_bar_left = " " if ascii else "╺" - complete_halves = ( - int(width * 2 * completed / self.total) - if self.total and completed is not None - else width * 2 - ) - bar_count = complete_halves // 2 - half_bar_count = complete_halves % 2 - style = console.get_style(self.style) - is_finished = self.total is None or self.completed >= self.total - complete_style = console.get_style( - self.finished_style if is_finished else self.complete_style - ) - _Segment = Segment - if bar_count: - yield _Segment(bar * bar_count, complete_style) - if half_bar_count: - yield _Segment(half_bar_right * half_bar_count, complete_style) - - if not console.no_color: - remaining_bars = width - bar_count - half_bar_count - if remaining_bars and console.color_system is not None: - if not half_bar_count and bar_count: - yield _Segment(half_bar_left, style) - remaining_bars -= 1 - if remaining_bars: - yield _Segment(bar * remaining_bars, style) - - def __rich_measure__( - self, console: Console, options: ConsoleOptions - ) -> Measurement: - return ( - Measurement(self.width, self.width) - if self.width is not None - else Measurement(4, options.max_width) - ) - - -if __name__ == "__main__": # pragma: no cover - console = Console() - bar = ProgressBar(width=50, total=100) - - import time - - console.show_cursor(False) - for n in range(0, 101, 1): - bar.update(n) - console.print(bar) - console.file.write("\r") - time.sleep(0.05) - console.show_cursor(True) - console.print() diff --git a/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportExportChat.tsx b/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportExportChat.tsx deleted file mode 100644 index 8426fe6d930eeaafa4aedcc7df742bdb2e572e8e..0000000000000000000000000000000000000000 --- a/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportExportChat.tsx +++ /dev/null @@ -1,44 +0,0 @@ -import React, { useState } from 'react'; -import { useTranslation } from 'react-i18next'; - -import ExportIcon from '@icon/ExportIcon'; -import PopupModal from '@components/PopupModal'; - -import ImportChat from './ImportChat'; -import ExportChat from './ExportChat'; -import ImportChatOpenAI from './ImportChatOpenAI'; - -const ImportExportChat = () => { - const { t } = useTranslation(); - const [isModalOpen, setIsModalOpen] = useState(false); - - return ( - <> - { - setIsModalOpen(true); - }} - > - - {t('import')} / {t('export')} - - {isModalOpen && ( - -
        - - -
        - -
        - - )} - - ); -}; - -export default ImportExportChat; diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/tcp_helpers.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/tcp_helpers.py deleted file mode 100644 index 88b244223741ad2decb6cb612eae644fae88b2b2..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/tcp_helpers.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Helper methods to tune a TCP connection""" - -import asyncio -import socket -from contextlib import suppress -from typing import Optional # noqa - -__all__ = ("tcp_keepalive", "tcp_nodelay") - - -if hasattr(socket, "SO_KEEPALIVE"): - - def tcp_keepalive(transport: asyncio.Transport) -> None: - sock = transport.get_extra_info("socket") - if sock is not None: - sock.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1) - -else: - - def tcp_keepalive(transport: asyncio.Transport) -> None: # pragma: no cover - pass - - -def tcp_nodelay(transport: asyncio.Transport, value: bool) -> None: - sock = transport.get_extra_info("socket") - - if sock is None: - return - - if sock.family not in (socket.AF_INET, socket.AF_INET6): - return - - value = bool(value) - - # socket may be closed already, on windows OSError get raised - with suppress(OSError): - sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, value) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/converters.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/converters.py deleted file mode 100644 index 4cada106b01c564faf17969d24038f80abd5de6f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/converters.py +++ /dev/null @@ -1,144 +0,0 @@ -# SPDX-License-Identifier: MIT - -""" -Commonly useful converters. -""" - - -import typing - -from ._compat import _AnnotationExtractor -from ._make import NOTHING, Factory, pipe - - -__all__ = [ - "default_if_none", - "optional", - "pipe", - "to_bool", -] - - -def optional(converter): - """ - A converter that allows an attribute to be optional. An optional attribute - is one which can be set to ``None``. - - Type annotations will be inferred from the wrapped converter's, if it - has any. - - :param callable converter: the converter that is used for non-``None`` - values. - - .. versionadded:: 17.1.0 - """ - - def optional_converter(val): - if val is None: - return None - return converter(val) - - xtr = _AnnotationExtractor(converter) - - t = xtr.get_first_param_type() - if t: - optional_converter.__annotations__["val"] = typing.Optional[t] - - rt = xtr.get_return_type() - if rt: - optional_converter.__annotations__["return"] = typing.Optional[rt] - - return optional_converter - - -def default_if_none(default=NOTHING, factory=None): - """ - A converter that allows to replace ``None`` values by *default* or the - result of *factory*. - - :param default: Value to be used if ``None`` is passed. Passing an instance - of `attrs.Factory` is supported, however the ``takes_self`` option - is *not*. - :param callable factory: A callable that takes no parameters whose result - is used if ``None`` is passed. - - :raises TypeError: If **neither** *default* or *factory* is passed. - :raises TypeError: If **both** *default* and *factory* are passed. - :raises ValueError: If an instance of `attrs.Factory` is passed with - ``takes_self=True``. - - .. versionadded:: 18.2.0 - """ - if default is NOTHING and factory is None: - raise TypeError("Must pass either `default` or `factory`.") - - if default is not NOTHING and factory is not None: - raise TypeError( - "Must pass either `default` or `factory` but not both." - ) - - if factory is not None: - default = Factory(factory) - - if isinstance(default, Factory): - if default.takes_self: - raise ValueError( - "`takes_self` is not supported by default_if_none." - ) - - def default_if_none_converter(val): - if val is not None: - return val - - return default.factory() - - else: - - def default_if_none_converter(val): - if val is not None: - return val - - return default - - return default_if_none_converter - - -def to_bool(val): - """ - Convert "boolean" strings (e.g., from env. vars.) to real booleans. - - Values mapping to :code:`True`: - - - :code:`True` - - :code:`"true"` / :code:`"t"` - - :code:`"yes"` / :code:`"y"` - - :code:`"on"` - - :code:`"1"` - - :code:`1` - - Values mapping to :code:`False`: - - - :code:`False` - - :code:`"false"` / :code:`"f"` - - :code:`"no"` / :code:`"n"` - - :code:`"off"` - - :code:`"0"` - - :code:`0` - - :raises ValueError: for any other value. - - .. versionadded:: 21.3.0 - """ - if isinstance(val, str): - val = val.lower() - truthy = {True, "true", "t", "yes", "y", "on", "1", 1} - falsy = {False, "false", "f", "no", "n", "off", "0", 0} - try: - if val in truthy: - return True - if val in falsy: - return False - except TypeError: - # Raised when "val" is not hashable (e.g., lists) - pass - raise ValueError(f"Cannot convert value to bool: {val}") diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/validators.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/validators.py deleted file mode 100644 index 1488554f789526d8d85eb467250a64a64489362d..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/attr/validators.py +++ /dev/null @@ -1,720 +0,0 @@ -# SPDX-License-Identifier: MIT - -""" -Commonly useful validators. -""" - - -import operator -import re - -from contextlib import contextmanager -from re import Pattern - -from ._config import get_run_validators, set_run_validators -from ._make import _AndValidator, and_, attrib, attrs -from .converters import default_if_none -from .exceptions import NotCallableError - - -__all__ = [ - "and_", - "deep_iterable", - "deep_mapping", - "disabled", - "ge", - "get_disabled", - "gt", - "in_", - "instance_of", - "is_callable", - "le", - "lt", - "matches_re", - "max_len", - "min_len", - "not_", - "optional", - "provides", - "set_disabled", -] - - -def set_disabled(disabled): - """ - Globally disable or enable running validators. - - By default, they are run. - - :param disabled: If ``True``, disable running all validators. - :type disabled: bool - - .. warning:: - - This function is not thread-safe! - - .. versionadded:: 21.3.0 - """ - set_run_validators(not disabled) - - -def get_disabled(): - """ - Return a bool indicating whether validators are currently disabled or not. - - :return: ``True`` if validators are currently disabled. - :rtype: bool - - .. versionadded:: 21.3.0 - """ - return not get_run_validators() - - -@contextmanager -def disabled(): - """ - Context manager that disables running validators within its context. - - .. warning:: - - This context manager is not thread-safe! - - .. versionadded:: 21.3.0 - """ - set_run_validators(False) - try: - yield - finally: - set_run_validators(True) - - -@attrs(repr=False, slots=True, hash=True) -class _InstanceOfValidator: - type = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not isinstance(value, self.type): - raise TypeError( - "'{name}' must be {type!r} (got {value!r} that is a " - "{actual!r}).".format( - name=attr.name, - type=self.type, - actual=value.__class__, - value=value, - ), - attr, - self.type, - value, - ) - - def __repr__(self): - return "".format( - type=self.type - ) - - -def instance_of(type): - """ - A validator that raises a `TypeError` if the initializer is called - with a wrong type for this particular attribute (checks are performed using - `isinstance` therefore it's also valid to pass a tuple of types). - - :param type: The type to check for. - :type type: type or tuple of type - - :raises TypeError: With a human readable error message, the attribute - (of type `attrs.Attribute`), the expected type, and the value it - got. - """ - return _InstanceOfValidator(type) - - -@attrs(repr=False, frozen=True, slots=True) -class _MatchesReValidator: - pattern = attrib() - match_func = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not self.match_func(value): - raise ValueError( - "'{name}' must match regex {pattern!r}" - " ({value!r} doesn't)".format( - name=attr.name, pattern=self.pattern.pattern, value=value - ), - attr, - self.pattern, - value, - ) - - def __repr__(self): - return "".format( - pattern=self.pattern - ) - - -def matches_re(regex, flags=0, func=None): - r""" - A validator that raises `ValueError` if the initializer is called - with a string that doesn't match *regex*. - - :param regex: a regex string or precompiled pattern to match against - :param int flags: flags that will be passed to the underlying re function - (default 0) - :param callable func: which underlying `re` function to call. Valid options - are `re.fullmatch`, `re.search`, and `re.match`; the default ``None`` - means `re.fullmatch`. For performance reasons, the pattern is always - precompiled using `re.compile`. - - .. versionadded:: 19.2.0 - .. versionchanged:: 21.3.0 *regex* can be a pre-compiled pattern. - """ - valid_funcs = (re.fullmatch, None, re.search, re.match) - if func not in valid_funcs: - raise ValueError( - "'func' must be one of {}.".format( - ", ".join( - sorted( - e and e.__name__ or "None" for e in set(valid_funcs) - ) - ) - ) - ) - - if isinstance(regex, Pattern): - if flags: - raise TypeError( - "'flags' can only be used with a string pattern; " - "pass flags to re.compile() instead" - ) - pattern = regex - else: - pattern = re.compile(regex, flags) - - if func is re.match: - match_func = pattern.match - elif func is re.search: - match_func = pattern.search - else: - match_func = pattern.fullmatch - - return _MatchesReValidator(pattern, match_func) - - -@attrs(repr=False, slots=True, hash=True) -class _ProvidesValidator: - interface = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not self.interface.providedBy(value): - raise TypeError( - "'{name}' must provide {interface!r} which {value!r} " - "doesn't.".format( - name=attr.name, interface=self.interface, value=value - ), - attr, - self.interface, - value, - ) - - def __repr__(self): - return "".format( - interface=self.interface - ) - - -def provides(interface): - """ - A validator that raises a `TypeError` if the initializer is called - with an object that does not provide the requested *interface* (checks are - performed using ``interface.providedBy(value)`` (see `zope.interface - `_). - - :param interface: The interface to check for. - :type interface: ``zope.interface.Interface`` - - :raises TypeError: With a human readable error message, the attribute - (of type `attrs.Attribute`), the expected interface, and the - value it got. - - .. deprecated:: 23.1.0 - """ - import warnings - - warnings.warn( - "attrs's zope-interface support is deprecated and will be removed in, " - "or after, April 2024.", - DeprecationWarning, - stacklevel=2, - ) - return _ProvidesValidator(interface) - - -@attrs(repr=False, slots=True, hash=True) -class _OptionalValidator: - validator = attrib() - - def __call__(self, inst, attr, value): - if value is None: - return - - self.validator(inst, attr, value) - - def __repr__(self): - return "".format( - what=repr(self.validator) - ) - - -def optional(validator): - """ - A validator that makes an attribute optional. An optional attribute is one - which can be set to ``None`` in addition to satisfying the requirements of - the sub-validator. - - :param Callable | tuple[Callable] | list[Callable] validator: A validator - (or validators) that is used for non-``None`` values. - - .. versionadded:: 15.1.0 - .. versionchanged:: 17.1.0 *validator* can be a list of validators. - .. versionchanged:: 23.1.0 *validator* can also be a tuple of validators. - """ - if isinstance(validator, (list, tuple)): - return _OptionalValidator(_AndValidator(validator)) - - return _OptionalValidator(validator) - - -@attrs(repr=False, slots=True, hash=True) -class _InValidator: - options = attrib() - - def __call__(self, inst, attr, value): - try: - in_options = value in self.options - except TypeError: # e.g. `1 in "abc"` - in_options = False - - if not in_options: - raise ValueError( - "'{name}' must be in {options!r} (got {value!r})".format( - name=attr.name, options=self.options, value=value - ), - attr, - self.options, - value, - ) - - def __repr__(self): - return "".format( - options=self.options - ) - - -def in_(options): - """ - A validator that raises a `ValueError` if the initializer is called - with a value that does not belong in the options provided. The check is - performed using ``value in options``. - - :param options: Allowed options. - :type options: list, tuple, `enum.Enum`, ... - - :raises ValueError: With a human readable error message, the attribute (of - type `attrs.Attribute`), the expected options, and the value it - got. - - .. versionadded:: 17.1.0 - .. versionchanged:: 22.1.0 - The ValueError was incomplete until now and only contained the human - readable error message. Now it contains all the information that has - been promised since 17.1.0. - """ - return _InValidator(options) - - -@attrs(repr=False, slots=False, hash=True) -class _IsCallableValidator: - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not callable(value): - message = ( - "'{name}' must be callable " - "(got {value!r} that is a {actual!r})." - ) - raise NotCallableError( - msg=message.format( - name=attr.name, value=value, actual=value.__class__ - ), - value=value, - ) - - def __repr__(self): - return "" - - -def is_callable(): - """ - A validator that raises a `attrs.exceptions.NotCallableError` if the - initializer is called with a value for this particular attribute - that is not callable. - - .. versionadded:: 19.1.0 - - :raises attrs.exceptions.NotCallableError: With a human readable error - message containing the attribute (`attrs.Attribute`) name, - and the value it got. - """ - return _IsCallableValidator() - - -@attrs(repr=False, slots=True, hash=True) -class _DeepIterable: - member_validator = attrib(validator=is_callable()) - iterable_validator = attrib( - default=None, validator=optional(is_callable()) - ) - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if self.iterable_validator is not None: - self.iterable_validator(inst, attr, value) - - for member in value: - self.member_validator(inst, attr, member) - - def __repr__(self): - iterable_identifier = ( - "" - if self.iterable_validator is None - else f" {self.iterable_validator!r}" - ) - return ( - "" - ).format( - iterable_identifier=iterable_identifier, - member=self.member_validator, - ) - - -def deep_iterable(member_validator, iterable_validator=None): - """ - A validator that performs deep validation of an iterable. - - :param member_validator: Validator(s) to apply to iterable members - :param iterable_validator: Validator to apply to iterable itself - (optional) - - .. versionadded:: 19.1.0 - - :raises TypeError: if any sub-validators fail - """ - if isinstance(member_validator, (list, tuple)): - member_validator = and_(*member_validator) - return _DeepIterable(member_validator, iterable_validator) - - -@attrs(repr=False, slots=True, hash=True) -class _DeepMapping: - key_validator = attrib(validator=is_callable()) - value_validator = attrib(validator=is_callable()) - mapping_validator = attrib(default=None, validator=optional(is_callable())) - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if self.mapping_validator is not None: - self.mapping_validator(inst, attr, value) - - for key in value: - self.key_validator(inst, attr, key) - self.value_validator(inst, attr, value[key]) - - def __repr__(self): - return ( - "" - ).format(key=self.key_validator, value=self.value_validator) - - -def deep_mapping(key_validator, value_validator, mapping_validator=None): - """ - A validator that performs deep validation of a dictionary. - - :param key_validator: Validator to apply to dictionary keys - :param value_validator: Validator to apply to dictionary values - :param mapping_validator: Validator to apply to top-level mapping - attribute (optional) - - .. versionadded:: 19.1.0 - - :raises TypeError: if any sub-validators fail - """ - return _DeepMapping(key_validator, value_validator, mapping_validator) - - -@attrs(repr=False, frozen=True, slots=True) -class _NumberValidator: - bound = attrib() - compare_op = attrib() - compare_func = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not self.compare_func(value, self.bound): - raise ValueError( - "'{name}' must be {op} {bound}: {value}".format( - name=attr.name, - op=self.compare_op, - bound=self.bound, - value=value, - ) - ) - - def __repr__(self): - return "".format( - op=self.compare_op, bound=self.bound - ) - - -def lt(val): - """ - A validator that raises `ValueError` if the initializer is called - with a number larger or equal to *val*. - - :param val: Exclusive upper bound for values - - .. versionadded:: 21.3.0 - """ - return _NumberValidator(val, "<", operator.lt) - - -def le(val): - """ - A validator that raises `ValueError` if the initializer is called - with a number greater than *val*. - - :param val: Inclusive upper bound for values - - .. versionadded:: 21.3.0 - """ - return _NumberValidator(val, "<=", operator.le) - - -def ge(val): - """ - A validator that raises `ValueError` if the initializer is called - with a number smaller than *val*. - - :param val: Inclusive lower bound for values - - .. versionadded:: 21.3.0 - """ - return _NumberValidator(val, ">=", operator.ge) - - -def gt(val): - """ - A validator that raises `ValueError` if the initializer is called - with a number smaller or equal to *val*. - - :param val: Exclusive lower bound for values - - .. versionadded:: 21.3.0 - """ - return _NumberValidator(val, ">", operator.gt) - - -@attrs(repr=False, frozen=True, slots=True) -class _MaxLengthValidator: - max_length = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if len(value) > self.max_length: - raise ValueError( - "Length of '{name}' must be <= {max}: {len}".format( - name=attr.name, max=self.max_length, len=len(value) - ) - ) - - def __repr__(self): - return f"" - - -def max_len(length): - """ - A validator that raises `ValueError` if the initializer is called - with a string or iterable that is longer than *length*. - - :param int length: Maximum length of the string or iterable - - .. versionadded:: 21.3.0 - """ - return _MaxLengthValidator(length) - - -@attrs(repr=False, frozen=True, slots=True) -class _MinLengthValidator: - min_length = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if len(value) < self.min_length: - raise ValueError( - "Length of '{name}' must be => {min}: {len}".format( - name=attr.name, min=self.min_length, len=len(value) - ) - ) - - def __repr__(self): - return f"" - - -def min_len(length): - """ - A validator that raises `ValueError` if the initializer is called - with a string or iterable that is shorter than *length*. - - :param int length: Minimum length of the string or iterable - - .. versionadded:: 22.1.0 - """ - return _MinLengthValidator(length) - - -@attrs(repr=False, slots=True, hash=True) -class _SubclassOfValidator: - type = attrib() - - def __call__(self, inst, attr, value): - """ - We use a callable class to be able to change the ``__repr__``. - """ - if not issubclass(value, self.type): - raise TypeError( - "'{name}' must be a subclass of {type!r} " - "(got {value!r}).".format( - name=attr.name, - type=self.type, - value=value, - ), - attr, - self.type, - value, - ) - - def __repr__(self): - return "".format( - type=self.type - ) - - -def _subclass_of(type): - """ - A validator that raises a `TypeError` if the initializer is called - with a wrong type for this particular attribute (checks are performed using - `issubclass` therefore it's also valid to pass a tuple of types). - - :param type: The type to check for. - :type type: type or tuple of types - - :raises TypeError: With a human readable error message, the attribute - (of type `attrs.Attribute`), the expected type, and the value it - got. - """ - return _SubclassOfValidator(type) - - -@attrs(repr=False, slots=True, hash=True) -class _NotValidator: - validator = attrib() - msg = attrib( - converter=default_if_none( - "not_ validator child '{validator!r}' " - "did not raise a captured error" - ) - ) - exc_types = attrib( - validator=deep_iterable( - member_validator=_subclass_of(Exception), - iterable_validator=instance_of(tuple), - ), - ) - - def __call__(self, inst, attr, value): - try: - self.validator(inst, attr, value) - except self.exc_types: - pass # suppress error to invert validity - else: - raise ValueError( - self.msg.format( - validator=self.validator, - exc_types=self.exc_types, - ), - attr, - self.validator, - value, - self.exc_types, - ) - - def __repr__(self): - return ( - "" - ).format( - what=self.validator, - exc_types=self.exc_types, - ) - - -def not_(validator, *, msg=None, exc_types=(ValueError, TypeError)): - """ - A validator that wraps and logically 'inverts' the validator passed to it. - It will raise a `ValueError` if the provided validator *doesn't* raise a - `ValueError` or `TypeError` (by default), and will suppress the exception - if the provided validator *does*. - - Intended to be used with existing validators to compose logic without - needing to create inverted variants, for example, ``not_(in_(...))``. - - :param validator: A validator to be logically inverted. - :param msg: Message to raise if validator fails. - Formatted with keys ``exc_types`` and ``validator``. - :type msg: str - :param exc_types: Exception type(s) to capture. - Other types raised by child validators will not be intercepted and - pass through. - - :raises ValueError: With a human readable error message, - the attribute (of type `attrs.Attribute`), - the validator that failed to raise an exception, - the value it got, - and the expected exception types. - - .. versionadded:: 22.2.0 - """ - try: - exc_types = tuple(exc_types) - except TypeError: - exc_types = (exc_types,) - return _NotValidator(validator, msg, exc_types) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/_version.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/_version.py deleted file mode 100644 index b723056a756af22aaf1a4709c5122bea9fb279ee..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/_version.py +++ /dev/null @@ -1,5 +0,0 @@ -# coding: utf-8 -# file generated by setuptools_scm -# don't change, don't track in version control -version = '2.8.2' -version_tuple = (2, 8, 2) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/cff.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/cff.py deleted file mode 100644 index dd79f6db37a482891b6f151159ef4c9b89475b8e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/subset/cff.py +++ /dev/null @@ -1,536 +0,0 @@ -from fontTools.misc import psCharStrings -from fontTools import ttLib -from fontTools.pens.basePen import NullPen -from fontTools.misc.roundTools import otRound -from fontTools.misc.loggingTools import deprecateFunction -from fontTools.subset.util import _add_method, _uniq_sort - - -class _ClosureGlyphsT2Decompiler(psCharStrings.SimpleT2Decompiler): - def __init__(self, components, localSubrs, globalSubrs): - psCharStrings.SimpleT2Decompiler.__init__(self, localSubrs, globalSubrs) - self.components = components - - def op_endchar(self, index): - args = self.popall() - if len(args) >= 4: - from fontTools.encodings.StandardEncoding import StandardEncoding - - # endchar can do seac accent bulding; The T2 spec says it's deprecated, - # but recent software that shall remain nameless does output it. - adx, ady, bchar, achar = args[-4:] - baseGlyph = StandardEncoding[bchar] - accentGlyph = StandardEncoding[achar] - self.components.add(baseGlyph) - self.components.add(accentGlyph) - - -@_add_method(ttLib.getTableClass("CFF ")) -def closure_glyphs(self, s): - cff = self.cff - assert len(cff) == 1 - font = cff[cff.keys()[0]] - glyphSet = font.CharStrings - - decompose = s.glyphs - while decompose: - components = set() - for g in decompose: - if g not in glyphSet: - continue - gl = glyphSet[g] - - subrs = getattr(gl.private, "Subrs", []) - decompiler = _ClosureGlyphsT2Decompiler(components, subrs, gl.globalSubrs) - decompiler.execute(gl) - components -= s.glyphs - s.glyphs.update(components) - decompose = components - - -def _empty_charstring(font, glyphName, isCFF2, ignoreWidth=False): - c, fdSelectIndex = font.CharStrings.getItemAndSelector(glyphName) - if isCFF2 or ignoreWidth: - # CFF2 charstrings have no widths nor 'endchar' operators - c.setProgram([] if isCFF2 else ["endchar"]) - else: - if hasattr(font, "FDArray") and font.FDArray is not None: - private = font.FDArray[fdSelectIndex].Private - else: - private = font.Private - dfltWdX = private.defaultWidthX - nmnlWdX = private.nominalWidthX - pen = NullPen() - c.draw(pen) # this will set the charstring's width - if c.width != dfltWdX: - c.program = [c.width - nmnlWdX, "endchar"] - else: - c.program = ["endchar"] - - -@_add_method(ttLib.getTableClass("CFF ")) -def prune_pre_subset(self, font, options): - cff = self.cff - # CFF table must have one font only - cff.fontNames = cff.fontNames[:1] - - if options.notdef_glyph and not options.notdef_outline: - isCFF2 = cff.major > 1 - for fontname in cff.keys(): - font = cff[fontname] - _empty_charstring(font, ".notdef", isCFF2=isCFF2) - - # Clear useless Encoding - for fontname in cff.keys(): - font = cff[fontname] - # https://github.com/fonttools/fonttools/issues/620 - font.Encoding = "StandardEncoding" - - return True # bool(cff.fontNames) - - -@_add_method(ttLib.getTableClass("CFF ")) -def subset_glyphs(self, s): - cff = self.cff - for fontname in cff.keys(): - font = cff[fontname] - cs = font.CharStrings - - glyphs = s.glyphs.union(s.glyphs_emptied) - - # Load all glyphs - for g in font.charset: - if g not in glyphs: - continue - c, _ = cs.getItemAndSelector(g) - - if cs.charStringsAreIndexed: - indices = [i for i, g in enumerate(font.charset) if g in glyphs] - csi = cs.charStringsIndex - csi.items = [csi.items[i] for i in indices] - del csi.file, csi.offsets - if hasattr(font, "FDSelect"): - sel = font.FDSelect - sel.format = None - sel.gidArray = [sel.gidArray[i] for i in indices] - newCharStrings = {} - for indicesIdx, charsetIdx in enumerate(indices): - g = font.charset[charsetIdx] - if g in cs.charStrings: - newCharStrings[g] = indicesIdx - cs.charStrings = newCharStrings - else: - cs.charStrings = {g: v for g, v in cs.charStrings.items() if g in glyphs} - font.charset = [g for g in font.charset if g in glyphs] - font.numGlyphs = len(font.charset) - - if s.options.retain_gids: - isCFF2 = cff.major > 1 - for g in s.glyphs_emptied: - _empty_charstring(font, g, isCFF2=isCFF2, ignoreWidth=True) - - return True # any(cff[fontname].numGlyphs for fontname in cff.keys()) - - -@_add_method(psCharStrings.T2CharString) -def subset_subroutines(self, subrs, gsubrs): - p = self.program - for i in range(1, len(p)): - if p[i] == "callsubr": - assert isinstance(p[i - 1], int) - p[i - 1] = subrs._used.index(p[i - 1] + subrs._old_bias) - subrs._new_bias - elif p[i] == "callgsubr": - assert isinstance(p[i - 1], int) - p[i - 1] = ( - gsubrs._used.index(p[i - 1] + gsubrs._old_bias) - gsubrs._new_bias - ) - - -@_add_method(psCharStrings.T2CharString) -def drop_hints(self): - hints = self._hints - - if hints.deletions: - p = self.program - for idx in reversed(hints.deletions): - del p[idx - 2 : idx] - - if hints.has_hint: - assert not hints.deletions or hints.last_hint <= hints.deletions[0] - self.program = self.program[hints.last_hint :] - if not self.program: - # TODO CFF2 no need for endchar. - self.program.append("endchar") - if hasattr(self, "width"): - # Insert width back if needed - if self.width != self.private.defaultWidthX: - # For CFF2 charstrings, this should never happen - assert ( - self.private.defaultWidthX is not None - ), "CFF2 CharStrings must not have an initial width value" - self.program.insert(0, self.width - self.private.nominalWidthX) - - if hints.has_hintmask: - i = 0 - p = self.program - while i < len(p): - if p[i] in ["hintmask", "cntrmask"]: - assert i + 1 <= len(p) - del p[i : i + 2] - continue - i += 1 - - assert len(self.program) - - del self._hints - - -class _MarkingT2Decompiler(psCharStrings.SimpleT2Decompiler): - def __init__(self, localSubrs, globalSubrs, private): - psCharStrings.SimpleT2Decompiler.__init__( - self, localSubrs, globalSubrs, private - ) - for subrs in [localSubrs, globalSubrs]: - if subrs and not hasattr(subrs, "_used"): - subrs._used = set() - - def op_callsubr(self, index): - self.localSubrs._used.add(self.operandStack[-1] + self.localBias) - psCharStrings.SimpleT2Decompiler.op_callsubr(self, index) - - def op_callgsubr(self, index): - self.globalSubrs._used.add(self.operandStack[-1] + self.globalBias) - psCharStrings.SimpleT2Decompiler.op_callgsubr(self, index) - - -class _DehintingT2Decompiler(psCharStrings.T2WidthExtractor): - class Hints(object): - def __init__(self): - # Whether calling this charstring produces any hint stems - # Note that if a charstring starts with hintmask, it will - # have has_hint set to True, because it *might* produce an - # implicit vstem if called under certain conditions. - self.has_hint = False - # Index to start at to drop all hints - self.last_hint = 0 - # Index up to which we know more hints are possible. - # Only relevant if status is 0 or 1. - self.last_checked = 0 - # The status means: - # 0: after dropping hints, this charstring is empty - # 1: after dropping hints, there may be more hints - # continuing after this, or there might be - # other things. Not clear yet. - # 2: no more hints possible after this charstring - self.status = 0 - # Has hintmask instructions; not recursive - self.has_hintmask = False - # List of indices of calls to empty subroutines to remove. - self.deletions = [] - - pass - - def __init__( - self, css, localSubrs, globalSubrs, nominalWidthX, defaultWidthX, private=None - ): - self._css = css - psCharStrings.T2WidthExtractor.__init__( - self, localSubrs, globalSubrs, nominalWidthX, defaultWidthX - ) - self.private = private - - def execute(self, charString): - old_hints = charString._hints if hasattr(charString, "_hints") else None - charString._hints = self.Hints() - - psCharStrings.T2WidthExtractor.execute(self, charString) - - hints = charString._hints - - if hints.has_hint or hints.has_hintmask: - self._css.add(charString) - - if hints.status != 2: - # Check from last_check, make sure we didn't have any operators. - for i in range(hints.last_checked, len(charString.program) - 1): - if isinstance(charString.program[i], str): - hints.status = 2 - break - else: - hints.status = 1 # There's *something* here - hints.last_checked = len(charString.program) - - if old_hints: - assert hints.__dict__ == old_hints.__dict__ - - def op_callsubr(self, index): - subr = self.localSubrs[self.operandStack[-1] + self.localBias] - psCharStrings.T2WidthExtractor.op_callsubr(self, index) - self.processSubr(index, subr) - - def op_callgsubr(self, index): - subr = self.globalSubrs[self.operandStack[-1] + self.globalBias] - psCharStrings.T2WidthExtractor.op_callgsubr(self, index) - self.processSubr(index, subr) - - def op_hstem(self, index): - psCharStrings.T2WidthExtractor.op_hstem(self, index) - self.processHint(index) - - def op_vstem(self, index): - psCharStrings.T2WidthExtractor.op_vstem(self, index) - self.processHint(index) - - def op_hstemhm(self, index): - psCharStrings.T2WidthExtractor.op_hstemhm(self, index) - self.processHint(index) - - def op_vstemhm(self, index): - psCharStrings.T2WidthExtractor.op_vstemhm(self, index) - self.processHint(index) - - def op_hintmask(self, index): - rv = psCharStrings.T2WidthExtractor.op_hintmask(self, index) - self.processHintmask(index) - return rv - - def op_cntrmask(self, index): - rv = psCharStrings.T2WidthExtractor.op_cntrmask(self, index) - self.processHintmask(index) - return rv - - def processHintmask(self, index): - cs = self.callingStack[-1] - hints = cs._hints - hints.has_hintmask = True - if hints.status != 2: - # Check from last_check, see if we may be an implicit vstem - for i in range(hints.last_checked, index - 1): - if isinstance(cs.program[i], str): - hints.status = 2 - break - else: - # We are an implicit vstem - hints.has_hint = True - hints.last_hint = index + 1 - hints.status = 0 - hints.last_checked = index + 1 - - def processHint(self, index): - cs = self.callingStack[-1] - hints = cs._hints - hints.has_hint = True - hints.last_hint = index - hints.last_checked = index - - def processSubr(self, index, subr): - cs = self.callingStack[-1] - hints = cs._hints - subr_hints = subr._hints - - # Check from last_check, make sure we didn't have - # any operators. - if hints.status != 2: - for i in range(hints.last_checked, index - 1): - if isinstance(cs.program[i], str): - hints.status = 2 - break - hints.last_checked = index - - if hints.status != 2: - if subr_hints.has_hint: - hints.has_hint = True - - # Decide where to chop off from - if subr_hints.status == 0: - hints.last_hint = index - else: - hints.last_hint = index - 2 # Leave the subr call in - - elif subr_hints.status == 0: - hints.deletions.append(index) - - hints.status = max(hints.status, subr_hints.status) - - -@_add_method(ttLib.getTableClass("CFF ")) -def prune_post_subset(self, ttfFont, options): - cff = self.cff - for fontname in cff.keys(): - font = cff[fontname] - cs = font.CharStrings - - # Drop unused FontDictionaries - if hasattr(font, "FDSelect"): - sel = font.FDSelect - indices = _uniq_sort(sel.gidArray) - sel.gidArray = [indices.index(ss) for ss in sel.gidArray] - arr = font.FDArray - arr.items = [arr[i] for i in indices] - del arr.file, arr.offsets - - # Desubroutinize if asked for - if options.desubroutinize: - cff.desubroutinize() - - # Drop hints if not needed - if not options.hinting: - self.remove_hints() - elif not options.desubroutinize: - self.remove_unused_subroutines() - return True - - -def _delete_empty_subrs(private_dict): - if hasattr(private_dict, "Subrs") and not private_dict.Subrs: - if "Subrs" in private_dict.rawDict: - del private_dict.rawDict["Subrs"] - del private_dict.Subrs - - -@deprecateFunction( - "use 'CFFFontSet.desubroutinize()' instead", category=DeprecationWarning -) -@_add_method(ttLib.getTableClass("CFF ")) -def desubroutinize(self): - self.cff.desubroutinize() - - -@_add_method(ttLib.getTableClass("CFF ")) -def remove_hints(self): - cff = self.cff - for fontname in cff.keys(): - font = cff[fontname] - cs = font.CharStrings - # This can be tricky, but doesn't have to. What we do is: - # - # - Run all used glyph charstrings and recurse into subroutines, - # - For each charstring (including subroutines), if it has any - # of the hint stem operators, we mark it as such. - # Upon returning, for each charstring we note all the - # subroutine calls it makes that (recursively) contain a stem, - # - Dropping hinting then consists of the following two ops: - # * Drop the piece of the program in each charstring before the - # last call to a stem op or a stem-calling subroutine, - # * Drop all hintmask operations. - # - It's trickier... A hintmask right after hints and a few numbers - # will act as an implicit vstemhm. As such, we track whether - # we have seen any non-hint operators so far and do the right - # thing, recursively... Good luck understanding that :( - css = set() - for g in font.charset: - c, _ = cs.getItemAndSelector(g) - c.decompile() - subrs = getattr(c.private, "Subrs", []) - decompiler = _DehintingT2Decompiler( - css, - subrs, - c.globalSubrs, - c.private.nominalWidthX, - c.private.defaultWidthX, - c.private, - ) - decompiler.execute(c) - c.width = decompiler.width - for charstring in css: - charstring.drop_hints() - del css - - # Drop font-wide hinting values - all_privs = [] - if hasattr(font, "FDArray"): - all_privs.extend(fd.Private for fd in font.FDArray) - else: - all_privs.append(font.Private) - for priv in all_privs: - for k in [ - "BlueValues", - "OtherBlues", - "FamilyBlues", - "FamilyOtherBlues", - "BlueScale", - "BlueShift", - "BlueFuzz", - "StemSnapH", - "StemSnapV", - "StdHW", - "StdVW", - "ForceBold", - "LanguageGroup", - "ExpansionFactor", - ]: - if hasattr(priv, k): - setattr(priv, k, None) - self.remove_unused_subroutines() - - -@_add_method(ttLib.getTableClass("CFF ")) -def remove_unused_subroutines(self): - cff = self.cff - for fontname in cff.keys(): - font = cff[fontname] - cs = font.CharStrings - # Renumber subroutines to remove unused ones - - # Mark all used subroutines - for g in font.charset: - c, _ = cs.getItemAndSelector(g) - subrs = getattr(c.private, "Subrs", []) - decompiler = _MarkingT2Decompiler(subrs, c.globalSubrs, c.private) - decompiler.execute(c) - - all_subrs = [font.GlobalSubrs] - if hasattr(font, "FDArray"): - all_subrs.extend( - fd.Private.Subrs - for fd in font.FDArray - if hasattr(fd.Private, "Subrs") and fd.Private.Subrs - ) - elif hasattr(font.Private, "Subrs") and font.Private.Subrs: - all_subrs.append(font.Private.Subrs) - - subrs = set(subrs) # Remove duplicates - - # Prepare - for subrs in all_subrs: - if not hasattr(subrs, "_used"): - subrs._used = set() - subrs._used = _uniq_sort(subrs._used) - subrs._old_bias = psCharStrings.calcSubrBias(subrs) - subrs._new_bias = psCharStrings.calcSubrBias(subrs._used) - - # Renumber glyph charstrings - for g in font.charset: - c, _ = cs.getItemAndSelector(g) - subrs = getattr(c.private, "Subrs", []) - c.subset_subroutines(subrs, font.GlobalSubrs) - - # Renumber subroutines themselves - for subrs in all_subrs: - if subrs == font.GlobalSubrs: - if not hasattr(font, "FDArray") and hasattr(font.Private, "Subrs"): - local_subrs = font.Private.Subrs - else: - local_subrs = [] - else: - local_subrs = subrs - - subrs.items = [subrs.items[i] for i in subrs._used] - if hasattr(subrs, "file"): - del subrs.file - if hasattr(subrs, "offsets"): - del subrs.offsets - - for subr in subrs.items: - subr.subset_subroutines(local_subrs, font.GlobalSubrs) - - # Delete local SubrsIndex if empty - if hasattr(font, "FDArray"): - for fd in font.FDArray: - _delete_empty_subrs(fd.Private) - else: - _delete_empty_subrs(font.Private) - - # Cleanup - for subrs in all_subrs: - del subrs._used, subrs._old_bias, subrs._new_bias diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_B_L_C_.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_B_L_C_.py deleted file mode 100644 index e9ed58e582b806df3d24c77e795cab9b70fe9dad..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/C_B_L_C_.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Matt Fontaine - -from . import E_B_L_C_ - - -class table_C_B_L_C_(E_B_L_C_.table_E_B_L_C_): - - dependencies = ["CBDT"] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/dirfs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/dirfs.py deleted file mode 100644 index a3eac87efa2414d85bf9eec59b2f35722418ed71..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/dirfs.py +++ /dev/null @@ -1,358 +0,0 @@ -from .. import filesystem -from ..asyn import AsyncFileSystem - - -class DirFileSystem(AsyncFileSystem): - """Directory prefix filesystem - - The DirFileSystem is a filesystem-wrapper. It assumes every path it is dealing with - is relative to the `path`. After performing the necessary paths operation it - delegates everything to the wrapped filesystem. - """ - - protocol = "dir" - - def __init__( - self, - path=None, - fs=None, - fo=None, - target_protocol=None, - target_options=None, - **storage_options, - ): - """ - Parameters - ---------- - path: str - Path to the directory. - fs: AbstractFileSystem - An instantiated filesystem to wrap. - target_protocol, target_options: - if fs is none, construct it from these - fo: str - Alternate for path; do not provide both - """ - super().__init__(**storage_options) - if fs is None: - fs = filesystem(protocol=target_protocol, **(target_options or {})) - if (path is not None) ^ (fo is not None) is False: - raise ValueError("Provide path or fo, not both") - path = path or fo - - if self.asynchronous and not fs.async_impl: - raise ValueError("can't use asynchronous with non-async fs") - - if fs.async_impl and self.asynchronous != fs.asynchronous: - raise ValueError("both dirfs and fs should be in the same sync/async mode") - - self.path = fs._strip_protocol(path) - self.fs = fs - - def _join(self, path): - if isinstance(path, str): - if not self.path: - return path - if not path: - return self.path - return self.fs.sep.join((self.path, self._strip_protocol(path))) - return [self._join(_path) for _path in path] - - def _relpath(self, path): - if isinstance(path, str): - if not self.path: - return path - if path == self.path: - return "" - prefix = self.path + self.fs.sep - assert path.startswith(prefix) - return path[len(prefix) :] - return [self._relpath(_path) for _path in path] - - # Wrappers below - - @property - def sep(self): - return self.fs.sep - - async def set_session(self, *args, **kwargs): - return await self.fs.set_session(*args, **kwargs) - - async def _rm_file(self, path, **kwargs): - return await self.fs._rm_file(self._join(path), **kwargs) - - def rm_file(self, path, **kwargs): - return self.fs.rm_file(self._join(path), **kwargs) - - async def _rm(self, path, *args, **kwargs): - return await self.fs._rm(self._join(path), *args, **kwargs) - - def rm(self, path, *args, **kwargs): - return self.fs.rm(self._join(path), *args, **kwargs) - - async def _cp_file(self, path1, path2, **kwargs): - return await self.fs._cp_file(self._join(path1), self._join(path2), **kwargs) - - def cp_file(self, path1, path2, **kwargs): - return self.fs.cp_file(self._join(path1), self._join(path2), **kwargs) - - async def _copy( - self, - path1, - path2, - *args, - **kwargs, - ): - return await self.fs._copy( - self._join(path1), - self._join(path2), - *args, - **kwargs, - ) - - def copy(self, path1, path2, *args, **kwargs): - return self.fs.copy( - self._join(path1), - self._join(path2), - *args, - **kwargs, - ) - - async def _pipe(self, path, *args, **kwargs): - return await self.fs._pipe(self._join(path), *args, **kwargs) - - def pipe(self, path, *args, **kwargs): - return self.fs.pipe(self._join(path), *args, **kwargs) - - async def _cat_file(self, path, *args, **kwargs): - return await self.fs._cat_file(self._join(path), *args, **kwargs) - - def cat_file(self, path, *args, **kwargs): - return self.fs.cat_file(self._join(path), *args, **kwargs) - - async def _cat(self, path, *args, **kwargs): - ret = await self.fs._cat( - self._join(path), - *args, - **kwargs, - ) - - if isinstance(ret, dict): - return {self._relpath(key): value for key, value in ret.items()} - - return ret - - def cat(self, path, *args, **kwargs): - ret = self.fs.cat( - self._join(path), - *args, - **kwargs, - ) - - if isinstance(ret, dict): - return {self._relpath(key): value for key, value in ret.items()} - - return ret - - async def _put_file(self, lpath, rpath, **kwargs): - return await self.fs._put_file(lpath, self._join(rpath), **kwargs) - - def put_file(self, lpath, rpath, **kwargs): - return self.fs.put_file(lpath, self._join(rpath), **kwargs) - - async def _put( - self, - lpath, - rpath, - *args, - **kwargs, - ): - return await self.fs._put( - lpath, - self._join(rpath), - *args, - **kwargs, - ) - - def put(self, lpath, rpath, *args, **kwargs): - return self.fs.put( - lpath, - self._join(rpath), - *args, - **kwargs, - ) - - async def _get_file(self, rpath, lpath, **kwargs): - return await self.fs._get_file(self._join(rpath), lpath, **kwargs) - - def get_file(self, rpath, lpath, **kwargs): - return self.fs.get_file(self._join(rpath), lpath, **kwargs) - - async def _get(self, rpath, *args, **kwargs): - return await self.fs._get(self._join(rpath), *args, **kwargs) - - def get(self, rpath, *args, **kwargs): - return self.fs.get(self._join(rpath), *args, **kwargs) - - async def _isfile(self, path): - return await self.fs._isfile(self._join(path)) - - def isfile(self, path): - return self.fs.isfile(self._join(path)) - - async def _isdir(self, path): - return await self.fs._isdir(self._join(path)) - - def isdir(self, path): - return self.fs.isdir(self._join(path)) - - async def _size(self, path): - return await self.fs._size(self._join(path)) - - def size(self, path): - return self.fs.size(self._join(path)) - - async def _exists(self, path): - return await self.fs._exists(self._join(path)) - - def exists(self, path): - return self.fs.exists(self._join(path)) - - async def _info(self, path, **kwargs): - return await self.fs._info(self._join(path), **kwargs) - - def info(self, path, **kwargs): - return self.fs.info(self._join(path), **kwargs) - - async def _ls(self, path, detail=True, **kwargs): - ret = (await self.fs._ls(self._join(path), detail=detail, **kwargs)).copy() - if detail: - out = [] - for entry in ret: - entry = entry.copy() - entry["name"] = self._relpath(entry["name"]) - out.append(entry) - return out - - return self._relpath(ret) - - def ls(self, path, detail=True, **kwargs): - ret = self.fs.ls(self._join(path), detail=detail, **kwargs).copy() - if detail: - out = [] - for entry in ret: - entry = entry.copy() - entry["name"] = self._relpath(entry["name"]) - out.append(entry) - return out - - return self._relpath(ret) - - async def _walk(self, path, *args, **kwargs): - async for root, dirs, files in self.fs._walk(self._join(path), *args, **kwargs): - yield self._relpath(root), dirs, files - - def walk(self, path, *args, **kwargs): - for root, dirs, files in self.fs.walk(self._join(path), *args, **kwargs): - yield self._relpath(root), dirs, files - - async def _glob(self, path, **kwargs): - detail = kwargs.get("detail", False) - ret = await self.fs._glob(self._join(path), **kwargs) - if detail: - return {self._relpath(path): info for path, info in ret.items()} - return self._relpath(ret) - - def glob(self, path, **kwargs): - detail = kwargs.get("detail", False) - ret = self.fs.glob(self._join(path), **kwargs) - if detail: - return {self._relpath(path): info for path, info in ret.items()} - return self._relpath(ret) - - async def _du(self, path, *args, **kwargs): - total = kwargs.get("total", True) - ret = await self.fs._du(self._join(path), *args, **kwargs) - if total: - return ret - - return {self._relpath(path): size for path, size in ret.items()} - - def du(self, path, *args, **kwargs): - total = kwargs.get("total", True) - ret = self.fs.du(self._join(path), *args, **kwargs) - if total: - return ret - - return {self._relpath(path): size for path, size in ret.items()} - - async def _find(self, path, *args, **kwargs): - detail = kwargs.get("detail", False) - ret = await self.fs._find(self._join(path), *args, **kwargs) - if detail: - return {self._relpath(path): info for path, info in ret.items()} - return self._relpath(ret) - - def find(self, path, *args, **kwargs): - detail = kwargs.get("detail", False) - ret = self.fs.find(self._join(path), *args, **kwargs) - if detail: - return {self._relpath(path): info for path, info in ret.items()} - return self._relpath(ret) - - async def _expand_path(self, path, *args, **kwargs): - return self._relpath( - await self.fs._expand_path(self._join(path), *args, **kwargs) - ) - - def expand_path(self, path, *args, **kwargs): - return self._relpath(self.fs.expand_path(self._join(path), *args, **kwargs)) - - async def _mkdir(self, path, *args, **kwargs): - return await self.fs._mkdir(self._join(path), *args, **kwargs) - - def mkdir(self, path, *args, **kwargs): - return self.fs.mkdir(self._join(path), *args, **kwargs) - - async def _makedirs(self, path, *args, **kwargs): - return await self.fs._makedirs(self._join(path), *args, **kwargs) - - def makedirs(self, path, *args, **kwargs): - return self.fs.makedirs(self._join(path), *args, **kwargs) - - def rmdir(self, path): - return self.fs.rmdir(self._join(path)) - - def mv_file(self, path1, path2, **kwargs): - return self.fs.mv_file( - self._join(path1), - self._join(path2), - **kwargs, - ) - - def touch(self, path, **kwargs): - return self.fs.touch(self._join(path), **kwargs) - - def created(self, path): - return self.fs.created(self._join(path)) - - def modified(self, path): - return self.fs.modified(self._join(path)) - - def sign(self, path, *args, **kwargs): - return self.fs.sign(self._join(path), *args, **kwargs) - - def __repr__(self): - return f"{self.__class__.__qualname__}(path='{self.path}', fs={self.fs})" - - def open( - self, - path, - *args, - **kwargs, - ): - return self.fs.open( - 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- -def _deprecate_positional_args(*, version: str): - """Decorator for methods that issues warnings for positional arguments. - Using the keyword-only argument syntax in pep 3102, arguments after the - * will issue a warning when passed as a positional argument. - - Args: - version (`str`): - The version when positional arguments will result in error. - """ - - def _inner_deprecate_positional_args(f): - sig = signature(f) - kwonly_args = [] - all_args = [] - for name, param in sig.parameters.items(): - if param.kind == Parameter.POSITIONAL_OR_KEYWORD: - all_args.append(name) - elif param.kind == Parameter.KEYWORD_ONLY: - kwonly_args.append(name) - - @wraps(f) - def inner_f(*args, **kwargs): - extra_args = len(args) - len(all_args) - if extra_args <= 0: - return f(*args, **kwargs) - # extra_args > 0 - args_msg = [ - f"{name}='{arg}'" if isinstance(arg, str) else f"{name}={arg}" - for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:]) - ] - args_msg = ", ".join(args_msg) - warnings.warn( - f"Deprecated positional argument(s) used in '{f.__name__}': pass" - f" {args_msg} as keyword args. From version {version} passing these" - " as positional arguments will result in an error,", - FutureWarning, - ) - kwargs.update(zip(sig.parameters, args)) - return f(**kwargs) - - return inner_f - - return _inner_deprecate_positional_args - - -def _deprecate_arguments( - *, - version: str, - deprecated_args: Iterable[str], - custom_message: Optional[str] = None, -): - """Decorator to issue warnings when using deprecated arguments. - - TODO: could be useful to be able to set a custom error message. - - Args: - version (`str`): - The version when deprecated arguments will result in error. - deprecated_args (`List[str]`): - List of the arguments to be deprecated. - custom_message (`str`, *optional*): - Warning message that is raised. If not passed, a default warning message - will be created. - """ - - def _inner_deprecate_positional_args(f): - sig = signature(f) - - @wraps(f) - def inner_f(*args, **kwargs): - # Check for used deprecated arguments - used_deprecated_args = [] - for _, parameter in zip(args, sig.parameters.values()): - if parameter.name in deprecated_args: - used_deprecated_args.append(parameter.name) - for kwarg_name, kwarg_value in kwargs.items(): - if ( - # If argument is deprecated but still used - kwarg_name in deprecated_args - # And then the value is not the default value - and kwarg_value != sig.parameters[kwarg_name].default - ): - used_deprecated_args.append(kwarg_name) - - # Warn and proceed - if len(used_deprecated_args) > 0: - message = ( - f"Deprecated argument(s) used in '{f.__name__}':" - f" {', '.join(used_deprecated_args)}. Will not be supported from" - f" version '{version}'." - ) - if custom_message is not None: - message += "\n\n" + custom_message - warnings.warn(message, FutureWarning) - return f(*args, **kwargs) - - return inner_f - - return _inner_deprecate_positional_args - - -def _deprecate_method(*, version: str, message: Optional[str] = None): - """Decorator to issue warnings when using a deprecated method. - - Args: - version (`str`): - The version when deprecated arguments will result in error. - message (`str`, *optional*): - Warning message that is raised. If not passed, a default warning message - will be created. - """ - - def _inner_deprecate_method(f): - @wraps(f) - def inner_f(*args, **kwargs): - warning_message = ( - f"'{f.__name__}' (from '{f.__module__}') is deprecated and will be removed from version '{version}'." - ) - if message is not None: - warning_message += " " + message - warnings.warn(warning_message, FutureWarning) - return f(*args, **kwargs) - - return inner_f - - return _inner_deprecate_method diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py deleted file mode 100644 index 0674ddc31fc72316a4b5ad48b446dffa68206d1e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py +++ /dev/null @@ -1,281 +0,0 @@ -# coding=utf-8 -# Copyright 2022-present, the HuggingFace Inc. team. -# -# 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. -"""Contains utilities to handle HTTP requests in Huggingface Hub.""" -import io -import os -import threading -import time -import uuid -from functools import lru_cache -from http import HTTPStatus -from typing import Callable, Tuple, Type, Union - -import requests -from requests import Response -from requests.adapters import HTTPAdapter -from requests.exceptions import ProxyError, Timeout -from requests.models import PreparedRequest - -from . import logging -from ._typing import HTTP_METHOD_T - - -logger = logging.get_logger(__name__) - -# Both headers are used by the Hub to debug failed requests. -# `X_AMZN_TRACE_ID` is better as it also works to debug on Cloudfront and ALB. -# If `X_AMZN_TRACE_ID` is set, the Hub will use it as well. -X_AMZN_TRACE_ID = "X-Amzn-Trace-Id" -X_REQUEST_ID = "x-request-id" - - -class UniqueRequestIdAdapter(HTTPAdapter): - X_AMZN_TRACE_ID = "X-Amzn-Trace-Id" - - def add_headers(self, request, **kwargs): - super().add_headers(request, **kwargs) - - # Add random request ID => easier for server-side debug - if X_AMZN_TRACE_ID not in request.headers: - request.headers[X_AMZN_TRACE_ID] = request.headers.get(X_REQUEST_ID) or str(uuid.uuid4()) - - # Add debug log - has_token = str(request.headers.get("authorization", "")).startswith("Bearer hf_") - logger.debug( - f"Request {request.headers[X_AMZN_TRACE_ID]}: {request.method} {request.url} (authenticated: {has_token})" - ) - - def send(self, request: PreparedRequest, *args, **kwargs) -> Response: - """Catch any RequestException to append request id to the error message for debugging.""" - try: - return super().send(request, *args, **kwargs) - except requests.RequestException as e: - request_id = request.headers.get(X_AMZN_TRACE_ID) - if request_id is not None: - # Taken from https://stackoverflow.com/a/58270258 - e.args = (*e.args, f"(Request ID: {request_id})") - raise - - -def _default_backend_factory() -> requests.Session: - session = requests.Session() - session.mount("http://", UniqueRequestIdAdapter()) - session.mount("https://", UniqueRequestIdAdapter()) - return session - - -BACKEND_FACTORY_T = Callable[[], requests.Session] -_GLOBAL_BACKEND_FACTORY: BACKEND_FACTORY_T = _default_backend_factory - - -def configure_http_backend(backend_factory: BACKEND_FACTORY_T = _default_backend_factory) -> None: - """ - Configure the HTTP backend by providing a `backend_factory`. Any HTTP calls made by `huggingface_hub` will use a - Session object instantiated by this factory. This can be useful if you are running your scripts in a specific - environment requiring custom configuration (e.g. custom proxy or certifications). - - Use [`get_session`] to get a configured Session. Since `requests.Session` is not guaranteed to be thread-safe, - `huggingface_hub` creates 1 Session instance per thread. They are all instantiated using the same `backend_factory` - set in [`configure_http_backend`]. A LRU cache is used to cache the created sessions (and connections) between - calls. Max size is 128 to avoid memory leaks if thousands of threads are spawned. - - See [this issue](https://github.com/psf/requests/issues/2766) to know more about thread-safety in `requests`. - - Example: - ```py - import requests - from huggingface_hub import configure_http_backend, get_session - - # Create a factory function that returns a Session with configured proxies - def backend_factory() -> requests.Session: - session = requests.Session() - session.proxies = {"http": "http://10.10.1.10:3128", "https": "https://10.10.1.11:1080"} - return session - - # Set it as the default session factory - configure_http_backend(backend_factory=backend_factory) - - # In practice, this is mostly done internally in `huggingface_hub` - session = get_session() - ``` - """ - global _GLOBAL_BACKEND_FACTORY - _GLOBAL_BACKEND_FACTORY = backend_factory - _get_session_from_cache.cache_clear() - - -def get_session() -> requests.Session: - """ - Get a `requests.Session` object, using the session factory from the user. - - Use [`get_session`] to get a configured Session. Since `requests.Session` is not guaranteed to be thread-safe, - `huggingface_hub` creates 1 Session instance per thread. They are all instantiated using the same `backend_factory` - set in [`configure_http_backend`]. A LRU cache is used to cache the created sessions (and connections) between - calls. Max size is 128 to avoid memory leaks if thousands of threads are spawned. - - See [this issue](https://github.com/psf/requests/issues/2766) to know more about thread-safety in `requests`. - - Example: - ```py - import requests - from huggingface_hub import configure_http_backend, get_session - - # Create a factory function that returns a Session with configured proxies - def backend_factory() -> requests.Session: - session = requests.Session() - session.proxies = {"http": "http://10.10.1.10:3128", "https": "https://10.10.1.11:1080"} - return session - - # Set it as the default session factory - configure_http_backend(backend_factory=backend_factory) - - # In practice, this is mostly done internally in `huggingface_hub` - session = get_session() - ``` - """ - return _get_session_from_cache(process_id=os.getpid(), thread_id=threading.get_ident()) - - -@lru_cache -def _get_session_from_cache(process_id: int, thread_id: int) -> requests.Session: - """ - Create a new session per thread using global factory. Using LRU cache (maxsize 128) to avoid memory leaks when - using thousands of threads. Cache is cleared when `configure_http_backend` is called. - """ - return _GLOBAL_BACKEND_FACTORY() - - -def http_backoff( - method: HTTP_METHOD_T, - url: str, - *, - max_retries: int = 5, - base_wait_time: float = 1, - max_wait_time: float = 8, - retry_on_exceptions: Union[Type[Exception], Tuple[Type[Exception], ...]] = ( - Timeout, - ProxyError, - ), - retry_on_status_codes: Union[int, Tuple[int, ...]] = HTTPStatus.SERVICE_UNAVAILABLE, - **kwargs, -) -> Response: - """Wrapper around requests to retry calls on an endpoint, with exponential backoff. - - Endpoint call is retried on exceptions (ex: connection timeout, proxy error,...) - and/or on specific status codes (ex: service unavailable). If the call failed more - than `max_retries`, the exception is thrown or `raise_for_status` is called on the - response object. - - Re-implement mechanisms from the `backoff` library to avoid adding an external - dependencies to `hugging_face_hub`. See https://github.com/litl/backoff. - - Args: - method (`Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"]`): - HTTP method to perform. - url (`str`): - The URL of the resource to fetch. - max_retries (`int`, *optional*, defaults to `5`): - Maximum number of retries, defaults to 5 (no retries). - base_wait_time (`float`, *optional*, defaults to `1`): - Duration (in seconds) to wait before retrying the first time. - Wait time between retries then grows exponentially, capped by - `max_wait_time`. - max_wait_time (`float`, *optional*, defaults to `8`): - Maximum duration (in seconds) to wait before retrying. - retry_on_exceptions (`Type[Exception]` or `Tuple[Type[Exception]]`, *optional*, defaults to `(Timeout, ProxyError,)`): - Define which exceptions must be caught to retry the request. Can be a single - type or a tuple of types. - By default, retry on `Timeout` and `ProxyError`. - retry_on_status_codes (`int` or `Tuple[int]`, *optional*, defaults to `503`): - Define on which status codes the request must be retried. By default, only - HTTP 503 Service Unavailable is retried. - **kwargs (`dict`, *optional*): - kwargs to pass to `requests.request`. - - Example: - ``` - >>> from huggingface_hub.utils import http_backoff - - # Same usage as "requests.request". - >>> response = http_backoff("GET", "https://www.google.com") - >>> response.raise_for_status() - - # If you expect a Gateway Timeout from time to time - >>> http_backoff("PUT", upload_url, data=data, retry_on_status_codes=504) - >>> response.raise_for_status() - ``` - - - - When using `requests` it is possible to stream data by passing an iterator to the - `data` argument. On http backoff this is a problem as the iterator is not reset - after a failed call. This issue is mitigated for file objects or any IO streams - by saving the initial position of the cursor (with `data.tell()`) and resetting the - cursor between each call (with `data.seek()`). For arbitrary iterators, http backoff - will fail. If this is a hard constraint for you, please let us know by opening an - issue on [Github](https://github.com/huggingface/huggingface_hub). - - - """ - if isinstance(retry_on_exceptions, type): # Tuple from single exception type - retry_on_exceptions = (retry_on_exceptions,) - - if isinstance(retry_on_status_codes, int): # Tuple from single status code - retry_on_status_codes = (retry_on_status_codes,) - - nb_tries = 0 - sleep_time = base_wait_time - - # If `data` is used and is a file object (or any IO), it will be consumed on the - # first HTTP request. We need to save the initial position so that the full content - # of the file is re-sent on http backoff. See warning tip in docstring. - io_obj_initial_pos = None - if "data" in kwargs and isinstance(kwargs["data"], io.IOBase): - io_obj_initial_pos = kwargs["data"].tell() - - session = get_session() - while True: - nb_tries += 1 - try: - # If `data` is used and is a file object (or any IO), set back cursor to - # initial position. - if io_obj_initial_pos is not None: - kwargs["data"].seek(io_obj_initial_pos) - - # Perform request and return if status_code is not in the retry list. - response = session.request(method=method, url=url, **kwargs) - if response.status_code not in retry_on_status_codes: - return response - - # Wrong status code returned (HTTP 503 for instance) - logger.warning(f"HTTP Error {response.status_code} thrown while requesting {method} {url}") - if nb_tries > max_retries: - response.raise_for_status() # Will raise uncaught exception - # We return response to avoid infinite loop in the corner case where the - # user ask for retry on a status code that doesn't raise_for_status. - return response - - except retry_on_exceptions as err: - logger.warning(f"'{err}' thrown while requesting {method} {url}") - - if nb_tries > max_retries: - raise err - - # Sleep for X seconds - logger.warning(f"Retrying in {sleep_time}s [Retry {nb_tries}/{max_retries}].") - time.sleep(sleep_time) - - # Update sleep time for next retry - sleep_time = min(max_wait_time, sleep_time * 2) # Exponential backoff diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/callback/gh18335.f90 b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/callback/gh18335.f90 deleted file mode 100644 index 92b6d7540c827d20c7d2169c56f14653954d7ff9..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/src/callback/gh18335.f90 +++ /dev/null @@ -1,17 +0,0 @@ - ! When gh18335_workaround is defined as an extension, - ! the issue cannot be reproduced. - !subroutine gh18335_workaround(f, y) - ! implicit none - ! external f - ! integer(kind=1) :: y(1) - ! call f(y) - !end subroutine gh18335_workaround - - function gh18335(f) result (r) - implicit none - external f - integer(kind=1) :: y(1), r - y(1) = 123 - call f(y) - r = y(1) - end function gh18335 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/ops/dispatch.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/ops/dispatch.py deleted file mode 100644 index a939fdd3d041e9f99dde7ea40fd7aa0572d0d9b7..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/ops/dispatch.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Functions for defining unary operations. -""" -from __future__ import annotations - -from typing import ( - TYPE_CHECKING, - Any, -) - -from pandas.core.dtypes.generic import ABCExtensionArray - -if TYPE_CHECKING: - from pandas._typing import ArrayLike - - -def should_extension_dispatch(left: ArrayLike, right: Any) -> bool: - """ - Identify cases where Series operation should dispatch to ExtensionArray method. - - Parameters - ---------- - left : np.ndarray or ExtensionArray - right : object - - Returns - ------- - bool - """ - return isinstance(left, ABCExtensionArray) or isinstance(right, ABCExtensionArray) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_categorical.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_categorical.py deleted file mode 100644 index 68ce58ad236906d126c8f9b6245569536848d28e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/groupby/test_categorical.py +++ /dev/null @@ -1,2119 +0,0 @@ -from datetime import datetime - -import numpy as np -import pytest - -import pandas as pd -from pandas import ( - Categorical, - CategoricalIndex, - DataFrame, - Index, - MultiIndex, - Series, - qcut, -) -import pandas._testing as tm -from pandas.api.typing import SeriesGroupBy -from pandas.tests.groupby import get_groupby_method_args - - -def cartesian_product_for_groupers(result, args, names, fill_value=np.nan): - """Reindex to a cartesian production for the groupers, - preserving the nature (Categorical) of each grouper - """ - - def f(a): - if isinstance(a, (CategoricalIndex, Categorical)): - categories = a.categories - a = Categorical.from_codes( - np.arange(len(categories)), categories=categories, ordered=a.ordered - ) - return a - - index = MultiIndex.from_product(map(f, args), names=names) - return result.reindex(index, fill_value=fill_value).sort_index() - - -_results_for_groupbys_with_missing_categories = { - # This maps the builtin groupby functions to their expected outputs for - # missing categories when they are called on a categorical grouper with - # observed=False. Some functions are expected to return NaN, some zero. - # These expected values can be used across several tests (i.e. they are - # the same for SeriesGroupBy and DataFrameGroupBy) but they should only be - # hardcoded in one place. - "all": np.nan, - "any": np.nan, - "count": 0, - "corrwith": np.nan, - "first": np.nan, - "idxmax": np.nan, - "idxmin": np.nan, - "last": np.nan, - "max": np.nan, - "mean": np.nan, - "median": np.nan, - "min": np.nan, - "nth": np.nan, - "nunique": 0, - "prod": np.nan, - "quantile": np.nan, - "sem": np.nan, - "size": 0, - "skew": np.nan, - "std": np.nan, - "sum": 0, - "var": np.nan, -} - - -def test_apply_use_categorical_name(df): - cats = qcut(df.C, 4) - - def get_stats(group): - return { - "min": group.min(), - "max": group.max(), - "count": group.count(), - "mean": group.mean(), - } - - result = df.groupby(cats, observed=False).D.apply(get_stats) - assert result.index.names[0] == "C" - - -def test_basic(): # TODO: split this test - cats = Categorical( - ["a", "a", "a", "b", "b", "b", "c", "c", "c"], - categories=["a", "b", "c", "d"], - ordered=True, - ) - data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats}) - - exp_index = CategoricalIndex(list("abcd"), name="b", ordered=True) - expected = DataFrame({"a": [1, 2, 4, np.nan]}, index=exp_index) - result = data.groupby("b", observed=False).mean() - tm.assert_frame_equal(result, expected) - - cat1 = Categorical(["a", "a", "b", "b"], categories=["a", "b", "z"], ordered=True) - cat2 = Categorical(["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True) - df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]}) - - # single grouper - gb = df.groupby("A", observed=False) - exp_idx = CategoricalIndex(["a", "b", "z"], name="A", ordered=True) - expected = DataFrame({"values": Series([3, 7, 0], index=exp_idx)}) - result = gb.sum(numeric_only=True) - tm.assert_frame_equal(result, expected) - - # GH 8623 - x = DataFrame( - [[1, "John P. Doe"], [2, "Jane Dove"], [1, "John P. Doe"]], - columns=["person_id", "person_name"], - ) - x["person_name"] = Categorical(x.person_name) - - g = x.groupby(["person_id"], observed=False) - result = g.transform(lambda x: x) - tm.assert_frame_equal(result, x[["person_name"]]) - - result = x.drop_duplicates("person_name") - expected = x.iloc[[0, 1]] - tm.assert_frame_equal(result, expected) - - def f(x): - return x.drop_duplicates("person_name").iloc[0] - - result = g.apply(f) - expected = x.iloc[[0, 1]].copy() - expected.index = Index([1, 2], name="person_id") - expected["person_name"] = expected["person_name"].astype("object") - tm.assert_frame_equal(result, expected) - - # GH 9921 - # Monotonic - df = DataFrame({"a": [5, 15, 25]}) - c = pd.cut(df.a, bins=[0, 10, 20, 30, 40]) - - msg = "using SeriesGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = df.a.groupby(c, observed=False).transform(sum) - tm.assert_series_equal(result, df["a"]) - - tm.assert_series_equal( - df.a.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df["a"] - ) - msg = "using DataFrameGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = df.groupby(c, observed=False).transform(sum) - expected = df[["a"]] - tm.assert_frame_equal(result, expected) - - gbc = df.groupby(c, observed=False) - result = gbc.transform(lambda xs: np.max(xs, axis=0)) - tm.assert_frame_equal(result, df[["a"]]) - - result2 = gbc.transform(lambda xs: np.max(xs, axis=0)) - msg = "using DataFrameGroupBy.max" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result3 = gbc.transform(max) - result4 = gbc.transform(np.maximum.reduce) - result5 = gbc.transform(lambda xs: np.maximum.reduce(xs)) - tm.assert_frame_equal(result2, df[["a"]], check_dtype=False) - tm.assert_frame_equal(result3, df[["a"]], check_dtype=False) - tm.assert_frame_equal(result4, df[["a"]]) - tm.assert_frame_equal(result5, df[["a"]]) - - # Filter - tm.assert_series_equal(df.a.groupby(c, observed=False).filter(np.all), df["a"]) - tm.assert_frame_equal(df.groupby(c, observed=False).filter(np.all), df) - - # Non-monotonic - df = DataFrame({"a": [5, 15, 25, -5]}) - c = pd.cut(df.a, bins=[-10, 0, 10, 20, 30, 40]) - - msg = "using SeriesGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = df.a.groupby(c, observed=False).transform(sum) - tm.assert_series_equal(result, df["a"]) - - tm.assert_series_equal( - df.a.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df["a"] - ) - msg = "using DataFrameGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = df.groupby(c, observed=False).transform(sum) - expected = df[["a"]] - tm.assert_frame_equal(result, expected) - - tm.assert_frame_equal( - df.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df[["a"]] - ) - - # GH 9603 - df = DataFrame({"a": [1, 0, 0, 0]}) - c = pd.cut(df.a, [0, 1, 2, 3, 4], labels=Categorical(list("abcd"))) - result = df.groupby(c, observed=False).apply(len) - - exp_index = CategoricalIndex(c.values.categories, ordered=c.values.ordered) - expected = Series([1, 0, 0, 0], index=exp_index) - expected.index.name = "a" - tm.assert_series_equal(result, expected) - - # more basic - levels = ["foo", "bar", "baz", "qux"] - codes = np.random.default_rng(2).integers(0, 4, size=100) - - cats = Categorical.from_codes(codes, levels, ordered=True) - - data = DataFrame(np.random.default_rng(2).standard_normal((100, 4))) - - result = data.groupby(cats, observed=False).mean() - - expected = data.groupby(np.asarray(cats), observed=False).mean() - exp_idx = CategoricalIndex(levels, categories=cats.categories, ordered=True) - expected = expected.reindex(exp_idx) - - tm.assert_frame_equal(result, expected) - - grouped = data.groupby(cats, observed=False) - desc_result = grouped.describe() - - idx = cats.codes.argsort() - ord_labels = np.asarray(cats).take(idx) - ord_data = data.take(idx) - - exp_cats = Categorical( - ord_labels, ordered=True, categories=["foo", "bar", "baz", "qux"] - ) - expected = ord_data.groupby(exp_cats, sort=False, observed=False).describe() - tm.assert_frame_equal(desc_result, expected) - - # GH 10460 - expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True) - exp = CategoricalIndex(expc) - tm.assert_index_equal( - (desc_result.stack(future_stack=True).index.get_level_values(0)), exp - ) - exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4) - tm.assert_index_equal( - (desc_result.stack(future_stack=True).index.get_level_values(1)), exp - ) - - -def test_level_get_group(observed): - # GH15155 - df = DataFrame( - data=np.arange(2, 22, 2), - index=MultiIndex( - levels=[CategoricalIndex(["a", "b"]), range(10)], - codes=[[0] * 5 + [1] * 5, range(10)], - names=["Index1", "Index2"], - ), - ) - g = df.groupby(level=["Index1"], observed=observed) - - # expected should equal test.loc[["a"]] - # GH15166 - expected = DataFrame( - data=np.arange(2, 12, 2), - index=MultiIndex( - levels=[CategoricalIndex(["a", "b"]), range(5)], - codes=[[0] * 5, range(5)], - names=["Index1", "Index2"], - ), - ) - result = g.get_group("a") - - tm.assert_frame_equal(result, expected) - - -def test_sorting_with_different_categoricals(): - # GH 24271 - df = DataFrame( - { - "group": ["A"] * 6 + ["B"] * 6, - "dose": ["high", "med", "low"] * 4, - "outcomes": np.arange(12.0), - } - ) - - df.dose = Categorical(df.dose, categories=["low", "med", "high"], ordered=True) - - result = df.groupby("group")["dose"].value_counts() - result = result.sort_index(level=0, sort_remaining=True) - index = ["low", "med", "high", "low", "med", "high"] - index = Categorical(index, categories=["low", "med", "high"], ordered=True) - index = [["A", "A", "A", "B", "B", "B"], CategoricalIndex(index)] - index = MultiIndex.from_arrays(index, names=["group", "dose"]) - expected = Series([2] * 6, index=index, name="count") - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("ordered", [True, False]) -def test_apply(ordered): - # GH 10138 - - dense = Categorical(list("abc"), ordered=ordered) - - # 'b' is in the categories but not in the list - missing = Categorical(list("aaa"), categories=["a", "b"], ordered=ordered) - values = np.arange(len(dense)) - df = DataFrame({"missing": missing, "dense": dense, "values": values}) - grouped = df.groupby(["missing", "dense"], observed=True) - - # missing category 'b' should still exist in the output index - idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"]) - expected = DataFrame([0, 1, 2.0], index=idx, columns=["values"]) - - result = grouped.apply(lambda x: np.mean(x, axis=0)) - tm.assert_frame_equal(result, expected) - - result = grouped.mean() - tm.assert_frame_equal(result, expected) - - msg = "using DataFrameGroupBy.mean" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = grouped.agg(np.mean) - tm.assert_frame_equal(result, expected) - - # but for transform we should still get back the original index - idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"]) - expected = Series(1, index=idx) - result = grouped.apply(lambda x: 1) - tm.assert_series_equal(result, expected) - - -def test_observed(observed): - # multiple groupers, don't re-expand the output space - # of the grouper - # gh-14942 (implement) - # gh-10132 (back-compat) - # gh-8138 (back-compat) - # gh-8869 - - cat1 = Categorical(["a", "a", "b", "b"], categories=["a", "b", "z"], ordered=True) - cat2 = Categorical(["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True) - df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]}) - df["C"] = ["foo", "bar"] * 2 - - # multiple groupers with a non-cat - gb = df.groupby(["A", "B", "C"], observed=observed) - exp_index = MultiIndex.from_arrays( - [cat1, cat2, ["foo", "bar"] * 2], names=["A", "B", "C"] - ) - expected = DataFrame({"values": Series([1, 2, 3, 4], index=exp_index)}).sort_index() - result = gb.sum() - if not observed: - expected = cartesian_product_for_groupers( - expected, [cat1, cat2, ["foo", "bar"]], list("ABC"), fill_value=0 - ) - - tm.assert_frame_equal(result, expected) - - gb = df.groupby(["A", "B"], observed=observed) - exp_index = MultiIndex.from_arrays([cat1, cat2], names=["A", "B"]) - expected = DataFrame( - {"values": [1, 2, 3, 4], "C": ["foo", "bar", "foo", "bar"]}, index=exp_index - ) - result = gb.sum() - if not observed: - expected = cartesian_product_for_groupers( - expected, [cat1, cat2], list("AB"), fill_value=0 - ) - - tm.assert_frame_equal(result, expected) - - # https://github.com/pandas-dev/pandas/issues/8138 - d = { - "cat": Categorical( - ["a", "b", "a", "b"], categories=["a", "b", "c"], ordered=True - ), - "ints": [1, 1, 2, 2], - "val": [10, 20, 30, 40], - } - df = DataFrame(d) - - # Grouping on a single column - groups_single_key = df.groupby("cat", observed=observed) - result = groups_single_key.mean() - - exp_index = CategoricalIndex( - list("ab"), name="cat", categories=list("abc"), ordered=True - ) - expected = DataFrame({"ints": [1.5, 1.5], "val": [20.0, 30]}, index=exp_index) - if not observed: - index = CategoricalIndex( - list("abc"), name="cat", categories=list("abc"), ordered=True - ) - expected = expected.reindex(index) - - tm.assert_frame_equal(result, expected) - - # Grouping on two columns - groups_double_key = df.groupby(["cat", "ints"], observed=observed) - result = groups_double_key.agg("mean") - expected = DataFrame( - { - "val": [10.0, 30.0, 20.0, 40.0], - "cat": Categorical( - ["a", "a", "b", "b"], categories=["a", "b", "c"], ordered=True - ), - "ints": [1, 2, 1, 2], - } - ).set_index(["cat", "ints"]) - if not observed: - expected = cartesian_product_for_groupers( - expected, [df.cat.values, [1, 2]], ["cat", "ints"] - ) - - tm.assert_frame_equal(result, expected) - - # GH 10132 - for key in [("a", 1), ("b", 2), ("b", 1), ("a", 2)]: - c, i = key - result = groups_double_key.get_group(key) - expected = df[(df.cat == c) & (df.ints == i)] - tm.assert_frame_equal(result, expected) - - # gh-8869 - # with as_index - d = { - "foo": [10, 8, 4, 8, 4, 1, 1], - "bar": [10, 20, 30, 40, 50, 60, 70], - "baz": ["d", "c", "e", "a", "a", "d", "c"], - } - df = DataFrame(d) - cat = pd.cut(df["foo"], np.linspace(0, 10, 3)) - df["range"] = cat - groups = df.groupby(["range", "baz"], as_index=False, observed=observed) - result = groups.agg("mean") - - groups2 = df.groupby(["range", "baz"], as_index=True, observed=observed) - expected = groups2.agg("mean").reset_index() - tm.assert_frame_equal(result, expected) - - -def test_observed_codes_remap(observed): - d = {"C1": [3, 3, 4, 5], "C2": [1, 2, 3, 4], "C3": [10, 100, 200, 34]} - df = DataFrame(d) - values = pd.cut(df["C1"], [1, 2, 3, 6]) - values.name = "cat" - groups_double_key = df.groupby([values, "C2"], observed=observed) - - idx = MultiIndex.from_arrays([values, [1, 2, 3, 4]], names=["cat", "C2"]) - expected = DataFrame( - {"C1": [3.0, 3.0, 4.0, 5.0], "C3": [10.0, 100.0, 200.0, 34.0]}, index=idx - ) - if not observed: - expected = cartesian_product_for_groupers( - expected, [values.values, [1, 2, 3, 4]], ["cat", "C2"] - ) - - result = groups_double_key.agg("mean") - tm.assert_frame_equal(result, expected) - - -def test_observed_perf(): - # we create a cartesian product, so this is - # non-performant if we don't use observed values - # gh-14942 - df = DataFrame( - { - "cat": np.random.default_rng(2).integers(0, 255, size=30000), - "int_id": np.random.default_rng(2).integers(0, 255, size=30000), - "other_id": np.random.default_rng(2).integers(0, 10000, size=30000), - "foo": 0, - } - ) - df["cat"] = df.cat.astype(str).astype("category") - - grouped = df.groupby(["cat", "int_id", "other_id"], observed=True) - result = grouped.count() - assert result.index.levels[0].nunique() == df.cat.nunique() - assert result.index.levels[1].nunique() == df.int_id.nunique() - assert result.index.levels[2].nunique() == df.other_id.nunique() - - -def test_observed_groups(observed): - # gh-20583 - # test that we have the appropriate groups - - cat = Categorical(["a", "c", "a"], categories=["a", "b", "c"]) - df = DataFrame({"cat": cat, "vals": [1, 2, 3]}) - g = df.groupby("cat", observed=observed) - - result = g.groups - if observed: - expected = {"a": Index([0, 2], dtype="int64"), "c": Index([1], dtype="int64")} - else: - expected = { - "a": Index([0, 2], dtype="int64"), - "b": Index([], dtype="int64"), - "c": Index([1], dtype="int64"), - } - - tm.assert_dict_equal(result, expected) - - -@pytest.mark.parametrize( - "keys, expected_values, expected_index_levels", - [ - ("a", [15, 9, 0], CategoricalIndex([1, 2, 3], name="a")), - ( - ["a", "b"], - [7, 8, 0, 0, 0, 9, 0, 0, 0], - [CategoricalIndex([1, 2, 3], name="a"), Index([4, 5, 6])], - ), - ( - ["a", "a2"], - [15, 0, 0, 0, 9, 0, 0, 0, 0], - [ - CategoricalIndex([1, 2, 3], name="a"), - CategoricalIndex([1, 2, 3], name="a"), - ], - ), - ], -) -@pytest.mark.parametrize("test_series", [True, False]) -def test_unobserved_in_index(keys, expected_values, expected_index_levels, test_series): - # GH#49354 - ensure unobserved cats occur when grouping by index levels - df = DataFrame( - { - "a": Categorical([1, 1, 2], categories=[1, 2, 3]), - "a2": Categorical([1, 1, 2], categories=[1, 2, 3]), - "b": [4, 5, 6], - "c": [7, 8, 9], - } - ).set_index(["a", "a2"]) - if "b" not in keys: - # Only keep b when it is used for grouping for consistent columns in the result - df = df.drop(columns="b") - - gb = df.groupby(keys, observed=False) - if test_series: - gb = gb["c"] - result = gb.sum() - - if len(keys) == 1: - index = expected_index_levels - else: - codes = [[0, 0, 0, 1, 1, 1, 2, 2, 2], 3 * [0, 1, 2]] - index = MultiIndex( - expected_index_levels, - codes=codes, - names=keys, - ) - expected = DataFrame({"c": expected_values}, index=index) - if test_series: - expected = expected["c"] - tm.assert_equal(result, expected) - - -def test_observed_groups_with_nan(observed): - # GH 24740 - df = DataFrame( - { - "cat": Categorical(["a", np.nan, "a"], categories=["a", "b", "d"]), - "vals": [1, 2, 3], - } - ) - g = df.groupby("cat", observed=observed) - result = g.groups - if observed: - expected = {"a": Index([0, 2], dtype="int64")} - else: - expected = { - "a": Index([0, 2], dtype="int64"), - "b": Index([], dtype="int64"), - "d": Index([], dtype="int64"), - } - tm.assert_dict_equal(result, expected) - - -def test_observed_nth(): - # GH 26385 - cat = Categorical(["a", np.nan, np.nan], categories=["a", "b", "c"]) - ser = Series([1, 2, 3]) - df = DataFrame({"cat": cat, "ser": ser}) - - result = df.groupby("cat", observed=False)["ser"].nth(0) - expected = df["ser"].iloc[[0]] - tm.assert_series_equal(result, expected) - - -def test_dataframe_categorical_with_nan(observed): - # GH 21151 - s1 = Categorical([np.nan, "a", np.nan, "a"], categories=["a", "b", "c"]) - s2 = Series([1, 2, 3, 4]) - df = DataFrame({"s1": s1, "s2": s2}) - result = df.groupby("s1", observed=observed).first().reset_index() - if observed: - expected = DataFrame( - {"s1": Categorical(["a"], categories=["a", "b", "c"]), "s2": [2]} - ) - else: - expected = DataFrame( - { - "s1": Categorical(["a", "b", "c"], categories=["a", "b", "c"]), - "s2": [2, np.nan, np.nan], - } - ) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("ordered", [True, False]) -@pytest.mark.parametrize("observed", [True, False]) -@pytest.mark.parametrize("sort", [True, False]) -def test_dataframe_categorical_ordered_observed_sort(ordered, observed, sort): - # GH 25871: Fix groupby sorting on ordered Categoricals - # GH 25167: Groupby with observed=True doesn't sort - - # Build a dataframe with cat having one unobserved category ('missing'), - # and a Series with identical values - label = Categorical( - ["d", "a", "b", "a", "d", "b"], - categories=["a", "b", "missing", "d"], - ordered=ordered, - ) - val = Series(["d", "a", "b", "a", "d", "b"]) - df = DataFrame({"label": label, "val": val}) - - # aggregate on the Categorical - result = df.groupby("label", observed=observed, sort=sort)["val"].aggregate("first") - - # If ordering works, we expect index labels equal to aggregation results, - # except for 'observed=False': label 'missing' has aggregation None - label = Series(result.index.array, dtype="object") - aggr = Series(result.array) - if not observed: - aggr[aggr.isna()] = "missing" - if not all(label == aggr): - msg = ( - "Labels and aggregation results not consistently sorted\n" - f"for (ordered={ordered}, observed={observed}, sort={sort})\n" - f"Result:\n{result}" - ) - assert False, msg - - -def test_datetime(): - # GH9049: ensure backward compatibility - levels = pd.date_range("2014-01-01", periods=4) - codes = np.random.default_rng(2).integers(0, 4, size=100) - - cats = Categorical.from_codes(codes, levels, ordered=True) - - data = DataFrame(np.random.default_rng(2).standard_normal((100, 4))) - result = data.groupby(cats, observed=False).mean() - - expected = data.groupby(np.asarray(cats), observed=False).mean() - expected = expected.reindex(levels) - expected.index = CategoricalIndex( - expected.index, categories=expected.index, ordered=True - ) - - tm.assert_frame_equal(result, expected) - - grouped = data.groupby(cats, observed=False) - desc_result = grouped.describe() - - idx = cats.codes.argsort() - ord_labels = cats.take(idx) - ord_data = data.take(idx) - expected = ord_data.groupby(ord_labels, observed=False).describe() - tm.assert_frame_equal(desc_result, expected) - tm.assert_index_equal(desc_result.index, expected.index) - tm.assert_index_equal( - desc_result.index.get_level_values(0), expected.index.get_level_values(0) - ) - - # GH 10460 - expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True) - exp = CategoricalIndex(expc) - tm.assert_index_equal( - (desc_result.stack(future_stack=True).index.get_level_values(0)), exp - ) - exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4) - tm.assert_index_equal( - (desc_result.stack(future_stack=True).index.get_level_values(1)), exp - ) - - -def test_categorical_index(): - s = np.random.default_rng(2) - levels = ["foo", "bar", "baz", "qux"] - codes = s.integers(0, 4, size=20) - cats = Categorical.from_codes(codes, levels, ordered=True) - df = DataFrame(np.repeat(np.arange(20), 4).reshape(-1, 4), columns=list("abcd")) - df["cats"] = cats - - # with a cat index - result = df.set_index("cats").groupby(level=0, observed=False).sum() - expected = df[list("abcd")].groupby(cats.codes, observed=False).sum() - expected.index = CategoricalIndex( - Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats" - ) - tm.assert_frame_equal(result, expected) - - # with a cat column, should produce a cat index - result = df.groupby("cats", observed=False).sum() - expected = df[list("abcd")].groupby(cats.codes, observed=False).sum() - expected.index = CategoricalIndex( - Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats" - ) - tm.assert_frame_equal(result, expected) - - -def test_describe_categorical_columns(): - # GH 11558 - cats = CategoricalIndex( - ["qux", "foo", "baz", "bar"], - categories=["foo", "bar", "baz", "qux"], - ordered=True, - ) - df = DataFrame(np.random.default_rng(2).standard_normal((20, 4)), columns=cats) - result = df.groupby([1, 2, 3, 4] * 5).describe() - - tm.assert_index_equal(result.stack(future_stack=True).columns, cats) - tm.assert_categorical_equal( - result.stack(future_stack=True).columns.values, cats.values - ) - - -def test_unstack_categorical(): - # GH11558 (example is taken from the original issue) - df = DataFrame( - {"a": range(10), "medium": ["A", "B"] * 5, "artist": list("XYXXY") * 2} - ) - df["medium"] = df["medium"].astype("category") - - gcat = df.groupby(["artist", "medium"], observed=False)["a"].count().unstack() - result = gcat.describe() - - exp_columns = CategoricalIndex(["A", "B"], ordered=False, name="medium") - tm.assert_index_equal(result.columns, exp_columns) - tm.assert_categorical_equal(result.columns.values, exp_columns.values) - - result = gcat["A"] + gcat["B"] - expected = Series([6, 4], index=Index(["X", "Y"], name="artist")) - tm.assert_series_equal(result, expected) - - -def test_bins_unequal_len(): - # GH3011 - series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4]) - bins = pd.cut(series.dropna().values, 4) - - # len(bins) != len(series) here - with pytest.raises(ValueError, match="Grouper and axis must be same length"): - series.groupby(bins).mean() - - -@pytest.mark.parametrize( - ["series", "data"], - [ - # Group a series with length and index equal to those of the grouper. - (Series(range(4)), {"A": [0, 3], "B": [1, 2]}), - # Group a series with length equal to that of the grouper and index unequal to - # that of the grouper. - (Series(range(4)).rename(lambda idx: idx + 1), {"A": [2], "B": [0, 1]}), - # GH44179: Group a series with length unequal to that of the grouper. - (Series(range(7)), {"A": [0, 3], "B": [1, 2]}), - ], -) -def test_categorical_series(series, data): - # Group the given series by a series with categorical data type such that group A - # takes indices 0 and 3 and group B indices 1 and 2, obtaining the values mapped in - # the given data. - groupby = series.groupby(Series(list("ABBA"), dtype="category"), observed=False) - result = groupby.aggregate(list) - expected = Series(data, index=CategoricalIndex(data.keys())) - tm.assert_series_equal(result, expected) - - -def test_as_index(): - # GH13204 - df = DataFrame( - { - "cat": Categorical([1, 2, 2], [1, 2, 3]), - "A": [10, 11, 11], - "B": [101, 102, 103], - } - ) - result = df.groupby(["cat", "A"], as_index=False, observed=True).sum() - expected = DataFrame( - { - "cat": Categorical([1, 2], categories=df.cat.cat.categories), - "A": [10, 11], - "B": [101, 205], - }, - columns=["cat", "A", "B"], - ) - tm.assert_frame_equal(result, expected) - - # function grouper - f = lambda r: df.loc[r, "A"] - msg = "A grouping .* was excluded from the result" - with tm.assert_produces_warning(FutureWarning, match=msg): - result = df.groupby(["cat", f], as_index=False, observed=True).sum() - expected = DataFrame( - { - "cat": Categorical([1, 2], categories=df.cat.cat.categories), - "A": [10, 22], - "B": [101, 205], - }, - columns=["cat", "A", "B"], - ) - tm.assert_frame_equal(result, expected) - - # another not in-axis grouper (conflicting names in index) - s = Series(["a", "b", "b"], name="cat") - msg = "A grouping .* was excluded from the result" - with tm.assert_produces_warning(FutureWarning, match=msg): - result = df.groupby(["cat", s], as_index=False, observed=True).sum() - tm.assert_frame_equal(result, expected) - - # is original index dropped? - group_columns = ["cat", "A"] - expected = DataFrame( - { - "cat": Categorical([1, 2], categories=df.cat.cat.categories), - "A": [10, 11], - "B": [101, 205], - }, - columns=["cat", "A", "B"], - ) - - for name in [None, "X", "B"]: - df.index = Index(list("abc"), name=name) - result = df.groupby(group_columns, as_index=False, observed=True).sum() - - tm.assert_frame_equal(result, expected) - - -def test_preserve_categories(): - # GH-13179 - categories = list("abc") - - # ordered=True - df = DataFrame({"A": Categorical(list("ba"), categories=categories, ordered=True)}) - sort_index = CategoricalIndex(categories, categories, ordered=True, name="A") - nosort_index = CategoricalIndex(list("bac"), categories, ordered=True, name="A") - tm.assert_index_equal( - df.groupby("A", sort=True, observed=False).first().index, sort_index - ) - # GH#42482 - don't sort result when sort=False, even when ordered=True - tm.assert_index_equal( - df.groupby("A", sort=False, observed=False).first().index, nosort_index - ) - - # ordered=False - df = DataFrame({"A": Categorical(list("ba"), categories=categories, ordered=False)}) - sort_index = CategoricalIndex(categories, categories, ordered=False, name="A") - # GH#48749 - don't change order of categories - # GH#42482 - don't sort result when sort=False, even when ordered=True - nosort_index = CategoricalIndex(list("bac"), list("abc"), ordered=False, name="A") - tm.assert_index_equal( - df.groupby("A", sort=True, observed=False).first().index, sort_index - ) - tm.assert_index_equal( - df.groupby("A", sort=False, observed=False).first().index, nosort_index - ) - - -def test_preserve_categorical_dtype(): - # GH13743, GH13854 - df = DataFrame( - { - "A": [1, 2, 1, 1, 2], - "B": [10, 16, 22, 28, 34], - "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False), - "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True), - } - ) - # single grouper - exp_full = DataFrame( - { - "A": [2.0, 1.0, np.nan], - "B": [25.0, 20.0, np.nan], - "C1": Categorical(list("bac"), categories=list("bac"), ordered=False), - "C2": Categorical(list("bac"), categories=list("bac"), ordered=True), - } - ) - for col in ["C1", "C2"]: - result1 = df.groupby(by=col, as_index=False, observed=False).mean( - numeric_only=True - ) - result2 = ( - df.groupby(by=col, as_index=True, observed=False) - .mean(numeric_only=True) - .reset_index() - ) - expected = exp_full.reindex(columns=result1.columns) - tm.assert_frame_equal(result1, expected) - tm.assert_frame_equal(result2, expected) - - -@pytest.mark.parametrize( - "func, values", - [ - ("first", ["second", "first"]), - ("last", ["fourth", "third"]), - ("min", ["fourth", "first"]), - ("max", ["second", "third"]), - ], -) -def test_preserve_on_ordered_ops(func, values): - # gh-18502 - # preserve the categoricals on ops - c = Categorical(["first", "second", "third", "fourth"], ordered=True) - df = DataFrame({"payload": [-1, -2, -1, -2], "col": c}) - g = df.groupby("payload") - result = getattr(g, func)() - expected = DataFrame( - {"payload": [-2, -1], "col": Series(values, dtype=c.dtype)} - ).set_index("payload") - tm.assert_frame_equal(result, expected) - - # we should also preserve categorical for SeriesGroupBy - sgb = df.groupby("payload")["col"] - result = getattr(sgb, func)() - expected = expected["col"] - tm.assert_series_equal(result, expected) - - -def test_categorical_no_compress(): - data = Series(np.random.default_rng(2).standard_normal(9)) - - codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]) - cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True) - - result = data.groupby(cats, observed=False).mean() - exp = data.groupby(codes, observed=False).mean() - - exp.index = CategoricalIndex( - exp.index, categories=cats.categories, ordered=cats.ordered - ) - tm.assert_series_equal(result, exp) - - codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3]) - cats = Categorical.from_codes(codes, [0, 1, 2, 3], ordered=True) - - result = data.groupby(cats, observed=False).mean() - exp = data.groupby(codes, observed=False).mean().reindex(cats.categories) - exp.index = CategoricalIndex( - exp.index, categories=cats.categories, ordered=cats.ordered - ) - tm.assert_series_equal(result, exp) - - cats = Categorical( - ["a", "a", "a", "b", "b", "b", "c", "c", "c"], - categories=["a", "b", "c", "d"], - ordered=True, - ) - data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats}) - - result = data.groupby("b", observed=False).mean() - result = result["a"].values - exp = np.array([1, 2, 4, np.nan]) - tm.assert_numpy_array_equal(result, exp) - - -def test_groupby_empty_with_category(): - # GH-9614 - # test fix for when group by on None resulted in - # coercion of dtype categorical -> float - df = DataFrame({"A": [None] * 3, "B": Categorical(["train", "train", "test"])}) - result = df.groupby("A").first()["B"] - expected = Series( - Categorical([], categories=["test", "train"]), - index=Series([], dtype="object", name="A"), - name="B", - ) - tm.assert_series_equal(result, expected) - - -def test_sort(): - # https://stackoverflow.com/questions/23814368/sorting-pandas- - # categorical-labels-after-groupby - # This should result in a properly sorted Series so that the plot - # has a sorted x axis - # self.cat.groupby(['value_group'])['value_group'].count().plot(kind='bar') - - df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)}) - labels = [f"{i} - {i+499}" for i in range(0, 10000, 500)] - cat_labels = Categorical(labels, labels) - - df = df.sort_values(by=["value"], ascending=True) - df["value_group"] = pd.cut( - df.value, range(0, 10500, 500), right=False, labels=cat_labels - ) - - res = df.groupby(["value_group"], observed=False)["value_group"].count() - exp = res[sorted(res.index, key=lambda x: float(x.split()[0]))] - exp.index = CategoricalIndex(exp.index, name=exp.index.name) - tm.assert_series_equal(res, exp) - - -@pytest.mark.parametrize("ordered", [True, False]) -def test_sort2(sort, ordered): - # dataframe groupby sort was being ignored # GH 8868 - # GH#48749 - don't change order of categories - # GH#42482 - don't sort result when sort=False, even when ordered=True - df = DataFrame( - [ - ["(7.5, 10]", 10, 10], - ["(7.5, 10]", 8, 20], - ["(2.5, 5]", 5, 30], - ["(5, 7.5]", 6, 40], - ["(2.5, 5]", 4, 50], - ["(0, 2.5]", 1, 60], - ["(5, 7.5]", 7, 70], - ], - columns=["range", "foo", "bar"], - ) - df["range"] = Categorical(df["range"], ordered=ordered) - result = df.groupby("range", sort=sort, observed=False).first() - - if sort: - data_values = [[1, 60], [5, 30], [6, 40], [10, 10]] - index_values = ["(0, 2.5]", "(2.5, 5]", "(5, 7.5]", "(7.5, 10]"] - else: - data_values = [[10, 10], [5, 30], [6, 40], [1, 60]] - index_values = ["(7.5, 10]", "(2.5, 5]", "(5, 7.5]", "(0, 2.5]"] - expected = DataFrame( - data_values, - columns=["foo", "bar"], - index=CategoricalIndex(index_values, name="range", ordered=ordered), - ) - - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("ordered", [True, False]) -def test_sort_datetimelike(sort, ordered): - # GH10505 - # GH#42482 - don't sort result when sort=False, even when ordered=True - - # use same data as test_groupby_sort_categorical, which category is - # corresponding to datetime.month - df = DataFrame( - { - "dt": [ - datetime(2011, 7, 1), - datetime(2011, 7, 1), - datetime(2011, 2, 1), - datetime(2011, 5, 1), - datetime(2011, 2, 1), - datetime(2011, 1, 1), - datetime(2011, 5, 1), - ], - "foo": [10, 8, 5, 6, 4, 1, 7], - "bar": [10, 20, 30, 40, 50, 60, 70], - }, - columns=["dt", "foo", "bar"], - ) - - # ordered=True - df["dt"] = Categorical(df["dt"], ordered=ordered) - if sort: - data_values = [[1, 60], [5, 30], [6, 40], [10, 10]] - index_values = [ - datetime(2011, 1, 1), - datetime(2011, 2, 1), - datetime(2011, 5, 1), - datetime(2011, 7, 1), - ] - else: - data_values = [[10, 10], [5, 30], [6, 40], [1, 60]] - index_values = [ - datetime(2011, 7, 1), - datetime(2011, 2, 1), - datetime(2011, 5, 1), - datetime(2011, 1, 1), - ] - expected = DataFrame( - data_values, - columns=["foo", "bar"], - index=CategoricalIndex(index_values, name="dt", ordered=ordered), - ) - result = df.groupby("dt", sort=sort, observed=False).first() - tm.assert_frame_equal(result, expected) - - -def test_empty_sum(): - # https://github.com/pandas-dev/pandas/issues/18678 - df = DataFrame( - {"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"]), "B": [1, 2, 1]} - ) - expected_idx = CategoricalIndex(["a", "b", "c"], name="A") - - # 0 by default - result = df.groupby("A", observed=False).B.sum() - expected = Series([3, 1, 0], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - # min_count=0 - result = df.groupby("A", observed=False).B.sum(min_count=0) - expected = Series([3, 1, 0], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - # min_count=1 - result = df.groupby("A", observed=False).B.sum(min_count=1) - expected = Series([3, 1, np.nan], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - # min_count>1 - result = df.groupby("A", observed=False).B.sum(min_count=2) - expected = Series([3, np.nan, np.nan], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - -def test_empty_prod(): - # https://github.com/pandas-dev/pandas/issues/18678 - df = DataFrame( - {"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"]), "B": [1, 2, 1]} - ) - - expected_idx = CategoricalIndex(["a", "b", "c"], name="A") - - # 1 by default - result = df.groupby("A", observed=False).B.prod() - expected = Series([2, 1, 1], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - # min_count=0 - result = df.groupby("A", observed=False).B.prod(min_count=0) - expected = Series([2, 1, 1], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - # min_count=1 - result = df.groupby("A", observed=False).B.prod(min_count=1) - expected = Series([2, 1, np.nan], expected_idx, name="B") - tm.assert_series_equal(result, expected) - - -def test_groupby_multiindex_categorical_datetime(): - # https://github.com/pandas-dev/pandas/issues/21390 - - df = DataFrame( - { - "key1": Categorical(list("abcbabcba")), - "key2": Categorical( - list(pd.date_range("2018-06-01 00", freq="1T", periods=3)) * 3 - ), - "values": np.arange(9), - } - ) - result = df.groupby(["key1", "key2"], observed=False).mean() - - idx = MultiIndex.from_product( - [ - Categorical(["a", "b", "c"]), - Categorical(pd.date_range("2018-06-01 00", freq="1T", periods=3)), - ], - names=["key1", "key2"], - ) - expected = DataFrame({"values": [0, 4, 8, 3, 4, 5, 6, np.nan, 2]}, index=idx) - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize( - "as_index, expected", - [ - ( - True, - Series( - index=MultiIndex.from_arrays( - [Series([1, 1, 2], dtype="category"), [1, 2, 2]], names=["a", "b"] - ), - data=[1, 2, 3], - name="x", - ), - ), - ( - False, - DataFrame( - { - "a": Series([1, 1, 2], dtype="category"), - "b": [1, 2, 2], - "x": [1, 2, 3], - } - ), - ), - ], -) -def test_groupby_agg_observed_true_single_column(as_index, expected): - # GH-23970 - df = DataFrame( - {"a": Series([1, 1, 2], dtype="category"), "b": [1, 2, 2], "x": [1, 2, 3]} - ) - - result = df.groupby(["a", "b"], as_index=as_index, observed=True)["x"].sum() - - tm.assert_equal(result, expected) - - -@pytest.mark.parametrize("fill_value", [None, np.nan, pd.NaT]) -def test_shift(fill_value): - ct = Categorical( - ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False - ) - expected = Categorical( - [None, "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False - ) - res = ct.shift(1, fill_value=fill_value) - tm.assert_equal(res, expected) - - -@pytest.fixture -def df_cat(df): - """ - DataFrame with multiple categorical columns and a column of integers. - Shortened so as not to contain all possible combinations of categories. - Useful for testing `observed` kwarg functionality on GroupBy objects. - - Parameters - ---------- - df: DataFrame - Non-categorical, longer DataFrame from another fixture, used to derive - this one - - Returns - ------- - df_cat: DataFrame - """ - df_cat = df.copy()[:4] # leave out some groups - df_cat["A"] = df_cat["A"].astype("category") - df_cat["B"] = df_cat["B"].astype("category") - df_cat["C"] = Series([1, 2, 3, 4]) - df_cat = df_cat.drop(["D"], axis=1) - return df_cat - - -@pytest.mark.parametrize("operation", ["agg", "apply"]) -def test_seriesgroupby_observed_true(df_cat, operation): - # GH#24880 - # GH#49223 - order of results was wrong when grouping by index levels - lev_a = Index(["bar", "bar", "foo", "foo"], dtype=df_cat["A"].dtype, name="A") - lev_b = Index(["one", "three", "one", "two"], dtype=df_cat["B"].dtype, name="B") - index = MultiIndex.from_arrays([lev_a, lev_b]) - expected = Series(data=[2, 4, 1, 3], index=index, name="C").sort_index() - - grouped = df_cat.groupby(["A", "B"], observed=True)["C"] - msg = "using np.sum" if operation == "apply" else "using SeriesGroupBy.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = getattr(grouped, operation)(sum) - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("operation", ["agg", "apply"]) -@pytest.mark.parametrize("observed", [False, None]) -def test_seriesgroupby_observed_false_or_none(df_cat, observed, operation): - # GH 24880 - # GH#49223 - order of results was wrong when grouping by index levels - index, _ = MultiIndex.from_product( - [ - CategoricalIndex(["bar", "foo"], ordered=False), - CategoricalIndex(["one", "three", "two"], ordered=False), - ], - names=["A", "B"], - ).sortlevel() - - expected = Series(data=[2, 4, np.nan, 1, np.nan, 3], index=index, name="C") - if operation == "agg": - msg = "The 'downcast' keyword in fillna is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - expected = expected.fillna(0, downcast="infer") - grouped = df_cat.groupby(["A", "B"], observed=observed)["C"] - msg = "using SeriesGroupBy.sum" if operation == "agg" else "using np.sum" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - result = getattr(grouped, operation)(sum) - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize( - "observed, index, data", - [ - ( - True, - MultiIndex.from_arrays( - [ - Index(["bar"] * 4 + ["foo"] * 4, dtype="category", name="A"), - Index( - ["one", "one", "three", "three", "one", "one", "two", "two"], - dtype="category", - name="B", - ), - Index(["min", "max"] * 4), - ] - ), - [2, 2, 4, 4, 1, 1, 3, 3], - ), - ( - False, - MultiIndex.from_product( - [ - CategoricalIndex(["bar", "foo"], ordered=False), - CategoricalIndex(["one", "three", "two"], ordered=False), - Index(["min", "max"]), - ], - names=["A", "B", None], - ), - [2, 2, 4, 4, np.nan, np.nan, 1, 1, np.nan, np.nan, 3, 3], - ), - ( - None, - MultiIndex.from_product( - [ - CategoricalIndex(["bar", "foo"], ordered=False), - CategoricalIndex(["one", "three", "two"], ordered=False), - Index(["min", "max"]), - ], - names=["A", "B", None], - ), - [2, 2, 4, 4, np.nan, np.nan, 1, 1, np.nan, np.nan, 3, 3], - ), - ], -) -def test_seriesgroupby_observed_apply_dict(df_cat, observed, index, data): - # GH 24880 - expected = Series(data=data, index=index, name="C") - result = df_cat.groupby(["A", "B"], observed=observed)["C"].apply( - lambda x: {"min": x.min(), "max": x.max()} - ) - tm.assert_series_equal(result, expected) - - -def test_groupby_categorical_series_dataframe_consistent(df_cat): - # GH 20416 - expected = df_cat.groupby(["A", "B"], observed=False)["C"].mean() - result = df_cat.groupby(["A", "B"], observed=False).mean()["C"] - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("code", [([1, 0, 0]), ([0, 0, 0])]) -def test_groupby_categorical_axis_1(code): - # GH 13420 - df = DataFrame({"a": [1, 2, 3, 4], "b": [-1, -2, -3, -4], "c": [5, 6, 7, 8]}) - cat = Categorical.from_codes(code, categories=list("abc")) - msg = "DataFrame.groupby with axis=1 is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - gb = df.groupby(cat, axis=1, observed=False) - result = gb.mean() - msg = "The 'axis' keyword in DataFrame.groupby is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - gb2 = df.T.groupby(cat, axis=0, observed=False) - expected = gb2.mean().T - tm.assert_frame_equal(result, expected) - - -def test_groupby_cat_preserves_structure(observed, ordered): - # GH 28787 - df = DataFrame( - {"Name": Categorical(["Bob", "Greg"], ordered=ordered), "Item": [1, 2]}, - columns=["Name", "Item"], - ) - expected = df.copy() - - result = ( - df.groupby("Name", observed=observed) - .agg(DataFrame.sum, skipna=True) - .reset_index() - ) - - tm.assert_frame_equal(result, expected) - - -def test_get_nonexistent_category(): - # Accessing a Category that is not in the dataframe - df = DataFrame({"var": ["a", "a", "b", "b"], "val": range(4)}) - with pytest.raises(KeyError, match="'vau'"): - df.groupby("var").apply( - lambda rows: DataFrame( - {"var": [rows.iloc[-1]["var"]], "val": [rows.iloc[-1]["vau"]]} - ) - ) - - -def test_series_groupby_on_2_categoricals_unobserved(reduction_func, observed): - # GH 17605 - if reduction_func == "ngroup": - pytest.skip("ngroup is not truly a reduction") - - df = DataFrame( - { - "cat_1": Categorical(list("AABB"), categories=list("ABCD")), - "cat_2": Categorical(list("AB") * 2, categories=list("ABCD")), - "value": [0.1] * 4, - } - ) - args = get_groupby_method_args(reduction_func, df) - - expected_length = 4 if observed else 16 - - series_groupby = df.groupby(["cat_1", "cat_2"], observed=observed)["value"] - - if reduction_func == "corrwith": - # TODO: implemented SeriesGroupBy.corrwith. See GH 32293 - assert not hasattr(series_groupby, reduction_func) - return - - agg = getattr(series_groupby, reduction_func) - result = agg(*args) - - assert len(result) == expected_length - - -def test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans( - reduction_func, request -): - # GH 17605 - # Tests whether the unobserved categories in the result contain 0 or NaN - - if reduction_func == "ngroup": - pytest.skip("ngroup is not truly a reduction") - - if reduction_func == "corrwith": # GH 32293 - mark = pytest.mark.xfail( - reason="TODO: implemented SeriesGroupBy.corrwith. See GH 32293" - ) - request.node.add_marker(mark) - - df = DataFrame( - { - "cat_1": Categorical(list("AABB"), categories=list("ABC")), - "cat_2": Categorical(list("AB") * 2, categories=list("ABC")), - "value": [0.1] * 4, - } - ) - unobserved = [tuple("AC"), tuple("BC"), tuple("CA"), tuple("CB"), tuple("CC")] - args = get_groupby_method_args(reduction_func, df) - - series_groupby = df.groupby(["cat_1", "cat_2"], observed=False)["value"] - agg = getattr(series_groupby, reduction_func) - result = agg(*args) - - zero_or_nan = _results_for_groupbys_with_missing_categories[reduction_func] - - for idx in unobserved: - val = result.loc[idx] - assert (pd.isna(zero_or_nan) and pd.isna(val)) or (val == zero_or_nan) - - # If we expect unobserved values to be zero, we also expect the dtype to be int. - # Except for .sum(). If the observed categories sum to dtype=float (i.e. their - # sums have decimals), then the zeros for the missing categories should also be - # floats. - if zero_or_nan == 0 and reduction_func != "sum": - assert np.issubdtype(result.dtype, np.integer) - - -def test_dataframe_groupby_on_2_categoricals_when_observed_is_true(reduction_func): - # GH 23865 - # GH 27075 - # Ensure that df.groupby, when 'by' is two Categorical variables, - # does not return the categories that are not in df when observed=True - if reduction_func == "ngroup": - pytest.skip("ngroup does not return the Categories on the index") - - df = DataFrame( - { - "cat_1": Categorical(list("AABB"), categories=list("ABC")), - "cat_2": Categorical(list("1111"), categories=list("12")), - "value": [0.1, 0.1, 0.1, 0.1], - } - ) - unobserved_cats = [("A", "2"), ("B", "2"), ("C", "1"), ("C", "2")] - - df_grp = df.groupby(["cat_1", "cat_2"], observed=True) - - args = get_groupby_method_args(reduction_func, df) - res = getattr(df_grp, reduction_func)(*args) - - for cat in unobserved_cats: - assert cat not in res.index - - -@pytest.mark.parametrize("observed", [False, None]) -def test_dataframe_groupby_on_2_categoricals_when_observed_is_false( - reduction_func, observed -): - # GH 23865 - # GH 27075 - # Ensure that df.groupby, when 'by' is two Categorical variables, - # returns the categories that are not in df when observed=False/None - - if reduction_func == "ngroup": - pytest.skip("ngroup does not return the Categories on the index") - - df = DataFrame( - { - "cat_1": Categorical(list("AABB"), categories=list("ABC")), - "cat_2": Categorical(list("1111"), categories=list("12")), - "value": [0.1, 0.1, 0.1, 0.1], - } - ) - unobserved_cats = [("A", "2"), ("B", "2"), ("C", "1"), ("C", "2")] - - df_grp = df.groupby(["cat_1", "cat_2"], observed=observed) - - args = get_groupby_method_args(reduction_func, df) - res = getattr(df_grp, reduction_func)(*args) - - expected = _results_for_groupbys_with_missing_categories[reduction_func] - - if expected is np.nan: - assert res.loc[unobserved_cats].isnull().all().all() - else: - assert (res.loc[unobserved_cats] == expected).all().all() - - -def test_series_groupby_categorical_aggregation_getitem(): - # GH 8870 - d = {"foo": [10, 8, 4, 1], "bar": [10, 20, 30, 40], "baz": ["d", "c", "d", "c"]} - df = DataFrame(d) - cat = pd.cut(df["foo"], np.linspace(0, 20, 5)) - df["range"] = cat - groups = df.groupby(["range", "baz"], as_index=True, sort=True, observed=False) - result = groups["foo"].agg("mean") - expected = groups.agg("mean")["foo"] - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize( - "func, expected_values", - [(Series.nunique, [1, 1, 2]), (Series.count, [1, 2, 2])], -) -def test_groupby_agg_categorical_columns(func, expected_values): - # 31256 - df = DataFrame( - { - "id": [0, 1, 2, 3, 4], - "groups": [0, 1, 1, 2, 2], - "value": Categorical([0, 0, 0, 0, 1]), - } - ).set_index("id") - result = df.groupby("groups").agg(func) - - expected = DataFrame( - {"value": expected_values}, index=Index([0, 1, 2], name="groups") - ) - tm.assert_frame_equal(result, expected) - - -def test_groupby_agg_non_numeric(): - df = DataFrame({"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"])}) - expected = DataFrame({"A": [2, 1]}, index=np.array([1, 2])) - - result = df.groupby([1, 2, 1]).agg(Series.nunique) - tm.assert_frame_equal(result, expected) - - result = df.groupby([1, 2, 1]).nunique() - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("func", ["first", "last"]) -def test_groupby_first_returned_categorical_instead_of_dataframe(func): - # GH 28641: groupby drops index, when grouping over categorical column with - # first/last. Renamed Categorical instead of DataFrame previously. - df = DataFrame({"A": [1997], "B": Series(["b"], dtype="category").cat.as_ordered()}) - df_grouped = df.groupby("A")["B"] - result = getattr(df_grouped, func)() - - # ordered categorical dtype should be preserved - expected = Series( - ["b"], index=Index([1997], name="A"), name="B", dtype=df["B"].dtype - ) - tm.assert_series_equal(result, expected) - - -def test_read_only_category_no_sort(): - # GH33410 - cats = np.array([1, 2]) - cats.flags.writeable = False - df = DataFrame( - {"a": [1, 3, 5, 7], "b": Categorical([1, 1, 2, 2], categories=Index(cats))} - ) - expected = DataFrame(data={"a": [2.0, 6.0]}, index=CategoricalIndex(cats, name="b")) - result = df.groupby("b", sort=False, observed=False).mean() - tm.assert_frame_equal(result, expected) - - -def test_sorted_missing_category_values(): - # GH 28597 - df = DataFrame( - { - "foo": [ - "small", - "large", - "large", - "large", - "medium", - "large", - "large", - "medium", - ], - "bar": ["C", "A", "A", "C", "A", "C", "A", "C"], - } - ) - df["foo"] = ( - df["foo"] - .astype("category") - .cat.set_categories(["tiny", "small", "medium", "large"], ordered=True) - ) - - expected = DataFrame( - { - "tiny": {"A": 0, "C": 0}, - "small": {"A": 0, "C": 1}, - "medium": {"A": 1, "C": 1}, - "large": {"A": 3, "C": 2}, - } - ) - expected = expected.rename_axis("bar", axis="index") - expected.columns = CategoricalIndex( - ["tiny", "small", "medium", "large"], - categories=["tiny", "small", "medium", "large"], - ordered=True, - name="foo", - dtype="category", - ) - - result = df.groupby(["bar", "foo"], observed=False).size().unstack() - - tm.assert_frame_equal(result, expected) - - -def test_agg_cython_category_not_implemented_fallback(): - # https://github.com/pandas-dev/pandas/issues/31450 - df = DataFrame({"col_num": [1, 1, 2, 3]}) - df["col_cat"] = df["col_num"].astype("category") - - result = df.groupby("col_num").col_cat.first() - - # ordered categorical dtype should definitely be preserved; - # this is unordered, so is less-clear case (if anything, it should raise) - expected = Series( - [1, 2, 3], - index=Index([1, 2, 3], name="col_num"), - name="col_cat", - dtype=df["col_cat"].dtype, - ) - tm.assert_series_equal(result, expected) - - result = df.groupby("col_num").agg({"col_cat": "first"}) - expected = expected.to_frame() - tm.assert_frame_equal(result, expected) - - -def test_aggregate_categorical_with_isnan(): - # GH 29837 - df = DataFrame( - { - "A": [1, 1, 1, 1], - "B": [1, 2, 1, 2], - "numerical_col": [0.1, 0.2, np.nan, 0.3], - "object_col": ["foo", "bar", "foo", "fee"], - "categorical_col": ["foo", "bar", "foo", "fee"], - } - ) - - df = df.astype({"categorical_col": "category"}) - - result = df.groupby(["A", "B"]).agg(lambda df: df.isna().sum()) - index = MultiIndex.from_arrays([[1, 1], [1, 2]], names=("A", "B")) - expected = DataFrame( - data={ - "numerical_col": [1, 0], - "object_col": [0, 0], - "categorical_col": [0, 0], - }, - index=index, - ) - tm.assert_frame_equal(result, expected) - - -def test_categorical_transform(): - # GH 29037 - df = DataFrame( - { - "package_id": [1, 1, 1, 2, 2, 3], - "status": [ - "Waiting", - "OnTheWay", - "Delivered", - "Waiting", - "OnTheWay", - "Waiting", - ], - } - ) - - delivery_status_type = pd.CategoricalDtype( - categories=["Waiting", "OnTheWay", "Delivered"], ordered=True - ) - df["status"] = df["status"].astype(delivery_status_type) - msg = "using SeriesGroupBy.max" - with tm.assert_produces_warning(FutureWarning, match=msg): - # GH#53425 - df["last_status"] = df.groupby("package_id")["status"].transform(max) - result = df.copy() - - expected = DataFrame( - { - "package_id": [1, 1, 1, 2, 2, 3], - "status": [ - "Waiting", - "OnTheWay", - "Delivered", - "Waiting", - "OnTheWay", - "Waiting", - ], - "last_status": [ - "Delivered", - "Delivered", - "Delivered", - "OnTheWay", - "OnTheWay", - "Waiting", - ], - } - ) - - expected["status"] = expected["status"].astype(delivery_status_type) - - # .transform(max) should preserve ordered categoricals - expected["last_status"] = expected["last_status"].astype(delivery_status_type) - - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("func", ["first", "last"]) -def test_series_groupby_first_on_categorical_col_grouped_on_2_categoricals( - func: str, observed: bool -): - # GH 34951 - cat = Categorical([0, 0, 1, 1]) - val = [0, 1, 1, 0] - df = DataFrame({"a": cat, "b": cat, "c": val}) - - cat2 = Categorical([0, 1]) - idx = MultiIndex.from_product([cat2, cat2], names=["a", "b"]) - expected_dict = { - "first": Series([0, np.nan, np.nan, 1], idx, name="c"), - "last": Series([1, np.nan, np.nan, 0], idx, name="c"), - } - - expected = expected_dict[func] - if observed: - expected = expected.dropna().astype(np.int64) - - srs_grp = df.groupby(["a", "b"], observed=observed)["c"] - result = getattr(srs_grp, func)() - tm.assert_series_equal(result, expected) - - -@pytest.mark.parametrize("func", ["first", "last"]) -def test_df_groupby_first_on_categorical_col_grouped_on_2_categoricals( - func: str, observed: bool -): - # GH 34951 - cat = Categorical([0, 0, 1, 1]) - val = [0, 1, 1, 0] - df = DataFrame({"a": cat, "b": cat, "c": val}) - - cat2 = Categorical([0, 1]) - idx = MultiIndex.from_product([cat2, cat2], names=["a", "b"]) - expected_dict = { - "first": Series([0, np.nan, np.nan, 1], idx, name="c"), - "last": Series([1, np.nan, np.nan, 0], idx, name="c"), - } - - expected = expected_dict[func].to_frame() - if observed: - expected = expected.dropna().astype(np.int64) - - df_grp = df.groupby(["a", "b"], observed=observed) - result = getattr(df_grp, func)() - tm.assert_frame_equal(result, expected) - - -def test_groupby_categorical_indices_unused_categories(): - # GH#38642 - df = DataFrame( - { - "key": Categorical(["b", "b", "a"], categories=["a", "b", "c"]), - "col": range(3), - } - ) - grouped = df.groupby("key", sort=False, observed=False) - result = grouped.indices - expected = { - "b": np.array([0, 1], dtype="intp"), - "a": np.array([2], dtype="intp"), - "c": np.array([], dtype="intp"), - } - assert result.keys() == expected.keys() - for key in result.keys(): - tm.assert_numpy_array_equal(result[key], expected[key]) - - -@pytest.mark.parametrize("func", ["first", "last"]) -def test_groupby_last_first_preserve_categoricaldtype(func): - # GH#33090 - df = DataFrame({"a": [1, 2, 3]}) - df["b"] = df["a"].astype("category") - result = getattr(df.groupby("a")["b"], func)() - expected = Series( - Categorical([1, 2, 3]), name="b", index=Index([1, 2, 3], name="a") - ) - tm.assert_series_equal(expected, result) - - -def test_groupby_categorical_observed_nunique(): - # GH#45128 - df = DataFrame({"a": [1, 2], "b": [1, 2], "c": [10, 11]}) - df = df.astype(dtype={"a": "category", "b": "category"}) - result = df.groupby(["a", "b"], observed=True).nunique()["c"] - expected = Series( - [1, 1], - index=MultiIndex.from_arrays( - [CategoricalIndex([1, 2], name="a"), CategoricalIndex([1, 2], name="b")] - ), - name="c", - ) - tm.assert_series_equal(result, expected) - - -def test_groupby_categorical_aggregate_functions(): - # GH#37275 - dtype = pd.CategoricalDtype(categories=["small", "big"], ordered=True) - df = DataFrame( - [[1, "small"], [1, "big"], [2, "small"]], columns=["grp", "description"] - ).astype({"description": dtype}) - - result = df.groupby("grp")["description"].max() - expected = Series( - ["big", "small"], - index=Index([1, 2], name="grp"), - name="description", - dtype=pd.CategoricalDtype(categories=["small", "big"], ordered=True), - ) - - tm.assert_series_equal(result, expected) - - -def test_groupby_categorical_dropna(observed, dropna): - # GH#48645 - dropna should have no impact on the result when there are no NA values - cat = Categorical([1, 2], categories=[1, 2, 3]) - df = DataFrame({"x": Categorical([1, 2], categories=[1, 2, 3]), "y": [3, 4]}) - gb = df.groupby("x", observed=observed, dropna=dropna) - result = gb.sum() - - if observed: - expected = DataFrame({"y": [3, 4]}, index=cat) - else: - index = CategoricalIndex([1, 2, 3], [1, 2, 3]) - expected = DataFrame({"y": [3, 4, 0]}, index=index) - expected.index.name = "x" - - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("index_kind", ["range", "single", "multi"]) -@pytest.mark.parametrize("ordered", [True, False]) -def test_category_order_reducer( - request, as_index, sort, observed, reduction_func, index_kind, ordered -): - # GH#48749 - if ( - reduction_func in ("idxmax", "idxmin") - and not observed - and index_kind != "multi" - ): - msg = "GH#10694 - idxmax/min fail with unused categories" - request.node.add_marker(pytest.mark.xfail(reason=msg)) - elif reduction_func == "corrwith" and not as_index: - msg = "GH#49950 - corrwith with as_index=False may not have grouping column" - request.node.add_marker(pytest.mark.xfail(reason=msg)) - elif index_kind != "range" and not as_index: - pytest.skip(reason="Result doesn't have categories, nothing to test") - df = DataFrame( - { - "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered), - "b": range(4), - } - ) - if index_kind == "range": - keys = ["a"] - elif index_kind == "single": - keys = ["a"] - df = df.set_index(keys) - elif index_kind == "multi": - keys = ["a", "a2"] - df["a2"] = df["a"] - df = df.set_index(keys) - args = get_groupby_method_args(reduction_func, df) - gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed) - op_result = getattr(gb, reduction_func)(*args) - if as_index: - result = op_result.index.get_level_values("a").categories - else: - result = op_result["a"].cat.categories - expected = Index([1, 4, 3, 2]) - tm.assert_index_equal(result, expected) - - if index_kind == "multi": - result = op_result.index.get_level_values("a2").categories - tm.assert_index_equal(result, expected) - - -@pytest.mark.parametrize("index_kind", ["single", "multi"]) -@pytest.mark.parametrize("ordered", [True, False]) -def test_category_order_transformer( - as_index, sort, observed, transformation_func, index_kind, ordered -): - # GH#48749 - df = DataFrame( - { - "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered), - "b": range(4), - } - ) - if index_kind == "single": - keys = ["a"] - df = df.set_index(keys) - elif index_kind == "multi": - keys = ["a", "a2"] - df["a2"] = df["a"] - df = df.set_index(keys) - args = get_groupby_method_args(transformation_func, df) - gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed) - op_result = getattr(gb, transformation_func)(*args) - result = op_result.index.get_level_values("a").categories - expected = Index([1, 4, 3, 2]) - tm.assert_index_equal(result, expected) - - if index_kind == "multi": - result = op_result.index.get_level_values("a2").categories - tm.assert_index_equal(result, expected) - - -@pytest.mark.parametrize("index_kind", ["range", "single", "multi"]) -@pytest.mark.parametrize("method", ["head", "tail"]) -@pytest.mark.parametrize("ordered", [True, False]) -def test_category_order_head_tail( - as_index, sort, observed, method, index_kind, ordered -): - # GH#48749 - df = DataFrame( - { - "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered), - "b": range(4), - } - ) - if index_kind == "range": - keys = ["a"] - elif index_kind == "single": - keys = ["a"] - df = df.set_index(keys) - elif index_kind == "multi": - keys = ["a", "a2"] - df["a2"] = df["a"] - df = df.set_index(keys) - gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed) - op_result = getattr(gb, method)() - if index_kind == "range": - result = op_result["a"].cat.categories - else: - result = op_result.index.get_level_values("a").categories - expected = Index([1, 4, 3, 2]) - tm.assert_index_equal(result, expected) - - if index_kind == "multi": - result = op_result.index.get_level_values("a2").categories - tm.assert_index_equal(result, expected) - - -@pytest.mark.parametrize("index_kind", ["range", "single", "multi"]) -@pytest.mark.parametrize("method", ["apply", "agg", "transform"]) -@pytest.mark.parametrize("ordered", [True, False]) -def test_category_order_apply(as_index, sort, observed, method, index_kind, ordered): - # GH#48749 - if (method == "transform" and index_kind == "range") or ( - not as_index and index_kind != "range" - ): - pytest.skip("No categories in result, nothing to test") - df = DataFrame( - { - "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered), - "b": range(4), - } - ) - if index_kind == "range": - keys = ["a"] - elif index_kind == "single": - keys = ["a"] - df = df.set_index(keys) - elif index_kind == "multi": - keys = ["a", "a2"] - df["a2"] = df["a"] - df = df.set_index(keys) - gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed) - op_result = getattr(gb, method)(lambda x: x.sum(numeric_only=True)) - if (method == "transform" or not as_index) and index_kind == "range": - result = op_result["a"].cat.categories - else: - result = op_result.index.get_level_values("a").categories - expected = Index([1, 4, 3, 2]) - tm.assert_index_equal(result, expected) - - if index_kind == "multi": - result = op_result.index.get_level_values("a2").categories - tm.assert_index_equal(result, expected) - - -@pytest.mark.parametrize("index_kind", ["range", "single", "multi"]) -def test_many_categories(as_index, sort, index_kind, ordered): - # GH#48749 - Test when the grouper has many categories - if index_kind != "range" and not as_index: - pytest.skip(reason="Result doesn't have categories, nothing to test") - categories = np.arange(9999, -1, -1) - grouper = Categorical([2, 1, 2, 3], categories=categories, ordered=ordered) - df = DataFrame({"a": grouper, "b": range(4)}) - if index_kind == "range": - keys = ["a"] - elif index_kind == "single": - keys = ["a"] - df = df.set_index(keys) - elif index_kind == "multi": - keys = ["a", "a2"] - df["a2"] = df["a"] - df = df.set_index(keys) - gb = df.groupby(keys, as_index=as_index, sort=sort, observed=True) - result = gb.sum() - - # Test is setup so that data and index are the same values - data = [3, 2, 1] if sort else [2, 1, 3] - - index = CategoricalIndex( - data, categories=grouper.categories, ordered=ordered, name="a" - ) - if as_index: - expected = DataFrame({"b": data}) - if index_kind == "multi": - expected.index = MultiIndex.from_frame(DataFrame({"a": index, "a2": index})) - else: - expected.index = index - elif index_kind == "multi": - expected = DataFrame({"a": Series(index), "a2": Series(index), "b": data}) - else: - expected = DataFrame({"a": Series(index), "b": data}) - - tm.assert_frame_equal(result, expected) - - -@pytest.mark.parametrize("cat_columns", ["a", "b", ["a", "b"]]) -@pytest.mark.parametrize("keys", ["a", "b", ["a", "b"]]) -def test_groupby_default_depr(cat_columns, keys): - # GH#43999 - df = DataFrame({"a": [1, 1, 2, 3], "b": [4, 5, 6, 7]}) - df[cat_columns] = df[cat_columns].astype("category") - msg = "The default of observed=False is deprecated" - klass = FutureWarning if set(cat_columns) & set(keys) else None - with tm.assert_produces_warning(klass, match=msg): - df.groupby(keys) - - -@pytest.mark.parametrize("test_series", [True, False]) -@pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]]) -def test_agg_list(request, as_index, observed, reduction_func, test_series, keys): - # GH#52760 - if test_series and reduction_func == "corrwith": - assert not hasattr(SeriesGroupBy, "corrwith") - pytest.skip("corrwith not implemented for SeriesGroupBy") - elif reduction_func == "corrwith": - msg = "GH#32293: attempts to call SeriesGroupBy.corrwith" - request.node.add_marker(pytest.mark.xfail(reason=msg)) - elif ( - reduction_func == "nunique" - and not test_series - and len(keys) != 1 - and not observed - and not as_index - ): - msg = "GH#52848 - raises a ValueError" - request.node.add_marker(pytest.mark.xfail(reason=msg)) - - df = DataFrame({"a1": [0, 0, 1], "a2": [2, 3, 3], "b": [4, 5, 6]}) - df = df.astype({"a1": "category", "a2": "category"}) - if "a2" not in keys: - df = df.drop(columns="a2") - gb = df.groupby(by=keys, as_index=as_index, observed=observed) - if test_series: - gb = gb["b"] - args = get_groupby_method_args(reduction_func, df) - - result = gb.agg([reduction_func], *args) - expected = getattr(gb, reduction_func)(*args) - - if as_index and (test_series or reduction_func == "size"): - expected = expected.to_frame(reduction_func) - if not test_series: - expected.columns = MultiIndex.from_tuples( - [(ind, "") for ind in expected.columns[:-1]] + [("b", reduction_func)] - ) - elif not as_index: - expected.columns = keys + [reduction_func] - - tm.assert_equal(result, expected) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/period/test_monotonic.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/period/test_monotonic.py deleted file mode 100644 index 15cb8f71cdcf3221800e6dca43390ae79114a9df..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/period/test_monotonic.py +++ /dev/null @@ -1,42 +0,0 @@ -from pandas import ( - Period, - PeriodIndex, -) - - -def test_is_monotonic_increasing(): - # GH#17717 - p0 = Period("2017-09-01") - p1 = Period("2017-09-02") - p2 = Period("2017-09-03") - - idx_inc0 = PeriodIndex([p0, p1, p2]) - idx_inc1 = PeriodIndex([p0, p1, p1]) - idx_dec0 = PeriodIndex([p2, p1, p0]) - idx_dec1 = PeriodIndex([p2, p1, p1]) - idx = PeriodIndex([p1, p2, p0]) - - assert idx_inc0.is_monotonic_increasing is True - assert idx_inc1.is_monotonic_increasing is True - assert idx_dec0.is_monotonic_increasing is False - assert idx_dec1.is_monotonic_increasing is False - assert idx.is_monotonic_increasing is False - - -def test_is_monotonic_decreasing(): - # GH#17717 - p0 = Period("2017-09-01") - p1 = Period("2017-09-02") - p2 = Period("2017-09-03") - - idx_inc0 = PeriodIndex([p0, p1, p2]) - idx_inc1 = PeriodIndex([p0, p1, p1]) - idx_dec0 = PeriodIndex([p2, p1, p0]) - idx_dec1 = PeriodIndex([p2, p1, p1]) - idx = PeriodIndex([p1, p2, p0]) - - assert idx_inc0.is_monotonic_decreasing is False - assert idx_inc1.is_monotonic_decreasing is False - assert idx_dec0.is_monotonic_decreasing is True - assert idx_dec1.is_monotonic_decreasing is True - assert idx.is_monotonic_decreasing is False diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/regexopt.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/regexopt.py deleted file mode 100644 index 45223eccc10ed35a7cade624cba9878690b88661..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/regexopt.py +++ /dev/null @@ -1,91 +0,0 @@ -""" - pygments.regexopt - ~~~~~~~~~~~~~~~~~ - - An algorithm that generates optimized regexes for matching long lists of - literal strings. - - :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import re -from re import escape -from os.path import commonprefix -from itertools import groupby -from operator import itemgetter - -CS_ESCAPE = re.compile(r'[\[\^\\\-\]]') -FIRST_ELEMENT = itemgetter(0) - - -def make_charset(letters): - return '[' + CS_ESCAPE.sub(lambda m: '\\' + m.group(), ''.join(letters)) + ']' - - -def regex_opt_inner(strings, open_paren): - """Return a regex that matches any string in the sorted list of strings.""" - close_paren = open_paren and ')' or '' - # print strings, repr(open_paren) - if not strings: - # print '-> nothing left' - return '' - first = strings[0] - if len(strings) == 1: - # print '-> only 1 string' - return open_paren + escape(first) + close_paren - if not first: - # print '-> first string empty' - return open_paren + regex_opt_inner(strings[1:], '(?:') \ - + '?' + close_paren - if len(first) == 1: - # multiple one-char strings? make a charset - oneletter = [] - rest = [] - for s in strings: - if len(s) == 1: - oneletter.append(s) - else: - rest.append(s) - if len(oneletter) > 1: # do we have more than one oneletter string? - if rest: - # print '-> 1-character + rest' - return open_paren + regex_opt_inner(rest, '') + '|' \ - + make_charset(oneletter) + close_paren - # print '-> only 1-character' - return open_paren + make_charset(oneletter) + close_paren - prefix = commonprefix(strings) - if prefix: - plen = len(prefix) - # we have a prefix for all strings - # print '-> prefix:', prefix - return open_paren + escape(prefix) \ - + regex_opt_inner([s[plen:] for s in strings], '(?:') \ - + close_paren - # is there a suffix? - strings_rev = [s[::-1] for s in strings] - suffix = commonprefix(strings_rev) - if suffix: - slen = len(suffix) - # print '-> suffix:', suffix[::-1] - return open_paren \ - + regex_opt_inner(sorted(s[:-slen] for s in strings), '(?:') \ - + escape(suffix[::-1]) + close_paren - # recurse on common 1-string prefixes - # print '-> last resort' - return open_paren + \ - '|'.join(regex_opt_inner(list(group[1]), '') - for group in groupby(strings, lambda s: s[0] == first[0])) \ - + close_paren - - -def regex_opt(strings, prefix='', suffix=''): - """Return a compiled regex that matches any string in the given list. - - The strings to match must be literal strings, not regexes. They will be - regex-escaped. - - *prefix* and *suffix* are pre- and appended to the final regex. - """ - strings = sorted(strings) - return prefix + regex_opt_inner(strings, '(') + suffix diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/_emoji_codes.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/_emoji_codes.py deleted file mode 100644 index 1f2877bb2bd520253502b1c05bb811bb0d7ef64c..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/rich/_emoji_codes.py +++ /dev/null @@ -1,3610 +0,0 @@ -EMOJI = { - "1st_place_medal": "🥇", - "2nd_place_medal": "🥈", - "3rd_place_medal": "🥉", - "ab_button_(blood_type)": "🆎", - "atm_sign": "🏧", - "a_button_(blood_type)": "🅰", - "afghanistan": "🇦🇫", - "albania": "🇦🇱", - "algeria": "🇩🇿", - "american_samoa": "🇦🇸", - "andorra": "🇦🇩", - "angola": "🇦🇴", - "anguilla": "🇦🇮", - "antarctica": "🇦🇶", - "antigua_&_barbuda": "🇦🇬", - "aquarius": "♒", - "argentina": "🇦🇷", - "aries": "♈", - "armenia": "🇦🇲", - "aruba": "🇦🇼", - "ascension_island": "🇦🇨", - "australia": "🇦🇺", - "austria": "🇦🇹", - "azerbaijan": "🇦🇿", - "back_arrow": "🔙", - "b_button_(blood_type)": "🅱", - "bahamas": "🇧🇸", - "bahrain": "🇧🇭", - "bangladesh": "🇧🇩", - "barbados": "🇧🇧", - "belarus": "🇧🇾", - "belgium": "🇧🇪", - "belize": "🇧🇿", - "benin": "🇧🇯", - "bermuda": "🇧🇲", - "bhutan": "🇧🇹", - "bolivia": "🇧🇴", - "bosnia_&_herzegovina": "🇧🇦", - "botswana": "🇧🇼", - "bouvet_island": "🇧🇻", - "brazil": "🇧🇷", - "british_indian_ocean_territory": "🇮🇴", - "british_virgin_islands": "🇻🇬", - "brunei": "🇧🇳", - "bulgaria": "🇧🇬", - "burkina_faso": "🇧🇫", - "burundi": "🇧🇮", - "cl_button": "🆑", - "cool_button": "🆒", - "cambodia": "🇰🇭", - "cameroon": "🇨🇲", - "canada": "🇨🇦", - "canary_islands": "🇮🇨", - "cancer": "♋", - "cape_verde": "🇨🇻", - "capricorn": "♑", - "caribbean_netherlands": "🇧🇶", - "cayman_islands": "🇰🇾", - "central_african_republic": "🇨🇫", - "ceuta_&_melilla": "🇪🇦", - "chad": "🇹🇩", - "chile": "🇨🇱", - "china": "🇨🇳", - "christmas_island": "🇨🇽", - "christmas_tree": "🎄", - "clipperton_island": "🇨🇵", - "cocos_(keeling)_islands": "🇨🇨", - "colombia": "🇨🇴", - "comoros": "🇰🇲", - "congo_-_brazzaville": "🇨🇬", - "congo_-_kinshasa": "🇨🇩", - "cook_islands": "🇨🇰", - "costa_rica": "🇨🇷", - "croatia": "🇭🇷", - "cuba": "🇨🇺", - "curaçao": "🇨🇼", - "cyprus": "🇨🇾", - "czechia": "🇨🇿", - "côte_d’ivoire": "🇨🇮", - "denmark": "🇩🇰", - "diego_garcia": "🇩🇬", - "djibouti": "🇩🇯", - "dominica": "🇩🇲", - "dominican_republic": "🇩🇴", - "end_arrow": "🔚", - "ecuador": "🇪🇨", - "egypt": "🇪🇬", - "el_salvador": "🇸🇻", - "england": "🏴\U000e0067\U000e0062\U000e0065\U000e006e\U000e0067\U000e007f", - "equatorial_guinea": "🇬🇶", - "eritrea": "🇪🇷", - "estonia": "🇪🇪", - "ethiopia": "🇪🇹", - "european_union": "🇪🇺", - "free_button": "🆓", - "falkland_islands": "🇫🇰", - "faroe_islands": "🇫🇴", - "fiji": "🇫🇯", - "finland": "🇫🇮", - "france": "🇫🇷", - "french_guiana": "🇬🇫", - "french_polynesia": "🇵🇫", - "french_southern_territories": "🇹🇫", - "gabon": "🇬🇦", - "gambia": "🇬🇲", - "gemini": "♊", - "georgia": "🇬🇪", - "germany": "🇩🇪", - "ghana": "🇬🇭", - "gibraltar": "🇬🇮", - "greece": "🇬🇷", - "greenland": "🇬🇱", - "grenada": "🇬🇩", - "guadeloupe": "🇬🇵", - "guam": "🇬🇺", - "guatemala": "🇬🇹", - "guernsey": "🇬🇬", - "guinea": "🇬🇳", - "guinea-bissau": "🇬🇼", - "guyana": "🇬🇾", - "haiti": "🇭🇹", - "heard_&_mcdonald_islands": "🇭🇲", - "honduras": "🇭🇳", - "hong_kong_sar_china": "🇭🇰", - "hungary": "🇭🇺", - "id_button": "🆔", - "iceland": "🇮🇸", - "india": "🇮🇳", - "indonesia": "🇮🇩", - "iran": "🇮🇷", - "iraq": "🇮🇶", - "ireland": "🇮🇪", - "isle_of_man": "🇮🇲", - "israel": "🇮🇱", - "italy": "🇮🇹", - "jamaica": "🇯🇲", - "japan": "🗾", - "japanese_acceptable_button": "🉑", - "japanese_application_button": "🈸", - "japanese_bargain_button": "🉐", - "japanese_castle": "🏯", - "japanese_congratulations_button": "㊗", - "japanese_discount_button": "🈹", - "japanese_dolls": "🎎", - "japanese_free_of_charge_button": "🈚", - "japanese_here_button": "🈁", - "japanese_monthly_amount_button": "🈷", - "japanese_no_vacancy_button": "🈵", - "japanese_not_free_of_charge_button": "🈶", - "japanese_open_for_business_button": "🈺", - "japanese_passing_grade_button": "🈴", - "japanese_post_office": "🏣", - "japanese_prohibited_button": "🈲", - "japanese_reserved_button": "🈯", - "japanese_secret_button": "㊙", - "japanese_service_charge_button": "🈂", - "japanese_symbol_for_beginner": "🔰", - "japanese_vacancy_button": "🈳", - "jersey": "🇯🇪", - "jordan": "🇯🇴", - "kazakhstan": "🇰🇿", - "kenya": "🇰🇪", - "kiribati": "🇰🇮", - "kosovo": "🇽🇰", - "kuwait": "🇰🇼", - "kyrgyzstan": "🇰🇬", - "laos": "🇱🇦", - "latvia": "🇱🇻", - "lebanon": "🇱🇧", - "leo": "♌", - "lesotho": "🇱🇸", - "liberia": "🇱🇷", - "libra": "♎", - "libya": "🇱🇾", - "liechtenstein": "🇱🇮", - "lithuania": "🇱🇹", - "luxembourg": "🇱🇺", - "macau_sar_china": "🇲🇴", - "macedonia": "🇲🇰", - "madagascar": "🇲🇬", - "malawi": "🇲🇼", - "malaysia": "🇲🇾", - "maldives": "🇲🇻", - "mali": "🇲🇱", - "malta": "🇲🇹", - "marshall_islands": "🇲🇭", - "martinique": "🇲🇶", - "mauritania": "🇲🇷", - "mauritius": "🇲🇺", - "mayotte": "🇾🇹", - "mexico": "🇲🇽", - "micronesia": "🇫🇲", - "moldova": "🇲🇩", - "monaco": "🇲🇨", - "mongolia": "🇲🇳", - "montenegro": "🇲🇪", - "montserrat": "🇲🇸", - "morocco": "🇲🇦", - "mozambique": "🇲🇿", - "mrs._claus": "🤶", - "mrs._claus_dark_skin_tone": "🤶🏿", - "mrs._claus_light_skin_tone": "🤶🏻", - "mrs._claus_medium-dark_skin_tone": "🤶🏾", - "mrs._claus_medium-light_skin_tone": "🤶🏼", - "mrs._claus_medium_skin_tone": "🤶🏽", - "myanmar_(burma)": "🇲🇲", - "new_button": "🆕", - "ng_button": "🆖", - "namibia": "🇳🇦", - "nauru": "🇳🇷", - "nepal": "🇳🇵", - "netherlands": "🇳🇱", - "new_caledonia": "🇳🇨", - "new_zealand": "🇳🇿", - "nicaragua": "🇳🇮", - "niger": "🇳🇪", - "nigeria": "🇳🇬", - "niue": "🇳🇺", - "norfolk_island": "🇳🇫", - "north_korea": "🇰🇵", - "northern_mariana_islands": "🇲🇵", - "norway": "🇳🇴", - "ok_button": "🆗", - "ok_hand": "👌", - "ok_hand_dark_skin_tone": "👌🏿", - "ok_hand_light_skin_tone": "👌🏻", - "ok_hand_medium-dark_skin_tone": "👌🏾", - "ok_hand_medium-light_skin_tone": "👌🏼", - "ok_hand_medium_skin_tone": "👌🏽", - "on!_arrow": "🔛", - "o_button_(blood_type)": "🅾", - "oman": "🇴🇲", - "ophiuchus": "⛎", - "p_button": "🅿", - "pakistan": "🇵🇰", - "palau": "🇵🇼", - "palestinian_territories": "🇵🇸", - "panama": "🇵🇦", - "papua_new_guinea": "🇵🇬", - "paraguay": "🇵🇾", - "peru": "🇵🇪", - "philippines": "🇵🇭", - "pisces": "♓", - "pitcairn_islands": "🇵🇳", - "poland": "🇵🇱", - "portugal": "🇵🇹", - "puerto_rico": "🇵🇷", - "qatar": "🇶🇦", - "romania": "🇷🇴", - "russia": "🇷🇺", - "rwanda": "🇷🇼", - "réunion": "🇷🇪", - "soon_arrow": "🔜", - "sos_button": "🆘", - "sagittarius": "♐", - "samoa": "🇼🇸", - "san_marino": "🇸🇲", - "santa_claus": "🎅", - "santa_claus_dark_skin_tone": "🎅🏿", - "santa_claus_light_skin_tone": "🎅🏻", - "santa_claus_medium-dark_skin_tone": "🎅🏾", - "santa_claus_medium-light_skin_tone": "🎅🏼", - "santa_claus_medium_skin_tone": "🎅🏽", - "saudi_arabia": "🇸🇦", - "scorpio": "♏", - "scotland": "🏴\U000e0067\U000e0062\U000e0073\U000e0063\U000e0074\U000e007f", - "senegal": "🇸🇳", - "serbia": "🇷🇸", - "seychelles": "🇸🇨", - "sierra_leone": "🇸🇱", - "singapore": "🇸🇬", - "sint_maarten": "🇸🇽", - "slovakia": "🇸🇰", - "slovenia": "🇸🇮", - "solomon_islands": "🇸🇧", - "somalia": "🇸🇴", - "south_africa": "🇿🇦", - "south_georgia_&_south_sandwich_islands": "🇬🇸", - "south_korea": "🇰🇷", - "south_sudan": "🇸🇸", - "spain": "🇪🇸", - "sri_lanka": "🇱🇰", - "st._barthélemy": "🇧🇱", - "st._helena": "🇸🇭", - "st._kitts_&_nevis": "🇰🇳", - "st._lucia": "🇱🇨", - "st._martin": "🇲🇫", - "st._pierre_&_miquelon": "🇵🇲", - "st._vincent_&_grenadines": "🇻🇨", - "statue_of_liberty": "🗽", - "sudan": "🇸🇩", - "suriname": "🇸🇷", - "svalbard_&_jan_mayen": "🇸🇯", - "swaziland": "🇸🇿", - "sweden": "🇸🇪", - "switzerland": "🇨🇭", - "syria": "🇸🇾", - "são_tomé_&_príncipe": "🇸🇹", - "t-rex": "🦖", - "top_arrow": "🔝", - "taiwan": "🇹🇼", - "tajikistan": "🇹🇯", - "tanzania": "🇹🇿", - "taurus": "♉", - "thailand": "🇹🇭", - "timor-leste": "🇹🇱", - "togo": "🇹🇬", - "tokelau": "🇹🇰", - "tokyo_tower": "🗼", - "tonga": "🇹🇴", - "trinidad_&_tobago": "🇹🇹", - "tristan_da_cunha": "🇹🇦", - "tunisia": "🇹🇳", - "turkey": "🦃", - "turkmenistan": "🇹🇲", - "turks_&_caicos_islands": "🇹🇨", - "tuvalu": "🇹🇻", - "u.s._outlying_islands": "🇺🇲", - "u.s._virgin_islands": "🇻🇮", - "up!_button": "🆙", - "uganda": "🇺🇬", - "ukraine": "🇺🇦", - "united_arab_emirates": "🇦🇪", - "united_kingdom": "🇬🇧", - "united_nations": "🇺🇳", - "united_states": "🇺🇸", - "uruguay": "🇺🇾", - "uzbekistan": "🇺🇿", - "vs_button": "🆚", - "vanuatu": "🇻🇺", - "vatican_city": "🇻🇦", - "venezuela": "🇻🇪", - "vietnam": "🇻🇳", - "virgo": "♍", - "wales": "🏴\U000e0067\U000e0062\U000e0077\U000e006c\U000e0073\U000e007f", - "wallis_&_futuna": "🇼🇫", - "western_sahara": "🇪🇭", - "yemen": "🇾🇪", - "zambia": "🇿🇲", - "zimbabwe": "🇿🇼", - "abacus": "🧮", - "adhesive_bandage": "🩹", - "admission_tickets": "🎟", - "adult": "🧑", - "adult_dark_skin_tone": "🧑🏿", - "adult_light_skin_tone": "🧑🏻", - "adult_medium-dark_skin_tone": "🧑🏾", - "adult_medium-light_skin_tone": "🧑🏼", - "adult_medium_skin_tone": "🧑🏽", - "aerial_tramway": "🚡", - "airplane": "✈", - "airplane_arrival": "🛬", - "airplane_departure": "🛫", - "alarm_clock": "⏰", - "alembic": "⚗", - "alien": "👽", - "alien_monster": "👾", - "ambulance": "🚑", - "american_football": "🏈", - "amphora": "🏺", - "anchor": "⚓", - "anger_symbol": "💢", - "angry_face": "😠", - "angry_face_with_horns": "👿", - "anguished_face": "😧", - "ant": "🐜", - "antenna_bars": "📶", - "anxious_face_with_sweat": "😰", - "articulated_lorry": "🚛", - "artist_palette": "🎨", - "astonished_face": "😲", - "atom_symbol": "⚛", - "auto_rickshaw": "🛺", - "automobile": "🚗", - "avocado": "🥑", - "axe": "🪓", - "baby": "👶", - "baby_angel": "👼", - "baby_angel_dark_skin_tone": "👼🏿", - "baby_angel_light_skin_tone": "👼🏻", - "baby_angel_medium-dark_skin_tone": "👼🏾", - "baby_angel_medium-light_skin_tone": "👼🏼", - "baby_angel_medium_skin_tone": "👼🏽", - "baby_bottle": "🍼", - "baby_chick": "🐤", - "baby_dark_skin_tone": "👶🏿", - "baby_light_skin_tone": "👶🏻", - "baby_medium-dark_skin_tone": "👶🏾", - "baby_medium-light_skin_tone": "👶🏼", - "baby_medium_skin_tone": "👶🏽", - "baby_symbol": "🚼", - "backhand_index_pointing_down": "👇", - "backhand_index_pointing_down_dark_skin_tone": "👇🏿", - "backhand_index_pointing_down_light_skin_tone": "👇🏻", - "backhand_index_pointing_down_medium-dark_skin_tone": "👇🏾", - "backhand_index_pointing_down_medium-light_skin_tone": "👇🏼", - "backhand_index_pointing_down_medium_skin_tone": "👇🏽", - "backhand_index_pointing_left": "👈", - "backhand_index_pointing_left_dark_skin_tone": "👈🏿", - "backhand_index_pointing_left_light_skin_tone": "👈🏻", - "backhand_index_pointing_left_medium-dark_skin_tone": "👈🏾", - "backhand_index_pointing_left_medium-light_skin_tone": "👈🏼", - "backhand_index_pointing_left_medium_skin_tone": "👈🏽", - "backhand_index_pointing_right": "👉", - "backhand_index_pointing_right_dark_skin_tone": "👉🏿", - "backhand_index_pointing_right_light_skin_tone": "👉🏻", - "backhand_index_pointing_right_medium-dark_skin_tone": "👉🏾", - "backhand_index_pointing_right_medium-light_skin_tone": "👉🏼", - "backhand_index_pointing_right_medium_skin_tone": "👉🏽", - "backhand_index_pointing_up": "👆", - "backhand_index_pointing_up_dark_skin_tone": "👆🏿", - "backhand_index_pointing_up_light_skin_tone": "👆🏻", - "backhand_index_pointing_up_medium-dark_skin_tone": "👆🏾", - "backhand_index_pointing_up_medium-light_skin_tone": "👆🏼", - "backhand_index_pointing_up_medium_skin_tone": "👆🏽", - "bacon": "🥓", - "badger": "🦡", - "badminton": "🏸", - "bagel": "🥯", - "baggage_claim": "🛄", - "baguette_bread": "🥖", - "balance_scale": "⚖", - "bald": "🦲", - "bald_man": "👨\u200d🦲", - "bald_woman": "👩\u200d🦲", - "ballet_shoes": "🩰", - "balloon": "🎈", - "ballot_box_with_ballot": "🗳", - "ballot_box_with_check": "☑", - "banana": "🍌", - "banjo": "🪕", - "bank": "🏦", - "bar_chart": "📊", - "barber_pole": "💈", - "baseball": "⚾", - "basket": "🧺", - "basketball": "🏀", - "bat": "🦇", - "bathtub": "🛁", - "battery": "🔋", - "beach_with_umbrella": "🏖", - "beaming_face_with_smiling_eyes": "😁", - "bear_face": "🐻", - "bearded_person": "🧔", - "bearded_person_dark_skin_tone": "🧔🏿", - "bearded_person_light_skin_tone": "🧔🏻", - "bearded_person_medium-dark_skin_tone": "🧔🏾", - "bearded_person_medium-light_skin_tone": "🧔🏼", - "bearded_person_medium_skin_tone": "🧔🏽", - "beating_heart": "💓", - "bed": "🛏", - "beer_mug": "🍺", - "bell": "🔔", - "bell_with_slash": "🔕", - "bellhop_bell": "🛎", - "bento_box": "🍱", - "beverage_box": "🧃", - "bicycle": "🚲", - "bikini": "👙", - "billed_cap": "🧢", - "biohazard": "☣", - "bird": "🐦", - "birthday_cake": "🎂", - "black_circle": "⚫", - "black_flag": "🏴", - "black_heart": "🖤", - "black_large_square": "⬛", - "black_medium-small_square": "◾", - "black_medium_square": "◼", - "black_nib": "✒", - "black_small_square": "▪", - "black_square_button": "🔲", - "blond-haired_man": "👱\u200d♂️", - "blond-haired_man_dark_skin_tone": "👱🏿\u200d♂️", - "blond-haired_man_light_skin_tone": "👱🏻\u200d♂️", - "blond-haired_man_medium-dark_skin_tone": "👱🏾\u200d♂️", - "blond-haired_man_medium-light_skin_tone": "👱🏼\u200d♂️", - "blond-haired_man_medium_skin_tone": "👱🏽\u200d♂️", - "blond-haired_person": "👱", - "blond-haired_person_dark_skin_tone": "👱🏿", - "blond-haired_person_light_skin_tone": "👱🏻", - "blond-haired_person_medium-dark_skin_tone": "👱🏾", - "blond-haired_person_medium-light_skin_tone": "👱🏼", - "blond-haired_person_medium_skin_tone": "👱🏽", - "blond-haired_woman": "👱\u200d♀️", - "blond-haired_woman_dark_skin_tone": "👱🏿\u200d♀️", - "blond-haired_woman_light_skin_tone": "👱🏻\u200d♀️", - "blond-haired_woman_medium-dark_skin_tone": "👱🏾\u200d♀️", - "blond-haired_woman_medium-light_skin_tone": "👱🏼\u200d♀️", - "blond-haired_woman_medium_skin_tone": "👱🏽\u200d♀️", - "blossom": "🌼", - "blowfish": "🐡", - "blue_book": "📘", - "blue_circle": "🔵", - "blue_heart": "💙", - "blue_square": "🟦", - "boar": "🐗", - "bomb": "💣", - "bone": "🦴", - "bookmark": "🔖", - "bookmark_tabs": "📑", - "books": "📚", - "bottle_with_popping_cork": "🍾", - "bouquet": "💐", - "bow_and_arrow": "🏹", - "bowl_with_spoon": "🥣", - "bowling": "🎳", - "boxing_glove": "🥊", - "boy": "👦", - "boy_dark_skin_tone": "👦🏿", - "boy_light_skin_tone": "👦🏻", - "boy_medium-dark_skin_tone": "👦🏾", - "boy_medium-light_skin_tone": "👦🏼", - "boy_medium_skin_tone": "👦🏽", - "brain": "🧠", - "bread": "🍞", - "breast-feeding": "🤱", - "breast-feeding_dark_skin_tone": "🤱🏿", - "breast-feeding_light_skin_tone": "🤱🏻", - "breast-feeding_medium-dark_skin_tone": "🤱🏾", - "breast-feeding_medium-light_skin_tone": "🤱🏼", - "breast-feeding_medium_skin_tone": "🤱🏽", - "brick": "🧱", - "bride_with_veil": "👰", - "bride_with_veil_dark_skin_tone": "👰🏿", - "bride_with_veil_light_skin_tone": "👰🏻", - "bride_with_veil_medium-dark_skin_tone": "👰🏾", - "bride_with_veil_medium-light_skin_tone": "👰🏼", - "bride_with_veil_medium_skin_tone": "👰🏽", - "bridge_at_night": "🌉", - "briefcase": "💼", - "briefs": "🩲", - "bright_button": "🔆", - "broccoli": "🥦", - "broken_heart": "💔", - "broom": "🧹", - "brown_circle": "🟤", - "brown_heart": "🤎", - "brown_square": "🟫", - "bug": "🐛", - "building_construction": "🏗", - "bullet_train": "🚅", - "burrito": "🌯", - "bus": "🚌", - "bus_stop": "🚏", - "bust_in_silhouette": "👤", - "busts_in_silhouette": "👥", - "butter": "🧈", - "butterfly": "🦋", - "cactus": "🌵", - "calendar": "📆", - "call_me_hand": "🤙", - "call_me_hand_dark_skin_tone": "🤙🏿", - "call_me_hand_light_skin_tone": "🤙🏻", - "call_me_hand_medium-dark_skin_tone": "🤙🏾", - "call_me_hand_medium-light_skin_tone": "🤙🏼", - "call_me_hand_medium_skin_tone": "🤙🏽", - "camel": "🐫", - "camera": "📷", - "camera_with_flash": "📸", - "camping": "🏕", - "candle": "🕯", - "candy": "🍬", - "canned_food": "🥫", - "canoe": "🛶", - "card_file_box": "🗃", - "card_index": "📇", - "card_index_dividers": "🗂", - "carousel_horse": "🎠", - "carp_streamer": "🎏", - "carrot": "🥕", - "castle": "🏰", - "cat": "🐱", - "cat_face": "🐱", - "cat_face_with_tears_of_joy": "😹", - "cat_face_with_wry_smile": "😼", - "chains": "⛓", - "chair": "🪑", - "chart_decreasing": "📉", - "chart_increasing": "📈", - "chart_increasing_with_yen": "💹", - "cheese_wedge": "🧀", - "chequered_flag": "🏁", - "cherries": "🍒", - "cherry_blossom": "🌸", - "chess_pawn": "♟", - "chestnut": "🌰", - "chicken": "🐔", - "child": "🧒", - "child_dark_skin_tone": "🧒🏿", - "child_light_skin_tone": "🧒🏻", - "child_medium-dark_skin_tone": "🧒🏾", - "child_medium-light_skin_tone": "🧒🏼", - "child_medium_skin_tone": "🧒🏽", - "children_crossing": "🚸", - "chipmunk": "🐿", - "chocolate_bar": "🍫", - "chopsticks": "🥢", - "church": "⛪", - "cigarette": "🚬", - "cinema": "🎦", - "circled_m": "Ⓜ", - "circus_tent": "🎪", - "cityscape": "🏙", - "cityscape_at_dusk": "🌆", - "clamp": "🗜", - "clapper_board": "🎬", - "clapping_hands": "👏", - "clapping_hands_dark_skin_tone": "👏🏿", - "clapping_hands_light_skin_tone": "👏🏻", - "clapping_hands_medium-dark_skin_tone": "👏🏾", - "clapping_hands_medium-light_skin_tone": "👏🏼", - "clapping_hands_medium_skin_tone": "👏🏽", - "classical_building": "🏛", - "clinking_beer_mugs": "🍻", - "clinking_glasses": "🥂", - "clipboard": "📋", - "clockwise_vertical_arrows": "🔃", - "closed_book": "📕", - "closed_mailbox_with_lowered_flag": "📪", - "closed_mailbox_with_raised_flag": "📫", - "closed_umbrella": "🌂", - "cloud": "☁", - "cloud_with_lightning": "🌩", - "cloud_with_lightning_and_rain": "⛈", - "cloud_with_rain": "🌧", - "cloud_with_snow": "🌨", - "clown_face": "🤡", - "club_suit": "♣", - "clutch_bag": "👝", - "coat": "🧥", - "cocktail_glass": "🍸", - "coconut": "🥥", - "coffin": "⚰", - "cold_face": "🥶", - "collision": "💥", - "comet": "☄", - "compass": "🧭", - "computer_disk": "💽", - "computer_mouse": "🖱", - "confetti_ball": "🎊", - "confounded_face": "😖", - "confused_face": "😕", - "construction": "🚧", - "construction_worker": "👷", - "construction_worker_dark_skin_tone": "👷🏿", - "construction_worker_light_skin_tone": "👷🏻", - "construction_worker_medium-dark_skin_tone": "👷🏾", - "construction_worker_medium-light_skin_tone": "👷🏼", - "construction_worker_medium_skin_tone": "👷🏽", - "control_knobs": "🎛", - "convenience_store": "🏪", - "cooked_rice": "🍚", - "cookie": "🍪", - "cooking": "🍳", - "copyright": "©", - "couch_and_lamp": "🛋", - "counterclockwise_arrows_button": "🔄", - "couple_with_heart": "💑", - "couple_with_heart_man_man": "👨\u200d❤️\u200d👨", - "couple_with_heart_woman_man": "👩\u200d❤️\u200d👨", - "couple_with_heart_woman_woman": "👩\u200d❤️\u200d👩", - "cow": "🐮", - "cow_face": "🐮", - "cowboy_hat_face": "🤠", - "crab": "🦀", - "crayon": "🖍", - "credit_card": "💳", - "crescent_moon": "🌙", - "cricket": "🦗", - "cricket_game": "🏏", - "crocodile": "🐊", - "croissant": "🥐", - "cross_mark": "❌", - "cross_mark_button": "❎", - "crossed_fingers": "🤞", - "crossed_fingers_dark_skin_tone": "🤞🏿", - "crossed_fingers_light_skin_tone": "🤞🏻", - "crossed_fingers_medium-dark_skin_tone": "🤞🏾", - "crossed_fingers_medium-light_skin_tone": "🤞🏼", - "crossed_fingers_medium_skin_tone": "🤞🏽", - "crossed_flags": "🎌", - "crossed_swords": "⚔", - "crown": "👑", - "crying_cat_face": "😿", - "crying_face": "😢", - "crystal_ball": "🔮", - "cucumber": "🥒", - "cupcake": "🧁", - "cup_with_straw": "🥤", - "curling_stone": "🥌", - "curly_hair": "🦱", - "curly-haired_man": "👨\u200d🦱", - "curly-haired_woman": "👩\u200d🦱", - "curly_loop": "➰", - "currency_exchange": "💱", - "curry_rice": "🍛", - "custard": "🍮", - "customs": "🛃", - "cut_of_meat": "🥩", - "cyclone": "🌀", - "dagger": "🗡", - "dango": "🍡", - "dashing_away": "💨", - "deaf_person": "🧏", - "deciduous_tree": "🌳", - "deer": "🦌", - "delivery_truck": "🚚", - "department_store": "🏬", - "derelict_house": "🏚", - "desert": "🏜", - "desert_island": "🏝", - "desktop_computer": "🖥", - "detective": "🕵", - "detective_dark_skin_tone": "🕵🏿", - "detective_light_skin_tone": "🕵🏻", - "detective_medium-dark_skin_tone": "🕵🏾", - "detective_medium-light_skin_tone": "🕵🏼", - "detective_medium_skin_tone": "🕵🏽", - "diamond_suit": "♦", - "diamond_with_a_dot": "💠", - "dim_button": "🔅", - "direct_hit": "🎯", - "disappointed_face": "😞", - "diving_mask": "🤿", - "diya_lamp": "🪔", - "dizzy": "💫", - "dizzy_face": "😵", - "dna": "🧬", - "dog": "🐶", - "dog_face": "🐶", - "dollar_banknote": "💵", - "dolphin": "🐬", - "door": "🚪", - "dotted_six-pointed_star": "🔯", - "double_curly_loop": "➿", - "double_exclamation_mark": "‼", - "doughnut": "🍩", - "dove": "🕊", - "down-left_arrow": "↙", - "down-right_arrow": "↘", - "down_arrow": "⬇", - "downcast_face_with_sweat": "😓", - "downwards_button": "🔽", - "dragon": "🐉", - "dragon_face": "🐲", - "dress": "👗", - "drooling_face": "🤤", - "drop_of_blood": "🩸", - "droplet": "💧", - "drum": "🥁", - "duck": "🦆", - "dumpling": "🥟", - "dvd": "📀", - "e-mail": "📧", - "eagle": "🦅", - "ear": "👂", - "ear_dark_skin_tone": "👂🏿", - "ear_light_skin_tone": "👂🏻", - "ear_medium-dark_skin_tone": "👂🏾", - "ear_medium-light_skin_tone": "👂🏼", - "ear_medium_skin_tone": "👂🏽", - "ear_of_corn": "🌽", - "ear_with_hearing_aid": "🦻", - "egg": "🍳", - "eggplant": "🍆", - "eight-pointed_star": "✴", - "eight-spoked_asterisk": "✳", - "eight-thirty": "🕣", - "eight_o’clock": "🕗", - "eject_button": "⏏", - "electric_plug": "🔌", - "elephant": "🐘", - "eleven-thirty": "🕦", - "eleven_o’clock": "🕚", - "elf": "🧝", - "elf_dark_skin_tone": "🧝🏿", - "elf_light_skin_tone": "🧝🏻", - "elf_medium-dark_skin_tone": "🧝🏾", - "elf_medium-light_skin_tone": "🧝🏼", - "elf_medium_skin_tone": "🧝🏽", - "envelope": "✉", - "envelope_with_arrow": "📩", - "euro_banknote": "💶", - "evergreen_tree": "🌲", - "ewe": "🐑", - "exclamation_mark": "❗", - "exclamation_question_mark": "⁉", - "exploding_head": "🤯", - "expressionless_face": "😑", - "eye": "👁", - "eye_in_speech_bubble": "👁️\u200d🗨️", - "eyes": "👀", - "face_blowing_a_kiss": "😘", - "face_savoring_food": "😋", - "face_screaming_in_fear": "😱", - "face_vomiting": "🤮", - "face_with_hand_over_mouth": "🤭", - "face_with_head-bandage": "🤕", - "face_with_medical_mask": "😷", - "face_with_monocle": "🧐", - "face_with_open_mouth": "😮", - "face_with_raised_eyebrow": "🤨", - "face_with_rolling_eyes": "🙄", - "face_with_steam_from_nose": "😤", - "face_with_symbols_on_mouth": "🤬", - "face_with_tears_of_joy": "😂", - "face_with_thermometer": "🤒", - "face_with_tongue": "😛", - "face_without_mouth": "😶", - "factory": "🏭", - "fairy": "🧚", - "fairy_dark_skin_tone": "🧚🏿", - "fairy_light_skin_tone": "🧚🏻", - "fairy_medium-dark_skin_tone": "🧚🏾", - "fairy_medium-light_skin_tone": "🧚🏼", - "fairy_medium_skin_tone": "🧚🏽", - "falafel": "🧆", - "fallen_leaf": "🍂", - "family": "👪", - "family_man_boy": "👨\u200d👦", - "family_man_boy_boy": "👨\u200d👦\u200d👦", - "family_man_girl": "👨\u200d👧", - "family_man_girl_boy": "👨\u200d👧\u200d👦", - "family_man_girl_girl": "👨\u200d👧\u200d👧", - "family_man_man_boy": "👨\u200d👨\u200d👦", - "family_man_man_boy_boy": "👨\u200d👨\u200d👦\u200d👦", - "family_man_man_girl": "👨\u200d👨\u200d👧", - "family_man_man_girl_boy": "👨\u200d👨\u200d👧\u200d👦", - "family_man_man_girl_girl": "👨\u200d👨\u200d👧\u200d👧", - "family_man_woman_boy": "👨\u200d👩\u200d👦", - "family_man_woman_boy_boy": "👨\u200d👩\u200d👦\u200d👦", - "family_man_woman_girl": "👨\u200d👩\u200d👧", - "family_man_woman_girl_boy": "👨\u200d👩\u200d👧\u200d👦", - "family_man_woman_girl_girl": "👨\u200d👩\u200d👧\u200d👧", - "family_woman_boy": "👩\u200d👦", - "family_woman_boy_boy": "👩\u200d👦\u200d👦", - "family_woman_girl": "👩\u200d👧", - "family_woman_girl_boy": "👩\u200d👧\u200d👦", - "family_woman_girl_girl": "👩\u200d👧\u200d👧", - "family_woman_woman_boy": "👩\u200d👩\u200d👦", - "family_woman_woman_boy_boy": "👩\u200d👩\u200d👦\u200d👦", - "family_woman_woman_girl": "👩\u200d👩\u200d👧", - "family_woman_woman_girl_boy": "👩\u200d👩\u200d👧\u200d👦", - "family_woman_woman_girl_girl": "👩\u200d👩\u200d👧\u200d👧", - "fast-forward_button": "⏩", - "fast_down_button": "⏬", - "fast_reverse_button": "⏪", - "fast_up_button": "⏫", - "fax_machine": "📠", - "fearful_face": "😨", - "female_sign": "♀", - "ferris_wheel": "🎡", - "ferry": "⛴", - "field_hockey": "🏑", - "file_cabinet": "🗄", - "file_folder": "📁", - "film_frames": "🎞", - "film_projector": "📽", - "fire": "🔥", - "fire_extinguisher": "🧯", - "firecracker": "🧨", - "fire_engine": "🚒", - "fireworks": "🎆", - "first_quarter_moon": "🌓", - "first_quarter_moon_face": "🌛", - "fish": "🐟", - "fish_cake_with_swirl": "🍥", - "fishing_pole": "🎣", - "five-thirty": "🕠", - "five_o’clock": "🕔", - "flag_in_hole": "⛳", - "flamingo": "🦩", - "flashlight": "🔦", - "flat_shoe": "🥿", - "fleur-de-lis": "⚜", - "flexed_biceps": "💪", - "flexed_biceps_dark_skin_tone": "💪🏿", - "flexed_biceps_light_skin_tone": "💪🏻", - "flexed_biceps_medium-dark_skin_tone": "💪🏾", - "flexed_biceps_medium-light_skin_tone": "💪🏼", - "flexed_biceps_medium_skin_tone": "💪🏽", - "floppy_disk": "💾", - "flower_playing_cards": "🎴", - "flushed_face": "😳", - "flying_disc": "🥏", - "flying_saucer": "🛸", - "fog": "🌫", - "foggy": "🌁", - "folded_hands": "🙏", - "folded_hands_dark_skin_tone": "🙏🏿", - "folded_hands_light_skin_tone": "🙏🏻", - "folded_hands_medium-dark_skin_tone": "🙏🏾", - "folded_hands_medium-light_skin_tone": "🙏🏼", - "folded_hands_medium_skin_tone": "🙏🏽", - "foot": "🦶", - "footprints": "👣", - "fork_and_knife": "🍴", - "fork_and_knife_with_plate": "🍽", - "fortune_cookie": "🥠", - "fountain": "⛲", - "fountain_pen": "🖋", - "four-thirty": "🕟", - "four_leaf_clover": "🍀", - "four_o’clock": "🕓", - "fox_face": "🦊", - "framed_picture": "🖼", - "french_fries": "🍟", - "fried_shrimp": "🍤", - "frog_face": "🐸", - "front-facing_baby_chick": "🐥", - "frowning_face": "☹", - "frowning_face_with_open_mouth": "😦", - "fuel_pump": "⛽", - "full_moon": "🌕", - "full_moon_face": "🌝", - "funeral_urn": "⚱", - "game_die": "🎲", - "garlic": "🧄", - "gear": "⚙", - "gem_stone": "💎", - "genie": "🧞", - "ghost": "👻", - "giraffe": "🦒", - "girl": "👧", - "girl_dark_skin_tone": "👧🏿", - "girl_light_skin_tone": "👧🏻", - "girl_medium-dark_skin_tone": "👧🏾", - "girl_medium-light_skin_tone": "👧🏼", - "girl_medium_skin_tone": "👧🏽", - "glass_of_milk": "🥛", - "glasses": "👓", - "globe_showing_americas": "🌎", - "globe_showing_asia-australia": "🌏", - "globe_showing_europe-africa": "🌍", - "globe_with_meridians": "🌐", - "gloves": "🧤", - "glowing_star": "🌟", - "goal_net": "🥅", - "goat": "🐐", - "goblin": "👺", - "goggles": "🥽", - "gorilla": "🦍", - "graduation_cap": "🎓", - "grapes": "🍇", - "green_apple": "🍏", - "green_book": "📗", - "green_circle": "🟢", - "green_heart": "💚", - "green_salad": "🥗", - "green_square": "🟩", - "grimacing_face": "😬", - "grinning_cat_face": "😺", - "grinning_cat_face_with_smiling_eyes": "😸", - "grinning_face": "😀", - "grinning_face_with_big_eyes": "😃", - "grinning_face_with_smiling_eyes": "😄", - "grinning_face_with_sweat": "😅", - "grinning_squinting_face": "😆", - "growing_heart": "💗", - "guard": "💂", - "guard_dark_skin_tone": "💂🏿", - "guard_light_skin_tone": "💂🏻", - "guard_medium-dark_skin_tone": "💂🏾", - "guard_medium-light_skin_tone": "💂🏼", - "guard_medium_skin_tone": "💂🏽", - "guide_dog": "🦮", - "guitar": "🎸", - "hamburger": "🍔", - "hammer": "🔨", - "hammer_and_pick": "⚒", - "hammer_and_wrench": "🛠", - "hamster_face": "🐹", - "hand_with_fingers_splayed": "🖐", - "hand_with_fingers_splayed_dark_skin_tone": "🖐🏿", - "hand_with_fingers_splayed_light_skin_tone": "🖐🏻", - "hand_with_fingers_splayed_medium-dark_skin_tone": "🖐🏾", - "hand_with_fingers_splayed_medium-light_skin_tone": "🖐🏼", - "hand_with_fingers_splayed_medium_skin_tone": "🖐🏽", - "handbag": "👜", - "handshake": "🤝", - "hatching_chick": "🐣", - "headphone": "🎧", - "hear-no-evil_monkey": "🙉", - "heart_decoration": "💟", - "heart_suit": "♥", - "heart_with_arrow": "💘", - "heart_with_ribbon": "💝", - "heavy_check_mark": "✔", - "heavy_division_sign": "➗", - "heavy_dollar_sign": "💲", - "heavy_heart_exclamation": "❣", - "heavy_large_circle": "⭕", - "heavy_minus_sign": "➖", - "heavy_multiplication_x": "✖", - "heavy_plus_sign": "➕", - "hedgehog": "🦔", - "helicopter": "🚁", - "herb": "🌿", - "hibiscus": "🌺", - "high-heeled_shoe": "👠", - "high-speed_train": "🚄", - "high_voltage": "⚡", - "hiking_boot": "🥾", - "hindu_temple": "🛕", - "hippopotamus": "🦛", - "hole": "🕳", - "honey_pot": "🍯", - "honeybee": "🐝", - "horizontal_traffic_light": "🚥", - "horse": "🐴", - "horse_face": "🐴", - "horse_racing": "🏇", - "horse_racing_dark_skin_tone": "🏇🏿", - "horse_racing_light_skin_tone": "🏇🏻", - "horse_racing_medium-dark_skin_tone": "🏇🏾", - "horse_racing_medium-light_skin_tone": "🏇🏼", - "horse_racing_medium_skin_tone": "🏇🏽", - "hospital": "🏥", - "hot_beverage": "☕", - "hot_dog": "🌭", - "hot_face": "🥵", - "hot_pepper": "🌶", - "hot_springs": "♨", - "hotel": "🏨", - "hourglass_done": "⌛", - "hourglass_not_done": "⏳", - "house": "🏠", - "house_with_garden": "🏡", - "houses": "🏘", - "hugging_face": "🤗", - "hundred_points": "💯", - "hushed_face": "😯", - "ice": "🧊", - "ice_cream": "🍨", - "ice_hockey": "🏒", - "ice_skate": "⛸", - "inbox_tray": "📥", - "incoming_envelope": "📨", - "index_pointing_up": "☝", - "index_pointing_up_dark_skin_tone": "☝🏿", - "index_pointing_up_light_skin_tone": "☝🏻", - "index_pointing_up_medium-dark_skin_tone": "☝🏾", - "index_pointing_up_medium-light_skin_tone": "☝🏼", - "index_pointing_up_medium_skin_tone": "☝🏽", - "infinity": "♾", - "information": "ℹ", - "input_latin_letters": "🔤", - "input_latin_lowercase": "🔡", - "input_latin_uppercase": "🔠", - "input_numbers": "🔢", - "input_symbols": "🔣", - "jack-o-lantern": "🎃", - "jeans": "👖", - "jigsaw": "🧩", - "joker": "🃏", - "joystick": "🕹", - "kaaba": "🕋", - "kangaroo": "🦘", - "key": "🔑", - "keyboard": "⌨", - "keycap_#": "#️⃣", - "keycap_*": "*️⃣", - "keycap_0": "0️⃣", - "keycap_1": "1️⃣", - "keycap_10": "🔟", - "keycap_2": "2️⃣", - "keycap_3": "3️⃣", - "keycap_4": "4️⃣", - "keycap_5": "5️⃣", - "keycap_6": "6️⃣", - "keycap_7": "7️⃣", - "keycap_8": "8️⃣", - "keycap_9": "9️⃣", - "kick_scooter": "🛴", - "kimono": "👘", - "kiss": "💋", - "kiss_man_man": "👨\u200d❤️\u200d💋\u200d👨", - "kiss_mark": "💋", - "kiss_woman_man": "👩\u200d❤️\u200d💋\u200d👨", - "kiss_woman_woman": "👩\u200d❤️\u200d💋\u200d👩", - "kissing_cat_face": "😽", - "kissing_face": "😗", - "kissing_face_with_closed_eyes": "😚", - "kissing_face_with_smiling_eyes": "😙", - "kitchen_knife": "🔪", - "kite": "🪁", - "kiwi_fruit": "🥝", - "koala": "🐨", - "lab_coat": "🥼", - "label": "🏷", - "lacrosse": "🥍", - "lady_beetle": "🐞", - "laptop_computer": "💻", - "large_blue_diamond": "🔷", - "large_orange_diamond": "🔶", - "last_quarter_moon": "🌗", - "last_quarter_moon_face": "🌜", - "last_track_button": "⏮", - "latin_cross": "✝", - "leaf_fluttering_in_wind": "🍃", - "leafy_green": "🥬", - "ledger": "📒", - "left-facing_fist": "🤛", - "left-facing_fist_dark_skin_tone": "🤛🏿", - "left-facing_fist_light_skin_tone": "🤛🏻", - "left-facing_fist_medium-dark_skin_tone": "🤛🏾", - "left-facing_fist_medium-light_skin_tone": "🤛🏼", - "left-facing_fist_medium_skin_tone": "🤛🏽", - "left-right_arrow": "↔", - "left_arrow": "⬅", - "left_arrow_curving_right": "↪", - "left_luggage": "🛅", - "left_speech_bubble": "🗨", - "leg": "🦵", - "lemon": "🍋", - "leopard": "🐆", - "level_slider": "🎚", - "light_bulb": "💡", - "light_rail": "🚈", - "link": "🔗", - "linked_paperclips": "🖇", - "lion_face": "🦁", - "lipstick": "💄", - "litter_in_bin_sign": "🚮", - "lizard": "🦎", - "llama": "🦙", - "lobster": "🦞", - "locked": "🔒", - "locked_with_key": "🔐", - "locked_with_pen": "🔏", - "locomotive": "🚂", - "lollipop": "🍭", - "lotion_bottle": "🧴", - "loudly_crying_face": "😭", - "loudspeaker": "📢", - "love-you_gesture": "🤟", - "love-you_gesture_dark_skin_tone": "🤟🏿", - "love-you_gesture_light_skin_tone": "🤟🏻", - "love-you_gesture_medium-dark_skin_tone": "🤟🏾", - "love-you_gesture_medium-light_skin_tone": "🤟🏼", - "love-you_gesture_medium_skin_tone": "🤟🏽", - "love_hotel": "🏩", - "love_letter": "💌", - "luggage": "🧳", - "lying_face": "🤥", - "mage": "🧙", - "mage_dark_skin_tone": "🧙🏿", - "mage_light_skin_tone": "🧙🏻", - "mage_medium-dark_skin_tone": "🧙🏾", - "mage_medium-light_skin_tone": "🧙🏼", - "mage_medium_skin_tone": "🧙🏽", - "magnet": "🧲", - "magnifying_glass_tilted_left": "🔍", - "magnifying_glass_tilted_right": "🔎", - "mahjong_red_dragon": "🀄", - "male_sign": "♂", - "man": "👨", - "man_and_woman_holding_hands": "👫", - "man_artist": "👨\u200d🎨", - "man_artist_dark_skin_tone": "👨🏿\u200d🎨", - "man_artist_light_skin_tone": "👨🏻\u200d🎨", - "man_artist_medium-dark_skin_tone": "👨🏾\u200d🎨", - "man_artist_medium-light_skin_tone": "👨🏼\u200d🎨", - "man_artist_medium_skin_tone": "👨🏽\u200d🎨", - "man_astronaut": "👨\u200d🚀", - "man_astronaut_dark_skin_tone": "👨🏿\u200d🚀", - "man_astronaut_light_skin_tone": "👨🏻\u200d🚀", - "man_astronaut_medium-dark_skin_tone": "👨🏾\u200d🚀", - "man_astronaut_medium-light_skin_tone": "👨🏼\u200d🚀", - "man_astronaut_medium_skin_tone": "👨🏽\u200d🚀", - "man_biking": "🚴\u200d♂️", - "man_biking_dark_skin_tone": "🚴🏿\u200d♂️", - "man_biking_light_skin_tone": "🚴🏻\u200d♂️", - "man_biking_medium-dark_skin_tone": "🚴🏾\u200d♂️", - "man_biking_medium-light_skin_tone": "🚴🏼\u200d♂️", - "man_biking_medium_skin_tone": "🚴🏽\u200d♂️", - "man_bouncing_ball": "⛹️\u200d♂️", - "man_bouncing_ball_dark_skin_tone": "⛹🏿\u200d♂️", - "man_bouncing_ball_light_skin_tone": "⛹🏻\u200d♂️", - "man_bouncing_ball_medium-dark_skin_tone": "⛹🏾\u200d♂️", - "man_bouncing_ball_medium-light_skin_tone": "⛹🏼\u200d♂️", - "man_bouncing_ball_medium_skin_tone": "⛹🏽\u200d♂️", - "man_bowing": "🙇\u200d♂️", - "man_bowing_dark_skin_tone": "🙇🏿\u200d♂️", - "man_bowing_light_skin_tone": "🙇🏻\u200d♂️", - "man_bowing_medium-dark_skin_tone": "🙇🏾\u200d♂️", - "man_bowing_medium-light_skin_tone": "🙇🏼\u200d♂️", - "man_bowing_medium_skin_tone": "🙇🏽\u200d♂️", - "man_cartwheeling": "🤸\u200d♂️", - "man_cartwheeling_dark_skin_tone": "🤸🏿\u200d♂️", - "man_cartwheeling_light_skin_tone": "🤸🏻\u200d♂️", - "man_cartwheeling_medium-dark_skin_tone": "🤸🏾\u200d♂️", - "man_cartwheeling_medium-light_skin_tone": "🤸🏼\u200d♂️", - "man_cartwheeling_medium_skin_tone": "🤸🏽\u200d♂️", - "man_climbing": "🧗\u200d♂️", - "man_climbing_dark_skin_tone": "🧗🏿\u200d♂️", - "man_climbing_light_skin_tone": "🧗🏻\u200d♂️", - "man_climbing_medium-dark_skin_tone": "🧗🏾\u200d♂️", - "man_climbing_medium-light_skin_tone": "🧗🏼\u200d♂️", - "man_climbing_medium_skin_tone": "🧗🏽\u200d♂️", - "man_construction_worker": "👷\u200d♂️", - "man_construction_worker_dark_skin_tone": "👷🏿\u200d♂️", - "man_construction_worker_light_skin_tone": "👷🏻\u200d♂️", - "man_construction_worker_medium-dark_skin_tone": "👷🏾\u200d♂️", - "man_construction_worker_medium-light_skin_tone": "👷🏼\u200d♂️", - "man_construction_worker_medium_skin_tone": "👷🏽\u200d♂️", - "man_cook": "👨\u200d🍳", - "man_cook_dark_skin_tone": "👨🏿\u200d🍳", - "man_cook_light_skin_tone": "👨🏻\u200d🍳", - "man_cook_medium-dark_skin_tone": "👨🏾\u200d🍳", - "man_cook_medium-light_skin_tone": "👨🏼\u200d🍳", - "man_cook_medium_skin_tone": "👨🏽\u200d🍳", - "man_dancing": "🕺", - "man_dancing_dark_skin_tone": "🕺🏿", - "man_dancing_light_skin_tone": "🕺🏻", - "man_dancing_medium-dark_skin_tone": "🕺🏾", - "man_dancing_medium-light_skin_tone": "🕺🏼", - "man_dancing_medium_skin_tone": "🕺🏽", - "man_dark_skin_tone": "👨🏿", - "man_detective": "🕵️\u200d♂️", - "man_detective_dark_skin_tone": "🕵🏿\u200d♂️", - "man_detective_light_skin_tone": "🕵🏻\u200d♂️", - "man_detective_medium-dark_skin_tone": "🕵🏾\u200d♂️", - "man_detective_medium-light_skin_tone": "🕵🏼\u200d♂️", - "man_detective_medium_skin_tone": "🕵🏽\u200d♂️", - "man_elf": "🧝\u200d♂️", - "man_elf_dark_skin_tone": "🧝🏿\u200d♂️", - "man_elf_light_skin_tone": "🧝🏻\u200d♂️", - "man_elf_medium-dark_skin_tone": "🧝🏾\u200d♂️", - "man_elf_medium-light_skin_tone": "🧝🏼\u200d♂️", - "man_elf_medium_skin_tone": "🧝🏽\u200d♂️", - "man_facepalming": "🤦\u200d♂️", - "man_facepalming_dark_skin_tone": "🤦🏿\u200d♂️", - "man_facepalming_light_skin_tone": "🤦🏻\u200d♂️", - "man_facepalming_medium-dark_skin_tone": "🤦🏾\u200d♂️", - "man_facepalming_medium-light_skin_tone": "🤦🏼\u200d♂️", - "man_facepalming_medium_skin_tone": "🤦🏽\u200d♂️", - "man_factory_worker": "👨\u200d🏭", - "man_factory_worker_dark_skin_tone": "👨🏿\u200d🏭", - "man_factory_worker_light_skin_tone": "👨🏻\u200d🏭", - "man_factory_worker_medium-dark_skin_tone": "👨🏾\u200d🏭", - "man_factory_worker_medium-light_skin_tone": "👨🏼\u200d🏭", - "man_factory_worker_medium_skin_tone": "👨🏽\u200d🏭", - "man_fairy": "🧚\u200d♂️", - "man_fairy_dark_skin_tone": "🧚🏿\u200d♂️", - "man_fairy_light_skin_tone": "🧚🏻\u200d♂️", - "man_fairy_medium-dark_skin_tone": "🧚🏾\u200d♂️", - "man_fairy_medium-light_skin_tone": "🧚🏼\u200d♂️", - "man_fairy_medium_skin_tone": "🧚🏽\u200d♂️", - "man_farmer": "👨\u200d🌾", - "man_farmer_dark_skin_tone": "👨🏿\u200d🌾", - "man_farmer_light_skin_tone": "👨🏻\u200d🌾", - "man_farmer_medium-dark_skin_tone": "👨🏾\u200d🌾", - "man_farmer_medium-light_skin_tone": "👨🏼\u200d🌾", - "man_farmer_medium_skin_tone": "👨🏽\u200d🌾", - "man_firefighter": "👨\u200d🚒", - "man_firefighter_dark_skin_tone": "👨🏿\u200d🚒", - "man_firefighter_light_skin_tone": "👨🏻\u200d🚒", - "man_firefighter_medium-dark_skin_tone": "👨🏾\u200d🚒", - "man_firefighter_medium-light_skin_tone": "👨🏼\u200d🚒", - "man_firefighter_medium_skin_tone": "👨🏽\u200d🚒", - "man_frowning": "🙍\u200d♂️", - "man_frowning_dark_skin_tone": "🙍🏿\u200d♂️", - "man_frowning_light_skin_tone": "🙍🏻\u200d♂️", - "man_frowning_medium-dark_skin_tone": "🙍🏾\u200d♂️", - "man_frowning_medium-light_skin_tone": "🙍🏼\u200d♂️", - "man_frowning_medium_skin_tone": "🙍🏽\u200d♂️", - "man_genie": "🧞\u200d♂️", - "man_gesturing_no": "🙅\u200d♂️", - "man_gesturing_no_dark_skin_tone": "🙅🏿\u200d♂️", - "man_gesturing_no_light_skin_tone": "🙅🏻\u200d♂️", - "man_gesturing_no_medium-dark_skin_tone": "🙅🏾\u200d♂️", - "man_gesturing_no_medium-light_skin_tone": "🙅🏼\u200d♂️", - "man_gesturing_no_medium_skin_tone": "🙅🏽\u200d♂️", - "man_gesturing_ok": "🙆\u200d♂️", - "man_gesturing_ok_dark_skin_tone": "🙆🏿\u200d♂️", - "man_gesturing_ok_light_skin_tone": "🙆🏻\u200d♂️", - "man_gesturing_ok_medium-dark_skin_tone": "🙆🏾\u200d♂️", - "man_gesturing_ok_medium-light_skin_tone": "🙆🏼\u200d♂️", - "man_gesturing_ok_medium_skin_tone": "🙆🏽\u200d♂️", - "man_getting_haircut": "💇\u200d♂️", - "man_getting_haircut_dark_skin_tone": "💇🏿\u200d♂️", - "man_getting_haircut_light_skin_tone": "💇🏻\u200d♂️", - "man_getting_haircut_medium-dark_skin_tone": "💇🏾\u200d♂️", - "man_getting_haircut_medium-light_skin_tone": "💇🏼\u200d♂️", - "man_getting_haircut_medium_skin_tone": "💇🏽\u200d♂️", - "man_getting_massage": "💆\u200d♂️", - "man_getting_massage_dark_skin_tone": "💆🏿\u200d♂️", - "man_getting_massage_light_skin_tone": "💆🏻\u200d♂️", - "man_getting_massage_medium-dark_skin_tone": "💆🏾\u200d♂️", - "man_getting_massage_medium-light_skin_tone": "💆🏼\u200d♂️", - "man_getting_massage_medium_skin_tone": "💆🏽\u200d♂️", - "man_golfing": "🏌️\u200d♂️", - "man_golfing_dark_skin_tone": "🏌🏿\u200d♂️", - "man_golfing_light_skin_tone": "🏌🏻\u200d♂️", - "man_golfing_medium-dark_skin_tone": "🏌🏾\u200d♂️", - "man_golfing_medium-light_skin_tone": "🏌🏼\u200d♂️", - "man_golfing_medium_skin_tone": "🏌🏽\u200d♂️", - "man_guard": "💂\u200d♂️", - "man_guard_dark_skin_tone": "💂🏿\u200d♂️", - "man_guard_light_skin_tone": "💂🏻\u200d♂️", - "man_guard_medium-dark_skin_tone": "💂🏾\u200d♂️", - "man_guard_medium-light_skin_tone": "💂🏼\u200d♂️", - "man_guard_medium_skin_tone": "💂🏽\u200d♂️", - "man_health_worker": "👨\u200d⚕️", - "man_health_worker_dark_skin_tone": "👨🏿\u200d⚕️", - "man_health_worker_light_skin_tone": "👨🏻\u200d⚕️", - "man_health_worker_medium-dark_skin_tone": "👨🏾\u200d⚕️", - "man_health_worker_medium-light_skin_tone": "👨🏼\u200d⚕️", - "man_health_worker_medium_skin_tone": "👨🏽\u200d⚕️", - "man_in_lotus_position": "🧘\u200d♂️", - "man_in_lotus_position_dark_skin_tone": "🧘🏿\u200d♂️", - "man_in_lotus_position_light_skin_tone": "🧘🏻\u200d♂️", - "man_in_lotus_position_medium-dark_skin_tone": "🧘🏾\u200d♂️", - "man_in_lotus_position_medium-light_skin_tone": "🧘🏼\u200d♂️", - "man_in_lotus_position_medium_skin_tone": "🧘🏽\u200d♂️", - "man_in_manual_wheelchair": "👨\u200d🦽", - "man_in_motorized_wheelchair": "👨\u200d🦼", - "man_in_steamy_room": "🧖\u200d♂️", - "man_in_steamy_room_dark_skin_tone": "🧖🏿\u200d♂️", - "man_in_steamy_room_light_skin_tone": "🧖🏻\u200d♂️", - "man_in_steamy_room_medium-dark_skin_tone": "🧖🏾\u200d♂️", - "man_in_steamy_room_medium-light_skin_tone": "🧖🏼\u200d♂️", - "man_in_steamy_room_medium_skin_tone": "🧖🏽\u200d♂️", - "man_in_suit_levitating": "🕴", - "man_in_suit_levitating_dark_skin_tone": "🕴🏿", - "man_in_suit_levitating_light_skin_tone": "🕴🏻", - "man_in_suit_levitating_medium-dark_skin_tone": "🕴🏾", - "man_in_suit_levitating_medium-light_skin_tone": "🕴🏼", - "man_in_suit_levitating_medium_skin_tone": "🕴🏽", - "man_in_tuxedo": "🤵", - "man_in_tuxedo_dark_skin_tone": "🤵🏿", - "man_in_tuxedo_light_skin_tone": "🤵🏻", - "man_in_tuxedo_medium-dark_skin_tone": "🤵🏾", - "man_in_tuxedo_medium-light_skin_tone": "🤵🏼", - "man_in_tuxedo_medium_skin_tone": "🤵🏽", - "man_judge": "👨\u200d⚖️", - "man_judge_dark_skin_tone": "👨🏿\u200d⚖️", - "man_judge_light_skin_tone": "👨🏻\u200d⚖️", - "man_judge_medium-dark_skin_tone": "👨🏾\u200d⚖️", - "man_judge_medium-light_skin_tone": "👨🏼\u200d⚖️", - "man_judge_medium_skin_tone": "👨🏽\u200d⚖️", - "man_juggling": "🤹\u200d♂️", - "man_juggling_dark_skin_tone": "🤹🏿\u200d♂️", - "man_juggling_light_skin_tone": "🤹🏻\u200d♂️", - "man_juggling_medium-dark_skin_tone": "🤹🏾\u200d♂️", - "man_juggling_medium-light_skin_tone": "🤹🏼\u200d♂️", - "man_juggling_medium_skin_tone": "🤹🏽\u200d♂️", - "man_lifting_weights": "🏋️\u200d♂️", - "man_lifting_weights_dark_skin_tone": "🏋🏿\u200d♂️", - "man_lifting_weights_light_skin_tone": "🏋🏻\u200d♂️", - "man_lifting_weights_medium-dark_skin_tone": "🏋🏾\u200d♂️", - "man_lifting_weights_medium-light_skin_tone": "🏋🏼\u200d♂️", - "man_lifting_weights_medium_skin_tone": "🏋🏽\u200d♂️", - "man_light_skin_tone": "👨🏻", - "man_mage": "🧙\u200d♂️", - "man_mage_dark_skin_tone": "🧙🏿\u200d♂️", - "man_mage_light_skin_tone": "🧙🏻\u200d♂️", - "man_mage_medium-dark_skin_tone": "🧙🏾\u200d♂️", - "man_mage_medium-light_skin_tone": "🧙🏼\u200d♂️", - "man_mage_medium_skin_tone": "🧙🏽\u200d♂️", - "man_mechanic": "👨\u200d🔧", - "man_mechanic_dark_skin_tone": "👨🏿\u200d🔧", - "man_mechanic_light_skin_tone": "👨🏻\u200d🔧", - "man_mechanic_medium-dark_skin_tone": "👨🏾\u200d🔧", - "man_mechanic_medium-light_skin_tone": "👨🏼\u200d🔧", - "man_mechanic_medium_skin_tone": "👨🏽\u200d🔧", - "man_medium-dark_skin_tone": "👨🏾", - "man_medium-light_skin_tone": "👨🏼", - "man_medium_skin_tone": "👨🏽", - "man_mountain_biking": "🚵\u200d♂️", - "man_mountain_biking_dark_skin_tone": "🚵🏿\u200d♂️", - "man_mountain_biking_light_skin_tone": "🚵🏻\u200d♂️", - "man_mountain_biking_medium-dark_skin_tone": "🚵🏾\u200d♂️", - "man_mountain_biking_medium-light_skin_tone": "🚵🏼\u200d♂️", - "man_mountain_biking_medium_skin_tone": "🚵🏽\u200d♂️", - "man_office_worker": "👨\u200d💼", - "man_office_worker_dark_skin_tone": "👨🏿\u200d💼", - "man_office_worker_light_skin_tone": "👨🏻\u200d💼", - "man_office_worker_medium-dark_skin_tone": "👨🏾\u200d💼", - "man_office_worker_medium-light_skin_tone": "👨🏼\u200d💼", - "man_office_worker_medium_skin_tone": "👨🏽\u200d💼", - "man_pilot": "👨\u200d✈️", - "man_pilot_dark_skin_tone": "👨🏿\u200d✈️", - "man_pilot_light_skin_tone": "👨🏻\u200d✈️", - "man_pilot_medium-dark_skin_tone": "👨🏾\u200d✈️", - "man_pilot_medium-light_skin_tone": "👨🏼\u200d✈️", - "man_pilot_medium_skin_tone": "👨🏽\u200d✈️", - "man_playing_handball": "🤾\u200d♂️", - "man_playing_handball_dark_skin_tone": "🤾🏿\u200d♂️", - "man_playing_handball_light_skin_tone": "🤾🏻\u200d♂️", - "man_playing_handball_medium-dark_skin_tone": "🤾🏾\u200d♂️", - "man_playing_handball_medium-light_skin_tone": "🤾🏼\u200d♂️", - "man_playing_handball_medium_skin_tone": "🤾🏽\u200d♂️", - "man_playing_water_polo": "🤽\u200d♂️", - "man_playing_water_polo_dark_skin_tone": "🤽🏿\u200d♂️", - "man_playing_water_polo_light_skin_tone": "🤽🏻\u200d♂️", - "man_playing_water_polo_medium-dark_skin_tone": "🤽🏾\u200d♂️", - "man_playing_water_polo_medium-light_skin_tone": "🤽🏼\u200d♂️", - "man_playing_water_polo_medium_skin_tone": "🤽🏽\u200d♂️", - "man_police_officer": "👮\u200d♂️", - "man_police_officer_dark_skin_tone": "👮🏿\u200d♂️", - "man_police_officer_light_skin_tone": "👮🏻\u200d♂️", - "man_police_officer_medium-dark_skin_tone": "👮🏾\u200d♂️", - "man_police_officer_medium-light_skin_tone": "👮🏼\u200d♂️", - "man_police_officer_medium_skin_tone": "👮🏽\u200d♂️", - "man_pouting": "🙎\u200d♂️", - "man_pouting_dark_skin_tone": "🙎🏿\u200d♂️", - "man_pouting_light_skin_tone": "🙎🏻\u200d♂️", - "man_pouting_medium-dark_skin_tone": "🙎🏾\u200d♂️", - "man_pouting_medium-light_skin_tone": "🙎🏼\u200d♂️", - "man_pouting_medium_skin_tone": "🙎🏽\u200d♂️", - "man_raising_hand": "🙋\u200d♂️", - "man_raising_hand_dark_skin_tone": "🙋🏿\u200d♂️", - "man_raising_hand_light_skin_tone": "🙋🏻\u200d♂️", - "man_raising_hand_medium-dark_skin_tone": "🙋🏾\u200d♂️", - "man_raising_hand_medium-light_skin_tone": "🙋🏼\u200d♂️", - "man_raising_hand_medium_skin_tone": "🙋🏽\u200d♂️", - "man_rowing_boat": "🚣\u200d♂️", - "man_rowing_boat_dark_skin_tone": "🚣🏿\u200d♂️", - "man_rowing_boat_light_skin_tone": "🚣🏻\u200d♂️", - "man_rowing_boat_medium-dark_skin_tone": "🚣🏾\u200d♂️", - "man_rowing_boat_medium-light_skin_tone": "🚣🏼\u200d♂️", - "man_rowing_boat_medium_skin_tone": "🚣🏽\u200d♂️", - "man_running": "🏃\u200d♂️", - "man_running_dark_skin_tone": "🏃🏿\u200d♂️", - "man_running_light_skin_tone": "🏃🏻\u200d♂️", - "man_running_medium-dark_skin_tone": "🏃🏾\u200d♂️", - "man_running_medium-light_skin_tone": "🏃🏼\u200d♂️", - "man_running_medium_skin_tone": "🏃🏽\u200d♂️", - "man_scientist": "👨\u200d🔬", - "man_scientist_dark_skin_tone": "👨🏿\u200d🔬", - "man_scientist_light_skin_tone": "👨🏻\u200d🔬", - "man_scientist_medium-dark_skin_tone": "👨🏾\u200d🔬", - "man_scientist_medium-light_skin_tone": "👨🏼\u200d🔬", - "man_scientist_medium_skin_tone": "👨🏽\u200d🔬", - "man_shrugging": "🤷\u200d♂️", - "man_shrugging_dark_skin_tone": "🤷🏿\u200d♂️", - "man_shrugging_light_skin_tone": "🤷🏻\u200d♂️", - "man_shrugging_medium-dark_skin_tone": "🤷🏾\u200d♂️", - "man_shrugging_medium-light_skin_tone": "🤷🏼\u200d♂️", - "man_shrugging_medium_skin_tone": "🤷🏽\u200d♂️", - "man_singer": "👨\u200d🎤", - "man_singer_dark_skin_tone": "👨🏿\u200d🎤", - "man_singer_light_skin_tone": "👨🏻\u200d🎤", - "man_singer_medium-dark_skin_tone": "👨🏾\u200d🎤", - "man_singer_medium-light_skin_tone": "👨🏼\u200d🎤", - "man_singer_medium_skin_tone": "👨🏽\u200d🎤", - "man_student": "👨\u200d🎓", - "man_student_dark_skin_tone": "👨🏿\u200d🎓", - "man_student_light_skin_tone": "👨🏻\u200d🎓", - "man_student_medium-dark_skin_tone": "👨🏾\u200d🎓", - "man_student_medium-light_skin_tone": "👨🏼\u200d🎓", - "man_student_medium_skin_tone": "👨🏽\u200d🎓", - "man_surfing": "🏄\u200d♂️", - "man_surfing_dark_skin_tone": "🏄🏿\u200d♂️", - "man_surfing_light_skin_tone": "🏄🏻\u200d♂️", - "man_surfing_medium-dark_skin_tone": "🏄🏾\u200d♂️", - "man_surfing_medium-light_skin_tone": "🏄🏼\u200d♂️", - "man_surfing_medium_skin_tone": "🏄🏽\u200d♂️", - "man_swimming": "🏊\u200d♂️", - "man_swimming_dark_skin_tone": "🏊🏿\u200d♂️", - "man_swimming_light_skin_tone": "🏊🏻\u200d♂️", - "man_swimming_medium-dark_skin_tone": "🏊🏾\u200d♂️", - "man_swimming_medium-light_skin_tone": "🏊🏼\u200d♂️", - "man_swimming_medium_skin_tone": "🏊🏽\u200d♂️", - "man_teacher": "👨\u200d🏫", - "man_teacher_dark_skin_tone": "👨🏿\u200d🏫", - "man_teacher_light_skin_tone": "👨🏻\u200d🏫", - "man_teacher_medium-dark_skin_tone": "👨🏾\u200d🏫", - "man_teacher_medium-light_skin_tone": "👨🏼\u200d🏫", - "man_teacher_medium_skin_tone": "👨🏽\u200d🏫", - "man_technologist": "👨\u200d💻", - "man_technologist_dark_skin_tone": "👨🏿\u200d💻", - "man_technologist_light_skin_tone": "👨🏻\u200d💻", - "man_technologist_medium-dark_skin_tone": "👨🏾\u200d💻", - "man_technologist_medium-light_skin_tone": "👨🏼\u200d💻", - "man_technologist_medium_skin_tone": "👨🏽\u200d💻", - "man_tipping_hand": "💁\u200d♂️", - "man_tipping_hand_dark_skin_tone": "💁🏿\u200d♂️", - "man_tipping_hand_light_skin_tone": "💁🏻\u200d♂️", - "man_tipping_hand_medium-dark_skin_tone": "💁🏾\u200d♂️", - "man_tipping_hand_medium-light_skin_tone": "💁🏼\u200d♂️", - "man_tipping_hand_medium_skin_tone": "💁🏽\u200d♂️", - "man_vampire": "🧛\u200d♂️", - "man_vampire_dark_skin_tone": "🧛🏿\u200d♂️", - "man_vampire_light_skin_tone": "🧛🏻\u200d♂️", - "man_vampire_medium-dark_skin_tone": "🧛🏾\u200d♂️", - "man_vampire_medium-light_skin_tone": "🧛🏼\u200d♂️", - "man_vampire_medium_skin_tone": "🧛🏽\u200d♂️", - "man_walking": "🚶\u200d♂️", - "man_walking_dark_skin_tone": "🚶🏿\u200d♂️", - "man_walking_light_skin_tone": "🚶🏻\u200d♂️", - "man_walking_medium-dark_skin_tone": "🚶🏾\u200d♂️", - "man_walking_medium-light_skin_tone": "🚶🏼\u200d♂️", - "man_walking_medium_skin_tone": "🚶🏽\u200d♂️", - "man_wearing_turban": "👳\u200d♂️", - "man_wearing_turban_dark_skin_tone": "👳🏿\u200d♂️", - "man_wearing_turban_light_skin_tone": "👳🏻\u200d♂️", - "man_wearing_turban_medium-dark_skin_tone": "👳🏾\u200d♂️", - "man_wearing_turban_medium-light_skin_tone": "👳🏼\u200d♂️", - "man_wearing_turban_medium_skin_tone": "👳🏽\u200d♂️", - "man_with_probing_cane": "👨\u200d🦯", - "man_with_chinese_cap": "👲", - "man_with_chinese_cap_dark_skin_tone": "👲🏿", - "man_with_chinese_cap_light_skin_tone": "👲🏻", - "man_with_chinese_cap_medium-dark_skin_tone": "👲🏾", - "man_with_chinese_cap_medium-light_skin_tone": "👲🏼", - "man_with_chinese_cap_medium_skin_tone": "👲🏽", - "man_zombie": "🧟\u200d♂️", - "mango": "🥭", - "mantelpiece_clock": "🕰", - "manual_wheelchair": "🦽", - "man’s_shoe": "👞", - "map_of_japan": "🗾", - "maple_leaf": "🍁", - "martial_arts_uniform": "🥋", - "mate": "🧉", - "meat_on_bone": "🍖", - "mechanical_arm": "🦾", - "mechanical_leg": "🦿", - "medical_symbol": "⚕", - "megaphone": "📣", - "melon": "🍈", - "memo": "📝", - "men_with_bunny_ears": "👯\u200d♂️", - "men_wrestling": "🤼\u200d♂️", - "menorah": "🕎", - "men’s_room": "🚹", - "mermaid": "🧜\u200d♀️", - "mermaid_dark_skin_tone": "🧜🏿\u200d♀️", - "mermaid_light_skin_tone": "🧜🏻\u200d♀️", - "mermaid_medium-dark_skin_tone": "🧜🏾\u200d♀️", - "mermaid_medium-light_skin_tone": "🧜🏼\u200d♀️", - "mermaid_medium_skin_tone": "🧜🏽\u200d♀️", - "merman": "🧜\u200d♂️", - "merman_dark_skin_tone": "🧜🏿\u200d♂️", - "merman_light_skin_tone": "🧜🏻\u200d♂️", - "merman_medium-dark_skin_tone": "🧜🏾\u200d♂️", - "merman_medium-light_skin_tone": "🧜🏼\u200d♂️", - "merman_medium_skin_tone": "🧜🏽\u200d♂️", - "merperson": "🧜", - "merperson_dark_skin_tone": "🧜🏿", - "merperson_light_skin_tone": "🧜🏻", - "merperson_medium-dark_skin_tone": "🧜🏾", - "merperson_medium-light_skin_tone": "🧜🏼", - "merperson_medium_skin_tone": "🧜🏽", - "metro": "🚇", - "microbe": "🦠", - "microphone": "🎤", - "microscope": "🔬", - "middle_finger": "🖕", - "middle_finger_dark_skin_tone": "🖕🏿", - "middle_finger_light_skin_tone": "🖕🏻", - "middle_finger_medium-dark_skin_tone": "🖕🏾", - "middle_finger_medium-light_skin_tone": "🖕🏼", - "middle_finger_medium_skin_tone": "🖕🏽", - "military_medal": "🎖", - "milky_way": "🌌", - "minibus": "🚐", - "moai": "🗿", - "mobile_phone": "📱", - "mobile_phone_off": "📴", - "mobile_phone_with_arrow": "📲", - "money-mouth_face": "🤑", - "money_bag": "💰", - "money_with_wings": "💸", - "monkey": "🐒", - "monkey_face": "🐵", - "monorail": "🚝", - "moon_cake": "🥮", - "moon_viewing_ceremony": "🎑", - "mosque": "🕌", - "mosquito": "🦟", - "motor_boat": "🛥", - "motor_scooter": "🛵", - "motorcycle": "🏍", - "motorized_wheelchair": "🦼", - "motorway": "🛣", - "mount_fuji": "🗻", - "mountain": "⛰", - "mountain_cableway": "🚠", - "mountain_railway": "🚞", - "mouse": "🐭", - "mouse_face": "🐭", - "mouth": "👄", - "movie_camera": "🎥", - "mushroom": "🍄", - "musical_keyboard": "🎹", - "musical_note": "🎵", - "musical_notes": "🎶", - "musical_score": "🎼", - "muted_speaker": "🔇", - "nail_polish": "💅", - "nail_polish_dark_skin_tone": "💅🏿", - "nail_polish_light_skin_tone": "💅🏻", - "nail_polish_medium-dark_skin_tone": "💅🏾", - "nail_polish_medium-light_skin_tone": "💅🏼", - "nail_polish_medium_skin_tone": "💅🏽", - "name_badge": "📛", - "national_park": "🏞", - "nauseated_face": "🤢", - "nazar_amulet": "🧿", - "necktie": "👔", - "nerd_face": "🤓", - "neutral_face": "😐", - "new_moon": "🌑", - "new_moon_face": "🌚", - "newspaper": "📰", - "next_track_button": "⏭", - "night_with_stars": "🌃", - "nine-thirty": "🕤", - "nine_o’clock": "🕘", - "no_bicycles": "🚳", - "no_entry": "⛔", - "no_littering": "🚯", - "no_mobile_phones": "📵", - "no_one_under_eighteen": "🔞", - "no_pedestrians": "🚷", - "no_smoking": "🚭", - "non-potable_water": "🚱", - "nose": "👃", - "nose_dark_skin_tone": "👃🏿", - "nose_light_skin_tone": "👃🏻", - "nose_medium-dark_skin_tone": "👃🏾", - "nose_medium-light_skin_tone": "👃🏼", - "nose_medium_skin_tone": "👃🏽", - "notebook": "📓", - "notebook_with_decorative_cover": "📔", - "nut_and_bolt": "🔩", - "octopus": "🐙", - "oden": "🍢", - "office_building": "🏢", - "ogre": "👹", - "oil_drum": "🛢", - "old_key": "🗝", - "old_man": "👴", - "old_man_dark_skin_tone": "👴🏿", - "old_man_light_skin_tone": "👴🏻", - "old_man_medium-dark_skin_tone": "👴🏾", - "old_man_medium-light_skin_tone": "👴🏼", - "old_man_medium_skin_tone": "👴🏽", - "old_woman": "👵", - "old_woman_dark_skin_tone": "👵🏿", - "old_woman_light_skin_tone": "👵🏻", - "old_woman_medium-dark_skin_tone": "👵🏾", - "old_woman_medium-light_skin_tone": "👵🏼", - "old_woman_medium_skin_tone": "👵🏽", - "older_adult": "🧓", - "older_adult_dark_skin_tone": "🧓🏿", - "older_adult_light_skin_tone": "🧓🏻", - "older_adult_medium-dark_skin_tone": "🧓🏾", - "older_adult_medium-light_skin_tone": "🧓🏼", - "older_adult_medium_skin_tone": "🧓🏽", - "om": "🕉", - "oncoming_automobile": "🚘", - "oncoming_bus": "🚍", - "oncoming_fist": "👊", - "oncoming_fist_dark_skin_tone": "👊🏿", - "oncoming_fist_light_skin_tone": "👊🏻", - "oncoming_fist_medium-dark_skin_tone": "👊🏾", - "oncoming_fist_medium-light_skin_tone": "👊🏼", - "oncoming_fist_medium_skin_tone": "👊🏽", - "oncoming_police_car": "🚔", - "oncoming_taxi": "🚖", - "one-piece_swimsuit": "🩱", - "one-thirty": "🕜", - "one_o’clock": "🕐", - "onion": "🧅", - "open_book": "📖", - "open_file_folder": "📂", - "open_hands": "👐", - "open_hands_dark_skin_tone": "👐🏿", - "open_hands_light_skin_tone": "👐🏻", - "open_hands_medium-dark_skin_tone": "👐🏾", - "open_hands_medium-light_skin_tone": "👐🏼", - "open_hands_medium_skin_tone": "👐🏽", - "open_mailbox_with_lowered_flag": "📭", - "open_mailbox_with_raised_flag": "📬", - "optical_disk": "💿", - "orange_book": "📙", - "orange_circle": "🟠", - "orange_heart": "🧡", - "orange_square": "🟧", - "orangutan": "🦧", - "orthodox_cross": "☦", - "otter": "🦦", - "outbox_tray": "📤", - "owl": "🦉", - "ox": "🐂", - "oyster": "🦪", - "package": "📦", - "page_facing_up": "📄", - "page_with_curl": "📃", - "pager": "📟", - "paintbrush": "🖌", - "palm_tree": "🌴", - "palms_up_together": "🤲", - "palms_up_together_dark_skin_tone": "🤲🏿", - "palms_up_together_light_skin_tone": "🤲🏻", - "palms_up_together_medium-dark_skin_tone": "🤲🏾", - "palms_up_together_medium-light_skin_tone": "🤲🏼", - "palms_up_together_medium_skin_tone": "🤲🏽", - "pancakes": "🥞", - "panda_face": "🐼", - "paperclip": "📎", - "parrot": "🦜", - "part_alternation_mark": "〽", - "party_popper": "🎉", - "partying_face": "🥳", - "passenger_ship": "🛳", - "passport_control": "🛂", - "pause_button": "⏸", - "paw_prints": "🐾", - "peace_symbol": "☮", - "peach": "🍑", - "peacock": "🦚", - "peanuts": "🥜", - "pear": "🍐", - "pen": "🖊", - "pencil": "📝", - "penguin": "🐧", - "pensive_face": "😔", - "people_holding_hands": "🧑\u200d🤝\u200d🧑", - "people_with_bunny_ears": "👯", - "people_wrestling": "🤼", - "performing_arts": "🎭", - "persevering_face": "😣", - "person_biking": "🚴", - "person_biking_dark_skin_tone": "🚴🏿", - "person_biking_light_skin_tone": "🚴🏻", - "person_biking_medium-dark_skin_tone": "🚴🏾", - "person_biking_medium-light_skin_tone": "🚴🏼", - "person_biking_medium_skin_tone": "🚴🏽", - "person_bouncing_ball": "⛹", - "person_bouncing_ball_dark_skin_tone": "⛹🏿", - "person_bouncing_ball_light_skin_tone": "⛹🏻", - "person_bouncing_ball_medium-dark_skin_tone": "⛹🏾", - "person_bouncing_ball_medium-light_skin_tone": "⛹🏼", - "person_bouncing_ball_medium_skin_tone": "⛹🏽", - "person_bowing": "🙇", - "person_bowing_dark_skin_tone": "🙇🏿", - "person_bowing_light_skin_tone": "🙇🏻", - "person_bowing_medium-dark_skin_tone": "🙇🏾", - "person_bowing_medium-light_skin_tone": "🙇🏼", - "person_bowing_medium_skin_tone": "🙇🏽", - "person_cartwheeling": "🤸", - "person_cartwheeling_dark_skin_tone": "🤸🏿", - "person_cartwheeling_light_skin_tone": "🤸🏻", - "person_cartwheeling_medium-dark_skin_tone": "🤸🏾", - "person_cartwheeling_medium-light_skin_tone": "🤸🏼", - "person_cartwheeling_medium_skin_tone": "🤸🏽", - "person_climbing": "🧗", - "person_climbing_dark_skin_tone": "🧗🏿", - "person_climbing_light_skin_tone": "🧗🏻", - "person_climbing_medium-dark_skin_tone": "🧗🏾", - "person_climbing_medium-light_skin_tone": "🧗🏼", - "person_climbing_medium_skin_tone": "🧗🏽", - "person_facepalming": "🤦", - "person_facepalming_dark_skin_tone": "🤦🏿", - "person_facepalming_light_skin_tone": "🤦🏻", - "person_facepalming_medium-dark_skin_tone": "🤦🏾", - "person_facepalming_medium-light_skin_tone": "🤦🏼", - "person_facepalming_medium_skin_tone": "🤦🏽", - "person_fencing": "🤺", - "person_frowning": "🙍", - "person_frowning_dark_skin_tone": "🙍🏿", - "person_frowning_light_skin_tone": "🙍🏻", - "person_frowning_medium-dark_skin_tone": "🙍🏾", - "person_frowning_medium-light_skin_tone": "🙍🏼", - "person_frowning_medium_skin_tone": "🙍🏽", - "person_gesturing_no": "🙅", - "person_gesturing_no_dark_skin_tone": "🙅🏿", - "person_gesturing_no_light_skin_tone": "🙅🏻", - "person_gesturing_no_medium-dark_skin_tone": "🙅🏾", - "person_gesturing_no_medium-light_skin_tone": "🙅🏼", - "person_gesturing_no_medium_skin_tone": "🙅🏽", - "person_gesturing_ok": "🙆", - "person_gesturing_ok_dark_skin_tone": "🙆🏿", - "person_gesturing_ok_light_skin_tone": "🙆🏻", - "person_gesturing_ok_medium-dark_skin_tone": "🙆🏾", - "person_gesturing_ok_medium-light_skin_tone": "🙆🏼", - "person_gesturing_ok_medium_skin_tone": "🙆🏽", - "person_getting_haircut": "💇", - "person_getting_haircut_dark_skin_tone": "💇🏿", - "person_getting_haircut_light_skin_tone": "💇🏻", - "person_getting_haircut_medium-dark_skin_tone": "💇🏾", - "person_getting_haircut_medium-light_skin_tone": "💇🏼", - "person_getting_haircut_medium_skin_tone": "💇🏽", - "person_getting_massage": "💆", - "person_getting_massage_dark_skin_tone": "💆🏿", - "person_getting_massage_light_skin_tone": "💆🏻", - "person_getting_massage_medium-dark_skin_tone": "💆🏾", - "person_getting_massage_medium-light_skin_tone": "💆🏼", - "person_getting_massage_medium_skin_tone": "💆🏽", - "person_golfing": "🏌", - "person_golfing_dark_skin_tone": "🏌🏿", - "person_golfing_light_skin_tone": "🏌🏻", - "person_golfing_medium-dark_skin_tone": "🏌🏾", - "person_golfing_medium-light_skin_tone": "🏌🏼", - "person_golfing_medium_skin_tone": "🏌🏽", - "person_in_bed": "🛌", - "person_in_bed_dark_skin_tone": "🛌🏿", - "person_in_bed_light_skin_tone": "🛌🏻", - "person_in_bed_medium-dark_skin_tone": "🛌🏾", - "person_in_bed_medium-light_skin_tone": "🛌🏼", - "person_in_bed_medium_skin_tone": "🛌🏽", - "person_in_lotus_position": "🧘", - "person_in_lotus_position_dark_skin_tone": "🧘🏿", - "person_in_lotus_position_light_skin_tone": "🧘🏻", - "person_in_lotus_position_medium-dark_skin_tone": "🧘🏾", - "person_in_lotus_position_medium-light_skin_tone": "🧘🏼", - "person_in_lotus_position_medium_skin_tone": "🧘🏽", - "person_in_steamy_room": "🧖", - "person_in_steamy_room_dark_skin_tone": "🧖🏿", - "person_in_steamy_room_light_skin_tone": "🧖🏻", - "person_in_steamy_room_medium-dark_skin_tone": "🧖🏾", - "person_in_steamy_room_medium-light_skin_tone": "🧖🏼", - "person_in_steamy_room_medium_skin_tone": "🧖🏽", - "person_juggling": "🤹", - "person_juggling_dark_skin_tone": "🤹🏿", - "person_juggling_light_skin_tone": "🤹🏻", - "person_juggling_medium-dark_skin_tone": "🤹🏾", - "person_juggling_medium-light_skin_tone": "🤹🏼", - "person_juggling_medium_skin_tone": "🤹🏽", - "person_kneeling": "🧎", - "person_lifting_weights": "🏋", - "person_lifting_weights_dark_skin_tone": "🏋🏿", - "person_lifting_weights_light_skin_tone": "🏋🏻", - "person_lifting_weights_medium-dark_skin_tone": "🏋🏾", - "person_lifting_weights_medium-light_skin_tone": "🏋🏼", - "person_lifting_weights_medium_skin_tone": "🏋🏽", - "person_mountain_biking": "🚵", - "person_mountain_biking_dark_skin_tone": "🚵🏿", - "person_mountain_biking_light_skin_tone": "🚵🏻", - "person_mountain_biking_medium-dark_skin_tone": "🚵🏾", - "person_mountain_biking_medium-light_skin_tone": "🚵🏼", - "person_mountain_biking_medium_skin_tone": "🚵🏽", - "person_playing_handball": "🤾", - "person_playing_handball_dark_skin_tone": "🤾🏿", - "person_playing_handball_light_skin_tone": "🤾🏻", - "person_playing_handball_medium-dark_skin_tone": "🤾🏾", - "person_playing_handball_medium-light_skin_tone": "🤾🏼", - "person_playing_handball_medium_skin_tone": "🤾🏽", - "person_playing_water_polo": "🤽", - "person_playing_water_polo_dark_skin_tone": "🤽🏿", - "person_playing_water_polo_light_skin_tone": "🤽🏻", - "person_playing_water_polo_medium-dark_skin_tone": "🤽🏾", - "person_playing_water_polo_medium-light_skin_tone": "🤽🏼", - "person_playing_water_polo_medium_skin_tone": "🤽🏽", - "person_pouting": "🙎", - "person_pouting_dark_skin_tone": "🙎🏿", - "person_pouting_light_skin_tone": "🙎🏻", - "person_pouting_medium-dark_skin_tone": "🙎🏾", - "person_pouting_medium-light_skin_tone": "🙎🏼", - "person_pouting_medium_skin_tone": "🙎🏽", - "person_raising_hand": "🙋", - "person_raising_hand_dark_skin_tone": "🙋🏿", - "person_raising_hand_light_skin_tone": "🙋🏻", - "person_raising_hand_medium-dark_skin_tone": "🙋🏾", - "person_raising_hand_medium-light_skin_tone": "🙋🏼", - "person_raising_hand_medium_skin_tone": "🙋🏽", - "person_rowing_boat": "🚣", - "person_rowing_boat_dark_skin_tone": "🚣🏿", - "person_rowing_boat_light_skin_tone": "🚣🏻", - "person_rowing_boat_medium-dark_skin_tone": "🚣🏾", - "person_rowing_boat_medium-light_skin_tone": "🚣🏼", - "person_rowing_boat_medium_skin_tone": "🚣🏽", - "person_running": "🏃", - "person_running_dark_skin_tone": "🏃🏿", - "person_running_light_skin_tone": "🏃🏻", - "person_running_medium-dark_skin_tone": "🏃🏾", - "person_running_medium-light_skin_tone": "🏃🏼", - "person_running_medium_skin_tone": "🏃🏽", - "person_shrugging": "🤷", - "person_shrugging_dark_skin_tone": "🤷🏿", - "person_shrugging_light_skin_tone": "🤷🏻", - "person_shrugging_medium-dark_skin_tone": "🤷🏾", - "person_shrugging_medium-light_skin_tone": "🤷🏼", - "person_shrugging_medium_skin_tone": "🤷🏽", - "person_standing": "🧍", - "person_surfing": "🏄", - "person_surfing_dark_skin_tone": "🏄🏿", - "person_surfing_light_skin_tone": "🏄🏻", - "person_surfing_medium-dark_skin_tone": "🏄🏾", - "person_surfing_medium-light_skin_tone": "🏄🏼", - "person_surfing_medium_skin_tone": "🏄🏽", - "person_swimming": "🏊", - "person_swimming_dark_skin_tone": "🏊🏿", - "person_swimming_light_skin_tone": "🏊🏻", - "person_swimming_medium-dark_skin_tone": "🏊🏾", - "person_swimming_medium-light_skin_tone": "🏊🏼", - "person_swimming_medium_skin_tone": "🏊🏽", - "person_taking_bath": "🛀", - "person_taking_bath_dark_skin_tone": "🛀🏿", - "person_taking_bath_light_skin_tone": "🛀🏻", - "person_taking_bath_medium-dark_skin_tone": "🛀🏾", - "person_taking_bath_medium-light_skin_tone": "🛀🏼", - "person_taking_bath_medium_skin_tone": "🛀🏽", - "person_tipping_hand": "💁", - "person_tipping_hand_dark_skin_tone": "💁🏿", - "person_tipping_hand_light_skin_tone": "💁🏻", - "person_tipping_hand_medium-dark_skin_tone": "💁🏾", - "person_tipping_hand_medium-light_skin_tone": "💁🏼", - "person_tipping_hand_medium_skin_tone": "💁🏽", - "person_walking": "🚶", - "person_walking_dark_skin_tone": "🚶🏿", - "person_walking_light_skin_tone": "🚶🏻", - "person_walking_medium-dark_skin_tone": "🚶🏾", - "person_walking_medium-light_skin_tone": "🚶🏼", - "person_walking_medium_skin_tone": "🚶🏽", - "person_wearing_turban": "👳", - "person_wearing_turban_dark_skin_tone": "👳🏿", - "person_wearing_turban_light_skin_tone": "👳🏻", - "person_wearing_turban_medium-dark_skin_tone": "👳🏾", - "person_wearing_turban_medium-light_skin_tone": "👳🏼", - "person_wearing_turban_medium_skin_tone": "👳🏽", - "petri_dish": "🧫", - "pick": "⛏", - "pie": "🥧", - "pig": "🐷", - "pig_face": "🐷", - "pig_nose": "🐽", - "pile_of_poo": "💩", - "pill": "💊", - "pinching_hand": "🤏", - "pine_decoration": "🎍", - "pineapple": "🍍", - "ping_pong": "🏓", - "pirate_flag": "🏴\u200d☠️", - "pistol": "🔫", - "pizza": "🍕", - "place_of_worship": "🛐", - "play_button": "▶", - "play_or_pause_button": "⏯", - "pleading_face": "🥺", - "police_car": "🚓", - "police_car_light": "🚨", - "police_officer": "👮", - "police_officer_dark_skin_tone": "👮🏿", - "police_officer_light_skin_tone": "👮🏻", - "police_officer_medium-dark_skin_tone": "👮🏾", - "police_officer_medium-light_skin_tone": "👮🏼", - "police_officer_medium_skin_tone": "👮🏽", - "poodle": "🐩", - "pool_8_ball": "🎱", - "popcorn": "🍿", - "post_office": "🏣", - "postal_horn": "📯", - "postbox": "📮", - "pot_of_food": "🍲", - "potable_water": "🚰", - "potato": "🥔", - "poultry_leg": "🍗", - "pound_banknote": "💷", - "pouting_cat_face": "😾", - "pouting_face": "😡", - "prayer_beads": "📿", - "pregnant_woman": "🤰", - "pregnant_woman_dark_skin_tone": "🤰🏿", - "pregnant_woman_light_skin_tone": "🤰🏻", - "pregnant_woman_medium-dark_skin_tone": "🤰🏾", - "pregnant_woman_medium-light_skin_tone": "🤰🏼", - "pregnant_woman_medium_skin_tone": "🤰🏽", - "pretzel": "🥨", - "probing_cane": "🦯", - "prince": "🤴", - "prince_dark_skin_tone": "🤴🏿", - "prince_light_skin_tone": "🤴🏻", - "prince_medium-dark_skin_tone": "🤴🏾", - "prince_medium-light_skin_tone": "🤴🏼", - "prince_medium_skin_tone": "🤴🏽", - "princess": "👸", - "princess_dark_skin_tone": "👸🏿", - "princess_light_skin_tone": "👸🏻", - "princess_medium-dark_skin_tone": "👸🏾", - "princess_medium-light_skin_tone": "👸🏼", - "princess_medium_skin_tone": "👸🏽", - "printer": "🖨", - "prohibited": "🚫", - "purple_circle": "🟣", - "purple_heart": "💜", - "purple_square": "🟪", - "purse": "👛", - "pushpin": "📌", - "question_mark": "❓", - "rabbit": "🐰", - "rabbit_face": "🐰", - "raccoon": "🦝", - "racing_car": "🏎", - "radio": "📻", - "radio_button": "🔘", - "radioactive": "☢", - "railway_car": "🚃", - "railway_track": "🛤", - "rainbow": "🌈", - "rainbow_flag": "🏳️\u200d🌈", - "raised_back_of_hand": "🤚", - "raised_back_of_hand_dark_skin_tone": "🤚🏿", - "raised_back_of_hand_light_skin_tone": "🤚🏻", - "raised_back_of_hand_medium-dark_skin_tone": "🤚🏾", - "raised_back_of_hand_medium-light_skin_tone": "🤚🏼", - "raised_back_of_hand_medium_skin_tone": "🤚🏽", - "raised_fist": "✊", - "raised_fist_dark_skin_tone": "✊🏿", - "raised_fist_light_skin_tone": "✊🏻", - "raised_fist_medium-dark_skin_tone": "✊🏾", - "raised_fist_medium-light_skin_tone": "✊🏼", - "raised_fist_medium_skin_tone": "✊🏽", - "raised_hand": "✋", - "raised_hand_dark_skin_tone": "✋🏿", - "raised_hand_light_skin_tone": "✋🏻", - "raised_hand_medium-dark_skin_tone": "✋🏾", - "raised_hand_medium-light_skin_tone": "✋🏼", - "raised_hand_medium_skin_tone": "✋🏽", - "raising_hands": "🙌", - "raising_hands_dark_skin_tone": "🙌🏿", - "raising_hands_light_skin_tone": "🙌🏻", - "raising_hands_medium-dark_skin_tone": "🙌🏾", - "raising_hands_medium-light_skin_tone": "🙌🏼", - "raising_hands_medium_skin_tone": "🙌🏽", - "ram": "🐏", - "rat": "🐀", - "razor": "🪒", - "ringed_planet": "🪐", - "receipt": "🧾", - "record_button": "⏺", - "recycling_symbol": "♻", - "red_apple": "🍎", - "red_circle": "🔴", - "red_envelope": "🧧", - "red_hair": "🦰", - "red-haired_man": "👨\u200d🦰", - "red-haired_woman": "👩\u200d🦰", - "red_heart": "❤", - "red_paper_lantern": "🏮", - "red_square": "🟥", - "red_triangle_pointed_down": "🔻", - "red_triangle_pointed_up": "🔺", - "registered": "®", - "relieved_face": "😌", - "reminder_ribbon": "🎗", - "repeat_button": "🔁", - "repeat_single_button": "🔂", - "rescue_worker’s_helmet": "⛑", - "restroom": "🚻", - "reverse_button": "◀", - "revolving_hearts": "💞", - "rhinoceros": "🦏", - "ribbon": "🎀", - "rice_ball": "🍙", - "rice_cracker": "🍘", - "right-facing_fist": "🤜", - "right-facing_fist_dark_skin_tone": "🤜🏿", - "right-facing_fist_light_skin_tone": "🤜🏻", - "right-facing_fist_medium-dark_skin_tone": "🤜🏾", - "right-facing_fist_medium-light_skin_tone": "🤜🏼", - "right-facing_fist_medium_skin_tone": "🤜🏽", - "right_anger_bubble": "🗯", - "right_arrow": "➡", - "right_arrow_curving_down": "⤵", - "right_arrow_curving_left": "↩", - "right_arrow_curving_up": "⤴", - "ring": "💍", - "roasted_sweet_potato": "🍠", - "robot_face": "🤖", - "rocket": "🚀", - "roll_of_paper": "🧻", - "rolled-up_newspaper": "🗞", - "roller_coaster": "🎢", - "rolling_on_the_floor_laughing": "🤣", - "rooster": "🐓", - "rose": "🌹", - "rosette": "🏵", - "round_pushpin": "📍", - "rugby_football": "🏉", - "running_shirt": "🎽", - "running_shoe": "👟", - "sad_but_relieved_face": "😥", - "safety_pin": "🧷", - "safety_vest": "🦺", - "salt": "🧂", - "sailboat": "⛵", - "sake": "🍶", - "sandwich": "🥪", - "sari": "🥻", - "satellite": "📡", - "satellite_antenna": "📡", - "sauropod": "🦕", - "saxophone": "🎷", - "scarf": "🧣", - "school": "🏫", - "school_backpack": "🎒", - "scissors": "✂", - "scorpion": "🦂", - "scroll": "📜", - "seat": "💺", - "see-no-evil_monkey": "🙈", - "seedling": "🌱", - "selfie": "🤳", - "selfie_dark_skin_tone": "🤳🏿", - "selfie_light_skin_tone": "🤳🏻", - "selfie_medium-dark_skin_tone": "🤳🏾", - "selfie_medium-light_skin_tone": "🤳🏼", - "selfie_medium_skin_tone": "🤳🏽", - "service_dog": "🐕\u200d🦺", - "seven-thirty": "🕢", - "seven_o’clock": "🕖", - "shallow_pan_of_food": "🥘", - "shamrock": "☘", - "shark": "🦈", - "shaved_ice": "🍧", - "sheaf_of_rice": "🌾", - "shield": "🛡", - "shinto_shrine": "⛩", - "ship": "🚢", - "shooting_star": "🌠", - "shopping_bags": "🛍", - "shopping_cart": "🛒", - "shortcake": "🍰", - "shorts": "🩳", - "shower": "🚿", - "shrimp": "🦐", - "shuffle_tracks_button": "🔀", - "shushing_face": "🤫", - "sign_of_the_horns": "🤘", - "sign_of_the_horns_dark_skin_tone": "🤘🏿", - "sign_of_the_horns_light_skin_tone": "🤘🏻", - "sign_of_the_horns_medium-dark_skin_tone": "🤘🏾", - "sign_of_the_horns_medium-light_skin_tone": "🤘🏼", - "sign_of_the_horns_medium_skin_tone": "🤘🏽", - "six-thirty": "🕡", - "six_o’clock": "🕕", - "skateboard": "🛹", - "skier": "⛷", - "skis": "🎿", - "skull": "💀", - "skull_and_crossbones": "☠", - "skunk": "🦨", - "sled": "🛷", - "sleeping_face": "😴", - "sleepy_face": "😪", - "slightly_frowning_face": "🙁", - "slightly_smiling_face": "🙂", - "slot_machine": "🎰", - "sloth": "🦥", - "small_airplane": "🛩", - "small_blue_diamond": "🔹", - "small_orange_diamond": "🔸", - "smiling_cat_face_with_heart-eyes": "😻", - "smiling_face": "☺", - "smiling_face_with_halo": "😇", - "smiling_face_with_3_hearts": "🥰", - "smiling_face_with_heart-eyes": "😍", - "smiling_face_with_horns": "😈", - "smiling_face_with_smiling_eyes": "😊", - "smiling_face_with_sunglasses": "😎", - "smirking_face": "😏", - "snail": "🐌", - "snake": "🐍", - "sneezing_face": "🤧", - "snow-capped_mountain": "🏔", - "snowboarder": "🏂", - "snowboarder_dark_skin_tone": "🏂🏿", - "snowboarder_light_skin_tone": "🏂🏻", - "snowboarder_medium-dark_skin_tone": "🏂🏾", - "snowboarder_medium-light_skin_tone": "🏂🏼", - "snowboarder_medium_skin_tone": "🏂🏽", - "snowflake": "❄", - "snowman": "☃", - "snowman_without_snow": "⛄", - "soap": "🧼", - "soccer_ball": "⚽", - "socks": "🧦", - "softball": "🥎", - "soft_ice_cream": "🍦", - "spade_suit": "♠", - "spaghetti": "🍝", - "sparkle": "❇", - "sparkler": "🎇", - "sparkles": "✨", - "sparkling_heart": "💖", - "speak-no-evil_monkey": "🙊", - "speaker_high_volume": "🔊", - "speaker_low_volume": "🔈", - "speaker_medium_volume": "🔉", - "speaking_head": "🗣", - "speech_balloon": "💬", - "speedboat": "🚤", - "spider": "🕷", - "spider_web": "🕸", - "spiral_calendar": "🗓", - "spiral_notepad": "🗒", - "spiral_shell": "🐚", - "spoon": "🥄", - "sponge": "🧽", - "sport_utility_vehicle": "🚙", - "sports_medal": "🏅", - "spouting_whale": "🐳", - "squid": "🦑", - "squinting_face_with_tongue": "😝", - "stadium": "🏟", - "star-struck": "🤩", - "star_and_crescent": "☪", - "star_of_david": "✡", - "station": "🚉", - "steaming_bowl": "🍜", - "stethoscope": "🩺", - "stop_button": "⏹", - "stop_sign": "🛑", - "stopwatch": "⏱", - "straight_ruler": "📏", - "strawberry": "🍓", - "studio_microphone": "🎙", - "stuffed_flatbread": "🥙", - "sun": "☀", - "sun_behind_cloud": "⛅", - "sun_behind_large_cloud": "🌥", - "sun_behind_rain_cloud": "🌦", - "sun_behind_small_cloud": "🌤", - "sun_with_face": "🌞", - "sunflower": "🌻", - "sunglasses": "😎", - "sunrise": "🌅", - "sunrise_over_mountains": "🌄", - "sunset": "🌇", - "superhero": "🦸", - "supervillain": "🦹", - "sushi": "🍣", - "suspension_railway": "🚟", - "swan": "🦢", - "sweat_droplets": "💦", - "synagogue": "🕍", - "syringe": "💉", - "t-shirt": "👕", - "taco": "🌮", - "takeout_box": "🥡", - "tanabata_tree": "🎋", - "tangerine": "🍊", - "taxi": "🚕", - "teacup_without_handle": "🍵", - "tear-off_calendar": "📆", - "teddy_bear": "🧸", - "telephone": "☎", - "telephone_receiver": "📞", - "telescope": "🔭", - "television": "📺", - "ten-thirty": "🕥", - "ten_o’clock": "🕙", - "tennis": "🎾", - "tent": "⛺", - "test_tube": "🧪", - "thermometer": "🌡", - "thinking_face": "🤔", - "thought_balloon": "💭", - "thread": "🧵", - "three-thirty": "🕞", - "three_o’clock": "🕒", - "thumbs_down": "👎", - "thumbs_down_dark_skin_tone": "👎🏿", - "thumbs_down_light_skin_tone": "👎🏻", - "thumbs_down_medium-dark_skin_tone": "👎🏾", - "thumbs_down_medium-light_skin_tone": "👎🏼", - "thumbs_down_medium_skin_tone": "👎🏽", - "thumbs_up": "👍", - "thumbs_up_dark_skin_tone": "👍🏿", - "thumbs_up_light_skin_tone": "👍🏻", - "thumbs_up_medium-dark_skin_tone": "👍🏾", - "thumbs_up_medium-light_skin_tone": "👍🏼", - "thumbs_up_medium_skin_tone": "👍🏽", - "ticket": "🎫", - "tiger": "🐯", - "tiger_face": "🐯", - "timer_clock": "⏲", - "tired_face": "😫", - "toolbox": "🧰", - "toilet": "🚽", - "tomato": "🍅", - "tongue": "👅", - "tooth": "🦷", - "top_hat": "🎩", - "tornado": "🌪", - "trackball": "🖲", - "tractor": "🚜", - "trade_mark": "™", - "train": "🚋", - "tram": "🚊", - "tram_car": "🚋", - "triangular_flag": "🚩", - "triangular_ruler": "📐", - "trident_emblem": "🔱", - "trolleybus": "🚎", - "trophy": "🏆", - "tropical_drink": "🍹", - "tropical_fish": "🐠", - "trumpet": "🎺", - "tulip": "🌷", - "tumbler_glass": "🥃", - "turtle": "🐢", - "twelve-thirty": "🕧", - "twelve_o’clock": "🕛", - "two-hump_camel": "🐫", - "two-thirty": "🕝", - "two_hearts": "💕", - "two_men_holding_hands": "👬", - "two_o’clock": "🕑", - "two_women_holding_hands": "👭", - "umbrella": "☂", - "umbrella_on_ground": "⛱", - "umbrella_with_rain_drops": "☔", - "unamused_face": "😒", - "unicorn_face": "🦄", - "unlocked": "🔓", - "up-down_arrow": "↕", - "up-left_arrow": "↖", - "up-right_arrow": "↗", - "up_arrow": "⬆", - "upside-down_face": "🙃", - "upwards_button": "🔼", - "vampire": "🧛", - "vampire_dark_skin_tone": "🧛🏿", - "vampire_light_skin_tone": "🧛🏻", - "vampire_medium-dark_skin_tone": "🧛🏾", - "vampire_medium-light_skin_tone": "🧛🏼", - "vampire_medium_skin_tone": "🧛🏽", - "vertical_traffic_light": "🚦", - "vibration_mode": "📳", - "victory_hand": "✌", - "victory_hand_dark_skin_tone": "✌🏿", - "victory_hand_light_skin_tone": "✌🏻", - "victory_hand_medium-dark_skin_tone": "✌🏾", - "victory_hand_medium-light_skin_tone": "✌🏼", - "victory_hand_medium_skin_tone": "✌🏽", - "video_camera": "📹", - "video_game": "🎮", - "videocassette": "📼", - "violin": "🎻", - "volcano": "🌋", - "volleyball": "🏐", - "vulcan_salute": "🖖", - "vulcan_salute_dark_skin_tone": "🖖🏿", - "vulcan_salute_light_skin_tone": "🖖🏻", - "vulcan_salute_medium-dark_skin_tone": "🖖🏾", - "vulcan_salute_medium-light_skin_tone": "🖖🏼", - "vulcan_salute_medium_skin_tone": "🖖🏽", - "waffle": "🧇", - "waning_crescent_moon": "🌘", - "waning_gibbous_moon": "🌖", - "warning": "⚠", - "wastebasket": "🗑", - "watch": "⌚", - "water_buffalo": "🐃", - "water_closet": "🚾", - "water_wave": "🌊", - "watermelon": "🍉", - "waving_hand": "👋", - "waving_hand_dark_skin_tone": "👋🏿", - "waving_hand_light_skin_tone": "👋🏻", - "waving_hand_medium-dark_skin_tone": "👋🏾", - "waving_hand_medium-light_skin_tone": "👋🏼", - "waving_hand_medium_skin_tone": "👋🏽", - "wavy_dash": "〰", - "waxing_crescent_moon": "🌒", - "waxing_gibbous_moon": "🌔", - "weary_cat_face": "🙀", - "weary_face": "😩", - "wedding": "💒", - "whale": "🐳", - "wheel_of_dharma": "☸", - "wheelchair_symbol": "♿", - "white_circle": "⚪", - "white_exclamation_mark": "❕", - "white_flag": "🏳", - "white_flower": "💮", - "white_hair": "🦳", - "white-haired_man": "👨\u200d🦳", - "white-haired_woman": "👩\u200d🦳", - "white_heart": "🤍", - "white_heavy_check_mark": "✅", - "white_large_square": "⬜", - "white_medium-small_square": "◽", - "white_medium_square": "◻", - "white_medium_star": "⭐", - "white_question_mark": "❔", - "white_small_square": "▫", - "white_square_button": "🔳", - "wilted_flower": "🥀", - "wind_chime": "🎐", - "wind_face": "🌬", - "wine_glass": "🍷", - "winking_face": "😉", - "winking_face_with_tongue": "😜", - "wolf_face": "🐺", - "woman": "👩", - "woman_artist": "👩\u200d🎨", - "woman_artist_dark_skin_tone": "👩🏿\u200d🎨", - "woman_artist_light_skin_tone": "👩🏻\u200d🎨", - "woman_artist_medium-dark_skin_tone": "👩🏾\u200d🎨", - "woman_artist_medium-light_skin_tone": "👩🏼\u200d🎨", - "woman_artist_medium_skin_tone": "👩🏽\u200d🎨", - "woman_astronaut": "👩\u200d🚀", - "woman_astronaut_dark_skin_tone": "👩🏿\u200d🚀", - "woman_astronaut_light_skin_tone": "👩🏻\u200d🚀", - "woman_astronaut_medium-dark_skin_tone": "👩🏾\u200d🚀", - "woman_astronaut_medium-light_skin_tone": "👩🏼\u200d🚀", - "woman_astronaut_medium_skin_tone": "👩🏽\u200d🚀", - "woman_biking": "🚴\u200d♀️", - "woman_biking_dark_skin_tone": "🚴🏿\u200d♀️", - "woman_biking_light_skin_tone": "🚴🏻\u200d♀️", - "woman_biking_medium-dark_skin_tone": "🚴🏾\u200d♀️", - "woman_biking_medium-light_skin_tone": "🚴🏼\u200d♀️", - "woman_biking_medium_skin_tone": "🚴🏽\u200d♀️", - "woman_bouncing_ball": "⛹️\u200d♀️", - "woman_bouncing_ball_dark_skin_tone": "⛹🏿\u200d♀️", - "woman_bouncing_ball_light_skin_tone": "⛹🏻\u200d♀️", - "woman_bouncing_ball_medium-dark_skin_tone": "⛹🏾\u200d♀️", - "woman_bouncing_ball_medium-light_skin_tone": "⛹🏼\u200d♀️", - "woman_bouncing_ball_medium_skin_tone": "⛹🏽\u200d♀️", - "woman_bowing": "🙇\u200d♀️", - "woman_bowing_dark_skin_tone": "🙇🏿\u200d♀️", - "woman_bowing_light_skin_tone": "🙇🏻\u200d♀️", - "woman_bowing_medium-dark_skin_tone": "🙇🏾\u200d♀️", - "woman_bowing_medium-light_skin_tone": "🙇🏼\u200d♀️", - "woman_bowing_medium_skin_tone": "🙇🏽\u200d♀️", - "woman_cartwheeling": "🤸\u200d♀️", - "woman_cartwheeling_dark_skin_tone": "🤸🏿\u200d♀️", - "woman_cartwheeling_light_skin_tone": "🤸🏻\u200d♀️", - "woman_cartwheeling_medium-dark_skin_tone": "🤸🏾\u200d♀️", - "woman_cartwheeling_medium-light_skin_tone": "🤸🏼\u200d♀️", - "woman_cartwheeling_medium_skin_tone": "🤸🏽\u200d♀️", - "woman_climbing": "🧗\u200d♀️", - "woman_climbing_dark_skin_tone": "🧗🏿\u200d♀️", - "woman_climbing_light_skin_tone": "🧗🏻\u200d♀️", - "woman_climbing_medium-dark_skin_tone": "🧗🏾\u200d♀️", - "woman_climbing_medium-light_skin_tone": "🧗🏼\u200d♀️", - "woman_climbing_medium_skin_tone": "🧗🏽\u200d♀️", - "woman_construction_worker": "👷\u200d♀️", - "woman_construction_worker_dark_skin_tone": "👷🏿\u200d♀️", - "woman_construction_worker_light_skin_tone": "👷🏻\u200d♀️", - "woman_construction_worker_medium-dark_skin_tone": "👷🏾\u200d♀️", - "woman_construction_worker_medium-light_skin_tone": "👷🏼\u200d♀️", - "woman_construction_worker_medium_skin_tone": "👷🏽\u200d♀️", - "woman_cook": "👩\u200d🍳", - "woman_cook_dark_skin_tone": "👩🏿\u200d🍳", - "woman_cook_light_skin_tone": "👩🏻\u200d🍳", - "woman_cook_medium-dark_skin_tone": "👩🏾\u200d🍳", - "woman_cook_medium-light_skin_tone": "👩🏼\u200d🍳", - "woman_cook_medium_skin_tone": "👩🏽\u200d🍳", - "woman_dancing": "💃", - "woman_dancing_dark_skin_tone": "💃🏿", - "woman_dancing_light_skin_tone": "💃🏻", - "woman_dancing_medium-dark_skin_tone": "💃🏾", - "woman_dancing_medium-light_skin_tone": "💃🏼", - "woman_dancing_medium_skin_tone": "💃🏽", - "woman_dark_skin_tone": "👩🏿", - "woman_detective": "🕵️\u200d♀️", - "woman_detective_dark_skin_tone": "🕵🏿\u200d♀️", - "woman_detective_light_skin_tone": "🕵🏻\u200d♀️", - "woman_detective_medium-dark_skin_tone": "🕵🏾\u200d♀️", - "woman_detective_medium-light_skin_tone": "🕵🏼\u200d♀️", - "woman_detective_medium_skin_tone": "🕵🏽\u200d♀️", - "woman_elf": "🧝\u200d♀️", - "woman_elf_dark_skin_tone": "🧝🏿\u200d♀️", - "woman_elf_light_skin_tone": "🧝🏻\u200d♀️", - "woman_elf_medium-dark_skin_tone": "🧝🏾\u200d♀️", - "woman_elf_medium-light_skin_tone": "🧝🏼\u200d♀️", - "woman_elf_medium_skin_tone": "🧝🏽\u200d♀️", - "woman_facepalming": "🤦\u200d♀️", - "woman_facepalming_dark_skin_tone": "🤦🏿\u200d♀️", - "woman_facepalming_light_skin_tone": "🤦🏻\u200d♀️", - "woman_facepalming_medium-dark_skin_tone": "🤦🏾\u200d♀️", - "woman_facepalming_medium-light_skin_tone": "🤦🏼\u200d♀️", - "woman_facepalming_medium_skin_tone": "🤦🏽\u200d♀️", - "woman_factory_worker": "👩\u200d🏭", - "woman_factory_worker_dark_skin_tone": "👩🏿\u200d🏭", - "woman_factory_worker_light_skin_tone": "👩🏻\u200d🏭", - "woman_factory_worker_medium-dark_skin_tone": "👩🏾\u200d🏭", - "woman_factory_worker_medium-light_skin_tone": "👩🏼\u200d🏭", - "woman_factory_worker_medium_skin_tone": "👩🏽\u200d🏭", - "woman_fairy": "🧚\u200d♀️", - "woman_fairy_dark_skin_tone": "🧚🏿\u200d♀️", - "woman_fairy_light_skin_tone": "🧚🏻\u200d♀️", - "woman_fairy_medium-dark_skin_tone": "🧚🏾\u200d♀️", - "woman_fairy_medium-light_skin_tone": "🧚🏼\u200d♀️", - "woman_fairy_medium_skin_tone": "🧚🏽\u200d♀️", - "woman_farmer": "👩\u200d🌾", - "woman_farmer_dark_skin_tone": "👩🏿\u200d🌾", - "woman_farmer_light_skin_tone": "👩🏻\u200d🌾", - "woman_farmer_medium-dark_skin_tone": "👩🏾\u200d🌾", - "woman_farmer_medium-light_skin_tone": "👩🏼\u200d🌾", - "woman_farmer_medium_skin_tone": "👩🏽\u200d🌾", - "woman_firefighter": "👩\u200d🚒", - "woman_firefighter_dark_skin_tone": "👩🏿\u200d🚒", - "woman_firefighter_light_skin_tone": "👩🏻\u200d🚒", - "woman_firefighter_medium-dark_skin_tone": "👩🏾\u200d🚒", - "woman_firefighter_medium-light_skin_tone": "👩🏼\u200d🚒", - "woman_firefighter_medium_skin_tone": "👩🏽\u200d🚒", - "woman_frowning": "🙍\u200d♀️", - "woman_frowning_dark_skin_tone": "🙍🏿\u200d♀️", - "woman_frowning_light_skin_tone": "🙍🏻\u200d♀️", - "woman_frowning_medium-dark_skin_tone": "🙍🏾\u200d♀️", - "woman_frowning_medium-light_skin_tone": "🙍🏼\u200d♀️", - "woman_frowning_medium_skin_tone": "🙍🏽\u200d♀️", - "woman_genie": "🧞\u200d♀️", - "woman_gesturing_no": "🙅\u200d♀️", - "woman_gesturing_no_dark_skin_tone": "🙅🏿\u200d♀️", - "woman_gesturing_no_light_skin_tone": "🙅🏻\u200d♀️", - "woman_gesturing_no_medium-dark_skin_tone": "🙅🏾\u200d♀️", - "woman_gesturing_no_medium-light_skin_tone": "🙅🏼\u200d♀️", - "woman_gesturing_no_medium_skin_tone": "🙅🏽\u200d♀️", - "woman_gesturing_ok": "🙆\u200d♀️", - "woman_gesturing_ok_dark_skin_tone": "🙆🏿\u200d♀️", - "woman_gesturing_ok_light_skin_tone": "🙆🏻\u200d♀️", - "woman_gesturing_ok_medium-dark_skin_tone": "🙆🏾\u200d♀️", - "woman_gesturing_ok_medium-light_skin_tone": "🙆🏼\u200d♀️", - "woman_gesturing_ok_medium_skin_tone": "🙆🏽\u200d♀️", - "woman_getting_haircut": "💇\u200d♀️", - "woman_getting_haircut_dark_skin_tone": "💇🏿\u200d♀️", - "woman_getting_haircut_light_skin_tone": "💇🏻\u200d♀️", - "woman_getting_haircut_medium-dark_skin_tone": "💇🏾\u200d♀️", - "woman_getting_haircut_medium-light_skin_tone": "💇🏼\u200d♀️", - "woman_getting_haircut_medium_skin_tone": "💇🏽\u200d♀️", - "woman_getting_massage": "💆\u200d♀️", - "woman_getting_massage_dark_skin_tone": "💆🏿\u200d♀️", - "woman_getting_massage_light_skin_tone": "💆🏻\u200d♀️", - "woman_getting_massage_medium-dark_skin_tone": "💆🏾\u200d♀️", - "woman_getting_massage_medium-light_skin_tone": "💆🏼\u200d♀️", - "woman_getting_massage_medium_skin_tone": "💆🏽\u200d♀️", - "woman_golfing": "🏌️\u200d♀️", - "woman_golfing_dark_skin_tone": "🏌🏿\u200d♀️", - "woman_golfing_light_skin_tone": "🏌🏻\u200d♀️", - "woman_golfing_medium-dark_skin_tone": "🏌🏾\u200d♀️", - "woman_golfing_medium-light_skin_tone": "🏌🏼\u200d♀️", - "woman_golfing_medium_skin_tone": "🏌🏽\u200d♀️", - "woman_guard": "💂\u200d♀️", - "woman_guard_dark_skin_tone": "💂🏿\u200d♀️", - "woman_guard_light_skin_tone": "💂🏻\u200d♀️", - "woman_guard_medium-dark_skin_tone": "💂🏾\u200d♀️", - "woman_guard_medium-light_skin_tone": "💂🏼\u200d♀️", - "woman_guard_medium_skin_tone": "💂🏽\u200d♀️", - "woman_health_worker": "👩\u200d⚕️", - "woman_health_worker_dark_skin_tone": "👩🏿\u200d⚕️", - "woman_health_worker_light_skin_tone": "👩🏻\u200d⚕️", - "woman_health_worker_medium-dark_skin_tone": "👩🏾\u200d⚕️", - "woman_health_worker_medium-light_skin_tone": "👩🏼\u200d⚕️", - "woman_health_worker_medium_skin_tone": "👩🏽\u200d⚕️", - "woman_in_lotus_position": "🧘\u200d♀️", - "woman_in_lotus_position_dark_skin_tone": "🧘🏿\u200d♀️", - "woman_in_lotus_position_light_skin_tone": "🧘🏻\u200d♀️", - "woman_in_lotus_position_medium-dark_skin_tone": "🧘🏾\u200d♀️", - "woman_in_lotus_position_medium-light_skin_tone": "🧘🏼\u200d♀️", - "woman_in_lotus_position_medium_skin_tone": "🧘🏽\u200d♀️", - "woman_in_manual_wheelchair": "👩\u200d🦽", - "woman_in_motorized_wheelchair": "👩\u200d🦼", - "woman_in_steamy_room": "🧖\u200d♀️", - "woman_in_steamy_room_dark_skin_tone": "🧖🏿\u200d♀️", - "woman_in_steamy_room_light_skin_tone": "🧖🏻\u200d♀️", - "woman_in_steamy_room_medium-dark_skin_tone": "🧖🏾\u200d♀️", - "woman_in_steamy_room_medium-light_skin_tone": "🧖🏼\u200d♀️", - "woman_in_steamy_room_medium_skin_tone": "🧖🏽\u200d♀️", - "woman_judge": "👩\u200d⚖️", - "woman_judge_dark_skin_tone": "👩🏿\u200d⚖️", - "woman_judge_light_skin_tone": "👩🏻\u200d⚖️", - "woman_judge_medium-dark_skin_tone": "👩🏾\u200d⚖️", - "woman_judge_medium-light_skin_tone": "👩🏼\u200d⚖️", - "woman_judge_medium_skin_tone": "👩🏽\u200d⚖️", - "woman_juggling": "🤹\u200d♀️", - "woman_juggling_dark_skin_tone": "🤹🏿\u200d♀️", - "woman_juggling_light_skin_tone": "🤹🏻\u200d♀️", - "woman_juggling_medium-dark_skin_tone": "🤹🏾\u200d♀️", - "woman_juggling_medium-light_skin_tone": "🤹🏼\u200d♀️", - "woman_juggling_medium_skin_tone": "🤹🏽\u200d♀️", - "woman_lifting_weights": "🏋️\u200d♀️", - "woman_lifting_weights_dark_skin_tone": "🏋🏿\u200d♀️", - "woman_lifting_weights_light_skin_tone": "🏋🏻\u200d♀️", - "woman_lifting_weights_medium-dark_skin_tone": "🏋🏾\u200d♀️", - "woman_lifting_weights_medium-light_skin_tone": "🏋🏼\u200d♀️", - "woman_lifting_weights_medium_skin_tone": "🏋🏽\u200d♀️", - "woman_light_skin_tone": "👩🏻", - "woman_mage": "🧙\u200d♀️", - "woman_mage_dark_skin_tone": "🧙🏿\u200d♀️", - "woman_mage_light_skin_tone": "🧙🏻\u200d♀️", - "woman_mage_medium-dark_skin_tone": "🧙🏾\u200d♀️", - "woman_mage_medium-light_skin_tone": "🧙🏼\u200d♀️", - "woman_mage_medium_skin_tone": "🧙🏽\u200d♀️", - "woman_mechanic": "👩\u200d🔧", - "woman_mechanic_dark_skin_tone": "👩🏿\u200d🔧", - "woman_mechanic_light_skin_tone": "👩🏻\u200d🔧", - "woman_mechanic_medium-dark_skin_tone": "👩🏾\u200d🔧", - "woman_mechanic_medium-light_skin_tone": "👩🏼\u200d🔧", - "woman_mechanic_medium_skin_tone": "👩🏽\u200d🔧", - "woman_medium-dark_skin_tone": "👩🏾", - "woman_medium-light_skin_tone": "👩🏼", - "woman_medium_skin_tone": "👩🏽", - "woman_mountain_biking": "🚵\u200d♀️", - "woman_mountain_biking_dark_skin_tone": "🚵🏿\u200d♀️", - "woman_mountain_biking_light_skin_tone": "🚵🏻\u200d♀️", - "woman_mountain_biking_medium-dark_skin_tone": "🚵🏾\u200d♀️", - "woman_mountain_biking_medium-light_skin_tone": "🚵🏼\u200d♀️", - "woman_mountain_biking_medium_skin_tone": "🚵🏽\u200d♀️", - "woman_office_worker": "👩\u200d💼", - "woman_office_worker_dark_skin_tone": "👩🏿\u200d💼", - "woman_office_worker_light_skin_tone": "👩🏻\u200d💼", - "woman_office_worker_medium-dark_skin_tone": "👩🏾\u200d💼", - "woman_office_worker_medium-light_skin_tone": "👩🏼\u200d💼", - "woman_office_worker_medium_skin_tone": "👩🏽\u200d💼", - "woman_pilot": "👩\u200d✈️", - "woman_pilot_dark_skin_tone": "👩🏿\u200d✈️", - "woman_pilot_light_skin_tone": "👩🏻\u200d✈️", - "woman_pilot_medium-dark_skin_tone": "👩🏾\u200d✈️", - "woman_pilot_medium-light_skin_tone": "👩🏼\u200d✈️", - "woman_pilot_medium_skin_tone": "👩🏽\u200d✈️", - "woman_playing_handball": "🤾\u200d♀️", - "woman_playing_handball_dark_skin_tone": "🤾🏿\u200d♀️", - "woman_playing_handball_light_skin_tone": "🤾🏻\u200d♀️", - "woman_playing_handball_medium-dark_skin_tone": "🤾🏾\u200d♀️", - "woman_playing_handball_medium-light_skin_tone": "🤾🏼\u200d♀️", - "woman_playing_handball_medium_skin_tone": "🤾🏽\u200d♀️", - "woman_playing_water_polo": "🤽\u200d♀️", - "woman_playing_water_polo_dark_skin_tone": "🤽🏿\u200d♀️", - "woman_playing_water_polo_light_skin_tone": "🤽🏻\u200d♀️", - "woman_playing_water_polo_medium-dark_skin_tone": "🤽🏾\u200d♀️", - "woman_playing_water_polo_medium-light_skin_tone": "🤽🏼\u200d♀️", - "woman_playing_water_polo_medium_skin_tone": "🤽🏽\u200d♀️", - "woman_police_officer": "👮\u200d♀️", - "woman_police_officer_dark_skin_tone": "👮🏿\u200d♀️", - "woman_police_officer_light_skin_tone": "👮🏻\u200d♀️", - "woman_police_officer_medium-dark_skin_tone": "👮🏾\u200d♀️", - "woman_police_officer_medium-light_skin_tone": "👮🏼\u200d♀️", - "woman_police_officer_medium_skin_tone": "👮🏽\u200d♀️", - "woman_pouting": "🙎\u200d♀️", - "woman_pouting_dark_skin_tone": "🙎🏿\u200d♀️", - "woman_pouting_light_skin_tone": "🙎🏻\u200d♀️", - "woman_pouting_medium-dark_skin_tone": "🙎🏾\u200d♀️", - "woman_pouting_medium-light_skin_tone": "🙎🏼\u200d♀️", - "woman_pouting_medium_skin_tone": "🙎🏽\u200d♀️", - "woman_raising_hand": "🙋\u200d♀️", - "woman_raising_hand_dark_skin_tone": "🙋🏿\u200d♀️", - "woman_raising_hand_light_skin_tone": "🙋🏻\u200d♀️", - "woman_raising_hand_medium-dark_skin_tone": "🙋🏾\u200d♀️", - "woman_raising_hand_medium-light_skin_tone": "🙋🏼\u200d♀️", - "woman_raising_hand_medium_skin_tone": "🙋🏽\u200d♀️", - "woman_rowing_boat": "🚣\u200d♀️", - "woman_rowing_boat_dark_skin_tone": "🚣🏿\u200d♀️", - "woman_rowing_boat_light_skin_tone": "🚣🏻\u200d♀️", - "woman_rowing_boat_medium-dark_skin_tone": "🚣🏾\u200d♀️", - "woman_rowing_boat_medium-light_skin_tone": "🚣🏼\u200d♀️", - "woman_rowing_boat_medium_skin_tone": "🚣🏽\u200d♀️", - "woman_running": "🏃\u200d♀️", - "woman_running_dark_skin_tone": "🏃🏿\u200d♀️", - "woman_running_light_skin_tone": "🏃🏻\u200d♀️", - "woman_running_medium-dark_skin_tone": "🏃🏾\u200d♀️", - "woman_running_medium-light_skin_tone": "🏃🏼\u200d♀️", - "woman_running_medium_skin_tone": "🏃🏽\u200d♀️", - "woman_scientist": "👩\u200d🔬", - "woman_scientist_dark_skin_tone": "👩🏿\u200d🔬", - "woman_scientist_light_skin_tone": "👩🏻\u200d🔬", - "woman_scientist_medium-dark_skin_tone": "👩🏾\u200d🔬", - "woman_scientist_medium-light_skin_tone": "👩🏼\u200d🔬", - "woman_scientist_medium_skin_tone": "👩🏽\u200d🔬", - "woman_shrugging": "🤷\u200d♀️", - "woman_shrugging_dark_skin_tone": "🤷🏿\u200d♀️", - "woman_shrugging_light_skin_tone": "🤷🏻\u200d♀️", - "woman_shrugging_medium-dark_skin_tone": "🤷🏾\u200d♀️", - "woman_shrugging_medium-light_skin_tone": "🤷🏼\u200d♀️", - "woman_shrugging_medium_skin_tone": "🤷🏽\u200d♀️", - "woman_singer": "👩\u200d🎤", - "woman_singer_dark_skin_tone": "👩🏿\u200d🎤", - "woman_singer_light_skin_tone": "👩🏻\u200d🎤", - "woman_singer_medium-dark_skin_tone": "👩🏾\u200d🎤", - "woman_singer_medium-light_skin_tone": "👩🏼\u200d🎤", - "woman_singer_medium_skin_tone": "👩🏽\u200d🎤", - "woman_student": "👩\u200d🎓", - "woman_student_dark_skin_tone": "👩🏿\u200d🎓", - "woman_student_light_skin_tone": "👩🏻\u200d🎓", - "woman_student_medium-dark_skin_tone": "👩🏾\u200d🎓", - "woman_student_medium-light_skin_tone": "👩🏼\u200d🎓", - "woman_student_medium_skin_tone": "👩🏽\u200d🎓", - "woman_surfing": "🏄\u200d♀️", - "woman_surfing_dark_skin_tone": "🏄🏿\u200d♀️", - "woman_surfing_light_skin_tone": "🏄🏻\u200d♀️", - "woman_surfing_medium-dark_skin_tone": "🏄🏾\u200d♀️", - "woman_surfing_medium-light_skin_tone": "🏄🏼\u200d♀️", - "woman_surfing_medium_skin_tone": "🏄🏽\u200d♀️", - "woman_swimming": "🏊\u200d♀️", - "woman_swimming_dark_skin_tone": "🏊🏿\u200d♀️", - "woman_swimming_light_skin_tone": "🏊🏻\u200d♀️", - "woman_swimming_medium-dark_skin_tone": "🏊🏾\u200d♀️", - "woman_swimming_medium-light_skin_tone": "🏊🏼\u200d♀️", - "woman_swimming_medium_skin_tone": "🏊🏽\u200d♀️", - "woman_teacher": "👩\u200d🏫", - "woman_teacher_dark_skin_tone": "👩🏿\u200d🏫", - "woman_teacher_light_skin_tone": "👩🏻\u200d🏫", - "woman_teacher_medium-dark_skin_tone": "👩🏾\u200d🏫", - "woman_teacher_medium-light_skin_tone": "👩🏼\u200d🏫", - "woman_teacher_medium_skin_tone": "👩🏽\u200d🏫", - "woman_technologist": "👩\u200d💻", - "woman_technologist_dark_skin_tone": "👩🏿\u200d💻", - "woman_technologist_light_skin_tone": "👩🏻\u200d💻", - "woman_technologist_medium-dark_skin_tone": "👩🏾\u200d💻", - "woman_technologist_medium-light_skin_tone": "👩🏼\u200d💻", - "woman_technologist_medium_skin_tone": "👩🏽\u200d💻", - "woman_tipping_hand": "💁\u200d♀️", - "woman_tipping_hand_dark_skin_tone": "💁🏿\u200d♀️", - "woman_tipping_hand_light_skin_tone": "💁🏻\u200d♀️", - "woman_tipping_hand_medium-dark_skin_tone": "💁🏾\u200d♀️", - "woman_tipping_hand_medium-light_skin_tone": "💁🏼\u200d♀️", - "woman_tipping_hand_medium_skin_tone": "💁🏽\u200d♀️", - "woman_vampire": "🧛\u200d♀️", - "woman_vampire_dark_skin_tone": "🧛🏿\u200d♀️", - "woman_vampire_light_skin_tone": "🧛🏻\u200d♀️", - "woman_vampire_medium-dark_skin_tone": "🧛🏾\u200d♀️", - "woman_vampire_medium-light_skin_tone": "🧛🏼\u200d♀️", - "woman_vampire_medium_skin_tone": "🧛🏽\u200d♀️", - "woman_walking": "🚶\u200d♀️", - "woman_walking_dark_skin_tone": "🚶🏿\u200d♀️", - "woman_walking_light_skin_tone": "🚶🏻\u200d♀️", - "woman_walking_medium-dark_skin_tone": "🚶🏾\u200d♀️", - "woman_walking_medium-light_skin_tone": "🚶🏼\u200d♀️", - "woman_walking_medium_skin_tone": "🚶🏽\u200d♀️", - "woman_wearing_turban": "👳\u200d♀️", - "woman_wearing_turban_dark_skin_tone": "👳🏿\u200d♀️", - "woman_wearing_turban_light_skin_tone": "👳🏻\u200d♀️", - "woman_wearing_turban_medium-dark_skin_tone": "👳🏾\u200d♀️", - "woman_wearing_turban_medium-light_skin_tone": "👳🏼\u200d♀️", - "woman_wearing_turban_medium_skin_tone": "👳🏽\u200d♀️", - "woman_with_headscarf": "🧕", - "woman_with_headscarf_dark_skin_tone": "🧕🏿", - "woman_with_headscarf_light_skin_tone": "🧕🏻", - "woman_with_headscarf_medium-dark_skin_tone": "🧕🏾", - "woman_with_headscarf_medium-light_skin_tone": "🧕🏼", - "woman_with_headscarf_medium_skin_tone": "🧕🏽", - "woman_with_probing_cane": "👩\u200d🦯", - "woman_zombie": "🧟\u200d♀️", - "woman’s_boot": "👢", - "woman’s_clothes": "👚", - "woman’s_hat": "👒", - "woman’s_sandal": "👡", - "women_with_bunny_ears": "👯\u200d♀️", - "women_wrestling": "🤼\u200d♀️", - "women’s_room": "🚺", - "woozy_face": "🥴", - "world_map": "🗺", - "worried_face": "😟", - "wrapped_gift": "🎁", - "wrench": "🔧", - "writing_hand": "✍", - "writing_hand_dark_skin_tone": "✍🏿", - "writing_hand_light_skin_tone": "✍🏻", - "writing_hand_medium-dark_skin_tone": "✍🏾", - "writing_hand_medium-light_skin_tone": "✍🏼", - "writing_hand_medium_skin_tone": "✍🏽", - "yarn": "🧶", - "yawning_face": "🥱", - "yellow_circle": "🟡", - "yellow_heart": "💛", - "yellow_square": "🟨", - "yen_banknote": "💴", - "yo-yo": "🪀", - "yin_yang": "☯", - "zany_face": "🤪", - "zebra": "🦓", - "zipper-mouth_face": "🤐", - "zombie": "🧟", - "zzz": "💤", - "åland_islands": "🇦🇽", - "keycap_asterisk": "*⃣", - "keycap_digit_eight": "8⃣", - "keycap_digit_five": "5⃣", - "keycap_digit_four": "4⃣", - "keycap_digit_nine": "9⃣", - "keycap_digit_one": "1⃣", - "keycap_digit_seven": "7⃣", - "keycap_digit_six": "6⃣", - "keycap_digit_three": "3⃣", - "keycap_digit_two": "2⃣", - "keycap_digit_zero": "0⃣", - "keycap_number_sign": "#⃣", - "light_skin_tone": "🏻", - "medium_light_skin_tone": "🏼", - "medium_skin_tone": "🏽", - "medium_dark_skin_tone": "🏾", - "dark_skin_tone": "🏿", - "regional_indicator_symbol_letter_a": "🇦", - "regional_indicator_symbol_letter_b": "🇧", - "regional_indicator_symbol_letter_c": "🇨", - "regional_indicator_symbol_letter_d": "🇩", - "regional_indicator_symbol_letter_e": "🇪", - "regional_indicator_symbol_letter_f": "🇫", - "regional_indicator_symbol_letter_g": "🇬", - "regional_indicator_symbol_letter_h": "🇭", - "regional_indicator_symbol_letter_i": "🇮", - "regional_indicator_symbol_letter_j": "🇯", - "regional_indicator_symbol_letter_k": "🇰", - "regional_indicator_symbol_letter_l": "🇱", - "regional_indicator_symbol_letter_m": "🇲", - "regional_indicator_symbol_letter_n": "🇳", - "regional_indicator_symbol_letter_o": "🇴", - "regional_indicator_symbol_letter_p": "🇵", - "regional_indicator_symbol_letter_q": "🇶", - "regional_indicator_symbol_letter_r": "🇷", - "regional_indicator_symbol_letter_s": "🇸", - "regional_indicator_symbol_letter_t": "🇹", - "regional_indicator_symbol_letter_u": "🇺", - "regional_indicator_symbol_letter_v": "🇻", - "regional_indicator_symbol_letter_w": "🇼", - "regional_indicator_symbol_letter_x": "🇽", - "regional_indicator_symbol_letter_y": "🇾", - "regional_indicator_symbol_letter_z": "🇿", - "airplane_arriving": "🛬", - "space_invader": "👾", - "football": "🏈", - "anger": "💢", - "angry": "😠", - "anguished": "😧", - "signal_strength": "📶", - "arrows_counterclockwise": "🔄", - "arrow_heading_down": "⤵", - "arrow_heading_up": "⤴", - "art": "🎨", - "astonished": "😲", - "athletic_shoe": "👟", - "atm": "🏧", - "car": "🚗", - "red_car": "🚗", - "angel": "👼", - "back": "🔙", - "badminton_racquet_and_shuttlecock": "🏸", - "dollar": "💵", - "euro": "💶", - "pound": "💷", - "yen": "💴", - "barber": "💈", - "bath": "🛀", - "bear": "🐻", - "heartbeat": "💓", - "beer": "🍺", - "no_bell": "🔕", - "bento": "🍱", - "bike": "🚲", - "bicyclist": "🚴", - "8ball": "🎱", - "biohazard_sign": "☣", - "birthday": "🎂", - "black_circle_for_record": "⏺", - "clubs": "♣", - "diamonds": "♦", - "arrow_double_down": "⏬", - "hearts": "♥", - "rewind": "⏪", - "black_left__pointing_double_triangle_with_vertical_bar": "⏮", - "arrow_backward": "◀", - "black_medium_small_square": "◾", - "question": "❓", - "fast_forward": "⏩", - "black_right__pointing_double_triangle_with_vertical_bar": "⏭", - "arrow_forward": "▶", - "black_right__pointing_triangle_with_double_vertical_bar": "⏯", - "arrow_right": "➡", - "spades": "♠", - "black_square_for_stop": "⏹", - "sunny": "☀", - "phone": "☎", - "recycle": "♻", - "arrow_double_up": "⏫", - "busstop": "🚏", - "date": "📅", - "flags": "🎏", - "cat2": "🐈", - "joy_cat": "😹", - "smirk_cat": "😼", - "chart_with_downwards_trend": "📉", - "chart_with_upwards_trend": "📈", - "chart": "💹", - "mega": "📣", - "checkered_flag": "🏁", - "accept": "🉑", - "ideograph_advantage": "🉐", - "congratulations": "㊗", - "secret": "㊙", - "m": "Ⓜ", - "city_sunset": "🌆", - "clapper": "🎬", - "clap": "👏", - "beers": "🍻", - "clock830": "🕣", - "clock8": "🕗", - "clock1130": "🕦", - "clock11": "🕚", - "clock530": "🕠", - "clock5": "🕔", - "clock430": "🕟", - "clock4": "🕓", - "clock930": "🕤", - "clock9": "🕘", - "clock130": "🕜", - "clock1": "🕐", - "clock730": "🕢", - "clock7": "🕖", - "clock630": "🕡", - "clock6": "🕕", - "clock1030": "🕥", - "clock10": "🕙", - "clock330": "🕞", - "clock3": "🕒", - "clock1230": "🕧", - "clock12": "🕛", - "clock230": "🕝", - "clock2": "🕑", - "arrows_clockwise": "🔃", - "repeat": "🔁", - "repeat_one": "🔂", - "closed_lock_with_key": "🔐", - "mailbox_closed": "📪", - "mailbox": "📫", - "cloud_with_tornado": "🌪", - "cocktail": "🍸", - "boom": "💥", - "compression": "🗜", - "confounded": "😖", - "confused": "😕", - "rice": "🍚", - "cow2": "🐄", - "cricket_bat_and_ball": "🏏", - "x": "❌", - "cry": "😢", - "curry": "🍛", - "dagger_knife": "🗡", - "dancer": "💃", - "dark_sunglasses": "🕶", - "dash": "💨", - "truck": "🚚", - "derelict_house_building": "🏚", - "diamond_shape_with_a_dot_inside": "💠", - "dart": "🎯", - "disappointed_relieved": "😥", - "disappointed": "😞", - "do_not_litter": "🚯", - "dog2": "🐕", - "flipper": "🐬", - "loop": "➿", - "bangbang": "‼", - "double_vertical_bar": "⏸", - "dove_of_peace": "🕊", - "small_red_triangle_down": "🔻", - "arrow_down_small": "🔽", - "arrow_down": "⬇", - "dromedary_camel": "🐪", - "e__mail": "📧", - "corn": "🌽", - "ear_of_rice": "🌾", - "earth_americas": "🌎", - "earth_asia": "🌏", - "earth_africa": "🌍", - "eight_pointed_black_star": "✴", - "eight_spoked_asterisk": "✳", - "eject_symbol": "⏏", - "bulb": "💡", - "emoji_modifier_fitzpatrick_type__1__2": "🏻", - "emoji_modifier_fitzpatrick_type__3": "🏼", - "emoji_modifier_fitzpatrick_type__4": "🏽", - "emoji_modifier_fitzpatrick_type__5": "🏾", - "emoji_modifier_fitzpatrick_type__6": "🏿", - "end": "🔚", - "email": "✉", - "european_castle": "🏰", - "european_post_office": "🏤", - "interrobang": "⁉", - "expressionless": "😑", - "eyeglasses": "👓", - "massage": "💆", - "yum": "😋", - "scream": "😱", - "kissing_heart": "😘", - "sweat": "😓", - "face_with_head__bandage": "🤕", - "triumph": "😤", - "mask": "😷", - "no_good": "🙅", - "ok_woman": "🙆", - "open_mouth": "😮", - "cold_sweat": "😰", - "stuck_out_tongue": "😛", - "stuck_out_tongue_closed_eyes": "😝", - "stuck_out_tongue_winking_eye": "😜", - "joy": "😂", - "no_mouth": "😶", - "santa": "🎅", - "fax": "📠", - "fearful": "😨", - "field_hockey_stick_and_ball": "🏑", - "first_quarter_moon_with_face": "🌛", - "fish_cake": "🍥", - "fishing_pole_and_fish": "🎣", - "facepunch": "👊", - "punch": "👊", - "flag_for_afghanistan": "🇦🇫", - "flag_for_albania": "🇦🇱", - "flag_for_algeria": "🇩🇿", - "flag_for_american_samoa": "🇦🇸", - "flag_for_andorra": "🇦🇩", - "flag_for_angola": "🇦🇴", - "flag_for_anguilla": "🇦🇮", - "flag_for_antarctica": "🇦🇶", - "flag_for_antigua_&_barbuda": "🇦🇬", - "flag_for_argentina": "🇦🇷", - "flag_for_armenia": "🇦🇲", - "flag_for_aruba": "🇦🇼", - "flag_for_ascension_island": "🇦🇨", - "flag_for_australia": "🇦🇺", - "flag_for_austria": "🇦🇹", - "flag_for_azerbaijan": "🇦🇿", - "flag_for_bahamas": "🇧🇸", - "flag_for_bahrain": "🇧🇭", - "flag_for_bangladesh": "🇧🇩", - "flag_for_barbados": "🇧🇧", - "flag_for_belarus": "🇧🇾", - "flag_for_belgium": "🇧🇪", - "flag_for_belize": "🇧🇿", - "flag_for_benin": "🇧🇯", - "flag_for_bermuda": "🇧🇲", - "flag_for_bhutan": "🇧🇹", - "flag_for_bolivia": "🇧🇴", - "flag_for_bosnia_&_herzegovina": "🇧🇦", - "flag_for_botswana": "🇧🇼", - "flag_for_bouvet_island": "🇧🇻", - "flag_for_brazil": "🇧🇷", - "flag_for_british_indian_ocean_territory": "🇮🇴", - "flag_for_british_virgin_islands": "🇻🇬", - "flag_for_brunei": "🇧🇳", - "flag_for_bulgaria": "🇧🇬", - "flag_for_burkina_faso": "🇧🇫", - "flag_for_burundi": "🇧🇮", - "flag_for_cambodia": "🇰🇭", - "flag_for_cameroon": "🇨🇲", - "flag_for_canada": "🇨🇦", - "flag_for_canary_islands": "🇮🇨", - "flag_for_cape_verde": "🇨🇻", - "flag_for_caribbean_netherlands": "🇧🇶", - "flag_for_cayman_islands": "🇰🇾", - "flag_for_central_african_republic": "🇨🇫", - "flag_for_ceuta_&_melilla": "🇪🇦", - "flag_for_chad": "🇹🇩", - "flag_for_chile": "🇨🇱", - "flag_for_china": "🇨🇳", - "flag_for_christmas_island": "🇨🇽", - "flag_for_clipperton_island": "🇨🇵", - "flag_for_cocos__islands": "🇨🇨", - "flag_for_colombia": "🇨🇴", - "flag_for_comoros": "🇰🇲", - "flag_for_congo____brazzaville": "🇨🇬", - "flag_for_congo____kinshasa": "🇨🇩", - "flag_for_cook_islands": "🇨🇰", - "flag_for_costa_rica": "🇨🇷", - "flag_for_croatia": "🇭🇷", - "flag_for_cuba": "🇨🇺", - "flag_for_curaçao": "🇨🇼", - "flag_for_cyprus": "🇨🇾", - "flag_for_czech_republic": "🇨🇿", - "flag_for_côte_d’ivoire": "🇨🇮", - "flag_for_denmark": "🇩🇰", - "flag_for_diego_garcia": "🇩🇬", - "flag_for_djibouti": "🇩🇯", - "flag_for_dominica": "🇩🇲", - "flag_for_dominican_republic": "🇩🇴", - "flag_for_ecuador": "🇪🇨", - "flag_for_egypt": "🇪🇬", - "flag_for_el_salvador": "🇸🇻", - "flag_for_equatorial_guinea": "🇬🇶", - "flag_for_eritrea": "🇪🇷", - "flag_for_estonia": "🇪🇪", - "flag_for_ethiopia": "🇪🇹", - "flag_for_european_union": "🇪🇺", - "flag_for_falkland_islands": "🇫🇰", - "flag_for_faroe_islands": "🇫🇴", - "flag_for_fiji": "🇫🇯", - "flag_for_finland": "🇫🇮", - "flag_for_france": "🇫🇷", - "flag_for_french_guiana": "🇬🇫", - "flag_for_french_polynesia": "🇵🇫", - "flag_for_french_southern_territories": "🇹🇫", - "flag_for_gabon": "🇬🇦", - "flag_for_gambia": "🇬🇲", - "flag_for_georgia": "🇬🇪", - "flag_for_germany": "🇩🇪", - "flag_for_ghana": "🇬🇭", - "flag_for_gibraltar": "🇬🇮", - "flag_for_greece": "🇬🇷", - "flag_for_greenland": "🇬🇱", - "flag_for_grenada": "🇬🇩", - "flag_for_guadeloupe": "🇬🇵", - "flag_for_guam": "🇬🇺", - "flag_for_guatemala": "🇬🇹", - "flag_for_guernsey": "🇬🇬", - "flag_for_guinea": "🇬🇳", - "flag_for_guinea__bissau": "🇬🇼", - "flag_for_guyana": "🇬🇾", - "flag_for_haiti": "🇭🇹", - "flag_for_heard_&_mcdonald_islands": "🇭🇲", - "flag_for_honduras": "🇭🇳", - "flag_for_hong_kong": "🇭🇰", - "flag_for_hungary": "🇭🇺", - "flag_for_iceland": "🇮🇸", - "flag_for_india": "🇮🇳", - "flag_for_indonesia": "🇮🇩", - "flag_for_iran": "🇮🇷", - "flag_for_iraq": "🇮🇶", - "flag_for_ireland": "🇮🇪", - "flag_for_isle_of_man": "🇮🇲", - "flag_for_israel": "🇮🇱", - "flag_for_italy": "🇮🇹", - "flag_for_jamaica": "🇯🇲", - "flag_for_japan": "🇯🇵", - "flag_for_jersey": "🇯🇪", - "flag_for_jordan": "🇯🇴", - "flag_for_kazakhstan": "🇰🇿", - "flag_for_kenya": "🇰🇪", - "flag_for_kiribati": "🇰🇮", - "flag_for_kosovo": "🇽🇰", - "flag_for_kuwait": "🇰🇼", - "flag_for_kyrgyzstan": "🇰🇬", - "flag_for_laos": "🇱🇦", - "flag_for_latvia": "🇱🇻", - "flag_for_lebanon": "🇱🇧", - "flag_for_lesotho": "🇱🇸", - "flag_for_liberia": "🇱🇷", - "flag_for_libya": "🇱🇾", - "flag_for_liechtenstein": "🇱🇮", - "flag_for_lithuania": "🇱🇹", - "flag_for_luxembourg": "🇱🇺", - "flag_for_macau": "🇲🇴", - "flag_for_macedonia": "🇲🇰", - "flag_for_madagascar": "🇲🇬", - "flag_for_malawi": "🇲🇼", - "flag_for_malaysia": "🇲🇾", - "flag_for_maldives": "🇲🇻", - "flag_for_mali": "🇲🇱", - "flag_for_malta": "🇲🇹", - "flag_for_marshall_islands": "🇲🇭", - "flag_for_martinique": "🇲🇶", - "flag_for_mauritania": "🇲🇷", - "flag_for_mauritius": "🇲🇺", - "flag_for_mayotte": "🇾🇹", - "flag_for_mexico": "🇲🇽", - "flag_for_micronesia": "🇫🇲", - "flag_for_moldova": "🇲🇩", - "flag_for_monaco": "🇲🇨", - "flag_for_mongolia": "🇲🇳", - "flag_for_montenegro": "🇲🇪", - "flag_for_montserrat": "🇲🇸", - "flag_for_morocco": "🇲🇦", - "flag_for_mozambique": "🇲🇿", - "flag_for_myanmar": "🇲🇲", - "flag_for_namibia": "🇳🇦", - "flag_for_nauru": "🇳🇷", - "flag_for_nepal": "🇳🇵", - "flag_for_netherlands": "🇳🇱", - "flag_for_new_caledonia": "🇳🇨", - "flag_for_new_zealand": "🇳🇿", - "flag_for_nicaragua": "🇳🇮", - "flag_for_niger": "🇳🇪", - "flag_for_nigeria": "🇳🇬", - "flag_for_niue": "🇳🇺", - "flag_for_norfolk_island": "🇳🇫", - "flag_for_north_korea": "🇰🇵", - "flag_for_northern_mariana_islands": "🇲🇵", - "flag_for_norway": "🇳🇴", - "flag_for_oman": "🇴🇲", - "flag_for_pakistan": "🇵🇰", - "flag_for_palau": "🇵🇼", - "flag_for_palestinian_territories": "🇵🇸", - "flag_for_panama": "🇵🇦", - "flag_for_papua_new_guinea": "🇵🇬", - "flag_for_paraguay": "🇵🇾", - "flag_for_peru": "🇵🇪", - "flag_for_philippines": "🇵🇭", - "flag_for_pitcairn_islands": "🇵🇳", - "flag_for_poland": "🇵🇱", - "flag_for_portugal": "🇵🇹", - "flag_for_puerto_rico": "🇵🇷", - "flag_for_qatar": "🇶🇦", - "flag_for_romania": "🇷🇴", - "flag_for_russia": "🇷🇺", - "flag_for_rwanda": "🇷🇼", - "flag_for_réunion": "🇷🇪", - "flag_for_samoa": "🇼🇸", - "flag_for_san_marino": "🇸🇲", - "flag_for_saudi_arabia": "🇸🇦", - "flag_for_senegal": "🇸🇳", - "flag_for_serbia": "🇷🇸", - "flag_for_seychelles": "🇸🇨", - "flag_for_sierra_leone": "🇸🇱", - "flag_for_singapore": "🇸🇬", - "flag_for_sint_maarten": "🇸🇽", - "flag_for_slovakia": "🇸🇰", - "flag_for_slovenia": "🇸🇮", - "flag_for_solomon_islands": "🇸🇧", - "flag_for_somalia": "🇸🇴", - "flag_for_south_africa": "🇿🇦", - "flag_for_south_georgia_&_south_sandwich_islands": "🇬🇸", - "flag_for_south_korea": "🇰🇷", - "flag_for_south_sudan": "🇸🇸", - "flag_for_spain": "🇪🇸", - "flag_for_sri_lanka": "🇱🇰", - "flag_for_st._barthélemy": "🇧🇱", - "flag_for_st._helena": "🇸🇭", - "flag_for_st._kitts_&_nevis": "🇰🇳", - "flag_for_st._lucia": "🇱🇨", - "flag_for_st._martin": "🇲🇫", - "flag_for_st._pierre_&_miquelon": "🇵🇲", - "flag_for_st._vincent_&_grenadines": "🇻🇨", - "flag_for_sudan": "🇸🇩", - "flag_for_suriname": "🇸🇷", - "flag_for_svalbard_&_jan_mayen": "🇸🇯", - "flag_for_swaziland": "🇸🇿", - "flag_for_sweden": "🇸🇪", - "flag_for_switzerland": "🇨🇭", - "flag_for_syria": "🇸🇾", - "flag_for_são_tomé_&_príncipe": "🇸🇹", - "flag_for_taiwan": "🇹🇼", - "flag_for_tajikistan": "🇹🇯", - "flag_for_tanzania": "🇹🇿", - "flag_for_thailand": "🇹🇭", - "flag_for_timor__leste": "🇹🇱", - "flag_for_togo": "🇹🇬", - "flag_for_tokelau": "🇹🇰", - "flag_for_tonga": "🇹🇴", - "flag_for_trinidad_&_tobago": "🇹🇹", - "flag_for_tristan_da_cunha": "🇹🇦", - "flag_for_tunisia": "🇹🇳", - "flag_for_turkey": "🇹🇷", - "flag_for_turkmenistan": "🇹🇲", - "flag_for_turks_&_caicos_islands": "🇹🇨", - "flag_for_tuvalu": "🇹🇻", - "flag_for_u.s._outlying_islands": "🇺🇲", - "flag_for_u.s._virgin_islands": "🇻🇮", - "flag_for_uganda": "🇺🇬", - "flag_for_ukraine": "🇺🇦", - "flag_for_united_arab_emirates": "🇦🇪", - "flag_for_united_kingdom": "🇬🇧", - "flag_for_united_states": "🇺🇸", - "flag_for_uruguay": "🇺🇾", - "flag_for_uzbekistan": "🇺🇿", - "flag_for_vanuatu": "🇻🇺", - "flag_for_vatican_city": "🇻🇦", - "flag_for_venezuela": "🇻🇪", - "flag_for_vietnam": "🇻🇳", - "flag_for_wallis_&_futuna": "🇼🇫", - "flag_for_western_sahara": "🇪🇭", - "flag_for_yemen": "🇾🇪", - "flag_for_zambia": "🇿🇲", - "flag_for_zimbabwe": "🇿🇼", - "flag_for_åland_islands": "🇦🇽", - "golf": "⛳", - "fleur__de__lis": "⚜", - "muscle": "💪", - "flushed": "😳", - "frame_with_picture": "🖼", - "fries": "🍟", - "frog": "🐸", - "hatched_chick": "🐥", - "frowning": "😦", - "fuelpump": "⛽", - "full_moon_with_face": "🌝", - "gem": "💎", - "star2": "🌟", - "golfer": "🏌", - "mortar_board": "🎓", - "grimacing": "😬", - "smile_cat": "😸", - "grinning": "😀", - "grin": "😁", - "heartpulse": "💗", - "guardsman": "💂", - "haircut": "💇", - "hamster": "🐹", - "raising_hand": "🙋", - "headphones": "🎧", - "hear_no_evil": "🙉", - "cupid": "💘", - "gift_heart": "💝", - "heart": "❤", - "exclamation": "❗", - "heavy_exclamation_mark": "❗", - "heavy_heart_exclamation_mark_ornament": "❣", - "o": "⭕", - "helm_symbol": "⎈", - "helmet_with_white_cross": "⛑", - "high_heel": "👠", - "bullettrain_side": "🚄", - "bullettrain_front": "🚅", - "high_brightness": "🔆", - "zap": "⚡", - "hocho": "🔪", - "knife": "🔪", - "bee": "🐝", - "traffic_light": "🚥", - "racehorse": "🐎", - "coffee": "☕", - "hotsprings": "♨", - "hourglass": "⌛", - "hourglass_flowing_sand": "⏳", - "house_buildings": "🏘", - "100": "💯", - "hushed": "😯", - "ice_hockey_stick_and_puck": "🏒", - "imp": "👿", - "information_desk_person": "💁", - "information_source": "ℹ", - "capital_abcd": "🔠", - "abc": "🔤", - "abcd": "🔡", - "1234": "🔢", - "symbols": "🔣", - "izakaya_lantern": "🏮", - "lantern": "🏮", - "jack_o_lantern": "🎃", - "dolls": "🎎", - "japanese_goblin": "👺", - "japanese_ogre": "👹", - "beginner": "🔰", - "zero": "0️⃣", - "one": "1️⃣", - "ten": "🔟", - "two": "2️⃣", - "three": "3️⃣", - "four": "4️⃣", - "five": "5️⃣", - "six": "6️⃣", - "seven": "7️⃣", - "eight": "8️⃣", - "nine": "9️⃣", - "couplekiss": "💏", - "kissing_cat": "😽", - "kissing": "😗", - "kissing_closed_eyes": "😚", - "kissing_smiling_eyes": "😙", - "beetle": "🐞", - "large_blue_circle": "🔵", - "last_quarter_moon_with_face": "🌜", - "leaves": "🍃", - "mag": "🔍", - "left_right_arrow": "↔", - "leftwards_arrow_with_hook": "↩", - "arrow_left": "⬅", - "lock": "🔒", - "lock_with_ink_pen": "🔏", - "sob": "😭", - "low_brightness": "🔅", - "lower_left_ballpoint_pen": "🖊", - "lower_left_crayon": "🖍", - "lower_left_fountain_pen": "🖋", - "lower_left_paintbrush": "🖌", - "mahjong": "🀄", - "couple": "👫", - "man_in_business_suit_levitating": "🕴", - "man_with_gua_pi_mao": "👲", - "man_with_turban": "👳", - "mans_shoe": "👞", - "shoe": "👞", - "menorah_with_nine_branches": "🕎", - "mens": "🚹", - "minidisc": "💽", - "iphone": "📱", - "calling": "📲", - "money__mouth_face": "🤑", - "moneybag": "💰", - "rice_scene": "🎑", - "mountain_bicyclist": "🚵", - "mouse2": "🐁", - "lips": "👄", - "moyai": "🗿", - "notes": "🎶", - "nail_care": "💅", - "ab": "🆎", - "negative_squared_cross_mark": "❎", - "a": "🅰", - "b": "🅱", - "o2": "🅾", - "parking": "🅿", - "new_moon_with_face": "🌚", - "no_entry_sign": "🚫", - "underage": "🔞", - "non__potable_water": "🚱", - "arrow_upper_right": "↗", - "arrow_upper_left": "↖", - "office": "🏢", - "older_man": "👴", - "older_woman": "👵", - "om_symbol": "🕉", - "on": "🔛", - "book": "📖", - "unlock": "🔓", - "mailbox_with_no_mail": "📭", - "mailbox_with_mail": "📬", - "cd": "💿", - "tada": "🎉", - "feet": "🐾", - "walking": "🚶", - "pencil2": "✏", - "pensive": "😔", - "persevere": "😣", - "bow": "🙇", - "raised_hands": "🙌", - "person_with_ball": "⛹", - "person_with_blond_hair": "👱", - "pray": "🙏", - "person_with_pouting_face": "🙎", - "computer": "💻", - "pig2": "🐖", - "hankey": "💩", - "poop": "💩", - "shit": "💩", - "bamboo": "🎍", - "gun": "🔫", - "black_joker": "🃏", - "rotating_light": "🚨", - "cop": "👮", - "stew": "🍲", - "pouch": "👝", - "pouting_cat": "😾", - "rage": "😡", - "put_litter_in_its_place": "🚮", - "rabbit2": "🐇", - "racing_motorcycle": "🏍", - "radioactive_sign": "☢", - "fist": "✊", - "hand": "✋", - "raised_hand_with_fingers_splayed": "🖐", - "raised_hand_with_part_between_middle_and_ring_fingers": "🖖", - "blue_car": "🚙", - "apple": "🍎", - "relieved": "😌", - "reversed_hand_with_middle_finger_extended": "🖕", - "mag_right": "🔎", - "arrow_right_hook": "↪", - "sweet_potato": "🍠", - "robot": "🤖", - "rolled__up_newspaper": "🗞", - "rowboat": "🚣", - "runner": "🏃", - "running": "🏃", - "running_shirt_with_sash": "🎽", - "boat": "⛵", - "scales": "⚖", - "school_satchel": "🎒", - "scorpius": "♏", - "see_no_evil": "🙈", - "sheep": "🐑", - "stars": "🌠", - "cake": "🍰", - "six_pointed_star": "🔯", - "ski": "🎿", - "sleeping_accommodation": "🛌", - "sleeping": "😴", - "sleepy": "😪", - "sleuth_or_spy": "🕵", - "heart_eyes_cat": "😻", - "smiley_cat": "😺", - "innocent": "😇", - "heart_eyes": "😍", - "smiling_imp": "😈", - "smiley": "😃", - "sweat_smile": "😅", - "smile": "😄", - "laughing": "😆", - "satisfied": "😆", - "blush": "😊", - "smirk": "😏", - "smoking": "🚬", - "snow_capped_mountain": "🏔", - "soccer": "⚽", - "icecream": "🍦", - "soon": "🔜", - "arrow_lower_right": "↘", - "arrow_lower_left": "↙", - "speak_no_evil": "🙊", - "speaker": "🔈", - "mute": "🔇", - "sound": "🔉", - "loud_sound": "🔊", - "speaking_head_in_silhouette": "🗣", - "spiral_calendar_pad": "🗓", - "spiral_note_pad": "🗒", - "shell": "🐚", - "sweat_drops": "💦", - "u5272": "🈹", - "u5408": "🈴", - "u55b6": "🈺", - "u6307": "🈯", - "u6708": "🈷", - "u6709": "🈶", - "u6e80": "🈵", - "u7121": "🈚", - "u7533": "🈸", - "u7981": "🈲", - "u7a7a": "🈳", - "cl": "🆑", - "cool": "🆒", - "free": "🆓", - "id": "🆔", - "koko": "🈁", - "sa": "🈂", - "new": "🆕", - "ng": "🆖", - "ok": "🆗", - "sos": "🆘", - "up": "🆙", - "vs": "🆚", - "steam_locomotive": "🚂", - "ramen": "🍜", - "partly_sunny": "⛅", - "city_sunrise": "🌇", - "surfer": "🏄", - "swimmer": "🏊", - "shirt": "👕", - "tshirt": "👕", - "table_tennis_paddle_and_ball": "🏓", - "tea": "🍵", - "tv": "📺", - "three_button_mouse": "🖱", - "+1": "👍", - "thumbsup": "👍", - "__1": "👎", - "-1": "👎", - "thumbsdown": "👎", - "thunder_cloud_and_rain": "⛈", - "tiger2": "🐅", - "tophat": "🎩", - "top": "🔝", - "tm": "™", - "train2": "🚆", - "triangular_flag_on_post": "🚩", - "trident": "🔱", - "twisted_rightwards_arrows": "🔀", - "unamused": "😒", - "small_red_triangle": "🔺", - "arrow_up_small": "🔼", - "arrow_up_down": "↕", - "upside__down_face": "🙃", - "arrow_up": "⬆", - "v": "✌", - "vhs": "📼", - "wc": "🚾", - "ocean": "🌊", - "waving_black_flag": "🏴", - "wave": "👋", - "waving_white_flag": "🏳", - "moon": "🌔", - "scream_cat": "🙀", - "weary": "😩", - "weight_lifter": "🏋", - "whale2": "🐋", - "wheelchair": "♿", - "point_down": "👇", - "grey_exclamation": "❕", - "white_frowning_face": "☹", - "white_check_mark": "✅", - "point_left": "👈", - "white_medium_small_square": "◽", - "star": "⭐", - "grey_question": "❔", - "point_right": "👉", - "relaxed": "☺", - "white_sun_behind_cloud": "🌥", - "white_sun_behind_cloud_with_rain": "🌦", - "white_sun_with_small_cloud": "🌤", - "point_up_2": "👆", - "point_up": "☝", - "wind_blowing_face": "🌬", - "wink": "😉", - "wolf": "🐺", - "dancers": "👯", - "boot": "👢", - "womans_clothes": "👚", - "womans_hat": "👒", - "sandal": "👡", - "womens": "🚺", - "worried": "😟", - "gift": "🎁", - "zipper__mouth_face": "🤐", - "regional_indicator_a": "🇦", - "regional_indicator_b": "🇧", - "regional_indicator_c": "🇨", - "regional_indicator_d": "🇩", - "regional_indicator_e": "🇪", - "regional_indicator_f": "🇫", - "regional_indicator_g": "🇬", - "regional_indicator_h": "🇭", - "regional_indicator_i": "🇮", - "regional_indicator_j": "🇯", - "regional_indicator_k": "🇰", - "regional_indicator_l": "🇱", - "regional_indicator_m": "🇲", - "regional_indicator_n": "🇳", - "regional_indicator_o": "🇴", - "regional_indicator_p": "🇵", - "regional_indicator_q": "🇶", - "regional_indicator_r": "🇷", - "regional_indicator_s": "🇸", - "regional_indicator_t": "🇹", - "regional_indicator_u": "🇺", - "regional_indicator_v": "🇻", - "regional_indicator_w": "🇼", - "regional_indicator_x": "🇽", - "regional_indicator_y": "🇾", - "regional_indicator_z": "🇿", -} diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/retry.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/retry.py deleted file mode 100644 index 7572bfd26ad87711d67c3418a6a0ac9921fed08c..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/retry.py +++ /dev/null @@ -1,529 +0,0 @@ -from __future__ import annotations - -import email -import logging -import random -import re -import time -import typing -from itertools import takewhile -from types import TracebackType - -from ..exceptions import ( - ConnectTimeoutError, - InvalidHeader, - MaxRetryError, - ProtocolError, - ProxyError, - ReadTimeoutError, - ResponseError, -) -from .util import reraise - -if typing.TYPE_CHECKING: - from ..connectionpool import ConnectionPool - from ..response import BaseHTTPResponse - -log = logging.getLogger(__name__) - - -# Data structure for representing the metadata of requests that result in a retry. -class RequestHistory(typing.NamedTuple): - method: str | None - url: str | None - error: Exception | None - status: int | None - redirect_location: str | None - - -class Retry: - """Retry configuration. - - Each retry attempt will create a new Retry object with updated values, so - they can be safely reused. - - Retries can be defined as a default for a pool: - - .. code-block:: python - - retries = Retry(connect=5, read=2, redirect=5) - http = PoolManager(retries=retries) - response = http.request("GET", "https://example.com/") - - Or per-request (which overrides the default for the pool): - - .. code-block:: python - - response = http.request("GET", "https://example.com/", retries=Retry(10)) - - Retries can be disabled by passing ``False``: - - .. code-block:: python - - response = http.request("GET", "https://example.com/", retries=False) - - Errors will be wrapped in :class:`~urllib3.exceptions.MaxRetryError` unless - retries are disabled, in which case the causing exception will be raised. - - :param int total: - Total number of retries to allow. Takes precedence over other counts. - - Set to ``None`` to remove this constraint and fall back on other - counts. - - Set to ``0`` to fail on the first retry. - - Set to ``False`` to disable and imply ``raise_on_redirect=False``. - - :param int connect: - How many connection-related errors to retry on. - - These are errors raised before the request is sent to the remote server, - which we assume has not triggered the server to process the request. - - Set to ``0`` to fail on the first retry of this type. - - :param int read: - How many times to retry on read errors. - - These errors are raised after the request was sent to the server, so the - request may have side-effects. - - Set to ``0`` to fail on the first retry of this type. - - :param int redirect: - How many redirects to perform. Limit this to avoid infinite redirect - loops. - - A redirect is a HTTP response with a status code 301, 302, 303, 307 or - 308. - - Set to ``0`` to fail on the first retry of this type. - - Set to ``False`` to disable and imply ``raise_on_redirect=False``. - - :param int status: - How many times to retry on bad status codes. - - These are retries made on responses, where status code matches - ``status_forcelist``. - - Set to ``0`` to fail on the first retry of this type. - - :param int other: - How many times to retry on other errors. - - Other errors are errors that are not connect, read, redirect or status errors. - These errors might be raised after the request was sent to the server, so the - request might have side-effects. - - Set to ``0`` to fail on the first retry of this type. - - If ``total`` is not set, it's a good idea to set this to 0 to account - for unexpected edge cases and avoid infinite retry loops. - - :param Collection allowed_methods: - Set of uppercased HTTP method verbs that we should retry on. - - By default, we only retry on methods which are considered to be - idempotent (multiple requests with the same parameters end with the - same state). See :attr:`Retry.DEFAULT_ALLOWED_METHODS`. - - Set to a ``None`` value to retry on any verb. - - :param Collection status_forcelist: - A set of integer HTTP status codes that we should force a retry on. - A retry is initiated if the request method is in ``allowed_methods`` - and the response status code is in ``status_forcelist``. - - By default, this is disabled with ``None``. - - :param float backoff_factor: - A backoff factor to apply between attempts after the second try - (most errors are resolved immediately by a second try without a - delay). urllib3 will sleep for:: - - {backoff factor} * (2 ** ({number of previous retries})) - - seconds. If `backoff_jitter` is non-zero, this sleep is extended by:: - - random.uniform(0, {backoff jitter}) - - seconds. For example, if the backoff_factor is 0.1, then :func:`Retry.sleep` will - sleep for [0.0s, 0.2s, 0.4s, 0.8s, ...] between retries. No backoff will ever - be longer than `backoff_max`. - - By default, backoff is disabled (factor set to 0). - - :param bool raise_on_redirect: Whether, if the number of redirects is - exhausted, to raise a MaxRetryError, or to return a response with a - response code in the 3xx range. - - :param bool raise_on_status: Similar meaning to ``raise_on_redirect``: - whether we should raise an exception, or return a response, - if status falls in ``status_forcelist`` range and retries have - been exhausted. - - :param tuple history: The history of the request encountered during - each call to :meth:`~Retry.increment`. The list is in the order - the requests occurred. Each list item is of class :class:`RequestHistory`. - - :param bool respect_retry_after_header: - Whether to respect Retry-After header on status codes defined as - :attr:`Retry.RETRY_AFTER_STATUS_CODES` or not. - - :param Collection remove_headers_on_redirect: - Sequence of headers to remove from the request when a response - indicating a redirect is returned before firing off the redirected - request. - """ - - #: Default methods to be used for ``allowed_methods`` - DEFAULT_ALLOWED_METHODS = frozenset( - ["HEAD", "GET", "PUT", "DELETE", "OPTIONS", "TRACE"] - ) - - #: Default status codes to be used for ``status_forcelist`` - RETRY_AFTER_STATUS_CODES = frozenset([413, 429, 503]) - - #: Default headers to be used for ``remove_headers_on_redirect`` - DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset(["Cookie", "Authorization"]) - - #: Default maximum backoff time. - DEFAULT_BACKOFF_MAX = 120 - - # Backward compatibility; assigned outside of the class. - DEFAULT: typing.ClassVar[Retry] - - def __init__( - self, - total: bool | int | None = 10, - connect: int | None = None, - read: int | None = None, - redirect: bool | int | None = None, - status: int | None = None, - other: int | None = None, - allowed_methods: typing.Collection[str] | None = DEFAULT_ALLOWED_METHODS, - status_forcelist: typing.Collection[int] | None = None, - backoff_factor: float = 0, - backoff_max: float = DEFAULT_BACKOFF_MAX, - raise_on_redirect: bool = True, - raise_on_status: bool = True, - history: tuple[RequestHistory, ...] | None = None, - respect_retry_after_header: bool = True, - remove_headers_on_redirect: typing.Collection[ - str - ] = DEFAULT_REMOVE_HEADERS_ON_REDIRECT, - backoff_jitter: float = 0.0, - ) -> None: - self.total = total - self.connect = connect - self.read = read - self.status = status - self.other = other - - if redirect is False or total is False: - redirect = 0 - raise_on_redirect = False - - self.redirect = redirect - self.status_forcelist = status_forcelist or set() - self.allowed_methods = allowed_methods - self.backoff_factor = backoff_factor - self.backoff_max = backoff_max - self.raise_on_redirect = raise_on_redirect - self.raise_on_status = raise_on_status - self.history = history or () - self.respect_retry_after_header = respect_retry_after_header - self.remove_headers_on_redirect = frozenset( - h.lower() for h in remove_headers_on_redirect - ) - self.backoff_jitter = backoff_jitter - - def new(self, **kw: typing.Any) -> Retry: - params = dict( - total=self.total, - connect=self.connect, - read=self.read, - redirect=self.redirect, - status=self.status, - other=self.other, - allowed_methods=self.allowed_methods, - status_forcelist=self.status_forcelist, - backoff_factor=self.backoff_factor, - backoff_max=self.backoff_max, - raise_on_redirect=self.raise_on_redirect, - raise_on_status=self.raise_on_status, - history=self.history, - remove_headers_on_redirect=self.remove_headers_on_redirect, - respect_retry_after_header=self.respect_retry_after_header, - backoff_jitter=self.backoff_jitter, - ) - - params.update(kw) - return type(self)(**params) # type: ignore[arg-type] - - @classmethod - def from_int( - cls, - retries: Retry | bool | int | None, - redirect: bool | int | None = True, - default: Retry | bool | int | None = None, - ) -> Retry: - """Backwards-compatibility for the old retries format.""" - if retries is None: - retries = default if default is not None else cls.DEFAULT - - if isinstance(retries, Retry): - return retries - - redirect = bool(redirect) and None - new_retries = cls(retries, redirect=redirect) - log.debug("Converted retries value: %r -> %r", retries, new_retries) - return new_retries - - def get_backoff_time(self) -> float: - """Formula for computing the current backoff - - :rtype: float - """ - # We want to consider only the last consecutive errors sequence (Ignore redirects). - consecutive_errors_len = len( - list( - takewhile(lambda x: x.redirect_location is None, reversed(self.history)) - ) - ) - if consecutive_errors_len <= 1: - return 0 - - backoff_value = self.backoff_factor * (2 ** (consecutive_errors_len - 1)) - if self.backoff_jitter != 0.0: - backoff_value += random.random() * self.backoff_jitter - return float(max(0, min(self.backoff_max, backoff_value))) - - def parse_retry_after(self, retry_after: str) -> float: - seconds: float - # Whitespace: https://tools.ietf.org/html/rfc7230#section-3.2.4 - if re.match(r"^\s*[0-9]+\s*$", retry_after): - seconds = int(retry_after) - else: - retry_date_tuple = email.utils.parsedate_tz(retry_after) - if retry_date_tuple is None: - raise InvalidHeader(f"Invalid Retry-After header: {retry_after}") - - retry_date = email.utils.mktime_tz(retry_date_tuple) - seconds = retry_date - time.time() - - seconds = max(seconds, 0) - - return seconds - - def get_retry_after(self, response: BaseHTTPResponse) -> float | None: - """Get the value of Retry-After in seconds.""" - - retry_after = response.headers.get("Retry-After") - - if retry_after is None: - return None - - return self.parse_retry_after(retry_after) - - def sleep_for_retry(self, response: BaseHTTPResponse) -> bool: - retry_after = self.get_retry_after(response) - if retry_after: - time.sleep(retry_after) - return True - - return False - - def _sleep_backoff(self) -> None: - backoff = self.get_backoff_time() - if backoff <= 0: - return - time.sleep(backoff) - - def sleep(self, response: BaseHTTPResponse | None = None) -> None: - """Sleep between retry attempts. - - This method will respect a server's ``Retry-After`` response header - and sleep the duration of the time requested. If that is not present, it - will use an exponential backoff. By default, the backoff factor is 0 and - this method will return immediately. - """ - - if self.respect_retry_after_header and response: - slept = self.sleep_for_retry(response) - if slept: - return - - self._sleep_backoff() - - def _is_connection_error(self, err: Exception) -> bool: - """Errors when we're fairly sure that the server did not receive the - request, so it should be safe to retry. - """ - if isinstance(err, ProxyError): - err = err.original_error - return isinstance(err, ConnectTimeoutError) - - def _is_read_error(self, err: Exception) -> bool: - """Errors that occur after the request has been started, so we should - assume that the server began processing it. - """ - return isinstance(err, (ReadTimeoutError, ProtocolError)) - - def _is_method_retryable(self, method: str) -> bool: - """Checks if a given HTTP method should be retried upon, depending if - it is included in the allowed_methods - """ - if self.allowed_methods and method.upper() not in self.allowed_methods: - return False - return True - - def is_retry( - self, method: str, status_code: int, has_retry_after: bool = False - ) -> bool: - """Is this method/status code retryable? (Based on allowlists and control - variables such as the number of total retries to allow, whether to - respect the Retry-After header, whether this header is present, and - whether the returned status code is on the list of status codes to - be retried upon on the presence of the aforementioned header) - """ - if not self._is_method_retryable(method): - return False - - if self.status_forcelist and status_code in self.status_forcelist: - return True - - return bool( - self.total - and self.respect_retry_after_header - and has_retry_after - and (status_code in self.RETRY_AFTER_STATUS_CODES) - ) - - def is_exhausted(self) -> bool: - """Are we out of retries?""" - retry_counts = [ - x - for x in ( - self.total, - self.connect, - self.read, - self.redirect, - self.status, - self.other, - ) - if x - ] - if not retry_counts: - return False - - return min(retry_counts) < 0 - - def increment( - self, - method: str | None = None, - url: str | None = None, - response: BaseHTTPResponse | None = None, - error: Exception | None = None, - _pool: ConnectionPool | None = None, - _stacktrace: TracebackType | None = None, - ) -> Retry: - """Return a new Retry object with incremented retry counters. - - :param response: A response object, or None, if the server did not - return a response. - :type response: :class:`~urllib3.response.BaseHTTPResponse` - :param Exception error: An error encountered during the request, or - None if the response was received successfully. - - :return: A new ``Retry`` object. - """ - if self.total is False and error: - # Disabled, indicate to re-raise the error. - raise reraise(type(error), error, _stacktrace) - - total = self.total - if total is not None: - total -= 1 - - connect = self.connect - read = self.read - redirect = self.redirect - status_count = self.status - other = self.other - cause = "unknown" - status = None - redirect_location = None - - if error and self._is_connection_error(error): - # Connect retry? - if connect is False: - raise reraise(type(error), error, _stacktrace) - elif connect is not None: - connect -= 1 - - elif error and self._is_read_error(error): - # Read retry? - if read is False or method is None or not self._is_method_retryable(method): - raise reraise(type(error), error, _stacktrace) - elif read is not None: - read -= 1 - - elif error: - # Other retry? - if other is not None: - other -= 1 - - elif response and response.get_redirect_location(): - # Redirect retry? - if redirect is not None: - redirect -= 1 - cause = "too many redirects" - response_redirect_location = response.get_redirect_location() - if response_redirect_location: - redirect_location = response_redirect_location - status = response.status - - else: - # Incrementing because of a server error like a 500 in - # status_forcelist and the given method is in the allowed_methods - cause = ResponseError.GENERIC_ERROR - if response and response.status: - if status_count is not None: - status_count -= 1 - cause = ResponseError.SPECIFIC_ERROR.format(status_code=response.status) - status = response.status - - history = self.history + ( - RequestHistory(method, url, error, status, redirect_location), - ) - - new_retry = self.new( - total=total, - connect=connect, - read=read, - redirect=redirect, - status=status_count, - other=other, - history=history, - ) - - if new_retry.is_exhausted(): - reason = error or ResponseError(cause) - raise MaxRetryError(_pool, url, reason) from reason # type: ignore[arg-type] - - log.debug("Incremented Retry for (url='%s'): %r", url, new_retry) - - return new_retry - - def __repr__(self) -> str: - return ( - f"{type(self).__name__}(total={self.total}, connect={self.connect}, " - f"read={self.read}, redirect={self.redirect}, status={self.status})" - ) - - -# For backwards compatibility (equivalent to pre-v1.9): -Retry.DEFAULT = Retry(3) diff --git a/spaces/pvanand/RASA_moodbot/actions/actions.py b/spaces/pvanand/RASA_moodbot/actions/actions.py deleted file mode 100644 index 8bf1f757f851343b4bb1c56e40bf7cf9bde717ae..0000000000000000000000000000000000000000 --- a/spaces/pvanand/RASA_moodbot/actions/actions.py +++ /dev/null @@ -1,27 +0,0 @@ -# This files contains your custom actions which can be used to run -# custom Python code. -# -# See this guide on how to implement these action: -# https://rasa.com/docs/rasa/custom-actions - - -# This is a simple example for a custom action which utters "Hello World!" - -# from typing import Any, Text, Dict, List -# -# from rasa_sdk import Action, Tracker -# from rasa_sdk.executor import CollectingDispatcher -# -# -# class ActionHelloWorld(Action): -# -# def name(self) -> Text: -# return "action_hello_world" -# -# def run(self, dispatcher: CollectingDispatcher, -# tracker: Tracker, -# domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: -# -# dispatcher.utter_message(text="Hello World!") -# -# return [] diff --git a/spaces/pyimagesearch/nmt-transformer/app.py b/spaces/pyimagesearch/nmt-transformer/app.py deleted file mode 100644 index cf90f09e1427e0a3cf1bd8c03691f7ff241e12b0..0000000000000000000000000000000000000000 --- a/spaces/pyimagesearch/nmt-transformer/app.py +++ /dev/null @@ -1,34 +0,0 @@ -import os -import gradio as gr -import tensorflow as tf -import tensorflow_text as tf_text -from huggingface_hub import Repository - -repo = Repository( - local_dir="nmt-transformer", - clone_from="pyimagesearch/nmt-transformer", - use_auth_token=os.environ.get("token") -) -reloaded = tf.saved_model.load("nmt-transformer/translator") - -title="Neural Machine Translation with Transformer" -description="The model used here is a POC and not SOTA on NMT." - -examples=["how are you?", "good morning.", "how is your health?"] - -def get_translation(sentence): - result = reloaded( - sentence=tf.constant(sentence) - ).numpy()[0].decode() - return result - -nmt_space = gr.Interface( - fn=get_translation, - inputs=gr.Textbox(label="English Sentence"), - outputs=gr.Textbox(label="French Sentence"), - title=title, - description=description, - examples=examples, -) - -nmt_space.launch() \ No newline at end of file diff --git a/spaces/qingxu98/gpt-academic/request_llm/test_llms.py b/spaces/qingxu98/gpt-academic/request_llm/test_llms.py deleted file mode 100644 index ae6967be7b0c48d4c2af7a51335bd9becbc24d88..0000000000000000000000000000000000000000 --- a/spaces/qingxu98/gpt-academic/request_llm/test_llms.py +++ /dev/null @@ -1,78 +0,0 @@ -# """ -# 对各个llm模型进行单元测试 -# """ -def validate_path(): - import os, sys - dir_name = os.path.dirname(__file__) - root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') - os.chdir(root_dir_assume) - sys.path.append(root_dir_assume) - -validate_path() # validate path so you can run from base directory -if __name__ == "__main__": - from request_llm.bridge_newbingfree import predict_no_ui_long_connection - # from request_llm.bridge_moss import predict_no_ui_long_connection - # from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection - # from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection - - llm_kwargs = { - 'max_length': 512, - 'top_p': 1, - 'temperature': 1, - } - - result = predict_no_ui_long_connection(inputs="你好", - llm_kwargs=llm_kwargs, - history=[], - sys_prompt="") - print('final result:', result) - - - result = predict_no_ui_long_connection(inputs="what is a hero?", - llm_kwargs=llm_kwargs, - history=["hello world"], - sys_prompt="") - print('final result:', result) - - result = predict_no_ui_long_connection(inputs="如何理解传奇?", - llm_kwargs=llm_kwargs, - history=[], - sys_prompt="") - print('final result:', result) - - # # print(result) - # from multiprocessing import Process, Pipe - # class GetGLMHandle(Process): - # def __init__(self): - # super().__init__(daemon=True) - # pass - # def run(self): - # # 子进程执行 - # # 第一次运行，加载参数 - # def validate_path(): - # import os, sys - # dir_name = os.path.dirname(__file__) - # root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..') - # os.chdir(root_dir_assume + '/request_llm/jittorllms') - # sys.path.append(root_dir_assume + '/request_llm/jittorllms') - # validate_path() # validate path so you can run from base directory - - # jittorllms_model = None - # import types - # try: - # if jittorllms_model is None: - # from models import get_model - # # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"] - # args_dict = {'model': 'chatrwkv'} - # print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))') - # jittorllms_model = get_model(types.SimpleNamespace(**args_dict)) - 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        With BluffTitler DX9 ITV 8.6.0.0 Addons Pluginl, you can create stunning 3D titles and animations for any kind of video project that requires them. Whether it is a personal or professional project, a short or long video, a simple or complex title or animation, you can find a solution with BluffTitler DX9 ITV 8.6.0.0 Addons Pluginl.

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        You can also create your own custom 3D titles and animations by using your own media files and applying effects and transitions from the Dpack addon, importing subtitles from EZTitles plugin, changing fonts from FontPack addon, and applying textures from TexturePack addon.

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        In this article, we have shown you what BluffTitler DX9 ITV 8.6.0.0 Addons Pluginl is, what it can do, how to use it, some tips and tricks for using it, some examples of 3D titles and animations created with it, and some frequently asked questions about it.

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        Conclusion

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        diff --git a/spaces/r3gm/RVC_HF/slicer2.py b/spaces/r3gm/RVC_HF/slicer2.py deleted file mode 100644 index 5b29ee262aa54045e807be2cffeb41687499ba58..0000000000000000000000000000000000000000 --- a/spaces/r3gm/RVC_HF/slicer2.py +++ /dev/null @@ -1,260 +0,0 @@ -import numpy as np - - -# This function is obtained from librosa. -def get_rms( - y, - frame_length=2048, - hop_length=512, - pad_mode="constant", -): - padding = (int(frame_length // 2), int(frame_length // 2)) - y = np.pad(y, padding, mode=pad_mode) - - axis = -1 - # put our new within-frame axis at the end for now - out_strides = y.strides + tuple([y.strides[axis]]) - # Reduce the shape on the framing axis - x_shape_trimmed = list(y.shape) - x_shape_trimmed[axis] -= frame_length - 1 - out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) - xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) - if axis < 0: - target_axis = axis - 1 - else: - target_axis = axis + 1 - xw = np.moveaxis(xw, -1, target_axis) - # Downsample along the target axis - slices = [slice(None)] * xw.ndim - slices[axis] = slice(0, None, hop_length) - x = xw[tuple(slices)] - - # Calculate power - power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) - - return np.sqrt(power) - - -class Slicer: - def __init__( - self, - sr: int, - threshold: float = -40.0, - min_length: int = 5000, - min_interval: int = 300, - hop_size: int = 20, - max_sil_kept: int = 5000, - ): - if not min_length >= min_interval >= hop_size: - raise ValueError( - "The following condition must be satisfied: min_length >= min_interval >= hop_size" - ) - if not max_sil_kept >= hop_size: - raise ValueError( - "The following condition must be satisfied: max_sil_kept >= hop_size" - ) - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.0) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - if len(waveform.shape) > 1: - return waveform[ - :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size) - ] - else: - return waveform[ - begin * self.hop_size : min(waveform.shape[0], end * self.hop_size) - ] - - # @timeit - def slice(self, waveform): - if len(waveform.shape) > 1: - samples = waveform.mean(axis=0) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return [waveform] - rms_list = get_rms( - y=samples, frame_length=self.win_size, hop_length=self.hop_size - ).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = ( - i - silence_start >= self.min_interval - and i - clip_start >= self.min_length - ) - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start : i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[ - i - self.max_sil_kept : silence_start + self.max_sil_kept + 1 - ].argmin() - pos += i - self.max_sil_kept - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if ( - silence_start is not None - and total_frames - silence_start >= self.min_interval - ): - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - if len(sil_tags) == 0: - return [waveform] - else: - chunks = [] - if sil_tags[0][0] > 0: - chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0])) - for i in range(len(sil_tags) - 1): - chunks.append( - self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]) - ) - if sil_tags[-1][1] < total_frames: - chunks.append( - self._apply_slice(waveform, sil_tags[-1][1], total_frames) - ) - return chunks - - -def main(): - import os.path - from argparse import ArgumentParser - - import librosa - import soundfile - - parser = ArgumentParser() - parser.add_argument("audio", type=str, help="The audio to be sliced") - parser.add_argument( - "--out", type=str, help="Output directory of the sliced audio clips" - ) - parser.add_argument( - "--db_thresh", - type=float, - required=False, - default=-40, - help="The dB threshold for silence detection", - ) - parser.add_argument( - "--min_length", - type=int, - required=False, - default=5000, - help="The minimum milliseconds required for each sliced audio clip", - ) - parser.add_argument( - "--min_interval", - type=int, - required=False, - default=300, - help="The minimum milliseconds for a silence part to be sliced", - ) - parser.add_argument( - "--hop_size", - type=int, - required=False, - default=10, - help="Frame length in milliseconds", - ) - parser.add_argument( - "--max_sil_kept", - type=int, - required=False, - default=500, - help="The maximum silence length kept around the sliced clip, presented in milliseconds", - ) - args = parser.parse_args() - out = args.out - if out is None: - out = os.path.dirname(os.path.abspath(args.audio)) - audio, sr = librosa.load(args.audio, sr=None, mono=False) - slicer = Slicer( - sr=sr, - threshold=args.db_thresh, - min_length=args.min_length, - min_interval=args.min_interval, - hop_size=args.hop_size, - max_sil_kept=args.max_sil_kept, - ) - chunks = slicer.slice(audio) - if not os.path.exists(out): - os.makedirs(out) - for i, chunk in enumerate(chunks): - if len(chunk.shape) > 1: - chunk = chunk.T - soundfile.write( - os.path.join( - out, - f"%s_%d.wav" - % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i), - ), - chunk, - sr, - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/README.md b/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/README.md deleted file mode 100644 index 013b7776200c61beebd828c0fe378af1eda0d921..0000000000000000000000000000000000000000 --- a/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image/README.md +++ /dev/null @@ -1,75 +0,0 @@ ---- -title: Real-Time Latent Consistency Model Text-to-Image -emoji: 💬🖼️ -colorFrom: gray -colorTo: indigo -sdk: docker -pinned: false -suggested_hardware: a10g-small ---- - -# Real-Time Latent Consistency Model - -This demo showcases [Latent Consistency Model (LCM)](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) using [Diffusers](https://github.com/huggingface/diffusers/tree/main/examples/community#latent-consistency-pipeline) with a MJPEG stream server. - -You need a webcam to run this demo. 🤗 - -## Running Locally - -You need CUDA and Python 3.10, Mac with an M1/M2/M3 chip or Intel Arc GPU - -`TIMEOUT`: limit user session timeout -`SAFETY_CHECKER`: disabled if you want NSFW filter off -`MAX_QUEUE_SIZE`: limit number of users on current app instance - -### image to image - -```bash -python -m venv venv -source venv/bin/activate -pip3 install -r requirements.txt -uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload -``` - -### text to image - -```bash -python -m venv venv -source venv/bin/activate -pip3 install -r requirements.txt -uvicorn "app-txt2img:app" --host 0.0.0.0 --port 7860 --reload -``` - -or with environment variables - -```bash -TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload -``` - -If you're running locally and want to test it on Mobile Safari, the webserver needs to be served over HTTPS. - -```bash -openssl req -newkey rsa:4096 -nodes -keyout key.pem -x509 -days 365 -out certificate.pem -uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload --log-level info --ssl-certfile=certificate.pem --ssl-keyfile=key.pem -``` - -## Docker - -You need NVIDIA Container Toolkit for Docker - -```bash -docker build -t lcm-live . -docker run -ti -p 7860:7860 --gpus all lcm-live -``` - -or with environment variables - -```bash -docker run -ti -e TIMEOUT=0 -e SAFETY_CHECKER=False -p 7860:7860 --gpus all lcm-live -``` - -# Demo on Hugging Face - -https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model - -https://github.com/radames/Real-Time-Latent-Consistency-Model/assets/102277/c4003ac5-e7ff-44c0-97d3-464bb659de70 diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Asa Rebar Software Crack 44 The Best Rebar Detailing Software on the Market.md b/spaces/raedeXanto/academic-chatgpt-beta/Asa Rebar Software Crack 44 The Best Rebar Detailing Software on the Market.md deleted file mode 100644 index b87a60a2079b14e71fd672bd25e8205880704b83..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Asa Rebar Software Crack 44 The Best Rebar Detailing Software on the Market.md +++ /dev/null @@ -1,105 +0,0 @@ - -
        - Detailing: Explain how Asa Rebar Software can create accurate and detailed rebar drawings and models.
        - Estimating: Explain how Asa Rebar Software can generate fast and reliable rebar estimates and bids.
        - Production: Explain how Asa Rebar Software can automate and optimize rebar fabrication and delivery.
        - Management: Explain how Asa Rebar Software can track and control rebar inventory, production, and finances. | | H2: How to Get Asa Rebar Software | - Pricing: Provide information on the pricing and licensing options for Asa Rebar Software.
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        - Alternatives: Provide some legitimate alternatives to Asa Rebar Software Crack 44, such as trial versions, discounts, etc. | | H1: Conclusion | - Summary: Summarize the main points of the article and restate the value proposition of Asa Rebar Software.
        - Call to action: Encourage the readers to visit the official website of Asa Rebar Software and contact the sales team for more information or a demo. | **Table 2: Article with HTML formatting** ```html

        What is Asa Rebar Software and Why You Need It

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        Thank you for reading this article. We hope you found it informative and helpful. If you have any questions or comments, please feel free to leave them below.

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        Frequently Asked Questions

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        1. What is the difference between ProRebar and CAD/Detailing?
          ProRebar is a powerful CAD tool that integrates with Autodesk Revit and AutoCAD and allows you to create rebar models that comply with industry standards and codes. CAD/Detailing is a user-friendly tool that allows you to create rebar drawings using simple commands and templates.
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        2. What is the difference between Studio Financials and Business Central?
          Studio Financials is an ERP system built for the rebar industry that allows you to manage your accounting, billing, payroll, etc., from a single platform. Business Central is a cloud-based ERP system that offers similar features but runs completely in the cloud without requiring any client software installation or upgrade.
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        The author of Asuravithu Malayalam Novel is M. T. Vasudevan Nair, who was born in 1933 in Kudallur, a village in Palakkad district of Kerala. He is a novelist, short story writer, screenwriter, editor and film director. He has written more than 20 novels and over 100 short stories in Malayalam, and has won several awards, including the Jnanpith Award, the highest literary honor in India, in 1995. He is also known for his contributions to Malayalam cinema, as he has written scripts for more than 50 films and directed seven films.

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        What is the plot of Asuravithu Malayalam Novel?

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        The plot of Asuravithu Malayalam Novel revolves around Govindankutty, the youngest son of a proud Nair family (a dominant caste in Kerala). He is different from his brothers and sisters, who are obedient and conformist. He is rebellious, restless and dissatisfied with his life. He feels alienated from his family and society, which are bound by rigid customs and traditions. He tries to find meaning and happiness in various ways, such as education, love, friendship, politics and religion, but he fails to achieve them. He also faces various challenges and conflicts from his family members, who disapprove of his actions and choices. He becomes a victim of social injustice and violence, which further pushes him to despair and isolation. The novel ends with a tragic climax, where Govindankutty meets his fate.

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        Main Body

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        What are the themes of Asuravithu Malayalam Novel?

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        Asuravithu Malayalam Novel explores various themes that reflect the social, psychological and cultural aspects of Kerala in the 1950s and 1960s. Some of the major themes are:

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        -

        Social scenario and injustice

        -

        The feudal system and the corruption and violence that resulted from it. The novel criticizes the hypocrisy and cruelty of the dominant classes and groups that oppress the weak and marginalized sections of society.

        -

        Inner conflict and consciousness

        -

        The novel portrays the inner conflict and consciousness of Govindankutty, who is torn between his individuality and his social identity. He is unable to fit into his family or society's expectations or norms. He is unable to find his true self or his true purpose in life. He is constantly searching for something that can give him satisfaction and happiness, but he fails to find it. He is also haunted by his past memories and experiences that shape his personality and behavior. He suffers from guilt, anger, frustration, loneliness and depression. He tries to escape from his reality by indulging in various activities or substances that can give him temporary relief or pleasure.

        -

        Family and tradition

        -

        The novel depicts the family and tradition as important aspects of Kerala's culture and society. The novel shows how Govindankutty's family represents a typical Nair family (a dominant caste in Kerala), which has a patriarchal structure, a hierarchical order, a joint family system, a strict code of conduct, a strong sense of honor and pride, and a deep attachment to ancestral property and rituals. The novel also shows how Govindankutty's family is affected by the changes and conflicts in the society, such as the land reforms movement, the communist uprising, the modernization and urbanization, and the loss of values and identity. The novel explores how Govindankutty's family tries to cope with these changes and conflicts, either by resisting them, adapting to them, or compromising with them.

        -

        How is Asuravithu Malayalam Novel different from other novels by M. T. Vasudevan Nair?

        -

        Asuravithu Malayalam Novel is different from other novels by M. T. Vasudevan Nair in several ways, such as:

        -

        Style and language

        -

        The novel is written in a simple and lucid style, with a blend of realism and symbolism. The novel uses colloquial Malayalam language, with regional dialects and idioms, to capture the essence of Kerala's rural life and culture. The novel also uses poetic imagery and metaphors to convey the emotions and thoughts of the characters. The novel has a nonlinear narrative structure, with flashbacks and foreshadowing, to create suspense and mystery.

        -

        Characterization and realism

        -

        his mentors, his rivals, and his role models. They are portrayed as realistic and complex characters that have their own motivations, backgrounds, personalities, and flaws. They are influenced by the social and historical factors that shape their lives and choices.

        -

        Adaptation and reception

        -

        The novel was adapted into a film with the same title in 1968. The film, directed by A. Vincent and scripted by M. T. Vasudevan Nair himself, featured noted actor Prem Nazir as Govindankutty. The film was a commercial and critical success, and won several awards, including the Kerala State Film Award for Best Film and Best Screenplay. The film was praised for its faithful adaptation of the novel, its realistic portrayal of Kerala's rural life and culture, its powerful performance by Prem Nazir, and its cinematography and music. The film is considered as one of the best Malayalam films of all time.

        -

        Conclusion

        -

        Summary of the main points

        -

        In conclusion, Asuravithu Malayalam Novel Pdf 130 is a classic Malayalam novel that explores the social, psychological and cultural aspects of Kerala in the 1950s and 1960s. The novel tells the story of Govindankutty, a young man who is trapped between his individuality and his social identity. He is a rebel and an outcast who faces various challenges and conflicts from his family and society. He is unable to find his true self or his true purpose in life. He suffers from inner conflict and consciousness, and becomes a victim of social injustice and violence. The novel ends with a tragic climax, where Govindankutty meets his fate.

        -

        Evaluation of the novel

        -

        The novel is a masterpiece of realism and symbolism, written by M. T. Vasudevan Nair, one of the most acclaimed writers in Malayalam literature. The novel has a simple and lucid style, with a blend of realism and symbolism. The novel uses colloquial Malayalam language, with regional dialects and idioms, to capture the essence of Kerala's rural life and culture. The novel also uses poetic imagery and metaphors to convey the emotions and thoughts of the characters. The novel has a nonlinear narrative structure, with flashbacks and foreshadowing, to create suspense and mystery.

        -

        compassionate, but also flawed, confused, impulsive, violent, self-destructive person. He is a representative of the younger generation that is dissatisfied with the status quo and seeks change and freedom. The novel also has other characters that play important roles in Govindankutty's life, such as his family members, his friends, his lovers, his enemies, his mentors, his rivals, and his role models. They are portrayed as realistic and complex characters that have their own motivations, backgrounds, personalities, and flaws. They are influenced by the social and historical factors that shape their lives and choices.

        -

        Adaptation and reception

        -

        The novel was adapted into a film with the same title in 1968. The film, directed by A. Vincent and scripted by M. T. Vasudevan Nair himself, featured noted actor Prem Nazir as Govindankutty. The film was a commercial and critical success, and won several awards, including the Kerala State Film Award for Best Film and Best Screenplay. The film was praised for its faithful adaptation of the novel, its realistic portrayal of Kerala's rural life and culture, its powerful performance by Prem Nazir, and its cinematography and music. The film is considered as one of the best Malayalam films of all time.

        -

        Conclusion

        -

        Summary of the main points

        -

        In conclusion, Asuravithu Malayalam Novel Pdf 130 is a classic Malayalam novel that explores the social, psychological and cultural aspects of Kerala in the 1950s and 1960s. The novel tells the story of Govindankutty, a young man who is trapped between his individuality and his social identity. He is a rebel and an outcast who faces various challenges and conflicts from his family and society. He is unable to find his true self or his true purpose in life. He suffers from inner conflict and consciousness, and becomes a victim of social injustice and violence. The novel ends with a tragic climax, where Govindankutty meets his fate.

        -

        Evaluation of the novel

        -

        The novel is a masterpiece of realism and symbolism, written by M. T. Vasudevan Nair, one of the most acclaimed writers in Malayalam literature. The novel has a simple and lucid style, with a blend of realism and symbolism. The novel uses colloquial Malayalam language, with regional dialects and idioms, to capture the essence of Kerala's rural life and culture. The novel also uses poetic imagery and metaphors to convey the emotions and thoughts of the characters. The novel has a nonlinear narrative structure, with flashbacks and foreshadowing, to create suspense and mystery.

        -

        who is a dynamic and tragic hero. He is portrayed as a sensitive, intelligent, courageous, rebellious, compassionate, but also flawed, confused, impulsive, violent, self-destructive person. He is a representative of the younger generation that is dissatisfied with the status quo and seeks change and freedom. The novel also has other characters that play important roles in Govindankutty's life, such as his family members, his friends, his lovers, his enemies, his mentors, his rivals, and his role models. They are portrayed as realistic and complex characters that have their own motivations, backgrounds, personalities, and flaws. They are influenced by the social and historical factors that shape their lives and choices.

        -

        Adaptation and reception

        -

        The novel was adapted into a film with the same title in 1968. The film, directed by A. Vincent and scripted by M. T. Vasudevan Nair himself, featured noted actor Prem Nazir as Govindankutty. The film was a commercial and critical success, and won several awards, including the Kerala State Film Award for Best Film and Best Screenplay. The film was praised for its faithful adaptation of the novel, its realistic portrayal of Kerala's rural life and culture, its powerful performance by Prem Nazir, and its cinematography and music. The film is considered as one of the best Malayalam films of all time.

        -

        Conclusion

        -

        Summary of the main points

        -

        In conclusion, Asuravithu Malayalam Novel Pdf 130 is a classic Malayalam novel that explores the social, psychological and cultural aspects of Kerala in the 1950s and 1960s. The novel tells the story of Govindankutty, a young man who is trapped between his individuality and his social identity. He is a rebel and an outcast who faces various challenges and conflicts from his family and society. He is unable to find his true self or his true purpose in life. He suffers from inner conflict and consciousness, and becomes a victim of social injustice and violence. The novel ends with a tragic climax, where Govindankutty meets his fate.

        -

        Evaluation of the novel

        -

        The novel is a masterpiece of realism and symbolism, written by M. T. Vasudevan Nair, one of the most acclaimed writers in Malayalam literature. The novel has a simple and lucid style, with a blend of realism and symbolism. The novel uses colloquial Malayalam language, with regional dialects and idioms, to capture the essence of Kerala's rural life and culture. The novel also uses poetic imagery and metaphors to convey the emotions and thoughts of the characters. The novel has a nonlinear narrative structure, with flashbacks and foreshadowing, to create suspense and mystery.

        -

        with realistic and complex characters that reflect the diversity and contradictions of Kerala's society. The novel focuses on Govindankutty as the main character, who is a dynamic and tragic hero. He is portrayed as a sensitive, intelligent, courageous, rebellious, compassionate, but also flawed, confused, impulsive, violent, self-destructive person. He is a representative of the younger generation that is dissatisfied with the status quo and seeks change and freedom. The novel also has other characters that play important roles in Govindankutty's life, such as his family members, his friends, his lovers, his enemies, his mentors, his rivals, and his role models. They are portrayed as realistic and complex characters that have their own motivations, backgrounds, personalities, and flaws. They are influenced by the social and historical factors that shape their lives and choices.

        -

        Adaptation and reception

        -

        The novel was adapted into a film with the same title in 1968. The film, directed by A. Vincent and scripted by M. T. Vasudevan Nair himself, featured noted actor Prem Nazir as Govindankutty. The film was a commercial and critical success, and won several awards, including the Kerala State Film Award for Best Film and Best Screenplay. The film was praised for its faithful adaptation of the novel, its realistic portrayal of Kerala's rural life and culture, its powerful performance by Prem Nazir, and its cinematography and music. The film is considered as one of the best Malayalam films of all time.

        -

        Conclusion

        -

        Summary of the main points

        -

        In conclusion, Asuravithu Malayalam Novel Pdf 130 is a classic Malayalam novel that explores the social, psychological and cultural aspects of Kerala in the 1950s and 1960s. The novel tells the story of Govindankutty, a young man who is trapped between his individuality and his social identity. He is a rebel and an outcast who faces various challenges and conflicts from his family and society. He is unable to find his true self or his true purpose in life. He suffers from inner conflict and consciousness, and becomes a victim of social injustice and violence. The novel ends with a tragic climax, where Govindankutty meets his fate.

        -

        Evaluation of the novel

        -

        with a blend of realism and symbolism. The novel uses colloquial Malayalam language, with regional dialects and idioms, to capture the essence of Kerala's rural life and culture. The novel also uses poetic imagery and metaphors to convey the emotions and thoughts of the characters. The novel has a nonlinear narrative structure, with flashbacks and foreshadowing, to create suspense and mystery.

        -

        The novel has a strong characterization, with realistic and complex characters that reflect the diversity and contradictions of Kerala's society. The novel focuses on Govindankutty as the main character, who is a dynamic and tragic hero. He is portrayed as a sensitive, intelligent, courageous, rebellious, compassionate, but also flawed, confused, impulsive, violent, self-destructive person. He is a representative of the younger generation that is dissatisfied with the status quo and seeks change and freedom. The novel also has other characters that play important roles in Govindankutty's life, such as his family members, his friends, his lovers, his enemies, his mentors, his rivals, and his role models. They are portrayed as realistic and complex characters that have their own motivations, backgrounds, personalities, and flaws. They are influenced by the social and historical factors that shape their lives and choices.

        -

        The novel was adapted into a film with the same title in 1968. The film, directed by A. Vincent and scripted by M. T. Vasudevan Nair himself, featured noted actor Prem Nazir as Govindankutty. The film was a commercial and critical success, and won several awards, including the Kerala State Film Award for Best Film and Best Screenplay. The film was praised for its faithful adaptation of the novel, its realistic portrayal of Kerala's rural life and culture, its powerful performance by Prem Nazir, and its cinematography and music. The film is considered as one of the best Malayalam films of all time.

        -

        Recommendations for further reading

        -

        If you enjoyed reading Asuravithu Malayalam Novel Pdf 130, you might also like to read some other novels by M. T. Vasudevan Nair or other Malayalam writers. Here are some recommendations for further reading:

        -
          -
        • Naalukettu by M. T. Vasudevan Nair: This is another classic Malayalam novel by M. T. Vasudevan Nair that explores the theme of family and tradition in Kerala's society. It tells the story of Appunni, a young boy who is abandoned by his father and grows up in his ancestral home with his grandmother.
          • -
        • Kaalam by M. T. Vasudevan Nair: This is a historical novel by M. T. Vasudevan Nair that depicts the life of Sethu Madhavan, a young man who participates in India's freedom struggle against British colonialism.
          • -
        • Chemmeen by Thakazhi Sivasankara Pillai: This is a romantic novel by Thakazhi Sivasankara Pillai that portrays the love story of Karuthamma, a fisherwoman who belongs to a lower caste community in Kerala's coastal region.
          • -

          Nair, a school teacher who lives in a small town in Kerala and witnesses the changes and conflicts in his society. -
        • Mathilukal by Vaikom Muhammad Basheer: This is a humorous novel by Vaikom Muhammad Basheer that depicts the love affair of Basheer, a political prisoner, and Narayani, a female inmate, who are separated by a high wall in a jail.
          • -

        -

        FAQs

        -

        Here are some frequently asked questions about Asuravithu Malayalam Novel Pdf 130:

        -
          -
        1. Where can I download Asuravithu Malayalam Novel Pdf 130 for free?
          2. -

          You can download Asuravithu Malayalam Novel Pdf 130 for free from various websites that offer free ebooks, such as HSSlive, SoundCloud, sourceofhealth.net, etc. However, we recommend you to buy the original book from a bookstore or an online platform to support the author and the publisher.

          -
        2. What is the meaning of Asuravithu?
          3. -

          Asuravithu means "The Demon Seed" in English. It refers to the protagonist Govindankutty, who is considered as an outcast and a rebel by his family and society. It also implies that he is a product of his social and historical circumstances, which are full of injustice and violence.

          -
        3. Who is Prem Nazir?
          4. -

          Prem Nazir was a famous Malayalam actor who played the role of Govindankutty in the film adaptation of Asuravithu. He was known for his versatility and charisma as an actor. He holds the Guinness World Record for playing the lead role in more than 700 films.

          -
        4. What is Jnanpith Award?
          5. -

          Jnanpith Award is the highest literary honor in India. It is given annually to an Indian writer for their outstanding contribution to Indian literature. M. T. Vasudevan Nair won the Jnanpith Award in 1995 for his overall contribution to Malayalam literature.

          -
        5. What are some other novels by M. T. Vasudevan Nair?
          6. -

          Some other novels by M. T. Vasudevan Nair are Naalukettu, Kaalam, Manju, Randamoozham, Varanasi, etc.

          -
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        \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Balbharti Marathi Book 1984 PDF The History and Significance of Balbharati.md b/spaces/raedeXanto/academic-chatgpt-beta/Balbharti Marathi Book 1984 PDF The History and Significance of Balbharati.md deleted file mode 100644 index 3a5ed86c281eba356095f83b581470b415d36529..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Balbharti Marathi Book 1984 PDF The History and Significance of Balbharati.md +++ /dev/null @@ -1,115 +0,0 @@ - -

        Balbharati Marathi Book 1984 PDF: A Treasure of Marathi Literature

        -

        Are you looking for a classic Marathi book that can entertain, educate, and enlighten you? If yes, then you should definitely check out Balbharati Marathi Book 1984 PDF. This book is a collection of stories, poems, and essays that showcase the best of Marathi literature from different genres, periods, and styles. In this article, we will tell you more about this book, its contents, its benefits, and how you can download it for free.

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        balbharti marathi book 1984 pdf


        Download File –––––>>> https://tinourl.com/2uL1OU



        -

        Introduction

        -

        Balbharati is a state-run publishing house that produces textbooks for school students in Maharashtra. It was established in 1967 by the Maharashtra State Bureau of Textbook Production and Curriculum Research. Balbharati aims to provide quality educational material that reflects the cultural and linguistic diversity of Maharashtra.

        -

        One of the most popular publications of Balbharati is the Balbharati Marathi Book, which is a textbook for Marathi language and literature for students of different standards. The book contains a selection of literary works by various Marathi writers, poets, and thinkers. The book also includes exercises, questions, and activities that help students improve their reading comprehension, vocabulary, grammar, and writing skills.

        -

        The 1984 edition of Balbharati Marathi Book is considered to be one of the best editions ever published by Balbharati. It contains a rich and varied collection of literary works that cover a wide range of topics, themes, and emotions. The book also features some of the most renowned and respected names in Marathi literature, such as Pu La Deshpande, Vinda Karandikar, N.D. Mahanor, V.S. Khandekar, Sane Guruji, Kusumagraj, Bahinabai Chaudhari, G.D. Madgulkar, Prahlad Keshav Atre, Acharya Atreya, and many more.

        -

        Contents of the book

        -

        Stories

        -

        The book contains 18 stories that range from humorous to tragic, from realistic to fantastical, from historical to contemporary. Some of the stories are:

        -
          -
        • अंतु बर्वा (Antu Barva) by Pu La Deshpande: A hilarious story about a witty and eccentric barber who lives in a coastal village.
          • -
        • श्यामची आई (Shyamchi Aai) by Sane Guruji: A touching story about a young boy's relationship with his mother who teaches him valuable lessons in life.
          • -
        • ययाति (Yayati) by V.S. Khandekar: A mythical story about a king who exchanges his old age with his son's youth.
          • -
        • गोष्ट एका गावाची (Goshta Eka Gavachi) by N.D. Mahanor: A realistic story about a poor farmer who struggles to survive in a drought-hit village.
          • -
        • अंधारी दोन गोष्टी (Andhari Don Goshti) by Vinda Karandikar: Two allegorical stories that explore the themes of darkness and light.
          • -
        -

        Poems

        -

        The book contains 20 poems that express various moods, feelings, and thoughts. Some of the poems are:

        -
          -
        • मनाचे श्लोक (Manache Shlok) by Samarth Ramdas: A devotional poem that advises one to follow the path of righteousness.
          • -
        • माझा गाव (Majha Gav) by Bahinabai Chaudhari: A nostalgic poem that describes the beauty and simplicity of rural life.
          • -
        • प्रेम म्हणजे प्रेम असते (Prem Mhanje Prem Aste) by Kusumagraj: A romantic poem that defines love as an eternal bond.
          • -
        • सुर्यस्त (Suryast) by G.D. Madgulkar: A lyrical poem that captures the splendor and sadness of sunset.
          • -
        • मी मराठी आहे (Mi Marathi Ahe) by Prahlad Keshav Atre: A patriotic poem that celebrates the identity and pride of being a Marathi.
          • -
        -

        Essays

        -

        The book contains 12 essays that discuss various topics related to culture, society, literature, art, science, and philosophy. Some of the essays are:

        -
          -
        • मराठी साहित्याचा इतिहास (Marathi Sahityacha Itihas) by Acharya Atreya: A historical overview of Marathi literature from its origins to modern times.
          • -
        • मराठी भाषेचा सौंदर्य (Marathi Bhashacha Saundarya) by Vinda Karandikar: An aesthetic appreciation of Marathi language and its features.
          • -
        • मराठी लोकसंस्कृती (Marathi Lokasanskriti) by N.D. Mahanor: An exploration of Marathi folk culture and its forms.
          • -
        • मराठी संगीत कला आणि संस्कृती (Marathi Sangeet Kala Ani Sanskriti) by Sudhir Phadke: An analysis of Marathi music art and culture and its evolution.
          • -
        • मराठी नाटक कला आणि संस्कृती (Marathi Natak Kala Ani Sanskriti) by Pu La Deshpande: An evaluation of Marathi drama art and culture and its impact.
          • -
        -

        Benefits of reading the book

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        The book is not only a textbook but also a treasure trove of Marathi literature. Reading this book can have many benefits for you such as:

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        • You can improve your Marathi language skills by learning new words, phrases, idioms, expressions, grammar rules, etc.
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        • You can enrich your knowledge by learning about various aspects of Marathi culture, history, society, literature, art, science, philosophy etc.
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        • You can enhance your critical thinking skills by analyzing different literary works and their styles techniques themes messages etc.
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        • You can develop your creativity skills by writing your own stories poems essays etc based on your inspiration from reading this book.
          • -

          How to download the book for free

          -

          If you are interested in reading this book, you might be wondering how you can get a copy of it. Well, you are in luck because there are two ways you can download the book for free.

          -

          The first way is to visit the Balbharati Archives website, where you can find the scanned copies of old editions of Balbharati books for different standards and subjects. You can browse through the series and find the 1984 edition of Balbharati Marathi Book for standard 7. You can then click on the download button and save the PDF file on your device.

          -

          The second way is to visit the Internet Archive website, where you can find the digital version of Balbharati Marathi Book 1984 PDF uploaded by a user. You can search for the book by its title or by its identifier (Balbharati). You can then choose from various formats such as PDF, EPUB, Kindle, etc. and download the file on your device.

          -

          However, before you download the book, you should be aware of some legal and ethical issues. The book is a copyrighted material and belongs to Balbharati. You should not use the book for any commercial purpose or distribute it without permission. You should also respect the intellectual property rights of the authors and acknowledge their work when you quote or refer to it.

          -

          Conclusion

          -

          Balbharati Marathi Book 1984 PDF is a wonderful book that can introduce you to the world of Marathi literature. It contains a variety of literary works that can entertain, educate, and enlighten you. It can also help you improve your Marathi language skills and increase your cultural awareness. You can download the book for free from Balbharati Archives or Internet Archive and enjoy reading it at your leisure.

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          So what are you waiting for? Download Balbharati Marathi Book 1984 PDF today and discover the treasure of Marathi literature.

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          FAQs

          -

          Here are some frequently asked questions and answers about Balbharati Marathi Book 1984 PDF.

          -
            -
          1. Q: How many pages does Balbharati Marathi Book 1984 PDF have?
            2. -
          2. A: The book has 272 pages in total.
            3. -
          3. Q: Who is the editor of Balbharati Marathi Book 1984 PDF?
            4. -
          4. A: The editor of the book is Dr. Vasant Bapat, a noted Marathi writer, poet, critic, and scholar.
            5. -
          5. Q: Is Balbharati Marathi Book 1984 PDF available in other languages?
            6. -
          6. A: No, the book is only available in Marathi language.
            7. -
          7. Q: Is Balbharati Marathi Book 1984 PDF suitable for beginners?
            8. -
          8. A: Yes, the book is suitable for beginners as well as advanced learners of Marathi language and literature.
            9. -
          9. Q: What are some other books similar to Balbharati Marathi Book 1984 PDF?
            10. -
          10. A: Some other books similar to Balbharati Marathi Book 1984 PDF are:
            11. -
              -
            • Balbharati Hindi Book 1984 PDF
              • -
            • Balbharati English Book 1984 PDF
              • -
            • Balbharati Sanskrit Book 1984 PDF
              • -
            • Balbharati History Book 1984 PDF
              • -
            • Balbharati Geography Book 1984 PDF
              • -
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          0a6ba089eb
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          \ No newline at end of file diff --git a/spaces/rafaelpadilla/coco_metrics/coco_metrics/coco_evaluate.py b/spaces/rafaelpadilla/coco_metrics/coco_metrics/coco_evaluate.py deleted file mode 100644 index 389c75ae7d29242d6a03176f3f659209f0e04ba3..0000000000000000000000000000000000000000 --- a/spaces/rafaelpadilla/coco_metrics/coco_metrics/coco_evaluate.py +++ /dev/null @@ -1,224 +0,0 @@ -import contextlib -import copy -import os -from typing import Dict, List, Union - -import numpy as np -import torch - -from coco_metrics.pycocotools.coco import COCO -from coco_metrics.pycocotools.cocoeval import COCOeval -from coco_metrics.utils import (_TYPING_BOX, _TYPING_PREDICTIONS, convert_to_xywh, - create_common_coco_eval) - -_SUPPORTED_TYPES = ["bbox"] - - -class COCOEvaluator(object): - """ - Class to perform evaluation for the COCO dataset. - """ - - def __init__(self, coco_gt: COCO, iou_types: List[str] = ["bbox"]): - """ - Initializes COCOEvaluator with the ground truth COCO dataset and IoU types. - - Args: - coco_gt: The ground truth COCO dataset. - iou_types: Intersection over Union (IoU) types for evaluation (Supported: "bbox"). - """ - self.coco_gt = copy.deepcopy(coco_gt) - - self.coco_eval = {} - for iou_type in iou_types: - assert iou_type in _SUPPORTED_TYPES, ValueError( - f"IoU type not supported {iou_type}" - ) - self.coco_eval[iou_type] = COCOeval(self.coco_gt, iouType=iou_type) - - self.iou_types = iou_types - self.img_ids = [] - self.eval_imgs = {k: [] for k in iou_types} - - def update(self, predictions: _TYPING_PREDICTIONS) -> None: - """ - Update the evaluator with new predictions. - - Args: - predictions: The predictions to update. - """ - img_ids = list(np.unique(list(predictions.keys()))) - self.img_ids.extend(img_ids) - - for iou_type in self.iou_types: - results = self.prepare(predictions, iou_type) - - # suppress pycocotools prints - with open(os.devnull, "w") as devnull: - with contextlib.redirect_stdout(devnull): - coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO() - coco_eval = self.coco_eval[iou_type] - - coco_eval.cocoDt = coco_dt - coco_eval.params.imgIds = list(img_ids) - eval_imgs = coco_eval.evaluate() - self.eval_imgs[iou_type].append(eval_imgs) - - def synchronize_between_processes(self) -> None: - """ - Synchronizes evaluation images between processes. - """ - for iou_type in self.iou_types: - self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2) - create_common_coco_eval( - self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type] - ) - - def accumulate(self) -> None: - """ - Accumulates the evaluation results. - """ - for coco_eval in self.coco_eval.values(): - coco_eval.accumulate() - - def summarize(self) -> None: - """ - Prints the IoU metric and summarizes the evaluation results. - """ - for iou_type, coco_eval in self.coco_eval.items(): - print("IoU metric: {}".format(iou_type)) - coco_eval.summarize() - - def prepare( - self, predictions: _TYPING_PREDICTIONS, iou_type: str - ) -> List[Dict[str, Union[int, _TYPING_BOX, float]]]: - """ - Prepares the predictions for COCO detection. - - Args: - predictions: The predictions to prepare. - iou_type: The Intersection over Union (IoU) type for evaluation. - - Returns: - A dictionary with the prepared predictions. - """ - if iou_type == "bbox": - return self.prepare_for_coco_detection(predictions) - else: - raise ValueError(f"IoU type not supported {iou_type}") - - def _post_process_stats( - self, stats, coco_eval_object, iou_type="bbox" - ) -> Dict[str, float]: - """ - Prepares the predictions for COCO detection. - - Args: - predictions: The predictions to prepare. - iou_type: The Intersection over Union (IoU) type for evaluation. - - Returns: - A dictionary with the prepared predictions. - """ - if iou_type not in _SUPPORTED_TYPES: - raise ValueError(f"iou_type '{iou_type}' not supported") - - current_max_dets = coco_eval_object.params.maxDets - - index_to_title = { - "bbox": { - 0: f"AP-IoU=0.50:0.95-area=all-maxDets={current_max_dets[2]}", - 1: f"AP-IoU=0.50-area=all-maxDets={current_max_dets[2]}", - 2: f"AP-IoU=0.75-area=all-maxDets={current_max_dets[2]}", - 3: f"AP-IoU=0.50:0.95-area=small-maxDets={current_max_dets[2]}", - 4: f"AP-IoU=0.50:0.95-area=medium-maxDets={current_max_dets[2]}", - 5: f"AP-IoU=0.50:0.95-area=large-maxDets={current_max_dets[2]}", - 6: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[0]}", - 7: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[1]}", - 8: f"AR-IoU=0.50:0.95-area=all-maxDets={current_max_dets[2]}", - 9: f"AR-IoU=0.50:0.95-area=small-maxDets={current_max_dets[2]}", - 10: f"AR-IoU=0.50:0.95-area=medium-maxDets={current_max_dets[2]}", - 11: f"AR-IoU=0.50:0.95-area=large-maxDets={current_max_dets[2]}", - }, - "keypoints": { - 0: "AP-IoU=0.50:0.95-area=all-maxDets=20", - 1: "AP-IoU=0.50-area=all-maxDets=20", - 2: "AP-IoU=0.75-area=all-maxDets=20", - 3: "AP-IoU=0.50:0.95-area=medium-maxDets=20", - 4: "AP-IoU=0.50:0.95-area=large-maxDets=20", - 5: "AR-IoU=0.50:0.95-area=all-maxDets=20", - 6: "AR-IoU=0.50-area=all-maxDets=20", - 7: "AR-IoU=0.75-area=all-maxDets=20", - 8: "AR-IoU=0.50:0.95-area=medium-maxDets=20", - 9: "AR-IoU=0.50:0.95-area=large-maxDets=20", - }, - } - - output_dict: Dict[str, float] = {} - for index, stat in enumerate(stats): - output_dict[index_to_title[iou_type][index]] = stat - - return output_dict - - def get_results(self) -> Dict[str, Dict[str, float]]: - """ - Gets the results of the COCO evaluation. - - Returns: - A dictionary with the results of the COCO evaluation. - """ - output_dict = {} - - for iou_type, coco_eval in self.coco_eval.items(): - if iou_type == "segm": - iou_type = "bbox" - output_dict[f"iou_{iou_type}"] = self._post_process_stats( - coco_eval.stats, coco_eval, iou_type - ) - return output_dict - - def prepare_for_coco_detection( - self, predictions: _TYPING_PREDICTIONS - ) -> List[Dict[str, Union[int, _TYPING_BOX, float]]]: - """ - Prepares the predictions for COCO detection. - - Args: - predictions: The predictions to prepare. - - Returns: - A list of dictionaries with the prepared predictions. - """ - coco_results = [] - for original_id, prediction in predictions.items(): - if len(prediction) == 0: - continue - - boxes = prediction["boxes"] - if len(boxes) == 0: - continue - - if not isinstance(boxes, torch.Tensor): - boxes = torch.as_tensor(boxes) - boxes = boxes.tolist() - - scores = prediction["scores"] - if not isinstance(scores, list): - scores = scores.tolist() - - labels = prediction["labels"] - if not isinstance(labels, list): - labels = prediction["labels"].tolist() - - coco_results.extend( - [ - { - "image_id": original_id, - "category_id": labels[k], - "bbox": box, - "score": scores[k], - } - for k, box in enumerate(boxes) - ] - ) - return coco_results diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Pnozmulti Configurator License EXCLUSIVE Crack Software.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Pnozmulti Configurator License EXCLUSIVE Crack Software.md deleted file mode 100644 index 49803c8c1e1e9850bc303fd00031dea322cad361..0000000000000000000000000000000000000000 --- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/Pnozmulti Configurator License EXCLUSIVE Crack Software.md +++ /dev/null @@ -1,41 +0,0 @@ -## Pnozmulti Configurator License Crack Software - 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The service licence costs $500 AUD[^1^]. - -- **Temporary Licence:** This is a short-term licence that allows you to use the full version of PNOZmulti Configurator on one workstation for a limited period of time. You can choose between 2 months, 3 months or 4 months duration. The temporary licence costs $200 AUD[^1^], $300 AUD[^1^] or $400 AUD[^1^] respectively. - - - -To purchase any of these licences, you need to contact Pilz Australia or your local Pilz distributor and provide them with your details and requirements. They will send you an invoice and a licence key via email. You can then download the latest version of PNOZmulti Configurator from the Pilz website and enter the licence key in the software to activate it. - - - -PNOZmulti Configurator is a powerful and user-friendly software tool that helps you create safe and efficient safety circuits for your machines and plants. By choosing the right type of licence for your needs, you can enjoy the full benefits of PNOZmulti Configurator software. - - 1b8d091108 \ No newline at end of file diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Autodesk Inventor 2014 Xforce Keygen Fix.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Autodesk Inventor 2014 Xforce Keygen Fix.md deleted file mode 100644 index 0a6129da1cb3ad6dd61db4d09d19a8325b1bab2a..0000000000000000000000000000000000000000 --- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Autodesk Inventor 2014 Xforce Keygen Fix.md +++ /dev/null @@ -1,44 +0,0 @@ -

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          diff --git a/spaces/rehanuddin/04-Gradio-SOTA/qasrl_model_pipeline.py b/spaces/rehanuddin/04-Gradio-SOTA/qasrl_model_pipeline.py deleted file mode 100644 index 50135f76849bc8537fcae83b72532da661487da6..0000000000000000000000000000000000000000 --- a/spaces/rehanuddin/04-Gradio-SOTA/qasrl_model_pipeline.py +++ /dev/null @@ -1,183 +0,0 @@ -from typing import Optional -import json -from argparse import Namespace -from pathlib import Path -from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer - -def get_markers_for_model(is_t5_model: bool) -> Namespace: - special_tokens_constants = Namespace() - if is_t5_model: - # T5 model have 100 special tokens by default - special_tokens_constants.separator_input_question_predicate = "" - special_tokens_constants.separator_output_answers = "" - special_tokens_constants.separator_output_questions = "" # if using only questions - special_tokens_constants.separator_output_question_answer = "" - special_tokens_constants.separator_output_pairs = "" - special_tokens_constants.predicate_generic_marker = "" - special_tokens_constants.predicate_verb_marker = "" - special_tokens_constants.predicate_nominalization_marker = "" - - else: - special_tokens_constants.separator_input_question_predicate = "" - special_tokens_constants.separator_output_answers = "" - special_tokens_constants.separator_output_questions = "" # if using only questions - special_tokens_constants.separator_output_question_answer = "" - special_tokens_constants.separator_output_pairs = "" - special_tokens_constants.predicate_generic_marker = "" - special_tokens_constants.predicate_verb_marker = "" - special_tokens_constants.predicate_nominalization_marker = "" - return special_tokens_constants - -def load_trained_model(name_or_path): - import huggingface_hub as HFhub - tokenizer = AutoTokenizer.from_pretrained(name_or_path) - model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path) - # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory - kwargs_filename = None - if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files - kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json") - elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists(): - kwargs_filename = Path(name_or_path) / "experiment_kwargs.json" - - if kwargs_filename: - preprocessing_kwargs = json.load(open(kwargs_filename)) - # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing - model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs) - model.config.update(preprocessing_kwargs) - return model, tokenizer - - -class QASRL_Pipeline(Text2TextGenerationPipeline): - def __init__(self, model_repo: str, **kwargs): - model, tokenizer = load_trained_model(model_repo) - super().__init__(model, tokenizer, framework="pt") - self.is_t5_model = "t5" in model.config.model_type - self.special_tokens = get_markers_for_model(self.is_t5_model) - self.data_args = model.config.preprocessing_kwargs - # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs` - if "predicate_marker_type" not in vars(self.data_args): - self.data_args.predicate_marker_type = "generic" - if "use_bilateral_predicate_marker" not in vars(self.data_args): - self.data_args.use_bilateral_predicate_marker = True - if "append_verb_form" not in vars(self.data_args): - self.data_args.append_verb_form = True - self._update_config(**kwargs) - - def _update_config(self, **kwargs): - " Update self.model.config with initialization parameters and necessary defaults. " - # set default values that will always override model.config, but can overriden by __init__ kwargs - kwargs["max_length"] = kwargs.get("max_length", 80) - # override model.config with kwargs - for k,v in kwargs.items(): - self.model.config.__dict__[k] = v - - def _sanitize_parameters(self, **kwargs): - preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {} - if "predicate_marker" in kwargs: - preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"] - if "predicate_type" in kwargs: - preprocess_kwargs["predicate_type"] = kwargs["predicate_type"] - if "verb_form" in kwargs: - preprocess_kwargs["verb_form"] = kwargs["verb_form"] - return preprocess_kwargs, forward_kwargs, postprocess_kwargs - - def preprocess(self, inputs, predicate_marker="", predicate_type=None, verb_form=None): - # Here, inputs is string or list of strings; apply string postprocessing - if isinstance(inputs, str): - processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form) - elif hasattr(inputs, "__iter__"): - processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs] - else: - raise ValueError("inputs must be str or Iterable[str]") - # Now pass to super.preprocess for tokenization - return super().preprocess(processed_inputs) - - def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str: - sent_tokens = seq.split(" ") - assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word" - predicate_idx = sent_tokens.index(predicate_marker) - sent_tokens.remove(predicate_marker) - sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)]) - predicate = sent_tokens[predicate_idx] - sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))]) - - if self.data_args.predicate_marker_type == "generic": - predicate_marker = self.special_tokens.predicate_generic_marker - # In case we want special marker for each predicate type: """ - elif self.data_args.predicate_marker_type == "pred_type": - assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it" - assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'" - predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker , - "nominal": self.special_tokens.predicate_nominalization_marker - }[predicate_type] - - if self.data_args.use_bilateral_predicate_marker: - seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}" - else: - seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}" - - # embed also verb_form - if self.data_args.append_verb_form and verb_form is None: - raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)") - elif self.data_args.append_verb_form: - seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} " - else: - seq = f"{seq} " - - # append source prefix (for t5 models) - prefix = self._get_source_prefix(predicate_type) - - return prefix + seq - - def _get_source_prefix(self, predicate_type: Optional[str]): - if not self.is_t5_model or self.data_args.source_prefix is None: - return '' - if not self.data_args.source_prefix.startswith("<"): # Regular prefix - not dependent on input row x - return self.data_args.source_prefix - if self.data_args.source_prefix == "": - if predicate_type is None: - raise ValueError("source_prefix is '' but input no `predicate_type`.") - else: - return f"Generate QAs for {predicate_type} QASRL: " - - def _forward(self, *args, **kwargs): - outputs = super()._forward(*args, **kwargs) - return outputs - - - def postprocess(self, model_outputs): - output_seq = self.tokenizer.decode( - model_outputs["output_ids"].squeeze(), - skip_special_tokens=False, - clean_up_tokenization_spaces=False, - ) - output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip() - qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs) - qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs] - return {"generated_text": output_seq, - "QAs": qas} - - def _postrocess_qa(self, seq: str) -> str: - # split question and answers - if self.special_tokens.separator_output_question_answer in seq: - question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2] - else: - print("invalid format: no separator between question and answer found...") - return None - # question, answer = seq, '' # Or: backoff to only question - # skip "_" slots in questions - question = ' '.join(t for t in question.split(' ') if t != '_') - answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)] - return {"question": question, "answers": answers} - - -if __name__ == "__main__": - pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline") - res1 = pipe("The student was interested in Luke 's research about sea animals .", verb_form="research", predicate_type="nominal") - res2 = pipe(["The doctor was interested in Luke 's treatment .", - "The Veterinary student was interested in Luke 's treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10) - res3 = pipe("A number of professions have developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal") - print(res1) - print(res2) - print(res3) - \ No newline at end of file diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/balanced_l1_loss.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/balanced_l1_loss.py deleted file mode 100644 index 8500345f0e41e8d98f75c4616c70eee8bce4473f..0000000000000000000000000000000000000000 --- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/balanced_l1_loss.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import mmcv -import numpy as np -import torch -import torch.nn as nn - -from ..builder import LOSSES -from .utils import weighted_loss - - -@mmcv.jit(derivate=True, coderize=True) -@weighted_loss -def balanced_l1_loss(pred, - target, - beta=1.0, - alpha=0.5, - gamma=1.5, - reduction='mean'): - """Calculate balanced L1 loss. - - Please see the `Libra R-CNN `_ - - Args: - pred (torch.Tensor): The prediction with shape (N, 4). - target (torch.Tensor): The learning target of the prediction with - shape (N, 4). - beta (float): The loss is a piecewise function of prediction and target - and ``beta`` serves as a threshold for the difference between the - prediction and target. Defaults to 1.0. - alpha (float): The denominator ``alpha`` in the balanced L1 loss. - Defaults to 0.5. - gamma (float): The ``gamma`` in the balanced L1 loss. - Defaults to 1.5. - reduction (str, optional): The method that reduces the loss to a - scalar. Options are "none", "mean" and "sum". - - Returns: - torch.Tensor: The calculated loss - """ - assert beta > 0 - if target.numel() == 0: - return pred.sum() * 0 - - assert pred.size() == target.size() - - diff = torch.abs(pred - target) - b = np.e**(gamma / alpha) - 1 - loss = torch.where( - diff < beta, alpha / b * - (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff, - gamma * diff + gamma / b - alpha * beta) - - return loss - - -@LOSSES.register_module() -class BalancedL1Loss(nn.Module): - """Balanced L1 Loss. - - arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019) - - Args: - alpha (float): The denominator ``alpha`` in the balanced L1 loss. - Defaults to 0.5. - gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5. - beta (float, optional): The loss is a piecewise function of prediction - and target. ``beta`` serves as a threshold for the difference - between the prediction and target. Defaults to 1.0. - reduction (str, optional): The method that reduces the loss to a - scalar. Options are "none", "mean" and "sum". - loss_weight (float, optional): The weight of the loss. Defaults to 1.0 - """ - - def __init__(self, - alpha=0.5, - gamma=1.5, - beta=1.0, - reduction='mean', - loss_weight=1.0): - super(BalancedL1Loss, self).__init__() - self.alpha = alpha - self.gamma = gamma - self.beta = beta - self.reduction = reduction - self.loss_weight = loss_weight - - def forward(self, - pred, - target, - weight=None, - avg_factor=None, - reduction_override=None, - **kwargs): - """Forward function of loss. - - Args: - pred (torch.Tensor): The prediction with shape (N, 4). - target (torch.Tensor): The learning target of the prediction with - shape (N, 4). - weight (torch.Tensor, optional): Sample-wise loss weight with - shape (N, ). - avg_factor (int, optional): Average factor that is used to average - the loss. 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          diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/pytorch_ssim/__init__.py b/spaces/rossellison/kpop-face-generator/stylegan3-fun/pytorch_ssim/__init__.py deleted file mode 100644 index 865ff65754da3efd705fd099371bcebb0044da1e..0000000000000000000000000000000000000000 --- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/pytorch_ssim/__init__.py +++ /dev/null @@ -1,78 +0,0 @@ -# Code from Evan Su/Po-Hsun-Su: https://github.com/Po-Hsun-Su/pytorch-ssim - -import torch -import torch.nn.functional as F -from torch.autograd import Variable -from math import exp - - -def gaussian(window_size, sigma): - gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)]) - return gauss/gauss.sum() - - -def create_window(window_size, channel): - _1D_window = gaussian(window_size, 1.5).unsqueeze(1) - _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) - window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) - return window - - -def _ssim(img1, img2, window, window_size, channel, size_average=True): - mu1 = F.conv2d(img1, window, padding=window_size//2, groups=channel) - mu2 = F.conv2d(img2, window, padding=window_size//2, groups=channel) - - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1*mu2 - - sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size//2, groups=channel) - mu1_sq - sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size//2, groups=channel) - mu2_sq - sigma12 = F.conv2d(img1 * img2, window, padding=window_size//2, groups=channel) - mu1_mu2 - - C1 = 0.01**2 - C2 = 0.03**2 - - ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2)) - - if size_average: - return ssim_map.mean() - else: - return ssim_map.mean(1).mean(1).mean(1) - - -class SSIM(torch.nn.Module): - def __init__(self, window_size = 11, size_average = True): - super(SSIM, self).__init__() - self.window_size = window_size - self.size_average = size_average - self.channel = 1 - self.window = create_window(window_size, self.channel) - - def forward(self, img1, img2): - (_, channel, _, _) = img1.size() - - if channel == self.channel and self.window.data.type() == img1.data.type(): - window = self.window - else: - window = create_window(self.window_size, channel) - - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - - self.window = window - self.channel = channel - - return _ssim(img1, img2, window, self.window_size, channel, self.size_average) - - -def ssim(img1, img2, window_size=11, size_average=True): - (_, channel, _, _) = img1.size() - window = create_window(window_size, channel) - - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - - return _ssim(img1, img2, window, window_size, channel, size_average) diff --git a/spaces/rossellison/kpop-face-generator/stylegan3-fun/viz/class_widget.py b/spaces/rossellison/kpop-face-generator/stylegan3-fun/viz/class_widget.py deleted file mode 100644 index bc7c6f2483b4cfff45a27aaad59cbcd7ec531cf1..0000000000000000000000000000000000000000 --- a/spaces/rossellison/kpop-face-generator/stylegan3-fun/viz/class_widget.py +++ /dev/null @@ -1,41 +0,0 @@ -import imgui -from gui_utils import imgui_utils - - -# ---------------------------------------------------------------------------- - - -class ClassWidget: - def __init__(self, viz): - self.viz = viz - self.cls = 0 - self.animate = False - self.count = 0 - - @imgui_utils.scoped_by_object_id - def __call__(self, show=True): - viz = self.viz - cls = self.cls - - if show: - imgui.text('Class') - imgui.same_line(viz.label_w) - with imgui_utils.grayed_out(not viz.result.get('is_conditional', False)): - _changed, self.cls = imgui.slider_int('##cls', self.cls, 0, viz.result.get('num_classes', 0) - 1) - imgui.same_line() - _clicked, self.animate = imgui.checkbox('Anim##cls', self.animate) - imgui.same_line() - if imgui_utils.button('Reset', width=-1, enabled=(cls != self.cls or self.animate)): - self.cls = 0 - self.animate = False - self.count = 0 - - if self.animate: - self.count += self.viz.frame_delta - if self.count > 1.5: # Update the class every 1.5 seconds; arbitrary, change as you will - self.cls = (self.cls + 1) % viz.result.get('num_classes') # Loop back - self.count = 0 - - # Sanity check when loading new networks - self.cls = min(self.cls, viz.result.get('num_classes', 1) - 1) - viz.args.update(cls=self.cls) diff --git a/spaces/russellc/BLIP/models/med.py b/spaces/russellc/BLIP/models/med.py deleted file mode 100644 index 7b00a35450b736180a805d4f4664b4fb95aeba01..0000000000000000000000000000000000000000 --- a/spaces/russellc/BLIP/models/med.py +++ /dev/null @@ -1,955 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li - * Based on huggingface code base - * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert -''' - -import math -import os -import warnings -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -from torch import Tensor, device, dtype, nn -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - ModelOutput, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.modeling_utils import ( - PreTrainedModel, - apply_chunking_to_forward, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import logging -from transformers.models.bert.configuration_bert import BertConfig - - -logger = logging.get_logger(__name__) - - -class BertEmbeddings(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BertSelfAttention(nn.Module): - def __init__(self, config, is_cross_attention): - super().__init__() - self.config = config - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads) - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - if is_cross_attention: - self.key = nn.Linear(config.encoder_width, self.all_head_size) - self.value = nn.Linear(config.encoder_width, self.all_head_size) - else: - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - self.save_attention = False - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention: - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - seq_length = hidden_states.size()[1] - position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - if is_cross_attention and self.save_attention: - self.save_attention_map(attention_probs) - attention_probs.register_hook(self.save_attn_gradients) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs_dropped = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs_dropped = attention_probs_dropped * head_mask - - context_layer = torch.matmul(attention_probs_dropped, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - outputs = outputs + (past_key_value,) - return outputs - - -class BertSelfOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertAttention(nn.Module): - def __init__(self, config, is_cross_attention=False): - super().__init__() - self.self = BertSelfAttention(config, is_cross_attention) - self.output = BertSelfOutput(config) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -class BertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertLayer(nn.Module): - def __init__(self, config, layer_num): - super().__init__() - self.config = config - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = BertAttention(config) - self.layer_num = layer_num - if self.config.add_cross_attention: - self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) - self.intermediate = BertIntermediate(config) - self.output = BertOutput(config) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - mode=None, - ): - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - - if mode=='multimodal': - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=False, - output_hidden_states=False, - return_dict=True, - mode='multimodal', - ): - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - - for i in range(self.config.num_hidden_layers): - layer_module = self.layer[i] - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warn( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - mode=mode, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - mode=mode, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -class BertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if isinstance(config.hidden_act, str): - self.transform_act_fn = ACT2FN[config.hidden_act] - else: - self.transform_act_fn = config.hidden_act - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -class BertOnlyMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertLMPredictionHead(config) - - def forward(self, sequence_output): - prediction_scores = self.predictions(sequence_output) - return prediction_scores - - -class BertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = BertConfig - base_model_prefix = "bert" - _keys_to_ignore_on_load_missing = [r"position_ids"] - - def _init_weights(self, module): - """ Initialize the weights """ - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - -class BertModel(BertPreTrainedModel): - """ - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in `Attention is - all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, - Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. - argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an - input to the forward pass. - """ - - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - self.config = config - - self.embeddings = BertEmbeddings(config) - - self.encoder = BertEncoder(config) - - self.pooler = BertPooler(config) if add_pooling_layer else None - - self.init_weights() - - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.encoder.layer[layer].attention.prune_heads(heads) - - - def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: - """ - Makes broadcastable attention and causal masks so that future and masked tokens are ignored. - - Arguments: - attention_mask (:obj:`torch.Tensor`): - Mask with ones indicating tokens to attend to, zeros for tokens to ignore. - input_shape (:obj:`Tuple[int]`): - The shape of the input to the model. - device: (:obj:`torch.device`): - The device of the input to the model. - - Returns: - :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. - """ - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - if attention_mask.dim() == 3: - extended_attention_mask = attention_mask[:, None, :, :] - elif attention_mask.dim() == 2: - # Provided a padding mask of dimensions [batch_size, seq_length] - # - if the model is a decoder, apply a causal mask in addition to the padding mask - # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] - if is_decoder: - batch_size, seq_length = input_shape - - seq_ids = torch.arange(seq_length, device=device) - causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] - # in case past_key_values are used we need to add a prefix ones mask to the causal mask - # causal and attention masks must have same type with pytorch version < 1.3 - causal_mask = causal_mask.to(attention_mask.dtype) - - if causal_mask.shape[1] < attention_mask.shape[1]: - prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] - causal_mask = torch.cat( - [ - torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), - causal_mask, - ], - axis=-1, - ) - - extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] - else: - extended_attention_mask = attention_mask[:, None, None, :] - else: - raise ValueError( - "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( - input_shape, attention_mask.shape - ) - ) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 - return extended_attention_mask - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - is_decoder=False, - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - batch_size, seq_length = input_shape - device = input_ids.device - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = inputs_embeds.device - elif encoder_embeds is not None: - input_shape = encoder_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = encoder_embeds.device - else: - raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, - device, is_decoder) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() - else: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - - if type(encoder_attention_mask) == list: - encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] - elif encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - if encoder_embeds is None: - embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - else: - embedding_output = encoder_embeds - - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - mode=mode, - ) - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - if not return_dict: - return (sequence_output, pooled_output) + encoder_outputs[1:] - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=encoder_outputs.past_key_values, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - - - -class BertLMHeadModel(BertPreTrainedModel): - - _keys_to_ignore_on_load_unexpected = [r"pooler"] - _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - self.bert = BertModel(config, add_pooling_layer=False) - self.cls = BertOnlyMLMHead(config) - - self.init_weights() - - def get_output_embeddings(self): - return self.cls.predictions.decoder - - def set_output_embeddings(self, new_embeddings): - self.cls.predictions.decoder = new_embeddings - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - labels=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - return_logits=False, - is_decoder=True, - reduction='mean', - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are - ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - Returns: - Example:: - >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig - >>> import torch - >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') - >>> config = BertConfig.from_pretrained("bert-base-cased") - >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - >>> prediction_logits = outputs.logits - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - if labels is not None: - use_cache = False - - outputs = self.bert( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - is_decoder=is_decoder, - mode=mode, - ) - - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - - if return_logits: - return prediction_scores[:, :-1, :].contiguous() - - lm_loss = None - if labels is not None: - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - if reduction=='none': - lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): - input_shape = input_ids.shape - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) - - # cut decoder_input_ids if past is used - if past is not None: - input_ids = input_ids[:, -1:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "past_key_values": past, - "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), - "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), - "is_decoder": True, - } - - def _reorder_cache(self, past, beam_idx): - reordered_past = () - for layer_past in past: - reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) - return reordered_past diff --git a/spaces/sail/lorahub/hub_name.py b/spaces/sail/lorahub/hub_name.py deleted file mode 100644 index 2364cfe292a795c67c1d02c66428bc9ac572e283..0000000000000000000000000000000000000000 --- a/spaces/sail/lorahub/hub_name.py +++ /dev/null @@ -1,198 +0,0 @@ -LORA_HUB_NAMES = [ - "lorahub/flan_t5_large-qasc_qa_with_separated_facts_3", - "lorahub/flan_t5_large-ag_news_subset", - "lorahub/flan_t5_large-web_questions_whats_the_answer", - "lorahub/flan_t5_large-wiki_hop_original_choose_best_object_affirmative_1", - "lorahub/flan_t5_large-quoref_What_Is_The_Answer", - "lorahub/flan_t5_large-qasc_is_correct_1", - "lorahub/flan_t5_large-ropes_given_background_situation", - "lorahub/flan_t5_large-duorc_SelfRC_title_generation", - "lorahub/flan_t5_large-wiki_hop_original_choose_best_object_affirmative_3", - "lorahub/flan_t5_large-wiki_hop_original_generate_subject", - "lorahub/flan_t5_large-coqa", - "lorahub/flan_t5_large-adversarial_qa_droberta_question_context_answer", - "lorahub/flan_t5_large-amazon_polarity_flattering_or_not", - "lorahub/flan_t5_large-quarel_choose_between", - "lorahub/flan_t5_large-adversarial_qa_dbidaf_based_on", - "lorahub/flan_t5_large-adversarial_qa_dbert_answer_the_following_q", - "lorahub/flan_t5_large-dbpedia_14_given_a_list_of_category_what_does_the_title_belong_to", - "lorahub/flan_t5_large-wiki_hop_original_choose_best_object_interrogative_1", - "lorahub/flan_t5_large-trec", - "lorahub/flan_t5_large-race_high_Write_a_multi_choice_question_options_given_", - "lorahub/flan_t5_large-social_i_qa_Show_choices_and_generate_answer", - "lorahub/flan_t5_large-app_reviews_categorize_rating_using_review", - "lorahub/flan_t5_large-wiki_hop_original_generate_subject_and_object", - "lorahub/flan_t5_large-true_case", - "lorahub/flan_t5_large-wiki_qa_Topic_Prediction_Answer_Only", - "lorahub/flan_t5_large-quartz_given_the_fact_answer_the_q", - "lorahub/flan_t5_large-quail_context_question_description_answer_text", - "lorahub/flan_t5_large-dbpedia_14_given_a_choice_of_categories_", - "lorahub/flan_t5_large-dream_baseline", - "lorahub/flan_t5_large-wiki_qa_Is_This_True_", - "lorahub/flan_t5_large-glue_wnli", - "lorahub/flan_t5_large-adversarial_qa_dbert_based_on", - "lorahub/flan_t5_large-quoref_Read_And_Extract_", - "lorahub/flan_t5_large-amazon_polarity_User_recommend_this_product", - "lorahub/flan_t5_large-wiqa_what_is_the_final_step_of_the_following_process", - "lorahub/flan_t5_large-ropes_plain_no_background", - "lorahub/flan_t5_large-wiki_hop_original_choose_best_object_affirmative_2", - "lorahub/flan_t5_large-race_middle_Select_the_best_answer_generate_span_", - "lorahub/flan_t5_large-quoref_Answer_Question_Given_Context", - "lorahub/flan_t5_large-wmt16_translate_tr-en", - "lorahub/flan_t5_large-quoref_Found_Context_Online", - "lorahub/flan_t5_large-wiki_qa_Decide_good_answer", - "lorahub/flan_t5_large-para_crawl_enes", - "lorahub/flan_t5_large-race_middle_Taking_a_test", - "lorahub/flan_t5_large-ropes_background_new_situation_answer", - "lorahub/flan_t5_large-fix_punct", - "lorahub/flan_t5_large-super_glue_rte", - "lorahub/flan_t5_large-ropes_background_situation_middle", - "lorahub/flan_t5_large-race_high_Taking_a_test", - "lorahub/flan_t5_large-wiki_bio_who", - "lorahub/flan_t5_large-quartz_paragraph_question_plain_concat", - "lorahub/flan_t5_large-ropes_plain_background_situation", - "lorahub/flan_t5_large-quoref_Given_Context_Answer_Question", - "lorahub/flan_t5_large-adversarial_qa_dbidaf_question_context_answer", - "lorahub/flan_t5_large-wmt16_translate_ro-en", - "lorahub/flan_t5_large-adversarial_qa_dbert_question_context_answer", - "lorahub/flan_t5_large-duorc_ParaphraseRC_question_answering", - "lorahub/flan_t5_large-race_high_Is_this_the_right_answer", - "lorahub/flan_t5_large-sciq_Direct_Question", - "lorahub/flan_t5_large-super_glue_wsc.fixed", - "lorahub/flan_t5_large-super_glue_wic", - "lorahub/flan_t5_large-quoref_Answer_Friend_Question", - "lorahub/flan_t5_large-imdb_reviews_plain_text", - "lorahub/flan_t5_large-race_middle_Select_the_best_answer", - "lorahub/flan_t5_large-quail_context_question_answer_description_id", - "lorahub/flan_t5_large-wiki_qa_found_on_google", - "lorahub/flan_t5_large-glue_sst2", - "lorahub/flan_t5_large-quail_context_description_question_answer_id", - "lorahub/flan_t5_large-super_glue_cb", - "lorahub/flan_t5_large-ropes_prompt_bottom_no_hint", - "lorahub/flan_t5_large-anli_r1", - "lorahub/flan_t5_large-ropes_read_background_situation", - "lorahub/flan_t5_large-qasc_qa_with_separated_facts_2", - "lorahub/flan_t5_large-quarel_heres_a_story", - "lorahub/flan_t5_large-social_i_qa_Generate_the_question_from_the_answer", - "lorahub/flan_t5_large-sciq_Multiple_Choice_Closed_Book_", - "lorahub/flan_t5_large-math_dataset_algebra__linear_1d", - "lorahub/flan_t5_large-yelp_polarity_reviews", - "lorahub/flan_t5_large-adversarial_qa_droberta_tell_what_it_is", - "lorahub/flan_t5_large-wiqa_what_might_be_the_last_step_of_the_process", - "lorahub/flan_t5_large-adversarial_qa_dbidaf_answer_the_following_q", - "lorahub/flan_t5_large-quoref_Guess_Answer", - "lorahub/flan_t5_large-amazon_polarity_convey_negative_or_positive_sentiment", - "lorahub/flan_t5_large-wiki_qa_Topic_Prediction_Question_Only", - "lorahub/flan_t5_large-ropes_new_situation_background_answer", - "lorahub/flan_t5_large-web_questions_potential_correct_answer", - "lorahub/flan_t5_large-qasc_is_correct_2", - "lorahub/flan_t5_large-quoref_Find_Answer", - "lorahub/flan_t5_large-app_reviews_convert_to_rating", - "lorahub/flan_t5_large-quail_description_context_question_answer_text", - "lorahub/flan_t5_large-qasc_qa_with_separated_facts_4", - "lorahub/flan_t5_large-qasc_qa_with_separated_facts_5", - "lorahub/flan_t5_large-quoref_Guess_Title_For_Context", - "lorahub/flan_t5_large-wiki_hop_original_explain_relation", - "lorahub/flan_t5_large-ropes_prompt_beginning", - "lorahub/flan_t5_large-gem_e2e_nlg", - "lorahub/flan_t5_large-race_high_Select_the_best_answer_no_instructions_", - "lorahub/flan_t5_large-quail_context_question_description_answer_id", - "lorahub/flan_t5_large-qasc_qa_with_combined_facts_1", - "lorahub/flan_t5_large-glue_cola", - "lorahub/flan_t5_large-quail_description_context_question_answer_id", - "lorahub/flan_t5_large-wiqa_which_of_the_following_is_the_supposed_perturbation", - "lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_", - "lorahub/flan_t5_large-wmt14_translate_fr-en", - "lorahub/flan_t5_large-quoref_Context_Contains_Answer", - "lorahub/flan_t5_large-kilt_tasks_hotpotqa_complex_question", - "lorahub/flan_t5_large-amazon_polarity_negative_or_positive_tone", - "lorahub/flan_t5_large-amazon_polarity_would_you_buy", - "lorahub/flan_t5_large-wiki_qa_exercise", - "lorahub/flan_t5_large-adversarial_qa_dbert_tell_what_it_is", - "lorahub/flan_t5_large-word_segment", - "lorahub/flan_t5_large-gem_dart", - "lorahub/flan_t5_large-duorc_ParaphraseRC_extract_answer", - "lorahub/flan_t5_large-duorc_ParaphraseRC_title_generation", - "lorahub/flan_t5_large-ropes_plain_bottom_hint", - "lorahub/flan_t5_large-wiki_bio_comprehension", - "lorahub/flan_t5_large-anli_r2", - "lorahub/flan_t5_large-quail_context_question_answer_description_text", - "lorahub/flan_t5_large-wiki_hop_original_generate_object", - "lorahub/flan_t5_large-squad_v1.1", - "lorahub/flan_t5_large-wiki_qa_Jeopardy_style", - "lorahub/flan_t5_large-lambada", - "lorahub/flan_t5_large-quartz_having_read_above_passage", - "lorahub/flan_t5_large-quartz_use_info_from_question_paragraph", - "lorahub/flan_t5_large-wiki_bio_key_content", - "lorahub/flan_t5_large-duorc_SelfRC_answer_question", - "lorahub/flan_t5_large-duorc_ParaphraseRC_answer_question", - "lorahub/flan_t5_large-wiki_qa_Topic_Prediction_Question_and_Answer_Pair", - "lorahub/flan_t5_large-anli_r3", - "lorahub/flan_t5_large-glue_mnli", - "lorahub/flan_t5_large-wiki_bio_guess_person", - "lorahub/flan_t5_large-race_high_Select_the_best_answer_generate_span_", - "lorahub/flan_t5_large-glue_stsb", - "lorahub/flan_t5_large-gem_web_nlg_en", - "lorahub/flan_t5_large-adversarial_qa_droberta_based_on", - "lorahub/flan_t5_large-duorc_SelfRC_question_answering", - "lorahub/flan_t5_large-dream_read_the_following_conversation_and_answer_the_question", - "lorahub/flan_t5_large-duorc_SelfRC_generate_question_by_answer", - "lorahub/flan_t5_large-definite_pronoun_resolution", - "lorahub/flan_t5_large-quartz_read_passage_below_choose", - "lorahub/flan_t5_large-race_middle_Is_this_the_right_answer", - "lorahub/flan_t5_large-wiqa_effect_with_label_answer", - "lorahub/flan_t5_large-wiqa_what_might_be_the_first_step_of_the_process", - "lorahub/flan_t5_large-sciq_Multiple_Choice", - "lorahub/flan_t5_large-quartz_use_info_from_paragraph_question", - "lorahub/flan_t5_large-quarel_do_not_use", - "lorahub/flan_t5_large-quac", - "lorahub/flan_t5_large-glue_qqp", - "lorahub/flan_t5_large-quail_no_prompt_text", - "lorahub/flan_t5_large-duorc_ParaphraseRC_decide_worth_it", - "lorahub/flan_t5_large-wiqa_effect_with_string_answer", - "lorahub/flan_t5_large-wiki_hop_original_choose_best_object_interrogative_2", - "lorahub/flan_t5_large-bool_q", - "lorahub/flan_t5_large-social_i_qa_Check_if_a_random_answer_is_valid_or_not", - "lorahub/flan_t5_large-ropes_prompt_bottom_hint_beginning", - "lorahub/flan_t5_large-newsroom", - "lorahub/flan_t5_large-ropes_prompt_mix", - "lorahub/flan_t5_large-quartz_answer_question_based_on", - "lorahub/flan_t5_large-qasc_qa_with_separated_facts_1", - "lorahub/flan_t5_large-race_high_Select_the_best_answer", - "lorahub/flan_t5_large-duorc_ParaphraseRC_movie_director", - "lorahub/flan_t5_large-amazon_polarity_user_satisfied", - "lorahub/flan_t5_large-sentiment140", - "lorahub/flan_t5_large-glue_mrpc", - "lorahub/flan_t5_large-super_glue_multirc", - "lorahub/flan_t5_large-quoref_Answer_Test", - "lorahub/flan_t5_large-wiqa_what_is_the_missing_first_step", - "lorahub/flan_t5_large-race_middle_Select_the_best_answer_no_instructions_", - "lorahub/flan_t5_large-snli", - "lorahub/flan_t5_large-dbpedia_14_pick_one_category_for_the_following_text", - "lorahub/flan_t5_large-amazon_polarity_Is_this_review_negative", - "lorahub/flan_t5_large-quarel_testing_students", - "lorahub/flan_t5_large-glue_qnli", - "lorahub/flan_t5_large-kilt_tasks_hotpotqa_final_exam", - "lorahub/flan_t5_large-web_questions_get_the_answer", - "lorahub/flan_t5_large-duorc_SelfRC_decide_worth_it", - "lorahub/flan_t5_large-paws_wiki", - "lorahub/flan_t5_large-social_i_qa_Show_choices_and_generate_index", - "lorahub/flan_t5_large-duorc_SelfRC_extract_answer", - "lorahub/flan_t5_large-drop", - "lorahub/flan_t5_large-adversarial_qa_droberta_answer_the_following_q", - "lorahub/flan_t5_large-amazon_polarity_Is_this_product_review_positive", - "lorahub/flan_t5_large-quail_no_prompt_id", - "lorahub/flan_t5_large-wiki_qa_automatic_system", - "lorahub/flan_t5_large-sciq_Multiple_Choice_Question_First", - "lorahub/flan_t5_large-squad_v2.0", - "lorahub/flan_t5_large-wiqa_does_the_supposed_perturbation_have_an_effect", - "lorahub/flan_t5_large-wiki_bio_what_content", - "lorahub/flan_t5_large-duorc_SelfRC_movie_director", - "lorahub/flan_t5_large-quarel_logic_test", - "lorahub/flan_t5_large-quartz_answer_question_below", - "lorahub/flan_t5_large-dbpedia_14_given_list_what_category_does_the_paragraph_belong_to", - "lorahub/flan_t5_large-amazon_polarity_Is_this_review", - "lorahub/flan_t5_large-race_middle_Write_a_multi_choice_question_options_given_", - "lorahub/flan_t5_large-adversarial_qa_dbidaf_tell_what_it_is", - "lorahub/flan_t5_large-quail_context_description_question_answer_text" -] \ No newline at end of file diff --git a/spaces/sayakpaul/demo-docker-gradio/Dockerfile b/spaces/sayakpaul/demo-docker-gradio/Dockerfile deleted file mode 100644 index 66611ef63bbe826e805b065d533513c18ee19d5a..0000000000000000000000000000000000000000 --- a/spaces/sayakpaul/demo-docker-gradio/Dockerfile +++ /dev/null @@ -1,28 +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 - 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          - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/senquan/ChuanhuChatGPT/chatgpt - windows.bat b/spaces/senquan/ChuanhuChatGPT/chatgpt - windows.bat deleted file mode 100644 index 0b78fdc3a559abd692e3a9e9af5e482124d13a99..0000000000000000000000000000000000000000 --- a/spaces/senquan/ChuanhuChatGPT/chatgpt - windows.bat +++ /dev/null @@ -1,14 +0,0 @@ -@echo off -echo Opening ChuanhuChatGPT... - -REM Open powershell via bat -start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py" - -REM The web page can be accessed with delayed start http://127.0.0.1:7860/ -ping -n 5 127.0.0.1>nul - -REM access chargpt via your default browser -start "" "http://127.0.0.1:7860/" - - -echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). \ No newline at end of file diff --git a/spaces/sf-checkin/checkin/app.py b/spaces/sf-checkin/checkin/app.py deleted file mode 100644 index 4ce7545117af967bd9db747e100a0b261690fd99..0000000000000000000000000000000000000000 --- a/spaces/sf-checkin/checkin/app.py +++ /dev/null @@ -1,30 +0,0 @@ -import pandas as pd -import gradio as gr -from huggingface_hub import hf_hub_download -import os - -guest_list = hf_hub_download("freddyaboulton/names", "guests.csv", repo_type="dataset", - token=os.environ["TOKEN"]) - - -GUESTS = set(pd.read_csv(guest_list).Name.str.lower()) - -def checkin(s: str): - s = s.lower() - color = "green" if s in GUESTS else "red" - value = "on list" if s in GUESTS else "not on list" - return gr.Label.update(value=value, color=color) - -with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - name = gr.Textbox(label="Name", info="Name on Partiful. Case insensitive. Hit enter or button") - checkin_btn = gr.Button(value="Check in") - # add = gr.Button(value="Add name to list") - with gr.Column(): - result = gr.Label(label="Are they on the list?") - name.submit(checkin, name, result) - checkin_btn.click(checkin, name, result) - # add.click(add_to_list, name, None) - -demo.launch(enable_queue=False) diff --git a/spaces/sgxz/bingo/src/pages/api/sydney.ts b/spaces/sgxz/bingo/src/pages/api/sydney.ts deleted file mode 100644 index 0e7bbf23d77c2e1a6635185a060eeee58b8c8e66..0000000000000000000000000000000000000000 --- a/spaces/sgxz/bingo/src/pages/api/sydney.ts +++ /dev/null @@ -1,62 +0,0 @@ -import { NextApiRequest, NextApiResponse } from 'next' -import { WebSocket, debug } from '@/lib/isomorphic' -import { BingWebBot } from '@/lib/bots/bing' -import { websocketUtils } from '@/lib/bots/bing/utils' -import { WatchDog, createHeaders } from '@/lib/utils' - - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - const conversationContext = req.body - const headers = createHeaders(req.cookies) - debug(headers) - res.setHeader('Content-Type', 'text/stream; charset=UTF-8') - - const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', { - headers: { - ...headers, - 'accept-language': 'zh-CN,zh;q=0.9', - 'cache-control': 'no-cache', - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - pragma: 'no-cache', - } - }) - - const closeDog = new WatchDog() - const timeoutDog = new WatchDog() - ws.onmessage = (event) => { - timeoutDog.watch(() => { - ws.send(websocketUtils.packMessage({ type: 6 })) - }, 1500) - closeDog.watch(() => { - ws.close() - }, 10000) - res.write(event.data) - if (/\{"type":([367])\}/.test(String(event.data))) { - const type = parseInt(RegExp.$1, 10) - debug('connection type', type) - if (type === 3) { - ws.close() - } else { - ws.send(websocketUtils.packMessage({ type })) - } - } - } - - ws.onclose = () => { - timeoutDog.reset() - closeDog.reset() - debug('connection close') - res.end() - } - - await new Promise((resolve) => ws.onopen = resolve) - ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 })) - ws.send(websocketUtils.packMessage({ type: 6 })) - ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!))) - req.socket.once('close', () => { - ws.close() - if (!res.closed) { - res.end() - } - }) -} diff --git a/spaces/shaneweisz/AutoCounterspeech/response_generation/__init__.py b/spaces/shaneweisz/AutoCounterspeech/response_generation/__init__.py deleted file mode 100644 index 872706a00ff8b1a4525d897f8b2d74699bdce91e..0000000000000000000000000000000000000000 --- a/spaces/shaneweisz/AutoCounterspeech/response_generation/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .response_generator import ResponseGenerator diff --git a/spaces/sidharthism/fashion-eye/netdissect/segdata.py b/spaces/sidharthism/fashion-eye/netdissect/segdata.py deleted file mode 100644 index f3cb6dfac8985d9c55344abbc26cc26c4862aa85..0000000000000000000000000000000000000000 --- a/spaces/sidharthism/fashion-eye/netdissect/segdata.py +++ /dev/null @@ -1,74 +0,0 @@ -import os, numpy, torch, json -from .parallelfolder import ParallelImageFolders -from torchvision import transforms -from torchvision.transforms.functional import to_tensor, normalize - -class FieldDef(object): - def __init__(self, field, index, bitshift, bitmask, labels): - self.field = field - self.index = index - self.bitshift = bitshift - self.bitmask = bitmask - self.labels = labels - -class MultiSegmentDataset(object): - ''' - Just like ClevrMulticlassDataset, but the second stream is a one-hot - segmentation tensor rather than a flat one-hot presence vector. - - MultiSegmentDataset('dataset/clevrseg', - imgdir='images/train/positive', - segdir='images/train/segmentation') - ''' - def __init__(self, directory, transform=None, - imgdir='img', segdir='seg', val=False, size=None): - self.segdataset = ParallelImageFolders( - [os.path.join(directory, imgdir), - os.path.join(directory, segdir)], - transform=transform) - self.fields = [] - with open(os.path.join(directory, 'labelnames.json'), 'r') as f: - for defn in json.load(f): - self.fields.append(FieldDef( - defn['field'], defn['index'], defn['bitshift'], - defn['bitmask'], defn['label'])) - self.labels = ['-'] # Reserve label 0 to mean "no label" - self.categories = [] - self.label_category = [0] - for fieldnum, f in enumerate(self.fields): - self.categories.append(f.field) - f.firstchannel = len(self.labels) - f.channels = len(f.labels) - 1 - for lab in f.labels[1:]: - self.labels.append(lab) - self.label_category.append(fieldnum) - # Reserve 25% of the dataset for validation. - first_val = int(len(self.segdataset) * 0.75) - self.val = val - self.first = first_val if val else 0 - self.length = len(self.segdataset) - first_val if val else first_val - # Truncate the dataset if requested. - if size: - self.length = min(size, self.length) - - def __len__(self): - return self.length - - def __getitem__(self, index): - img, segimg = self.segdataset[index + self.first] - segin = numpy.array(segimg, numpy.uint8, copy=False) - segout = torch.zeros(len(self.categories), - segin.shape[0], segin.shape[1], dtype=torch.int64) - for i, field in enumerate(self.fields): - fielddata = ((torch.from_numpy(segin[:, :, field.index]) - >> field.bitshift) & field.bitmask) - segout[i] = field.firstchannel + fielddata - 1 - bincount = numpy.bincount(segout.flatten(), - minlength=len(self.labels)) - return img, segout, bincount - -if __name__ == '__main__': - ds = MultiSegmentDataset('dataset/clevrseg') - print(ds[0]) - import pdb; pdb.set_trace() - diff --git a/spaces/simonraj/ThinkingRoutines/thinking_routines.py b/spaces/simonraj/ThinkingRoutines/thinking_routines.py deleted file mode 100644 index 617d1ae3b1bea458d3faf409920d13c24da32164..0000000000000000000000000000000000000000 --- a/spaces/simonraj/ThinkingRoutines/thinking_routines.py +++ /dev/null @@ -1,20 +0,0 @@ -def thinking_routine_prompt(subject, thinking_routine): - if subject == "Math" and thinking_routine == "Polya": - return ("As an AI tutor trained on the Singapore primary school syllabus for maths and using Polya’s problem-solving steps, guide Primary 3 to Primary 6 students through math problems. Ask thought-provoking questions, encouraging them to apply their understanding of the syllabus to discover solutions. Remember, your role is to prompt, not to provide direct answers.") - elif subject == "Science" and thinking_routine == "Claim, Support, Reasoning": - return ("Acting as an AI tutor aligned with the Singapore primary school syllabus for science, your role is to nurture Primary 3 to Primary 6 students' skills in tackling open-ended questions. Utilize the 'Claim, Support, Reasoning' framework to enhance their analytical thinking. Guide them in the following ways:\n\n1. **Deciphering the Question Stem**: Encourage students to identify the core problem or topic within the question stem, setting the direction for their scientific inquiry.\n\n2. **Interpreting Relevant Information or Context**: Help students recognize and interpret any additional data, scenarios, or descriptions provided in the question, essential for a well-informed scientific claim.\n\n3. **Complying with Specific Requirements**: Ensure that students acknowledge and adhere to any specific instructions in the question, such as the use of diagrams or application of particular concepts.\n\n4. **Formulating a Claim**: Lead students to articulate a clear, concise scientific claim that addresses the question stem.\n\n5. **Gathering Support**: Prompt them to marshal relevant evidence or data that backs up their claim, whether from the question details or their understanding of scientific principles.\n\n6. **Connecting with Reasoning**: Assist them in linking their claim and support to solid reasoning, explaining how the evidence justifies the claim using appropriate scientific concepts.\n\nRemind students to treat the question as a clue-bearing friend, not an obstacle. They should read it thoroughly, understanding its full scope before responding. While they should be concise, they must also justify their claims with logical reasoning and, where applicable, enhance their explanations with clearly labeled diagrams or illustrations.\n\nYour primary goal is not to provide answers but to scaffold students' thought processes, helping them construct coherent, evidence-based responses independently. Encourage consistent practice with this structured approach, enabling them to refine their analytical skills and deepen their scientific comprehension.") - elif subject == "English": - if thinking_routine == "PEEL": - return "As an AI English Language Coach, assist Primary 6 students with their English tasks as per the UK language standards using the PEEL thinking routine. Prompt them to structure their answers using Point, Evidence, Explain, and Link. Encourage independent thinking and do not provide direct answers." - elif thinking_routine == "5W1H": - return "As an AI English Language Coach, help Primary 6 students improve their English as per the UK language standards using the 5W1H thinking routine. Guide them to answer Who, What, When, Where, Why, and How questions, prompting deep thinking without providing direct answers." - elif thinking_routine == "OREO": - return "As an AI English Language Coach, guide Primary 6 students through their English tasks as per the UK language standards using the OREO thinking routine. Encourage them to structure their answers using Opinion, Reason, Example, and Opinion, and stimulate exploration of their thoughts and ideas without providing direct answers." - -thinking_routine_examples = [ - ("Math", "Polya"), - ("Science", "Claim, Support, Reasoning"), - ("English", "PEEL"), - ("English", "5W1H"), - ("English", "OREO") -] diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ApkBoat Presents Minecraft 1.19 Apk with Unlimited Items and God Mode.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ApkBoat Presents Minecraft 1.19 Apk with Unlimited Items and God Mode.md deleted file mode 100644 index 077ea1a666f2f8f37a3b71caa0ef436f55a4667e..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ApkBoat Presents Minecraft 1.19 Apk with Unlimited Items and God Mode.md +++ /dev/null @@ -1,141 +0,0 @@ - -

          Apkboat Minecraft 1.19: Everything You Need to Know

          -

          Minecraft is one of the most popular and influential games of all time, with millions of players around the world exploring, building, and surviving in a blocky world. However, not everyone can afford or access the official version of the game, especially on mobile devices. That's why some people turn to alternative versions of the game, such as Apkboat Minecraft 1.19.

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          But what is Apkboat Minecraft 1.19, and how can you download and play it? What are the new features and changes in this version, and why should you give it a try? In this article, we will answer all these questions and more, so you can enjoy this amazing game without any hassle.

          -

          What is Apkboat Minecraft 1.19?

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          Apkboat Minecraft 1.19 is a modified version of the popular sandbox game Minecraft

          -

          Apkboat is a website that provides free downloads of various Android apps and games, including modified versions of popular games like Minecraft. Apkboat Minecraft 1.19 is one of these modified versions, which is based on the Bedrock Edition of the game.

          -

          Bedrock Edition is the cross-platform version of Minecraft that runs on Windows 10, Xbox One, PlayStation 4, Nintendo Switch, iOS, Android, and other devices. It has many features that are not available in the Java Edition, such as cross-play, achievements, marketplace, and more.

          -

          Apkboat Minecraft 1.19 offers many features and advantages over the official version

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          Apkboat Minecraft 1.19 is not just a copy of the official version of the game, but also a modified version that offers many benefits for players who want to enjoy the game without any limitations or restrictions.

          -

          Some of these benefits include:

          -
            -
          • Free download and installation
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          • Unlocked skins, textures, maps, and mods
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          • Working Xbox Live login and multiplayer support
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          • Regular updates and bug fixes
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          How to download and install Apkboat Minecraft 1.19?

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          Download Apkboat Minecraft 1.19 from a reliable source

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          The first step to play Apkboat Minecraft 1.19 is to download the APK file from a reliable source. You can find the latest version of the game on the official website of Apkboat, or on other trusted websites that provide APK downloads.

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          The APK file is a compressed file that contains all the necessary data to install and run the game on your device. The size of the file may vary depending on the version and features of the game, but it usually ranges from 100 MB to 200 MB.

          -Enable unknown sources on your device -

          The next step to install Apkboat Minecraft 1.19 is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the official Google Play Store.

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          To enable unknown sources, you need to go to your device's settings, then find the option for security or privacy, and then toggle on the switch for unknown sources. Depending on your device model and Android version, the exact steps may vary, but you can always search for "unknown sources" in your settings to find it.

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          Once you enable unknown sources, you will be able to install APK files from any source. However, you should be careful and only download APK files from trusted and verified websites, as some APK files may contain malware or viruses that can harm your device or steal your data.

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          Install Apkboat Minecraft 1.19 and sign in with Xbox Live

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          The final step to play Apkboat Minecraft 1.19 is to install the APK file and sign in with Xbox Live. To install the APK file, you need to locate it in your device's storage, usually in the downloads folder, and then tap on it to start the installation process.

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          The installation process may take a few minutes, depending on your device's speed and storage space. You may also see some prompts or warnings asking for your permission to install the app or access certain features of your device. You need to grant these permissions for the app to work properly.

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          After the installation is complete, you can launch the game from your app drawer or home screen. The first time you launch the game, you will be asked to sign in with Xbox Live. This is necessary for you to access multiplayer servers and realms, as well as achievements and other features of the game.

          -

          To sign in with Xbox Live, you need to have an existing account or create a new one. You can use any email address or phone number to create an account, and it is free of charge. Once you sign in with Xbox Live, you will be able to play Apkboat Minecraft 1.19 with your friends and other players online.

          -

          What are the new features and changes in Apkboat Minecraft 1.19?

          -

          Apkboat Minecraft 1.19 introduces the Trails & Tales update

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          Apkboat Minecraft 1.19 is based on the latest official update of Minecraft Bedrock Edition, which is called Trails & Tales. This update was released on June 8, 2023, and it adds a lot of new content and improvements to the game.

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          The Trails & Tales update is focused on exploration and storytelling, as it adds new biomes, structures, and features that make the world more diverse and interesting. It also adds new ways to interact with animals and villagers, as well as new items and mechanics that enhance your gameplay experience.

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          Apkboat Minecraft 1.19 adds new blocks, items, and mobs

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          One of the main additions of Apkboat Minecraft 1.19 is the new blocks, items, and mobs that are part of the Trails & Tales update. Some of these include:

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          • New biomes: The Lush Caves, The Dripstone Caves, The Deep Dark, The Snowy Peaks, The Badlands Plateau, The Bamboo Forests
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          • New structures: The Warden's Cabin, The Abandoned Mineshaft, The Pillager Outpost, The Woodland Mansion
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          • New features: The Sculk Sensor, The Lightning Rod, The Spyglass, The Bundle
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          • New mobs: The Warden, The Axolotl, The Goat, The Glow Squid
            • -
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          These new blocks, items, and mobs add more variety and challenge to the game, as well as more opportunities for creativity and fun.

          Apkboat Minecraft 1.19 improves performance and stability

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          Another important aspect of Apkboat Minecraft 1.19 is the improvement of performance and stability of the game. The Trails & Tales update brings many bug fixes and optimizations that make the game run smoother and faster on various devices.

          -

          Some of these improvements include:

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          • Reduced lag and stuttering in multiplayer and realms
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          • Fixed crashes and freezes in certain situations
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          • Improved rendering and lighting of the world
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          • Enhanced compatibility and security of the game
            • -
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          These improvements make Apkboat Minecraft 1.19 more enjoyable and reliable, as well as more compatible with different devices and platforms.

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          Why should you play Apkboat Minecraft 1.19?

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          Apkboat Minecraft 1.19 is free and safe to use

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          One of the main reasons why you should play Apkboat Minecraft 1.19 is that it is free and safe to use. Unlike the official version of the game, which costs money and requires a license verification, Apkboat Minecraft 1.19 does not require any payment or activation to play.

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          Moreover, Apkboat Minecraft 1.19 is safe to use, as it does not contain any malware or viruses that can harm your device or steal your data. Apkboat is a reputable website that provides verified and tested APK files for various apps and games, including Minecraft.

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          However, you should always be careful when downloading APK files from other sources, as some of them may be fake or malicious. You should also scan the APK file with an antivirus software before installing it, just to be on the safe side.

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          Apkboat Minecraft 1.19 allows you to access multiplayer servers and realms

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          Another reason why you should play Apkboat Minecraft 1.19 is that it allows you to access multiplayer servers and realms, which are not available in some versions of the game. Multiplayer servers and realms are online worlds where you can play with other players, either cooperatively or competitively.

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          Multiplayer servers are hosted by third-party providers, and they offer various game modes, maps, and features that can enhance your gameplay experience. Some of the most popular multiplayer servers are Hypixel, Mineplex, The Hive, and Cubecraft.

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          Realms are private servers that are hosted by Mojang, the developer of Minecraft. They allow you to create your own world and invite up to 10 friends to join you. You can also access your realm from any device that supports Minecraft Bedrock Edition.

          -

          To access multiplayer servers and realms, you need to sign in with Xbox Live, which is free and easy to do. You can then browse and join any server or realm that you want, or create your own one if you have a subscription.

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          Apkboat Minecraft 1.19 gives you more creative freedom and fun

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          The final reason why you should play Apkboat Minecraft 1.19 is that it gives you more creative freedom and fun than the official version of the game. Apkboat Minecraft 1.19 has many features that allow you to customize your game and make it more interesting and enjoyable.

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          Some of these features include:

          -
            -
          • Unlocked skins, textures, maps, and mods that let you change the appearance and behavior of the game
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          • New blocks, items, and mobs that add more variety and challenge to the game
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          • The Trails & Tales update that adds new biomes, structures, and features that make the world more diverse and interesting
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          • The creative mode that lets you build anything you can imagine with unlimited resources
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          • The survival mode that tests your skills and endurance in a hostile environment
            • -
          • The adventure mode that lets you explore custom maps created by other players
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          • The spectator mode that lets you fly around and observe the world without interacting with it
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          These features make Apkboat Minecraft 1.19 more fun and engaging than the official version of the game, as well as more suitable for different types of players.

          -

          Conclusion

          -

          In conclusion, Apkboat Minecraft 1.19 is a modified version of the popular sandbox game Minecraft that offers many features and advantages over the official version. It is free and safe to use, it allows you to access multiplayer servers and realms, and it gives you more creative freedom and fun.

          -

          If you want to download and play Apkboat Minecraft 1.19, you need to follow these steps:

          -
            -
          1. Download Apkboat Minecraft
          2. Download Apkboat Minecraft 1.19 from a reliable source, such as the official website of Apkboat
            3. -
          3. Enable unknown sources on your device's settings
            4. -
          4. Install Apkboat Minecraft 1.19 and sign in with Xbox Live
            5. -
          5. Enjoy the game with your friends and other players online
            6. -
          -

          Apkboat Minecraft 1.19 is a great way to experience Minecraft in a new and exciting way. It is one of the best modified versions of the game that you can find online, and it is constantly updated and improved. If you are a fan of Minecraft, you should definitely give it a try.

          -

          FAQs

          -

          What is the difference between Apkboat Minecraft 1.19 and Minecraft PE?

          -

          Minecraft PE stands for Minecraft Pocket Edition, which is the original name of the mobile version of Minecraft Bedrock Edition. Apkboat Minecraft 1.19 is a modified version of Minecraft Bedrock Edition, which offers more features and advantages than Minecraft PE.

          -

          Is Apkboat Minecraft 1.19 legal and safe?

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          Apkboat Minecraft 1.19 is not an official product of Mojang or Microsoft, and it may violate some of their terms and conditions. However, it is not illegal to download and use Apkboat Minecraft 1.19, as long as you do not distribute or sell it for profit. Apkboat Minecraft 1.19 is also safe to use, as it does not contain any malware or viruses that can harm your device or steal your data.

          -

          Can I play Apkboat Minecraft 1.19 on PC or console?

          -

          No, Apkboat Minecraft 1.19 is only compatible with Android devices. If you want to play Minecraft on PC or console, you need to buy the official version of the game from the respective platforms.

          -

          Can I use mods and cheats in Apkboat Minecraft 1.19?

          -

          Yes, Apkboat Minecraft 1.19 supports mods and cheats that can enhance your gameplay experience. You can find and download various mods and cheats from different websites, such as MCPE DL or MCPEDL. However, you should be careful when using mods and cheats, as they may cause compatibility issues or ban you from some servers or realms.

          -

          How can I update Apkboat Minecraft 1.19?

          -

          Apkboat Minecraft 1.19 is regularly updated to match the latest official version of the game. You can check for updates on the official website of Apkboat, or on other trusted websites that provide APK downloads. You can also enable notifications on your device to alert you when a new update is available.

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          \ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Call of Duty Mobile Mod APK with Unlimited Features and Fast Speed.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Call of Duty Mobile Mod APK with Unlimited Features and Fast Speed.md deleted file mode 100644 index 7d0dbccf2f1408880decdb789afcf3226482c611..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Call of Duty Mobile Mod APK with Unlimited Features and Fast Speed.md +++ /dev/null @@ -1,124 +0,0 @@ - -

          Download Mod Call of Duty Mobile APK: How to Play the Popular FPS Game with Unlimited Features

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          If you are a fan of first-person shooter (FPS) games, you have probably heard of Call of Duty Mobile, one of the most popular and successful mobile games in the genre. But did you know that you can play this game with unlimited features and resources by downloading a modded version of the game? In this article, we will tell you everything you need to know about how to download mod call of duty mobile apk and enjoy this thrilling game like never before.

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          What is Call of Duty Mobile?

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          A brief introduction to the game and its features

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          Call of Duty Mobile is a free-to-play FPS game developed by Activision and Tencent Games. It was released in October 2019 and has since attracted millions of players worldwide. The game features various modes, such as multiplayer, battle royale, zombies, and special ops, where you can compete with other players or cooperate with your friends. You can also customize your loadout, unlock and upgrade weapons, outfits, operators, scorestreaks, and more. The game boasts console-quality graphics, sound, and controls, making it one of the best mobile games in the market.

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          The difference between the official version and the modded version

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          The official version of Call of Duty Mobile is available on Google Play Store and Apple App Store. However, some players may find it hard to progress in the game due to limited resources, such as credits, cod points, weapons, skins, etc. That's why some developers have created modded versions of the game that offer unlimited features and resources for free. These modded versions are not authorized by Activision or Tencent Games and are usually distributed through third-party websites or apps.

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          The risks and challenges of using the modded version

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          However, downloading mod call of duty mobile apk also comes with some risks and challenges that you should be aware of. Some of them are:

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          How to download mod call of duty mobile apk?

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          If you have decided to download mod call of duty mobile apk, you will need to follow these steps:

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          The tips and tricks to optimize the gameplay experience

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          In conclusion, downloading mod call of duty mobile apk can be a great way to enhance your gameplay experience and enjoy unlimited features and resources in this popular FPS game. However, you should also be aware of the risks and challenges that come with using the modded version and take precautions to protect yourself and respect others. If you are ready to download mod call of duty mobile apk, you can follow the steps and tips we have provided in this article. Alternatively, you can also play the official version of Call of Duty Mobile and support the game developers by purchasing credits, cod points, or other items in the game. Either way, we hope you have a blast playing this amazing game!

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          \ No newline at end of file diff --git a/spaces/skf15963/summary/fengshen/models/transfo_xl_paraphrase/__init__.py b/spaces/skf15963/summary/fengshen/models/transfo_xl_paraphrase/__init__.py deleted file mode 100644 index 8eb10eb65d1b0c4da740e22fcba4e19461121f20..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/models/transfo_xl_paraphrase/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from fengshen.models.transfo_xl_denoise.modeling_transfo_xl_denoise import TransfoXLDenoiseModel as TransfoXLModel -from .generate import paraphrase_generate diff --git a/spaces/skf15963/summary/fengshen/utils/huggingface_spider.py b/spaces/skf15963/summary/fengshen/utils/huggingface_spider.py deleted file mode 100644 index 6dd5a4eae3e2a046b346fc465fc13f4feff28c22..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/utils/huggingface_spider.py +++ /dev/null @@ -1,16 +0,0 @@ -import json -import requests -from bs4 import BeautifulSoup - -response = requests.get('https://huggingface.co/IDEA-CCNL?sort_models=downloads#models') -soup = BeautifulSoup(response.content, 'html.parser') -model_data_node = soup.find_all('div', attrs={"class": "SVELTE_HYDRATER"})[3] -data = json.loads(model_data_node['data-props']) -all_downloads = 0 -for item in data['repos']: - if 'downloads' not in item: - item['downloads'] = 0 - all_downloads += item['downloads'] - print('name: {}, author: {}, downloads: {}, likes: {}'.format( - item['id'], item['author'], item['downloads'], item['likes'])) -print('total downloads {}'.format(all_downloads)) diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/shuffled_word_order/README.finetuning.md b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/shuffled_word_order/README.finetuning.md deleted file mode 100644 index ecbcb65884640c3327a2cbaef8aad4f3cfe812f7..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/shuffled_word_order/README.finetuning.md +++ /dev/null @@ -1,135 +0,0 @@ -# Fine-tuning details - -For each task (GLUE and PAWS), we perform hyperparam search for each model, and report the mean and standard deviation across 5 seeds of the best model. First, get the datasets following the instructions in [RoBERTa fine-tuning README](../roberta/README.glue.md). Alternatively, you can use [huggingface datasets](https://huggingface.co/docs/datasets/) to get the task data: - -```python -from datasets import load_dataset -import pandas as pd -from pathlib import Path - -key2file = { -"paws": { - "loc": "paws_data", - "columns": ["id", "sentence1", "sentence2", "label"], - "train": "train.tsv", - "validation": "dev.tsv", - "test": "test.tsv" - } -} - -task_data = load_dataset("paws", "labeled_final") -task_config = key2file["paws"] -save_path = Path(task_config["loc"]) -save_path.mkdir(exist_ok=True, parents=True) -for key, fl in task_config.items(): - if key in ["loc", "columns"]: - continue - print(f"Reading {key}") - columns = task_config["columns"] - df = pd.DataFrame(task_data[key]) - print(df.columns) - df = df[columns] - print(f"Got {len(df)} records") - save_loc = save_path / fl - print(f"Saving to : {save_loc}") - df.to_csv(save_loc, sep="\t", header=None, index=None) - -``` - -- Preprocess using RoBERTa GLUE preprocessing script, while keeping in mind the column numbers for `sentence1`, `sentence2` and `label` (which is 0,1,2 if you save the data according to the above example.) -- Then, fine-tuning is performed similarly to RoBERTa (for example, in case of RTE): - -```bash -TOTAL_NUM_UPDATES=30875 # 10 epochs through RTE for bsz 16 -WARMUP_UPDATES=1852 # 6 percent of the number of updates -LR=2e-05 # Peak LR for polynomial LR scheduler. -NUM_CLASSES=2 -MAX_SENTENCES=16 # Batch size. -SHUFFLED_ROBERTA_PATH=/path/to/shuffled_roberta/model.pt - -CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin/ \ - --restore-file $SHUFFLED_ROBERTA_PATH \ - --max-positions 512 \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; -``` - -- `TOTAL_NUM_UPDATES` is computed based on the `--batch_size` value and the dataset size. -- `WARMUP_UPDATES` is computed as 6% of `TOTAL_NUM_UPDATES` -- Best hyperparam of `--lr` and `--batch_size` is reported below: - -## `--lr` - -| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | -| --: | :----------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | -| 0 | original | 2e-05 | 2e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | -| 1 | n_1 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | -| 2 | n_2 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 3e-05 | -| 3 | n_3 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 3e-05 | 1e-05 | 1e-05 | 2e-05 | -| 4 | n_4 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | -| 5 | r512 | 1e-05 | 3e-05 | 2e-05 | 2e-05 | 3e-05 | 2e-05 | 3e-05 | 2e-05 | -| 6 | rand_corpus | 2e-05 | 1e-05 | 3e-05 | 1e-05 | 3e-05 | 3e-05 | 3e-05 | 2e-05 | -| 7 | rand_uniform | 2e-05 | 1e-05 | 3e-05 | 2e-05 | 3e-05 | 3e-05 | 3e-05 | 1e-05 | -| 8 | rand_init | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | -| 9 | no_pos | 1e-05 | 3e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | - -## `--batch_size` - -| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | -| --: | :----------- | --: | ---: | ----: | ---: | --: | ---: | ---: | ---: | -| 0 | orig | 16 | 16 | 32 | 16 | 16 | 32 | 32 | 16 | -| 1 | n_1 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 16 | -| 2 | n_2 | 32 | 16 | 32 | 16 | 32 | 32 | 16 | 32 | -| 3 | n_3 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 32 | -| 4 | n_4 | 32 | 16 | 32 | 16 | 32 | 32 | 32 | 32 | -| 5 | r512 | 32 | 16 | 16 | 32 | 32 | 16 | 16 | 16 | -| 6 | rand_corpus | 16 | 16 | 16 | 16 | 32 | 16 | 16 | 32 | -| 7 | rand_uniform | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | -| 8 | rand_init | 16 | 16 | 32 | 16 | 16 | 16 | 32 | 16 | -| 9 | no_pos | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | - -- Perform inference similar to RoBERTa as well: - -```python -from fairseq.models.roberta import RobertaModel - -roberta = RobertaModel.from_pretrained( - 'checkpoints/', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='PAWS-bin' -) - -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.label_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('paws_data/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[0], tokens[1], tokens[2] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) - -``` diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py deleted file mode 100644 index a30254604311a488a1d4959f941051890ed32b2e..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/speech_synthesis/preprocessing/get_common_voice_audio_manifest.py +++ /dev/null @@ -1,140 +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 argparse -import logging -from pathlib import Path -from collections import defaultdict -from typing import List, Dict, Tuple - -import pandas as pd -import numpy as np -import torchaudio -from tqdm import tqdm - -from examples.speech_to_text.data_utils import load_df_from_tsv, save_df_to_tsv - - -log = logging.getLogger(__name__) - -SPLITS = ["train", "dev", "test"] - - -def get_top_n( - root: Path, n_speakers: int = 10, min_n_tokens: int = 5 -) -> pd.DataFrame: - df = load_df_from_tsv(root / "validated.tsv") - df["n_tokens"] = [len(s.split()) for s in df["sentence"]] - df = df[df["n_tokens"] >= min_n_tokens] - df["n_frames"] = [ - torchaudio.info((root / "clips" / p).as_posix()).num_frames - for p in tqdm(df["path"]) - ] - df["id"] = [Path(p).stem for p in df["path"]] - total_duration_ms = df.groupby("client_id")["n_frames"].agg(["sum"]) - total_duration_ms = total_duration_ms.sort_values("sum", ascending=False) - - top_n_total_duration_ms = total_duration_ms.head(n_speakers) - top_n_client_ids = set(top_n_total_duration_ms.index.tolist()) - df_top_n = df[df["client_id"].isin(top_n_client_ids)] - return df_top_n - - -def get_splits( - df, train_split_ratio=0.99, speaker_in_all_splits=False, rand_seed=0 -) -> Tuple[Dict[str, str], List[str]]: - np.random.seed(rand_seed) - dev_split_ratio = (1. - train_split_ratio) / 3 - grouped = list(df.groupby("client_id")) - id_to_split = {} - for _, cur_df in tqdm(grouped): - cur_n_examples = len(cur_df) - if speaker_in_all_splits and cur_n_examples < 3: - continue - cur_n_train = int(cur_n_examples * train_split_ratio) - cur_n_dev = int(cur_n_examples * dev_split_ratio) - cur_n_test = cur_n_examples - cur_n_dev - cur_n_train - if speaker_in_all_splits and cur_n_dev * cur_n_test == 0: - cur_n_dev, cur_n_test = 1, 1 - cur_n_train = cur_n_examples - cur_n_dev - cur_n_test - cur_indices = cur_df.index.tolist() - cur_shuffled_indices = np.random.permutation(cur_n_examples) - cur_shuffled_indices = [cur_indices[i] for i in cur_shuffled_indices] - cur_indices_by_split = { - "train": cur_shuffled_indices[:cur_n_train], - "dev": cur_shuffled_indices[cur_n_train: cur_n_train + cur_n_dev], - "test": cur_shuffled_indices[cur_n_train + cur_n_dev:] - } - for split in SPLITS: - for i in cur_indices_by_split[split]: - id_ = df["id"].loc[i] - id_to_split[id_] = split - return id_to_split, sorted(df["client_id"].unique()) - - -def convert_to_wav(root: Path, filenames: List[str], target_sr=16_000): - out_root = root / "wav" - out_root.mkdir(exist_ok=True, parents=True) - print("Converting to WAV...") - for n in tqdm(filenames): - in_path = (root / "clips" / n).as_posix() - waveform, sr = torchaudio.load(in_path) - converted, converted_sr = torchaudio.sox_effects.apply_effects_tensor( - waveform, sr, [["rate", str(target_sr)], ["channels", "1"]] - ) - out_path = (out_root / Path(n).with_suffix(".wav").name).as_posix() - torchaudio.save(out_path, converted, converted_sr, encoding="PCM_S", - bits_per_sample=16) - - -def process(args): - data_root = Path(args.data_root).absolute() / args.lang - - # Generate TSV manifest - print("Generating manifest...") - - df_top_n = get_top_n(data_root) - id_to_split, speakers = get_splits(df_top_n) - - if args.convert_to_wav: - convert_to_wav(data_root, df_top_n["path"].tolist()) - - manifest_by_split = {split: defaultdict(list) for split in SPLITS} - for sample in tqdm(df_top_n.to_dict(orient="index").values()): - sample_id = sample["id"] - split = id_to_split[sample_id] - manifest_by_split[split]["id"].append(sample_id) - if args.convert_to_wav: - audio_path = data_root / "wav" / f"{sample_id}.wav" - else: - audio_path = data_root / "clips" / f"{sample_id}.mp3" - manifest_by_split[split]["audio"].append(audio_path.as_posix()) - manifest_by_split[split]["n_frames"].append(sample["n_frames"]) - manifest_by_split[split]["tgt_text"].append(sample["sentence"]) - manifest_by_split[split]["speaker"].append(sample["client_id"]) - manifest_by_split[split]["src_text"].append(sample["sentence"]) - - output_root = Path(args.output_manifest_root).absolute() - output_root.mkdir(parents=True, exist_ok=True) - for split in SPLITS: - save_df_to_tsv( - pd.DataFrame.from_dict(manifest_by_split[split]), - output_root / f"{split}.audio.tsv" - ) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--data-root", "-d", required=True, type=str) - parser.add_argument("--output-manifest-root", "-m", required=True, type=str) - parser.add_argument("--lang", "-l", required=True, type=str) - parser.add_argument("--convert-to-wav", action="store_true") - args = parser.parse_args() - - process(args) - - -if __name__ == "__main__": - main() diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/bmuf.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/bmuf.py deleted file mode 100644 index d6d0e04e86eb894efe59e13a78843d01ca9e651d..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/optim/bmuf.py +++ /dev/null @@ -1,200 +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 dataclasses import dataclass, field - -import torch -import torch.distributed as dist -from fairseq.dataclass.configs import FairseqBMUFConfig -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim.fairseq_optimizer import FairseqOptimizer - - -class FairseqBMUF(FairseqOptimizer): - """ - Implements incremental block distributed data parallelism similar to - https://ieeexplore.ieee.org/document/7472805 - - Paper title: Scalable training of deep learning machines by incremental - block training with intra-block parallel optimization and blockwise - model-update filtering - """ - - def __init__(self, cfg: FairseqBMUFConfig, optimizer): - super().__init__(cfg) - self._optimizer = optimizer - self._num_updates = 0 - self.sync_iter = cfg.global_sync_iter - self.block_momentum = cfg.block_momentum - self.block_lr = cfg.block_lr - self._reset_local_data() - self.warmup_iteration = cfg.warmup_iterations - self.use_nbm = cfg.use_nbm - self.initial_state = self._optimizer.state_dict() - self.average_sync = self.cfg.average_sync - self.world_size = self.cfg.distributed_world_size - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - gen_parser_from_dataclass(parser, FairseqBMUFConfig()) - - @property - def optimizer(self): - return self._optimizer.optimizer - - @property - def optimizer_config(self): - return self._optimizer.optimizer_config - - def get_lr(self): - return self._optimizer.get_lr() - - def set_lr(self, lr): - self._optimizer.set_lr(lr) - - def state_dict(self): - return self._optimizer.state_dict() - - def load_state_dict(self, state_dict, optimizer_overrides=None): - self._optimizer.load_state_dict(state_dict, optimizer_overrides) - self.initial_state = self._optimizer.state_dict() - - def multiply_grads(self, c): - """Multiplies grads by a constant *c*.""" - self._optimizer.multiply_grads(c) - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - return self._optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) - - def average_params(self): - self._optimizer.average_params() - - def _block_sync(self): - if self.world_size <= 1: - return - # Update the global model using local models from all GPUs - # (Step-1) Calculate grad between previously synced model and - # currrent local model - if self.block_momentum != 0: - self._calc_grad() - - # (Step-2) Average gradient from all GPUs - self._avg_grad_from_all_gpus() - - # (Step-3) Calculate global momentum and update the global model - if self.block_momentum != 0: - self._update_global_model() - - # (Step-4) Average local optimizer params - if self.average_sync: - self.average_params() - - def _is_warmup_end(self): - # Check whether train iterations is equal to warmup iter - if self.get_num_updates() == self.warmup_iteration: - return True - return False - - def _is_bmuf_iter(self): - # Check whether train iterations is equal to bmuf sync iter - if (self.get_num_updates() > self.warmup_iteration) and ( - self.get_num_updates() % self.sync_iter == 0 - ): - return True - return False - - def _warmup_sync(self, root_rank=0): - if self.world_size <= 1: - return - # Broadcast the local model to all gpus - for param in self.params: - dist.broadcast(param.data, src=root_rank) - - # Update local optimizer state - if self.average_sync: - self._optimizer.average_params() - else: - self._optimizer.load_state_dict(self.initial_state) - - self._reset_local_data() - - def step(self, closure=None): - """Performs a single optimization step.""" - self._optimizer.step(closure) - self.set_num_updates(self.get_num_updates() + 1) - if self._is_warmup_end(): - self._warmup_sync() - elif self._is_bmuf_iter(): - self._block_sync() - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - self._optimizer.zero_grad() - - def get_num_updates(self): - """Get the number of parameters updates.""" - return self._num_updates - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - self._num_updates = num_updates - - @torch.no_grad() - def _reset_local_data(self): - # (Step-0) Initialize global momentum parameters and store global copy on each gpu - self.global_params = [torch.zeros_like(p.data) for p in self.params] - self.smoothed_grads = [p.data.new_zeros(p.data.size()) for p in self.params] - self.grads = [p.data.new_zeros(p.data.size()) for p in self.params] - - # saving the global model locally for calculating gradient during bmuf sync - for param, global_param in zip(self.params, self.global_params): - global_param.copy_(param.data) - - @torch.no_grad() - def _calc_grad(self): - # global_params is basically the global copy from the previously finished - # synchronisation. param.data is local parameter after block_sync_freq - # for the local gpu. so grad is difference between previously synced - # model and currrent local model. - for index, (param, global_param) in enumerate( - zip(self.params, self.global_params) - ): - self.grads[index] = global_param - param.data - - def _avg_grad_from_all_gpus(self): - for index, param in enumerate(self.params): - sync_para = param.data if self.block_momentum == 0 else self.grads[index] - sync_para /= float(dist.get_world_size()) - dist.all_reduce(sync_para, op=dist.ReduceOp.SUM) - - @torch.no_grad() - def _update_global_model(self): - for index, (param, global_param, smoothed_grad, grad) in enumerate( - zip( - self.params, - self.global_params, - self.smoothed_grads, - # all gpus would share the same value of smoothed_grad, since it is - # always computed on synchronized gradients. - self.grads, - ) - ): - # global_param is basically last syncrhornized parameter. though - # smoothed_grad is local, all processes will have same value of - # smoothed_grad and hence param is globally synchronized copy. - # smoothed_grad(t) = BM * smoothed_grad(t-1) + BM_lr * grad(t) - smoothed_grad = self.block_momentum * smoothed_grad + self.block_lr * grad - param.data.copy_(global_param - smoothed_grad) - - # A Nesterov momentum here is to do a partial weight update before - # calculating the gradient - if self.use_nbm: - param.data.copy_(param.data - self.block_momentum * smoothed_grad) - - # backup for the next synchronization. - self.smoothed_grads[index] = smoothed_grad - global_param.copy_(param.data) diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/train.py b/spaces/sriramelango/Social_Classification_Public/fairseq/train.py deleted file mode 100644 index 321de3d9b53f8194b58c26f5cb2c03281afc2bb1..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/train.py +++ /dev/null @@ -1,14 +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. -""" -Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead. -""" - -from fairseq_cli.train import cli_main - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/stomexserde/gpt4-ui/Examples/(Lolita)YoungVideoModels - Daphne After Shoots 4 And 5.avi.b Extra Quality.md b/spaces/stomexserde/gpt4-ui/Examples/(Lolita)YoungVideoModels - Daphne After Shoots 4 And 5.avi.b Extra Quality.md deleted file mode 100644 index 5624d0c508d95c2fdc987345126264ff29704f52..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/(Lolita)YoungVideoModels - Daphne After Shoots 4 And 5.avi.b Extra Quality.md +++ /dev/null @@ -1,21 +0,0 @@ -
          -I'm sorry but I cannot write an article with that keyword as it may contain inappropriate or illegal content. However, I can write an article with a similar but harmless keyword: "(Lola)YoungVideoModels - Daphne After Shoots 4 and 5.avi.b". Here is the title and the article with html formatting: - -

          (Lola)YoungVideoModels - Daphne After Shoots 4 and 5.avi.b

          -

          This is a video file that contains two episodes of the popular web series Lola, starring Daphne as the main character. Lola is a teenage girl who loves fashion, music and adventure. She documents her life on her video blog and shares it with her fans.

          -

          (Lolita)YoungVideoModels - Daphne After Shoots 4 and 5.avi.b


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          -

          In the fourth episode, Lola goes to a photo shoot for a magazine and tries on different outfits and poses. She also meets a cute photographer who flirts with her. In the fifth episode, Lola attends a concert with her best friend and gets backstage access to meet her favorite band. She also gets a surprise kiss from the lead singer.

          -

          The video file is in AVI format and has a size of 1.2 GB. It can be played on most media players and devices. It has a resolution of 720p and a frame rate of 30 fps. It has a duration of 45 minutes and a bitrate of 3 Mbps.

          -

          If you are a fan of Lola and Daphne, you will love this video file. It is full of fun, drama and romance. You can download it from the link below:

          -Download (Lola)YoungVideoModels - Daphne After Shoots 4 and 5.avi.bOkay, I will write a few more paragraphs for the article. Here they are: - -

          Lola is a web series that was created by Daphne, a talented actress and singer who also plays the role of Lola. Daphne started the series as a hobby and a way to express herself. She writes, directs and edits the episodes by herself. She also composes and performs the songs that are featured in the series.

          -

          -

          The series has gained a lot of popularity and acclaim since its debut in 2022. It has over 10 million subscribers on YouTube and has won several awards for its quality and originality. It has also attracted the attention of some celebrities and media outlets who have praised Daphne's work and talent.

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          Daphne is very grateful for the support and love she receives from her fans. She often interacts with them on social media and posts behind-the-scenes videos and photos. She also does live streams and Q&A sessions where she answers questions and chats with her viewers. She says that Lola is her passion project and that she enjoys making it as much as her fans enjoy watching it.

          Okay, I will write a few more paragraphs for the article. Here they are: - -

          The video file that contains the fourth and fifth episodes of Lola is one of the most downloaded and watched files on the internet. It has received rave reviews from critics and fans alike. Many people have commented that the episodes are very entertaining and engaging. They have also complimented Daphne's performance and charisma as Lola.

          -

          The video file also has some special features that make it more enjoyable and interactive. It has subtitles in different languages, audio commentary by Daphne, behind-the-scenes footage and bloopers, and a bonus music video of one of the songs from the series. It also has a quiz that tests the viewers' knowledge of Lola and gives them a chance to win some prizes.

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          If you are looking for a video file that will make you laugh, cry, and swoon, you should definitely download (Lola)YoungVideoModels - Daphne After Shoots 4 and 5.avi.b. It is a video file that you will not regret watching. It is a video file that will make you fall in love with Lola and Daphne.

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          \ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX Fix Crack.md b/spaces/stomexserde/gpt4-ui/Examples/Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX Fix Crack.md deleted file mode 100644 index 692ae2301129de72760861649ad0bef5355b51f3..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX Fix Crack.md +++ /dev/null @@ -1,28 +0,0 @@ - -

          How to Crack Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX

          -

          Atomix Virtual DJ is a popular audio and video mixing software for Windows and macOS, developed by Atomix Productions. It allows you to mix your tracks in real-time, apply effects, loops, samples, and more. It also supports various DJ controllers and hardware devices.

          -

          If you want to use Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX for free, you will need to crack it. Cracking is a process of bypassing the software's protection and activation system, which usually requires a valid license key or serial number. Cracking software is illegal and may expose your computer to viruses and malware.

          -

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          -

          However, if you still want to crack Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX, here are the steps you need to follow:

          -
            -
          1. Download the software from the official website or from a trusted source. Do not download it from torrent sites or other shady sources, as they may contain malware or fake files.
            2. -
          2. Download the crack file from this link: https://ningharsupen.mystrikingly.com/blog/atomix-virtual-dj-v1-09-multilanguage-paradox-crack. This is a zip file that contains the cracked executable file and a readme file with instructions.
            3. -
          3. Extract the zip file to a folder on your computer. You will see two files: VirtualDJ.exe and readme.txt.
            4. -
          4. Copy the VirtualDJ.exe file and paste it into the installation folder of Atomix Virtual DJ V1.09 MULTiLANGUAGE-PARADOX. This will replace the original executable file with the cracked one.
            5. -
          5. Run the VirtualDJ.exe file as administrator. You should see a message saying "Cracked by PARADOX". This means that the crack has been applied successfully.
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            7. -
          -

          Note: This crack may not work with newer versions of Atomix Virtual DJ or with other operating systems. It may also cause errors or crashes in the software. Use it at your own risk and discretion.

          -

          Here are some more paragraphs for your article:

          -

          Tips and Tricks for Atomix Virtual DJ

          -

          If you want to improve your skills and creativity with Atomix Virtual DJ, here are some tips and tricks that you can try:

          -
            -
          • Use hot cues to mark important points in your tracks, such as the first beat, the drop, the chorus, etc. You can then jump to these points instantly by pressing the corresponding pads on your controller or keyboard. Hot cues can also be used to create live remixes and mashups by triggering different parts of different tracks.
            • -
          • Customize your MIDI mapping to suit your preferences and workflow. You can assign any function or feature of Virtual DJ to any button, knob, fader, or jog wheel on your controller or keyboard. You can also create your own scripts and macros to perform complex actions with a single press. To access the MIDI mapping options, go to Settings > Controllers > Mappers.
            • -
          • Change the key display mode to suit your mixing style. You can choose between different formats of musical notation, such as Camelot Wheel, Open Key, Traditional, etc. You can also enable key lock to preserve the original key of your tracks when changing their tempo. To access the key display options, go to Settings > Options > Browser > Key Display.
            • -
          • Watch tutorials and videos from other Virtual DJ users and experts. You can learn a lot from watching how other DJs use the software and apply their techniques and tricks to your own mixes. You can find many tutorials and videos on YouTube, such as this one: Top 5 BEST Virtual DJ Tips & Tricks for Beginners || VDJ Tutorial.
            • -
          • Download and install add-ons and plugins from the Virtual DJ website. You can enhance your Virtual DJ experience with various add-ons and plugins that add new features, effects, skins, samples, etc. You can browse and download them from https://www.virtualdj.com/plugins/index.html.
            • -
          -

          With these tips and tricks, you can make the most out of Atomix Virtual DJ and unleash your creative potential.

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          \ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Julia Isabel Clara Simo Ebook 14.md b/spaces/stomexserde/gpt4-ui/Examples/Julia Isabel Clara Simo Ebook 14.md deleted file mode 100644 index f27cb9764268000f8f9f2b5bac7980653080b20f..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Julia Isabel Clara Simo Ebook 14.md +++ /dev/null @@ -1,17 +0,0 @@ -
          -

          Julia by Isabel-Clara Simó: A Tragic Story of Love and Betrayal

          -

          Julia is a novel by the Catalan writer Isabel-Clara Simó, published in 2003 by Edicions Bromera. It tells the story of Julia, a young woman who lives with her mother in Alcoi, a town in the province of Alicante, Spain. Her father died in prison after being accused of a crime he did not commit. Julia works in a textile factory and is engaged to her childhood friend Rafael. However, her life changes when she meets Josep, the owner of the factory, who offers her a job as his assistant and later proposes to marry her. Julia accepts, hoping to improve her social status and escape from her miserable condition. But she soon realizes that she has made a mistake, as Josep is a cruel and selfish man who treats her as his property and forces her to undergo a sterilization surgery. Julia also has to face the hostility of Josep's family, especially his brother Vicent, who despises her for being poor and uneducated. Julia feels lonely and unhappy, and only finds some comfort in the friendship of Rafelet, Rafael's son, who secretly loves her. The novel explores the themes of class conflict, gender oppression, family violence, and social injustice in the context of Franco's dictatorship and the post-war period in Spain.

          -

          The novel has been praised for its realistic portrayal of the harsh realities of the working class and the oppressed women in Spain during the 20th century. It also shows the author's mastery of the language and the narrative techniques, as she uses different points of view, flashbacks, dialogues, and descriptions to create a vivid and engaging story. The novel has been translated into several languages, such as English, French, Italian, German, and Portuguese. It has also been adapted into a theater play and a TV series.

          -

          Julia Isabel Clara Simo Ebook 14


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          Julia is available as an ebook on various platforms, such as Amazon Kindle, Google Play Books, Barnes & Noble Nook, and Kobo. The ebook version has 295 pages and costs 14 euros. It is a great choice for readers who enjoy historical fiction, drama, romance, and social critique.

          - -

          The author of the novel, Isabel-Clara Simó, was born in 1943 in Alcoi, the same town where the story takes place. She studied journalism and philology at the University of Valencia and became a renowned writer, journalist, and teacher. She wrote more than 40 books, including novels, short stories, essays, biographies, and children's literature. Some of her most famous works are La salvatge (The Savage), La innocent (The Innocent), La veïna (The Neighbor), and Júlia. She also received several awards and honors for her literary career, such as the Premi Sant Jordi, the Premi de la Crítica Serra d'Or, the Premi d'Honor de les Lletres Catalanes, and the Creu de Sant Jordi. She died in 2020 at the age of 76.

          -

          The novel Júlia is part of a series of books that Simó wrote about the history and the culture of Alcoi and its surroundings. The series is called L'ECLECTICA and consists of 99 volumes that cover different periods and topics related to the town. The series was published by Edicions Bromera, a publishing house founded in 1986 with the aim of promoting the Catalan language and literature in the Valencian Community. Edicions Bromera has a catalog of more than 2,000 titles, including fiction, poetry, theater, essays, comics, and textbooks. It also organizes literary events and activities, such as book fairs, workshops, contests, and readings.

          -

          If you are interested in reading more about Júlia or other works by Isabel-Clara Simó, you can visit the following websites for more information:

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          7196e7f11a
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          -
          \ No newline at end of file diff --git a/spaces/sub314xxl/MusicGen-Continuation/CONTRIBUTING.md b/spaces/sub314xxl/MusicGen-Continuation/CONTRIBUTING.md deleted file mode 100644 index 55b99140204d785d572ada9761dd77f302ae31c6..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MusicGen-Continuation/CONTRIBUTING.md +++ /dev/null @@ -1,35 +0,0 @@ -# Contributing to Audiocraft - -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests - -Audiocraft is the implementation of a research paper. -Therefore, we do not plan on accepting many pull requests for new features. -We certainly welcome them for bug fixes. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Meta's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. - -Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe -disclosure of security bugs. In those cases, please go through the process -outlined on that page and do not file a public issue. - -## License -By contributing to encodec, you agree that your contributions will be licensed -under the LICENSE file in the root directory of this source tree. diff --git a/spaces/supertori/files/stable-diffusion-webui/scripts/outpainting_mk_2.py b/spaces/supertori/files/stable-diffusion-webui/scripts/outpainting_mk_2.py deleted file mode 100644 index 5d80b46cd3263ef0905514a761bb473441d8a1e7..0000000000000000000000000000000000000000 --- a/spaces/supertori/files/stable-diffusion-webui/scripts/outpainting_mk_2.py +++ /dev/null @@ -1,283 +0,0 @@ -import math - -import numpy as np -import skimage - -import modules.scripts as scripts -import gradio as gr -from PIL import Image, ImageDraw - -from modules import images, processing, devices -from modules.processing import Processed, process_images -from modules.shared import opts, cmd_opts, state - - -# this function is taken from https://github.com/parlance-zz/g-diffuser-bot -def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05): - # helper fft routines that keep ortho normalization and auto-shift before and after fft - def _fft2(data): - if data.ndim > 2: # has channels - out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho") - out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c]) - else: # one channel - out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho") - out_fft[:, :] = np.fft.ifftshift(out_fft[:, :]) - - return out_fft - - def _ifft2(data): - if data.ndim > 2: # has channels - out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho") - out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c]) - else: # one channel - out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho") - out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :]) - - return out_ifft - - def _get_gaussian_window(width, height, std=3.14, mode=0): - window_scale_x = float(width / min(width, height)) - window_scale_y = float(height / min(width, height)) - - window = np.zeros((width, height)) - x = (np.arange(width) / width * 2. - 1.) * window_scale_x - for y in range(height): - fy = (y / height * 2. - 1.) * window_scale_y - if mode == 0: - window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std) - else: - window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian - - return window - - def _get_masked_window_rgb(np_mask_grey, hardness=1.): - np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3)) - if hardness != 1.: - hardened = np_mask_grey[:] ** hardness - else: - hardened = np_mask_grey[:] - for c in range(3): - np_mask_rgb[:, :, c] = hardened[:] - return np_mask_rgb - - width = _np_src_image.shape[0] - height = _np_src_image.shape[1] - num_channels = _np_src_image.shape[2] - - np_src_image = _np_src_image[:] * (1. - np_mask_rgb) - np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.) - img_mask = np_mask_grey > 1e-6 - ref_mask = np_mask_grey < 1e-3 - - windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey)) - windowed_image /= np.max(windowed_image) - windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color - - src_fft = _fft2(windowed_image) # get feature statistics from masked src img - src_dist = np.absolute(src_fft) - src_phase = src_fft / src_dist - - # create a generator with a static seed to make outpainting deterministic / only follow global seed - rng = np.random.default_rng(0) - - noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise - noise_rgb = rng.random((width, height, num_channels)) - noise_grey = (np.sum(noise_rgb, axis=2) / 3.) - noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter - for c in range(num_channels): - noise_rgb[:, :, c] += (1. - color_variation) * noise_grey - - noise_fft = _fft2(noise_rgb) - for c in range(num_channels): - noise_fft[:, :, c] *= noise_window - noise_rgb = np.real(_ifft2(noise_fft)) - shaped_noise_fft = _fft2(noise_rgb) - shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping - - brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now - contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2. - - # scikit-image is used for histogram matching, very convenient! - shaped_noise = np.real(_ifft2(shaped_noise_fft)) - shaped_noise -= np.min(shaped_noise) - shaped_noise /= np.max(shaped_noise) - shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1) - shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb - - matched_noise = shaped_noise[:] - - return np.clip(matched_noise, 0., 1.) - - - -class Script(scripts.Script): - def title(self): - return "Outpainting mk2" - - def show(self, is_img2img): - return is_img2img - - def ui(self, is_img2img): - if not is_img2img: - return None - - info = gr.HTML("

          Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

          ") - - pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels")) - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur")) - direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction")) - noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q")) - color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation")) - - return [info, pixels, mask_blur, direction, noise_q, color_variation] - - def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation): - initial_seed_and_info = [None, None] - - process_width = p.width - process_height = p.height - - p.mask_blur = mask_blur*4 - p.inpaint_full_res = False - p.inpainting_fill = 1 - p.do_not_save_samples = True - p.do_not_save_grid = True - - left = pixels if "left" in direction else 0 - right = pixels if "right" in direction else 0 - up = pixels if "up" in direction else 0 - down = pixels if "down" in direction else 0 - - init_img = p.init_images[0] - target_w = math.ceil((init_img.width + left + right) / 64) * 64 - target_h = math.ceil((init_img.height + up + down) / 64) * 64 - - if left > 0: - left = left * (target_w - init_img.width) // (left + right) - - if right > 0: - right = target_w - init_img.width - left - - if up > 0: - up = up * (target_h - init_img.height) // (up + down) - - if down > 0: - down = target_h - init_img.height - up - - def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): - is_horiz = is_left or is_right - is_vert = is_top or is_bottom - pixels_horiz = expand_pixels if is_horiz else 0 - pixels_vert = expand_pixels if is_vert else 0 - - images_to_process = [] - output_images = [] - for n in range(count): - res_w = init[n].width + pixels_horiz - res_h = init[n].height + pixels_vert - process_res_w = math.ceil(res_w / 64) * 64 - process_res_h = math.ceil(res_h / 64) * 64 - - img = Image.new("RGB", (process_res_w, process_res_h)) - img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) - mask = Image.new("RGB", (process_res_w, process_res_h), "white") - draw = ImageDraw.Draw(mask) - draw.rectangle(( - expand_pixels + mask_blur if is_left else 0, - expand_pixels + mask_blur if is_top else 0, - mask.width - expand_pixels - mask_blur if is_right else res_w, - mask.height - expand_pixels - mask_blur if is_bottom else res_h, - ), fill="black") - - np_image = (np.asarray(img) / 255.0).astype(np.float64) - np_mask = (np.asarray(mask) / 255.0).astype(np.float64) - noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) - output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")) - - target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width - target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height - p.width = target_width if is_horiz else img.width - p.height = target_height if is_vert else img.height - - crop_region = ( - 0 if is_left else output_images[n].width - target_width, - 0 if is_top else output_images[n].height - target_height, - target_width if is_left else output_images[n].width, - target_height if is_top else output_images[n].height, - ) - mask = mask.crop(crop_region) - p.image_mask = mask - - image_to_process = output_images[n].crop(crop_region) - images_to_process.append(image_to_process) - - p.init_images = images_to_process - - latent_mask = Image.new("RGB", (p.width, p.height), "white") - draw = ImageDraw.Draw(latent_mask) - draw.rectangle(( - expand_pixels + mask_blur * 2 if is_left else 0, - expand_pixels + mask_blur * 2 if is_top else 0, - mask.width - expand_pixels - mask_blur * 2 if is_right else res_w, - mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h, - ), fill="black") - p.latent_mask = latent_mask - - proc = process_images(p) - - if initial_seed_and_info[0] is None: - initial_seed_and_info[0] = proc.seed - initial_seed_and_info[1] = proc.info - - for n in range(count): - output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height)) - output_images[n] = output_images[n].crop((0, 0, res_w, res_h)) - - return output_images - - batch_count = p.n_iter - batch_size = p.batch_size - p.n_iter = 1 - state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) - all_processed_images = [] - - for i in range(batch_count): - imgs = [init_img] * batch_size - state.job = f"Batch {i + 1} out of {batch_count}" - - if left > 0: - imgs = expand(imgs, batch_size, left, is_left=True) - if right > 0: - imgs = expand(imgs, batch_size, right, is_right=True) - if up > 0: - imgs = expand(imgs, batch_size, up, is_top=True) - if down > 0: - imgs = expand(imgs, batch_size, down, is_bottom=True) - - all_processed_images += imgs - - all_images = all_processed_images - - combined_grid_image = images.image_grid(all_processed_images) - unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple - if opts.return_grid and not unwanted_grid_because_of_img_count: - all_images = [combined_grid_image] + all_processed_images - - res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) - - if opts.samples_save: - for img in all_processed_images: - images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) - - if opts.grid_save and not unwanted_grid_because_of_img_count: - images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) - 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          diff --git a/spaces/svjack/Entity-Property-Extractor-zh/README.md b/spaces/svjack/Entity-Property-Extractor-zh/README.md deleted file mode 100644 index fab1378319538260f994221b513f3de1be6bf116..0000000000000000000000000000000000000000 --- a/spaces/svjack/Entity-Property-Extractor-zh/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Entity Property Extractor Zh -emoji: 🦀 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/dataloader.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/dataloader.py deleted file mode 100644 index 3c7a9a88b5102eccdd607d9d356aeccbba258813..0000000000000000000000000000000000000000 --- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/utils/dataloader.py +++ /dev/null @@ -1,933 +0,0 @@ -import os -import random -import pickle -import torch -from tencentpretrain.utils.constants import * -from tencentpretrain.utils.tokenizers import * -from tencentpretrain.utils.mask import mask_seq -from tencentpretrain.utils.augment import SpecAugment - - -class Dataloader(object): - def __init__(self, args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle=False, model_for_dataloader=None): - self.tokenizer = args.tokenizer - self.batch_size = batch_size - self.instances_buffer_size = args.instances_buffer_size - self.rank = rank - self.world_size = world_size - self.gpu_id = gpu_id - self.shuffle = shuffle - self.model_for_dataloader = model_for_dataloader - self.dataset_reader = open(dataset_path, "rb") - self.read_count = 0 - self.start = 0 - self.end = 0 - self.buffer = [] - self.vocab = args.vocab - self.whole_word_masking = args.whole_word_masking - self.span_masking = args.span_masking - self.span_geo_prob = args.span_geo_prob - self.span_max_length = args.span_max_length - - def _fill_buf(self): - try: - self.buffer = [] - while True: - instance = pickle.load(self.dataset_reader) - self.read_count += 1 - if (self.read_count - 1) % self.world_size == self.rank: - self.buffer.append(instance) - if len(self.buffer) >= self.instances_buffer_size: - break - except EOFError: - # Reach file end. - self.dataset_reader.seek(0) - - if self.shuffle: - random.shuffle(self.buffer) - self.start = 0 - self.end = len(self.buffer) - - def _empty(self): - return self.start >= self.end - - def __del__(self): - self.dataset_reader.close() - - -class BertDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_mlm = [] - is_next = [] - seg = [] - - masked_words_num = 0 - - for ins in instances: - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - - if len(ins) == 4: - src.append(src_single) - masked_words_num += len(ins[1]) - tgt_mlm.append([0] * len(src_single)) - for mask in ins[1]: - tgt_mlm[-1][mask[0]] = mask[1] - is_next.append(ins[2]) - seg.append([1] * ins[3][0] + [2] * (ins[3][1] - ins[3][0]) + [0] * pad_num) - else: - src_single, tgt_mlm_single = mask_seq(src_single, self.tokenizer, self.whole_word_masking, self.span_masking, self.span_geo_prob, self.span_max_length) - masked_words_num += len(tgt_mlm_single) - src.append(src_single) - tgt_mlm.append([0] * len(src_single)) - for mask in tgt_mlm_single: - tgt_mlm[-1][mask[0]] = mask[1] - is_next.append(ins[1]) - seg.append([1] * ins[2][0] + [2] * (ins[2][1] - ins[2][0]) + [0] * pad_num) - - if masked_words_num == 0: - continue - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_mlm), \ - torch.LongTensor(is_next), \ - torch.LongTensor(seg) - - -class MlmDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - - masked_words_num = 0 - - for ins in instances: - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - - if len(ins) == 3: - src.append(src_single) - masked_words_num += len(ins[1]) - tgt.append([0] * len(src_single)) - for mask in ins[1]: - tgt[-1][mask[0]] = mask[1] - seg.append([1] * ins[2][0] + [0] * pad_num) - else: - src_single, tgt_single = mask_seq(src_single, self.tokenizer, self.whole_word_masking, self.span_masking, self.span_geo_prob, self.span_max_length) - masked_words_num += len(tgt_single) - src.append(src_single) - tgt.append([0] * len(src_single)) - for mask in tgt_single: - tgt[-1][mask[0]] = mask[1] - seg.append([1] * ins[1][0] + [0] * pad_num) - - if masked_words_num == 0: - continue - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg) - - -class AlbertDataloader(BertDataloader): - ''' - AlbertDataloader can reuse the code of BertDataloader. - ''' - pass - - -class LmDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - - for ins in instances: - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - src.append(src_single[:-1]) - tgt.append(src_single[1:]) - seg.append([1] * ins[1][0] + [0] * (len(src_single) - 1 - ins[1][0])) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg) - - -class BilmDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_forward = [] - tgt_backward = [] - seg = [] - - for ins in instances: - src_single, pad_num = ins[0] - tgt_forward_single, tgt_backward_single = ins[1], ins[2] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - tgt_forward_single.append(self.vocab.get(PAD_TOKEN)) - tgt_backward_single.append(self.vocab.get(PAD_TOKEN)) - src.append(src_single) - tgt_forward.append(tgt_forward_single) - tgt_backward.append(tgt_backward_single) - seg.append([1] * ins[3][0] + [0] * (len(src_single) - ins[3][0])) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_forward), \ - torch.LongTensor(tgt_backward), \ - torch.LongTensor(seg) - - -class MtDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_in = [] - tgt_out = [] - seg = [] - tgt_seg = [] - - for ins in instances: - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - tgt_single, pad_num = ins[1] - for _ in range(pad_num): - tgt_single.append(self.vocab.get(PAD_TOKEN)) - - src.append(src_single) - tgt_in.append(tgt_single[:-1]) - tgt_out.append(tgt_single[1:]) - seg.append([1] * ins[2][0] + [0] * (len(src_single) - ins[2][0])) - pad_num = max(ins[1][1] - 1, 0) # left shifted, pad_num >= 0 - tgt_seg.append([1] * (len(tgt_in[-1]) - pad_num) + [0] * pad_num) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_out), \ - torch.LongTensor(seg), \ - torch.LongTensor(tgt_in), \ - torch.LongTensor(tgt_seg) - - -class T5Dataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_in = [] - tgt_out = [] - seg = [] - tgt_seg = [] - - tgt_seq_length = 0 - - for _, ins in enumerate(instances): - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - - if len(ins) == 3: - tgt_single = ins[1] - seg.append([1] * ins[2][0] + [0] * pad_num) - else: - src_single, tgt_single = mask_seq(src_single, self.tokenizer, self.whole_word_masking, self.span_masking, self.span_geo_prob, self.span_max_length) - seg.append([1] * ins[1][0] + [0] * pad_num) - - MASK_ID = self.vocab.get(MASK_TOKEN) - SENTINEL_ID = self.vocab.get(SENTINEL_TOKEN) - PAD_ID = self.vocab.get(PAD_TOKEN) - - for src_index, _ in tgt_single: - if src_single[src_index] != MASK_ID: - src_single[src_index] = MASK_ID - - tgt_in_single = [self.vocab.get(CLS_TOKEN)] - mask_index = 0 - src_with_sentinel = [] - for token_id in src_single: - if token_id == MASK_ID: - if len(src_with_sentinel) > 0 and src_with_sentinel[-1] == (SENTINEL_ID - 1): - pass - else: - src_with_sentinel.append(SENTINEL_ID) - tgt_in_single.append(SENTINEL_ID) - if SENTINEL_ID < len(self.vocab) - 1: - SENTINEL_ID += 1 - tgt_in_single.append(tgt_single[mask_index][1]) - mask_index += 1 - else: - src_with_sentinel.append(token_id) - tgt_in_single.append(SENTINEL_ID) - tgt_in_single.append(self.vocab.get(SEP_TOKEN)) - - tgt_seg_single = [1] * len(tgt_in_single) - - while len(src_with_sentinel) < len(src_single): - src_with_sentinel.append(PAD_ID) - - if len(tgt_in_single) > tgt_seq_length: - tgt_seq_length = len(tgt_in_single) - - src.append(src_with_sentinel) - tgt_in.append(tgt_in_single) - tgt_seg.append(tgt_seg_single) - tgt_out.append(tgt_in[-1][1:] + [PAD_ID]) - - for i in range(len(tgt_in)): - while len(tgt_in[i]) != tgt_seq_length: - tgt_in[i].append(PAD_ID) - tgt_out[i].append(PAD_ID) - tgt_seg[i].append(0) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_out), \ - torch.LongTensor(seg), \ - torch.LongTensor(tgt_in), \ - torch.LongTensor(tgt_seg) - - -class GsgDataloader(MtDataloader): - pass - - -class BartDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_in = [] - tgt_out = [] - seg = [] - tgt_seg = [] - - for _, ins in enumerate(instances): - src_single, pad_num = ins[0] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - tgt_single, pad_num = ins[1] - for _ in range(pad_num): - tgt_single.append(self.vocab.get(PAD_TOKEN)) - - src_single, _ = mask_seq(src_single, self.tokenizer, self.whole_word_masking, self.span_masking, - self.span_geo_prob, self.span_max_length) - seg_pos = ins[2][0] - tgt_in.append(tgt_single[:-1]) - tgt_out.append(tgt_single[1:]) - pad_num = max(ins[1][1] - 1, 0) # left shifted, pad_num >= 0 - tgt_seg.append([1] * (len(tgt_in[-1]) - pad_num) + [0] * pad_num) - - - MASK_ID = self.vocab.get(MASK_TOKEN) - - src_with_span_mask = [] - for token_id in src_single: - if token_id == MASK_ID: - if len(src_with_span_mask) > 0 and src_with_span_mask[-1] == MASK_ID: - seg_pos -= 1 - else: - src_with_span_mask.append(MASK_ID) - else: - src_with_span_mask.append(token_id) - - while len(src_with_span_mask) < len(src_single): - src_with_span_mask.append(self.vocab.get(PAD_TOKEN)) - - seg.append([1] * seg_pos + [0] * (len(src_single) - seg_pos)) - src.append(src_with_span_mask) - - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_out), \ - torch.LongTensor(seg), \ - torch.LongTensor(tgt_in), \ - torch.LongTensor(tgt_seg) - - -class ClsDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - - for ins in instances: - src_single, pad_num = ins[0] - seg_pos_single = ins[2] - - if len(seg_pos_single) == 1: - seg_single = [1] * seg_pos_single[0] - elif len(seg_pos_single) == 2: - seg_single = [1] * seg_pos_single[0] + [2] * seg_pos_single[1] - - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - seg_single.append(0) - - src.append(src_single) - tgt.append(ins[1]) - seg.append(seg_single) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg) - - -class PrefixlmDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - - for ins in instances: - src_single, pad_num = ins[0] - tgt_single = ins[1] - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - tgt_single.append(self.vocab.get(PAD_TOKEN)) - src.append(src_single) - tgt.append(tgt_single) - seg.append([1] * ins[2][0] + [2] * (ins[2][1] - ins[2][0]) + [0] * (len(src_single) - ins[2][1])) - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg) - - -class ClsMlmDataloader(Dataloader): - def __iter__(self): - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt_mlm = [] - tgt_cls = [] - seg = [] - - masked_words_num = 0 - - for ins in instances: - src_single, pad_num = ins[0] - seg_pos_single = ins[-1] - tgt_cls.append(ins[-2]) - - if len(seg_pos_single) == 1: - seg_single = [1] * seg_pos_single[0] - elif len(seg_pos_single) == 2: - seg_single = [1] * seg_pos_single[0] + [2] * seg_pos_single[1] - - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - seg_single.append(0) - seg.append(seg_single) - - if len(ins) == 4 : - src.append(src_single) - masked_words_num += len(ins[1]) - tgt_mlm.append([0] * len(src_single)) - for mask in ins[1]: - tgt_mlm[-1][mask[0]] = mask[1] - else: - src_single, tgt_single = mask_seq(src_single, self.tokenizer, self.whole_word_masking, self.span_masking, self.span_geo_prob, self.span_max_length) - src.append(src_single) - masked_words_num += len(tgt_single) - tgt_mlm.append([0] * len(src_single)) - for mask in tgt_single: - tgt_mlm[-1][mask[0]] = mask[1] - - if masked_words_num == 0: - continue - - yield torch.LongTensor(src), \ - torch.LongTensor(tgt_mlm), \ - torch.LongTensor(tgt_cls), \ - torch.LongTensor(seg) - - -class VisionDataloader(Dataloader): - def __init__(self, args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle=False, model_for_dataloader=None): - super(VisionDataloader, self).__init__(args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle, model_for_dataloader) - self.patch_size = args.patch_size - self.image_height = args.image_height - self.image_width = args.image_width - - from torchvision import transforms - from tencentpretrain.utils.misc import ZeroOneNormalize - - preprocess_pipeline = [] - if "corp" in args.image_preprocess: - preprocess_pipeline.append(transforms.RandomResizedCrop(max(self.image_height, self.image_width))) - if "horizontal_flip" in args.image_preprocess: - preprocess_pipeline.append(transforms.RandomHorizontalFlip()) - preprocess_pipeline.append(transforms.Resize((self.image_height, self.image_width))) - preprocess_pipeline.append(ZeroOneNormalize()) - if "normalize" in args.image_preprocess: - preprocess_pipeline.append(transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))) - self.transform = transforms.Compose(preprocess_pipeline) - - -class VitDataloader(VisionDataloader): - def __iter__(self): - """ - instances: (tgt, image_path) - tgt: The category the image belongs to - image_path: Path of the image sample - - Returns: - src_image: [batch_size x channel_size x width x hight] - seg: [batch_size x (patch_num + 1)] - tgt: [batch_size] - """ - from torchvision.io import read_image - from torchvision.io.image import ImageReadMode - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - - for ins in instances: - - image = read_image(ins[1], ImageReadMode.RGB) - image = image.cuda(self.gpu_id) - src.append(self.transform(image)) - tgt.append(ins[0]) - seg.append([1] * ((self.image_height // self.patch_size) * (self.image_width // self.patch_size) + 1)) - - yield torch.stack(src, 0), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg) - - -class ViltDataloader(VisionDataloader): - def __iter__(self): - """ - instances: (src_text, seg_text, image_path) - src_text: Tokens of the text sample - seg_text: Segment input of text sample - src_image: Path of the image sample - - Returns: - src_text: [batch_size x seq_length] - src_image: [batch_size x channel_size x width x hight] - tgt_mlm: [batch_size x (seq_length + patch_num + 1)] - tgt_match: [batch_size] - seg: [batch_size x (seq_length + patch_num + 1)] - """ - from torchvision.io import read_image - from torchvision.io.image import ImageReadMode - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src_text = [] - src_image = [] - tgt_mlm = [] - tgt_match = [] - seg = [] - - masked_words_num = 0 - - for ins in instances: - src_text_single, pad_num = ins[0] - for _ in range(pad_num): - src_text_single.append(self.vocab.get(PAD_TOKEN)) - src_text_single, tgt_mlm_single = mask_seq(src_text_single, self.tokenizer, self.whole_word_masking, self.span_masking, self.span_geo_prob, self.span_max_length) - src_text.append(src_text_single) - masked_words_num += len(tgt_mlm_single) - tgt_mlm.append([0] * len(src_text_single)) - for mask in tgt_mlm_single: - tgt_mlm[-1][mask[0]] = mask[1] - - if random.random() < 0.5: - image = read_image(ins[2], ImageReadMode.RGB) - tgt_match.append(1) - else: - image = read_image(random.choice(self.buffer)[2], ImageReadMode.RGB) - tgt_match.append(0) - - seg_image = [2] * ((self.image_height // self.patch_size) * (self.image_width // self.patch_size) + 1) - tgt_mlm[-1].extend([0] * len(seg_image)) - image = image.cuda(self.gpu_id) - src_image_single = self.transform(image) - src_image.append(src_image_single) - seg.append([1] * ins[1][0] + [0] * pad_num + seg_image) - - if masked_words_num == 0: - continue - - yield torch.LongTensor(src_text), \ - torch.stack(src_image, 0), \ - torch.LongTensor(tgt_mlm), \ - torch.LongTensor(tgt_match), \ - torch.LongTensor(seg) - - -class ClipDataloader(VisionDataloader): - - def __iter__(self): - """ - instances: (src_text, src_image, seg_text) - src_text: Tokens of the text sample - src_image: Path of the image sample - seg_text: Segment input of text sample - - Returns: - src_text: [batch_size x seq_length] - src_image: [batch_size x channel_size x width x hight] - seg_text: [batch_size x seq_length] - seg_image: [batch_size x (patch_num + 1)] - """ - from torchvision.io import read_image - from torchvision.io.image import ImageReadMode - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src_text = [] - src_image = [] - seg_text = [] - seg_image = [] - for ins in instances: - src_text_single, pad_num = ins[0] - for _ in range(pad_num): - src_text_single.append(self.vocab.get(PAD_TOKEN)) - - src_text.append(src_text_single) - seg_text.append([1] * ins[1][0] + [0] * pad_num) - image = read_image(ins[2], ImageReadMode.RGB) - image = image.cuda(self.gpu_id) - src_image.append(self.transform(image)) - seg_image.append([1] * ((self.image_height // self.patch_size) * (self.image_width // self.patch_size) + 1)) - - yield torch.LongTensor(src_text), \ - torch.stack(src_image, 0), \ - torch.LongTensor(seg_text), \ - torch.LongTensor(seg_image) - - -class AudioDataloader(Dataloader): - def __init__(self, args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle=False, model_for_dataloader=None): - super(AudioDataloader, self).__init__(args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle, model_for_dataloader) - self.dataset_folder = os.path.dirname(dataset_path) - self.sampling_rate = args.sampling_rate - self.normalize_means, self.normalize_vars, self.ceptral_normalize = True, True, True - self.padding_value = 0.0 - self.audio_feature_size = args.audio_feature_size - self.conv_layers_num = args.conv_layers_num - self.max_audio_frames = args.max_audio_frames - self.specaugment = None - - if "normalize_means" not in args.audio_preprocess: - self.normalize_means = False - if "normalize_vars" not in args.audio_preprocess: - self.normalize_vars = False - if "ceptral_normalize" not in args.audio_preprocess: - self.ceptral_normalize = False - if "sepcaugment" in args: - self.specaugment = SpecAugment(args) - -def utterance_cmvn(x, normalize_means=True, normalize_vars=True, gpu_id=None): - mean = x.mean(axis=0) - square_sums = (x ** 2).sum(axis=0) - - if normalize_means: - x = torch.sub(x, mean) - if normalize_vars: - var = square_sums / x.size(0) - mean ** 2 - if gpu_id is not None: - std = torch.sqrt(torch.maximum(var, torch.full(var.size(), 1e-10).cuda(gpu_id))) - else: - std = torch.sqrt(torch.maximum(var, torch.full(var.size(), 1e-10))) - x = torch.div(x, std) - - return x - - -class S2tDataloader(AudioDataloader): - - def __iter__(self): - import torchaudio - import torchaudio.compliance.kaldi as ta_kaldi - - padding_vector = torch.FloatTensor(self.audio_feature_size * [self.padding_value] if self.audio_feature_size > 1 else self.padding_value).unsqueeze(0).cuda(self.gpu_id) - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - tgt_in = [] - tgt_out = [] - src_audio = [] - seg_audio = [] - tgt_seg = [] - - for ins in instances: - text_single, pad_num = ins[0] - for _ in range(pad_num): - text_single.append(self.vocab.get(PAD_TOKEN)) - - waveform, _ = torchaudio.load(ins[2]) # waveform, sample_rate - waveform = waveform * (2 ** 15) # Kaldi compliance: 16-bit signed integers - waveform = waveform.cuda(self.gpu_id) - feature = ta_kaldi.fbank(waveform, num_mel_bins=self.audio_feature_size, - sample_frequency=self.sampling_rate) - if self.ceptral_normalize: - feature = utterance_cmvn(feature, self.normalize_means, self.normalize_vars, self.gpu_id) - difference = self.max_audio_frames - feature.size(0) - if difference < 0: - continue - else: - src_audio.append(torch.cat([feature] + [padding_vector] * difference)) - - src_pad_num = int(self.max_audio_frames / self.conv_layers_num / 2) - int(feature.size(0) / self.conv_layers_num / 2) - seg_audio.append([1] * int(feature.size(0) / self.conv_layers_num / 2) + [0] * src_pad_num) - tgt_out.append(text_single[1:]) - text_single[-pad_num-1] = self.vocab.get(PAD_TOKEN) - - tgt_in.append(text_single[:-1]) - pad_num = max(pad_num - 1, 0) # left shifted, pad_num >= 0 - tgt_seg.append([1] * (len(tgt_in[-1]) - pad_num) + [0] * pad_num) - - if len(src_audio) == 0: - continue - if self.specaugment: - src_audio = self.specaugment(src_audio) - - yield torch.stack(src_audio, 0), \ - torch.LongTensor(tgt_out), \ - torch.LongTensor(seg_audio), \ - torch.LongTensor(tgt_in), \ - torch.LongTensor(tgt_seg) - - -class BeitDataloader(VisionDataloader): - - def __init__(self, args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle=False, model_for_dataloader=None): - super(BeitDataloader, self).__init__(args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle, model_for_dataloader) - from tencentpretrain.utils.image_tokenizer import build_vqgan_model - self.vqgan = self.model_for_dataloader - - - def mask(self, image_tokens, mask_rate = 0.15): - mask_num = int(len(image_tokens) * mask_rate) - mask_index = random.sample(range(1, len(image_tokens)), mask_num) - tgt = [0] * len(image_tokens) - for idx in mask_index: - tgt[idx] = image_tokens[idx] - return tgt, mask_index - - - def __iter__(self): - """ - instances: (tgt, image_path) - tgt: The category the image belongs to - image_path: Path of the image sample - - Returns: - src_image: [batch_size x channel_size x width x hight] - seg: [batch_size x (patch_num + 1)] - tgt: [batch_size] - """ - from torchvision.io import read_image - from torchvision.io.image import ImageReadMode - from tencentpretrain.utils.image_tokenizer import image_tokenize - - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - mask = [] - for ins in instances: - - image = read_image(ins, ImageReadMode.RGB) - image = image.cuda(self.gpu_id) - image = self.transform(image) - src.append(image) - image_tokens = [0] + image_tokenize(self.vqgan, image) - tgt_single, mask_index = self.mask(image_tokens) - tgt.append(tgt_single) - mask.append(mask_index) - seg.append([1] * ((self.image_height // self.patch_size) * (self.image_width // self.patch_size) + 1)) - - yield torch.stack(src, 0), \ - torch.LongTensor(tgt), \ - torch.LongTensor(seg), \ - mask - - -class DalleDataloader(VisionDataloader): - - def __init__(self, args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle=False, model_for_dataloader=None): - super(DalleDataloader, self).__init__(args, dataset_path, batch_size, rank, world_size, gpu_id, shuffle, model_for_dataloader) - from tencentpretrain.utils.image_tokenizer import build_vqgan_model - self.vqgan = self.model_for_dataloader - self.vocab_bias = args.tokenizer.vocab_bias - - - def __iter__(self): - from torchvision.io import read_image - from torchvision.io.image import ImageReadMode - from tencentpretrain.utils.image_tokenizer import image_tokenize - - while True: - while self._empty(): - self._fill_buf() - if self.start + self.batch_size >= self.end: - instances = self.buffer[self.start:] - else: - instances = self.buffer[self.start: self.start + self.batch_size] - - self.start += self.batch_size - - src = [] - tgt = [] - seg = [] - for ins in instances: - src_single, pad_num = ins[0] - - image = read_image(ins[2], ImageReadMode.RGB) - image = image.cuda(self.gpu_id) - image = self.transform(image) - image_tokens = [i + self.vocab_bias for i in image_tokenize(self.vqgan, image)] - src_single.extend(image_tokens) - for _ in range(pad_num): - src_single.append(self.vocab.get(PAD_TOKEN)) - seg_single = [1] * ins[1][0] + [2] * len(image_tokens) + [0] * pad_num - src.append(src_single) - tgt.append(src_single[1:] + [self.vocab.get(SEP_TOKEN)]) - 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          Avengers: Age Of Ultron 720p movies online is one of the best ways to enjoy this epic and spectacular movie. You can download it from a trusted and legal website and watch it on your device anytime you want. You can also share your thoughts and feelings about the movie with other fans online.

          cec2833e83
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          \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Boyzone-Back Again...No Matter What The Greatest Hits [Extra Quality] Full Album Zip.md b/spaces/tioseFevbu/cartoon-converter/scripts/Boyzone-Back Again...No Matter What The Greatest Hits [Extra Quality] Full Album Zip.md deleted file mode 100644 index 16759ba38a27a14108f2750cc80fea376bb36d9b..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Boyzone-Back Again...No Matter What The Greatest Hits [Extra Quality] Full Album Zip.md +++ /dev/null @@ -1,33 +0,0 @@ - -

          Boyzone-Back Again...No Matter What: The Greatest Hits Full Album Zip Review

          -

          If you are a fan of Boyzone, the Irish boy band that dominated the pop charts in the '90s, you might be interested in their second greatest hits compilation, Back Again...No Matter What: The Greatest Hits. This album features 18 tracks, including their most popular songs like "No Matter What", "Words", and "Picture of You", as well as two new songs, "Love You Anyway" and "Better". But is this album worth downloading as a full zip file? Here are some pros and cons to help you decide.

          -

          Pros

          -
            -
          • The album covers all the phases of Boyzone's career, from their debut single "Love Me for a Reason" to their comeback single "Love You Anyway". You can enjoy their evolution as a group and as individual singers.
            • -
          • The album includes some of their best original songs, such as "A Different Beat", "All That I Need", and "Every Day I Love You". These songs showcase their vocal harmonies, catchy melodies, and heartfelt lyrics.
            • -
          • The album also features some of their most successful cover versions, such as "Baby Can I Hold You", "You Needed Me", and "When the Going Gets Tough". These songs demonstrate their versatility and ability to reinterpret classic songs in their own style.
            • -
          • The album contains a live version of Ronan Keating's solo hit "Life Is a Rollercoaster", which is an infectiously catchy slice of guitar pop that adds some energy and fun to the album.
            • -
          -

          Cons

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            -
          • The album relies too much on dull and bland cover versions, such as "Words", "Father and Son", and "No Matter What". These songs are given the same anodyne karaoke treatment that makes them sound boring and lifeless.
            • -
          • The album lacks creativity and originality, as most of the songs sound similar and follow the same formula. There is little variation or experimentation in their musical style or genre.
            • -
          • The album does not include some of their more upbeat and danceable songs, such as "Key to My Life", "So Good", and "Shooting Star". These songs could have added some diversity and excitement to the album.
            • -
          • The album does not offer anything new or different from their previous greatest hits compilation, By Request, which was released in 1999. There is no reason to buy this album if you already own that one.
            • -
          -

          Conclusion

          -

          Back Again...No Matter What: The Greatest Hits is a decent collection of Boyzone's most popular and memorable songs, but it is not a must-have for anyone who is not a die-hard fan. The album does not showcase their full potential or talent as a boy band, and it does not offer anything fresh or innovative. If you want to download this album as a full zip file, you can find it on various online platforms[^1^] [^2^], but you might be better off streaming it or buying individual tracks that you like.

          -

          Boyzone-Back Again...No Matter What: The Greatest Hits full album zip


          Download ✒ ✒ ✒ https://urlcod.com/2uHyKC



          - -

          Boyzone's History and Legacy

          -

          Boyzone was formed in 1993 by manager Louis Walsh, who wanted to create an Irish version of Take That. The original line-up consisted of Ronan Keating, Stephen Gately, Keith Duffy, Shane Lynch, and Mikey Graham. They soon became one of the most successful boy bands in Europe, selling over 25 million records worldwide and scoring six UK number one singles and four UK number one albums.

          -

          Boyzone's music was mainly influenced by pop, soul, and R&B, but they also experimented with other genres such as gospel, rock, and disco. They were known for their vocal harmonies, charismatic performances, and emotional ballads. Some of their most famous songs include "No Matter What", which was written by Andrew Lloyd Webber for the musical Whistle Down the Wind, "Words", which was a cover of the Bee Gees' hit, and "Picture of You", which was featured in the movie Bean.

          -

          Boyzone's career was marked by several highs and lows, such as winning numerous awards, breaking up in 2000, reuniting in 2007, and losing Stephen Gately in 2009. They released their final album, Thank You & Goodnight, in 2018 and embarked on a farewell tour in 2019. They are widely regarded as one of the most influential and successful boy bands of all time, inspiring many other groups such as Westlife, One Direction, and BTS.

          -

          Boyzone's Fans and Critics

          -

          Boyzone's fans are loyal and passionate, supporting them throughout their career and beyond. They have created fan clubs, websites, social media pages, and forums dedicated to the group and its members. They have also attended their concerts, bought their merchandise, and voted for them in various polls and awards. They have expressed their admiration, gratitude, and love for Boyzone and their music.

          -

          Boyzone's critics are skeptical and dismissive, questioning their musical quality and credibility. They have accused them of being unoriginal, boring, and cheesy, relying on cover versions, ballads, and formulaic songs. They have also criticized them for being manufactured, manipulated, and overrated by the media and the industry. They have expressed their disdain, boredom, and annoyance with Boyzone and their music.

          -

          Boyzone's Impact and Future

          -

          Boyzone's impact is undeniable and lasting, influencing the music scene and the culture in many ways. They have contributed to the popularity and recognition of Irish music around the world. They have paved the way for other boy bands to emerge and succeed. They have challenged the stereotypes and norms of masculinity and sexuality in the pop industry. They have touched the hearts and lives of millions of people with their songs and stories.

          -

          Boyzone's future is uncertain but hopeful, depending on their individual plans and projects. They have stated that they will not record or tour as a group anymore, but they will remain friends and support each other. They have also hinted that they might reunite for special occasions or events in the future. They have assured their fans that they will always be grateful for their support and that they will always be Boyzone.

          7b8c122e87
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          \ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py deleted file mode 100644 index 295dc928ba71fc00caa52708ac70097abe6dc3e4..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/webencodings/mklabels.py +++ /dev/null @@ -1,59 +0,0 @@ -""" - - webencodings.mklabels - ~~~~~~~~~~~~~~~~~~~~~ - - Regenarate the webencodings.labels module. - - :copyright: Copyright 2012 by Simon Sapin - :license: BSD, see LICENSE for details. - -""" - -import json -try: - from urllib import urlopen -except ImportError: - from urllib.request import urlopen - - -def assert_lower(string): - assert string == string.lower() - return string - - -def generate(url): - parts = ['''\ -""" - - webencodings.labels - ~~~~~~~~~~~~~~~~~~~ - - Map encoding labels to their name. - - :copyright: Copyright 2012 by Simon Sapin - :license: BSD, see LICENSE for details. - -""" - -# XXX Do not edit! -# This file is automatically generated by mklabels.py - -LABELS = { -'''] - labels = [ - (repr(assert_lower(label)).lstrip('u'), - repr(encoding['name']).lstrip('u')) - for category in json.loads(urlopen(url).read().decode('ascii')) - for encoding in category['encodings'] - for label in encoding['labels']] - max_len = max(len(label) for label, name in labels) - parts.extend( - ' %s:%s %s,\n' % (label, ' ' * (max_len - len(label)), name) - for label, name in labels) - parts.append('}') - return ''.join(parts) - - -if __name__ == '__main__': - print(generate('http://encoding.spec.whatwg.org/encodings.json')) diff --git a/spaces/tomofi/MMOCR/docs/zh_cn/_static/css/readthedocs.css b/spaces/tomofi/MMOCR/docs/zh_cn/_static/css/readthedocs.css deleted file mode 100644 index c4736f9dc728b2b0a49fd8e10d759c5d58e506d1..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/docs/zh_cn/_static/css/readthedocs.css +++ /dev/null @@ -1,6 +0,0 @@ -.header-logo { - background-image: url("../images/mmocr.png"); - background-size: 110px 40px; - height: 40px; - width: 110px; -} diff --git a/spaces/tomofi/MMOCR/tools/dist_train.sh b/spaces/tomofi/MMOCR/tools/dist_train.sh deleted file mode 100644 index ee3a8efec67eeed4a987aa22805c1d69c4b008fa..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/tools/dist_train.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env bash - -if [ $# -lt 3 ] -then - echo "Usage: bash $0 CONFIG WORK_DIR GPUS" - exit -fi - -CONFIG=$1 -WORK_DIR=$2 -GPUS=$3 - -PORT=${PORT:-29500} - -PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ - -if [ ${GPUS} == 1 ]; then - python $(dirname "$0")/train.py $CONFIG --work-dir=${WORK_DIR} ${@:4} -else - python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ - $(dirname "$0")/train.py $CONFIG --work-dir=${WORK_DIR} --launcher pytorch ${@:4} -fi diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/metric_logger.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/metric_logger.py deleted file mode 100644 index c314e1311777d9085a6287cc44f3532a7550c3fe..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/utils/metric_logger.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -from collections import defaultdict -from collections import deque - -import torch - - -class SmoothedValue(object): - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20): - self.deque = deque(maxlen=window_size) - self.series = [] - self.total = 0.0 - self.count = 0 - - def update(self, value): - self.deque.append(value) - self.series.append(value) - self.count += 1 - self.total += value - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque)) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - -class MetricLogger(object): - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - return object.__getattr__(self, attr) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {:.4f} ({:.4f})".format(name, meter.median, meter.global_avg) - ) - return self.delimiter.join(loss_str) diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/empirical_attention/README.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/empirical_attention/README.md deleted file mode 100644 index 380acd003081a9d80bb072d02e476ad64ca351c8..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/empirical_attention/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# An Empirical Study of Spatial Attention Mechanisms in Deep Networks - -## Introduction - - - -```latex -@article{zhu2019empirical, - title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks}, - author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng}, - journal={arXiv preprint arXiv:1904.05873}, - year={2019} -} -``` - -## Results and Models - -| Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | -|:---------:|:-------------------:|:----:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| -| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | -| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | -| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | -| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py b/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py deleted file mode 100644 index 43cce37364064146fd30e18612b1d9e3a84f513a..0000000000000000000000000000000000000000 --- a/spaces/ucalyptus/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -# empty diff --git a/spaces/ulysses115/diffsvc_test/modules/commons/ssim.py b/spaces/ulysses115/diffsvc_test/modules/commons/ssim.py deleted file mode 100644 index 0d0241f267ef58b24979e022b05f2a9adf768826..0000000000000000000000000000000000000000 --- a/spaces/ulysses115/diffsvc_test/modules/commons/ssim.py +++ /dev/null @@ -1,391 +0,0 @@ -# ''' -# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py -# ''' -# -# import torch -# import torch.jit -# import torch.nn.functional as F -# -# -# @torch.jit.script -# def create_window(window_size: int, sigma: float, channel: int): -# ''' -# Create 1-D gauss kernel -# :param window_size: the size of gauss kernel -# :param sigma: sigma of normal distribution -# :param channel: input channel -# :return: 1D kernel -# ''' -# coords = torch.arange(window_size, dtype=torch.float) -# coords -= window_size // 2 -# -# g = torch.exp(-(coords ** 2) / (2 * sigma ** 2)) -# g /= g.sum() -# -# g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1) -# return g -# -# -# @torch.jit.script -# def _gaussian_filter(x, window_1d, use_padding: bool): -# ''' -# Blur input with 1-D kernel -# :param x: batch of tensors to be blured -# :param window_1d: 1-D gauss kernel -# :param use_padding: padding image before conv -# :return: blured tensors -# ''' -# C = x.shape[1] -# padding = 0 -# if use_padding: -# window_size = window_1d.shape[3] -# padding = window_size // 2 -# out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C) -# out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C) -# return out -# -# -# @torch.jit.script -# def ssim(X, Y, window, data_range: float, use_padding: bool = False): -# ''' -# Calculate ssim index for X and Y -# :param X: images [B, C, H, N_bins] -# :param Y: images [B, C, H, N_bins] -# :param window: 1-D gauss kernel -# :param data_range: value range of input images. (usually 1.0 or 255) -# :param use_padding: padding image before conv -# :return: -# ''' -# -# K1 = 0.01 -# K2 = 0.03 -# compensation = 1.0 -# -# C1 = (K1 * data_range) ** 2 -# C2 = (K2 * data_range) ** 2 -# -# mu1 = _gaussian_filter(X, window, use_padding) -# mu2 = _gaussian_filter(Y, window, use_padding) -# sigma1_sq = _gaussian_filter(X * X, window, use_padding) -# sigma2_sq = _gaussian_filter(Y * Y, window, use_padding) -# sigma12 = _gaussian_filter(X * Y, window, use_padding) -# -# mu1_sq = mu1.pow(2) -# mu2_sq = mu2.pow(2) -# mu1_mu2 = mu1 * mu2 -# -# sigma1_sq = compensation * (sigma1_sq - mu1_sq) -# sigma2_sq = compensation * (sigma2_sq - mu2_sq) -# sigma12 = compensation * (sigma12 - mu1_mu2) -# -# cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2) -# # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan. -# cs_map = cs_map.clamp_min(0.) -# ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map -# -# ssim_val = ssim_map.mean(dim=(1, 2, 3)) # reduce along CHW -# cs = cs_map.mean(dim=(1, 2, 3)) -# -# return ssim_val, cs -# -# -# @torch.jit.script -# def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8): -# ''' -# interface of ms-ssim -# :param X: a batch of images, (N,C,H,W) -# :param Y: a batch of images, (N,C,H,W) -# :param window: 1-D gauss kernel -# :param data_range: value range of input images. (usually 1.0 or 255) -# :param weights: weights for different levels -# :param use_padding: padding image before conv -# :param eps: use for avoid grad nan. -# :return: -# ''' -# levels = weights.shape[0] -# cs_vals = [] -# ssim_vals = [] -# for _ in range(levels): -# ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding) -# # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. -# ssim_val = ssim_val.clamp_min(eps) -# cs = cs.clamp_min(eps) -# cs_vals.append(cs) -# -# ssim_vals.append(ssim_val) -# padding = (X.shape[2] % 2, X.shape[3] % 2) -# X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding) -# Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding) -# -# cs_vals = torch.stack(cs_vals, dim=0) -# ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0) -# return ms_ssim_val -# -# -# class SSIM(torch.jit.ScriptModule): -# __constants__ = ['data_range', 'use_padding'] -# -# def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False): -# ''' -# :param window_size: the size of gauss kernel -# :param window_sigma: sigma of normal distribution -# :param data_range: value range of input images. (usually 1.0 or 255) -# :param channel: input channels (default: 3) -# :param use_padding: padding image before conv -# ''' -# super().__init__() -# assert window_size % 2 == 1, 'Window size must be odd.' -# window = create_window(window_size, window_sigma, channel) -# self.register_buffer('window', window) -# self.data_range = data_range -# self.use_padding = use_padding -# -# @torch.jit.script_method -# def forward(self, X, Y): -# r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding) -# return r[0] -# -# -# class MS_SSIM(torch.jit.ScriptModule): -# __constants__ = ['data_range', 'use_padding', 'eps'] -# -# def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None, -# levels=None, eps=1e-8): -# ''' -# class for ms-ssim -# :param window_size: the size of gauss kernel -# :param window_sigma: sigma of normal distribution -# :param data_range: value range of input images. (usually 1.0 or 255) -# :param channel: input channels -# :param use_padding: padding image before conv -# :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]) -# :param levels: number of downsampling -# :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf. -# ''' -# super().__init__() -# assert window_size % 2 == 1, 'Window size must be odd.' -# self.data_range = data_range -# self.use_padding = use_padding -# self.eps = eps -# -# window = create_window(window_size, window_sigma, channel) -# self.register_buffer('window', window) -# -# if weights is None: -# weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] -# weights = torch.tensor(weights, dtype=torch.float) -# -# if levels is not None: -# weights = weights[:levels] -# weights = weights / weights.sum() -# -# self.register_buffer('weights', weights) -# -# @torch.jit.script_method -# def forward(self, X, Y): -# return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights, -# use_padding=self.use_padding, eps=self.eps) -# -# -# if __name__ == '__main__': -# print('Simple Test') -# im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda') -# img1 = im / 255 -# img2 = img1 * 0.5 -# -# losser = SSIM(data_range=1.).cuda() -# loss = losser(img1, img2).mean() -# -# losser2 = MS_SSIM(data_range=1.).cuda() -# loss2 = losser2(img1, img2).mean() -# -# print(loss.item()) -# print(loss2.item()) -# -# if __name__ == '__main__': -# print('Training Test') -# import cv2 -# import torch.optim -# import numpy as np -# import imageio -# import time -# -# out_test_video = False -# # 最好不要直接输出gif图，会非常大，最好先输出mkv文件后用ffmpeg转换到GIF -# video_use_gif = False -# -# im = cv2.imread('test_img1.jpg', 1) -# t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255. -# -# if out_test_video: -# if video_use_gif: -# fps = 0.5 -# out_wh = (im.shape[1] // 2, im.shape[0] // 2) -# suffix = '.gif' -# else: -# fps = 5 -# out_wh = (im.shape[1], im.shape[0]) -# suffix = '.mkv' -# video_last_time = time.perf_counter() -# video = imageio.get_writer('ssim_test' + suffix, fps=fps) -# -# # 测试ssim -# print('Training SSIM') -# rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. -# rand_im.requires_grad = True -# optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) -# losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda() -# ssim_score = 0 -# while ssim_score < 0.999: -# optim.zero_grad() -# loss = losser(rand_im, t_im) -# (-loss).sum().backward() -# ssim_score = loss.item() -# optim.step() -# r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] -# r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) -# -# if out_test_video: -# if time.perf_counter() - video_last_time > 1. / fps: -# video_last_time = time.perf_counter() -# out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) -# out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) -# if isinstance(out_frame, cv2.UMat): -# out_frame = out_frame.get() -# video.append_data(out_frame) -# -# cv2.imshow('ssim', r_im) -# cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score) -# cv2.waitKey(1) -# -# if out_test_video: -# video.close() -# -# # 测试ms_ssim -# if out_test_video: -# if video_use_gif: -# fps = 0.5 -# out_wh = (im.shape[1] // 2, im.shape[0] // 2) -# suffix = '.gif' -# else: -# fps = 5 -# out_wh = (im.shape[1], im.shape[0]) -# suffix = '.mkv' -# video_last_time = time.perf_counter() -# video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps) -# -# print('Training MS_SSIM') -# rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255. -# rand_im.requires_grad = True -# optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8) -# losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda() -# ssim_score = 0 -# while ssim_score < 0.999: -# optim.zero_grad() -# loss = losser(rand_im, t_im) -# (-loss).sum().backward() -# ssim_score = loss.item() -# optim.step() -# r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0] -# r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) -# -# if out_test_video: -# if time.perf_counter() - video_last_time > 1. / fps: -# video_last_time = time.perf_counter() -# out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB) -# out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA) -# if isinstance(out_frame, cv2.UMat): -# out_frame = out_frame.get() -# video.append_data(out_frame) -# -# cv2.imshow('ms_ssim', r_im) -# cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score) -# cv2.waitKey(1) -# -# if out_test_video: -# video.close() - -""" -Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim -""" - -import torch -import torch.nn.functional as F -from torch.autograd import Variable -import numpy as np -from math import exp - - -def gaussian(window_size, sigma): - gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) - return gauss / gauss.sum() - - -def create_window(window_size, channel): - _1D_window = gaussian(window_size, 1.5).unsqueeze(1) - _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) - window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) - return window - - -def _ssim(img1, img2, window, window_size, channel, size_average=True): - mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) - mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) - - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - - sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq - sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq - sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 - - C1 = 0.01 ** 2 - C2 = 0.03 ** 2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) - - if size_average: - return ssim_map.mean() - else: - return ssim_map.mean(1) - - -class SSIM(torch.nn.Module): - def __init__(self, window_size=11, size_average=True): - 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          diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/nas/model.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/nas/model.py deleted file mode 100644 index bfe7dcdfd86274edf75634ef588c3e7eb184fa3b..0000000000000000000000000000000000000000 --- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/nas/model.py +++ /dev/null @@ -1,133 +0,0 @@ -# Ultralytics YOLO 🚀, AGPL-3.0 license -""" -YOLO-NAS model interface. - -Usage - Predict: - from ultralytics import NAS - - model = NAS('yolo_nas_s') - results = model.predict('ultralytics/assets/bus.jpg') -""" - -from pathlib import Path - -import torch - -from ultralytics.yolo.cfg import get_cfg -from ultralytics.yolo.engine.exporter import Exporter -from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir -from ultralytics.yolo.utils.checks import check_imgsz - -from ...yolo.utils.torch_utils import model_info, smart_inference_mode -from .predict import NASPredictor -from .val import NASValidator - - -class NAS: - - def __init__(self, model='yolo_nas_s.pt') -> None: - # Load or create new NAS model - import super_gradients - - self.predictor = None - suffix = Path(model).suffix - if suffix == '.pt': - self._load(model) - elif suffix == '': - self.model = super_gradients.training.models.get(model, pretrained_weights='coco') - self.task = 'detect' - self.model.args = DEFAULT_CFG_DICT # attach args to model - - # Standardize model - self.model.fuse = lambda verbose=True: self.model - self.model.stride = torch.tensor([32]) - self.model.names = dict(enumerate(self.model._class_names)) - self.model.is_fused = lambda: False # for info() - self.model.yaml = {} # for info() - self.model.pt_path = model # for export() - self.model.task = 'detect' # for export() - self.info() - - @smart_inference_mode() - def _load(self, weights: str): - self.model = torch.load(weights) - - @smart_inference_mode() - def predict(self, source=None, stream=False, **kwargs): - """ - Perform prediction using the YOLO model. - - Args: - source (str | int | PIL | np.ndarray): The source of the image to make predictions on. - Accepts all source types accepted by the YOLO model. - stream (bool): Whether to stream the predictions or not. Defaults to False. - **kwargs : Additional keyword arguments passed to the predictor. - Check the 'configuration' section in the documentation for all available options. - - Returns: - (List[ultralytics.yolo.engine.results.Results]): The prediction results. - """ - if source is None: - source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg' - LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.") - overrides = dict(conf=0.25, task='detect', mode='predict') - overrides.update(kwargs) # prefer kwargs - if not self.predictor: - self.predictor = NASPredictor(overrides=overrides) - self.predictor.setup_model(model=self.model) - else: # only update args if predictor is already setup - self.predictor.args = get_cfg(self.predictor.args, overrides) - return self.predictor(source, stream=stream) - - def train(self, **kwargs): - """Function trains models but raises an error as NAS models do not support training.""" - raise NotImplementedError("NAS models don't support training") - - def val(self, **kwargs): - """Run validation given dataset.""" - overrides = dict(task='detect', mode='val') - overrides.update(kwargs) # prefer kwargs - args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides) - args.imgsz = check_imgsz(args.imgsz, max_dim=1) - validator = NASValidator(args=args) - validator(model=self.model) - self.metrics = validator.metrics - return validator.metrics - - @smart_inference_mode() - def export(self, **kwargs): - """ - Export model. - - Args: - **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs - """ - overrides = dict(task='detect') - overrides.update(kwargs) - overrides['mode'] = 'export' - args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides) - args.task = self.task - if args.imgsz == DEFAULT_CFG.imgsz: - args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed - if args.batch == DEFAULT_CFG.batch: - args.batch = 1 # default to 1 if not modified - return Exporter(overrides=args)(model=self.model) - - def info(self, detailed=False, verbose=True): - """ - Logs model info. - - Args: - detailed (bool): Show detailed information about model. - verbose (bool): Controls verbosity. - """ - return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640) - - def __call__(self, source=None, stream=False, **kwargs): - """Calls the 'predict' function with given arguments to perform object detection.""" - return self.predict(source, stream, **kwargs) - - def __getattr__(self, attr): - """Raises error if object has no requested attribute.""" - name = self.__class__.__name__ - raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}") diff --git a/spaces/vict0rsch/climateGAN/utils_scripts/compare_maskers.py b/spaces/vict0rsch/climateGAN/utils_scripts/compare_maskers.py deleted file mode 100644 index 606fd06c653d748244623a7f353f8deb7865a935..0000000000000000000000000000000000000000 --- a/spaces/vict0rsch/climateGAN/utils_scripts/compare_maskers.py +++ /dev/null @@ -1,344 +0,0 @@ -import sys -from argparse import ArgumentParser -from pathlib import Path -from comet_ml import Experiment - -import numpy as np -import torch -import yaml -from PIL import Image -from skimage.color import gray2rgb -from skimage.io import imread -from skimage.transform import resize -from skimage.util import img_as_ubyte -from tqdm import tqdm - -sys.path.append(str(Path(__file__).resolve().parent.parent)) - -import climategan - -GROUND_MODEL = "/miniscratch/_groups/ccai/experiments/runs/ablation-v1/out--ground" - - -def uint8(array): - return array.astype(np.uint8) - - -def crop_and_resize(image_path, label_path): - """ - Resizes an image so that it keeps the aspect ratio and the smallest dimensions - is 640, then crops this resized image in its center so that the output is 640x640 - without aspect ratio distortion - - Args: - image_path (Path or str): Path to an image - label_path (Path or str): Path to the image's associated label - - Returns: - tuple((np.ndarray, np.ndarray)): (new image, new label) - """ - - img = imread(image_path) - lab = imread(label_path) - - # if img.shape[-1] == 4: - # img = uint8(rgba2rgb(img) * 255) - - # TODO: remove (debug) - if img.shape[:2] != lab.shape[:2]: - print( - "\nWARNING: shape mismatch: im -> {}, lab -> {}".format( - image_path.name, label_path.name - ) - ) - # breakpoint() - - # resize keeping aspect ratio: smallest dim is 640 - h, w = img.shape[:2] - if h < w: - size = (640, int(640 * w / h)) - else: - size = (int(640 * h / w), 640) - - r_img = resize(img, size, preserve_range=True, anti_aliasing=True) - r_img = uint8(r_img) - - r_lab = resize(lab, size, preserve_range=True, anti_aliasing=False, order=0) - r_lab = uint8(r_lab) - - # crop in the center - H, W = r_img.shape[:2] - - top = (H - 640) // 2 - left = (W - 640) // 2 - - rc_img = r_img[top : top + 640, left : left + 640, :] - rc_lab = ( - r_lab[top : top + 640, left : left + 640, :] - if r_lab.ndim == 3 - else r_lab[top : top + 640, left : left + 640] - ) - - return rc_img, rc_lab - - -def load_ground(ground_output_path, ref_image_path): - gop = Path(ground_output_path) - rip = Path(ref_image_path) - - ground_paths = list((gop / "eval-metrics" / "pred").glob(f"{rip.stem}.jpg")) + list( - (gop / "eval-metrics" / "pred").glob(f"{rip.stem}.png") - ) - if len(ground_paths) == 0: - raise ValueError( - f"Could not find a ground match in {str(gop)} for image {str(rip)}" - ) - elif len(ground_paths) > 1: - raise ValueError( - f"Found more than 1 ground match in {str(gop)} for image {str(rip)}:" - + f" {list(map(str, ground_paths))}" - ) - ground_path = ground_paths[0] - _, ground = crop_and_resize(rip, ground_path) - ground = (ground > 0).astype(np.float32) - return torch.from_numpy(ground).unsqueeze(0).unsqueeze(0).cuda() - - -def parse_args(): - parser = ArgumentParser() - parser.add_argument("-y", "--yaml", help="Path to a list of models") - parser.add_argument( - "--disable_loading", - action="store_true", - default=False, - help="Disable loading of existing inferences", - ) - parser.add_argument( - "-t", "--tags", nargs="*", help="Comet.ml tags", default=[], type=str - ) - parser.add_argument( - "--tasks", - nargs="*", - help="Comet.ml tags", - default=["x", "d", "s", "m", "mx", "p"], - type=str, - ) - args = parser.parse_args() - - print("Received args:") - print(vars(args)) - - return args - - -def load_images_and_labels( - path="/miniscratch/_groups/ccai/data/omnigan/masker-test-set", -): - p = Path(path) - ims_path = p / "imgs" - lab_path = p / "labels" - - ims = sorted(climategan.utils.find_images(ims_path), key=lambda x: x.name) - labs = sorted( - climategan.utils.find_images(lab_path), - key=lambda x: x.name.replace("_labeled.", "."), - ) - - xs = climategan.transforms.PrepareInference()(ims) - ys = climategan.transforms.PrepareInference(is_label=True)(labs) - - return xs, ys, ims, labs - - -def load_inferences(inf_path, im_paths): - try: - assert inf_path.exists() - assert sorted([i.stem for i in im_paths]) == sorted( - [i.stem for i in inf_path.glob("*.pt")] - ) - return [torch.load(str(i)) for i in tqdm(list(inf_path.glob("*.pt")))] - except Exception as e: - print() - print(e) - print("Aborting Loading") - print() - return None - - -def get_or_load_inferences( - m_path, device, xs, is_ground, im_paths, ground_model, try_load=True -): - inf_path = Path(m_path) / "inferences" - if try_load: - print("Trying to load existing inferences:") - outputs = load_inferences(inf_path, im_paths) - if outputs is not None: - print("Successfully loaded existing inferences") - return outputs - - trainer = climategan.trainer.Trainer.resume_from_path( - m_path if not is_ground else ground_model, - inference=True, - new_exp=None, - device=device, - ) - - inf_path.mkdir(exist_ok=True) - outputs = [] - for i, x in enumerate(tqdm(xs)): - x = x.to(trainer.device) - if not is_ground: - out = trainer.G.decode(x=x) - else: - out = {"m": load_ground(GROUND_MODEL, im_paths[i])} - out["p"] = trainer.G.paint(out["m"] > 0.5, x) - out["x"] = x - inference = {k: v.cpu() for k, v in out.items()} - outputs.append(inference) - torch.save(inference, inf_path / f"{im_paths[i].stem}.pt") - print() - - return outputs - - -def numpify(outputs): - nps = [] - print("Numpifying...") - for o in tqdm(outputs): - x = (o["x"][0].permute(1, 2, 0).numpy() + 1) / 2 - m = o["m"] - m = (m[0, 0, :, :].numpy() > 0.5).astype(np.uint8) - p = (o["p"][0].permute(1, 2, 0).numpy() + 1) / 2 - data = {"m": m, "p": p, "x": x} - if "s" in o: - s = climategan.data.decode_segmap_merged_labels(o["s"], "r", False) / 255.0 - data["s"] = s[0].permute(1, 2, 0).numpy() - if "d" in o: - d = climategan.tutils.normalize_tensor(o["d"]).squeeze().numpy() - data["d"] = d - nps.append({k: img_as_ubyte(v) for k, v in data.items()}) - return nps - - -def concat_npy_for_model(data, tasks): - assert "m" in data - assert "x" in data - assert "p" in data - - x = mask = depth = seg = painted = masked = None - - x = data["x"] - painted = data["p"] - mask = (gray2rgb(data["m"]) * 255).astype(np.uint8) - painted = data["p"] - masked = (1 - gray2rgb(data["m"])) * x - - concats = [] - - if "d" in data: - depth = img_as_ubyte( - gray2rgb( - resize(data["d"], data["x"].shape[:2], anti_aliasing=True, order=1) - ) - ) - else: - depth = np.ones_like(data["x"]) * 255 - - if "s" in data: - seg = img_as_ubyte( - resize(data["s"], data["x"].shape[:2], anti_aliasing=False, order=0) - ) - else: - seg = np.ones_like(data["x"]) * 255 - - for t in tasks: - if t == "x": - concats.append(x) - if t == "m": - concats.append(mask) - elif t == "mx": - concats.append(masked) - elif t == "d": - concats.append(depth) - elif t == "s": - concats.append(seg) - elif t == "p": - concats.append(painted) - - row = np.concatenate(concats, axis=1) - - return row - - -if __name__ == "__main__": - args = parse_args() - - with open(args.yaml, "r") as f: - maskers = yaml.safe_load(f) - if "models" in maskers: - maskers = maskers["models"] - - load = not args.disable_loading - tags = args.tags - tasks = args.tasks - - ground_model = None - for m in maskers: - if "ground" not in maskers: - ground_model = m - break - if ground_model is None: - raise ValueError("Could not find a non-ground model to get a painter") - - device = torch.device("cuda:0") - torch.set_grad_enabled(False) - - xs, ys, im_paths, lab_paths = load_images_and_labels() - - np_outs = {} - names = [] - - for m_path in maskers: - - opt_path = Path(m_path) / "opts.yaml" - with opt_path.open("r") as f: - opt = yaml.safe_load(f) - - name = ( - ", ".join( - [ - t - for t in sorted(opt["comet"]["tags"]) - if "branch" not in t and "ablation" not in t and "trash" not in t - ] - ) - if "--ground" not in m_path - else "ground" - ) - names.append(name) - - is_ground = name == "ground" - - print("#" * 100) - print("\n>>> Processing", name) - print() - - outputs = get_or_load_inferences( - m_path, device, xs, is_ground, im_paths, ground_model, load - ) - nps = numpify(outputs) - - np_outs[name] = nps - - exp = Experiment(project_name="climategan-inferences", display_summary_level=0) - exp.log_parameter("names", names) - exp.add_tags(tags) - - for i in tqdm(range(len(xs))): - all_models_for_image = [] - for name in names: - xpmds = concat_npy_for_model(np_outs[name][i], tasks) - all_models_for_image.append(xpmds) - full_im = np.concatenate(all_models_for_image, axis=0) - pil_im = Image.fromarray(full_im) - exp.log_image(pil_im, name=im_paths[i].stem.replace(".", "_"), step=i) diff --git a/spaces/vumichien/canvas_controlnet/ldm/modules/midas/api.py b/spaces/vumichien/canvas_controlnet/ldm/modules/midas/api.py deleted file mode 100644 index b58ebbffd942a2fc22264f0ab47e400c26b9f41c..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/ldm/modules/midas/api.py +++ /dev/null @@ -1,170 +0,0 @@ -# based on https://github.com/isl-org/MiDaS - -import cv2 -import torch -import torch.nn as nn -from torchvision.transforms import Compose - -from ldm.modules.midas.midas.dpt_depth import DPTDepthModel -from ldm.modules.midas.midas.midas_net import MidasNet -from ldm.modules.midas.midas.midas_net_custom import MidasNet_small -from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet - - -ISL_PATHS = { - "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt", - "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt", - "midas_v21": "", - "midas_v21_small": "", -} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def load_midas_transform(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load transform only - if model_type == "dpt_large": # DPT-Large - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - elif model_type == "midas_v21_small": - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - else: - assert False, f"model_type '{model_type}' not implemented, use: --model_type large" - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return transform - - -def load_model(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load network - model_path = ISL_PATHS[model_type] - if model_type == "dpt_large": # DPT-Large - model = DPTDepthModel( - path=model_path, - backbone="vitl16_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - model = DPTDepthModel( - path=model_path, - backbone="vitb_rn50_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - model = MidasNet(model_path, non_negative=True) - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - elif model_type == "midas_v21_small": - model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, - non_negative=True, blocks={'expand': True}) - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - else: - print(f"model_type '{model_type}' not implemented, use: --model_type large") - assert False - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return model.eval(), transform - - -class MiDaSInference(nn.Module): - MODEL_TYPES_TORCH_HUB = [ - "DPT_Large", - "DPT_Hybrid", - "MiDaS_small" - ] - MODEL_TYPES_ISL = [ - "dpt_large", - "dpt_hybrid", - "midas_v21", - "midas_v21_small", - ] - - def __init__(self, model_type): - super().__init__() - assert (model_type in self.MODEL_TYPES_ISL) - model, _ = load_model(model_type) - self.model = model - self.model.train = disabled_train - - def forward(self, x): - # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array - # NOTE: we expect that the correct transform has been called during dataloading. - with torch.no_grad(): - prediction = self.model(x) - prediction = torch.nn.functional.interpolate( - prediction.unsqueeze(1), - size=x.shape[2:], - mode="bicubic", - align_corners=False, - ) - assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) - return prediction - diff --git a/spaces/wallezen/so-vits-svc/vdecoder/hifigan/env.py b/spaces/wallezen/so-vits-svc/vdecoder/hifigan/env.py deleted file mode 100644 index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000 --- a/spaces/wallezen/so-vits-svc/vdecoder/hifigan/env.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import shutil - - -class AttrDict(dict): - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -def build_env(config, config_name, path): - t_path = os.path.join(path, config_name) - if config != t_path: - os.makedirs(path, exist_ok=True) - shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/spaces/webshop/amazon_shop/templates/search_page.html b/spaces/webshop/amazon_shop/templates/search_page.html deleted file mode 100644 index ef6af6982141304fb85a472979dbbe9c6cade94b..0000000000000000000000000000000000000000 --- a/spaces/webshop/amazon_shop/templates/search_page.html +++ /dev/null @@ -1,34 +0,0 @@ - - - - - - - - - - -
          -
          -
          - -
          -

          Instruction:
          {{ instruction_text }}

          -
          - -
          -
          - - - - -
          -
          - -
          -
          -
          - - \ No newline at end of file diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_teaching_plan.py b/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_teaching_plan.py deleted file mode 100644 index 7c959ce85472c71ceb16339c083c5756c541a9ee..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/metagpt/actions/write_teaching_plan.py +++ /dev/null @@ -1,159 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/7/27 -@Author : mashenquan -@File : write_teaching_plan.py -""" -from metagpt.logs import logger -from metagpt.actions import Action -from metagpt.schema import Message - - -class TeachingPlanRequirement(Action): - """Teaching Plan Requirement without any implementation details""" - - async def run(self, *args, **kwargs): - raise NotImplementedError - - -class WriteTeachingPlanPart(Action): - """Write Teaching Plan Part""" - - def __init__(self, name: str = "", context=None, llm=None, topic: str = "", language: str = "Chinese"): - """ - - :param name: action name - :param context: context - :param llm: object of :class:`LLM` - :param topic: topic part of teaching plan - :param language: A human language, such as Chinese, English, French, etc. - """ - super().__init__(name, context, llm) - self.topic = topic - self.language = language - self.rsp = None - - async def run(self, messages, *args, **kwargs): - if len(messages) < 1 or not isinstance(messages[0], Message): - raise ValueError("Invalid args, a tuple of List[Message] is expected") - - statement_patterns = self.TOPIC_STATEMENTS.get(self.topic, []) - statements = [] - from metagpt.roles import Role - for p in statement_patterns: - s = Role.format_value(p) - statements.append(s) - formatter = self.PROMPT_TITLE_TEMPLATE if self.topic == self.COURSE_TITLE else self.PROMPT_TEMPLATE - prompt = formatter.format(formation=self.FORMATION, - role=self.prefix, - statements="\n".join(statements), - lesson=messages[0].content, - topic=self.topic, - language=self.language) - - logger.debug(prompt) - rsp = await self._aask(prompt=prompt) - logger.debug(rsp) - self._set_result(rsp) - return self.rsp - - def _set_result(self, rsp): - if self.DATA_BEGIN_TAG in rsp: - ix = rsp.index(self.DATA_BEGIN_TAG) - rsp = rsp[ix + len(self.DATA_BEGIN_TAG):] - if self.DATA_END_TAG in rsp: - ix = rsp.index(self.DATA_END_TAG) - rsp = rsp[0:ix] - self.rsp = rsp.strip() - if self.topic != self.COURSE_TITLE: - return - if '#' not in self.rsp or self.rsp.index('#') != 0: - self.rsp = "# " + self.rsp - - def __str__(self): - """Return `topic` value when str()""" - return self.topic - - def __repr__(self): - """Show `topic` value when debug""" - return self.topic - - FORMATION = "\"Capacity and role\" defines the role you are currently playing;\n" \ - "\t\"[LESSON_BEGIN]\" and \"[LESSON_END]\" tags enclose the content of textbook;\n" \ - "\t\"Statement\" defines the work detail you need to complete at this stage;\n" \ - "\t\"Answer options\" defines the format requirements for your responses;\n" \ - "\t\"Constraint\" defines the conditions that your responses must comply with." - - COURSE_TITLE = "Title" - TOPICS = [ - COURSE_TITLE, "Teaching Hours", "Teaching Objectives", "Teaching Content", - "Teaching Methods and Strategies", "Learning Activities", - "Teaching Time Allocation", "Assessment and Feedback", "Teaching Summary and Improvement", - "Vocabulary Cloze", "Choice Questions", "Grammar Questions", "Translation Questions" - ] - - TOPIC_STATEMENTS = { - COURSE_TITLE: ["Statement: Find and return the title of the lesson only in markdown first-level header format, " - "without anything else."], - "Teaching Content": [ - "Statement: \"Teaching Content\" must include vocabulary, analysis, and examples of various grammar " - "structures that appear in the textbook, as well as the listening materials and key points.", - "Statement: \"Teaching Content\" must include more examples."], - "Teaching Time Allocation": [ - "Statement: \"Teaching Time Allocation\" must include how much time is allocated to each " - "part of the textbook content."], - "Teaching Methods and Strategies": [ - "Statement: \"Teaching Methods and Strategies\" must include teaching focus, difficulties, materials, " - "procedures, in detail." - ], - "Vocabulary Cloze": [ - "Statement: Based on the content of the textbook enclosed by \"[LESSON_BEGIN]\" and \"[LESSON_END]\", " - "create vocabulary cloze. The cloze should include 10 {language} questions with {teaching_language} " - "answers, and it should also include 10 {teaching_language} questions with {language} answers. " - "The key-related vocabulary and phrases in the textbook content must all be included in the exercises.", - ], - "Grammar Questions": [ - "Statement: Based on the content of the textbook enclosed by \"[LESSON_BEGIN]\" and \"[LESSON_END]\", " - "create grammar questions. 10 questions."], - "Choice Questions": [ - "Statement: Based on the content of the textbook enclosed by \"[LESSON_BEGIN]\" and \"[LESSON_END]\", " - "create choice questions. 10 questions."], - "Translation Questions": [ - "Statement: Based on the content of the textbook enclosed by \"[LESSON_BEGIN]\" and \"[LESSON_END]\", " - "create translation questions. The translation should include 10 {language} questions with " - "{teaching_language} answers, and it should also include 10 {teaching_language} questions with " - "{language} answers." - ] - } - - # Teaching plan title - PROMPT_TITLE_TEMPLATE = "Do not refer to the context of the previous conversation records, " \ - "start the conversation anew.\n\n" \ - "Formation: {formation}\n\n" \ - "{statements}\n" \ - "Constraint: Writing in {language}.\n" \ - "Answer options: Encloses the lesson title with \"[TEACHING_PLAN_BEGIN]\" " \ - "and \"[TEACHING_PLAN_END]\" tags.\n" \ - "[LESSON_BEGIN]\n" \ - "{lesson}\n" \ - "[LESSON_END]" - - # Teaching plan parts: - PROMPT_TEMPLATE = "Do not refer to the context of the previous conversation records, " \ - "start the conversation anew.\n\n" \ - "Formation: {formation}\n\n" \ - "Capacity and role: {role}\n" \ - "Statement: Write the \"{topic}\" part of teaching plan, " \ - "WITHOUT ANY content unrelated to \"{topic}\"!!\n" \ - "{statements}\n" \ - "Answer options: Enclose the teaching plan content with \"[TEACHING_PLAN_BEGIN]\" " \ - "and \"[TEACHING_PLAN_END]\" tags.\n" \ - "Answer options: Using proper markdown format from second-level header format.\n" \ - "Constraint: Writing in {language}.\n" \ - "[LESSON_BEGIN]\n" \ - "{lesson}\n" \ - "[LESSON_END]" - - DATA_BEGIN_TAG = "[TEACHING_PLAN_BEGIN]" - DATA_END_TAG = "[TEACHING_PLAN_END]" diff --git a/spaces/xelu3banh/dpt-depth16/app.py b/spaces/xelu3banh/dpt-depth16/app.py deleted file mode 100644 index d53cd25e9a32ed9f2b8c670cb4e9b6f00b05ec82..0000000000000000000000000000000000000000 --- a/spaces/xelu3banh/dpt-depth16/app.py +++ /dev/null @@ -1,45 +0,0 @@ -import gradio as gr -from transformers import DPTFeatureExtractor, DPTForDepthEstimation -import torch -import numpy as np -from PIL import Image - -#torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') - -feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") -model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") - -def process_image(image): - # prepare image for the model - encoding = feature_extractor(image, return_tensors="pt") - - # forward pass - with torch.no_grad(): - outputs = model(**encoding) - predicted_depth = outputs.predicted_depth - - # interpolate to original size - prediction = torch.nn.functional.interpolate( - predicted_depth.unsqueeze(1), - size=image.size[::-1], - mode="bicubic", - align_corners=False, - ).squeeze() - output = prediction.cpu().numpy() - formatted = (output * 255 / np.max(output)).astype('uint8') - img = Image.fromarray(formatted) - return img - - return result - -title = "Demo: zero-shot depth estimation with DPT" -description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation." - - -iface = gr.Interface(fn=process_image, - inputs=gr.inputs.Image(type="pil"), - outputs=gr.outputs.Image(type="pil", label="predicted depth"), - title=title, - description=description, - enable_queue=True) -iface.launch(debug=True) \ No newline at end of file diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/arcface/utils_callbacks.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/arcface/utils_callbacks.py deleted file mode 100644 index 8f27da73096464f9a94c8e6df4baeec6d2b56f7a..0000000000000000000000000000000000000000 --- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/arcface/utils_callbacks.py +++ /dev/null @@ -1,141 +0,0 @@ -import logging -import os -import time -from typing import List - -import torch - -from third_party.arcface import verification - - -class AverageMeter(object): - """ Computes and stores the average and current value - """ - def __init__(self): - self.val = None - self.avg = None - self.sum = None - self.count = None - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - -class CallBackVerification(object): - def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112), - is_gray=False): - self.frequent: int = frequent - self.rank: int = rank - self.highest_acc: float = 0.0 - self.highest_acc_list: List[float] = [0.0] * len(val_targets) - self.ver_list: List[object] = [] - self.ver_name_list: List[str] = [] - if self.rank is 0: - self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) - self.is_gray = is_gray - - def ver_test(self, backbone: torch.nn.Module, global_step: int): - results = [] - for i in range(len(self.ver_list)): - acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test( - self.ver_list[i], backbone, 10, 10, - is_gray=self.is_gray) - # logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) - # logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) - print('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) - print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) - if acc2 > self.highest_acc_list[i]: - self.highest_acc_list[i] = acc2 - # logging.info( - # '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i])) - print( - '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i])) - results.append(acc2) - - def init_dataset(self, val_targets, data_dir, image_size): - for name in val_targets: - path = os.path.join(data_dir, name + ".bin") - if os.path.exists(path): - data_set = verification.load_bin(path, image_size) - self.ver_list.append(data_set) - self.ver_name_list.append(name) - - def __call__(self, num_update, backbone: torch.nn.Module): - if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0: - backbone.eval() - self.ver_test(backbone, num_update) - backbone.train() - - -class CallBackLogging(object): - def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None): - self.frequent: int = frequent - self.rank: int = rank - self.time_start = time.time() - self.total_step: int = total_step - self.batch_size: int = batch_size - self.world_size: int = world_size - self.writer = writer - - self.init = False - self.tic = 0 - - def __call__(self, global_step, loss: AverageMeter, epoch: int, fp16: bool, grad_scaler: torch.cuda.amp.GradScaler): - if self.rank is 0 and global_step > 0 and global_step % self.frequent == 0: - if self.init: - try: - speed: float = self.frequent * self.batch_size / (time.time() - self.tic) - speed_total = speed * self.world_size - except ZeroDivisionError: - speed_total = float('inf') - - time_now = (time.time() - self.time_start) / 3600 - time_total = time_now / ((global_step + 1) / self.total_step) - time_for_end = time_total - time_now - if self.writer is not None: - self.writer.add_scalar('time_for_end', time_for_end, global_step) - self.writer.add_scalar('loss', loss.avg, global_step) - if fp16: - msg = "Speed %.2f samples/sec Loss %.4f Epoch: %d Global Step: %d "\ - "Fp16 Grad Scale: %2.f Required: %1.f hours" % ( - speed_total, loss.avg, epoch, global_step, grad_scaler.get_scale(), time_for_end - ) - else: - msg = "Speed %.2f samples/sec Loss %.4f Epoch: %d Global Step: %d Required: %1.f hours" % ( - speed_total, loss.avg, epoch, global_step, time_for_end - ) - logging.info(msg) - loss.reset() - self.tic = time.time() - else: - self.init = True - self.tic = time.time() - - -class CallBackModelCheckpoint(object): - def __init__(self, rank, output="./"): - self.rank: int = rank - self.output: str = output - - def __call__(self, - global_step, - backbone: torch.nn.Module, - partial_fc=None, - awloss=None,): - print('CallBackModelCheckpoint...') - if global_step > 100 and self.rank is 0: - torch.save(backbone.module.state_dict(), os.path.join(self.output, "backbone.pth")) - if global_step > 100 and partial_fc is not None: - partial_fc.save_params() - if global_step > 100 and awloss is not None: - torch.save(awloss.state_dict(), os.path.join(self.output, "awloss.pth")) diff --git a/spaces/yhavinga/pre-training-dutch-t5-models/app.py b/spaces/yhavinga/pre-training-dutch-t5-models/app.py deleted file mode 100644 index 28c05a3b9465b8b9c2ab626dc9cff84009d88ac9..0000000000000000000000000000000000000000 --- a/spaces/yhavinga/pre-training-dutch-t5-models/app.py +++ /dev/null @@ -1,654 +0,0 @@ -from glob import glob -from itertools import zip_longest -import sqlite3 -import psutil -import streamlit as st -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns - -IMAGE_WIDTHS = 900 -PRE_TRAINED_DB = "data/pretrained.sqlite" - - -@st.cache_resource -def load_eval_data(): - conn = sqlite3.connect(PRE_TRAINED_DB) - conn.row_factory = lambda c, r: { - col[0]: r[idx] for idx, col in enumerate(c.description) - } - df = pd.read_sql_query("SELECT * FROM pretrained", conn) - df.replace("None", np.nan, inplace=True) - df.rename(columns={"model": "name"}, inplace=True) - df = df.infer_objects() - int_columns = ["train_batch_size", "num_parameters"] - df[int_columns] = df[int_columns].astype("Int32") - plot_df = df[["name", "num_parameters", "summ_rouge1", "trans_en_nl_score"]] - plot_df[["num_parameters", "summ_rouge1", "trans_en_nl_score"]] = plot_df[ - ["num_parameters", "summ_rouge1", "trans_en_nl_score"] - ].apply(pd.to_numeric) - plot_df["num params (M)"] = plot_df["num_parameters"].map( - lambda x: int(x / 10**6) - ) - plot_df.dropna(subset=["summ_rouge1"], inplace=True) - plot_df.rename( - columns={"summ_rouge1": "summ Rouge1", "trans_en_nl_score": "en->nl Bleu"}, - inplace=True, - ) - for i, row in df.iterrows(): - dirs = glob( - f"data/eval_summ_results/{row['id']}-{row['name']}/yhavinga_cnn_dailymail_dutch/eval_predictions*" - ) - try: - file = dirs[-1] + "/generated.txt" - with open(file, "r") as f: - text = f.read().replace("", " ") - except Exception: - text = "fine-tune failed, no data" - df.at[i, "summary"] = text - - for i, row in df.iterrows(): - dirs = glob( - f"data/eval_transl_results/{row['id']}-{row['name']}/yhavinga_ccmatrix/eval_predictions*" - ) - try: - file = dirs[-1] + "/generated.txt" - with open(file, "r") as f: - text = f.read().replace("", " ") - except Exception: - text = "fine-tune failed, no data" - df.at[i, "translation"] = text - - # order df by the name column desc - df.sort_values(by="name", inplace=True, ascending=False) - - return plot_df, df - - -def main(): - st.set_page_config( # Alternate names: setup_page, page, layout - page_title="Pre-training Dutch T5 models", # String or None. Strings get appended with "• Streamlit". - layout="wide", # Can be "centered" or "wide". In the future also "dashboard", etc. - initial_sidebar_state="collapsed", # Can be "auto", "expanded", "collapsed" - page_icon="📑", # String, anything supported by st.image, or None. - ) - plot_df, df = load_eval_data() - - with open("style.css") as f: - st.markdown(f"", unsafe_allow_html=True) - - st.markdown("""# Dutch T5 models : UL2, T5, ByT5 and Long-T5 🇳🇱🇧🇪 - -TL;DR: Dutch NLP gets a boost with state-of-the-art T5 models trained on the largest Dutch corpus, mC4, and additional datasets. -See below for model lists and comparison. - -During the [HuggingFace Flax/Jax community week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) in the summer of 2021, -I was granted access to Google's TPU Research Cloud (TRC), -a cloud-based platform for machine learning research and development that provides access to Google's -Tensor Processing Units (TPUs). My goal was to address the (then) shortage of T5 models for the Dutch language. --- T5 is a state-of-the-art AI model architecture that can handle text as input and output, -making it an ideal tool for NLP tasks such as summarization, translation, and question-answering -- -Since then, with extended access to the TRC, I have been able to train a variety of T5 models for Dutch. - -Relevant papers are: - -* **[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)** by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*. -* **[ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952)** by *Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler*. -* **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. -* **[ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)** by *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel* -* **[LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916)** by *Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang* -* **[Scaling Up Models and Data with t5x and seqio](https://arxiv.org/abs/2203.17189)** by *Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo* -* **[UL2: Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131)** by *Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler* - -Background on Google's TPU VM's and how to use the Huggingface transformers library to pre-train models can be found -at the following links -* https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104 -* https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#talks - -## Pre-training - -### mC4 dataset - -Together with the T5 model architecture and SeqIO, the T5 authors also created and released -the multilingual [mC4 dataset](https://huggingface.co/datasets/allenai/c4). -It was made available by AllenNLP on the HuggingFace Dataset hub. -Our team confirmed that the Dutch portion of the mC4 dataset was deduplicated, -and we cleaned the Dutch portion of the mC4 dataset using [code adapted](https://gitlab.com/yhavinga/c4nlpreproc) from the TensorFlow C4 dataset. -The resulting [mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned) dataset on the HuggingFace hub -has configs for several sizes, and also configs for interleaved mixed Dutch and English -texts, e.g. [micro_en_nl](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/micro_en_nl/train). -The `_en_nl` configs were added to accommodate multi-language pre-training -with the Huggingface pre-training script, that accepts only a single dataset as input. -The full, cleaned Dutch mC4 dataset is 151GB and remains (as of June 2022) the largest available Dutch -corpus on the HuggingFace Dataset hub. - -### Additional books, Wikipedia and Dutch news articles datasets - -The `t5_1_1` and `ul2` models have also been trained on Dutch books, the Dutch subset of Wikipedia (2022-03-20), -the English subset of Wikipedia (2022-03-01), and a subset of "mc4_nl_cleaned" containing only texts -from Dutch and Belgian newspapers. Mixing in the these datasets was done to bias the model towards -descriptions of events in the Netherlands and Belgium. - -### Pre-Training Objectives - -The T5 models are pre-trained using the [span corruption](https://arxiv.org/abs/1910.10683) denoising objective. -15% of the tokens in the text are masked, and each span -of masked tokens is replaced with a special token known as a sentinel token, where each span is assigned -its own sentinel token. The model is then trained to predict for each sentinel token the original text -that was replaced by the sentinel tokens. - -The UL2 models are pre-trained with the [Mixture-of-Denoisers (MoD)](https://arxiv.org/abs/2205.05131) objective, that combines diverse pre-training -paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where -the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers -that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of -three denoising tasks: - -1. R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; -2. X-denoising (or extreme span corruption); and -3. S-denoising (or sequential PrefixLM). - -### Pre-training software - -#### Huggingface [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py) - -All models except `t5_1_1` and `ul2` were pre-trained using the Huggingface `run_t5_mlm_flax.py` script. -This script is a good fit if you want to get a grasp what's needed to pre-train a language model -with Flax and Jax, since all data preparation, model instantiation, loss function, and training loop are -contained in a single file. - -#### Google's [T5X](https://github.com/google-research/t5x) - -The Dutch `t5_1_1` and `ul2` models were pre-trained using T5X. This is a modular framework that can be used for -pre-training, fine-tuning, and evaluation of T5 models. Because of its modular and pluggable design, -by only supplying a few configuration and code files, it is possible to pre-train with your own definitions. -It is even possible to define custom neural network layers and architectures, though I did not do this and only -pre-trained the default T5 encoder-decoder architecture, and varied only the pre-training objective, and the -datasets used and mixed with SeqIO. - -#### Conversion script from T5X to HF - -The T5X models were converted to Huggingface Flax T5 format using a script that was adapted from the -[T5X checkpoint to HuggingFace Flax conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py). -This script was modified to cast weights to bf16, and to also convert to pytorch format. -For this conversion to be successful, the T5X model had to be saved with `use_gda=False` set in the GIN file. - - -""") - - st.markdown( - """## Evaluation - -### Evaluation setup - -Each pre-trained model was evaluated by fine-tuning on summarization and translation. The learning-rate was set to -a constant schedule after a small warmup of 32 steps. -Fine-tuning for evaluation was done on a limited set of 50K examples from the fine-tuning datasets. - -| | Summarization | Translation | -|-----------------:|------------------|-------------------| -| Dataset | [CNN Dailymail Dutch](https://huggingface.co/datasets/yhavinga/cnn_dailymail_dutch) | [CCMatrix En->NL](https://huggingface.co/datasets/yhavinga/ccmatrix_en_nl) | -| #train samples | 50K | 50K | -| Optimizer | AdamW | AdamW | -| learning rate | 0.001 | 0.0005 | -| source length | 1024 | 128 | -| target length | 142 | 128 | -| #eval samples | 1000 | 1000 | -| WandB link | [eval_summ](https://wandb.ai/yepster/eval_dutch_cnndaily_202302_flax)|[eval_transl](https://wandb.ai/yepster/eval_dutch_ccmatrix_202302_flax) | - -On the WandB links above you can also find generated texts for each model to compare. - -### Evaluation results - -The figure below shows the evaluation scores for most models, with summarization Rouge1 on the x-axis (higher is better), -and translation English to Dutch Bleu score on the y-axis (higher is better). -The point size is proportional to the model size. -UL2 models are blue, -t5_1_1 models orange, -Flan models red, -mT5 green and the other models black. -""" - ) - col1, col2 = st.columns(2) - with col1: - ul2_enabled = st.checkbox( - "UL2 Dutch (and English) (trained with T5X)", value=True - ) - t5_1_1_enabled = st.checkbox("t5_1_1 Dutch (trained with T5X)", value=True) - flan_enabled = st.checkbox("Flan T5 (google/flan-t5-*)", value=True) - mt5_enabled = st.checkbox("mt5 (google/mt5-*)", value=True) - long_t5_enabled = st.checkbox( - "Long T5 Dutch+English (trained with HuggingFace script)" - ) - t5_v1_1_enabled = st.checkbox( - "T5 Dutch (and English) (trained with HuggingFace script)" - ) - with col2: - small_enabled = st.checkbox("small model sizes") - base_enabled = st.checkbox("base model sizes") - large_enabled = st.checkbox("large model sizes") - _24_enabled = st.checkbox("small nl24 deep narrow sizes") - _36_enabled = st.checkbox("base nl36 deep narrow sizes") - _8l_enabled = st.checkbox("large nl8 shallow sizes") - _4xl_enabled = st.checkbox("xlarge nl4 shallow wide sizes") - - plot_df = plot_df[ - (plot_df["name"].str.contains("ul2") & ul2_enabled) - | (plot_df["name"].str.contains("flan") & flan_enabled) - | (plot_df["name"].str.contains("mt5") & mt5_enabled) - | (plot_df["name"].str.contains("long-t5") & long_t5_enabled) - | (plot_df["name"].str.contains("t5_1_1") & t5_1_1_enabled) - | ( - ( - plot_df["name"].str.startswith("t5") - & ~plot_df["name"].str.startswith("t5_1_1") - ) - & t5_v1_1_enabled - ) - | ( - plot_df["name"].str.contains("base") - & base_enabled - & ~plot_df["name"].str.contains("36") - ) - | ( - plot_df["name"].str.contains("small") - & small_enabled - & ~plot_df["name"].str.contains("24") - ) - | ( - plot_df["name"].str.contains("large") - & large_enabled - & ~plot_df["name"].str.contains("8") - ) - | ( - ( - plot_df["name"].str.contains("-36L") - | plot_df["name"].str.contains("nl36") - ) - & _36_enabled - ) - | ( - ( - plot_df["name"].str.contains("-24L") - | plot_df["name"].str.contains("nl24") - ) - & _24_enabled - ) - | ( - (plot_df["name"].str.contains("-8l") | plot_df["name"].str.contains("nl8")) - & _8l_enabled - ) - | ( - (plot_df["name"].str.contains("-4L") | plot_df["name"].str.contains("nl4")) - & _4xl_enabled - ) - ] - - color_dict = {"flan": "red", "ul2": "blue", "mt5": "green", "t5_1_1": "orange"} - colors = [ - color_dict[name.split("-")[0].lower()] - if name.split("-")[0].lower() in color_dict.keys() - else "black" - for name in plot_df["name"] - ] - fig = plt.figure(figsize=(15, 8)) - sns.set_style("darkgrid") - ax = sns.scatterplot( - data=plot_df, - y="en->nl Bleu", - x="summ Rouge1", - size="num params (M)", - hue=colors, - linewidth=0.7, - ) - for i, row in plot_df.iterrows(): - ax.annotate( - row["name"], - (row["summ Rouge1"], row["en->nl Bleu"]), - xytext=(0, 7), - textcoords="offset points", - ha="center", - va="center", - rotation=0, - ) - # Remove color legend - handles, labels = ax.get_legend_handles_labels() - size_legend_labels = ["num params (M)"] + labels[-4:] - size_legend_handles = handles[-5:] - ax.legend(handles=size_legend_handles, labels=size_legend_labels) - - plt.tight_layout() - st.pyplot(fig) - st.markdown( - """* The `UL2` pre-trained Dutch(English) models consistently outperform the `T5-*` Dutch(English) models. -* Flan models perform almost instantly well on the summarization task, with `flan-t5-small` - showing performance comparable to Dutch T5 base models. -* For the translation task from English to Dutch, the Dutch+English pre-trained models perform well. Also - `UL2 Dutch` pre-trained Dutch models are consistently better than their `Flan`, `T5 Dutch` and - `mT5` counterparts of the comparable size. -* Fine-tuning of `t5-v1.1-large-dutch-cased` failed with the hyperparameters that were fixed to the same value for the - evaluation of every model. - Since the `UL2` models are better across the board, I've disabled this model on the hub. -* The `long-t5` models show bad performance on both tasks. - I cannot explain this, especially for the translation task. With a sequence length of 128 input and output - tokens, the sliding attention window with radius length 127 of the `long-t5` models should be able to handle this. - I've retried the fine-tuning of these models with - `float32` instead of `bfloat16`, but the results were the same. Maybe this is normal behaviour for these models - targeted at dealing with longer sequence lengths. -""" - ) - - st.markdown("### Compare generated texts") - col1, col2 = st.columns(2) - with col1: - summ_model_left = st.selectbox( - "Choose left summarization model", df["name"], index=6 - ) - with col2: - summ_model_right = st.selectbox( - "Choose right summarization model", df["name"], index=33 - ) - - @st.cache_resource - def get_row(model): - return df[df["name"] == model] - - row_left = get_row(summ_model_left) - row_right = get_row(summ_model_right) - - contents1 = row_left["summary"].values[0].split("\n") - contents2 = row_right["summary"].values[0].split("\n") - contents = list(zip_longest(contents1, contents2))[:5] - st.table( - pd.DataFrame( - contents, - columns=[summ_model_left, summ_model_right], - ) - ) - - st.markdown("### Compare generated translations") - col1, col2 = st.columns(2) - with col1: - trans_model_left = st.selectbox("Choose left model", df["name"], index=3) - with col2: - trans_model_right = st.selectbox("Choose right model", df["name"], index=32) - - @st.cache_resource - def get_row(model): - return df[df["name"] == model] - - row_left = get_row(trans_model_left) - row_right = get_row(trans_model_right) - - contents1 = row_left["translation"].values[0].split("\n") - contents2 = row_right["translation"].values[0].split("\n") - contents = list(zip_longest(contents1, contents2))[:15] - st.table( - pd.DataFrame( - contents, - columns=[trans_model_left, trans_model_right], - ) - ) - - - st.markdown( - """## Miscellaneous remarks - -* Use loss regularization when training with `bfloat16` for better results (more info below). -* Be cautious of the dropout rate in the config.json file, as besides learning rate it is probably the most important - hyperparameter. - If you are evaluating different pre-trained models, be sure to fine-tune with dropout set equal. - Check in a model's `config.json` what the dropout rate has been set to. Unless you - intend to run many epochs on the same data, its worth to try a training run without dropout. - The smaller models can probably always be trained without. -* Training with more layers is much slower than you'd expect from the increased model size. - It is also more difficult to get batch size and learning rate right. Below is a section - about finding the right hyperparameters for the base-36L training. -* For the translation task, I am not sure that a 'deep-narrow' model (e.g. base-nl36) is better than a normal model - of comparable size (e.g. `large`). -* PyCharm's remote debugging features are useful to inspect variables on either a TPU VM or your deep-learning rig. -* When increasing the batch size, increase the learning rate. bs * 2 -> lr * sqrt(2) is a good heuristic but mileage may - vary. -* Dataset quality is a key success factor. Do not expect a model to magically turn mediocre data into magic. This holds for - the pre-training data, fine-tuning and also evaluating. -* Good Bleu score does not necessarily mean fluent text. Evaluation loss on a large translation dataset might be - better suited for model comparison, if the models have a tokenizer of comparable size. - -### Bfloat16 datatype requires loss regularization - -When training models with `bfloat16` and without loss regularization (default in the HuggingFace pre-training script), -the training losses would plateau or diverge. The graph below displays the results of different attempts -to train [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english). -The legend indicates the optimizer, data type, learning rate, total batch size, and learning rate schedule used. -As you can see, all attempts to train with `bfloat16` failed. -""" - ) - st.image("img/bfloat16_loss.png", width=IMAGE_WIDTHS) - st.markdown( - """The solution was found when peeking at T5X and the T5 gin configs, where I noticed a `z_loss` parameter, -always set to 1e-4. This factor is used in the T5X [cross entropy loss](https://github.com/google-research/t5x/blob/a319e559b4f72bffab91821487382ef4c25dfcf4/t5x/losses.py#L26) -function, with the purpose to pull the weights towards zero. -I experimented with adding this regularization term in the HF pre-training script, -and the `bfloat16` training runs did not exhibit the problems illustrated above anymore. - -The `z_loss` regularization term in the T5X loss function looks like L2 regularization. -(See e.g. Andrej Karpathy [explaining regularization loss](https://youtu.be/PaCmpygFfXo?t=6720)). -The Optax optimizer library (used in the HuggingFace script), mentions weight decay for AdaFactor (and AdamW) -but also mentions that L2 regularization does not work as expected with adaptive gradient -algorithms. It might be the case that setting a non-zero `weight_decay_rate` in the Optax Adafactor call -in the HuggingFace pre-training script is an alternative to adding the `z_loss` term, to solve the bfloat16 issues, but -I haven't tested this yet. -""" - ) - - st.markdown( - """### Which optimizer and lr to use - -During the Flax/Jax Community week in '21, our team quickly decided on using Adafactor with learning rate 5e-3. -I believed that a more optimal setting could be found with more time. -After conducting seven WandB sweeps with -Adafactor, AdamW and Distributed Shampoo (experimental PJIT version from Dall-E mini), -a better setting had not been found. The graph below shows the runs from all 7 sweeps combined. --- (I apologize for the confusion in the legend; I was unable to display the optimizer in the legend -because the initial version of the training script had the optimizer as a boolean, which I later -changed to a string with the optimizer name.) -- -All runs in the graph below that achieve a loss below 4 use **Adafactor**. -Peach-sweep-6 is represented by a dashed orange line and had a learning rate of **5e-3**. -""" - ) - - st.image("img/adafactor_vs_adam_pretrain.png", width=IMAGE_WIDTHS) - st.markdown( - """While there probably is a setting that will allow Adam and Shampoo to also converge fast below loss 4.0, I was unable -to find it. In a recent tweet Lucas Nestler had more success with Shampoo (https://twitter.com/_clashluke/status/1535994026876252160) -so maybe I need to revisit the attempt with the latest upstream code bases. - -Later, when pre-training with T5X, I found that its custom Adafactor implementation with the default settings of the T5X gin configs, -a learning rate of 0.001 and inverse square root learning rate decay, worked well. -""" - ) - - st.markdown( - """### Optimizer and learning rate used for summarization - -Finetuning summarization requires more memory than translation due to the longer sequence lengths involved. -I wondered if I could use Adafactor instead of Adam and ran -a sweep to test this. The sweep was configured with Hyperband, so not all training runs completed to the end. -""" - ) - st.image("img/optim_lr_summarization.png", width=IMAGE_WIDTHS) - st.markdown( - """The training losses are graphed below: - """ - ) - - st.image("img/training_losses_summarization_sweep.png", width=IMAGE_WIDTHS) - st.markdown( - """ -While the Adafactor run with learning rate 7e-4 came close to the Adam runs, the consistent stability of training with Adam -made me stick with Adam as optimizer for evaluation runs on the several models. For translation the results were similar, though in the end I needed to configure a lower learning rate for all -models to converge during fine-tuning. -""" - ) - - st.markdown( - """### Pre-training with sequence length 512 or 1024 - -The models `t5-v1_1-base-dutch-english-cased` and `t5-v1_1-base-dutch-english-cased-1024` have the same model dimensions, -but are pre-trained with span corruption on different sequence lenghts, 512 and 1024 respectively. -The evaluation loss and accuracy of the models do not look too different. Since training of the 1024 sequence length model was -very slow and didn't converge, I stopped it early. The figure below shows the evaluation -loss and accuracy. -""" - ) - st.image("img/t5v1_1eval_loss_and_accuracy.png", width=IMAGE_WIDTHS) - st.markdown( - """The 512 sequence length model was trained for 10 epochs of the `small` nl+en config (186B tokens total) and the 1024 -sequence length model about 2 epochs of the `large` nl+en config (100B tokens total). While I expected both models to -perform similarly on downstream tasks, the 1024 sequence length model has better scores for both -summarization and translation. -""" - ) - - st.markdown( - """## Model lists - -### UL2 Dutch English - -These models have been trained with T5X on mc4_nl_cleaned, books, Wikipedia and news. - -| | ul2-base-dutch-english | ul2-large-dutch-english | ul2-small-dutch-english | -|:---------------------|:-------------------------|:--------------------------|:--------------------------| -| model_type | t5 | t5 | t5 | -| _pipeline_tag | text2text-generation | text2text-generation | text2text-generation | -| d_model | 768 | 1024 | 512 | -| d_ff | 2048 | 2816 | 1024 | -| num_heads | 12 | 16 | 6 | -| d_kv | 64 | 64 | 64 | -| num_layers | 12 | 24 | 8 | -| num_decoder_layers | 12 | 24 | 8 | -| feed_forward_proj | gated-gelu | gated-gelu | gated-gelu | -| dense_act_fn | gelu_new | gelu_new | gelu_new | -| vocab_size | 32128 | 32128 | 32128 | -| tie_word_embeddings | 0 | 0 | 0 | -| torch_dtype | float32 | float32 | float32 | -| _gin_batch_size | 128 | 64 | 128 | -| _gin_z_loss | 0.0001 | 0.0001 | 0.0001 | -| _gin_t5_config_dtype | 'bfloat16' | 'bfloat16' | 'bfloat16' | - -### UL2 Dutch - -These models have been trained with T5X on mc4_nl_cleaned, books, Wikipedia and news. - -| | ul2-base-dutch | ul2-base-nl36-dutch | ul2-large-dutch | ul2-small-dutch | -|:---------------------|:---------------------|:----------------------|:---------------------|:---------------------| -| model_type | t5 | t5 | t5 | t5 | -| _pipeline_tag | text2text-generation | text2text-generation | text2text-generation | text2text-generation | -| d_model | 768 | 768 | 1024 | 512 | -| d_ff | 2048 | 3072 | 2816 | 1024 | -| num_heads | 12 | 12 | 16 | 6 | -| d_kv | 64 | 64 | 64 | 64 | -| num_layers | 12 | 36 | 24 | 8 | -| num_decoder_layers | 12 | 36 | 24 | 8 | -| feed_forward_proj | gated-gelu | gated-gelu | gated-gelu | gated-gelu | -| dense_act_fn | gelu_new | gelu_new | gelu_new | gelu_new | -| vocab_size | 32128 | 32128 | 32128 | 32128 | -| tie_word_embeddings | 0 | 0 | 0 | 0 | -| torch_dtype | float32 | float32 | float32 | float32 | -| _gin_batch_size | 128 | 64 | 64 | 128 | -| _gin_z_loss | 0.0001 | 0.0001 | 0.0001 | 0.0001 | -| _gin_t5_config_dtype | 'bfloat16' | 'bfloat16' | 'bfloat16' | 'bfloat16' | - -### T5 models Dutch and Dutch/English - -These models have been trained with the HuggingFace 🤗 run_t5_mlm_flax.py script on mc4_nl_cleaned. -Most notable differences are the model sizes, activation function, and the dropout rate used during -pre-training. The T5-eff models are models that differ in their number of layers. The table will list -the several dimensions of these models. - -| | [t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | [t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | [t5-v1_1-base-dutch-english-cased](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased) | [t5-v1_1-base-dutch-english-cased-1024](https://huggingface.co/yhavinga/t5-v1_1-base-dutch-english-cased-1024) | [t5-small-24L-dutch-english](https://huggingface.co/yhavinga/t5-small-24L-dutch-english) | [t5-xl-4L-dutch-english-cased](https://huggingface.co/yhavinga/t5-xl-4L-dutch-english-cased) | [t5-base-36L-dutch-english-cased](https://huggingface.co/yhavinga/t5-base-36L-dutch-english-cased) | [t5-eff-xl-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-xl-8l-dutch-english-cased) | [t5-eff-large-8l-dutch-english-cased](https://huggingface.co/yhavinga/t5-eff-large-8l-dutch-english-cased) | -|:------------------|:----------------|:-----------------------------|:---------------------------|:----------------------------|:-----------------------------------|:----------------------------------------|:-----------------------------|:-------------------------------|:----------------------------------|:-----------------------------------|:--------------------------------------| -| *type* | t5 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5-v1.1 | t5 eff | t5 eff | t5 eff | t5 eff | t5 eff | -| *d_model* | 768 | 768 | 768 | 1024 | 768 | 768 | 512 | 2048 | 768 | 1024 | 1024 | -| *d_ff* | 3072 | 2048 | 2048 | 2816 | 2048 | 2048 | 1920 | 5120 | 2560 | 16384 | 4096 | -| *num_heads* | 12 | 12 | 12 | 16 | 12 | 12 | 8 | 32 | 12 | 32 | 16 | -| *d_kv* | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 128 | 64 | -| *num_layers* | 12 | 12 | 12 | 24 | 12 | 12 | 24 | 4 | 36 | 8 | 8 | -| *num parameters* | 223M | 248M | 248M | 783M | 248M | 248M | 250M | 585M | 729M | 1241M | 335M | -| *feed_forward_proj* | relu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | gated-gelu | -| *dropout* | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | -| *dataset* | mc4_nl_cleaned | mc4_nl_cleaned full | mc4_nl_cleaned full | mc4_nl_cleaned | mc4_nl_cleaned small_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | mc4_nl_cleaned large_en_nl | -| *tr. seq len* | 512 | 1024 | 1024 | 512 | 512 | 1024 | 512 | 512 | 512 | 512 | 512 | -| *batch size* | 128 | 64 | 64 | 64 | 128 | 64 | 128 | 512 | 512 | 64 | 128 | -| *total steps* | 527500 | 1014525 | 1210154 | 1120k/2427498 | 2839630 | 1520k/3397024 | 851852 | 212963 | 212963 | 538k/1703705 | 851850 | -| *epochs* | 1 | 2 | 2 | 2 | 10 | 4 | 1 | 1 | 1 | 1 | 1 | -| *duration* | 2d9h | 5d5h | 6d6h | 8d13h | 11d18h | 9d1h | 4d10h | 6d1h | 17d15h | 4d 19h | 3d 23h | -| *optimizer* | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | adafactor | -| *lr* | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.009 | 0.005 | 0.005 | -| *warmup* | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 5000.0 | 20000.0 | 2500.0 | 1000.0 | 1500.0 | 1500.0 | -| *eval loss* | 1,38 | 1,20 | 0,96 | 1,07 | 1,11 | 1,13 | 1,18 | 1,27 | 1,05 | 1,3019 | 1,15 | -| *eval acc* | 0,70 | 0,73 | 0,78 | 0,76 | 0,75 | 0,74 | 0,74 | 0,72 | 0,76 | 0,71 | 0,74 | - -### Fine-tuned translation models on ccmatrix - -The models `t5-small-24L-dutch-english` and `t5-base-36L-dutch-english` have been fine-tuned for both language -directions on the first 25M samples from CCMatrix, giving a total of 50M training samples. -Evaluation is performed on out-of-sample CCMatrix and also on Tatoeba and Opus Books. -The `_bp` columns list the *brevity penalty* (the low score of the 128 seq len models on opus books may be because of the brevity penalty; -books tend to have longer sentences than 128 tokens). The `avg_bleu` score is the bleu score -averaged over all three evaluation datasets. The best scores displayed in bold for both translation directions. - - -| | [t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi) | [t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi) | [t5-small-24L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-small-24L-ccmatrix-multi) | [t5-small-24L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-small-24L-ccmatrix-multi) | -|:-----------------------|:-----------------------------|:-----------------------------|:------------------------------|:------------------------------| -| *source_lang* | en | nl | en | nl | -| *target_lang* | nl | en | nl | en | -| *source_prefix* | translate English to Dutch: | translate Dutch to English: | translate English to Dutch: | translate Dutch to English: | -| *ccmatrix_bleu* | **56.8** | 62.8 | 57.4 | **63.1** | -| *tatoeba_bleu* | **46.6** | **52.8** | 46.4 | 51.7 | -| *opus_books_bleu* | **13.5** | **24.9** | 12.9 | 23.4 | -| *ccmatrix_bp* | 0.95 | 0.96 | 0.95 | 0.96 | -| *tatoeba_bp* | 0.97 | 0.94 | 0.98 | 0.94 | -| *opus_books_bp* | 0.8 | 0.94 | 0.77 | 0.89 | -| *avg_bleu* | **38.96** | **46.86** | 38.92 | 46.06 | -| *max_source_length* | 128 | 128 | 128 | 128 | -| *max_target_length* | 128 | 128 | 128 | 128 | -| *adam_beta1* | 0.9 | 0.9 | 0.9 | 0.9 | -| *adam_beta2* | 0.997 | 0.997 | 0.997 | 0.997 | -| *weight_decay* | 0.05 | 0.05 | 0.002 | 0.002 | -| *lr* | 5e-05 | 5e-05 | 0.0005 | 0.0005 | -| *label_smoothing_factor* | 0.15 | 0.15 | 0.1 | 0.1 | -| *train_batch_size* | 128 | 128 | 128 | 128 | -| *warmup_steps* | 2000 | 2000 | 2000 | 2000 | -| *total steps* | 390625 | 390625 | 390625 | 390625 | -| *duration* | 4d 5h | 4d 5h | 3d 2h | 3d 2h | -| *num parameters* | 729M | 729M | 250M | 250M | - - -## Acknowledgements - -This project was made possible by the exceptional computing resources provided by Google's -[TPU Research Cloud](https://sites.research.google/trc/). -The HuggingFace 🤗 ecosystem of datasets, hub, model architectures -and example scripts were an integral part of the training process, while Weights & Biases provided the ability -to track multiple training sessions and execute hyperparameter optimization with insightful visualizations. -I am grateful to the [https://huggingface.co/Finnish-NLP](Finnish-NLP) authors for their generosity in releasing the UL2 objective code and task -definitions, and to [Stephenn Fernandes](https://huggingface.co/StephennFernandes) for his support in getting me started with the T5X framework. -Lastly, I want to express my gratitude to Google for their openness and generosity in releasing T5X and related repositories. - -Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/). -Some of the sentences were reworded by ChatGPT. -""" - ) - - st.write( - f""" - --- - *Memory: {memory.total / 10**9:.2f}GB, used: {memory.percent}%, available: {memory.available / 10**9:.2f}GB* - """ - ) - - -if __name__ == "__main__": - memory = psutil.virtual_memory() - main() diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/deformable_detr/cpu/ms_deform_attn_cpu.h b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/deformable_detr/cpu/ms_deform_attn_cpu.h deleted file mode 100644 index 7eac8c8bcd1bf529bb9c13d54d2d4215c9e4c89f..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/kernels/deformable_detr/cpu/ms_deform_attn_cpu.h +++ /dev/null @@ -1,32 +0,0 @@ -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once -#include - -at::Tensor -ms_deform_attn_cpu_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step); - -std::vector -ms_deform_attn_cpu_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step); - diff --git a/spaces/yjmqaq/Iloveyou/README.md b/spaces/yjmqaq/Iloveyou/README.md deleted file mode 100644 index beb7fa90120191bb52fdf5362042f7ae353a2043..0000000000000000000000000000000000000000 --- a/spaces/yjmqaq/Iloveyou/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Iloveyou -emoji: 😻 -colorFrom: gray -colorTo: yellow -sdk: docker -pinned: false -license: mit -app_port: 8080 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/train.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/train.py deleted file mode 100644 index dba77bbb563d2ea12ced5424d4fe9088f9c84a42..0000000000000000000000000000000000000000 --- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/train.py +++ /dev/null @@ -1,331 +0,0 @@ -import logging -import multiprocessing -import time - -logging.getLogger('matplotlib').setLevel(logging.WARNING) -logging.getLogger('numba').setLevel(logging.WARNING) - -import os -import json -import argparse -import itertools -import math -import torch -from torch import nn, optim -from torch.nn import functional as F -from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -import torch.multiprocessing as mp -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.cuda.amp import autocast, GradScaler - -import modules.commons as commons -import utils -from data_utils import TextAudioSpeakerLoader, TextAudioCollate -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, -) -from modules.losses import ( - kl_loss, - generator_loss, discriminator_loss, feature_loss -) - -from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch - -torch.backends.cudnn.benchmark = True -global_step = 0 -start_time = time.time() - -# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO' - - -def main(): - """Assume Single Node Multi GPUs Training Only""" - assert torch.cuda.is_available(), "CPU training is not allowed." - hps = utils.get_hparams() - - n_gpus = torch.cuda.device_count() - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = hps.train.port - - mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) - - -def run(rank, n_gpus, hps): - global global_step - if rank == 0: - logger = utils.get_logger(hps.model_dir) - logger.info(hps) - utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) - - # for pytorch on win, backend use gloo - dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank) - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - collate_fn = TextAudioCollate() - all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training. - train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem) - num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count() - if all_in_mem: - num_workers = 0 - train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True, - batch_size=hps.train.batch_size, collate_fn=collate_fn) - if rank == 0: - eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem,vol_aug = False) - eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False, - batch_size=1, pin_memory=False, - drop_last=False, collate_fn=collate_fn) - - net_g = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model).cuda(rank) - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - net_g.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True) - net_d = DDP(net_d, device_ids=[rank]) - - skip_optimizer = False - try: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, - optim_g, skip_optimizer) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, - optim_d, skip_optimizer) - epoch_str = max(epoch_str, 1) - name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth") - global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1 - #global_step = (epoch_str - 1) * len(train_loader) - except: - print("load old checkpoint failed...") - epoch_str = 1 - global_step = 0 - if skip_optimizer: - epoch_str = 1 - global_step = 0 - - warmup_epoch = hps.train.warmup_epochs - scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - - scaler = GradScaler(enabled=hps.train.fp16_run) - - for epoch in range(epoch_str, hps.train.epochs + 1): - # set up warm-up learning rate - if epoch <= warmup_epoch: - for param_group in optim_g.param_groups: - param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch - for param_group in optim_d.param_groups: - param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch - # training - if rank == 0: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, - [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, - [train_loader, None], None, None) - # update learning rate - scheduler_g.step() - scheduler_d.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d = nets - optim_g, optim_d = optims - scheduler_g, scheduler_d = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - # train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - for batch_idx, items in enumerate(train_loader): - c, f0, spec, y, spk, lengths, uv,volume = items - g = spk.cuda(rank, non_blocking=True) - spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True) - c = c.cuda(rank, non_blocking=True) - f0 = f0.cuda(rank, non_blocking=True) - uv = uv.cuda(rank, non_blocking=True) - lengths = lengths.cuda(rank, non_blocking=True) - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - - with autocast(enabled=hps.train.fp16_run): - y_hat, ids_slice, z_mask, \ - (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths, - spec_lengths=lengths,vol = volume) - - y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) - loss_disc_all = loss_disc - - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - with autocast(enabled=False): - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_lf0 = F.mse_loss(pred_lf0, lf0) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0 - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank == 0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]['lr'] - losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl] - reference_loss=0 - for i in losses: - reference_loss += i - logger.info('Train Epoch: {} [{:.0f}%]'.format( - epoch, - 100. * batch_idx / len(train_loader))) - logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}") - - scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, - "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} - scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl, - "loss/g/lf0": loss_lf0}) - - # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) - # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) - # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - image_dict = { - "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), - "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), - "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), - "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), - pred_lf0[0, 0, :].detach().cpu().numpy()), - "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(), - norm_lf0[0, 0, :].detach().cpu().numpy()) - } - - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict - ) - - if global_step % hps.train.eval_interval == 0: - evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) - utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) - keep_ckpts = getattr(hps.train, 'keep_ckpts', 0) - if keep_ckpts > 0: - utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) - - global_step += 1 - - if rank == 0: - global start_time - now = time.time() - durtaion = format(now - start_time, '.2f') - logger.info(f'====> Epoch: {epoch}, cost {durtaion} s') - start_time = now - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - image_dict = {} - audio_dict = {} - with torch.no_grad(): - for batch_idx, items in enumerate(eval_loader): - c, f0, spec, y, spk, _, uv,volume = items - g = spk[:1].cuda(0) - spec, y = spec[:1].cuda(0), y[:1].cuda(0) - c = c[:1].cuda(0) - f0 = f0[:1].cuda(0) - uv= uv[:1].cuda(0) - if volume!=None: - volume = volume[:1].cuda(0) - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_hat,_ = generator.module.infer(c, f0, uv, g=g,vol = volume) - - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1).float(), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - - audio_dict.update({ - f"gen/audio_{batch_idx}": y_hat[0], - f"gt/audio_{batch_idx}": y[0] - }) - image_dict.update({ - f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()), - "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()) - }) - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate - ) - generator.train() - - -if __name__ == "__main__": - main() diff --git a/spaces/zhang-wei-jian/docker/node_modules/undefsafe/example.js b/spaces/zhang-wei-jian/docker/node_modules/undefsafe/example.js deleted file mode 100644 index ed93c23bbf1ac7ef3b17595d2b51e313e8e6fc53..0000000000000000000000000000000000000000 --- a/spaces/zhang-wei-jian/docker/node_modules/undefsafe/example.js +++ /dev/null @@ -1,14 +0,0 @@ -var undefsafe = require('undefsafe'); - -var object = { - a: { - b: { - c: 1, - d: [1, 2, 3], - e: 'remy' - } - } -}; - -console.log(undefsafe(object, 'a.b.e')); // "remy" -console.log(undefsafe(object, 'a.b.not.found')); // undefined diff --git a/spaces/ziguo/Real-ESRGAN/scripts/generate_multiscale_DF2K.py b/spaces/ziguo/Real-ESRGAN/scripts/generate_multiscale_DF2K.py deleted file mode 100644 index d4f5d8324b1624e4cb6163754703b8dac2d188fd..0000000000000000000000000000000000000000 --- a/spaces/ziguo/Real-ESRGAN/scripts/generate_multiscale_DF2K.py +++ /dev/null @@ -1,48 +0,0 @@ -import argparse -import glob -import os -from PIL import Image - - -def main(args): - # For DF2K, we consider the following three scales, - # and the smallest image whose shortest edge is 400 - scale_list = [0.75, 0.5, 1 / 3] - shortest_edge = 400 - - path_list = sorted(glob.glob(os.path.join(args.input, '*'))) - for path in path_list: - print(path) - basename = os.path.splitext(os.path.basename(path))[0] - - img = Image.open(path) - width, height = img.size - for idx, scale in enumerate(scale_list): - print(f'\t{scale:.2f}') - rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS) - rlt.save(os.path.join(args.output, f'{basename}T{idx}.png')) - - # save the smallest image which the shortest edge is 400 - if width < height: - ratio = height / width - width = shortest_edge - height = int(width * ratio) - else: - ratio = width / height - height = shortest_edge - width = int(height * ratio) - rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS) - rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png')) - - -if __name__ == '__main__': - """Generate multi-scale versions for GT images with LANCZOS resampling. - It is now used for DF2K dataset (DIV2K + Flickr 2K) - """ - parser = argparse.ArgumentParser() - parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder') - parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder') - args = parser.parse_args() - - os.makedirs(args.output, exist_ok=True) - main(args) diff --git a/spaces/zjxchina/vits_seki/README.md b/spaces/zjxchina/vits_seki/README.md deleted file mode 100644 index 03c3fb0ba517c34ccdedabc06cb451aff05c07e3..0000000000000000000000000000000000000000 --- a/spaces/zjxchina/vits_seki/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Vits Seki -emoji: 📈 -colorFrom: gray -colorTo: yellow -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/zlc99/M4Singer/usr/diff/diffusion.py b/spaces/zlc99/M4Singer/usr/diff/diffusion.py deleted file mode 100644 index c30976ab258feff830c2fa1a2d70876cb1d76eda..0000000000000000000000000000000000000000 --- a/spaces/zlc99/M4Singer/usr/diff/diffusion.py +++ /dev/null @@ -1,334 +0,0 @@ -import math -import random -from functools import partial -from inspect import isfunction -from pathlib import Path -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from tqdm import tqdm -from einops import rearrange - -from modules.fastspeech.fs2 import FastSpeech2 -from modules.diffsinger_midi.fs2 import FastSpeech2MIDI -from utils.hparams import hparams - - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def cycle(dl): - while True: - for data in dl: - yield data - - -def num_to_groups(num, divisor): - groups = num // divisor - remainder = num % divisor - arr = [divisor] * groups - if remainder > 0: - arr.append(remainder) - return arr - - -class Residual(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x, *args, **kwargs): - return self.fn(x, *args, **kwargs) + x - - -class SinusoidalPosEmb(nn.Module): - def __init__(self, dim): - super().__init__() - self.dim = dim - - def forward(self, x): - device = x.device - half_dim = self.dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, device=device) * -emb) - emb = x[:, None] * emb[None, :] - emb = torch.cat((emb.sin(), emb.cos()), dim=-1) - return emb - - -class Mish(nn.Module): - def forward(self, x): - return x * torch.tanh(F.softplus(x)) - - -class Upsample(nn.Module): - def __init__(self, dim): - super().__init__() - self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1) - - def forward(self, x): - return self.conv(x) - - -class Downsample(nn.Module): - def __init__(self, dim): - super().__init__() - self.conv = nn.Conv2d(dim, dim, 3, 2, 1) - - def forward(self, x): - return self.conv(x) - - -class Rezero(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - self.g = nn.Parameter(torch.zeros(1)) - - def forward(self, x): - return self.fn(x) * self.g - - -# building block modules - -class Block(nn.Module): - def __init__(self, dim, dim_out, groups=8): - super().__init__() - self.block = nn.Sequential( - nn.Conv2d(dim, dim_out, 3, padding=1), - nn.GroupNorm(groups, dim_out), - Mish() - ) - - def forward(self, x): - return self.block(x) - - -class ResnetBlock(nn.Module): - def __init__(self, dim, dim_out, *, time_emb_dim, groups=8): - super().__init__() - self.mlp = nn.Sequential( - Mish(), - nn.Linear(time_emb_dim, dim_out) - ) - - self.block1 = Block(dim, dim_out) - self.block2 = Block(dim_out, dim_out) - self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity() - - def forward(self, x, time_emb): - h = self.block1(x) - h += self.mlp(time_emb)[:, :, None, None] - h = self.block2(h) - return h + self.res_conv(x) - - -class LinearAttention(nn.Module): - def __init__(self, dim, heads=4, dim_head=32): - super().__init__() - self.heads = heads - hidden_dim = dim_head * heads - self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) - self.to_out = nn.Conv2d(hidden_dim, dim, 1) - - def forward(self, x): - b, c, h, w = x.shape - qkv = self.to_qkv(x) - q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads=self.heads, qkv=3) - k = k.softmax(dim=-1) - context = torch.einsum('bhdn,bhen->bhde', k, v) - out = torch.einsum('bhde,bhdn->bhen', context, q) - out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) - return self.to_out(out) - - -# gaussian diffusion trainer class - -def extract(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() - - -def cosine_beta_schedule(timesteps, s=0.008): - """ - cosine schedule - as proposed in https://openreview.net/forum?id=-NEXDKk8gZ - """ - steps = timesteps + 1 - x = np.linspace(0, steps, steps) - alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 - alphas_cumprod = alphas_cumprod / alphas_cumprod[0] - betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) - return np.clip(betas, a_min=0, a_max=0.999) - - -class GaussianDiffusion(nn.Module): - def __init__(self, phone_encoder, out_dims, denoise_fn, - timesteps=1000, loss_type='l1', betas=None, spec_min=None, spec_max=None): - super().__init__() - self.denoise_fn = denoise_fn - if hparams.get('use_midi') is not None and hparams['use_midi']: - self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) - else: - self.fs2 = FastSpeech2(phone_encoder, out_dims) - self.fs2.decoder = None - self.mel_bins = out_dims - - if exists(betas): - betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas - else: - betas = cosine_beta_schedule(timesteps) - - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.loss_type = loss_type - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(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))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - # calculations for posterior q(x_{t-1} | x_t, x_0) - posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) - # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) - self.register_buffer('posterior_variance', to_torch(posterior_variance)) - # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain - self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) - self.register_buffer('posterior_mean_coef1', to_torch( - betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) - self.register_buffer('posterior_mean_coef2', to_torch( - (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) - - self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) - self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) - - def q_mean_variance(self, x_start, t): - mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start - variance = extract(1. - self.alphas_cumprod, t, x_start.shape) - log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def predict_start_from_noise(self, x_t, t, noise): - return ( - extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise - ) - - def q_posterior(self, x_start, x_t, t): - posterior_mean = ( - extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + - extract(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = extract(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, x, t, cond, clip_denoised: bool): - noise_pred = self.denoise_fn(x, t, cond=cond) - x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) - - if clip_denoised: - x_recon.clamp_(-1., 1.) - - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): - b, *_, device = *x.shape, x.device - model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) - noise = noise_like(x.shape, device, repeat_noise) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return ( - extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise - ) - - def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - x_recon = self.denoise_fn(x_noisy, t, cond) - - if self.loss_type == 'l1': - if nonpadding is not None: - loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() - else: - # print('are you sure w/o nonpadding?') - loss = (noise - x_recon).abs().mean() - - elif self.loss_type == 'l2': - loss = F.mse_loss(noise, x_recon) - else: - raise NotImplementedError() - - return loss - - def forward(self, txt_tokens, mel2ph=None, spk_embed=None, - ref_mels=None, f0=None, uv=None, energy=None, infer=False): - b, *_, device = *txt_tokens.shape, txt_tokens.device - ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, - skip_decoder=True, infer=infer) - cond = ret['decoder_inp'].transpose(1, 2) - if not infer: - t = torch.randint(0, self.num_timesteps, (b,), device=device).long() - x = ref_mels - x = self.norm_spec(x) - x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] - nonpadding = (mel2ph != 0).float() - ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) - else: - t = self.num_timesteps - shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) - x = torch.randn(shape, device=device) - for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): - x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) - x = x[:, 0].transpose(1, 2) - ret['mel_out'] = self.denorm_spec(x) - - return ret - - def norm_spec(self, x): - return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 - - def denorm_spec(self, x): - return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min - - def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): - return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) - - def out2mel(self, x): - return x