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spaces/1acneusushi/gradio-2dmoleculeeditor/data/8bf Download BEST Full.md

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-<h1>8bf Download Full: How to Enhance Your Image Editing with Free Plugins</h1>
-<p>If you are an avid user of Photoshop or other image editing software, you may have heard of <strong>8bf files</strong>. These are files that contain <strong>Photoshop filter plug-ins</strong>, which are extensions that add extra functionality, such as new image filters, to Photoshop and compatible programs. These plug-ins can help you customize your Photoshop experience and create stunning images with ease.</p>
-<p>In this article, we will show you <strong>how to download and install 8bf plugins</strong> from reliable sources, and <strong>how to use them</strong> in your image editing projects. We will also introduce you to some of the <strong>best 8bf plugins</strong> that you can get for free, and how they can enhance your creative and professional image editing. Whether you are a beginner or an expert, you will find something useful and interesting in this article.</p>
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- <h2>How to Download and Install 8bf Plugins</h2>
-<p>The first step to using 8bf plugins is to <strong>download them</strong> from the internet. There are many websites that offer free or paid plugins for Photoshop and other image editing software, but not all of them are trustworthy or compatible. You need to be careful when choosing where to download your plugins from, and make sure they are safe and suitable for your software version.</p>
-<p>One of the best places to get free Photoshop plug-ins is <strong>Adobe's own website</strong>. You can sort the hundreds of free resources by rating, popularity, or date added, and find what you need easily. These plug-ins are installed differently than the others on this list. You must have a free Adobe account and the Creative Cloud program installed to use them.</p>
-<p>Another good source of free Photoshop filters and plug-ins is <strong>Lifewire</strong>, which has compiled a list of five best sites for free Photoshop filters and plug-ins. You can find links to these sites on their page, along with directions on how to install them.</p>
-<p>Once you have downloaded your desired plugin, you need to <strong>install it</strong> on your computer. The installation process may vary depending on the file format and the software you are using, but here are some general steps that you can follow:</p>
-<ul>
-<li>If the plugin is downloaded in <code>.8bf</code> format, you need to copy and paste the file into Photoshop's filter folder. On Windows, it's usually here: <code>C:\Program Files\Adobe\Adobe Photoshop (version)\Plug-ins\Filters\</code>. However, if putting the filter in that folder doesn't work, try this one: <code>C:\Program Files\Common Files\Adobe\Plug-Ins\CC</code>.</li>
-<li>If the plugin is downloaded in <code>.zxp </code> format, you need to use a program called <strong>Adobe Extension Manager</strong> or <strong>ZXPInstaller</strong> to install it. You can download these programs for free from their official websites . Once you have them, you can drag and drop the zxp file into the program and follow the instructions.</li>
-<li>If the plugin is downloaded in <code>.exe</code> format, you need to run the file and follow the installation wizard. Make sure you choose the correct destination folder for your Photoshop version.</li>
-</ul>
-<p>After you have installed your plugin, you need to <strong>access it</strong> from your image editing software. In Photoshop, you can usually find your plugins under the <strong>window menu</strong>, under <strong>extensions</strong> or <strong>filters</strong>. You can also use the <strong>search bar</strong> at the top of Photoshop to find your plugin by name. Once you have opened your plugin, you can use it as instructed by the developer.</p>
- <h2>Best 8bf Plugins for Creative and Professional Image Editing</h2>
-<p>Now that you know how to download and install 8bf plugins, you may be wondering which ones are worth trying. There are thousands of plugins available online, but not all of them are equally useful or high-quality. To help you narrow down your choices, we have selected some of the <strong>best 8bf plugins</strong> that you can get for free, and how they can enhance your creative and professional image editing.</p>
-<p></p>
- <h3>Adobe's Free Photoshop Plug-ins</h3>
-<p>If you want to get the most out of your Photoshop experience, you should definitely check out Adobe's own collection of free plug-ins. These plug-ins are designed by Adobe experts and offer a huge variety of features and effects that can improve your workflow and creativity. Some of the most popular and useful plug-ins are:</p>
-<ul>
-<li><strong>Kuler Panel</strong>: This plug-in allows you to create, explore, and share color themes that you can use in your projects. You can browse thousands of color combinations created by other users, or create your own using various color rules and modes. You can also sync your themes with other Adobe products, such as Illustrator or InDesign.</li>
-<li><strong>Lens Profile Creator</strong>: This plug-in helps you correct lens distortions, such as barrel or pincushion distortion, vignetting, or chromatic aberration. You can create custom profiles for your lenses based on calibration images, or use predefined profiles for common lenses. You can also share your profiles with other users or download profiles created by others.</li>
-<li><strong>Perspective Warp</strong>: This plug-in allows you to adjust the perspective of your images without distorting them. You can create multiple planes in your image and manipulate them independently, or merge them into a single plane. You can also change the viewpoint of your image, such as changing from a bird's eye view to a worm's eye view.</li>
-<li><strong>Pixlr-o-matic</strong>: This plug-in lets you add retro effects to your images with just a few clicks. You can choose from hundreds of filters, overlays, and borders to create vintage-looking photos. You can also mix and match different effects to create your own unique style.</li>
-<li><strong>Social Kit Pro</strong>: This plug-in helps you create professional-looking social media graphics, such as cover photos, profile pictures, or ads. You can choose from various templates that match the dimensions and guidelines of different social platforms, such as Facebook, Twitter, or YouTube. You can also customize your graphics with text, shapes, images, or logos.</li>
-</ul>
- <h3>Mehdi's Free Photoshop Filters</h3>
-<p>If you are looking for some simple but powerful filters that can transform your images in amazing ways, you should try Mehdi's free Photoshop filters. These filters are created by Mehdi Rabah, a French developer who has been making Photoshop plugins since 2002. His website offers dozens of filters with detailed explanations and examples of what they do. Some of his most popular and useful filters are:</p>
-<ul>
-<li><strong>Kaleidoscope 2.1</strong>: This filter allows you to create kaleidoscopic patterns from any image. You can adjust the number of segments, the angle, the zoom, and the offset of the pattern. You can also apply different blending modes and colors to create stunning effects.</li>
-<li><strong>Weaver 2.0</strong>: This filter allows you to create realistic woven textures from any image. You can adjust the size, the shape, the color, and the opacity of the threads. You can also apply different effects, such as embossing or shadowing, to make the texture more 3D.</li>
-<li><strong>Seamless Border 2.0</strong>: This filter allows you to create seamless borders from any image. You can adjust the width, the height, the angle, and the offset of the border. You can also apply different effects, such as mirroring, flipping, or rotating, to make the border more interesting.</li>
-<li><strong>Sorting Tiles 1.1</strong>: This filter allows you to create mosaic-like effects from any image. You can adjust the size, the shape, the color, and the order of the tiles. You can also apply different effects, such as blurring, sharpening, or inverting, to make the tiles more varied.</li>
-<li><strong>Wavy Lab 1.1</strong>: This filter allows you to create wavy patterns from any image. You can adjust the frequency, the amplitude, the phase, and the direction of the waves. You can also apply different effects, such as colorization, gradient, or transparency, to make the waves more colorful.</li>
-</ul>
- <h3>The Plugin Site's Free Photoshop Filters</h3>
-<p>If you want to get a lot of filters for a single download, you should check out The Plugin Site's free Photoshop filters. These filters are created by Harald Heim, a German developer who has been making Photoshop plugins since 1997. His website offers a single download that contains 70 image effects that can be applied to any image. Some of his most popular and useful filters are:</p>
-<ul>
-<li><strong>Color MegaMix 1.1</strong>: This filter allows you to modify the colors of your image in various ways. You can choose from 20 color modes and adjust the intensity and contrast of each mode. You can also mix different modes together to create new color effects.</li>
-<li><strong>Contrast Mask 1.0</strong>: This filter allows you to enhance the contrast and details of your image without losing quality. You can adjust the strength and radius of the contrast mask and apply it to different tonal ranges of your image.</li>
-<li><strong>Edge Detector 1.0</strong>: This filter allows you to detect and highlight the edges of your image in various ways. You can choose from 10 edge modes and adjust the threshold and smoothness of each mode. You can also invert or colorize the edges to create different effects.</li>
-<li><strong>Old Movie 1.0</strong>: This filter allows you to simulate the look of old movies on your image. You can adjust the amount and size of scratches, dust, hair, jitter, flicker, and noise on your image. You can also change the color and brightness of your image to make it look more aged.</li>
-<li><strong>Posterizer 1.0</strong>: This filter allows you to reduce the number of colors in your image and create poster-like effects. You can adjust the number of colors and levels for each color channel and apply dithering or smoothing to your image.</li>
-</ul>
- <h3>Lokas Software's Free 3D Shadow Filter</h3>
-<p>If you want to add realistic shadows to your images, you should try Lokas Software's free 3D Shadow filter. This filter is created by Lokas Software, a Russian company that specializes in graphics software development since 1997. Their website offers a free filter that can create various types of shadows from any image or text layer. Some of the features of this filter are:</p>
-<ul>
-<li><strong>Shadow Type</strong>: You can choose from four types of shadows: drop shadow, perspective shadow, inner shadow, or reflection shadow.</li>
-<li><strong>Shadow Position</strong>: You can adjust the angle, distance, scale, and perspective of your shadow.</li>
-<li><strong>Shadow Color</strong>: You can choose any color for your shadow or use a gradient or a texture.</li>
-<li><strong>Shadow Quality</strong>: You can adjust the opacity, blur, noise, and softness of your shadow.</li>
-<li><strong>Shadow Effects</strong>: You can apply various effects to your shadow, such as glow, bevel, emboss, or contour.</li>
-</ul>
- <h3>Flaticon</h3>
-<p>If you need icons for your projects, you should check out Flaticon. Flaticon is a website that offers a large collection of free icons in various formats: PNG, SVG, EPS, PSD, or BASE 64. You can browse thousands of icons by category or keyword or use their online editor to customize them. Some of the benefits of using Flaticon are:</p>
-<ul>
-<li><strong>Variety</strong>: You can find icons for any topic or theme you need: business, education, health, technology, etc. You can also find icons in different styles: flat, outline, 3D, hand-drawn, etc.</li>
-<li><strong>Quality</strong>: You can download icons in high resolution and vector format, which means they can be scaled and edited without losing quality. You can also use their online editor to change the color, size, orientation, or shape of the icons.</li>
-<li><strong>Compatibility</strong>: You can use Flaticon's icons in any software or platform that supports images. You can also use their Photoshop plugin to access and insert icons directly from Photoshop's window menu.</li>
-<li><strong>License</strong>: You can use Flaticon's icons for free for personal and commercial projects, as long as you credit the author and Flaticon. You can also get a premium subscription to access more icons and features without attribution.</li>
-</ul>
- <h3>Ink</h3>
-<p>If you are a designer who works with developers, you should try Ink. Ink is a Photoshop plugin that helps you create comprehensive design specifications for your projects. You can use Ink to generate useful information about your layers, such as dimensions, typography, colors, effects, etc. You can also export your design specifications as a PNG file or a HTML document. Some of the advantages of using Ink are:</p>
-<ul>
-<li><strong>Accuracy</strong>: You can ensure that your design is implemented exactly as you intended by providing precise and detailed information about your layers. You can also avoid misunderstandings and errors by communicating clearly with your developers.</li>
-<li><strong>Efficiency</strong>: You can save time and effort by generating design specifications automatically with Ink. You don't have to manually measure, label, or document your layers. You can also update your specifications easily if you make any changes to your design.</li>
-<li><strong>Convenience</strong>: You can access Ink from Photoshop's window menu or by using a keyboard shortcut. You can also customize Ink's settings to suit your preferences and needs. You can choose which information to include or exclude, how to format it, and how to export it.</li>
-<li><strong>Compatibility</strong>: You can use Ink with any version of Photoshop from CS6 to CC 2021. You can also use Ink with any language or operating system that supports Photoshop.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, 8bf plugins are files that contain Photoshop filter plug-ins, which are extensions that add extra functionality to Photoshop and compatible programs. These plug-ins can help you customize your Photoshop experience and create stunning images with ease.</p>
-<p>To use 8bf plugins, you need to download them from reliable sources, install them on your computer, and access them from your image editing software. In this article, we have shown you how to do that, and introduced you to some of the best 8bf plugins that you can get for free.</p>
-<p>We hope you have found this article useful and informative. If you want to learn more about 8bf plugins and how to use them in your projects, you can check out the following resources:</p>
-<ul>
-<li><a href="">Adobe's Free Photoshop Plug-ins</a></li>
-<li><a href="">Lifewire's List of Free Photoshop Filters and Plug-ins</a></li>
-<li><a href="">Mehdi's Free Photoshop Filters</a></li>
-<li><a href="">The Plugin Site's Free Photoshop Filters</a></li>
-<li><a href="">Lokas Software's Free 3D Shadow Filter</a></li>
-<li><a href="">Flaticon</a></li>
-<li><a href="">Ink</a></li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about 8bf plugins and their answers:</p>
- <h4>What is the difference between filters and plugins?</h4>
-<p>Filters are a type of plugin that apply specific effects or transformations to an image or a layer. Plugins are a broader term that includes filters as well as other extensions that add extra functionality to Photoshop or compatible programs.</p>
- <h4>How can I uninstall or disable a plugin that I don't need?</h4>
-<p>To uninstall a plugin, you need to delete the file from the folder where you installed it. To disable a plugin temporarily, you can rename the file extension from <code>.8bf</code> or <code>.zxp</code> to something else, such as <code>.bak</code>. To enable it again, you need to rename it back to its original extension.</p>
- <h4>Are there any risks or drawbacks of using 8bf plugins?</h4>
-<p>Using 8bf plugins is generally safe and beneficial, as long as you download them from reputable sources and install them correctly. However, there are some potential risks or drawbacks that you should be aware of, such as:</p>
-<ul>
-<li><strong>Compatibility issues</strong>: Some plugins may not work well with your software version or operating system. They may cause errors, crashes, or performance issues. To avoid this, you should always check the compatibility and requirements of the plugins before installing them.</li>
-<li><strong>Security issues</strong>: Some plugins may contain malware or viruses that can harm your computer or compromise your data. To avoid this, you should always scan the files with an antivirus program before installing them. You should also only download plugins from trusted sources and avoid clicking on suspicious links or pop-ups.</li>
-<li><strong>Quality issues</strong>: Some plugins may not be well-designed or well-maintained. They may have bugs, glitches, or limitations that can affect your image quality or user experience. To avoid this, you should always read the reviews and ratings of the plugins before installing them. You should also update your plugins regularly and report any problems to the developers.</li>
-</ul>
- <h4>How can I update or troubleshoot my plugins?</h4>
-<p>To update your plugins, you need to check the websites of the developers for any new versions or updates. You can also use programs like Adobe Extension Manager or ZXPInstaller to manage your plugins and check for updates. To troubleshoot your plugins, you need to identify the source of the problem and try some common solutions, such as:</p>
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-<li><strong>Restarting your software or computer</strong>: This can help resolve any temporary issues or conflicts that may cause your plugins to malfunction.</li>
-<li><strong>Reinstalling your plugins</strong>: This can help fix any corrupted or missing files that may prevent your plugins from working properly.</li>
-<li><strong>Disabling other plugins</strong>: This can help determine if there is any incompatibility or interference between your plugins that may cause errors or crashes.</li>
-<li><strong>Contacting the developers</strong>: This can help get support and guidance from the creators of the plugins. You can find their contact information on their websites or in the plugin documentation.</li>
-</ul>
- <h4>Where can I find more resources and tutorials on using 8bf plugins?</h4>
-<p>If you want to learn more about using 8bf plugins in your projects, you can find many resources and tutorials online. Some of the best ones are:</p>
-<ul>
-<li><a href="">Photoshop Essentials: How to Install Plugins in Photoshop</a></li>
-<li><a href="">Photoshop Tutorials: 50 Best Photoshop Plugins for 2021</a></li>
-<li><a href="">Creative Bloq: The Best Photoshop Plugins for Designers</a></li>
-<li><a href="">Envato Tuts+: How to Use Photoshop Plugins: A Beginner's Guide</a></li>
-<li><a href="">YouTube: Photoshop Plugin Tutorials Playlist</a></li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Edius 5 Free Download Full Version with Key 64 Bit What You Need to Know.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fonelab For Ios WORK Crack.md

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-<p>If you are a fan of racing games, you might have heard of Asphalt Nitro 2, a mobile game developed and published by Gameloft as part of the Asphalt series. But did you know that there is a modded version of the game that allows you to play it at 60 frames per second (FPS) and enjoy unlimited money and other features? In this article, we will review Asphalt Nitro 2 Mod APK 60 FPS, a modified version of the game that enhances your gaming experience. We will also tell you how to download and install it, and how to play it with some tips and tricks.</p>
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-<h3>A racing game for low-end devices</h3>
-<p>Asphalt Nitro 2 is an arcade racing game that was announced in 2021 and is currently available in beta for Android users. It is basically Asphalt but for low-end devices, as it offers so much excitement in a compact (50 MB) package. It is designed to run smoothly on a wide range of mobile devices, including phones with weaker hardware specs.</p>
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-<p>Asphalt Nitro 2 Mod APK 60 FPS is a modified version of the game that enhances your gaming experience by unlocking some features that are not available in the original version. For example, you can play the game at 60 FPS, which makes the graphics smoother and more realistic. You can also enjoy unlimited money, which means you can buy any car or upgrade you want without worrying about the cost. Moreover, you can access all the cars and tracks without having to complete any missions or challenges.</p>
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-<p>If you want to try Asphalt Nitro 2 Mod APK 60 FPS, you will need to follow these steps:</p>
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-<li>Go to this link  and download the mod APK file.</li>
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-<p>Asphalt Nitro 2 Mod APK 60 FPS offers four game modes: Career, Quick Race, Multiplayer, and Events. In Career mode, you can complete various missions and challenges to earn money and reputation. In Quick Race mode, you can choose any track and car and race against AI opponents. In Multiplayer mode, you can race against other players online and compete for rankings and rewards. In Events mode, you can participate in limited-time events and win exclusive prizes.</p>
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-<p>If you are a fan of shooting games, you might have heard of Doodle Army 2: Mini Militia, a popular multiplayer game that lets you battle with up to 12 players online or offline. The game offers various modes, weapons, maps, and customization options to make your gaming experience more fun and exciting.</p>
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-<p>Playing Mini Militia Hile APK 4.3. 4 is very similar to playing the original game, except that you have more features and advantages in the modded version. Here are some tips and tricks for playing Mini Militia Hile APK 4.3. 4:</p>
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-<li>Choose your mode wisely: You can play in different modes such as survival, deathmatch, team deathmatch, capture the flag, etc. Choose the mode that suits your skills and preferences.</li>
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-<p>Like any other modded version of a game, Mini Militia Hile APK 4.3. 4 has its own pros and cons. Here are some of them:</p>
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-<p>Mini Militia Hile APK 4.3. 4 is a modded version of Doodle Army 2: Mini Militia, a popular multiplayer shooting game that lets you battle with up to 12 players online or offline. The modded version gives you unlimited access to everything in the game, such as ammo, health, jetpack, pro pack, and more. With this modded version, you can dominate the battlefield and have more fun with your friends.</p>
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-<p>We hope you enjoyed reading this article and learned something new about Mini Militia Hile APK 4.3. 4. If you have any questions or feedback, feel free to leave a comment below. Thank you for your time and attention!</p>
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-<li>A: Yes, Mini Militia Hile APK 4.3. 4 is safe to download and use as long as you download it from a trusted source and scan it with an antivirus before installing it on your device.</li>
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-<p>Getting Over It with Bennett Foddy is a punishing climbing game that was released in 2017 by Bennett Foddy, an Australian game developer and professor of game design. The game is inspired by a 2002 B-Game classic called Sexy Hiking, which was created by Jazzuo. The game is also a homage to other games that are known for their difficulty and frustration, such as QWOP, Flappy Bird, and Dark Souls.</p>
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-<h3>The main features and challenges of the game</h3>
-<p>The game has a simple premise: you control a man named Diogenes who is stuck in a metal pot. You use your mouse to move a hammer that can hook onto objects and surfaces. Your goal is to climb up an enormous mountain that is filled with various obstacles, such as rocks, trees, furniture, pipes, barrels, and more. The game has no checkpoints or save points, so if you fall down, you have to start over from where you landed. The game also has no end, so you can keep climbing as long as you want.</p>
-<p>The game is designed to be hard and frustrating, as it requires precise mouse movements and timing. The physics of the game are also unpredictable and sometimes unfair, as you can slip, bounce, or fly off in unexpected directions. The game also features a voice-over commentary by Bennett Foddy himself, who will make philosophical observations, sarcastic remarks, or motivational quotes depending on your progress. Some players may find his voice soothing and helpful, while others may find it annoying and mocking.</p>
-<h3>The rewards and achievements of the game</h3>
-<p>The game does not have any explicit rewards or achievements for completing it, but it does offer some hidden surprises and secrets for those who manage to reach the top of the mountain. There is also a sense of satisfaction and accomplishment that comes from overcoming the challenges and difficulties of the game. The game also allows you to share your success or failure with other players through online leaderboards or chat rooms.</p>
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-<h2>Why download the latest mod APK?</h2>
-<h3>The benefits of using a modded version of the game</h3>
-<p>A modded version of the game is a modified version that has some changes or additions that are not present in the original version. A modded version can offer some benefits for players who want to have a different or better experience with the game. For example, a modded version can:</p>
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-<li>Unlock all the features and content of the game without paying any money</li>
-<li>Remove any ads or in-app <p>purchases that may interrupt or distract you from the game</li>
-<li>Add some extra features or options that can enhance the gameplay experience, such as custom skins, cheats, hacks, or mods</li>
-<li>Fix some bugs or errors that may affect the performance or stability of the game</li>
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-<h3>The mod features that enhance the gameplay experience</h3>
-<p>The latest mod APK for Getting Over It with Bennett Foddy has some amazing features that can make the game more enjoyable and fun. Some of these features are:</p>
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-<li>Skip level: You can skip any level that you find too hard or boring and go to the next one</li>
-<li>God mode: You can become invincible and immune to any harm or danger</li>
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-<h3>The compatibility and security of the mod APK</h3>
-<p>The latest mod APK for Getting Over It with Bennett Foddy is compatible with most Android devices that have Android 4.1 or higher. The mod APK file size is about 120 MB, so you need to have enough storage space on your device. The mod APK is also safe and secure to use, as it does not contain any viruses, malware, or spyware. The mod APK does not require any root access or special permissions to install or run.</p>
-<h2>How to download and install the latest mod APK?</h2>
-<h3>The steps to find and download the mod APK file</h3>
-<p>If you want to download and install the latest mod APK for Getting Over It with Bennett Foddy, you need to follow these simple steps:</p>
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-<p>After you have downloaded the mod APK file, you need to install and run it on your device. Here are the steps to do so:</p>
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-<li>Follow the instructions on the screen and agree to the terms and conditions to install the mod APK file.</li>
-<li>Wait for the installation process to finish and then launch the game from your app drawer or home screen.</li>
-<li>Enjoy playing Getting Over It with Bennett Foddy with all the mod features enabled.</li>
-</ol> <h3>How to enjoy the game and have fun with it</h3>
-<p>The final and most important aspect of playing Getting Over It with Bennett Foddy is to enjoy the game and have fun with it. The game is not meant to be a torture or a punishment, but a challenge and a reward. Here are some tips and tricks to help you with that:</p>
-<ul>
-<li>Appreciate the game's art and design, which are inspired by real-life locations, objects, and artworks. You can also admire the game's graphics and sound effects, which are realistic and immersive.</li>
-<li>Explore the game's world and discover its secrets and easter eggs. You can also try to find different paths or shortcuts that can lead you to new places or surprises.</li>
-<li>Express yourself and your creativity through the game. You can customize your pot and hammer with different skins or items that you can buy or unlock. You can also use the game as a platform to create your own art or content, such as videos, memes, or fan art.</li>
-<li>Connect with other players and the game's community. You can join online chat rooms or leaderboards to chat or compete with other players. You can also watch or follow other players' streams or videos to learn from them or support them.</li>
-<li>Challenge yourself and set your own goals or rules. You can try to beat the game in the fastest time possible, or in the most difficult way possible. You can also try to play the game with different settings or modes, such as inverted controls, no mouse, or blindfolded.</li>
-</ul>
-<h2>Conclusion and FAQs</h2>
-<p>In conclusion, Getting Over It with Bennett Foddy is a game that will make you experience a range of emotions and sensations, from anger and frustration to joy and satisfaction. It is a game that will challenge your patience, skill, and perseverance, but also reward you with a unique and memorable experience. If you want to play this game with some extra features and advantages, you can download and install the latest mod APK version that we have explained in this article. We hope that this article has helped you understand more about this game and how to play it better. Here are some FAQs that you may have:</p>
-<table>
-<tr><td><b>Q: How long does it take to beat the game?</b></td><td><b>A: It depends on your skill level and luck, but some players have reported beating the game in less than 10 minutes, while others have spent hours or days on it.</b></td></tr>
-<tr><td><b>Q: Is there a way to save or pause the game?</b></td><td><b>A: No, there is no way to save or pause the game. The game is meant to be played in one sitting, without any interruptions or distractions.</b></td></tr>
-<tr><td><b>Q: Is there a multiplayer mode in the game?</b></td><td><b>A: No, there is no multiplayer mode in the game. The game is meant to be played solo, without any help or interference from other players.</b></td></tr>
-<tr><td><b>Q: Is there a sequel or a spin-off of the game?</b></td><td><b>A: No, there is no sequel or a spin-off of the game. The game is meant to be a standalone project, without any plans for future updates or expansions.</b></td></tr>
-<tr><td><b>Q: Is there a way to contact Bennett Foddy or give him feedback?</b></td><td><b>A: Yes, you can contact Bennett Foddy through his website (https://www.foddy.net/) or his Twitter account (@bfod). You can also give him feedback through his email ([email protected]) or his Steam page (https://store.steampowered.com/app/240720/Getting_Over_It_with_Bennett_Foddy/).</b></td></tr>
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-<h1>Seven Deadly Sins Grand Cross APK Download: A Cinematic Anime Game for Mobile</h1>
- <p>If you are a fan of anime and manga, you might have heard of The Seven Deadly Sins, a popular series that follows the adventures of a group of legendary knights in a fantasy world. If you want to experience the story and battles of The Seven Deadly Sins on your mobile device, you should check out Seven Deadly Sins Grand Cross, a cinematic anime game that will immerse you in the world of Britannia. In this article, we will tell you what Seven Deadly Sins Grand Cross is, how to download its APK file, what are its main features, what are some tips and tricks for playing it, and what are some reviews of it.</p>
- <h2>What is Seven Deadly Sins Grand Cross?</h2>
- <p>Seven Deadly Sins Grand Cross is a mobile RPG based on the popular anime and manga series The Seven Deadly Sins. It is developed by Netmarble, a leading mobile game company, and is available on Android and iOS platforms. Here are some of the reasons why you should play Seven Deadly Sins Grand Cross:</p>
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- <h3>A mobile RPG based on the popular anime and manga series</h3>
- <p>Seven Deadly Sins Grand Cross lets you play as Meliodas, the leader of the Seven Deadly Sins, and his companions as they embark on an epic quest to save the kingdom from the tyranny of the Holy Knights. You will meet familiar characters from the series, such as Elizabeth, Ban, King, Diane, Gowther, Merlin, Escanor, Hawk, and many more. You will also encounter enemies and allies from different races, such as humans, fairies, giants, demons, goddesses, vampires, etc. You will be able to relive the memorable scenes and events from the anime and manga, such as the Boar Hat Tavern, the Forest of White Dreams, the Capital of the Dead, etc.</p>
- <h3>A game that recreates the original story and battles with high-quality 3D graphics and voice acting</h3>
- <p>Seven Deadly Sins Grand Cross is not just a simple adaptation of the series. It is a game that recreates the original story and battles with high-quality 3D graphics and voice acting. The game uses a cinematic approach to present the story, with cutscenes that feature stunning animations and dialogues. The game also uses a card-based combat system that allows you to use different skills and ultimate moves based on your character's abilities. The game also includes original voice dialogues from the voice actors of the anime series, such as Yuki Kaji, Sora Amamiya, Misaki Kuno, Aoi Yuki, Tatsuhisa Suzuki, Jun Fukuyama, Yuhei Takagi, Maaya Sakamoto, and Tomokazu Sugita. You will feel like you are watching the anime as you play the game.</p>
- <h3>A game that offers various features and content for fans and newcomers alike</h3>
- <p>Seven Deadly Sins Grand Cross is not just a game for fans of the series. It is also a game that offers various features and content for newcomers and casual players. You can explore the vast world of Britannia and interact with different characters and locations. You can also customize your own tavern and collect various items and costumes. You can also join a knighthood and cooperate with other players in guild wars and events. You can also enjoy mini-games, such as cooking, fishing, card battles, etc. There is always something new and exciting to do in Seven Deadly Sins Grand Cross.</p>
- <h2>How to download Seven Deadly Sins Grand Cross APK?</h2>
- <p>If you want to play Seven Deadly Sins Grand Cross on your mobile device, you will need to download its APK file. APK stands for Android Package Kit, which is a file format that contains all the elements needed to install an app on an Android device. Here are some of the ways you can download Seven Deadly Sins Grand Cross APK:</p>
- <h3>The official sources for Android and iOS devices</h3>
- <p>The easiest and safest way to download Seven Deadly Sins Grand Cross APK is to use the official sources for Android and iOS devices. You can simply go to the Google Play Store or the App Store and search for the game. Then, you can tap on the install button and wait for the download to finish. You will need about 4 GB of free space on your device to install the game. You will also need a stable internet connection to play the game online.</p>
- <h3>The alternative sources for Android devices</h3>
- <p>If you cannot access the official sources for some reason, or if you want to download an older version of the game, you can use alternative sources for Android devices. These are websites that offer APK files of various apps and games for free. However, you should be careful when using these sources, as some of them may contain malware or viruses that can harm your device or steal your personal information. You should only use trusted and reputable websites that have positive reviews and ratings from other users. Some examples of these websites are APKPure.com, APKMirror.com, and APKCombo.com. To download Seven Deadly Sins Grand Cross APK from these websites, you will need to follow these steps:</p>
- - Go to the website of your choice and search for Seven Deadly Sins Grand Cross. - Choose the version of the game that you want to download and tap on the download button. - Wait for the download to finish and locate the APK file on your device. - Before installing the APK file, you will 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. - Tap on the APK file and follow the instructions to install the game. - Enjoy playing Seven Deadly Sins Grand Cross on your device. <h3>The precautions and requirements for installing the APK file</h3>
- <p>Before installing Seven Deadly Sins Grand Cross APK on your device, you should take some precautions and meet some requirements to ensure a smooth and safe gaming experience. Here are some of them:</p>
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- - Make sure that your device meets the minimum system requirements for the game. According to the official website, you will need at least Android 4.4 or iOS 9.0, 2 GB of RAM, 4 GB of free space, and a compatible processor. - Make sure that your device has enough battery power or is plugged into a charger while installing the game. - Make sure that your device has a stable internet connection while downloading and installing the game. - Make sure that you have enough data or Wi-Fi bandwidth to download the game, as it is quite large in size. - Make sure that you have enough storage space on your device to install the game and its updates. - Make sure that you backup your data before installing the game, in case something goes wrong or you need to uninstall it later. - Make sure that you scan the APK file with an antivirus or security app before installing it, to check for any malware or viruses. - Make sure that you only install the game from trusted sources, as mentioned above. <h2>What are the main features of Seven Deadly Sins Grand Cross?</h2>
- <p>Seven Deadly Sins Grand Cross is a game that offers a lot of features and content for players to enjoy. Here are some of the main features of the game:</p>
- <h3>Dynamic combat with skill rank up system and ultimate moves</h3>
- <p>Seven Deadly Sins Grand Cross uses a card-based combat system that allows you to use different skills and ultimate moves based on your character's abilities. You can choose from four cards per turn, each with a different effect and cost. You can also combine cards of the same type to rank them up and increase their power and range. You can also use ultimate moves that are unique to each character and can deal massive damage to your enemies. The combat system is dynamic and strategic, as you have to consider the enemy's attributes, the card order, the card fusion, the card effects, etc.</p>
- <h3>Various PvE systems that reflect the original anime</h3>
- <p>Seven Deadly Sins Grand Cross offers various PvE systems that reflect the original anime and manga series. You can follow the main quest line that follows the story of The Seven Deadly Sins, or you can explore the side quests that feature different characters and events. You can also participate in special events that are based on the anime episodes, such as the Vaizel Fighting Festival, the Kingdom Infiltration Arc, etc. You can also challenge various bosses and enemies that appear in the series, such as the Demon Clan, the Ten Commandments, etc. You can also collect various rewards and items from completing these PvE systems.</p>
- <h3>Unique character appearances and costumes</h3>
- <p>Seven Deadly Sins Grand Cross features unique character appearances and costumes that are faithful to the original anime and manga series. You can collect and customize various characters from the series, each with their own skills, stats, and personalities. You can also unlock and equip different costumes for your characters, such as their original outfits, their casual outfits, their seasonal outfits, etc. You can also change their hairstyles, accessories, weapons, etc. You can also view your characters in 3D models and interact with them in various ways.</p>
- <h3>Thorough and authentic implementation of the original anime</h3>
- <p>Seven Deadly Sins Grand Cross is a game that is thorough and authentic in implementing the original anime and manga series. The game uses high-quality 3D graphics and voice acting to recreate the original story and battles of The Seven Deadly Sins. The game also includes original soundtracks and sound effects from the anime series, such as the opening and ending songs, the background music, the character voices, etc. The game also includes original scenes and dialogues from the anime series, such as the comedic moments, the emotional moments, the plot twists, etc. The game also includes original content and stories that are exclusive to the game, such as new characters, new events, new quests, etc.</p>
- <h3>Real-time PvP and guild content</h3>
- <p>Seven Deadly Sins Grand Cross is not only a game for solo players. It is also a game that offers real-time PvP and guild content for multiplayer players. You can compete with other players in various PvP modes, such as Death Match, Elite Demon Battle, Knighthood Boss Battle, etc. You can also join a knighthood and cooperate with other players in guild wars and events. You can also chat with other players in real-time and share your strategies and tips. You can also trade items and cards with other players in the market.</p>
- <h2>What are some tips and tricks for playing Seven Deadly Sins Grand Cross?</h2>
- <p>If you are new to Seven Deadly Sins Grand Cross or want to improve your gameplay skills, here are some tips and tricks for playing the game:</p>
- <h3>Prioritize the main quest line</h3>
- <p>The main quest line is the best way to progress through the game and unlock new features and content. The main quest line follows the story of The Seven Deadly Sins and rewards you with various items and resources, such as gold, gems, stamina potions, equipment, etc. The main quest line also unlocks new areas and locations for you to explore and complete side quests. The main quest line also increases your player level and rank, which allows you to access more content and modes.</p>
- <h3>Create card fusions without forcing them</h3>
- <p>Card fusion is a key element of the combat system in Seven Deadly Sins Grand Cross. Card fusion allows you to combine cards of the same type to rank them up and increase their power and range. However, you should not force card fusion by using cards that are not optimal for the situation. For example, you should not use a heal card to create a fusion if you do not need to heal. You should also not use a debuff card to create a fusion if the enemy is immune to debuffs. You should always consider the enemy's attributes, the card effects, and the card order before creating card fusions. You should also save some cards for the next turn, as they will be automatically ranked up.</p>
- <h3>Put the auto battle and x2 speed feature to good use</h3>
- <p>Seven Deadly Sins Grand Cross has an auto battle and x2 speed feature that can help you save time and effort when playing the game. The auto battle feature allows the game to choose and use cards for you based on a preset strategy. The x2 speed feature allows the game to run faster and skip some animations. You can use these features when you are farming resources, completing easy quests, or replaying stages that you have already cleared. However, you should not rely on these features too much, as they may not be optimal for some situations. For example, you should not use the auto battle feature when you are facing a boss or a difficult enemy, as the game may not use the best cards or strategy for you. You should also not use the x2 speed feature when you are watching cutscenes or enjoying the story, as you may miss some important details or emotions.</p>
- <h3>Manage your resources wisely</h3>
- <p>Seven Deadly Sins Grand Cross is a game that requires you to manage your resources wisely. You will need various resources to upgrade your characters, equipment, tavern, etc. Some of the main resources are gold, gems, stamina, anvils, hammers, awakening stones, etc. You can obtain these resources from various sources, such as quests, events, rewards, shops, etc. However, you should not spend these resources recklessly, as they may be limited or scarce. You should always prioritize the most important or urgent upgrades and save some resources for future needs. You should also avoid wasting resources on unnecessary or inefficient upgrades.</p>
- <h3>Join a knighthood and participate in events</h3>
- <p>Seven Deadly Sins Grand Cross is a game that encourages you to join a knighthood and participate in events. A knighthood is a guild that allows you to cooperate and communicate with other players. You can join an existing knighthood or create your own knighthood with your friends. By joining a knighthood, you can access various benefits and features, such as guild wars, guild bosses, guild shop, guild chat, etc. You can also earn guild coins and guild points that can be used to buy items or rank up your knighthood. By participating in events, you can access various content and rewards that are exclusive to the event period. You can participate in events such as festivals, collabs, special quests, etc. You can also earn event coins and event points that can be used to buy items or exchange for prizes.</p>
- <h2>What are some reviews of Seven Deadly Sins Grand Cross?</h2>
- <p>Seven Deadly Sins Grand Cross is a game that has received positive reviews from critics and players alike. Here are some of the reviews of the game:</p>
- <h3>A positive review from TheGamer.com</h3>
- <p>TheGamer.com gave Seven Deadly Sins Grand Cross a score of 4 out of 5 stars and praised its graphics, combat system, story mode, and voice acting. The reviewer wrote:</p>
- <blockquote>
-<p>"Seven Deadly Sins: Grand Cross is one of the best looking anime games on the market right now...The combat system is simple yet satisfying...The story mode is well done and faithful to the source material...The voice acting is top notch..."</p>
-</blockquote>
- <h3>A positive review from IGN.com</h3>
- <p>IGN.com gave Seven Deadly Sins Grand Cross a score of 8 out of 10 and praised its gameplay variety , graphics, story, and characters. The reviewer wrote:</p>
- <blockquote>
-<p>"Seven Deadly Sins: Grand Cross is a well-made and polished RPG that offers a lot of gameplay variety...The graphics are stunning and the animations are smooth...The story is engaging and faithful to the anime...The characters are diverse and likable..."</p>
-</blockquote>
- <h3>A positive review from KINCIR.com</h3>
- <p>KINCIR.com gave Seven Deadly Sins Grand Cross a score of 8.5 out of 10 and praised its gameplay mechanics, customization options, and sound quality. The reviewer wrote:</p>
- <blockquote>
-<p>"Seven Deadly Sins: Grand Cross is a game that has a lot of gameplay mechanics that are fun and challenging...The customization options are abundant and satisfying...The sound quality is excellent and immersive..."</p>
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- <h3>A positive review from Metacritic.com</h3>
- <p>Metacritic.com gave Seven Deadly Sins Grand Cross a score of 86 out of 100 based on the ratings of 12 critics and 32 users. The website also showed some of the positive user reviews, such as:</p>
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-<p>"This game is amazing. The graphics are beautiful, the gameplay is smooth, the story is captivating, and the characters are awesome. I love this game so much."</p>
-<p>"This game is one of the best anime games I have ever played. It has everything I want in a game: great story, great combat, great customization, great voice acting, great music, etc. I highly recommend this game to anyone who likes anime or RPGs."</p>
-<p>"This game is a masterpiece. It is a perfect adaptation of the anime and manga series. It is a game that respects the fans and the source material. It is a game that deserves more recognition and appreciation."</p>
-</blockquote>
- <h2>Conclusion</h2>
- <p>Seven Deadly Sins Grand Cross is a cinematic anime game for mobile that is based on the popular anime and manga series The Seven Deadly Sins. It is a game that recreates the original story and battles with high-quality 3D graphics and voice acting. It is a game that offers various features and content for fans and newcomers alike. It is a game that has received positive reviews from critics and players alike. If you want to play Seven Deadly Sins Grand Cross on your mobile device, you can download its APK file from the official sources or the alternative sources, as long as you take some precautions and meet some requirements. You can also use some tips and tricks to improve your gameplay skills and enjoy the game more. Seven Deadly Sins Grand Cross is a game that will immerse you in the world of Britannia and make you feel like you are part of The Seven Deadly Sins.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Seven Deadly Sins Grand Cross:</p>
- <h3>Q: Is Seven Deadly Sins Grand Cross free to play?</h3>
-<p>A: Yes, Seven Deadly Sins Grand Cross is free to play. However, it also offers in-app purchases that can enhance your gaming experience.</p>
- <h3>Q: Is Seven Deadly Sins Grand Cross available in my country?</h3>
-<p>A: Seven Deadly Sins Grand Cross is available in most countries around the world. However, some regions may have different versions or servers of the game. You can check the official website or the official social media pages for more information.</p>
- <h3>Q: Is Seven Deadly Sins Grand Cross compatible with my device?</h3>
-<p>A: Seven Deadly Sins Grand Cross is compatible with most Android and iOS devices that meet the minimum system requirements. However, some devices may experience performance issues or bugs due to various factors. You can check the official website or contact the customer support for more information.</p>
- <h3>Q: How can I contact the customer support of Seven Deadly Sins Grand Cross?</h3>
-<p>A: You can contact the customer support of Seven Deadly Sins Grand Cross by using the in-game inquiry feature or by sending an email to [email protected].</p>
- <h3>Q: How can I get more information about Seven Deadly Sins Grand Cross?</h3>
-<p>A: You can get more information about Seven Deadly Sins Grand Cross by visiting the official website, following the official social media pages, joining the official community forums, or watching the official YouTube channel.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/A00001/bingothoo/src/components/turn-counter.tsx

@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    <div className="turn-counter">
-      <div className="text">
-        <span>{throttling.numUserMessagesInConversation}</span>
-        <span> 共 </span>
-        <span>{throttling.maxNumUserMessagesInConversation}</span>
-      </div>
-      <div className="indicator"></div>
-    </div>
-  )
-}

spaces/AFCMEgypt/WCB/app.py

@@ -1,122 +0,0 @@
-
-#Import Required Packages
-import numpy as np
-import gradio as gr
-#from google.colab.patches import cv2_imshow
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import skimage 
-import imutils
-from imutils import contours 
-import math
-def cube (v):
-  return v**3
-def sqrtabs (v) :
-  return math.sqrt(abs(v))
-def figplota(xvalues):
-  fig = plt.figure()
-  plt.plot(xvalues, figure=fig)
-  return fig
-def quant(imageinput):
-  #@title Please Input the Lateral Flow Assay Image
-  # read image using openCV
-  #path = "/content/l1.jpg"
-  image = cv2.imread(imageinput)#imageinput
-  target = "PKU" 
-  #print(image)
-  #cv2_imshow(image)
-  # Convert the image to grayscale 
-  BGR2RGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-  gray = cv2.cvtColor(BGR2RGB, cv2.COLOR_RGB2GRAY)
-  #print(gray)
-  #cv2_imshow(gray)
-  # Invert the image to negative scale
-  negative = cv2.bitwise_not(gray)
-  negativeimage = negative.copy() #save a copy to avoid disrupting the image contour
-  #print(negativeimage)
-  #cv2_imshow(negativeimage)
-  # Minimize the noisy effects of artificats using Gaussian blur (helps with minimizing the effect of noisy artifactual bright-spots)
-  blur = cv2.GaussianBlur(negativeimage, (11, 11), 0)
-  #print(blur)
-  #cv2_imshow(blur)
-  # Binarize Image
-  threshold = float(cv2.meanStdDev(blur)[0]) + 0.6*float(cv2.meanStdDev(blur)[1])
-  imgthreshold = cv2.threshold(blur, threshold, 255, cv2.THRESH_BINARY)[1]
-  #print(imgthreshold)
-  #cv2_imshow(image_thresh)
-  # Reducing noise noise through eroding & eroding 
-  imgeroding = cv2.erode(imgthreshold, None, iterations=1)
-  zeronoise = cv2.dilate(imgeroding, None, iterations=1)
-  #print(zeronoise)
-  #cv2_imshow(zeronoise)
-  # CCA the threshold Image
-  import skimage.measure
-  labels = skimage.measure.label(zeronoise, background=0)
-  masking = np.zeros(zeronoise.shape, dtype="uint8")
-  for label in np.unique(labels):
-      if label == 0:
-          continue
-      MaskL = np.zeros(zeronoise.shape, dtype="uint8")
-      MaskL[labels == label] = 255
-      numPixels = cv2.countNonZero(MaskL)
-      if numPixels > masking.shape[1]*3:
-          masking = cv2.add(masking, MaskL)    
-  #cv2_imshow(mask)
-  # Find the contours and sort, please change from bottom-to-top to top-to-bottom accordingly
-  contourss = cv2.findContours(masking.copy(), cv2.RETR_EXTERNAL,
-      cv2.CHAIN_APPROX_SIMPLE)
-  contourss = imutils.grab_contours(contourss)
-  contourss = contours.sort_contours(contourss, method="bottom-to-top")[0] #change here accordingly
-  final= []
-  if len(contourss) > 1:
-      for (i, c) in enumerate(contourss):
-          # draw the bright spot on the image for the control and sample band 
-          x, y, width, height = cv2.boundingRect(c)
-          final.append(negativeimage[y:y+height, x:x+width])
-          rect = cv2.minAreaRect(c)
-          box = cv2.boxPoints(rect)
-          # convert all coordinates floating point values to int
-          box = np.int0(box)
-          # draw a rectangle
-          cv2.drawContours(image, [box], 0, (0, 0, 255), thickness=2)
-
-  elif len(contourss) == 1:
-          # draw the bright spot on the image for the control band
-          for (i, c) in enumerate(contourss):
-              x, y, width, height = cv2.boundingRect(c)
-              final.append(negativeimage[y:y+height, x:x+width])
-              rect = cv2.minAreaRect(c)
-              box = cv2.boxPoints(rect)
-              # convert all coordinates floating point values to int
-              box = np.int0(box)
-              # draw a rectangle
-              cv2.drawContours(image, [box], 0, (0, 0, 255), thickness=2)
-
-    
-          
-  # Return error message for unclear tests
-  else :
-      print("No Bands Detected")
-  #print(image)
-  #cv2_imshow(image)
-  # generate signal ratio of sample to control band, you can change according to sorting of bands
-
-  ratio1 = cv2.meanStdDev(final[0])[0]
-  ratio=((cube(math.cos(sqrtabs(ratio1  - -0.393284)) + 2.2783713) / pow(math.cos(y), 0.20675313)) - (math.exp(math.cos(math.cos((sqrtabs(math.tan(cube(ratio1)) - (ratio1 +math.tan(math.sin(ratio1)))) / 0.44953698) * 0.9778089))) + (-2.3363407 / ratio1)))
-  thresho = 20
-  sig=final[0][0]
-  #signal=plt.plot(sig,figure=plt.figure())
-  if ratio >= thresho:
-    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "Classic PKU, needs urgent medical treatment")
-  elif ratio >= 2 and ratio <6:
-    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "Likely PKU phenotype.")
-  elif ratio >= 6 and ratio <12:
-    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "PKU and dietary restriction is recommended")
-  elif ratio >=12 and ratio <20:
-    xx=str("The test band signal [" + str(ratio) + "mg/dl] shows a " + target +"-POSITIVE test." +" " + "PKU and need medical attention for risk of intellectuall impairment")
-  else:
-    xx=str("The test band signal[" + str(ratio) + "mg/dl] shows a " + target +"-NEGATIVE test.")
-  return xx,figplota(sig),cv2.resize(image, (20,60), interpolation = cv2.INTER_AREA) #cv2.resize(signal, (20,40), interpolation = cv2.INTER_AREA)#,cv2.resize(signal, (20,40), interpolation = cv2.INTER_AREA)
-iface = gr.Interface(quant, gr.Image(type="filepath"), outputs=["text","plot","image"],debug=True)
-iface.launch()

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/hifigan/modules.py

@@ -1,332 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from pathlib import Path
-import yaml
-import numpy as np
-from argparse import Namespace
-LRELU_SLOPE = 0.1
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-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)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        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):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        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, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        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):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-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
-        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(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(2),
-            DiscriminatorP(3),
-            DiscriminatorP(5),
-            DiscriminatorP(7),
-            DiscriminatorP(11),
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            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, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i-1](y)
-                y_hat = self.meanpools[i-1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss*2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1-dr)**2)
-        g_loss = torch.mean(dg**2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-class VocoderHifigan(object):
-    def __init__(self, ckpt_vocoder,device='cuda'):
-        
-        with open(os.path.join(ckpt_vocoder,'args.yml'), 'r') as f:
-            vocoder_args = Namespace(**yaml.load(f, Loader=yaml.UnsafeLoader))
-
-        self.generator = Generator(vocoder_args)
-        netG_path = os.path.join(ckpt_vocoder,'best_netG.pt')
-        if os.path.exists(netG_path):
-            vocoder_sd = torch.load(netG_path, map_location='cpu')
-            self.generator.load_state_dict(vocoder_sd['generator'])
-        self.generator.eval()
-
-        self.device = device
-        self.generator.to(self.device)
-
-    def vocode(self, spec, global_step=None):
-        with torch.no_grad():
-            if isinstance(spec,np.ndarray):
-                spec = torch.from_numpy(spec).unsqueeze(0)
-            spec = spec.to(dtype=torch.float32,device=self.device)
-            return self.generator(spec).squeeze().cpu().numpy()
-
-class VocoderHifigan_noload(object):
-    def __init__(self, vocoder_args,device='cuda'):
-        self.generator = Generator(vocoder_args)
-        self.generator.eval()
-
-        self.device = device
-        self.generator.to(self.device)
-
-    def vocode(self, spec, global_step=None):
-        with torch.no_grad():
-            if isinstance(spec,np.ndarray):
-                spec = torch.from_numpy(spec).unsqueeze(0)
-            spec = spec.to(dtype=torch.float32,device=self.device)
-            return self.generator(spec).squeeze().cpu().numpy()

spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/base_binarizer.py

@@ -1,412 +0,0 @@
-import json
-import os
-import random
-from re import L
-import traceback
-from functools import partial
-
-import numpy as np
-from resemblyzer import VoiceEncoder
-from tqdm import tqdm
-
-from transformers import AutoTokenizer
-
-# import utils.commons.single_thread_env  # NOQA
-from text_to_speech.utils.audio import librosa_wav2spec
-from text_to_speech.utils.audio.align import get_mel2ph, mel2token_to_dur
-from text_to_speech.utils.audio.cwt import get_lf0_cwt, get_cont_lf0
-from text_to_speech.utils.audio.pitch.utils import f0_to_coarse
-from text_to_speech.utils.audio.pitch_extractors import extract_pitch_simple
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.commons.indexed_datasets import IndexedDatasetBuilder
-from text_to_speech.utils.commons.multiprocess_utils import multiprocess_run_tqdm
-from text_to_speech.utils.os_utils import remove_file, copy_file
-
-np.seterr(divide='ignore', invalid='ignore')
-
-
-class BinarizationError(Exception):
-    pass
-
-sentence2graph_parser = None
-bert_tokenizer = None
-use_graph = False
-use_bpe = True
-
-
-class BaseBinarizer:
-    def __init__(self, processed_data_dir=None):
-        if processed_data_dir is None:
-            processed_data_dir = hparams['processed_data_dir']
-        self.processed_data_dir = processed_data_dir
-        self.binarization_args = hparams['binarization_args']
-        self.items = {}
-        self.item_names = []
-
-        global sentence2graph_parser
-        global use_graph
-        global use_bpe
-        global bert_tokenizer
-        if use_graph:
-            from text_to_speech.modules.tts.syntaspeech.syntactic_graph_buider import Sentence2GraphParser
-
-        if hparams['ds_name'] in ['libritts', 'librispeech']:
-            # Unfortunately, we found when processing libritts with multi-processing will incur pytorch.multiprocessing ERROR
-            # so we use single thread with cuda graph builder 
-            # it take about 20 hours in a PC with 24-cores-cpu and a RTX2080Ti to process the whole LibriTTS
-            # so run the binarization and take a break!
-            if use_graph:
-                sentence2graph_parser = Sentence2GraphParser("en", use_gpu=True)
-            if use_bpe:
-                model_name = 'bert-base-uncased'
-                tokenizer_kwargs = {'cache_dir': None, 'use_fast': True, 'revision': 'main', 'use_auth_token': None}
-                bert_tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
-        elif hparams['ds_name'] == 'ljspeech':
-            # use multi-processing, thus gpu is disabled
-            # it takes about 30 minutes for binarization
-            if use_graph:
-                sentence2graph_parser = Sentence2GraphParser("en", use_gpu=False)
-            if use_bpe: 
-                model_name = 'bert-base-uncased'
-                tokenizer_kwargs = {'cache_dir': None, 'use_fast': True, 'revision': 'main', 'use_auth_token': None}
-                bert_tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
-        elif hparams['preprocess_args']['txt_processor'] == 'zh':
-            # use multi-processing, thus gpu is disabled
-            # it takes about 30 minutes for binarization
-            if use_graph:
-                sentence2graph_parser = Sentence2GraphParser("zh", use_gpu=False)
-            if use_bpe:
-                model_name = 'bert-base-chinese'
-                tokenizer_kwargs = {'cache_dir': None, 'use_fast': True, 'revision': 'main', 'use_auth_token': None}
-                bert_tokenizer = AutoTokenizer.from_pretrained(model_name, **tokenizer_kwargs)
-        else:
-            pass
-
-    def load_meta_data(self):
-        processed_data_dir = self.processed_data_dir
-        items_list = json.load(open(f"{processed_data_dir}/metadata.json"))
-        for r in tqdm(items_list, desc='Loading meta data.'):
-            item_name = r['item_name']
-            self.items[item_name] = r
-            self.item_names.append(item_name)
-        if self.binarization_args['shuffle']:
-            random.seed(1234)
-            random.shuffle(self.item_names)
-
-    @property
-    def train_item_names(self):
-        range_ = self._convert_range(self.binarization_args['train_range'])
-        return self.item_names[range_[0]:range_[1]]
-
-    @property
-    def valid_item_names(self):
-        range_ = self._convert_range(self.binarization_args['valid_range'])
-        return self.item_names[range_[0]:range_[1]]
-
-    @property
-    def test_item_names(self):
-        range_ = self._convert_range(self.binarization_args['test_range'])
-        return self.item_names[range_[0]:range_[1]]
-
-    def _convert_range(self, range_):
-        if range_[1] == -1:
-            range_[1] = len(self.item_names)
-        return range_
-
-    def meta_data(self, prefix):
-        if prefix == 'valid':
-            item_names = self.valid_item_names
-        elif prefix == 'test':
-            item_names = self.test_item_names
-        else:
-            item_names = self.train_item_names
-        for item_name in item_names:
-            yield self.items[item_name]
-
-    def process(self):
-        self.load_meta_data()
-        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
-        for fn in ['phone_set.json', 'word_set.json', 'spk_map.json']:
-            remove_file(f"{hparams['binary_data_dir']}/{fn}")
-            copy_file(f"{hparams['processed_data_dir']}/{fn}", f"{hparams['binary_data_dir']}/{fn}")
-        if hparams['ds_name'] in ['ljspeech', 'biaobei', 'wenetspeech']:
-            self.process_data('valid')
-            self.process_data('test')
-            self.process_data('train')
-        elif hparams['ds_name'] in ['libritts', 'librispeech']:
-            self.process_data_single_processing('valid')
-            self.process_data_single_processing('test')
-            self.process_data_single_processing('train')
-        else:
-            self.process_data('valid')
-            self.process_data('test')
-            self.process_data('train')
-            # raise NotImplementedError
-
-    def process_data(self, prefix):
-        data_dir = hparams['binary_data_dir']
-        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-        meta_data = list(self.meta_data(prefix))
-        process_item = partial(self.process_item, binarization_args=self.binarization_args)
-        ph_lengths = []
-        mel_lengths = []
-        total_sec = 0
-        items = []
-        args = [{'item': item} for item in meta_data]
-
-        for item_id, item in multiprocess_run_tqdm(process_item, args, desc='Processing data'):
-            if item is not None:
-                items.append(item)
-        if self.binarization_args['with_spk_embed']:
-            args = [{'wav': item['wav']} for item in items]
-            for item_id, spk_embed in multiprocess_run_tqdm(
-                    self.get_spk_embed, args,
-                    init_ctx_func=lambda wid: {'voice_encoder': VoiceEncoder().cuda()}, num_workers=4,
-                    desc='Extracting spk embed'):
-                items[item_id]['spk_embed'] = spk_embed
-
-        for item in items:
-            if not self.binarization_args['with_wav'] and 'wav' in item:
-                del item['wav']
-            builder.add_item(item)
-            mel_lengths.append(item['len'])
-            assert item['len'] > 0, (item['item_name'], item['txt'], item['mel2ph'])
-            if 'ph_len' in item:
-                ph_lengths.append(item['ph_len'])
-            total_sec += item['sec']
-        builder.finalize()
-        np.save(f'{data_dir}/{prefix}_lengths.npy', mel_lengths)
-        if len(ph_lengths) > 0:
-            np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_lengths)
-        print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    def process_data_single_processing(self, prefix):
-        data_dir = hparams['binary_data_dir']
-        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-        meta_data = list(self.meta_data(prefix))
-        ph_lengths = []
-        mel_lengths = []
-        total_sec = 0
-
-        if self.binarization_args['with_spk_embed']:
-            voice_encoder = VoiceEncoder().cuda()
-        for raw_item in tqdm(meta_data):
-            item = self.process_item(raw_item, self.binarization_args)
-            if item is None: 
-                continue
-            if item is not None:
-                if use_graph:
-                    if item['dgl_graph'].num_nodes() != np.array(item['ph2word']).max():
-                        print(f"Skip Item: {item['item_name']} word nodes number incorrect!")
-                        continue
-
-            if self.binarization_args['with_spk_embed']:
-                spk_embed = self.get_spk_embed(item['wav'],  {'voice_encoder': voice_encoder})
-                item['spk_embed'] = spk_embed
-
-            if not self.binarization_args['with_wav'] and 'wav' in item:
-                del item['wav']
-            builder.add_item(item)
-            mel_lengths.append(item['len'])
-            assert item['len'] > 0, (item['item_name'], item['txt'], item['mel2ph'])
-            if 'ph_len' in item:
-                ph_lengths.append(item['ph_len'])
-            total_sec += item['sec']
-        builder.finalize()
-        np.save(f'{data_dir}/{prefix}_lengths.npy', mel_lengths)
-        if len(ph_lengths) > 0:
-            np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_lengths)
-        print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    # def process_data_single_processing(self, prefix):
-    #     data_dir = hparams['binary_data_dir']
-    #     builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-    #     meta_data = list(self.meta_data(prefix))
-    #     ph_lengths = []
-    #     mel_lengths = []
-    #     total_sec = 0
-    #     items = []
-    #     args = [{'item': item} for item in meta_data]
-
-    #     for raw_item in tqdm(meta_data):
-    #         item = self.process_item(raw_item, self.binarization_args)
-    #         if item is not None:
-    #             if item['dgl_graph'].num_nodes() != np.array(item['ph2word']).max():
-    #                 print(f"Skip Item: {item['item_name']} word nodes number incorrect!")
-    #                 continue
-
-    #             items.append(item)
-
-    #     if self.binarization_args['with_spk_embed']:
-    #         args = [{'wav': item['wav']} for item in items]
-    #         for item_id, spk_embed in multiprocess_run_tqdm(
-    #                 self.get_spk_embed, args,
-    #                 init_ctx_func=lambda wid: {'voice_encoder': VoiceEncoder().cuda()}, num_workers=4,
-    #                 desc='Extracting spk embed'):
-    #             items[item_id]['spk_embed'] = spk_embed
-
-    #     for item in items:
-    #         if not self.binarization_args['with_wav'] and 'wav' in item:
-    #             del item['wav']
-    #         builder.add_item(item)
-    #         mel_lengths.append(item['len'])
-    #         assert item['len'] > 0, (item['item_name'], item['txt'], item['mel2ph'])
-    #         if 'ph_len' in item:
-    #             ph_lengths.append(item['ph_len'])
-    #         total_sec += item['sec']
-    #     builder.finalize()
-    #     np.save(f'{data_dir}/{prefix}_lengths.npy', mel_lengths)
-    #     if len(ph_lengths) > 0:
-    #         np.save(f'{data_dir}/{prefix}_ph_lengths.npy', ph_lengths)
-    #     print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    @classmethod
-    def process_item(cls, item, binarization_args):
-        try:
-            item['ph_len'] = len(item['ph_token'])
-            item_name = item['item_name']
-            wav_fn = item['wav_fn']
-            wav, mel = cls.process_audio(wav_fn, item, binarization_args)
-        except Exception as e:
-            print(f"| Skip item ({e}) for index error. item_name: {item_name}, wav_fn: {wav_fn}")
-            return None
-        try:
-            n_bos_frames, n_eos_frames = 0, 0
-            if binarization_args['with_align']:
-                tg_fn = f"{hparams['processed_data_dir']}/mfa_outputs/{item_name}.TextGrid"
-                item['tg_fn'] = tg_fn
-                cls.process_align(tg_fn, item)
-                if binarization_args['trim_eos_bos']:
-                    n_bos_frames = item['dur'][0]
-                    n_eos_frames = item['dur'][-1]
-                    T = len(mel)
-                    item['mel'] = mel[n_bos_frames:T - n_eos_frames]
-
-                    item['mel2ph'] = item['mel2ph'][n_bos_frames:T - n_eos_frames]
-                    item['mel2word'] = item['mel2word'][n_bos_frames:T - n_eos_frames]
-                    item['dur'] = item['dur'][1:-1]
-                    item['dur_word'] = item['dur_word'][1:-1]
-                    item['len'] = item['mel'].shape[0]
-                    item['wav'] = wav[n_bos_frames * hparams['hop_size']:len(wav) - n_eos_frames * hparams['hop_size']]
-            if binarization_args['with_f0']:
-                cls.process_pitch(item, n_bos_frames, n_eos_frames)
-        except BinarizationError as e:
-            print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}")
-            return None
-        except Exception as e:
-            traceback.print_exc()
-            print(f"| Skip item. item_name: {item_name}, wav_fn: {wav_fn}")
-            return None
-
-        # if item['mel'].shape[0] < 64:
-        #     print(f"Skip Item: {item['item_name']} Mel-spectrogram is shorter than 64!")
-        #     return None
-        # fix one bad case of stanza
-        if item['txt'].endswith('yn .'):
-            item['txt'] = item['txt'][:-4]+'y .'
-        if use_graph:
-            try:
-                language = sentence2graph_parser.language
-                if language == 'en':
-                    dgl_graph, etypes = sentence2graph_parser.parse(item['txt'])
-                elif language == 'zh':
-                    dgl_graph, etypes = sentence2graph_parser.parse(item['txt'], item['word'].split(" "), item['ph_gb_word'].split(" "))
-                else:
-                    raise NotImplementedError
-                item['dgl_graph'] = dgl_graph
-                item['edge_types'] = etypes
-            except:
-                print(f"| Dependency Parsing Error! Skip item. item_name: {item_name}, wav_fn: {wav_fn}")
-                return None
-
-        if use_bpe:
-            sent = item['word'][6:-6] # discard the <BOS> and <EOS>, because the bert_tokenizer cannot recognize them.
-            bert_tokens = bert_tokenizer.tokenize(sent)
-            input_ids = bert_tokenizer.convert_tokens_to_ids(bert_tokens)
-            input_ids.insert(0, 101) # add [CLS] to represent [BOS]
-            input_ids.append(102) # add [SEP] to represent [EOS]
-            
-            bert_tokens.insert(0, '<BOS>')
-            bert_tokens.append('<EOS>')
-            bert_token2word = []
-            word_idx = 0
-            for i in range(len(bert_tokens)):
-                if not bert_tokens[i].startswith("##"): # this token is a independent word
-                    word_idx += 1
-                bert_token2word.append(word_idx)
-
-            item['bert_token'] = bert_tokens
-            item['bert_input_ids'] = input_ids
-            item['bert_token2word'] = bert_token2word
-            item['bert_attention_mask'] = [1 for _ in range(len(bert_tokens))]
-            item['bert_token_type_ids'] = [0 for _ in range(len(bert_tokens))]
-
-        return item
-
-    @classmethod
-    def process_audio(cls, wav_fn, res, binarization_args):
-        wav2spec_dict = librosa_wav2spec(
-            wav_fn,
-            fft_size=hparams['fft_size'],
-            hop_size=hparams['hop_size'],
-            win_length=hparams['win_size'],
-            num_mels=hparams['audio_num_mel_bins'],
-            fmin=hparams['fmin'],
-            fmax=hparams['fmax'],
-            sample_rate=hparams['audio_sample_rate'],
-            loud_norm=hparams['loud_norm'])
-        mel = wav2spec_dict['mel']
-        wav = wav2spec_dict['wav'].astype(np.float16)
-        if binarization_args['with_linear']:
-            res['linear'] = wav2spec_dict['linear']
-        res.update({'mel': mel, 'wav': wav, 'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]})
-        return wav, mel
-
-    @staticmethod
-    def process_align(tg_fn, item):
-        ph = item['ph']
-        mel = item['mel']
-        ph_token = item['ph_token']
-        if tg_fn is not None and os.path.exists(tg_fn):
-            mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams['hop_size'], hparams['audio_sample_rate'],
-                                     hparams['binarization_args']['min_sil_duration'])
-        else:
-            raise BinarizationError(f"Align not found")
-        if np.array(mel2ph).max() - 1 >= len(ph_token):
-            raise BinarizationError(
-                f"Align does not match: mel2ph.max() - 1: {np.array(mel2ph).max() - 1}, len(phone_encoded): {len(ph_token)}")
-        item['mel2ph'] = mel2ph
-        item['dur'] = dur
-
-        ph2word = item['ph2word']
-        mel2word = [ph2word[p - 1] for p in item['mel2ph']]
-        item['mel2word'] = mel2word  # [T_mel]
-        dur_word = mel2token_to_dur(mel2word, len(item['word_token']))
-        item['dur_word'] = dur_word.tolist()  # [T_word]
-
-    @staticmethod
-    def process_pitch(item, n_bos_frames, n_eos_frames):
-        wav, mel = item['wav'], item['mel']
-        f0 = extract_pitch_simple(item['wav'])
-        if sum(f0) == 0:
-            raise BinarizationError("Empty f0")
-        assert len(mel) == len(f0), (len(mel), len(f0))
-        pitch_coarse = f0_to_coarse(f0)
-        item['f0'] = f0
-        item['pitch'] = pitch_coarse
-        if hparams['binarization_args']['with_f0cwt']:
-            uv, cont_lf0_lpf = get_cont_lf0(f0)
-            logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
-            cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
-            cwt_spec, scales = get_lf0_cwt(cont_lf0_lpf_norm)
-            item['cwt_spec'] = cwt_spec
-            item['cwt_mean'] = logf0s_mean_org
-            item['cwt_std'] = logf0s_std_org
-
-    @staticmethod
-    def get_spk_embed(wav, ctx):
-        return ctx['voice_encoder'].embed_utterance(wav.astype(float))
-
-    @property
-    def num_workers(self):
-        return int(os.getenv('N_PROC', hparams.get('N_PROC', os.cpu_count())))

spaces/AIZeroToHero/Video-Automatic-Speech-Recognition/streaming.py

@@ -1,66 +0,0 @@
-import subprocess
-
-import numpy as np
-
-
-def ffmpeg_stream(youtube_url, sampling_rate=16_000, chunk_duration_ms=5000, pad_duration_ms=200):
-    """
-    Helper function to read an audio file through ffmpeg.
-    """
-    chunk_len = int(sampling_rate * chunk_duration_ms / 1000)
-    pad_len = int(sampling_rate * pad_duration_ms / 1000)
-    read_chunk_len = chunk_len + pad_len * 2
-
-    ar = f"{sampling_rate}"
-    ac = "1"
-    format_for_conversion = "f32le"
-    dtype = np.float32
-    size_of_sample = 4
-
-    ffmpeg_command = [
-        "ffmpeg",
-        "-i",
-        "pipe:",
-        "-ac",
-        ac,
-        "-ar",
-        ar,
-        "-f",
-        format_for_conversion,
-        "-hide_banner",
-        "-loglevel",
-        "quiet",
-        "pipe:1",
-    ]
-
-    ytdl_command = ["yt-dlp", "-f", "bestaudio", youtube_url, "--quiet", "-o", "-"]
-
-    try:
-        ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, bufsize=-1)
-        ytdl_process = subprocess.Popen(ytdl_command, stdout=ffmpeg_process.stdin)
-    except FileNotFoundError:
-        raise ValueError("ffmpeg was not found but is required to stream audio files from filename")
-
-    acc = b""
-    leftover = np.zeros((0,), dtype=np.float32)
-    while ytdl_process.poll() is None:
-        buflen = read_chunk_len * size_of_sample
-
-        raw = ffmpeg_process.stdout.read(buflen)
-        if raw == b"":
-            break
-
-        if len(acc) + len(raw) > buflen:
-            acc = raw
-        else:
-            acc += raw
-
-        audio = np.frombuffer(acc, dtype=dtype)
-        audio = np.concatenate([leftover, audio])
-        if len(audio) < pad_len * 2:
-            # TODO: handle end of stream better than this
-            break
-        yield audio
-
-        leftover = audio[-pad_len * 2 :]
-        read_chunk_len = chunk_len

spaces/ASJMO/freegpt/g4f/Provider/Providers/You.py

@@ -1,24 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://you.com'
-model = 'gpt-3.5-turbo'
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages}, separators=(',', ':'))
-    
-    cmd = ['python3', f'{path}/helpers/you.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8') #[:-1]

spaces/AbeShinzo0708/AI_Kishida_Fumio_speaker/hooks/hook-streamlit.py

@@ -1,3 +0,0 @@
-from PyInstaller.utils.hooks import copy_metadata
-
-datas = copy_metadata('streamlit')

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/stores/pendingMessageIdToRetry.ts

@@ -1,4 +0,0 @@
-import type { Message } from "$lib/types/Message";
-import { writable } from "svelte/store";
-
-export const pendingMessageIdToRetry = writable<Message["id"] | null>(null);

spaces/AchyuthGamer/OpenGPT/server/website.py

@@ -1,58 +0,0 @@
-from flask import render_template, redirect, url_for, request, session
-from flask_babel import refresh
-from time import time
-from os import urandom
-from server.babel import get_locale, get_languages
-
-
-class Website:
-    def __init__(self, bp, url_prefix) -> None:
-        self.bp = bp
-        self.url_prefix = url_prefix
-        self.routes = {
-            '/': {
-                'function': lambda: redirect(url_for('._index')),
-                'methods': ['GET', 'POST']
-            },
-            '/chat/': {
-                'function': self._index,
-                'methods': ['GET', 'POST']
-            },
-            '/chat/<conversation_id>': {
-                'function': self._chat,
-                'methods': ['GET', 'POST']
-            },
-            '/change-language': {
-                'function': self.change_language,
-                'methods': ['POST']
-            },
-            '/get-locale': {
-                'function': self.get_locale,
-                'methods': ['GET']
-            },
-            '/get-languages': {
-                'function': self.get_languages,
-                'methods': ['GET']
-            }
-        }
-
-    def _chat(self, conversation_id):
-        if '-' not in conversation_id:
-            return redirect(url_for('._index'))
-
-        return render_template('index.html', chat_id=conversation_id, url_prefix=self.url_prefix)
-
-    def _index(self):
-        return render_template('index.html', chat_id=f'{urandom(4).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{hex(int(time() * 1000))[2:]}', url_prefix=self.url_prefix)
-
-    def change_language(self):
-        data = request.get_json()
-        session['language'] = data.get('language')
-        refresh()
-        return '', 204
-
-    def get_locale(self):
-        return get_locale()
-    
-    def get_languages(self):  
-        return get_languages()

spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/decision_maker/vertical.py

@@ -1,58 +0,0 @@
-from __future__ import annotations
-import asyncio
-from colorama import Fore
-
-from typing import TYPE_CHECKING, List
-
-from . import decision_maker_registry
-from .base import BaseDecisionMaker
-from agentverse.logging import typewriter_log, logger
-
-if TYPE_CHECKING:
-    from agentverse.agents import BaseAgent, SolverAgent, CriticAgent
-    from agentverse.message import Message, CriticMessage, SolverMessage
-
-
-@decision_maker_registry.register("vertical")
-class VerticalDecisionMaker(BaseDecisionMaker):
-    """
-    Discuss in a vertical manner.
-    """
-
-    name: str = "vertical"
-
-    async def astep(
-        self,
-        agents: List[BaseAgent],
-        task_description: str,
-        previous_plan: str = "No solution yet.",
-        advice: str = "No advice yet.",
-        *args,
-        **kwargs,
-    ) -> List[SolverMessage]:
-        # Here we assume that the first agent is the solver.
-        # The rest of the agents are the reviewers.
-        reviews: List[CriticMessage] = await asyncio.gather(
-            *[
-                agent.astep(previous_plan, advice, task_description)
-                for agent in agents[1:]
-            ]
-        )
-        logger.info("", "Reviews:", Fore.YELLOW)
-        logger.info(
-            "",
-            "\n".join([f"[{review.sender}]: {review.content}" for review in reviews]),
-            Fore.YELLOW,
-        )
-
-        nonempty_reviews = []
-        for review in reviews:
-            if not review.is_agree and review.content != "":
-                nonempty_reviews.append(review)
-        agents[0].add_message_to_memory(nonempty_reviews)
-        result = agents[0].step(previous_plan, advice, task_description)
-        agents[0].add_message_to_memory([result])
-        return [result]
-
-    def reset(self):
-        pass

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/inputtext/InputText.js

@@ -1,2 +0,0 @@
-import InputText from '../../../plugins/inputtext.js';
-export default InputText;

spaces/Alex132/togethercomputer-LLaMA-2-7B-32K/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/togethercomputer/LLaMA-2-7B-32K").launch()

spaces/AlexWang/lama/models/ade20k/segm_lib/utils/th.py

@@ -1,41 +0,0 @@
-import torch
-from torch.autograd import Variable
-import numpy as np
-import collections
-
-__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
-
-def as_variable(obj):
-    if isinstance(obj, Variable):
-        return obj
-    if isinstance(obj, collections.Sequence):
-        return [as_variable(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_variable(v) for k, v in obj.items()}
-    else:
-        return Variable(obj)
-
-def as_numpy(obj):
-    if isinstance(obj, collections.Sequence):
-        return [as_numpy(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_numpy(v) for k, v in obj.items()}
-    elif isinstance(obj, Variable):
-        return obj.data.cpu().numpy()
-    elif torch.is_tensor(obj):
-        return obj.cpu().numpy()
-    else:
-        return np.array(obj)
-
-def mark_volatile(obj):
-    if torch.is_tensor(obj):
-        obj = Variable(obj)
-    if isinstance(obj, Variable):
-        obj.no_grad = True
-        return obj
-    elif isinstance(obj, collections.Mapping):
-        return {k: mark_volatile(o) for k, o in obj.items()}
-    elif isinstance(obj, collections.Sequence):
-        return [mark_volatile(o) for o in obj]
-    else:
-        return obj

spaces/AlhitawiMohammed22/CER_Hu-Evaluation-Metrics/eval_cer.py

@@ -1,145 +0,0 @@
-""" Character Error Ratio (CER) metric. """
-from typing import List
-import datasets, evaluate , jiwer
-import jiwer.transforms as tr
-from datasets.config import PY_VERSION
-from packaging import version
-
-
-if PY_VERSION < version.parse("3.8"):
-    import importlib_metadata
-else:
-    import importlib.metadata as importlib_metadata
-
-SENTENCE_DELIMITER = ""
-
-if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
-
-    class SentencesToListOfCharacters(tr.AbstractTransform):
-        def __init__(self, sentence_delimiter: str = " "):
-            self.sentence_delimiter = sentence_delimiter
-
-        def process_string(self, s: str):
-            return list(s)
-
-        def process_list(self, inp: List[str]):
-            chars = []
-            for sent_idx, sentence in enumerate(inp):
-                chars.extend(self.process_string(sentence))
-                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(inp) - 1:
-                    chars.append(self.sentence_delimiter)
-            return chars
-
-    cer_transform = tr.Compose(
-        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
-    )
-else:
-    cer_transform = tr.Compose(
-        [
-            tr.RemoveMultipleSpaces(),
-            tr.Strip(),
-            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
-            tr.ReduceToListOfListOfChars(),
-        ]
-    )
-
-
-_CITATION = """\
-@inproceedings{inproceedings,
-    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
-    year = {2004},
-    month = {01},
-    pages = {},
-    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
-}
-"""
-
-
-_DESCRIPTION = """\
-Character error rate (CER) is a standard metric of the performance of an automatic speech recognition system.
-
-CER is similar to Word Error Rate (WER) but operates on characters instead of words. Please refer to the docs of WER for further information.
-
-The character error rate can be computed as:
-
-CER = (S + D + I) / N = (S + D + I) / (S + D + C)
-
-where
-
-S is the number of substitutions,
-D is the number of deletions,
-I is the number of insertions,
-C is the number of correct characters,
-N is the number of characters in the reference (N=S+D+C).
-
-CER's output is not always a number between 0 and 1, particularly when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
-performance of the ASR system with a CER of 0 being a perfect score.
-"""
-
-_KWARGS_DESCRIPTION = """
-Computes CER score of transcribed segments against references.
-Args:
-    references: list of references for each speech input.
-    predictions: list of transcriptions to score.
-    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for a more accurate result.
-Returns:
-    (float): the character error rate
-
-Examples for the Hungarian Language:
-    >>> # Colab usage 
-    >>> !pip install evaluate jiwer
-    >>> import evaluate 
-    >>> from evaluate import load  
-
-    >>> predictions = ["ez a jóslat", "van egy másik minta is"]
-    >>> references = ["ez a hivatkozás", "van még egy"]
-    >>> cer = evaluate.load("cer")
-    >>> cer_score = cer.compute(predictions=predictions, references=references)
-    >>> print(cer_score)
-    >>> 0.9615384615384616
-"""
-
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class CER(evaluate.Metric):
-    def _info(self):
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "predictions": datasets.Value("string", id="sequence"),
-                    "references": datasets.Value("string", id="sequence"),
-                }
-            ),
-            codebase_urls=["https://github.com/jitsi/jiwer/"],
-            reference_urls=[
-                "https://en.wikipedia.org/wiki/Word_error_rate",
-                "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
-            ],
-        )
-
-    def _compute(self, predictions, references, concatenate_texts=False):
-        if concatenate_texts:
-            return jiwer.compute_measures(
-                references,
-                predictions,
-                truth_transform=cer_transform,
-                hypothesis_transform=cer_transform,
-            )["wer"]
-
-        incorrect = 0
-        total = 0
-        for prediction, reference in zip(predictions, references):
-            measures = jiwer.compute_measures(
-                reference,
-                prediction,
-                truth_transform=cer_transform,
-                hypothesis_transform=cer_transform,
-            )
-            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
-            total += measures["substitutions"] + measures["deletions"] + measures["hits"]
-
-        return incorrect / total

spaces/Altinas/vits-uma-genshin-honkais/text/__init__.py

@@ -1,57 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence, clean_text
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''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
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text

spaces/Amrrs/DragGan-Inversion/PTI/training/coaches/single_id_coach.py

@@ -1,80 +0,0 @@
-import os
-import torch
-from tqdm import tqdm
-from PTI.configs import paths_config, hyperparameters, global_config
-from PTI.training.coaches.base_coach import BaseCoach
-from PTI.utils.log_utils import log_images_from_w
-
-
-class SingleIDCoach(BaseCoach):
-    def __init__(self, data_loader, use_wandb):
-        super().__init__(data_loader, use_wandb)
-
-    def train(self):
-        w_path_dir = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}"
-        os.makedirs(w_path_dir, exist_ok=True)
-        os.makedirs(f"{w_path_dir}/{paths_config.pti_results_keyword}", exist_ok=True)
-
-        use_ball_holder = True
-        w_pivot = None
-        fname, image = next(iter(self.data_loader))
-        print("NANANAN", fname)
-        image_name = fname[0]
-
-        self.restart_training()
-
-        embedding_dir = f"{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}"
-        os.makedirs(embedding_dir, exist_ok=True)
-
-        if hyperparameters.use_last_w_pivots:
-            w_pivot = self.load_inversions(w_path_dir, image_name)
-
-        elif not hyperparameters.use_last_w_pivots or w_pivot is None:
-            w_pivot = self.calc_inversions(image, image_name)
-        torch.save(w_pivot, f"{embedding_dir}/0.pt")
-        # w_pivot = w_pivot.detach().clone().to(global_config.device)
-        w_pivot = w_pivot.to(global_config.device)
-
-        log_images_counter = 0
-        real_images_batch = image.to(global_config.device)
-
-        for i in tqdm(range(hyperparameters.max_pti_steps)):
-            generated_images = self.forward(w_pivot)
-            loss, l2_loss_val, loss_lpips = self.calc_loss(
-                generated_images,
-                real_images_batch,
-                image_name,
-                self.G,
-                use_ball_holder,
-                w_pivot,
-            )
-
-            self.optimizer.zero_grad()
-
-            if loss_lpips <= hyperparameters.LPIPS_value_threshold:
-                break
-
-            loss.backward()
-            self.optimizer.step()
-
-            use_ball_holder = (
-                global_config.training_step
-                % hyperparameters.locality_regularization_interval
-                == 0
-            )
-
-            if (
-                self.use_wandb
-                and log_images_counter % global_config.image_rec_result_log_snapshot
-                == 0
-            ):
-                log_images_from_w([w_pivot], self.G, [image_name])
-
-            global_config.training_step += 1
-            log_images_counter += 1
-
-        torch.save(
-            self.G,
-            f"{paths_config.checkpoints_dir}/model_{global_config.run_name}_{image_name}.pt",
-        )
-        return self.G, w_pivot

spaces/Amrrs/image-caption-with-vit-gpt2/README.md

@@ -1,46 +0,0 @@
----
-title: Image Caption With Vit Gpt2
-emoji: 👀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 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.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py

@@ -1,578 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
-
-from ...models import PriorTransformer
-from ...schedulers import UnCLIPScheduler
-from ...utils import (
-    BaseOutput,
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
-        >>> import torch
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior")
-        >>> pipe_prior.to("cuda")
-
-        >>> prompt = "red cat, 4k photo"
-        >>> out = pipe_prior(prompt)
-        >>> image_emb = out.image_embeds
-        >>> negative_image_emb = out.negative_image_embeds
-
-        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
-        >>> pipe.to("cuda")
-
-        >>> image = pipe(
-        ...     prompt,
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=negative_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=100,
-        ... ).images
-
-        >>> image[0].save("cat.png")
-        ```
-"""
-
-EXAMPLE_INTERPOLATE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline
-        >>> from diffusers.utils import load_image
-        >>> import PIL
-
-        >>> import torch
-        >>> from torchvision import transforms
-
-        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-
-        >>> img1 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... )
-
-        >>> img2 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/starry_night.jpeg"
-        ... )
-
-        >>> images_texts = ["a cat", img1, img2]
-        >>> weights = [0.3, 0.3, 0.4]
-        >>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)
-
-        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-        >>> pipe.to("cuda")
-
-        >>> image = pipe(
-        ...     "",
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=zero_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=150,
-        ... ).images[0]
-
-        >>> image.save("starry_cat.png")
-        ```
-"""
-
-
-@dataclass
-class KandinskyPriorPipelineOutput(BaseOutput):
-    """
-    Output class for KandinskyPriorPipeline.
-
-    Args:
-        image_embeds (`torch.FloatTensor`)
-            clip image embeddings for text prompt
-        negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)
-            clip image embeddings for unconditional tokens
-    """
-
-    image_embeds: Union[torch.FloatTensor, np.ndarray]
-    negative_image_embeds: Union[torch.FloatTensor, np.ndarray]
-
-
-class KandinskyPriorPipeline(DiffusionPipeline):
-    """
-    Pipeline for generating image prior for Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen image-encoder.
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        scheduler ([`UnCLIPScheduler`]):
-            A scheduler to be used in combination with `prior` to generate image embedding.
-    """
-
-    _exclude_from_cpu_offload = ["prior"]
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        image_encoder: CLIPVisionModelWithProjection,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        scheduler: UnCLIPScheduler,
-        image_processor: CLIPImageProcessor,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            image_processor=image_processor,
-        )
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
-    def interpolate(
-        self,
-        images_and_prompts: List[Union[str, PIL.Image.Image, torch.FloatTensor]],
-        weights: List[float],
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        negative_prior_prompt: Optional[str] = None,
-        negative_prompt: str = "",
-        guidance_scale: float = 4.0,
-        device=None,
-    ):
-        """
-        Function invoked when using the prior pipeline for interpolation.
-
-        Args:
-            images_and_prompts (`List[Union[str, PIL.Image.Image, torch.FloatTensor]]`):
-                list of prompts and images to guide the image generation.
-            weights: (`List[float]`):
-                list of weights for each condition in `images_and_prompts`
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            negative_prior_prompt (`str`, *optional*):
-                The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        device = device or self.device
-
-        if len(images_and_prompts) != len(weights):
-            raise ValueError(
-                f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length"
-            )
-
-        image_embeddings = []
-        for cond, weight in zip(images_and_prompts, weights):
-            if isinstance(cond, str):
-                image_emb = self(
-                    cond,
-                    num_inference_steps=num_inference_steps,
-                    num_images_per_prompt=num_images_per_prompt,
-                    generator=generator,
-                    latents=latents,
-                    negative_prompt=negative_prior_prompt,
-                    guidance_scale=guidance_scale,
-                ).image_embeds
-
-            elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
-                if isinstance(cond, PIL.Image.Image):
-                    cond = (
-                        self.image_processor(cond, return_tensors="pt")
-                        .pixel_values[0]
-                        .unsqueeze(0)
-                        .to(dtype=self.image_encoder.dtype, device=device)
-                    )
-
-                image_emb = self.image_encoder(cond)["image_embeds"]
-
-            else:
-                raise ValueError(
-                    f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor`  but is {type(cond)}"
-                )
-
-            image_embeddings.append(image_emb * weight)
-
-        image_emb = torch.cat(image_embeddings).sum(dim=0, keepdim=True)
-
-        out_zero = self(
-            negative_prompt,
-            num_inference_steps=num_inference_steps,
-            num_images_per_prompt=num_images_per_prompt,
-            generator=generator,
-            latents=latents,
-            negative_prompt=negative_prior_prompt,
-            guidance_scale=guidance_scale,
-        )
-        zero_image_emb = out_zero.negative_image_embeds if negative_prompt == "" else out_zero.image_embeds
-
-        return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    def get_zero_embed(self, batch_size=1, device=None):
-        device = device or self.device
-        zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
-            device=device, dtype=self.image_encoder.dtype
-        )
-        zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
-        zero_image_emb = zero_image_emb.repeat(batch_size, 1)
-        return zero_image_emb
-
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        text_mask = text_inputs.attention_mask.bool().to(device)
-
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-
-        text_encoder_output = self.text_encoder(text_input_ids.to(device))
-
-        prompt_embeds = text_encoder_output.text_embeds
-        text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
-            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
-
-            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.prior]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.prior_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.image_encoder, device, prev_module_hook=self.prior_hook)
-
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        guidance_scale: float = 4.0,
-        output_type: Optional[str] = "pt",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            output_type (`str`, *optional*, defaults to `"pt"`):
-                The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
-                (`torch.Tensor`).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        if isinstance(prompt, str):
-            prompt = [prompt]
-        elif not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if isinstance(negative_prompt, str):
-            negative_prompt = [negative_prompt]
-        elif not isinstance(negative_prompt, list) and negative_prompt is not None:
-            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
-
-        # if the negative prompt is defined we double the batch size to
-        # directly retrieve the negative prompt embedding
-        if negative_prompt is not None:
-            prompt = prompt + negative_prompt
-            negative_prompt = 2 * negative_prompt
-
-        device = self._execution_device
-
-        batch_size = len(prompt)
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # prior
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        prior_timesteps_tensor = self.scheduler.timesteps
-
-        embedding_dim = self.prior.config.embedding_dim
-
-        latents = self.prepare_latents(
-            (batch_size, embedding_dim),
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            predicted_image_embedding = self.prior(
-                latent_model_input,
-                timestep=t,
-                proj_embedding=prompt_embeds,
-                encoder_hidden_states=text_encoder_hidden_states,
-                attention_mask=text_mask,
-            ).predicted_image_embedding
-
-            if do_classifier_free_guidance:
-                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
-                predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (
-                    predicted_image_embedding_text - predicted_image_embedding_uncond
-                )
-
-            if i + 1 == prior_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = prior_timesteps_tensor[i + 1]
-
-            latents = self.scheduler.step(
-                predicted_image_embedding,
-                timestep=t,
-                sample=latents,
-                generator=generator,
-                prev_timestep=prev_timestep,
-            ).prev_sample
-
-        latents = self.prior.post_process_latents(latents)
-
-        image_embeddings = latents
-
-        # if negative prompt has been defined, we retrieve split the image embedding into two
-        if negative_prompt is None:
-            zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
-
-            if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-                self.final_offload_hook.offload()
-        else:
-            image_embeddings, zero_embeds = image_embeddings.chunk(2)
-
-            if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-                self.prior_hook.offload()
-
-        if output_type not in ["pt", "np"]:
-            raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}")
-
-        if output_type == "np":
-            image_embeddings = image_embeddings.cpu().numpy()
-            zero_embeds = zero_embeds.cpu().numpy()
-
-        if not return_dict:
-            return (image_embeddings, zero_embeds)
-
-        return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_image_processor.py

@@ -1,149 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-import PIL
-import torch
-
-from diffusers.image_processor import VaeImageProcessor
-
-
-class ImageProcessorTest(unittest.TestCase):
-    @property
-    def dummy_sample(self):
-        batch_size = 1
-        num_channels = 3
-        height = 8
-        width = 8
-
-        sample = torch.rand((batch_size, num_channels, height, width))
-
-        return sample
-
-    def to_np(self, image):
-        if isinstance(image[0], PIL.Image.Image):
-            return np.stack([np.array(i) for i in image], axis=0)
-        elif isinstance(image, torch.Tensor):
-            return image.cpu().numpy().transpose(0, 2, 3, 1)
-        return image
-
-    def test_vae_image_processor_pt(self):
-        image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
-
-        input_pt = self.dummy_sample
-        input_np = self.to_np(input_pt)
-
-        for output_type in ["pt", "np", "pil"]:
-            out = image_processor.postprocess(
-                image_processor.preprocess(input_pt),
-                output_type=output_type,
-            )
-            out_np = self.to_np(out)
-            in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert (
-                np.abs(in_np - out_np).max() < 1e-6
-            ), f"decoded output does not match input for output_type {output_type}"
-
-    def test_vae_image_processor_np(self):
-        image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
-        input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
-
-        for output_type in ["pt", "np", "pil"]:
-            out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type)
-
-            out_np = self.to_np(out)
-            in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert (
-                np.abs(in_np - out_np).max() < 1e-6
-            ), f"decoded output does not match input for output_type {output_type}"
-
-    def test_vae_image_processor_pil(self):
-        image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
-
-        input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
-        input_pil = image_processor.numpy_to_pil(input_np)
-
-        for output_type in ["pt", "np", "pil"]:
-            out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type)
-            for i, o in zip(input_pil, out):
-                in_np = np.array(i)
-                out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
-                assert (
-                    np.abs(in_np - out_np).max() < 1e-6
-                ), f"decoded output does not match input for output_type {output_type}"
-
-    def test_preprocess_input_3d(self):
-        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
-
-        input_pt_4d = self.dummy_sample
-        input_pt_3d = input_pt_4d.squeeze(0)
-
-        out_pt_4d = image_processor.postprocess(
-            image_processor.preprocess(input_pt_4d),
-            output_type="np",
-        )
-        out_pt_3d = image_processor.postprocess(
-            image_processor.preprocess(input_pt_3d),
-            output_type="np",
-        )
-
-        input_np_4d = self.to_np(self.dummy_sample)
-        input_np_3d = input_np_4d.squeeze(0)
-
-        out_np_4d = image_processor.postprocess(
-            image_processor.preprocess(input_np_4d),
-            output_type="np",
-        )
-        out_np_3d = image_processor.postprocess(
-            image_processor.preprocess(input_np_3d),
-            output_type="np",
-        )
-
-        assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6
-        assert np.abs(out_np_4d - out_np_3d).max() < 1e-6
-
-    def test_preprocess_input_list(self):
-        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
-
-        input_pt_4d = self.dummy_sample
-        input_pt_list = list(input_pt_4d)
-
-        out_pt_4d = image_processor.postprocess(
-            image_processor.preprocess(input_pt_4d),
-            output_type="np",
-        )
-
-        out_pt_list = image_processor.postprocess(
-            image_processor.preprocess(input_pt_list),
-            output_type="np",
-        )
-
-        input_np_4d = self.to_np(self.dummy_sample)
-        list(input_np_4d)
-
-        out_np_4d = image_processor.postprocess(
-            image_processor.preprocess(input_pt_4d),
-            output_type="np",
-        )
-
-        out_np_list = image_processor.postprocess(
-            image_processor.preprocess(input_pt_list),
-            output_type="np",
-        )
-
-        assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6
-        assert np.abs(out_np_4d - out_np_list).max() < 1e-6

spaces/Andy1621/uniformer_image_detection/mmdet/core/mask/__init__.py

@@ -1,8 +0,0 @@
-from .mask_target import mask_target
-from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks
-from .utils import encode_mask_results, split_combined_polys
-
-__all__ = [
-    'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks',
-    'PolygonMasks', 'encode_mask_results'
-]

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/paa_head.py

@@ -1,671 +0,0 @@
-import numpy as np
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import multi_apply, multiclass_nms
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-from mmdet.models import HEADS
-from mmdet.models.dense_heads import ATSSHead
-
-EPS = 1e-12
-try:
-    import sklearn.mixture as skm
-except ImportError:
-    skm = None
-
-
-def levels_to_images(mlvl_tensor):
-    """Concat multi-level feature maps by image.
-
-    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
-    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
-    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
-    concat elements in same image of all level along first dimension.
-
-    Args:
-        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
-            corresponding level. Each element is of shape (N, C, H, W)
-
-    Returns:
-        list[torch.Tensor]: A list that contains N tensors and each tensor is
-            of shape (num_elements, C)
-    """
-    batch_size = mlvl_tensor[0].size(0)
-    batch_list = [[] for _ in range(batch_size)]
-    channels = mlvl_tensor[0].size(1)
-    for t in mlvl_tensor:
-        t = t.permute(0, 2, 3, 1)
-        t = t.view(batch_size, -1, channels).contiguous()
-        for img in range(batch_size):
-            batch_list[img].append(t[img])
-    return [torch.cat(item, 0) for item in batch_list]
-
-
-@HEADS.register_module()
-class PAAHead(ATSSHead):
-    """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
-    Prediction for Object Detection.
-
-    Code is modified from the `official github repo
-    <https://github.com/kkhoot/PAA/blob/master/paa_core
-    /modeling/rpn/paa/loss.py>`_.
-
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2007.08103>`_ .
-
-    Args:
-        topk (int): Select topk samples with smallest loss in
-            each level.
-        score_voting (bool): Whether to use score voting in post-process.
-        covariance_type : String describing the type of covariance parameters
-            to be used in :class:`sklearn.mixture.GaussianMixture`.
-            It must be one of:
-
-            - 'full': each component has its own general covariance matrix
-            - 'tied': all components share the same general covariance matrix
-            - 'diag': each component has its own diagonal covariance matrix
-            - 'spherical': each component has its own single variance
-            Default: 'diag'. From 'full' to 'spherical', the gmm fitting
-            process is faster yet the performance could be influenced. For most
-            cases, 'diag' should be a good choice.
-    """
-
-    def __init__(self,
-                 *args,
-                 topk=9,
-                 score_voting=True,
-                 covariance_type='diag',
-                 **kwargs):
-        # topk used in paa reassign process
-        self.topk = topk
-        self.with_score_voting = score_voting
-        self.covariance_type = covariance_type
-        super(PAAHead, self).__init__(*args, **kwargs)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             iou_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            iou_preds (list[Tensor]): iou_preds for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
-                boxes can be ignored when are computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss gmm_assignment.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-        )
-        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
-         pos_gt_index) = cls_reg_targets
-        cls_scores = levels_to_images(cls_scores)
-        cls_scores = [
-            item.reshape(-1, self.cls_out_channels) for item in cls_scores
-        ]
-        bbox_preds = levels_to_images(bbox_preds)
-        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
-        iou_preds = levels_to_images(iou_preds)
-        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
-        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
-                                       cls_scores, bbox_preds, labels,
-                                       labels_weight, bboxes_target,
-                                       bboxes_weight, pos_inds)
-
-        with torch.no_grad():
-            reassign_labels, reassign_label_weight, \
-                reassign_bbox_weights, num_pos = multi_apply(
-                    self.paa_reassign,
-                    pos_losses_list,
-                    labels,
-                    labels_weight,
-                    bboxes_weight,
-                    pos_inds,
-                    pos_gt_index,
-                    anchor_list)
-            num_pos = sum(num_pos)
-        # convert all tensor list to a flatten tensor
-        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
-        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
-        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
-        labels = torch.cat(reassign_labels, 0).view(-1)
-        flatten_anchors = torch.cat(
-            [torch.cat(item, 0) for item in anchor_list])
-        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
-        bboxes_target = torch.cat(bboxes_target,
-                                  0).view(-1, bboxes_target[0].size(-1))
-
-        pos_inds_flatten = ((labels >= 0)
-                            &
-                            (labels < self.num_classes)).nonzero().reshape(-1)
-
-        losses_cls = self.loss_cls(
-            cls_scores,
-            labels,
-            labels_weight,
-            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
-        if num_pos:
-            pos_bbox_pred = self.bbox_coder.decode(
-                flatten_anchors[pos_inds_flatten],
-                bbox_preds[pos_inds_flatten])
-            pos_bbox_target = bboxes_target[pos_inds_flatten]
-            iou_target = bbox_overlaps(
-                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
-            losses_iou = self.loss_centerness(
-                iou_preds[pos_inds_flatten],
-                iou_target.unsqueeze(-1),
-                avg_factor=num_pos)
-            losses_bbox = self.loss_bbox(
-                pos_bbox_pred,
-                pos_bbox_target,
-                iou_target.clamp(min=EPS),
-                avg_factor=iou_target.sum())
-        else:
-            losses_iou = iou_preds.sum() * 0
-            losses_bbox = bbox_preds.sum() * 0
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
-
-    def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
-                     bbox_target, bbox_weight, pos_inds):
-        """Calculate loss of all potential positive samples obtained from first
-        match process.
-
-        Args:
-            anchors (list[Tensor]): Anchors of each scale.
-            cls_score (Tensor): Box scores of single image with shape
-                (num_anchors, num_classes)
-            bbox_pred (Tensor): Box energies / deltas of single image
-                with shape (num_anchors, 4)
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_target (dict): Regression target of each anchor with
-                shape (num_anchors, 4).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-
-        Returns:
-            Tensor: Losses of all positive samples in single image.
-        """
-        if not len(pos_inds):
-            return cls_score.new([]),
-        anchors_all_level = torch.cat(anchors, 0)
-        pos_scores = cls_score[pos_inds]
-        pos_bbox_pred = bbox_pred[pos_inds]
-        pos_label = label[pos_inds]
-        pos_label_weight = label_weight[pos_inds]
-        pos_bbox_target = bbox_target[pos_inds]
-        pos_bbox_weight = bbox_weight[pos_inds]
-        pos_anchors = anchors_all_level[pos_inds]
-        pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
-
-        # to keep loss dimension
-        loss_cls = self.loss_cls(
-            pos_scores,
-            pos_label,
-            pos_label_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_bbox = self.loss_bbox(
-            pos_bbox_pred,
-            pos_bbox_target,
-            pos_bbox_weight,
-            avg_factor=self.loss_cls.loss_weight,
-            reduction_override='none')
-
-        loss_cls = loss_cls.sum(-1)
-        pos_loss = loss_bbox + loss_cls
-        return pos_loss,
-
-    def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
-                     pos_inds, pos_gt_inds, anchors):
-        """Fit loss to GMM distribution and separate positive, ignore, negative
-        samples again with GMM model.
-
-        Args:
-            pos_losses (Tensor): Losses of all positive samples in
-                single image.
-            label (Tensor): classification target of each anchor with
-                shape (num_anchors,)
-            label_weight (Tensor): Classification loss weight of each
-                anchor with shape (num_anchors).
-            bbox_weight (Tensor): Bbox weight of each anchor with shape
-                (num_anchors, 4).
-            pos_inds (Tensor): Index of all positive samples got from
-                first assign process.
-            pos_gt_inds (Tensor): Gt_index of all positive samples got
-                from first assign process.
-            anchors (list[Tensor]): Anchors of each scale.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - label (Tensor): classification target of each anchor after
-                  paa assign, with shape (num_anchors,)
-                - label_weight (Tensor): Classification loss weight of each
-                  anchor after paa assign, with shape (num_anchors).
-                - bbox_weight (Tensor): Bbox weight of each anchor with shape
-                  (num_anchors, 4).
-                - num_pos (int): The number of positive samples after paa
-                  assign.
-        """
-        if not len(pos_inds):
-            return label, label_weight, bbox_weight, 0
-        label = label.clone()
-        label_weight = label_weight.clone()
-        bbox_weight = bbox_weight.clone()
-        num_gt = pos_gt_inds.max() + 1
-        num_level = len(anchors)
-        num_anchors_each_level = [item.size(0) for item in anchors]
-        num_anchors_each_level.insert(0, 0)
-        inds_level_interval = np.cumsum(num_anchors_each_level)
-        pos_level_mask = []
-        for i in range(num_level):
-            mask = (pos_inds >= inds_level_interval[i]) & (
-                pos_inds < inds_level_interval[i + 1])
-            pos_level_mask.append(mask)
-        pos_inds_after_paa = [label.new_tensor([])]
-        ignore_inds_after_paa = [label.new_tensor([])]
-        for gt_ind in range(num_gt):
-            pos_inds_gmm = []
-            pos_loss_gmm = []
-            gt_mask = pos_gt_inds == gt_ind
-            for level in range(num_level):
-                level_mask = pos_level_mask[level]
-                level_gt_mask = level_mask & gt_mask
-                value, topk_inds = pos_losses[level_gt_mask].topk(
-                    min(level_gt_mask.sum(), self.topk), largest=False)
-                pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
-                pos_loss_gmm.append(value)
-            pos_inds_gmm = torch.cat(pos_inds_gmm)
-            pos_loss_gmm = torch.cat(pos_loss_gmm)
-            # fix gmm need at least two sample
-            if len(pos_inds_gmm) < 2:
-                continue
-            device = pos_inds_gmm.device
-            pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
-            pos_inds_gmm = pos_inds_gmm[sort_inds]
-            pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
-            min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
-            means_init = np.array([min_loss, max_loss]).reshape(2, 1)
-            weights_init = np.array([0.5, 0.5])
-            precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1)  # full
-            if self.covariance_type == 'spherical':
-                precisions_init = precisions_init.reshape(2)
-            elif self.covariance_type == 'diag':
-                precisions_init = precisions_init.reshape(2, 1)
-            elif self.covariance_type == 'tied':
-                precisions_init = np.array([[1.0]])
-            if skm is None:
-                raise ImportError('Please run "pip install sklearn" '
-                                  'to install sklearn first.')
-            gmm = skm.GaussianMixture(
-                2,
-                weights_init=weights_init,
-                means_init=means_init,
-                precisions_init=precisions_init,
-                covariance_type=self.covariance_type)
-            gmm.fit(pos_loss_gmm)
-            gmm_assignment = gmm.predict(pos_loss_gmm)
-            scores = gmm.score_samples(pos_loss_gmm)
-            gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
-            scores = torch.from_numpy(scores).to(device)
-
-            pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
-                gmm_assignment, scores, pos_inds_gmm)
-            pos_inds_after_paa.append(pos_inds_temp)
-            ignore_inds_after_paa.append(ignore_inds_temp)
-
-        pos_inds_after_paa = torch.cat(pos_inds_after_paa)
-        ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
-        reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
-        reassign_ids = pos_inds[reassign_mask]
-        label[reassign_ids] = self.num_classes
-        label_weight[ignore_inds_after_paa] = 0
-        bbox_weight[reassign_ids] = 0
-        num_pos = len(pos_inds_after_paa)
-        return label, label_weight, bbox_weight, num_pos
-
-    def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
-        """A general separation scheme for gmm model.
-
-        It separates a GMM distribution of candidate samples into three
-        parts, 0 1 and uncertain areas, and you can implement other
-        separation schemes by rewriting this function.
-
-        Args:
-            gmm_assignment (Tensor): The prediction of GMM which is of shape
-                (num_samples,). The 0/1 value indicates the distribution
-                that each sample comes from.
-            scores (Tensor): The probability of sample coming from the
-                fit GMM distribution. The tensor is of shape (num_samples,).
-            pos_inds_gmm (Tensor): All the indexes of samples which are used
-                to fit GMM model. The tensor is of shape (num_samples,)
-
-        Returns:
-            tuple[Tensor]: The indices of positive and ignored samples.
-
-                - pos_inds_temp (Tensor): Indices of positive samples.
-                - ignore_inds_temp (Tensor): Indices of ignore samples.
-        """
-        # The implementation is (c) in Fig.3 in origin paper instead of (b).
-        # You can refer to issues such as
-        # https://github.com/kkhoot/PAA/issues/8 and
-        # https://github.com/kkhoot/PAA/issues/9.
-        fgs = gmm_assignment == 0
-        pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
-        if fgs.nonzero().numel():
-            _, pos_thr_ind = scores[fgs].topk(1)
-            pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
-            ignore_inds_temp = pos_inds_gmm.new_tensor([])
-        return pos_inds_temp, ignore_inds_temp
-
-    def get_targets(
-        self,
-        anchor_list,
-        valid_flag_list,
-        gt_bboxes_list,
-        img_metas,
-        gt_bboxes_ignore_list=None,
-        gt_labels_list=None,
-        label_channels=1,
-        unmap_outputs=True,
-    ):
-        """Get targets for PAA head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. We direct
-        return the results from _get_targets_single instead map it to levels
-        by images_to_levels function.
-
-        Args:
-            anchor_list (list[list[Tensor]]): Multi level anchors of each
-                image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, 4).
-            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
-                each image. The outer list indicates images, and the inner list
-                corresponds to feature levels of the image. Each element of
-                the inner list is a tensor of shape (num_anchors, )
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_labels_list (list[Tensor]): Ground truth labels of each box.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - labels (list[Tensor]): Labels of all anchors, each with
-                    shape (num_anchors,).
-                - label_weights (list[Tensor]): Label weights of all anchor.
-                    each with shape (num_anchors,).
-                - bbox_targets (list[Tensor]): BBox targets of all anchors.
-                    each with shape (num_anchors, 4).
-                - bbox_weights (list[Tensor]): BBox weights of all anchors.
-                    each with shape (num_anchors, 4).
-                - pos_inds (list[Tensor]): Contains all index of positive
-                    sample in all anchor.
-                - gt_inds (list[Tensor]): Contains all gt_index of positive
-                    sample in all anchor.
-        """
-
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-        concat_anchor_list = []
-        concat_valid_flag_list = []
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        results = multi_apply(
-            self._get_targets_single,
-            concat_anchor_list,
-            concat_valid_flag_list,
-            gt_bboxes_list,
-            gt_bboxes_ignore_list,
-            gt_labels_list,
-            img_metas,
-            label_channels=label_channels,
-            unmap_outputs=unmap_outputs)
-
-        (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
-         valid_neg_inds, sampling_result) = results
-
-        # Due to valid flag of anchors, we have to calculate the real pos_inds
-        # in origin anchor set.
-        pos_inds = []
-        for i, single_labels in enumerate(labels):
-            pos_mask = (0 <= single_labels) & (
-                single_labels < self.num_classes)
-            pos_inds.append(pos_mask.nonzero().view(-1))
-
-        gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                gt_inds)
-
-    def _get_targets_single(self,
-                            flat_anchors,
-                            valid_flags,
-                            gt_bboxes,
-                            gt_bboxes_ignore,
-                            gt_labels,
-                            img_meta,
-                            label_channels=1,
-                            unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        This method is same as `AnchorHead._get_targets_single()`.
-        """
-        assert unmap_outputs, 'We must map outputs back to the original' \
-            'set of anchors in PAAhead'
-        return super(ATSSHead, self)._get_targets_single(
-            flat_anchors,
-            valid_flags,
-            gt_bboxes,
-            gt_bboxes_ignore,
-            gt_labels,
-            img_meta,
-            label_channels=1,
-            unmap_outputs=True)
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    iou_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        This method is almost same as `ATSSHead._get_bboxes()`.
-        We use sqrt(iou_preds * cls_scores) in NMS process instead of just
-        cls_scores. Besides, score voting is used when `` score_voting``
-        is set to True.
-        """
-        assert with_nms, 'PAA only supports "with_nms=True" now'
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_iou_preds = []
-        for cls_score, bbox_pred, iou_preds, anchors in zip(
-                cls_scores, bbox_preds, iou_preds, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-            iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size,
-                                                              -1).sigmoid()
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                iou_preds = iou_preds[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_iou_preds.append(iou_preds)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-        batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1)
-        batch_mlvl_nms_scores = (batch_mlvl_scores *
-                                 batch_mlvl_iou_preds[..., None]).sqrt()
-
-        det_results = []
-        for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                              batch_mlvl_nms_scores):
-            det_bbox, det_label = multiclass_nms(
-                mlvl_bboxes,
-                mlvl_scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=None)
-            if self.with_score_voting and len(det_bbox) > 0:
-                det_bbox, det_label = self.score_voting(
-                    det_bbox, det_label, mlvl_bboxes, mlvl_scores,
-                    cfg.score_thr)
-            det_results.append(tuple([det_bbox, det_label]))
-
-        return det_results
-
-    def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
-                     mlvl_nms_scores, score_thr):
-        """Implementation of score voting method works on each remaining boxes
-        after NMS procedure.
-
-        Args:
-            det_bboxes (Tensor): Remaining boxes after NMS procedure,
-                with shape (k, 5), each dimension means
-                (x1, y1, x2, y2, score).
-            det_labels (Tensor): The label of remaining boxes, with shape
-                (k, 1),Labels are 0-based.
-            mlvl_bboxes (Tensor): All boxes before the NMS procedure,
-                with shape (num_anchors,4).
-            mlvl_nms_scores (Tensor): The scores of all boxes which is used
-                in the NMS procedure, with shape (num_anchors, num_class)
-            mlvl_iou_preds (Tensor): The predictions of IOU of all boxes
-                before the NMS procedure, with shape (num_anchors, 1)
-            score_thr (float): The score threshold of bboxes.
-
-        Returns:
-            tuple: Usually returns a tuple containing voting results.
-
-                - det_bboxes_voted (Tensor): Remaining boxes after
-                    score voting procedure, with shape (k, 5), each
-                    dimension means (x1, y1, x2, y2, score).
-                - det_labels_voted (Tensor): Label of remaining bboxes
-                    after voting, with shape (num_anchors,).
-        """
-        candidate_mask = mlvl_nms_scores > score_thr
-        candidate_mask_nonzeros = candidate_mask.nonzero()
-        candidate_inds = candidate_mask_nonzeros[:, 0]
-        candidate_labels = candidate_mask_nonzeros[:, 1]
-        candidate_bboxes = mlvl_bboxes[candidate_inds]
-        candidate_scores = mlvl_nms_scores[candidate_mask]
-        det_bboxes_voted = []
-        det_labels_voted = []
-        for cls in range(self.cls_out_channels):
-            candidate_cls_mask = candidate_labels == cls
-            if not candidate_cls_mask.any():
-                continue
-            candidate_cls_scores = candidate_scores[candidate_cls_mask]
-            candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
-            det_cls_mask = det_labels == cls
-            det_cls_bboxes = det_bboxes[det_cls_mask].view(
-                -1, det_bboxes.size(-1))
-            det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
-                                               candidate_cls_bboxes)
-            for det_ind in range(len(det_cls_bboxes)):
-                single_det_ious = det_candidate_ious[det_ind]
-                pos_ious_mask = single_det_ious > 0.01
-                pos_ious = single_det_ious[pos_ious_mask]
-                pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
-                pos_scores = candidate_cls_scores[pos_ious_mask]
-                pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
-                       pos_scores)[:, None]
-                voted_box = torch.sum(
-                    pis * pos_bboxes, dim=0) / torch.sum(
-                        pis, dim=0)
-                voted_score = det_cls_bboxes[det_ind][-1:][None, :]
-                det_bboxes_voted.append(
-                    torch.cat((voted_box[None, :], voted_score), dim=1))
-                det_labels_voted.append(cls)
-
-        det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
-        det_labels_voted = det_labels.new_tensor(det_labels_voted)
-        return det_bboxes_voted, det_labels_voted

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py

@@ -1,27 +0,0 @@
-from mmdet.models.builder import HEADS
-from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
-from .fcn_mask_head import FCNMaskHead
-
-
-@HEADS.register_module()
-class SCNetMaskHead(FCNMaskHead):
-    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
-
-    Args:
-        conv_to_res (bool, optional): if True, change the conv layers to
-            ``SimplifiedBasicBlock``.
-    """
-
-    def __init__(self, conv_to_res=True, **kwargs):
-        super(SCNetMaskHead, self).__init__(**kwargs)
-        self.conv_to_res = conv_to_res
-        if conv_to_res:
-            assert self.conv_kernel_size == 3
-            self.num_res_blocks = self.num_convs // 2
-            self.convs = ResLayer(
-                SimplifiedBasicBlock,
-                self.in_channels,
-                self.conv_out_channels,
-                self.num_res_blocks,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg)

spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/apcnet_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

spaces/Armored-Atom/Image-To-Motion/app.py

@@ -1,128 +0,0 @@
-import gradio as gr
-import os
-import shutil
-import torch
-from PIL import Image
-import argparse
-import pathlib
-
-os.system("git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model")
-os.chdir("Thin-Plate-Spline-Motion-Model")
-os.system("mkdir checkpoints")
-os.system("wget -c https://cloud.tsinghua.edu.cn/f/da8d61d012014b12a9e4/?dl=1 -O checkpoints/vox.pth.tar")
-
-
-
-title = "# Thin-Plate Spline Motion Model for Image Animation"
-DESCRIPTION = '''### Gradio demo for <b>Thin-Plate Spline Motion Model for Image Animation</b>, CVPR 2022. <a href='https://arxiv.org/abs/2203.14367'>[Paper]</a><a href='https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model'>[Github Code]</a>
-
-<img id="overview" alt="overview" src="https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model/raw/main/assets/vox.gif" />
-'''
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.Image-Animation-using-Thin-Plate-Spline-Motion-Model" />'
-
-
-def get_style_image_path(style_name: str) -> str:
-    base_path = 'assets'
-    filenames = {
-        'source': 'source.png',
-        'driving': 'driving.mp4',
-    }
-    return f'{base_path}/{filenames[style_name]}'
-
-
-def get_style_image_markdown_text(style_name: str) -> str:
-    url = get_style_image_path(style_name)
-    return f'<img id="style-image" src="{url}" alt="style image">'
-
-
-def update_style_image(style_name: str) -> dict:
-    text = get_style_image_markdown_text(style_name)
-    return gr.Markdown.update(value=text)
-
-
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-
-def set_example_video(example: list) -> dict:
-    return gr.Video.update(value=example[0])
-
-def inference(img,vid):
-  if not os.path.exists('temp'):
-    os.system('mkdir temp')
-  
-  img.save("temp/image.jpg", "JPEG")
-  os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4' --cpu")
-  return './temp/result.mp4'
-  
-
-
-def main():
-    with gr.Blocks(theme="huggingface", css='style.css') as demo:
-        gr.Markdown(title)
-        gr.Markdown(DESCRIPTION)
-
-        with gr.Box():
-            gr.Markdown('''## Step 1 (Provide Input Face Image)
-- Drop an image containing a face to the **Input Image**.
-    - If there are multiple faces in the image, use Edit button in the upper right corner and crop the input image beforehand.
-''')
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        input_image = gr.Image(label='Input Image',
-                                               type="pil")
-                        
-            with gr.Row():
-                paths = sorted(pathlib.Path('assets').glob('*.png'))
-                example_images = gr.Dataset(components=[input_image],
-                                            samples=[[path.as_posix()]
-                                                     for path in paths])
-
-        with gr.Box():
-            gr.Markdown('''## Step 2 (Select Driving Video)
-- Select **Style Driving Video for the face image animation**.
-''')
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        driving_video = gr.Video(label='Driving Video',
-                                               format="mp4")
-
-            with gr.Row():
-                paths = sorted(pathlib.Path('assets').glob('*.mp4'))
-                example_video = gr.Dataset(components=[driving_video],
-                                            samples=[[path.as_posix()]
-                                                     for path in paths])
-
-        with gr.Box():
-            gr.Markdown('''## Step 3 (Generate Animated Image based on the Video)
-- Hit the **Generate** button. (Note: As it runs on the CPU, it takes ~ 3 minutes to generate final results.)
-''')
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        generate_button = gr.Button('Generate')
-
-                with gr.Column():
-                    result = gr.Video(type="file", label="Output")
-        gr.Markdown(FOOTER)
-        generate_button.click(fn=inference,
-                              inputs=[
-                                  input_image,
-                                  driving_video
-                              ],
-                              outputs=result)
-        example_images.click(fn=set_example_image,
-                             inputs=example_images,
-                             outputs=example_images.components)
-        example_video.click(fn=set_example_video,
-                             inputs=example_video,
-                             outputs=example_video.components)
-
-    demo.launch(
-        enable_queue=True,
-        debug=True
-    )
-
-if __name__ == '__main__':
-    main()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/spinners.py

@@ -1,159 +0,0 @@
-import contextlib
-import itertools
-import logging
-import sys
-import time
-from typing import IO, Generator, Optional
-
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.logging import get_indentation
-
-logger = logging.getLogger(__name__)
-
-
-class SpinnerInterface:
-    def spin(self) -> None:
-        raise NotImplementedError()
-
-    def finish(self, final_status: str) -> None:
-        raise NotImplementedError()
-
-
-class InteractiveSpinner(SpinnerInterface):
-    def __init__(
-        self,
-        message: str,
-        file: Optional[IO[str]] = None,
-        spin_chars: str = "-\\|/",
-        # Empirically, 8 updates/second looks nice
-        min_update_interval_seconds: float = 0.125,
-    ):
-        self._message = message
-        if file is None:
-            file = sys.stdout
-        self._file = file
-        self._rate_limiter = RateLimiter(min_update_interval_seconds)
-        self._finished = False
-
-        self._spin_cycle = itertools.cycle(spin_chars)
-
-        self._file.write(" " * get_indentation() + self._message + " ... ")
-        self._width = 0
-
-    def _write(self, status: str) -> None:
-        assert not self._finished
-        # Erase what we wrote before by backspacing to the beginning, writing
-        # spaces to overwrite the old text, and then backspacing again
-        backup = "\b" * self._width
-        self._file.write(backup + " " * self._width + backup)
-        # Now we have a blank slate to add our status
-        self._file.write(status)
-        self._width = len(status)
-        self._file.flush()
-        self._rate_limiter.reset()
-
-    def spin(self) -> None:
-        if self._finished:
-            return
-        if not self._rate_limiter.ready():
-            return
-        self._write(next(self._spin_cycle))
-
-    def finish(self, final_status: str) -> None:
-        if self._finished:
-            return
-        self._write(final_status)
-        self._file.write("\n")
-        self._file.flush()
-        self._finished = True
-
-
-# Used for dumb terminals, non-interactive installs (no tty), etc.
-# We still print updates occasionally (once every 60 seconds by default) to
-# act as a keep-alive for systems like Travis-CI that take lack-of-output as
-# an indication that a task has frozen.
-class NonInteractiveSpinner(SpinnerInterface):
-    def __init__(self, message: str, min_update_interval_seconds: float = 60.0) -> None:
-        self._message = message
-        self._finished = False
-        self._rate_limiter = RateLimiter(min_update_interval_seconds)
-        self._update("started")
-
-    def _update(self, status: str) -> None:
-        assert not self._finished
-        self._rate_limiter.reset()
-        logger.info("%s: %s", self._message, status)
-
-    def spin(self) -> None:
-        if self._finished:
-            return
-        if not self._rate_limiter.ready():
-            return
-        self._update("still running...")
-
-    def finish(self, final_status: str) -> None:
-        if self._finished:
-            return
-        self._update(f"finished with status '{final_status}'")
-        self._finished = True
-
-
-class RateLimiter:
-    def __init__(self, min_update_interval_seconds: float) -> None:
-        self._min_update_interval_seconds = min_update_interval_seconds
-        self._last_update: float = 0
-
-    def ready(self) -> bool:
-        now = time.time()
-        delta = now - self._last_update
-        return delta >= self._min_update_interval_seconds
-
-    def reset(self) -> None:
-        self._last_update = time.time()
-
-
-@contextlib.contextmanager
-def open_spinner(message: str) -> Generator[SpinnerInterface, None, None]:
-    # Interactive spinner goes directly to sys.stdout rather than being routed
-    # through the logging system, but it acts like it has level INFO,
-    # i.e. it's only displayed if we're at level INFO or better.
-    # Non-interactive spinner goes through the logging system, so it is always
-    # in sync with logging configuration.
-    if sys.stdout.isatty() and logger.getEffectiveLevel() <= logging.INFO:
-        spinner: SpinnerInterface = InteractiveSpinner(message)
-    else:
-        spinner = NonInteractiveSpinner(message)
-    try:
-        with hidden_cursor(sys.stdout):
-            yield spinner
-    except KeyboardInterrupt:
-        spinner.finish("canceled")
-        raise
-    except Exception:
-        spinner.finish("error")
-        raise
-    else:
-        spinner.finish("done")
-
-
-HIDE_CURSOR = "\x1b[?25l"
-SHOW_CURSOR = "\x1b[?25h"
-
-
-@contextlib.contextmanager
-def hidden_cursor(file: IO[str]) -> Generator[None, None, None]:
-    # The Windows terminal does not support the hide/show cursor ANSI codes,
-    # even via colorama. So don't even try.
-    if WINDOWS:
-        yield
-    # We don't want to clutter the output with control characters if we're
-    # writing to a file, or if the user is running with --quiet.
-    # See https://github.com/pypa/pip/issues/3418
-    elif not file.isatty() or logger.getEffectiveLevel() > logging.INFO:
-        yield
-    else:
-        file.write(HIDE_CURSOR)
-        try:
-            yield
-        finally:
-            file.write(SHOW_CURSOR)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/build_clib.py

@@ -1,208 +0,0 @@
-"""distutils.command.build_clib
-
-Implements the Distutils 'build_clib' command, to build a C/C++ library
-that is included in the module distribution and needed by an extension
-module."""
-
-
-# XXX this module has *lots* of code ripped-off quite transparently from
-# build_ext.py -- not surprisingly really, as the work required to build
-# a static library from a collection of C source files is not really all
-# that different from what's required to build a shared object file from
-# a collection of C source files.  Nevertheless, I haven't done the
-# necessary refactoring to account for the overlap in code between the
-# two modules, mainly because a number of subtle details changed in the
-# cut 'n paste.  Sigh.
-
-import os
-from distutils.core import Command
-from distutils.errors import DistutilsSetupError
-from distutils.sysconfig import customize_compiler
-from distutils import log
-
-
-def show_compilers():
-    from distutils.ccompiler import show_compilers
-
-    show_compilers()
-
-
-class build_clib(Command):
-
-    description = "build C/C++ libraries used by Python extensions"
-
-    user_options = [
-        ('build-clib=', 'b', "directory to build C/C++ libraries to"),
-        ('build-temp=', 't', "directory to put temporary build by-products"),
-        ('debug', 'g', "compile with debugging information"),
-        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
-        ('compiler=', 'c', "specify the compiler type"),
-    ]
-
-    boolean_options = ['debug', 'force']
-
-    help_options = [
-        ('help-compiler', None, "list available compilers", show_compilers),
-    ]
-
-    def initialize_options(self):
-        self.build_clib = None
-        self.build_temp = None
-
-        # List of libraries to build
-        self.libraries = None
-
-        # Compilation options for all libraries
-        self.include_dirs = None
-        self.define = None
-        self.undef = None
-        self.debug = None
-        self.force = 0
-        self.compiler = None
-
-    def finalize_options(self):
-        # This might be confusing: both build-clib and build-temp default
-        # to build-temp as defined by the "build" command.  This is because
-        # I think that C libraries are really just temporary build
-        # by-products, at least from the point of view of building Python
-        # extensions -- but I want to keep my options open.
-        self.set_undefined_options(
-            'build',
-            ('build_temp', 'build_clib'),
-            ('build_temp', 'build_temp'),
-            ('compiler', 'compiler'),
-            ('debug', 'debug'),
-            ('force', 'force'),
-        )
-
-        self.libraries = self.distribution.libraries
-        if self.libraries:
-            self.check_library_list(self.libraries)
-
-        if self.include_dirs is None:
-            self.include_dirs = self.distribution.include_dirs or []
-        if isinstance(self.include_dirs, str):
-            self.include_dirs = self.include_dirs.split(os.pathsep)
-
-        # XXX same as for build_ext -- what about 'self.define' and
-        # 'self.undef' ?
-
-    def run(self):
-        if not self.libraries:
-            return
-
-        # Yech -- this is cut 'n pasted from build_ext.py!
-        from distutils.ccompiler import new_compiler
-
-        self.compiler = new_compiler(
-            compiler=self.compiler, dry_run=self.dry_run, force=self.force
-        )
-        customize_compiler(self.compiler)
-
-        if self.include_dirs is not None:
-            self.compiler.set_include_dirs(self.include_dirs)
-        if self.define is not None:
-            # 'define' option is a list of (name,value) tuples
-            for (name, value) in self.define:
-                self.compiler.define_macro(name, value)
-        if self.undef is not None:
-            for macro in self.undef:
-                self.compiler.undefine_macro(macro)
-
-        self.build_libraries(self.libraries)
-
-    def check_library_list(self, libraries):
-        """Ensure that the list of libraries is valid.
-
-        `library` is presumably provided as a command option 'libraries'.
-        This method checks that it is a list of 2-tuples, where the tuples
-        are (library_name, build_info_dict).
-
-        Raise DistutilsSetupError if the structure is invalid anywhere;
-        just returns otherwise.
-        """
-        if not isinstance(libraries, list):
-            raise DistutilsSetupError("'libraries' option must be a list of tuples")
-
-        for lib in libraries:
-            if not isinstance(lib, tuple) and len(lib) != 2:
-                raise DistutilsSetupError("each element of 'libraries' must a 2-tuple")
-
-            name, build_info = lib
-
-            if not isinstance(name, str):
-                raise DistutilsSetupError(
-                    "first element of each tuple in 'libraries' "
-                    "must be a string (the library name)"
-                )
-
-            if '/' in name or (os.sep != '/' and os.sep in name):
-                raise DistutilsSetupError(
-                    "bad library name '%s': "
-                    "may not contain directory separators" % lib[0]
-                )
-
-            if not isinstance(build_info, dict):
-                raise DistutilsSetupError(
-                    "second element of each tuple in 'libraries' "
-                    "must be a dictionary (build info)"
-                )
-
-    def get_library_names(self):
-        # Assume the library list is valid -- 'check_library_list()' is
-        # called from 'finalize_options()', so it should be!
-        if not self.libraries:
-            return None
-
-        lib_names = []
-        for (lib_name, build_info) in self.libraries:
-            lib_names.append(lib_name)
-        return lib_names
-
-    def get_source_files(self):
-        self.check_library_list(self.libraries)
-        filenames = []
-        for (lib_name, build_info) in self.libraries:
-            sources = build_info.get('sources')
-            if sources is None or not isinstance(sources, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'sources' must be present and must be "
-                    "a list of source filenames" % lib_name
-                )
-
-            filenames.extend(sources)
-        return filenames
-
-    def build_libraries(self, libraries):
-        for (lib_name, build_info) in libraries:
-            sources = build_info.get('sources')
-            if sources is None or not isinstance(sources, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'sources' must be present and must be "
-                    "a list of source filenames" % lib_name
-                )
-            sources = list(sources)
-
-            log.info("building '%s' library", lib_name)
-
-            # First, compile the source code to object files in the library
-            # directory.  (This should probably change to putting object
-            # files in a temporary build directory.)
-            macros = build_info.get('macros')
-            include_dirs = build_info.get('include_dirs')
-            objects = self.compiler.compile(
-                sources,
-                output_dir=self.build_temp,
-                macros=macros,
-                include_dirs=include_dirs,
-                debug=self.debug,
-            )
-
-            # Now "link" the object files together into a static library.
-            # (On Unix at least, this isn't really linking -- it just
-            # builds an archive.  Whatever.)
-            self.compiler.create_static_lib(
-                objects, lib_name, output_dir=self.build_clib, debug=self.debug
-            )

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/extend.md

@@ -1,141 +0,0 @@
-# Extend Detectron2's Defaults
-
-__Research is about doing things in new ways__.
-This brings a tension in how to create abstractions in code,
-which is a challenge for any research engineering project of a significant size:
-
-1. On one hand, it needs to have very thin abstractions to allow for the possibility of doing
-   everything in new ways. It should be reasonably easy to break existing
-   abstractions and replace them with new ones.
-
-2. On the other hand, such a project also needs reasonably high-level
-   abstractions, so that users can easily do things in standard ways,
-   without worrying too much about the details that only certain researchers care about.
-
-In detectron2, there are two types of interfaces that address this tension together:
-
-1. Functions and classes that take a config (`cfg`) argument
-   created from a yaml file
-   (sometimes with few extra arguments).
-
-   Such functions and classes implement
-   the "standard default" behavior: it will read what it needs from a given
-   config and do the "standard" thing.
-   Users only need to load an expert-made config and pass it around, without having to worry about
-   which arguments are used and what they all mean.
-
-   See [Yacs Configs](configs.md) for a detailed tutorial.
-
-2. Functions and classes that have well-defined explicit arguments.
-
-   Each of these is a small building block of the entire system.
-   They require users' expertise to understand what each argument should be,
-   and require more effort to stitch together to a larger system.
-   But they can be stitched together in more flexible ways.
-
-   When you need to implement something not supported by the "standard defaults"
-   included in detectron2, these well-defined components can be reused.
-
-   The [LazyConfig system](lazyconfigs.md) relies on such functions and classes.
-
-3. A few functions and classes are implemented with the
-   [@configurable](../modules/config.html#detectron2.config.configurable)
-   decorator - they can be called with either a config, or with explicit arguments, or a mixture of both.
-   Their explicit argument interfaces are currently experimental.
-
-   As an example, a Mask R-CNN model can be built in the following ways:
-
-   1. Config-only:
-      ```python
-      # load proper yaml config file, then
-      model = build_model(cfg)
-      ```
-
-   2. Mixture of config and additional argument overrides:
-      ```python
-      model = GeneralizedRCNN(
-        cfg,
-        roi_heads=StandardROIHeads(cfg, batch_size_per_image=666),
-        pixel_std=[57.0, 57.0, 57.0])
-      ```
-
-   3. Full explicit arguments:
-   <details>
-   <summary>
-   (click to expand)
-   </summary>
-
-   ```python
-   model = GeneralizedRCNN(
-       backbone=FPN(
-           ResNet(
-               BasicStem(3, 64, norm="FrozenBN"),
-               ResNet.make_default_stages(50, stride_in_1x1=True, norm="FrozenBN"),
-               out_features=["res2", "res3", "res4", "res5"],
-           ).freeze(2),
-           ["res2", "res3", "res4", "res5"],
-           256,
-           top_block=LastLevelMaxPool(),
-       ),
-       proposal_generator=RPN(
-           in_features=["p2", "p3", "p4", "p5", "p6"],
-           head=StandardRPNHead(in_channels=256, num_anchors=3),
-           anchor_generator=DefaultAnchorGenerator(
-               sizes=[[32], [64], [128], [256], [512]],
-               aspect_ratios=[0.5, 1.0, 2.0],
-               strides=[4, 8, 16, 32, 64],
-               offset=0.0,
-           ),
-           anchor_matcher=Matcher([0.3, 0.7], [0, -1, 1], allow_low_quality_matches=True),
-           box2box_transform=Box2BoxTransform([1.0, 1.0, 1.0, 1.0]),
-           batch_size_per_image=256,
-           positive_fraction=0.5,
-           pre_nms_topk=(2000, 1000),
-           post_nms_topk=(1000, 1000),
-           nms_thresh=0.7,
-       ),
-       roi_heads=StandardROIHeads(
-           num_classes=80,
-           batch_size_per_image=512,
-           positive_fraction=0.25,
-           proposal_matcher=Matcher([0.5], [0, 1], allow_low_quality_matches=False),
-           box_in_features=["p2", "p3", "p4", "p5"],
-           box_pooler=ROIPooler(7, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"),
-           box_head=FastRCNNConvFCHead(
-               ShapeSpec(channels=256, height=7, width=7), conv_dims=[], fc_dims=[1024, 1024]
-           ),
-           box_predictor=FastRCNNOutputLayers(
-               ShapeSpec(channels=1024),
-               test_score_thresh=0.05,
-               box2box_transform=Box2BoxTransform((10, 10, 5, 5)),
-               num_classes=80,
-           ),
-           mask_in_features=["p2", "p3", "p4", "p5"],
-           mask_pooler=ROIPooler(14, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"),
-           mask_head=MaskRCNNConvUpsampleHead(
-               ShapeSpec(channels=256, width=14, height=14),
-               num_classes=80,
-               conv_dims=[256, 256, 256, 256, 256],
-           ),
-       ),
-       pixel_mean=[103.530, 116.280, 123.675],
-       pixel_std=[1.0, 1.0, 1.0],
-       input_format="BGR",
-   )
-   ```
-
-   </details>
-
-
-If you only need the standard behavior, the [Beginner's Tutorial](./getting_started.md)
-should suffice. If you need to extend detectron2 to your own needs,
-see the following tutorials for more details:
-
-* Detectron2 includes a few standard datasets. To use custom ones, see
-  [Use Custom Datasets](./datasets.md).
-* Detectron2 contains the standard logic that creates a data loader for training/testing from a
-  dataset, but you can write your own as well. See [Use Custom Data Loaders](./data_loading.md).
-* Detectron2 implements many standard detection models, and provide ways for you
-  to overwrite their behaviors. See [Use Models](./models.md) and [Write Models](./write-models.md).
-* Detectron2 provides a default training loop that is good for common training tasks.
-  You can customize it with hooks, or write your own loop instead. See [training](./training.md).

spaces/Benson/text-generation/Examples/Agua Clasificacin Rompecabezas Mod Apk Descargar.md

@@ -1,48 +0,0 @@
-
-<h1>Tipo de agua Jigsaw Mod APK Descargar: Un divertido y relajante juego de puzzle</h1>
- <p>¿Te encantan los juegos de puzzle que desafían tu cerebro y calman tus nervios? Si es así, entonces deberías probar Water Sort Jigsaw, un juego único y adictivo que combina clasificación de agua y rompecabezas. En este juego, tienes que ordenar diferentes colores de agua en tubos separados y completar hermosas imágenes con el agua ordenada. Suena fácil, ¿verdad? Bueno, no tan rápido. Tienes que tener cuidado de no mezclar los colores o desbordar los tubos, o tendrás que empezar de nuevo. Water Sort Jigsaw es un juego que pondrá a prueba tu lógica, paciencia y creatividad. </p>
- <h2>¿Qué es el rompecabezas de clasificación de agua? </h2>
- <p>Water Sort Jigsaw es un juego de puzzle desarrollado por IEC Global Pty Ltd, una empresa especializada en juegos casuales y educativos para todas las edades. El juego fue lanzado en 2020 y se ha descargado más de 10 millones de veces en Google Play Store. El juego tiene una calificación de 4.4 de 5 estrellas, con miles de comentarios positivos de jugadores satisfechos. </p>
-<h2>agua clasificación rompecabezas mod apk descargar</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6KnM">https://bltlly.com/2v6KnM</a></b></p><br /><br />
- <h3>Cómo jugar Agua Ordenar rompecabezas</h3>
- <p>El juego de Water Sort Jigsaw es simple e intuitivo. Tienes un conjunto de tubos llenos de diferentes colores de agua. Su objetivo es ordenar el agua por color en tubos separados. Solo puede verter agua de un tubo a otro si los colores coinciden o si el tubo está vacío. También puedes usar tubos vacíos como almacenamiento temporal de agua. Tienes que ordenar toda el agua de los tubos para completar el nivel. </p>
- <p>A medida que avanzas a través de los niveles, también desbloquearás diferentes rompecabezas que puedes completar con el agua ordenada. Los rompecabezas se basan en varios temas, como animales, naturaleza, comida, arte y más. Usted puede elegir el nivel de dificultad de los puzzles, de fácil a difícil. Los rompecabezas son una gran manera de relajarse y disfrutar de los gráficos coloridos del juego. </p>
- <h3> ¿Por qué descargar agua Ordenar Jigsaw mod apk? </h3>
-
- <h2>Características de Water Sort Jigsaw mod apk</h2>
- <h3>Niveles y puzzles ilimitados</h3>
- <p>Una de las mejores características de Water Sort Jigsaw mod apk es que le da acceso ilimitado a todos los niveles y rompecabezas en el juego. No tienes que esperar nuevas actualizaciones o pagar por contenido premium. Puedes jugar todo lo que quieras y disfrutar de interminables horas de diversión y entretenimiento. </p>
- <h3>Gráficos coloridos y sonidos relajantes</h3>
- <p>Otra característica de Water Sort Jigsaw mod apk es que mejora los gráficos y sonidos del juego. El apk mod hace que los colores más vibrante y realista, haciendo el juego más atractivo y atractivo. El mod apk también mejora la calidad de sonido y añade más relajante música y efectos de sonido para el juego. El juego se vuelve más envolvente y relajante con el mod apk. </p>
- <h3>No se requieren anuncios ni internet</h3>
- <p>Una tercera característica de Water Sort Jigsaw mod apk es que elimina todos los anuncios molestos y pop-ups que interrumpen su juego. No tienes que ver anuncios para desbloquear niveles u obtener recompensas. Puedes jugar sin distracciones ni interrupciones. El mod apk también le permite jugar sin conexión, sin necesidad de una conexión a Internet. Puede jugar el juego en cualquier momento y en cualquier lugar que desee. </p>
- <h3>Fácil de instalar y usar</h3>
- <p>Una cuarta característica de Water Sort Jigsaw mod apk es que es muy fácil de instalar y usar. Usted no necesita raíz de su dispositivo o pasar por pasos complicados para obtener el apk mod. Solo tiene que descargar el archivo apk mod de una fuente de confianza y siga las instrucciones simples a continuación. El mod apk es compatible con la mayoría de los dispositivos Android y se ejecuta sin problemas sin errores o problemas técnicos. </p>
-<p></p>
- <h2>¿Cómo descargar e instalar Water Sort Jigsaw mod apk? </h2>
- <p>Si usted está interesado en la descarga y la instalación de Water Sort Jigsaw mod apk, puede seguir estos sencillos pasos:</p>
- <h3>Paso 1: Descargar el archivo apk mod de una fuente de confianza</h3>
-
- <p><a href="">Clasificación del agua Jigsaw Mod APK Descargar</a></p>
- <h3>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h3>
- <p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo y busque opciones de seguridad o privacidad. Luego, encuentre la opción que dice fuentes desconocidas o permita la instalación desde fuentes desconocidas y enciéndala. </p>
- <h3>Paso 3: Instalar el archivo apk mod y disfrutar del juego</h3>
- <p>El tercer y último paso es instalar el archivo apk mod y disfrutar del juego. Para hacer esto, busque el archivo descargado en su dispositivo y toque en él. Luego, siga las instrucciones en pantalla para completar el proceso de instalación. Una vez hecho esto, puedes iniciar el juego y comenzar a jugar con funciones y beneficios ilimitados. </p>
- <h2>Conclusión</h2>
- <p>Water Sort Jigsaw es un divertido y relajante juego de puzzle que te mantendrá entretenido durante horas. Es una gran manera de ejercitar el cerebro y aliviar el estrés. Si usted quiere disfrutar del juego con más características y beneficios, usted debe descargar Water Sort Jigsaw mod apk. El apk mod le da acceso ilimitado a todos los niveles y rompecabezas, mejora los gráficos y sonidos, elimina anuncios y requisitos de Internet, y es fácil de instalar y usar. Puede descargar agua Ordenar Jigsaw mod apk desde el enlace de abajo y empezar a clasificar el agua y completar rompecabezas. </p>
- <h4>Preguntas frecuentes</h4>
- <p>Aquí hay algunas preguntas frecuentes sobre Water Sort Jigsaw mod apk:</p>
- <ul>
-<li><b> ¿Es seguro descargar Jigsaw mod apk? </b></li>
-<li>Sí, Agua Ordenar Jigsaw mod apk es seguro de descargar, siempre y cuando se obtiene de una fuente de confianza. El archivo apk mod es libre de virus y no contiene ningún código malicioso o malware. </li>
-<li><b>¿Requiere acceso de raíz Jigsaw mod apk? </b></li>
-
-<li><b>¿Puedo actualizar la clasificación de agua Jigsaw mod apk? </b></li>
-<li>No, Agua Ordenar Jigsaw mod apk no es compatible con las actualizaciones de la versión oficial. Si desea actualizar el juego, usted tiene que desinstalar el apk mod e instalar la última versión de la Google Play Store.</li>
-<li><b>¿Puedo jugar Water Sort Jigsaw con mis amigos? </b></li>
-<li>No, Water Sort Jigsaw no tiene un modo multijugador o una función social. Solo puede jugar el juego en solitario y sin conexión. </li>
-<li><b>¿Puedo personalizar la configuración del juego? </b></li>
-<li>Sí, Water Sort Jigsaw te permite personalizar algunos de los ajustes del juego, como sonido, música, vibración, idioma y nivel de dificultad. Puedes acceder a estos ajustes desde el menú principal del juego. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Bmw Drift Apk.md

@@ -1,54 +0,0 @@
-
-<h1>BMW deriva APK: Un divertido y realista juego de deriva para Android</h1>
-<p>Si usted es un fan de la deriva y los coches de BMW, te encantará BMW Drift APK, un juego que le permite experimentar la emoción de deslizarse de lado en varios modelos del fabricante de automóviles alemán. En este artículo, le diremos qué es BMW Drift APK, cómo descargarlo e instalarlo, cómo jugarlo, y algunos consejos y trucos para mejorar sus habilidades de deriva. </p>
-<h2>¿Qué es BMW Drift APK? </h2>
-<p>BMW Drift APK es un juego que simula la técnica de conducción de la deriva, donde el conductor sobrevirajes intencionalmente y pierde la tracción manteniendo el control y la dirección. El juego le permite elegir entre diferentes modelos de BMW, como el M3, M5, Z4, X6, y más, y la deriva en varias pistas, como calles de la ciudad, carreteras, carreteras de montaña, y circuitos de carreras. </p>
-<h2>bmw drift apk</h2><br /><p><b><b>DOWNLOAD</b> &#11088; <a href="https://bltlly.com/2v6J5l">https://bltlly.com/2v6J5l</a></b></p><br /><br />
-<h3>Características del juego</h3>
-<p>BMW Drift APK tiene muchas características que lo convierten en un juego de deriva divertido y realista para dispositivos Android. Algunas de estas características son:</p>
-<h4>Física y gráficos realistas</h4>
-<p>El juego utiliza la física avanzada y los motores gráficos para crear una experiencia de conducción realista. Se puede ver el humo de los neumáticos, las chispas de su parachoques, el daño a su coche, y los reflejos en las ventanas. También puede sentir la transferencia de peso, la inercia, el agarre y la retroalimentación de su automóvil a medida que se deriva. </p>
-<h4>Coches y ajustes personalizables</h4>
-<p>El juego te permite personalizar la apariencia y el rendimiento de tu coche. Puedes cambiar el color, las ruedas, los spoilers, los escapes y más. También puede ajustar el motor, la suspensión, los frenos, los neumáticos, el diferencial y la dirección de su automóvil para adaptarse a su estilo de conducción. También puedes ajustar la configuración del juego, como el ángulo de la cámara, los efectos de sonido, el volumen de música y el nivel de dificultad. </p>
-<h4>Múltiples modos de juego y desafíos</h4>
-
-<h2>Cómo descargar e instalar BMW deriva APK? </h2>
-<p>Si desea descargar e instalar BMW Drift APK en su dispositivo Android, es necesario seguir estos pasos:</p>
-<h3>Descargar el archivo APK de una fuente de confianza</h3>
-<p>El primer paso es descargar el archivo APK de BMW Drift APK de una fuente de confianza. Puede utilizar este enlace para descargarlo de forma segura. El tamaño del archivo es de unos 50 MB.</p>
-<h3>Habilitar fuentes desconocidas en su dispositivo</h3>
-<p>El siguiente paso es habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
-<h3>Instalar el archivo APK y lanzar el juego</h3>
-<p>El paso final es instalar el archivo APK y lanzar el juego. Para hacer esto, busque el archivo descargado en su administrador de archivos y toque en él. Siga las instrucciones en la pantalla para instalar el juego. Una vez que la instalación haya terminado, puede abrir el juego y disfrutar de la deriva. </p>
-<h2>Cómo jugar BMW deriva APK? </h2>
-<p>Jugar BMW Drift APK es fácil y divertido. Aquí están los pasos básicos para jugar el juego:</p>
-<p></p>
-<h3>Elige tu coche y pista</h3>
-<p>Lo primero que tienes que hacer es elegir tu coche y pista. Puedes seleccionar entre una variedad de modelos BMW, como el M3, M5, Z4, X6 y más. También puede elegir entre diferentes pistas, como calles de la ciudad, carreteras, carreteras de montaña y circuitos de carreras. También puede personalizar la apariencia y el rendimiento de su coche antes de empezar a deriva. </p>
-<h3>Utilice los controles para dirigir, acelerar, frenar y la deriva</h3>
-<p>Lo siguiente que tienes que hacer es utilizar los controles para dirigir, acelerar, frenar y la deriva. Puede utilizar los botones en pantalla o el sensor de inclinación de su dispositivo para controlar su automóvil. También puede usar el botón de freno de mano para iniciar una deriva. El juego te mostrará un indicador de deriva que te dice qué tan bien estás a la deriva. Cuanto más te desvíes, más puntos y recompensas ganarás. </p>
-
-<p>Lo último que tienes que hacer es ganar puntos y recompensas por tus habilidades de deriva. El juego te dará puntos basados en el ángulo, la velocidad, la duración y la distancia de tus derivas. También puede ganar puntos extra mediante la realización de combos, como encadenar múltiples derivaciones juntos o la deriva cerca de los obstáculos. Puede utilizar los puntos y recompensas para desbloquear nuevos coches y pistas, o actualizar los existentes. </p>
-<h2> Consejos y trucos para BMW deriva APK</h2>
-<p>Si desea mejorar sus habilidades de deriva y disfrutar del juego más, aquí hay algunos consejos y trucos para BMW Drift APK:</p>
-<h3>Aprende los fundamentos de las técnicas de deriva</h3>
-<p>El primer consejo es aprender los fundamentos de las técnicas de deriva. La deriva no se trata solo de deslizarse hacia los lados, sino también de controlar el equilibrio y la dirección de su coche. Hay diferentes tipos de derivaciones, como derivas de potencia, derivas de freno, derivas de patada de embrague, derivas de freno de mano, y más. Puedes aprender más sobre estas técnicas en tutoriales o videos en línea. </p>
-<h3>Práctica en diferentes pistas y coches</h3>
-<p>El segundo consejo es practicar en diferentes pistas y coches. Cada pista y coche tiene sus propias características y desafíos. Algunas pistas pueden tener esquinas estrechas, carriles estrechos o superficies resbaladizas. Algunos coches pueden tener más potencia, agarre o peso que otros. Practicando en diferentes pistas y coches, aprenderás cómo adaptarte a diferentes situaciones y mejorar tus habilidades de deriva. </p>
-<h3>Ajustar la configuración para adaptarse a sus preferencias y el rendimiento del dispositivo</h3>
-<p>El tercer consejo es ajustar la configuración para adaptarse a sus preferencias y el rendimiento del dispositivo. Puedes cambiar la configuración del juego, como el ángulo de la cámara, los efectos de sonido, el volumen de la música y el nivel de dificultad. También puede ajustar la configuración de su automóvil, como el motor, la suspensión, los frenos, los neumáticos, el diferencial y la dirección. Ajustando los ajustes, puedes hacer el juego más agradable y cómodo para ti. </p>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre BMW Drift APK:</p>
-<tabla>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>¿Es BMW Drift APK gratis? </td><td>Sí, BMW Drift APK es gratis para descargar y jugar. </td></tr>
-<tr><td>¿Es seguro BMW Drift APK? </td><td>Sí, BMW Drift APK es seguro si lo descarga de una fuente de confianza como este enlace. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas. </td></tr>
-<tr><td>Es BMW Dr ift APK compatible con mi dispositivo? </td><td>BMW Drift APK es compatible con la mayoría de los dispositivos Android que tienen Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener problemas de rendimiento o compatibilidad dependiendo de sus especificaciones y configuraciones. </td></tr>
-<tr><td>¿Puedo jugar BMW Drift APK offline? </td><td>Sí, puede jugar BMW Drift APK offline en modo libre y modo carrera. Sin embargo, necesitará una conexión a Internet para jugar en modo online y acceder a algunas características, como tablas de clasificación y actualizaciones. </td></tr>
-<tr><td>¿Puedo jugar BMW Drift APK con un controlador? </td><td>Sí, puede jugar BMW Drift APK con un controlador si su dispositivo lo admite. Puede conectar su controlador a través de Bluetooth o USB y configurar los botones en la configuración del juego. </td></tr>
-</tabla>
-<p>Espero que este artículo le ha ayudado a aprender más sobre BMW Drift APK y cómo disfrutarlo. Si tienes alguna pregunta o comentario, por favor deja un comentario abajo. Happy drifting! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar El Juego Mod Township Offline.md

@@ -1,79 +0,0 @@
-
-<h1>Cómo descargar el juego Mod Township sin conexión gratis</h1>
-<p>Si usted está buscando un juego divertido y relajante que combina la construcción de la ciudad y la agricultura, entonces usted debe probar Township. Township es un popular juego móvil que te permite crear tu ciudad de ensueño, cosechar cosechas, comerciar con otros países, administrar un zoológico y más. ¿Pero qué pasa si quieres jugar Township sin conexión a Internet? ¿O qué pasa si quieres obtener recursos ilimitados, monedas y dinero en efectivo en el juego? En este artículo, te mostraremos cómo descargar el juego mod Township offline gratis. También explicaremos qué es un mod de juego, cómo puede mejorar su experiencia de juego y cuáles son los beneficios y riesgos de descargar el juego mod Township sin conexión. </p>
-<h2>descargar el juego mod township offline</h2><br /><p><b><b>Download Zip</b> ---> <a href="https://bltlly.com/2v6JJV">https://bltlly.com/2v6JJV</a></b></p><br /><br />
- <h2>¿Qué es el municipio y por qué usted debe jugar</h2>
-<p>Municipio es una mezcla única de la construcción de la ciudad y la agricultura que fue desarrollado por Playrix. Está disponible para dispositivos Android, iOS, Windows, Xbox One, PlayStation 4 y Nintendo Switch. En Township, puedes construir tu ciudad de ensueño desde cero, utilizando varios edificios y decoraciones que puedes personalizar a tu gusto. También puede cultivar y procesar cultivos en sus granjas y fábricas, vender bienes para desarrollar su ciudad, comerciar con islas exóticas, restaurantes abiertos, cines y otros edificios comunitarios, explorar la mina en busca de recursos y artefactos, administrar su propio zoológico con animales de todo el mundo, y más. Township es un juego que ofrece infinitas posibilidades de creatividad y diversión. </p>
- <p>Township tiene muchas características y actividades para disfrutar. Puedes jugar con tus amigos de Facebook y Google+, hacer nuevos amigos en la comunidad de juegos, crear tus propios clanes, participar en eventos y competiciones de temporada, completar misiones y pedidos de tu pueblo, recoger banderas del país y monumentos famosos para tu ciudad, ver animaciones divertidas de sus personajes, y mucho más. Township es un juego que nunca se vuelve aburrido. </p>
-
- <h2>¿Qué es un mod de juego y cómo puede mejorar su experiencia de juego</h2>
-<p>Un mod de juego es una modificación o alteración del juego original que cambia algunos aspectos del mismo. Un mod de juego puede ser creado por cualquiera que tenga las habilidades y herramientas para hacerlo. Un mod de juego se puede descargar desde varios sitios web o plataformas que los alojan. Puedes instalar un mod de juego en tu dispositivo siguiendo algunas instrucciones o usando algún software. </p>
-<p></p>
- <p>Un mod de juego puede agregar nuevo contenido, características o elementos de juego al juego. Por ejemplo, un mod de juego puede introducir nuevos personajes, objetos, mapas, misiones, modos o géneros al juego. Por ejemplo, un mod de juego puede convertir un juego de estrategia en un juego de rol, o un juego de carreras en un juego de supervivencia zombie. Un mod de juego también puede mejorar los gráficos, el sonido o la interfaz del juego. Por ejemplo, un mod de juego puede mejorar la resolución, texturas, iluminación o efectos del juego, o agregar nueva música, voz o subtítulos al juego. Un mod de juego también puede corregir errores, mejorar el rendimiento o personalizar el juego según tus preferencias. Por ejemplo, un mod de juego puede eliminar fallos, errores o fallos del juego, o aumentar la velocidad, estabilidad o compatibilidad del juego. Un mod de juego también puede cambiar la dificultad, el equilibrio o la mecánica del juego. Por ejemplo, un mod de juego puede hacer el juego más fácil o más difícil, más realista o más fantasía, más divertido o más desafiante. </p>
- <p>Un mod de juego puede mejorar tu experiencia de juego al darte más opciones, variedad y disfrute en el juego. Un mod de juego puede hacer que el juego sea más interesante, emocionante o inmersivo. Un mod de juego también puede extender la vida útil del juego añadiendo nuevo contenido o valor de repetición al juego. Un mod de juego también puede satisfacer tu curiosidad o creatividad permitiéndote explorar nuevas posibilidades o crear tus propios escenarios en el juego. </p>
- <h2>Cómo descargar el juego Mod Township sin conexión gratis</h2>
-
- <h3>Encuentra una fuente confiable y segura para el juego mod</h3>
-<p>Hay muchos sitios web o plataformas que ofrecen mods de juegos para Township y otros juegos. Sin embargo, no todos son confiables o seguros. Algunos de ellos pueden contener archivos falsos, obsoletos o dañados que pueden no funcionar correctamente o dañar su dispositivo. Algunos de ellos también pueden tener anuncios maliciosos, ventanas emergentes o enlaces que pueden redirigirle a sitios no deseados o peligrosos. Por lo tanto, debes ser cuidadoso y selectivo al elegir una fuente para el mod del juego. </p>
- <p>Una forma de encontrar una fuente confiable y segura para el mod del juego es hacer una investigación y leer algunos comentarios de otros usuarios que han descargado y usado el mod del juego. También puedes consultar las valoraciones, comentarios, comentarios o testimonios de otros usuarios que han descargado y utilizado el mod del juego. También puede buscar algunas recomendaciones o sugerencias de sitios de renombre, blogs, foros o comunidades que están relacionados con Township o juegos de azar en general. </p>
- <p>Otra manera de encontrar una fuente confiable y segura para el mod del juego es usar algunas herramientas o software que pueden ayudarlo a escanear y verificar los archivos antes de descargarlos. Puede usar algunos programas antivirus, detectores de malware, comprobadores de archivos o gestores de descargas que pueden ayudarlo a detectar y eliminar cualquier virus, malware, spyware, adware, troyanos, gusanos u otras amenazas de los archivos. También puede utilizar algunas herramientas o software que pueden ayudarle a comparar y verificar los archivos con los archivos originales del juego para asegurarse de que son compatibles y auténticos. </p>
- <h3>Descargue el archivo mod del juego e instálelo en su dispositivo</h3>
-
- <p>Para descargar el archivo mod del juego, debe seguir el enlace o botón proporcionado por la fuente y guardar el archivo en su dispositivo. Es posible que necesite permitir algunos permisos o acceso a su dispositivo o navegador para descargar el archivo. También es posible que necesite desactivar algunos ajustes de seguridad o características en su dispositivo o navegador para descargar el archivo. Por ejemplo, puede que necesite habilitar fuentes desconocidas o deshabilitar programas antivirus en su dispositivo para descargar el archivo. </p>
- <p>Para instalar el archivo mod del juego, necesita localizar y abrir el archivo en su dispositivo. Es posible que necesite extraer o descomprimir el archivo primero si es un archivo comprimido. También es posible que tenga que desinstalar o eliminar el juego original primero si ya está instalado en su dispositivo. También es posible que tenga que hacer una copia de seguridad o guardar primero el progreso o los datos del juego si desea conservarlos. Luego, debes seguir las instrucciones o pasos proporcionados por la fuente o el propio archivo para instalar el mod del juego en tu dispositivo. Es posible que necesite permitir algunos permisos o acceso a su dispositivo o aplicación para instalar el archivo. También es posible que necesite reiniciar su dispositivo o aplicación después de instalar el archivo. </p>
- <h3>Inicie el mod del juego y disfrute jugando Township offline</h3>
-<p>Después de instalar el archivo de mod del juego, puede iniciar el mod del juego y disfrutar jugando Township sin conexión. Puedes encontrar y abrir el icono o la aplicación de mod del juego en tu dispositivo. Es posible que veas algunos cambios o diferencias en el logotipo, el título, la interfaz o el contenido del juego en comparación con el juego original. También puede ver algunas notificaciones o mensajes de la fuente o el archivo en sí sobre las características o configuraciones del mod del juego. Puedes ajustarlos o personalizarlos según tus preferencias. </p>
-
- <h2>Beneficios y riesgos de descargar el juego Mod Township sin conexión</h2>
-<p>Descargar el juego mod Township offline tiene sus beneficios y riesgos. Estos son algunos de ellos:</p>
- <h3>Beneficios de descargar el juego mod Township offline</h3>
-<tabla>
-<tr>
-<th>Beneficio</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Puedes jugar a Township sin conexión a internet</td>
-<td>No necesitas preocuparte por tener una conexión a Internet estable o rápida para jugar a Township. Puedes jugar a Township en cualquier momento y en cualquier lugar que desees, incluso si no estás conectado. También puede guardar su uso de datos o la duración de la batería jugando Township offline. </td>
-</tr>
-<tr>
-<td>Puedes acceder a recursos ilimitados, monedas y dinero en efectivo en el juego</td>
-<td>No necesitas esperar a que tus recursos crezcan o se repongan en el juego. Usted no necesita gastar su dinero real para comprar monedas o dinero en efectivo en el juego. Puedes tener recursos ilimitados, monedas y dinero en el juego que puedes usar para construir, mejorar o expandir tu ciudad, granja, zoológico y más. </td>
-</tr>
-<tr>
-<td>Puedes desbloquear todos los edificios, decoraciones y animales del juego</td>
-<td>No necesitas subir de nivel o completar ciertas tareas para desbloquear todos los edificios, decoraciones y animales del juego. Puedes tener acceso a todos los elementos y opciones del juego que puedes usar para personalizar y embellecer tu ciudad, granja, zoológico y más. </td>
-</tr>
-</tabla>
- <h3>Riesgos de descargar el juego mod Township offline</h3>
-<tabla>
-<tr>
-<th>Riesgo</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Usted puede encontrar problemas de compatibilidad o errores en el juego mod</td>
-<td>El mod del juego puede no funcionar correctamente o sin problemas en su dispositivo o aplicación. Es posible que el mod del juego no sea compatible con el modelo de dispositivo, el sistema operativo, la versión de la aplicación u otros factores. El mod del juego también puede tener algunos errores, fallas o errores que pueden afectar su juego o rendimiento. </td>
-</tr>
-<tr>
-<td>Puede violar los términos de servicio o la política de privacidad del desarrollador del juego</td>
-
-</tr>
-<tr>
-<td>Puede exponer su dispositivo a malware o virus desde el archivo de mod del juego</td>
-<td>El archivo mod del juego puede contener algún código malicioso o software que puede dañar su dispositivo o aplicación. El archivo mod del juego también puede tener algunos anuncios ocultos, ventanas emergentes o enlaces que pueden redirigirlo a sitios no deseados o peligrosos. Al descargar e instalar el archivo mod del juego, puede exponer su dispositivo a malware o virus que pueden dañar su dispositivo o aplicación. </td>
-</tr>
-</tabla>
- <h2>Conclusión y preguntas frecuentes</h2>
-<p>En conclusión, descargar el juego mod Township sin conexión es una manera de disfrutar jugando Township sin conexión a Internet y con recursos ilimitados, monedas, dinero en efectivo y artículos en el juego. Sin embargo, descargar el juego mod Township offline también tiene algunos riesgos como problemas de compatibilidad, términos de violaciones de servicio y exposición a malware. Por lo tanto, debe ser cuidadoso y responsable al descargar y usar el juego mod Township offline. Necesitas encontrar una fuente confiable y segura para el mod del juego, descargar e instalar el archivo del mod del juego correctamente, y lanzar y jugar el mod del juego con precaución. También es necesario respetar los derechos e intereses del desarrollador del juego y otros jugadores. También debes ser consciente de las posibles consecuencias de descargar y usar el juego mod Township offline. Aquí hay algunas preguntas frecuentes que pueden ayudarle a aprender más acerca de cómo descargar el juego mod Township offline: <h4>Q: ¿Puedo jugar Township online con el mod del juego? </h4>
-<p>A: No, no se puede jugar Township en línea con el mod del juego. El mod del juego está diseñado para funcionar solo sin conexión. Si intenta jugar Township en línea con el mod del juego, puede encontrar algunos errores o problemas. También puede correr el riesgo de ser detectado o reportado por el desarrollador del juego u otros jugadores. </p>
- <h4>Q: ¿Puedo actualizar Township con el mod del juego? </h4>
-
- <h4>P: ¿Puedo restaurar mi juego original de Township después de usar el mod de juego? </h4>
-<p>A: Sí, puedes restaurar tu juego original de Township después de usar el mod de juego. Necesitas desinstalar o eliminar el archivo de mod de juego de tu dispositivo. También necesitas reinstalar o descargar el juego original de Township desde la fuente oficial. También es posible que necesite restaurar o recuperar el progreso del juego original de Township o los datos de su copia de seguridad o almacenamiento en la nube. </p>
- <h4>Q: ¿Puedo usar otros mods de juego para Township? </h4>
-<p>A: Sí, puedes usar otros mods de juegos para Township. Hay muchos tipos diferentes de mods de juego para Township que ofrecen diferentes características o funciones. Sin embargo, debes ser cuidadoso y selectivo al elegir y usar otros mods de juego para Township. Necesitas asegurarte de que sean confiables, seguros, compatibles y actualizados. </p>
- <h4>Q: ¿Puedo crear mi propio mod de juego para Township? </h4>
-<p>A: Sí, puedes crear tu propio mod de juego para Township si tienes las habilidades y herramientas para hacerlo. Necesitas tener algún conocimiento y experiencia en programación, codificación, hacking o modificación de juegos. También necesitas tener algunas herramientas o software que te ayuden a crear, editar, probar o distribuir tu propio mod de juego para Township. Sin embargo, debes ser respetuoso y ético al crear tu propio mod de juego para Township. Es necesario seguir las reglas y reglamentos del desarrollador de juegos y la comunidad de juegos. También necesitas dar crédito y reconocimiento a las fuentes originales o creadores de tu propio mod de juego para Township.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/lib/utils/sha256.ts

@@ -1,7 +0,0 @@
-export async function sha256(input: string): Promise<string> {
-	const utf8 = new TextEncoder().encode(input);
-	const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
-	const hashArray = Array.from(new Uint8Array(hashBuffer));
-	const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join("");
-	return hashHex;
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/attr.py

@@ -1,72 +0,0 @@
-# Copyright 2015 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
-#
-# https://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.
-from botocore.docs.params import ResponseParamsDocumenter
-
-from boto3.docs.utils import get_identifier_description
-
-
-class ResourceShapeDocumenter(ResponseParamsDocumenter):
-    EVENT_NAME = 'resource-shape'
-
-
-def document_attribute(
-    section,
-    service_name,
-    resource_name,
-    attr_name,
-    event_emitter,
-    attr_model,
-    include_signature=True,
-):
-    if include_signature:
-        full_attr_name = f"{section.context.get('qualifier', '')}{attr_name}"
-        section.style.start_sphinx_py_attr(full_attr_name)
-    # Note that an attribute may have one, may have many, or may have no
-    # operations that back the resource's shape. So we just set the
-    # operation_name to the resource name if we ever to hook in and modify
-    # a particular attribute.
-    ResourceShapeDocumenter(
-        service_name=service_name,
-        operation_name=resource_name,
-        event_emitter=event_emitter,
-    ).document_params(section=section, shape=attr_model)
-
-
-def document_identifier(
-    section,
-    resource_name,
-    identifier_model,
-    include_signature=True,
-):
-    if include_signature:
-        full_identifier_name = (
-            f"{section.context.get('qualifier', '')}{identifier_model.name}"
-        )
-        section.style.start_sphinx_py_attr(full_identifier_name)
-    description = get_identifier_description(
-        resource_name, identifier_model.name
-    )
-    section.write(f'*(string)* {description}')
-
-
-def document_reference(section, reference_model, include_signature=True):
-    if include_signature:
-        full_reference_name = (
-            f"{section.context.get('qualifier', '')}{reference_model.name}"
-        )
-        section.style.start_sphinx_py_attr(full_reference_name)
-    reference_type = f'(:py:class:`{reference_model.resource.type}`) '
-    section.write(reference_type)
-    section.include_doc_string(
-        f'The related {reference_model.name} if set, otherwise ``None``.'
-    )

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/candidates.py

@@ -1,552 +0,0 @@
-import logging
-import sys
-from typing import TYPE_CHECKING, Any, FrozenSet, Iterable, Optional, Tuple, Union, cast
-
-from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
-from pip._vendor.packaging.version import Version
-
-from pip._internal.exceptions import (
-    HashError,
-    InstallationSubprocessError,
-    MetadataInconsistent,
-)
-from pip._internal.metadata import BaseDistribution
-from pip._internal.models.link import Link, links_equivalent
-from pip._internal.models.wheel import Wheel
-from pip._internal.req.constructors import (
-    install_req_from_editable,
-    install_req_from_line,
-)
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.direct_url_helpers import direct_url_from_link
-from pip._internal.utils.misc import normalize_version_info
-
-from .base import Candidate, CandidateVersion, Requirement, format_name
-
-if TYPE_CHECKING:
-    from .factory import Factory
-
-logger = logging.getLogger(__name__)
-
-BaseCandidate = Union[
-    "AlreadyInstalledCandidate",
-    "EditableCandidate",
-    "LinkCandidate",
-]
-
-# Avoid conflicting with the PyPI package "Python".
-REQUIRES_PYTHON_IDENTIFIER = cast(NormalizedName, "<Python from Requires-Python>")
-
-
-def as_base_candidate(candidate: Candidate) -> Optional[BaseCandidate]:
-    """The runtime version of BaseCandidate."""
-    base_candidate_classes = (
-        AlreadyInstalledCandidate,
-        EditableCandidate,
-        LinkCandidate,
-    )
-    if isinstance(candidate, base_candidate_classes):
-        return candidate
-    return None
-
-
-def make_install_req_from_link(
-    link: Link, template: InstallRequirement
-) -> InstallRequirement:
-    assert not template.editable, "template is editable"
-    if template.req:
-        line = str(template.req)
-    else:
-        line = link.url
-    ireq = install_req_from_line(
-        line,
-        user_supplied=template.user_supplied,
-        comes_from=template.comes_from,
-        use_pep517=template.use_pep517,
-        isolated=template.isolated,
-        constraint=template.constraint,
-        global_options=template.global_options,
-        hash_options=template.hash_options,
-        config_settings=template.config_settings,
-    )
-    ireq.original_link = template.original_link
-    ireq.link = link
-    ireq.extras = template.extras
-    return ireq
-
-
-def make_install_req_from_editable(
-    link: Link, template: InstallRequirement
-) -> InstallRequirement:
-    assert template.editable, "template not editable"
-    ireq = install_req_from_editable(
-        link.url,
-        user_supplied=template.user_supplied,
-        comes_from=template.comes_from,
-        use_pep517=template.use_pep517,
-        isolated=template.isolated,
-        constraint=template.constraint,
-        permit_editable_wheels=template.permit_editable_wheels,
-        global_options=template.global_options,
-        hash_options=template.hash_options,
-        config_settings=template.config_settings,
-    )
-    ireq.extras = template.extras
-    return ireq
-
-
-def _make_install_req_from_dist(
-    dist: BaseDistribution, template: InstallRequirement
-) -> InstallRequirement:
-    if template.req:
-        line = str(template.req)
-    elif template.link:
-        line = f"{dist.canonical_name} @ {template.link.url}"
-    else:
-        line = f"{dist.canonical_name}=={dist.version}"
-    ireq = install_req_from_line(
-        line,
-        user_supplied=template.user_supplied,
-        comes_from=template.comes_from,
-        use_pep517=template.use_pep517,
-        isolated=template.isolated,
-        constraint=template.constraint,
-        global_options=template.global_options,
-        hash_options=template.hash_options,
-        config_settings=template.config_settings,
-    )
-    ireq.satisfied_by = dist
-    return ireq
-
-
-class _InstallRequirementBackedCandidate(Candidate):
-    """A candidate backed by an ``InstallRequirement``.
-
-    This represents a package request with the target not being already
-    in the environment, and needs to be fetched and installed. The backing
-    ``InstallRequirement`` is responsible for most of the leg work; this
-    class exposes appropriate information to the resolver.
-
-    :param link: The link passed to the ``InstallRequirement``. The backing
-        ``InstallRequirement`` will use this link to fetch the distribution.
-    :param source_link: The link this candidate "originates" from. This is
-        different from ``link`` when the link is found in the wheel cache.
-        ``link`` would point to the wheel cache, while this points to the
-        found remote link (e.g. from pypi.org).
-    """
-
-    dist: BaseDistribution
-    is_installed = False
-
-    def __init__(
-        self,
-        link: Link,
-        source_link: Link,
-        ireq: InstallRequirement,
-        factory: "Factory",
-        name: Optional[NormalizedName] = None,
-        version: Optional[CandidateVersion] = None,
-    ) -> None:
-        self._link = link
-        self._source_link = source_link
-        self._factory = factory
-        self._ireq = ireq
-        self._name = name
-        self._version = version
-        self.dist = self._prepare()
-
-    def __str__(self) -> str:
-        return f"{self.name} {self.version}"
-
-    def __repr__(self) -> str:
-        return "{class_name}({link!r})".format(
-            class_name=self.__class__.__name__,
-            link=str(self._link),
-        )
-
-    def __hash__(self) -> int:
-        return hash((self.__class__, self._link))
-
-    def __eq__(self, other: Any) -> bool:
-        if isinstance(other, self.__class__):
-            return links_equivalent(self._link, other._link)
-        return False
-
-    @property
-    def source_link(self) -> Optional[Link]:
-        return self._source_link
-
-    @property
-    def project_name(self) -> NormalizedName:
-        """The normalised name of the project the candidate refers to"""
-        if self._name is None:
-            self._name = self.dist.canonical_name
-        return self._name
-
-    @property
-    def name(self) -> str:
-        return self.project_name
-
-    @property
-    def version(self) -> CandidateVersion:
-        if self._version is None:
-            self._version = self.dist.version
-        return self._version
-
-    def format_for_error(self) -> str:
-        return "{} {} (from {})".format(
-            self.name,
-            self.version,
-            self._link.file_path if self._link.is_file else self._link,
-        )
-
-    def _prepare_distribution(self) -> BaseDistribution:
-        raise NotImplementedError("Override in subclass")
-
-    def _check_metadata_consistency(self, dist: BaseDistribution) -> None:
-        """Check for consistency of project name and version of dist."""
-        if self._name is not None and self._name != dist.canonical_name:
-            raise MetadataInconsistent(
-                self._ireq,
-                "name",
-                self._name,
-                dist.canonical_name,
-            )
-        if self._version is not None and self._version != dist.version:
-            raise MetadataInconsistent(
-                self._ireq,
-                "version",
-                str(self._version),
-                str(dist.version),
-            )
-
-    def _prepare(self) -> BaseDistribution:
-        try:
-            dist = self._prepare_distribution()
-        except HashError as e:
-            # Provide HashError the underlying ireq that caused it. This
-            # provides context for the resulting error message to show the
-            # offending line to the user.
-            e.req = self._ireq
-            raise
-        except InstallationSubprocessError as exc:
-            # The output has been presented already, so don't duplicate it.
-            exc.context = "See above for output."
-            raise
-
-        self._check_metadata_consistency(dist)
-        return dist
-
-    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
-        requires = self.dist.iter_dependencies() if with_requires else ()
-        for r in requires:
-            yield self._factory.make_requirement_from_spec(str(r), self._ireq)
-        yield self._factory.make_requires_python_requirement(self.dist.requires_python)
-
-    def get_install_requirement(self) -> Optional[InstallRequirement]:
-        return self._ireq
-
-
-class LinkCandidate(_InstallRequirementBackedCandidate):
-    is_editable = False
-
-    def __init__(
-        self,
-        link: Link,
-        template: InstallRequirement,
-        factory: "Factory",
-        name: Optional[NormalizedName] = None,
-        version: Optional[CandidateVersion] = None,
-    ) -> None:
-        source_link = link
-        cache_entry = factory.get_wheel_cache_entry(source_link, name)
-        if cache_entry is not None:
-            logger.debug("Using cached wheel link: %s", cache_entry.link)
-            link = cache_entry.link
-        ireq = make_install_req_from_link(link, template)
-        assert ireq.link == link
-        if ireq.link.is_wheel and not ireq.link.is_file:
-            wheel = Wheel(ireq.link.filename)
-            wheel_name = canonicalize_name(wheel.name)
-            assert name == wheel_name, f"{name!r} != {wheel_name!r} for wheel"
-            # Version may not be present for PEP 508 direct URLs
-            if version is not None:
-                wheel_version = Version(wheel.version)
-                assert version == wheel_version, "{!r} != {!r} for wheel {}".format(
-                    version, wheel_version, name
-                )
-
-        if cache_entry is not None:
-            assert ireq.link.is_wheel
-            assert ireq.link.is_file
-            if cache_entry.persistent and template.link is template.original_link:
-                ireq.cached_wheel_source_link = source_link
-            if cache_entry.origin is not None:
-                ireq.download_info = cache_entry.origin
-            else:
-                # Legacy cache entry that does not have origin.json.
-                # download_info may miss the archive_info.hashes field.
-                ireq.download_info = direct_url_from_link(
-                    source_link, link_is_in_wheel_cache=cache_entry.persistent
-                )
-
-        super().__init__(
-            link=link,
-            source_link=source_link,
-            ireq=ireq,
-            factory=factory,
-            name=name,
-            version=version,
-        )
-
-    def _prepare_distribution(self) -> BaseDistribution:
-        preparer = self._factory.preparer
-        return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True)
-
-
-class EditableCandidate(_InstallRequirementBackedCandidate):
-    is_editable = True
-
-    def __init__(
-        self,
-        link: Link,
-        template: InstallRequirement,
-        factory: "Factory",
-        name: Optional[NormalizedName] = None,
-        version: Optional[CandidateVersion] = None,
-    ) -> None:
-        super().__init__(
-            link=link,
-            source_link=link,
-            ireq=make_install_req_from_editable(link, template),
-            factory=factory,
-            name=name,
-            version=version,
-        )
-
-    def _prepare_distribution(self) -> BaseDistribution:
-        return self._factory.preparer.prepare_editable_requirement(self._ireq)
-
-
-class AlreadyInstalledCandidate(Candidate):
-    is_installed = True
-    source_link = None
-
-    def __init__(
-        self,
-        dist: BaseDistribution,
-        template: InstallRequirement,
-        factory: "Factory",
-    ) -> None:
-        self.dist = dist
-        self._ireq = _make_install_req_from_dist(dist, template)
-        self._factory = factory
-
-        # This is just logging some messages, so we can do it eagerly.
-        # The returned dist would be exactly the same as self.dist because we
-        # set satisfied_by in _make_install_req_from_dist.
-        # TODO: Supply reason based on force_reinstall and upgrade_strategy.
-        skip_reason = "already satisfied"
-        factory.preparer.prepare_installed_requirement(self._ireq, skip_reason)
-
-    def __str__(self) -> str:
-        return str(self.dist)
-
-    def __repr__(self) -> str:
-        return "{class_name}({distribution!r})".format(
-            class_name=self.__class__.__name__,
-            distribution=self.dist,
-        )
-
-    def __hash__(self) -> int:
-        return hash((self.__class__, self.name, self.version))
-
-    def __eq__(self, other: Any) -> bool:
-        if isinstance(other, self.__class__):
-            return self.name == other.name and self.version == other.version
-        return False
-
-    @property
-    def project_name(self) -> NormalizedName:
-        return self.dist.canonical_name
-
-    @property
-    def name(self) -> str:
-        return self.project_name
-
-    @property
-    def version(self) -> CandidateVersion:
-        return self.dist.version
-
-    @property
-    def is_editable(self) -> bool:
-        return self.dist.editable
-
-    def format_for_error(self) -> str:
-        return f"{self.name} {self.version} (Installed)"
-
-    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
-        if not with_requires:
-            return
-        for r in self.dist.iter_dependencies():
-            yield self._factory.make_requirement_from_spec(str(r), self._ireq)
-
-    def get_install_requirement(self) -> Optional[InstallRequirement]:
-        return None
-
-
-class ExtrasCandidate(Candidate):
-    """A candidate that has 'extras', indicating additional dependencies.
-
-    Requirements can be for a project with dependencies, something like
-    foo[extra].  The extras don't affect the project/version being installed
-    directly, but indicate that we need additional dependencies. We model that
-    by having an artificial ExtrasCandidate that wraps the "base" candidate.
-
-    The ExtrasCandidate differs from the base in the following ways:
-
-    1. It has a unique name, of the form foo[extra]. This causes the resolver
-       to treat it as a separate node in the dependency graph.
-    2. When we're getting the candidate's dependencies,
-       a) We specify that we want the extra dependencies as well.
-       b) We add a dependency on the base candidate.
-          See below for why this is needed.
-    3. We return None for the underlying InstallRequirement, as the base
-       candidate will provide it, and we don't want to end up with duplicates.
-
-    The dependency on the base candidate is needed so that the resolver can't
-    decide that it should recommend foo[extra1] version 1.0 and foo[extra2]
-    version 2.0. Having those candidates depend on foo=1.0 and foo=2.0
-    respectively forces the resolver to recognise that this is a conflict.
-    """
-
-    def __init__(
-        self,
-        base: BaseCandidate,
-        extras: FrozenSet[str],
-    ) -> None:
-        self.base = base
-        self.extras = extras
-
-    def __str__(self) -> str:
-        name, rest = str(self.base).split(" ", 1)
-        return "{}[{}] {}".format(name, ",".join(self.extras), rest)
-
-    def __repr__(self) -> str:
-        return "{class_name}(base={base!r}, extras={extras!r})".format(
-            class_name=self.__class__.__name__,
-            base=self.base,
-            extras=self.extras,
-        )
-
-    def __hash__(self) -> int:
-        return hash((self.base, self.extras))
-
-    def __eq__(self, other: Any) -> bool:
-        if isinstance(other, self.__class__):
-            return self.base == other.base and self.extras == other.extras
-        return False
-
-    @property
-    def project_name(self) -> NormalizedName:
-        return self.base.project_name
-
-    @property
-    def name(self) -> str:
-        """The normalised name of the project the candidate refers to"""
-        return format_name(self.base.project_name, self.extras)
-
-    @property
-    def version(self) -> CandidateVersion:
-        return self.base.version
-
-    def format_for_error(self) -> str:
-        return "{} [{}]".format(
-            self.base.format_for_error(), ", ".join(sorted(self.extras))
-        )
-
-    @property
-    def is_installed(self) -> bool:
-        return self.base.is_installed
-
-    @property
-    def is_editable(self) -> bool:
-        return self.base.is_editable
-
-    @property
-    def source_link(self) -> Optional[Link]:
-        return self.base.source_link
-
-    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
-        factory = self.base._factory
-
-        # Add a dependency on the exact base
-        # (See note 2b in the class docstring)
-        yield factory.make_requirement_from_candidate(self.base)
-        if not with_requires:
-            return
-
-        # The user may have specified extras that the candidate doesn't
-        # support. We ignore any unsupported extras here.
-        valid_extras = self.extras.intersection(self.base.dist.iter_provided_extras())
-        invalid_extras = self.extras.difference(self.base.dist.iter_provided_extras())
-        for extra in sorted(invalid_extras):
-            logger.warning(
-                "%s %s does not provide the extra '%s'",
-                self.base.name,
-                self.version,
-                extra,
-            )
-
-        for r in self.base.dist.iter_dependencies(valid_extras):
-            requirement = factory.make_requirement_from_spec(
-                str(r), self.base._ireq, valid_extras
-            )
-            if requirement:
-                yield requirement
-
-    def get_install_requirement(self) -> Optional[InstallRequirement]:
-        # We don't return anything here, because we always
-        # depend on the base candidate, and we'll get the
-        # install requirement from that.
-        return None
-
-
-class RequiresPythonCandidate(Candidate):
-    is_installed = False
-    source_link = None
-
-    def __init__(self, py_version_info: Optional[Tuple[int, ...]]) -> None:
-        if py_version_info is not None:
-            version_info = normalize_version_info(py_version_info)
-        else:
-            version_info = sys.version_info[:3]
-        self._version = Version(".".join(str(c) for c in version_info))
-
-    # We don't need to implement __eq__() and __ne__() since there is always
-    # only one RequiresPythonCandidate in a resolution, i.e. the host Python.
-    # The built-in object.__eq__() and object.__ne__() do exactly what we want.
-
-    def __str__(self) -> str:
-        return f"Python {self._version}"
-
-    @property
-    def project_name(self) -> NormalizedName:
-        return REQUIRES_PYTHON_IDENTIFIER
-
-    @property
-    def name(self) -> str:
-        return REQUIRES_PYTHON_IDENTIFIER
-
-    @property
-    def version(self) -> CandidateVersion:
-        return self._version
-
-    def format_for_error(self) -> str:
-        return f"Python {self.version}"
-
-    def iter_dependencies(self, with_requires: bool) -> Iterable[Optional[Requirement]]:
-        return ()
-
-    def get_install_requirement(self) -> Optional[InstallRequirement]:
-        return None

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/cascade_rcnn.py

@@ -1,245 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import torch
-from torch import nn
-from torch.autograd.function import Function
-
-from detectron2.layers import ShapeSpec
-from detectron2.structures import Boxes, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-
-from ..box_regression import Box2BoxTransform
-from ..matcher import Matcher
-from ..poolers import ROIPooler
-from .box_head import build_box_head
-from .fast_rcnn import FastRCNNOutputLayers, fast_rcnn_inference
-from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-
-
-class _ScaleGradient(Function):
-    @staticmethod
-    def forward(ctx, input, scale):
-        ctx.scale = scale
-        return input
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        return grad_output * ctx.scale, None
-
-
-@ROI_HEADS_REGISTRY.register()
-class CascadeROIHeads(StandardROIHeads):
-    def _init_box_head(self, cfg, input_shape):
-        # fmt: off
-        pooler_resolution        = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_scales            = tuple(1.0 / input_shape[k].stride for k in self.in_features)
-        sampling_ratio           = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type              = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS
-        cascade_ious             = cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS
-        self.num_cascade_stages  = len(cascade_ious)
-        assert len(cascade_bbox_reg_weights) == self.num_cascade_stages
-        assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,  \
-            "CascadeROIHeads only support class-agnostic regression now!"
-        assert cascade_ious[0] == cfg.MODEL.ROI_HEADS.IOU_THRESHOLDS[0]
-        # fmt: on
-
-        in_channels = [input_shape[f].channels for f in self.in_features]
-        # Check all channel counts are equal
-        assert len(set(in_channels)) == 1, in_channels
-        in_channels = in_channels[0]
-
-        self.box_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        pooled_shape = ShapeSpec(
-            channels=in_channels, width=pooler_resolution, height=pooler_resolution
-        )
-
-        self.box_head = nn.ModuleList()
-        self.box_predictor = nn.ModuleList()
-        self.box2box_transform = []
-        self.proposal_matchers = []
-        for k in range(self.num_cascade_stages):
-            box_head = build_box_head(cfg, pooled_shape)
-            self.box_head.append(box_head)
-            self.box_predictor.append(
-                FastRCNNOutputLayers(
-                    cfg,
-                    box_head.output_shape,
-                    box2box_transform=Box2BoxTransform(weights=cascade_bbox_reg_weights[k]),
-                )
-            )
-
-            if k == 0:
-                # The first matching is done by the matcher of ROIHeads (self.proposal_matcher).
-                self.proposal_matchers.append(None)
-            else:
-                self.proposal_matchers.append(
-                    Matcher([cascade_ious[k]], [0, 1], allow_low_quality_matches=False)
-                )
-
-    def forward(self, images, features, proposals, targets=None):
-        del images
-        if self.training:
-            proposals = self.label_and_sample_proposals(proposals, targets)
-
-        if self.training:
-            # Need targets to box head
-            losses = self._forward_box(features, proposals, targets)
-            losses.update(self._forward_mask(features, proposals))
-            losses.update(self._forward_keypoint(features, proposals))
-            return proposals, losses
-        else:
-            pred_instances = self._forward_box(features, proposals)
-            pred_instances = self.forward_with_given_boxes(features, pred_instances)
-            return pred_instances, {}
-
-    def _forward_box(self, features, proposals, targets=None):
-        """
-        Args:
-            features, targets: the same as in
-                Same as in :meth:`ROIHeads.forward`.
-            proposals (list[Instances]): the per-image object proposals with
-                their matching ground truth.
-                Each has fields "proposal_boxes", and "objectness_logits",
-                "gt_classes", "gt_boxes".
-        """
-        features = [features[f] for f in self.in_features]
-        head_outputs = []  # (predictor, predictions, proposals)
-        prev_pred_boxes = None
-        image_sizes = [x.image_size for x in proposals]
-        for k in range(self.num_cascade_stages):
-            if k > 0:
-                # The output boxes of the previous stage are used to create the input
-                # proposals of the next stage.
-                proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes)
-                if self.training:
-                    proposals = self._match_and_label_boxes(proposals, k, targets)
-            predictions = self._run_stage(features, proposals, k)
-            prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals)
-            head_outputs.append((self.box_predictor[k], predictions, proposals))
-
-        if self.training:
-            losses = {}
-            storage = get_event_storage()
-            for stage, (predictor, predictions, proposals) in enumerate(head_outputs):
-                with storage.name_scope("stage{}".format(stage)):
-                    stage_losses = predictor.losses(predictions, proposals)
-                losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()})
-            return losses
-        else:
-            # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1)
-            scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs]
-
-            # Average the scores across heads
-            scores = [
-                sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages)
-                for scores_per_image in zip(*scores_per_stage)
-            ]
-            # Use the boxes of the last head
-            predictor, predictions, proposals = head_outputs[-1]
-            boxes = predictor.predict_boxes(predictions, proposals)
-            pred_instances, _ = fast_rcnn_inference(
-                boxes,
-                scores,
-                image_sizes,
-                predictor.test_score_thresh,
-                predictor.test_nms_thresh,
-                predictor.test_topk_per_image,
-            )
-            return pred_instances
-
-    @torch.no_grad()
-    def _match_and_label_boxes(self, proposals, stage, targets):
-        """
-        Match proposals with groundtruth using the matcher at the given stage.
-        Label the proposals as foreground or background based on the match.
-
-        Args:
-            proposals (list[Instances]): One Instances for each image, with
-                the field "proposal_boxes".
-            stage (int): the current stage
-            targets (list[Instances]): the ground truth instances
-
-        Returns:
-            list[Instances]: the same proposals, but with fields "gt_classes" and "gt_boxes"
-        """
-        num_fg_samples, num_bg_samples = [], []
-        for proposals_per_image, targets_per_image in zip(proposals, targets):
-            match_quality_matrix = pairwise_iou(
-                targets_per_image.gt_boxes, proposals_per_image.proposal_boxes
-            )
-            # proposal_labels are 0 or 1
-            matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix)
-            if len(targets_per_image) > 0:
-                gt_classes = targets_per_image.gt_classes[matched_idxs]
-                # Label unmatched proposals (0 label from matcher) as background (label=num_classes)
-                gt_classes[proposal_labels == 0] = self.num_classes
-                gt_boxes = targets_per_image.gt_boxes[matched_idxs]
-            else:
-                gt_classes = torch.zeros_like(matched_idxs) + self.num_classes
-                gt_boxes = Boxes(
-                    targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4))
-                )
-            proposals_per_image.gt_classes = gt_classes
-            proposals_per_image.gt_boxes = gt_boxes
-
-            num_fg_samples.append((proposal_labels == 1).sum().item())
-            num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1])
-
-        # Log the number of fg/bg samples in each stage
-        storage = get_event_storage()
-        storage.put_scalar(
-            "stage{}/roi_head/num_fg_samples".format(stage),
-            sum(num_fg_samples) / len(num_fg_samples),
-        )
-        storage.put_scalar(
-            "stage{}/roi_head/num_bg_samples".format(stage),
-            sum(num_bg_samples) / len(num_bg_samples),
-        )
-        return proposals
-
-    def _run_stage(self, features, proposals, stage):
-        """
-        Args:
-            features (list[Tensor]): #lvl input features to ROIHeads
-            proposals (list[Instances]): #image Instances, with the field "proposal_boxes"
-            stage (int): the current stage
-
-        Returns:
-            Same output as `FastRCNNOutputLayers.forward()`.
-        """
-        box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
-        # The original implementation averages the losses among heads,
-        # but scale up the parameter gradients of the heads.
-        # This is equivalent to adding the losses among heads,
-        # but scale down the gradients on features.
-        box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages)
-        box_features = self.box_head[stage](box_features)
-        return self.box_predictor[stage](box_features)
-
-    def _create_proposals_from_boxes(self, boxes, image_sizes):
-        """
-        Args:
-            boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4
-            image_sizes (list[tuple]): list of image shapes in (h, w)
-
-        Returns:
-            list[Instances]: per-image proposals with the given boxes.
-        """
-        # Just like RPN, the proposals should not have gradients
-        boxes = [Boxes(b.detach()) for b in boxes]
-        proposals = []
-        for boxes_per_image, image_size in zip(boxes, image_sizes):
-            boxes_per_image.clip(image_size)
-            if self.training:
-                # do not filter empty boxes at inference time,
-                # because the scores from each stage need to be aligned and added later
-                boxes_per_image = boxes_per_image[boxes_per_image.nonempty()]
-            prop = Instances(image_size)
-            prop.proposal_boxes = boxes_per_image
-            proposals.append(prop)
-        return proposals

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/PointRend/train_net.py

@@ -1,110 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-"""
-PointRend Training Script.
-
-This script is a simplified version of the training script in detectron2/tools.
-"""
-
-import os
-import torch
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import MetadataCatalog
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
-from detectron2.evaluation import (
-    CityscapesEvaluator,
-    COCOEvaluator,
-    DatasetEvaluators,
-    LVISEvaluator,
-    verify_results,
-)
-
-from point_rend import add_pointrend_config
-
-
-class Trainer(DefaultTrainer):
-    """
-    We use the "DefaultTrainer" which contains a number pre-defined logic for
-    standard training workflow. They may not work for you, especially if you
-    are working on a new research project. In that case you can use the cleaner
-    "SimpleTrainer", or write your own training loop.
-    """
-
-    @classmethod
-    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
-        """
-        Create evaluator(s) for a given dataset.
-        This uses the special metadata "evaluator_type" associated with each builtin dataset.
-        For your own dataset, you can simply create an evaluator manually in your
-        script and do not have to worry about the hacky if-else logic here.
-        """
-        if output_folder is None:
-            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
-        evaluator_list = []
-        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-        if evaluator_type == "lvis":
-            return LVISEvaluator(dataset_name, cfg, True, output_folder)
-        if evaluator_type == "coco":
-            return COCOEvaluator(dataset_name, cfg, True, output_folder)
-        if evaluator_type == "cityscapes":
-            assert (
-                torch.cuda.device_count() >= comm.get_rank()
-            ), "CityscapesEvaluator currently do not work with multiple machines."
-            return CityscapesEvaluator(dataset_name)
-        if len(evaluator_list) == 0:
-            raise NotImplementedError(
-                "no Evaluator for the dataset {} with the type {}".format(
-                    dataset_name, evaluator_type
-                )
-            )
-        if len(evaluator_list) == 1:
-            return evaluator_list[0]
-        return DatasetEvaluators(evaluator_list)
-
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    add_pointrend_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-
-    if args.eval_only:
-        model = Trainer.build_model(cfg)
-        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-            cfg.MODEL.WEIGHTS, resume=args.resume
-        )
-        res = Trainer.test(cfg, model)
-        if comm.is_main_process():
-            verify_results(cfg, res)
-        return res
-
-    trainer = Trainer(cfg)
-    trainer.resume_or_load(resume=args.resume)
-    return trainer.train()
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )

spaces/CVPR/LIVE/pydiffvg/shape.py

@@ -1,172 +0,0 @@
-import torch
-import svgpathtools
-import math
-
-class Circle:
-    def __init__(self, radius, center, stroke_width = torch.tensor(1.0), id = ''):
-        self.radius = radius
-        self.center = center
-        self.stroke_width = stroke_width
-        self.id = id
-
-class Ellipse:
-    def __init__(self, radius, center, stroke_width = torch.tensor(1.0), id = ''):
-        self.radius = radius
-        self.center = center
-        self.stroke_width = stroke_width
-        self.id = id
-
-class Path:
-    def __init__(self,
-                 num_control_points,
-                 points,
-                 is_closed,
-                 stroke_width = torch.tensor(1.0),
-                 id = '',
-                 use_distance_approx = False):
-        self.num_control_points = num_control_points
-        self.points = points
-        self.is_closed = is_closed
-        self.stroke_width = stroke_width
-        self.id = id
-        self.use_distance_approx = use_distance_approx
-
-class Polygon:
-    def __init__(self, points, is_closed, stroke_width = torch.tensor(1.0), id = ''):
-        self.points = points
-        self.is_closed = is_closed
-        self.stroke_width = stroke_width
-        self.id = id
-
-class Rect:
-    def __init__(self, p_min, p_max, stroke_width = torch.tensor(1.0), id = ''):
-        self.p_min = p_min
-        self.p_max = p_max
-        self.stroke_width = stroke_width
-        self.id = id
-
-class ShapeGroup:
-    def __init__(self,
-                 shape_ids,
-                 fill_color,
-                 use_even_odd_rule = True,
-                 stroke_color = None,
-                 shape_to_canvas = torch.eye(3),
-                 id = ''):
-        self.shape_ids = shape_ids
-        self.fill_color = fill_color
-        self.use_even_odd_rule = use_even_odd_rule
-        self.stroke_color = stroke_color
-        self.shape_to_canvas = shape_to_canvas
-        self.id = id
-
-def from_svg_path(path_str, shape_to_canvas = torch.eye(3), force_close = False):
-    path = svgpathtools.parse_path(path_str)
-    if len(path) == 0:
-        return []
-    ret_paths = []
-    subpaths = path.continuous_subpaths()
-    for subpath in subpaths:
-        if subpath.isclosed():
-            if len(subpath) > 1 and isinstance(subpath[-1], svgpathtools.Line) and subpath[-1].length() < 1e-5:
-                subpath.remove(subpath[-1])
-                subpath[-1].end = subpath[0].start # Force closing the path
-                subpath.end = subpath[-1].end
-                assert(subpath.isclosed())
-        else:
-            beg = subpath[0].start
-            end = subpath[-1].end
-            if abs(end - beg) < 1e-5:
-                subpath[-1].end = beg # Force closing the path
-                subpath.end = subpath[-1].end
-                assert(subpath.isclosed())
-            elif force_close:
-                subpath.append(svgpathtools.Line(end, beg))
-                subpath.end = subpath[-1].end
-                assert(subpath.isclosed())
-
-        num_control_points = []
-        points = []
-
-        for i, e in enumerate(subpath):
-            if i == 0:
-                points.append((e.start.real, e.start.imag))
-            else:
-                # Must begin from the end of previous segment
-                assert(e.start.real == points[-1][0])
-                assert(e.start.imag == points[-1][1])
-            if isinstance(e, svgpathtools.Line):
-                num_control_points.append(0)
-            elif isinstance(e, svgpathtools.QuadraticBezier):
-                num_control_points.append(1)
-                points.append((e.control.real, e.control.imag))
-            elif isinstance(e, svgpathtools.CubicBezier):
-                num_control_points.append(2)
-                points.append((e.control1.real, e.control1.imag))
-                points.append((e.control2.real, e.control2.imag))
-            elif isinstance(e, svgpathtools.Arc):
-                # Convert to Cubic curves
-                # https://www.joecridge.me/content/pdf/bezier-arcs.pdf
-                start = e.theta * math.pi / 180.0
-                stop = (e.theta + e.delta) * math.pi / 180.0
-
-                sign = 1.0
-                if stop < start:
-                    sign = -1.0
-
-                epsilon = 0.00001
-                debug = abs(e.delta) >= 90.0
-                while (sign * (stop - start) > epsilon):
-                    arc_to_draw = stop - start
-                    if arc_to_draw > 0.0:
-                        arc_to_draw = min(arc_to_draw, 0.5 * math.pi)
-                    else:
-                        arc_to_draw = max(arc_to_draw, -0.5 * math.pi)
-                    alpha = arc_to_draw / 2.0
-                    cos_alpha = math.cos(alpha)
-                    sin_alpha = math.sin(alpha)
-                    cot_alpha = 1.0 / math.tan(alpha)
-                    phi = start + alpha
-                    cos_phi = math.cos(phi)
-                    sin_phi = math.sin(phi)
-                    lambda_ = (4.0 - cos_alpha) / 3.0
-                    mu = sin_alpha + (cos_alpha - lambda_) * cot_alpha
-                    last = sign * (stop - (start + arc_to_draw)) <= epsilon
-                    num_control_points.append(2)
-                    rx = e.radius.real
-                    ry = e.radius.imag
-                    cx = e.center.real
-                    cy = e.center.imag
-                    rot = e.phi * math.pi / 180.0
-                    cos_rot = math.cos(rot)
-                    sin_rot = math.sin(rot)
-                    x = lambda_ * cos_phi + mu * sin_phi
-                    y = lambda_ * sin_phi - mu * cos_phi
-                    xx = x * cos_rot - y * sin_rot
-                    yy = x * sin_rot + y * cos_rot
-                    points.append((cx + rx * xx, cy + ry * yy))
-                    x = lambda_ * cos_phi - mu * sin_phi
-                    y = lambda_ * sin_phi + mu * cos_phi
-                    xx = x * cos_rot - y * sin_rot
-                    yy = x * sin_rot + y * cos_rot
-                    points.append((cx + rx * xx, cy + ry * yy))
-                    if not last:
-                        points.append((cx + rx * math.cos(rot + start + arc_to_draw),
-                                       cy + ry * math.sin(rot + start + arc_to_draw)))
-                    start += arc_to_draw
-                    first = False
-            if i != len(subpath) - 1:
-                points.append((e.end.real, e.end.imag))
-            else:
-                if subpath.isclosed():
-                    # Must end at the beginning of first segment
-                    assert(e.end.real == points[0][0])
-                    assert(e.end.imag == points[0][1])
-                else:
-                    points.append((e.end.real, e.end.imag))
-        points = torch.tensor(points)
-        points = torch.cat((points, torch.ones([points.shape[0], 1])), dim = 1) @ torch.transpose(shape_to_canvas, 0, 1)
-        points = points / points[:, 2:3]
-        points = points[:, :2].contiguous()
-        ret_paths.append(Path(torch.tensor(num_control_points), points, subpath.isclosed()))
-    return ret_paths

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/logical.h

@@ -1,22 +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 <thrust/detail/config.h>
-
-// this system has no special version of this algorithm 
-

spaces/CVPR/WALT/mmdet/models/roi_heads/mask_heads/fcn_occmask_head.py

@@ -1,570 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer
-from mmcv.ops.carafe import CARAFEPack
-from mmcv.runner import auto_fp16, force_fp32
-from torch.nn.modules.utils import _pair
-
-from mmdet.core import mask_target
-from mmdet.models.builder import HEADS, build_loss
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024**3  # 1 GB memory limit
-
-
-@HEADS.register_module()
-class FCNOccMaskHead(nn.Module):
-
-    def __init__(self,
-                 num_convs=4,
-                 roi_feat_size=14,
-                 in_channels=256,
-                 conv_kernel_size=3,
-                 conv_out_channels=256,
-                 num_classes=80,
-                 class_agnostic=False,
-                 upsample_cfg=dict(type='deconv', scale_factor=2),
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 loss_mask=dict(
-                     type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)):
-        super(FCNOccMaskHead, self).__init__()
-        self.upsample_cfg = upsample_cfg.copy()
-        if self.upsample_cfg['type'] not in [
-                None, 'deconv', 'nearest', 'bilinear', 'carafe'
-        ]:
-            raise ValueError(
-                f'Invalid upsample method {self.upsample_cfg["type"]}, '
-                'accepted methods are "deconv", "nearest", "bilinear", '
-                '"carafe"')
-        self.num_convs = num_convs
-        # WARN: roi_feat_size is reserved and not used
-        self.roi_feat_size = _pair(roi_feat_size)
-        self.in_channels = in_channels
-        self.conv_kernel_size = conv_kernel_size
-        self.conv_out_channels = conv_out_channels
-        self.upsample_method = self.upsample_cfg.get('type')
-        self.scale_factor = self.upsample_cfg.pop('scale_factor', None)
-        self.num_classes = num_classes
-        self.class_agnostic = class_agnostic
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.fp16_enabled = False
-        self.loss_mask = build_loss(loss_mask)
-
-        self.convs = nn.ModuleList()
-        for i in range(self.num_convs):
-            if i ==0:
-                in_channels_change = in_channels*2
-            else:
-                in_channels_change = in_channels
-
-            in_channels = (
-                self.in_channels if i == 0 else self.conv_out_channels)
-            padding = (self.conv_kernel_size - 1) // 2
-            self.convs.append(
-                ConvModule(
-                    in_channels_change,
-                    self.conv_out_channels,
-                    self.conv_kernel_size,
-                    padding=padding,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg))
-
-        self.convs_occluder = nn.ModuleList()
-        for i in range(self.num_convs):
-            in_channels = (
-                self.in_channels if i == 0 else self.conv_out_channels)
-            padding = (self.conv_kernel_size - 1) // 2
-            self.convs_occluder.append(
-                ConvModule(
-                    in_channels,
-                    self.conv_out_channels,
-                    self.conv_kernel_size,
-                    padding=padding,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg))
-
-        upsample_in_channels = (
-            self.conv_out_channels if self.num_convs > 0 else in_channels)
-        upsample_cfg_ = self.upsample_cfg.copy()
-        if self.upsample_method is None:
-            self.upsample = None
-        elif self.upsample_method == 'deconv':
-            upsample_cfg_.update(
-                in_channels=upsample_in_channels,
-                out_channels=self.conv_out_channels,
-                kernel_size=self.scale_factor,
-                stride=self.scale_factor)
-            self.upsample = build_upsample_layer(upsample_cfg_)
-        elif self.upsample_method == 'carafe':
-            upsample_cfg_.update(
-                channels=upsample_in_channels, scale_factor=self.scale_factor)
-            self.upsample = build_upsample_layer(upsample_cfg_)
-        else:
-            # suppress warnings
-            align_corners = (None
-                             if self.upsample_method == 'nearest' else False)
-            upsample_cfg_.update(
-                scale_factor=self.scale_factor,
-                mode=self.upsample_method,
-                align_corners=align_corners)
-            self.upsample = build_upsample_layer(upsample_cfg_)
-
-        out_channels = 1 if self.class_agnostic else self.num_classes
-        logits_in_channel = (
-            self.conv_out_channels
-            if self.upsample_method == 'deconv' else upsample_in_channels)
-        self.conv_logits = Conv2d(logits_in_channel, out_channels, 1)
-        self.conv_logits_occluder = Conv2d(logits_in_channel, out_channels, 1)
-        self.relu = nn.ReLU(inplace=True)
-        self.debug_imgs = None
-
-    def init_weights(self):
-        for m in [self.upsample, self.conv_logits]:
-            if m is None:
-                continue
-            elif isinstance(m, CARAFEPack):
-                m.init_weights()
-            else:
-                nn.init.kaiming_normal_(
-                    m.weight, mode='fan_out', nonlinearity='relu')
-                nn.init.constant_(m.bias, 0)
-
-    @auto_fp16()
-    def forward(self, x):
-        y = x.clone()
-        for conv in self.convs_occluder:
-            y = conv(y)
-        x = torch.cat((x, y), 1)
-        for conv in self.convs:
-            x = conv(x)
-        if self.upsample is not None:
-            x = self.upsample(x)
-            if self.upsample_method == 'deconv':
-                x = self.relu(x)
-        if self.upsample is not None:
-            y = self.upsample(y)
-            if self.upsample_method == 'deconv':
-                y = self.relu(y)
-        mask_pred = self.conv_logits(x)
-        mask_occluder_pred = self.conv_logits_occluder(y)
-        return mask_pred, mask_occluder_pred
-
-    def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg):
-        pos_proposals = [res.pos_bboxes for res in sampling_results]
-        pos_assigned_gt_inds = [
-            res.pos_assigned_gt_inds for res in sampling_results
-        ]
-        mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
-                                   gt_masks, rcnn_train_cfg)
-        return mask_targets
-
-    @force_fp32(apply_to=('mask_pred', ))
-    def loss(self, mask_pred, mask_targets, labels):
-        """
-        Example:
-            >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import *  # NOQA
-            >>> N = 7  # N = number of extracted ROIs
-            >>> C, H, W = 11, 32, 32
-            >>> # Create example instance of FCN Mask Head.
-            >>> # There are lots of variations depending on the configuration
-            >>> self = FCNMaskHead(num_classes=C, num_convs=1)
-            >>> inputs = torch.rand(N, self.in_channels, H, W)
-            >>> mask_pred = self.forward(inputs)
-            >>> sf = self.scale_factor
-            >>> labels = torch.randint(0, C, size=(N,))
-            >>> # With the default properties the mask targets should indicate
-            >>> # a (potentially soft) single-class label
-            >>> mask_targets = torch.rand(N, H * sf, W * sf)
-            >>> loss = self.loss(mask_pred, mask_targets, labels)
-            >>> print('loss = {!r}'.format(loss))
-        """
-        mask_full_pred, mask_occ_pred = mask_pred
-        loss = dict()
-        if mask_full_pred.size(0) == 0:
-            loss_mask_vis = mask_full_pred.sum()
-        else:
-            if self.class_agnostic:
-                loss_mask = self.loss_mask(mask_full_pred, mask_targets,
-                                           torch.zeros_like(labels))
-            else:
-                #print(mask_pred[:,0:1].shape, mask_targets[0::2].shape, labels.shape)
-                loss_mask_vis = self.loss_mask(mask_full_pred[:,0:1], mask_targets[0::2], labels)
-        loss['loss_mask_vis'] = loss_mask_vis
-
-        if mask_occ_pred.size(0) == 0:
-            loss_mask = mask_occ_pred.sum()
-        else:
-            if self.class_agnostic:
-                loss_mask = self.loss_mask(mask_occ_pred, mask_targets,
-                                           torch.zeros_like(labels))
-            else:
-                loss_mask_occ = self.loss_mask(mask_occ_pred[:,0:1], mask_targets[1::2], labels)
-        loss['loss_mask_occ'] = loss_mask_occ
-        return loss
-
-    def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,
-                      ori_shape, scale_factor, rescale):
-        """Get segmentation masks from mask_pred and bboxes.
-        Args:
-            mask_pred (Tensor or ndarray): shape (n, #class, h, w).
-                For single-scale testing, mask_pred is the direct output of
-                model, whose type is Tensor, while for multi-scale testing,
-                it will be converted to numpy array outside of this method.
-            det_bboxes (Tensor): shape (n, 4/5)
-            det_labels (Tensor): shape (n, )
-            rcnn_test_cfg (dict): rcnn testing config
-            ori_shape (Tuple): original image height and width, shape (2,)
-            scale_factor(float | Tensor): If ``rescale is True``, box
-                coordinates are divided by this scale factor to fit
-                ``ori_shape``.
-            rescale (bool): If True, the resulting masks will be rescaled to
-                ``ori_shape``.
-        Returns:
-            list[list]: encoded masks. The c-th item in the outer list
-                corresponds to the c-th class. Given the c-th outer list, the
-                i-th item in that inner list is the mask for the i-th box with
-                class label c.
-        Example:
-            >>> import mmcv
-            >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import *  # NOQA
-            >>> N = 7  # N = number of extracted ROIs
-            >>> C, H, W = 11, 32, 32
-            >>> # Create example instance of FCN Mask Head.
-            >>> self = FCNMaskHead(num_classes=C, num_convs=0)
-            >>> inputs = torch.rand(N, self.in_channels, H, W)
-            >>> mask_pred = self.forward(inputs)
-            >>> # Each input is associated with some bounding box
-            >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N)
-            >>> det_labels = torch.randint(0, C, size=(N,))
-            >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, })
-            >>> ori_shape = (H * 4, W * 4)
-            >>> scale_factor = torch.FloatTensor((1, 1))
-            >>> rescale = False
-            >>> # Encoded masks are a list for each category.
-            >>> encoded_masks = self.get_seg_masks(
-            >>>     mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape,
-            >>>     scale_factor, rescale
-            >>> )
-            >>> assert len(encoded_masks) == C
-            >>> assert sum(list(map(len, encoded_masks))) == N
-        """
-        if isinstance(mask_pred, torch.Tensor):
-            mask_pred = mask_pred.sigmoid()
-        else:
-            mask_pred = det_bboxes.new_tensor(mask_pred)
-
-        device = mask_pred.device
-        cls_segms = [[] for _ in range(self.num_classes)
-                     ]  # BG is not included in num_classes
-        bboxes = det_bboxes[:, :4]
-        labels = det_labels
-
-        if rescale:
-            img_h, img_w = ori_shape[:2]
-        else:
-            if isinstance(scale_factor, float):
-                img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32)
-                img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32)
-            else:
-                w_scale, h_scale = scale_factor[0], scale_factor[1]
-                img_h = np.round(ori_shape[0] * h_scale.item()).astype(
-                    np.int32)
-                img_w = np.round(ori_shape[1] * w_scale.item()).astype(
-                    np.int32)
-            scale_factor = 1.0
-
-        if not isinstance(scale_factor, (float, torch.Tensor)):
-            scale_factor = bboxes.new_tensor(scale_factor)
-        bboxes = bboxes / scale_factor
-
-        if torch.onnx.is_in_onnx_export():
-            # TODO: Remove after F.grid_sample is supported.
-            from torchvision.models.detection.roi_heads \
-                import paste_masks_in_image
-            masks = paste_masks_in_image(mask_pred, bboxes, ori_shape[:2])
-            thr = rcnn_test_cfg.get('mask_thr_binary', 0)
-            if thr > 0:
-                masks = masks >= thr
-            return masks
-
-        N = len(mask_pred)
-        # The actual implementation split the input into chunks,
-        # and paste them chunk by chunk.
-        if device.type == 'cpu':
-            # CPU is most efficient when they are pasted one by one with
-            # skip_empty=True, so that it performs minimal number of
-            # operations.
-            num_chunks = N
-        else:
-            # GPU benefits from parallelism for larger chunks,
-            # but may have memory issue
-            num_chunks = int(
-                np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
-            assert (num_chunks <=
-                    N), 'Default GPU_MEM_LIMIT is too small; try increasing it'
-        chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
-        threshold = rcnn_test_cfg.mask_thr_binary
-        im_mask = torch.zeros(
-            N,
-            img_h,
-            img_w,
-            device=device,
-            dtype=torch.bool if threshold >= 0 else torch.uint8)
-
-        if not self.class_agnostic:
-            mask_pred = mask_pred[range(N), labels][:, None]
-
-        for inds in chunks:
-            masks_chunk, spatial_inds = _do_paste_mask(
-                mask_pred[inds],
-                bboxes[inds],
-                img_h,
-                img_w,
-                skip_empty=device.type == 'cpu')
-
-            if threshold >= 0:
-                masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
-            else:
-                # for visualization and debugging
-                masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
-            im_mask[(inds, ) + spatial_inds] = masks_chunk
-
-        for i in range(N):
-            cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy())
-        return cls_segms
-
-    def get_seg_masks1(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,
-                      ori_shape, scale_factor, rescale):
-        """Get segmentation masks from mask_pred and bboxes.
-
-        Args:
-            mask_pred (Tensor or ndarray): shape (n, #class, h, w).
-                For single-scale testing, mask_pred is the direct output of
-                model, whose type is Tensor, while for multi-scale testing,
-                it will be converted to numpy array outside of this method.
-            det_bboxes (Tensor): shape (n, 4/5)
-            det_labels (Tensor): shape (n, )
-            rcnn_test_cfg (dict): rcnn testing config
-            ori_shape (Tuple): original image height and width, shape (2,)
-            scale_factor(float | Tensor): If ``rescale is True``, box
-                coordinates are divided by this scale factor to fit
-                ``ori_shape``.
-            rescale (bool): If True, the resulting masks will be rescaled to
-                ``ori_shape``.
-
-        Returns:
-            list[list]: encoded masks. The c-th item in the outer list
-                corresponds to the c-th class. Given the c-th outer list, the
-                i-th item in that inner list is the mask for the i-th box with
-                class label c.
-
-        Example:
-            >>> import mmcv
-            >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import *  # NOQA
-            >>> N = 7  # N = number of extracted ROIs
-            >>> C, H, W = 11, 32, 32
-            >>> # Create example instance of FCN Mask Head.
-            >>> self = FCNMaskHead(num_classes=C, num_convs=0)
-            >>> inputs = torch.rand(N, self.in_channels, H, W)
-            >>> mask_pred = self.forward(inputs)
-            >>> # Each input is associated with some bounding box
-            >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N)
-            >>> det_labels = torch.randint(0, C, size=(N,))
-            >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, })
-            >>> ori_shape = (H * 4, W * 4)
-            >>> scale_factor = torch.FloatTensor((1, 1))
-            >>> rescale = False
-            >>> # Encoded masks are a list for each category.
-            >>> encoded_masks = self.get_seg_masks(
-            >>>     mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape,
-            >>>     scale_factor, rescale
-            >>> )
-            >>> assert len(encoded_masks) == C
-            >>> assert sum(list(map(len, encoded_masks))) == N
-        """
-        if isinstance(mask_pred, torch.Tensor):
-            mask_pred = mask_pred.sigmoid()
-        else:
-            mask_pred = det_bboxes.new_tensor(mask_pred)
-
-        device = mask_pred.device
-        cls_segms = [[] for _ in range(self.num_classes)
-                     ]  # BG is not included in num_classes
-        bboxes = det_bboxes[:, :4]
-        labels = det_labels
-        labels = torch.cat((labels, torch.tensor(([1]))))
-        bboxes = torch.cat((bboxes, bboxes))
-        #print(labels,torch.tensor(([1])))
-        #asas
-
-        if rescale:
-            img_h, img_w = ori_shape[:2]
-        else:
-            if isinstance(scale_factor, float):
-                img_h = np.round(ori_shape[0] * scale_factor).astype(np.int32)
-                img_w = np.round(ori_shape[1] * scale_factor).astype(np.int32)
-            else:
-                w_scale, h_scale = scale_factor[0], scale_factor[1]
-                img_h = np.round(ori_shape[0] * h_scale.item()).astype(
-                    np.int32)
-                img_w = np.round(ori_shape[1] * w_scale.item()).astype(
-                    np.int32)
-            scale_factor = 1.0
-
-        if not isinstance(scale_factor, (float, torch.Tensor)):
-            scale_factor = bboxes.new_tensor(scale_factor)
-        bboxes = bboxes / scale_factor
-
-        if torch.onnx.is_in_onnx_export():
-            # TODO: Remove after F.grid_sample is supported.
-            from torchvision.models.detection.roi_heads \
-                import paste_masks_in_image
-            masks = paste_masks_in_image(mask_pred, bboxes, ori_shape[:2])
-            thr = rcnn_test_cfg.get('mask_thr_binary', 0)
-            if thr > 0:
-                masks = masks >= thr
-            return masks
-
-        N = len(mask_pred)
-        # The actual implementation split the input into chunks,
-        # and paste them chunk by chunk.
-        if device.type == 'cpu':
-            # CPU is most efficient when they are pasted one by one with
-            # skip_empty=True, so that it performs minimal number of
-            # operations.
-            num_chunks = N
-        else:
-            # GPU benefits from parallelism for larger chunks,
-            # but may have memory issue
-            num_chunks = int(
-                np.ceil(N * img_h * img_w * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
-            assert (num_chunks <=
-                    N), 'Default GPU_MEM_LIMIT is too small; try increasing it'
-        chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
-        threshold = rcnn_test_cfg.mask_thr_binary
-        im_mask = torch.zeros(
-            N,
-            img_h,
-            img_w,
-            device=device,
-            dtype=torch.bool if threshold >= 0 else torch.uint8)
-
-        if not self.class_agnostic:
-            mask_pred = mask_pred[range(N), labels][:, None]
-        #print('-----------------------------')
-        #print(chunks)
-
-        for inds in chunks:
-            #print(mask_pred[inds].shape, bboxes[inds].shape)
-            masks_chunk, spatial_inds = _do_paste_mask(
-                mask_pred[0:1],
-                bboxes[inds],
-                img_h,
-                img_w,
-                skip_empty=device.type == 'cpu')
-            masks_chunk_occ, spatial_inds_occ = _do_paste_mask(
-                mask_pred[1:2],
-                bboxes[inds],
-                img_h,
-                img_w,
-                skip_empty=device.type == 'cpu')
-
-
-            if threshold >= 0:
-                masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
-                masks_chunk_occ = (masks_chunk_occ >= threshold).to(dtype=torch.bool)
-            else:
-                # for visualization and debugging
-                masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
-            im_mask[([0], ) + spatial_inds] = masks_chunk
-            im_mask[([1], ) + spatial_inds] = masks_chunk_occ
-
-
-        for i in range(N):
-            cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy())
-        #print(cls_segms)
-        return cls_segms
-
-
-def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
-    """Paste instance masks according to boxes.
-
-    This implementation is modified from
-    https://github.com/facebookresearch/detectron2/
-
-    Args:
-        masks (Tensor): N, 1, H, W
-        boxes (Tensor): N, 4
-        img_h (int): Height of the image to be pasted.
-        img_w (int): Width of the image to be pasted.
-        skip_empty (bool): Only paste masks within the region that
-            tightly bound all boxes, and returns the results this region only.
-            An important optimization for CPU.
-
-    Returns:
-        tuple: (Tensor, tuple). The first item is mask tensor, the second one
-            is the slice object.
-        If skip_empty == False, the whole image will be pasted. It will
-            return a mask of shape (N, img_h, img_w) and an empty tuple.
-        If skip_empty == True, only area around the mask will be pasted.
-            A mask of shape (N, h', w') and its start and end coordinates
-            in the original image will be returned.
-    """
-    # On GPU, paste all masks together (up to chunk size)
-    # by using the entire image to sample the masks
-    # Compared to pasting them one by one,
-    # this has more operations but is faster on COCO-scale dataset.
-    device = masks.device
-    if skip_empty:
-        x0_int, y0_int = torch.clamp(
-            boxes.min(dim=0).values.floor()[:2] - 1,
-            min=0).to(dtype=torch.int32)
-        x1_int = torch.clamp(
-            boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
-        y1_int = torch.clamp(
-            boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
-    else:
-        x0_int, y0_int = 0, 0
-        x1_int, y1_int = img_w, img_h
-    x0, y0, x1, y1 = torch.split(boxes, 1, dim=1)  # each is Nx1
-
-    N = masks.shape[0]
-
-    img_y = torch.arange(
-        y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
-    img_x = torch.arange(
-        x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
-    img_y = (img_y - y0) / (y1 - y0) * 2 - 1
-    img_x = (img_x - x0) / (x1 - x0) * 2 - 1
-    # img_x, img_y have shapes (N, w), (N, h)
-    if torch.isinf(img_x).any():
-        inds = torch.where(torch.isinf(img_x))
-        img_x[inds] = 0
-    if torch.isinf(img_y).any():
-        inds = torch.where(torch.isinf(img_y))
-        img_y[inds] = 0
-
-    gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
-    gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
-    grid = torch.stack([gx, gy], dim=3)
-
-    if torch.onnx.is_in_onnx_export():
-        raise RuntimeError(
-            'Exporting F.grid_sample from Pytorch to ONNX is not supported.')
-    img_masks = F.grid_sample(
-        masks.to(dtype=torch.float32), grid, align_corners=False)
-
-    if skip_empty:
-        return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
-    else:
-        return img_masks[:, 0], ()

spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/audio_text.py

@@ -1,36 +0,0 @@
-import json
-
-import requests
-
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-cfg = Config()
-
-
-def read_audio_from_file(audio_path):
-    audio_path = path_in_workspace(audio_path)
-    with open(audio_path, "rb") as audio_file:
-        audio = audio_file.read()
-    return read_audio(audio)
-
-
-def read_audio(audio):
-    model = cfg.huggingface_audio_to_text_model
-    api_url = f"https://api-inference.huggingface.co/models/{model}"
-    api_token = cfg.huggingface_api_token
-    headers = {"Authorization": f"Bearer {api_token}"}
-
-    if api_token is None:
-        raise ValueError(
-            "You need to set your Hugging Face API token in the config file."
-        )
-
-    response = requests.post(
-        api_url,
-        headers=headers,
-        data=audio,
-    )
-
-    text = json.loads(response.content.decode("utf-8"))["text"]
-    return "The audio says: " + text