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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Diablo 2 D2se Mod Manager 15 The Best Way to Enjoy Diablo II in 2023.md
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<h1><b>Diablo 2 D2se Mod Manager 15: How to Install and Use It</b></h1>
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<p>If you are a fan of Diablo 2, you probably know that there are many mods available for the game that can enhance your gameplay experience. However, installing and managing multiple mods can be a hassle, especially if they are not compatible with each other or with the latest version of the game. That's where Diablo 2 D2se Mod Manager 15 comes in handy. In this article, we will explain what this mod manager is, how to download and install it, and how to use it to play your favorite mods for Diablo 2.</p>
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<h2>Diablo 2 D2se Mod Manager 15</h2><br /><p><b><b>DOWNLOAD</b> ☑ <a href="https://byltly.com/2uKwk7">https://byltly.com/2uKwk7</a></b></p><br /><br />
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<h2><b>What is Diablo 2 D2se Mod Manager 15?</b></h2>
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<p>Diablo 2 D2se Mod Manager 15 is a tool that allows you to easily install and switch between different mods for Diablo 2. It works by creating a separate folder for each mod, so you don't have to worry about overwriting or deleting any files from your original game directory. You can also create multiple profiles for different mods, so you can play them with different settings and characters.</p>
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<p>Some of the features of Diablo 2 D2se Mod Manager 15 are:</p>
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<ul>
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<li>It supports all versions of Diablo 2 from 1.07 to 1.14d.</li>
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<li>It supports both single-player and multiplayer modes.</li>
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<li>It has a user-friendly interface that shows you all the available mods and their descriptions.</li>
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<li>It allows you to customize various options for each mod, such as resolution, window mode, sound, language, etc.</li>
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<li>It automatically detects and fixes any compatibility issues between mods and the game.</li>
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<li>It lets you backup and restore your save files and configuration files.</li>
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</ul>
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<p>The benefits of using Diablo 2 D2se Mod Manager 15 are:</p>
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<ul>
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<li>You can enjoy a variety of mods for Diablo 2 without having to install them manually or worry about conflicts.</li>
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<li>You can switch between different mods with just a few clicks.</li>
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<li>You can keep your original game files intact and avoid any errors or crashes.</li>
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<li>You can explore new features and content that the mods offer, such as new items, skills, quests, enemies, etc.</li>
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</ul>
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<h2><b>How to Download and Install Diablo 2 D2se Mod Manager 15?</b></h2>
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<p>To download and install Diablo 2 D2se Mod Manager 15, you need to have the following requirements:</p>
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<ul>
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<li>A PC running Windows XP or later.</li>
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<li>A copy of Diablo 2 and its expansion Lord of Destruction installed on your PC.</li>
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<li>A minimum of 4 GB of free disk space.</li>
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</ul>
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<p>The compatibility of Diablo 2 D2se Mod Manager 15 is:</p>
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<p>How to install Diablo 2 D2se Mod Manager 15<br />
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Diablo 2 D2se Mod Manager 15 download link<br />
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Best mods for Diablo 2 D2se Mod Manager 15<br />
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Diablo 2 D2se Mod Manager 15 tutorial<br />
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Diablo 2 D2se Mod Manager 15 compatibility issues<br />
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Diablo 2 D2se Mod Manager 15 review<br />
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Diablo 2 D2se Mod Manager 15 error fix<br />
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Diablo 2 D2se Mod Manager 15 features<br />
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Diablo 2 D2se Mod Manager 15 update<br />
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Diablo 2 D2se Mod Manager 15 vs PlugY<br />
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Diablo 2 D2se Mod Manager 15 guide<br />
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Diablo 2 D2se Mod Manager 15 mod list<br />
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Diablo 2 D2se Mod Manager 15 screenshots<br />
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Diablo 2 D2se Mod Manager 15 system requirements<br />
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Diablo 2 D2se Mod Manager 15 tips and tricks<br />
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Diablo 2 D2se Mod Manager 15 forum<br />
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Diablo 2 D2se Mod Manager 15 video<br />
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Diablo 2 D2se Mod Manager 15 wiki<br />
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Diablo 2 D2se Mod Manager 15 reddit<br />
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Diablo 2 D2se Mod Manager 15 steam<br />
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Diablo 2 D2se Mod Manager 15 mac<br />
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Diablo 2 D2se Mod Manager 15 windows 10<br />
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Diablo 2 D2se Mod Manager 15 multiplayer<br />
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Diablo 2 D2se Mod Manager 15 cheats<br />
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Diablo 2 D2se Mod Manager 15 mods reddit<br />
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Diablo 2 D2se Mod Manager 15 plugy mod<br />
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Diablo 2 D2se Mod Manager 15 median xl mod<br />
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Diablo 2 D2se Mod Manager 15 path of diablo mod<br />
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Diablo 2 D2se Mod Manager 15 project diablo mod<br />
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Diablo 2 D2se Mod Manager 15 eastern sun mod<br />
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Diablo 2 D2se Mod Manager 15 ressurected mod<br />
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Diablo 2 D2se Mod Manager 15 dbrunski125 mod<br />
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Diablo 2 D2se Mod Manager 15 mrllamasc mod<br />
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Diablo 2 D2se Mod Manager 15 sigma mod<br />
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Diablo</p>
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<ul>
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<li>It works with both CD-ROM and digital versions of the game.</li>
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<li>It works with both vanilla and patched versions of the game.</li>
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<li>It works with both online and offline modes of the game.</li>
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</ul>
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<p>The steps to download and install Diablo 2 D2se Mod Manager 15 are:</p>
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<ol>
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<li>Go to <a href="https://www.moddb.com/mods/diablo-ii-dual-screen-hud/downloads/diablo-ii-dual-screen-hud-v10">this link</a> and click on the "Download Now" button. This will download a zip file containing the mod manager.</li>
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<li>Extract the zip file to a location of your choice. You will see a folder named "D2SE" inside it.</li>
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<li>Copy the "D2SE" folder and paste it into your Diablo 2 installation directory. This is usually located at C:\Program Files (x86)\Diablo II or C:\Program Files\Diablo II.</li>
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<li>Run the "D2SE.exe" file inside the "D2SE" folder. This will launch the mod manager.</li>
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</ol>
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<p>If you encounter any issues during the installation process, such as missing DLL files or permission errors, you can try the following solutions:</p>
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<ul>
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<li>Make sure you have administrator rights on your PC. You can right-click on the "D2SE.exe" file and select "Run as administrator".</li>
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<li>Make sure you have installed Microsoft Visual C++ Redistributable Package. You can download it from <a href="https://www.microsoft.com/en-us/download/details.aspx?id=5555">here</a>.</li>
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<li>Make sure you have disabled any antivirus or firewall software that might interfere with the installation process.</li>
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</ul>
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<h2><b>How to Use Diablo 2 D2se Mod Manager 15?</b></h2>
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<p>To use Diablo 2 D2se Mod Manager 15, you need to follow these steps:</p>
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<h3><b>How to launch and configure the mod manager</b></h3>
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<ol>
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<li>Run the "D2SE.exe" file inside the "D2SE" folder. This will launch the mod manager.</li>
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<li>You will see a window with a list of all the available mods for Diablo 2. You can scroll through them using the arrow keys or the mouse wheel. You can also use the search box at the top right corner to find a specific mod by name or keyword.</li>
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<li>To select a mod, click on its name or press Enter. You will see a preview image of the mod on the right side of the window. You will also see some information about the mod below it, such as its version, author, description, website, etc.</li>
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<li>To configure some options for the selected mod, click on the "Settings" button at the bottom right corner of the window. You will see a new window with several tabs that allow you to adjust various settings for each mod, such as resolution, window mode, sound, language, etc. You can also enable or disable some features that are specific to each mod, such as plug-ins, cheats, tweaks, etc. To apply your changes, click on "Save & Exit". To cancel your changes, click on "Exit without saving".</li>
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</ol>
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<h3><b>How to browse and select different mods for Diablo 2</b></h3>
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<ol>
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<li>To browse through different mods for Diablo 2, use the arrow keys or the mouse wheel to scroll through them in the main window of the mod manager. You can also use the search box at the top right corner to find a specific mod by name or keyword.</li>
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<li>To select a mod, click on its name or press Enter. You will see a preview image of the mod on the right side of the window. You will also see some information about the mod below it, such as its version, author, description, website, etc. </li><li>To start playing the selected mod, click on the "Start PlugY" button at the bottom left corner of the window. This will launch the game with the mod enabled. You can also press F9 to do the same. You will see a splash screen with the logo of the mod and some loading messages. </li><li>To exit the game and return to the mod manager, press Alt+F4 or click on the X button at the top right corner of the game window. You will see a confirmation message asking if you want to quit. Click on "Yes" or press Enter. You will be back in the main window of the mod manager. </li></ol>
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<ol start="5">
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<li>To create and manage multiple profiles for different mods, click on the "Profiles" button at the bottom right corner of the main window of the mod manager. You will see a new window with a list of all the profiles you have created. A profile is a set of settings and save files that are associated with a specific mod. You can have multiple profiles for the same mod or different mods.</li>
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<li>To create a new profile, click on the "New" button at the top left corner of the window. You will see a dialog box asking you to enter a name for the new profile. Type a name and click on "OK" or press Enter. You will see a new profile added to the list with the default settings and save files for the selected mod.</li>
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<li>To edit an existing profile, click on its name in the list or press Enter. You will see a window with several tabs that allow you to adjust various settings for the profile, such as resolution, window mode, sound, language, etc. You can also enable or disable some features that are specific to each mod, such as plug-ins, cheats, tweaks, etc. To apply your changes, click on "Save & Exit". To cancel your changes, click on "Exit without saving".</li>
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<li>To delete an existing profile, click on its name in the list and then click on the "Delete" button at the top left corner of the window. You will see a confirmation message asking if you want to delete the profile. Click on "Yes" or press Enter. You will see the profile removed from the list.</li>
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<li>To switch between different profiles, click on their names in the list or use the arrow keys to select them. You will see a preview image of the mod and some information about the profile on the right side of the window. To start playing with the selected profile, click on "Start PlugY" or press F9.</li>
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</ol>
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<h3><b>How to update and uninstall Diablo 2 D2se Mod Manager 15?</b></h3>
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<ol>
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<li>To update Diablo 2 D2se Mod Manager 15, go to <a href="https://www.moddb.com/mods/diablo-ii-dual-screen-hud/downloads/diablo-ii-dual-screen-hud-v10">this link</a> and check if there is a newer version available. If there is, download it and follow the same steps as above to install it. The mod manager will automatically overwrite the old files and keep your profiles and settings intact.</li>
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<li>To uninstall Diablo 2 D2se Mod Manager 15, go to your Diablo 2 installation directory and delete the "D2SE" folder. This will remove all the files and folders related to the mod manager. You can also delete any mods that you have downloaded and installed using the mod manager.</li>
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</ol>
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<h2><b>Conclusion</b></h2>
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<p>Diablo 2 D2se Mod Manager 15 is a great tool for Diablo 2 players who want to enjoy different mods for the game without any hassle. It allows you to easily install and switch between different mods, as well as customize various options for each mod. It also lets you create and manage multiple profiles for different mods, so you can play them with different settings and characters. It is compatible with all versions of Diablo 2 and supports both single-player and multiplayer modes.</p>
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<p>If you are interested in trying out Diablo 2 D2se Mod Manager 15, you can download it from <a href="https://www.moddb.com/mods/diablo-ii-dual-screen-hud/downloads/diablo-ii-dual-screen-hud-v10">here</a> and follow our guide on how to install and use it. We hope you have fun playing Diablo 2 with your favorite mods!</p>
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<h4><b>FAQs</b></h4>
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<p>Here are some frequently asked questions about Diablo 2 D2se Mod Manager 15:</p>
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<ul>
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<li><b>Q: What are some of the best mods for Diablo 2?</b></li>
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<li>A: There are many mods for Diablo 2 that offer different features and content for the game. Some of the most popular ones are Median XL, PlugY, Path of Diablo, Eastern Sun, Zy-El, etc. You can find more mods at <a href="https://www.moddb.com/games/diablo-2/mods">this link</a>.</li>
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<li><b>Q: Can I play online with Diablo 2 D2se Mod Manager 15?</b></li>
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<li>A: Yes, you can play online with Diablo 2 D2se Mod Manager 15. However, you need to make sure that you are using a mod that is compatible with online mode and that you are playing on a server that supports that mod. Otherwise, you might encounter errors or bans.</li>
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<li><b>Q: Can I use cheats or hacks with Diablo 2 D2se Mod Manager 15?</b></li>
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<li>A: Yes, you can use cheats or hacks with Diablo 2 D2se Mod Manager 15. However, we do not recommend doing so as it might ruin your gameplay experience or cause problems with other players or servers. Use them at your own risk.</li>
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<li><b>Q: How can I contact the developer of Diablo 2 D2se Mod Manager 15?</b></li>
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<li>A: You can contact the developer of Diablo 2 D2se Mod Manager 15 by visiting his website at <a href="http://dualscreenhud.com/">this link</a>. You can also leave a comment or feedback at <a href="https://www.moddb.com/mods/diablo-ii-dual-screen-hud">this link</a>.</li>
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<li><b>Q: How can I support the development of Diablo 2 D2se Mod Manager 15?</b></li>
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<li>A: You can support the development of Diablo 2 D2se Mod Manager 15 by donating to the developer via PayPal at <a href="https://www.paypal.com/paypalme/DSHUD">this link</a>. You can also share this article with your friends who might be interested in playing Diablo 2 with mods.</li>
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</ul>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Guitar Rig 5 Effects BEST.md
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| 1 |
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<h1>Guitar Rig 5 Effects: How to Create Amazing Guitar Tones</h1>
|
| 3 |
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<p>Guitar Rig 5 is a software that allows you to create and customize your own guitar tones using a variety of effects, amps, cabinets, and mics. Whether you want to emulate your favorite artists, experiment with new sounds, or record your own music, Guitar Rig 5 can help you achieve your goals.</p>
|
| 4 |
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<h2>guitar rig 5 effects</h2><br /><p><b><b>Download</b> →→→ <a href="https://byltly.com/2uKvX0">https://byltly.com/2uKvX0</a></b></p><br /><br />
|
| 5 |
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<p>But how do you use Guitar Rig 5 effects to create amazing guitar tones? What are the different types of effects and how do they work? How can you combine and tweak them to suit your style and preferences? In this article, we will answer these questions and show you how to use Guitar Rig 5 effects to create amazing guitar tones.</p>
|
| 6 |
-
<h2>What are Guitar Rig 5 Effects?</h2>
|
| 7 |
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<p>Guitar Rig 5 effects are digital simulations of various devices that can modify the sound of your guitar. They can be divided into four categories: distortion, modulation, delay, and reverb. Each category has several subtypes that offer different variations and options. Here is a brief overview of each category and its subtypes:</p>
|
| 8 |
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<ul>
|
| 9 |
-
<li>Distortion: This category includes effects that add distortion, overdrive, fuzz, or saturation to your guitar sound. They can make your sound more aggressive, crunchy, or warm. Some examples of distortion effects are Tube Screamer, Big Muff, Rat, and Screamer.</li>
|
| 10 |
-
<li>Modulation: This category includes effects that modulate the frequency, amplitude, or phase of your guitar sound. They can create subtle or dramatic changes in your sound, such as chorus, flanger, phaser, tremolo, vibrato, or wah-wah. Some examples of modulation effects are Chorus/Flanger, Phaser Nine, Tremolo/Rotary, and Wah-Wah.</li>
|
| 11 |
-
<li>Delay: This category includes effects that create echoes or repetitions of your guitar sound. They can add depth, space, or movement to your sound. Some examples of delay effects are Delay Man, Echoes, Memory Man, and Twin Delay.</li>
|
| 12 |
-
<li>Reverb: This category includes effects that simulate the sound of different environments or spaces. They can add ambience, dimension, or realism to your sound. Some examples of reverb effects are Hall Reverb, Plate Reverb, Spring Reverb, and Studio Reverb.</li>
|
| 13 |
-
</ul>
|
| 14 |
-
<h2>How to Use Guitar Rig 5 Effects?</h2>
|
| 15 |
-
<p>To use Guitar Rig 5 effects, you need to have a guitar, an audio interface, a computer with Guitar Rig 5 installed, and a pair of headphones or speakers. You also need to connect your guitar to the audio interface using a cable and set up the audio settings in Guitar Rig 5.</p>
|
| 16 |
-
<p></p>
|
| 17 |
-
<p>Once you have everything ready, you can start using Guitar Rig 5 effects by following these steps:</p>
|
| 18 |
-
<ol>
|
| 19 |
-
<li>Open Guitar Rig 5 and select a preset or create a new one. A preset is a combination of effects that are already configured for a specific sound. You can choose from hundreds of presets that are included in Guitar Rig 5 or download more from the online library. You can also create your own presets by adding and arranging effects in the rack.</li>
|
| 20 |
-
<li>Add an effect to the rack by dragging it from the browser on the left side of the screen. You can add as many effects as you want and place them in any order you like. You can also adjust the parameters of each effect by using the knobs and sliders on the right side of the screen.</li>
|
| 21 |
-
<li>Play your guitar and listen to how the effect changes your sound. You can also use the bypass button to turn the effect on or off or use the solo button to isolate the effect from the rest of the rack.</li>
|
| 22 |
-
<li>Save your preset by clicking on the save button on the top right corner of the screen. You can name your preset and assign it to a category and a bank for easy access later.</li>
|
| 23 |
-
</ol>
|
| 24 |
-
<h2>How to Create Amazing Guitar Tones with Guitar Rig 5 Effects?</h2>
|
| 25 |
-
<p>To create amazing guitar tones with Guitar Rig 5 effects, you need to experiment with different combinations and settings of effects until you find the ones that suit your taste and style. There is no right or wrong way to use Guitar Rig 5 effects; it all depends on your personal preference and creativity.</p>
|
| 26 |
-
<p>However, here are some general tips and guidelines that can help you create</p> ddb901b051<br />
|
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<br />
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spaces/1gistliPinn/ChatGPT4/Examples/EM Client Pro 7.2.37472.0 Multilingual Free __TOP__ Download Full Crack.md
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@@ -1,86 +0,0 @@
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| 1 |
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<h2>eM Client Pro 7.2.37472.0 Multilingual Free Download Full Crack</h2><br /><p><b><b>Download</b> 🔗 <a href="https://imgfil.com/2uy1zw">https://imgfil.com/2uy1zw</a></b></p><br /><br />
|
| 2 |
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| 3 |
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It can manage all your mail accounts and you can schedule emails too. The most interesting thing in this email client is that it can manage multiple email accounts.
|
| 4 |
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|
| 5 |
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Not only this application will help you to manage your email, but it will help you to filter spam emails. This feature is very powerful to manage multiple email accounts. It has many other features like:
|
| 6 |
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| 7 |
-
Filters and Rules
|
| 8 |
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|
| 9 |
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Automatic Scheduling
|
| 10 |
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| 11 |
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Organize the Email
|
| 12 |
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| 13 |
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Themes
|
| 14 |
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| 15 |
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Replying and Forwarding
|
| 16 |
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Filter Spam
|
| 18 |
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| 19 |
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Emoji
|
| 20 |
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Compose Messages
|
| 22 |
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Attachments
|
| 24 |
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| 25 |
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Group Mail
|
| 26 |
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| 27 |
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... and many other features
|
| 28 |
-
|
| 29 |
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How to use
|
| 30 |
-
|
| 31 |
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Step 1: Download Email Client software from eM Client official website. The Download file will open.
|
| 32 |
-
|
| 33 |
-
Step 2: Install the application and you are done.
|
| 34 |
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| 35 |
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Step 3: Open the application and start using it.
|
| 36 |
-
|
| 37 |
-
Features of eM Client
|
| 38 |
-
|
| 39 |
-
It has a lot of useful features for emailing. Those features are like:
|
| 40 |
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|
| 41 |
-
Better design and interface
|
| 42 |
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|
| 43 |
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Better email preview
|
| 44 |
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| 45 |
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Different filters like Spam, Junk and so on
|
| 46 |
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|
| 47 |
-
There is a lot of functions to sort emails
|
| 48 |
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|
| 49 |
-
You can also mark emails as read/unread
|
| 50 |
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|
| 51 |
-
Inbox list, star list and Filing system
|
| 52 |
-
|
| 53 |
-
Replying to an email is easy, just type R and choose to reply in a specific email.
|
| 54 |
-
|
| 55 |
-
You can send message to multiple users with the help of this feature.
|
| 56 |
-
|
| 57 |
-
Other features of Email Client
|
| 58 |
-
|
| 59 |
-
It has a lot of features. Those features are like:
|
| 60 |
-
|
| 61 |
-
Filter spam emails
|
| 62 |
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|
| 63 |
-
Many filters to make your inbox more organized
|
| 64 |
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|
| 65 |
-
Automatic sending and retrieving email
|
| 66 |
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|
| 67 |
-
Emojis
|
| 68 |
-
|
| 69 |
-
Email Client comes with lots of different features. All the features are free and they do not ask for any fee.
|
| 70 |
-
|
| 71 |
-
Step 3: Download Email Client software from eM Client official website. The Download file will open.
|
| 72 |
-
|
| 73 |
-
Step 4: Install the application and you are done.
|
| 74 |
-
|
| 75 |
-
Step 5: Open the application and start using it.
|
| 76 |
-
|
| 77 |
-
Conclusion
|
| 78 |
-
|
| 79 |
-
eM Client is a great application to handle multiple emails and to filter the spam emails. This application is best for multiple users to send and receive emails.
|
| 80 |
-
|
| 81 |
-
One Reply to “em Client Review & Review of eM Client”
|
| 82 |
-
|
| 83 |
-
I believe it is better than Outlook because I am using it for the last five years. I have searched a lot 4fefd39f24<br />
|
| 84 |
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<br />
|
| 85 |
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<br />
|
| 86 |
-
<p></p>
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spaces/1line/AutoGPT/autogpt/app.py
DELETED
|
@@ -1,330 +0,0 @@
|
|
| 1 |
-
""" Command and Control """
|
| 2 |
-
import json
|
| 3 |
-
from typing import Dict, List, NoReturn, Union
|
| 4 |
-
|
| 5 |
-
from autogpt.agent.agent_manager import AgentManager
|
| 6 |
-
from autogpt.commands.analyze_code import analyze_code
|
| 7 |
-
from autogpt.commands.audio_text import read_audio_from_file
|
| 8 |
-
from autogpt.commands.execute_code import (
|
| 9 |
-
execute_python_file,
|
| 10 |
-
execute_shell,
|
| 11 |
-
execute_shell_popen,
|
| 12 |
-
)
|
| 13 |
-
from autogpt.commands.file_operations import (
|
| 14 |
-
append_to_file,
|
| 15 |
-
delete_file,
|
| 16 |
-
download_file,
|
| 17 |
-
read_file,
|
| 18 |
-
search_files,
|
| 19 |
-
write_to_file,
|
| 20 |
-
)
|
| 21 |
-
from autogpt.commands.git_operations import clone_repository
|
| 22 |
-
from autogpt.commands.google_search import google_official_search, google_search
|
| 23 |
-
from autogpt.commands.image_gen import generate_image
|
| 24 |
-
from autogpt.commands.improve_code import improve_code
|
| 25 |
-
from autogpt.commands.twitter import send_tweet
|
| 26 |
-
from autogpt.commands.web_requests import scrape_links, scrape_text
|
| 27 |
-
from autogpt.commands.web_selenium import browse_website
|
| 28 |
-
from autogpt.commands.write_tests import write_tests
|
| 29 |
-
from autogpt.config import Config
|
| 30 |
-
from autogpt.json_utils.json_fix_llm import fix_and_parse_json
|
| 31 |
-
from autogpt.memory import get_memory
|
| 32 |
-
from autogpt.processing.text import summarize_text
|
| 33 |
-
from autogpt.speech import say_text
|
| 34 |
-
|
| 35 |
-
CFG = Config()
|
| 36 |
-
AGENT_MANAGER = AgentManager()
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
def is_valid_int(value: str) -> bool:
|
| 40 |
-
"""Check if the value is a valid integer
|
| 41 |
-
|
| 42 |
-
Args:
|
| 43 |
-
value (str): The value to check
|
| 44 |
-
|
| 45 |
-
Returns:
|
| 46 |
-
bool: True if the value is a valid integer, False otherwise
|
| 47 |
-
"""
|
| 48 |
-
try:
|
| 49 |
-
int(value)
|
| 50 |
-
return True
|
| 51 |
-
except ValueError:
|
| 52 |
-
return False
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def get_command(response_json: Dict):
|
| 56 |
-
"""Parse the response and return the command name and arguments
|
| 57 |
-
|
| 58 |
-
Args:
|
| 59 |
-
response_json (json): The response from the AI
|
| 60 |
-
|
| 61 |
-
Returns:
|
| 62 |
-
tuple: The command name and arguments
|
| 63 |
-
|
| 64 |
-
Raises:
|
| 65 |
-
json.decoder.JSONDecodeError: If the response is not valid JSON
|
| 66 |
-
|
| 67 |
-
Exception: If any other error occurs
|
| 68 |
-
"""
|
| 69 |
-
try:
|
| 70 |
-
if "command" not in response_json:
|
| 71 |
-
return "Error:", "Missing 'command' object in JSON"
|
| 72 |
-
|
| 73 |
-
if not isinstance(response_json, dict):
|
| 74 |
-
return "Error:", f"'response_json' object is not dictionary {response_json}"
|
| 75 |
-
|
| 76 |
-
command = response_json["command"]
|
| 77 |
-
if not isinstance(command, dict):
|
| 78 |
-
return "Error:", "'command' object is not a dictionary"
|
| 79 |
-
|
| 80 |
-
if "name" not in command:
|
| 81 |
-
return "Error:", "Missing 'name' field in 'command' object"
|
| 82 |
-
|
| 83 |
-
command_name = command["name"]
|
| 84 |
-
|
| 85 |
-
# Use an empty dictionary if 'args' field is not present in 'command' object
|
| 86 |
-
arguments = command.get("args", {})
|
| 87 |
-
|
| 88 |
-
return command_name, arguments
|
| 89 |
-
except json.decoder.JSONDecodeError:
|
| 90 |
-
return "Error:", "Invalid JSON"
|
| 91 |
-
# All other errors, return "Error: + error message"
|
| 92 |
-
except Exception as e:
|
| 93 |
-
return "Error:", str(e)
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
def map_command_synonyms(command_name: str):
|
| 97 |
-
"""Takes the original command name given by the AI, and checks if the
|
| 98 |
-
string matches a list of common/known hallucinations
|
| 99 |
-
"""
|
| 100 |
-
synonyms = [
|
| 101 |
-
("write_file", "write_to_file"),
|
| 102 |
-
("create_file", "write_to_file"),
|
| 103 |
-
("search", "google"),
|
| 104 |
-
]
|
| 105 |
-
for seen_command, actual_command_name in synonyms:
|
| 106 |
-
if command_name == seen_command:
|
| 107 |
-
return actual_command_name
|
| 108 |
-
return command_name
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def execute_command(command_name: str, arguments):
|
| 112 |
-
"""Execute the command and return the result
|
| 113 |
-
|
| 114 |
-
Args:
|
| 115 |
-
command_name (str): The name of the command to execute
|
| 116 |
-
arguments (dict): The arguments for the command
|
| 117 |
-
|
| 118 |
-
Returns:
|
| 119 |
-
str: The result of the command
|
| 120 |
-
"""
|
| 121 |
-
try:
|
| 122 |
-
command_name = map_command_synonyms(command_name.lower())
|
| 123 |
-
if command_name == "google":
|
| 124 |
-
# Check if the Google API key is set and use the official search method
|
| 125 |
-
# If the API key is not set or has only whitespaces, use the unofficial
|
| 126 |
-
# search method
|
| 127 |
-
key = CFG.google_api_key
|
| 128 |
-
if key and key.strip() and key != "your-google-api-key":
|
| 129 |
-
google_result = google_official_search(arguments["input"])
|
| 130 |
-
return google_result
|
| 131 |
-
else:
|
| 132 |
-
google_result = google_search(arguments["input"])
|
| 133 |
-
|
| 134 |
-
# google_result can be a list or a string depending on the search results
|
| 135 |
-
if isinstance(google_result, list):
|
| 136 |
-
safe_message = [
|
| 137 |
-
google_result_single.encode("utf-8", "ignore")
|
| 138 |
-
for google_result_single in google_result
|
| 139 |
-
]
|
| 140 |
-
else:
|
| 141 |
-
safe_message = google_result.encode("utf-8", "ignore")
|
| 142 |
-
|
| 143 |
-
return safe_message.decode("utf-8")
|
| 144 |
-
elif command_name == "memory_add":
|
| 145 |
-
memory = get_memory(CFG)
|
| 146 |
-
return memory.add(arguments["string"])
|
| 147 |
-
elif command_name == "start_agent":
|
| 148 |
-
return start_agent(
|
| 149 |
-
arguments["name"], arguments["task"], arguments["prompt"]
|
| 150 |
-
)
|
| 151 |
-
elif command_name == "message_agent":
|
| 152 |
-
return message_agent(arguments["key"], arguments["message"])
|
| 153 |
-
elif command_name == "list_agents":
|
| 154 |
-
return list_agents()
|
| 155 |
-
elif command_name == "delete_agent":
|
| 156 |
-
return delete_agent(arguments["key"])
|
| 157 |
-
elif command_name == "get_text_summary":
|
| 158 |
-
return get_text_summary(arguments["url"], arguments["question"])
|
| 159 |
-
elif command_name == "get_hyperlinks":
|
| 160 |
-
return get_hyperlinks(arguments["url"])
|
| 161 |
-
elif command_name == "clone_repository":
|
| 162 |
-
return clone_repository(
|
| 163 |
-
arguments["repository_url"], arguments["clone_path"]
|
| 164 |
-
)
|
| 165 |
-
elif command_name == "read_file":
|
| 166 |
-
return read_file(arguments["file"])
|
| 167 |
-
elif command_name == "write_to_file":
|
| 168 |
-
return write_to_file(arguments["file"], arguments["text"])
|
| 169 |
-
elif command_name == "append_to_file":
|
| 170 |
-
return append_to_file(arguments["file"], arguments["text"])
|
| 171 |
-
elif command_name == "delete_file":
|
| 172 |
-
return delete_file(arguments["file"])
|
| 173 |
-
elif command_name == "search_files":
|
| 174 |
-
return search_files(arguments["directory"])
|
| 175 |
-
elif command_name == "download_file":
|
| 176 |
-
if not CFG.allow_downloads:
|
| 177 |
-
return "Error: You do not have user authorization to download files locally."
|
| 178 |
-
return download_file(arguments["url"], arguments["file"])
|
| 179 |
-
elif command_name == "browse_website":
|
| 180 |
-
return browse_website(arguments["url"], arguments["question"])
|
| 181 |
-
# TODO: Change these to take in a file rather than pasted code, if
|
| 182 |
-
# non-file is given, return instructions "Input should be a python
|
| 183 |
-
# filepath, write your code to file and try again"
|
| 184 |
-
elif command_name == "analyze_code":
|
| 185 |
-
return analyze_code(arguments["code"])
|
| 186 |
-
elif command_name == "improve_code":
|
| 187 |
-
return improve_code(arguments["suggestions"], arguments["code"])
|
| 188 |
-
elif command_name == "write_tests":
|
| 189 |
-
return write_tests(arguments["code"], arguments.get("focus"))
|
| 190 |
-
elif command_name == "execute_python_file": # Add this command
|
| 191 |
-
return execute_python_file(arguments["file"])
|
| 192 |
-
elif command_name == "execute_shell":
|
| 193 |
-
if CFG.execute_local_commands:
|
| 194 |
-
return execute_shell(arguments["command_line"])
|
| 195 |
-
else:
|
| 196 |
-
return (
|
| 197 |
-
"You are not allowed to run local shell commands. To execute"
|
| 198 |
-
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
|
| 199 |
-
"in your config. Do not attempt to bypass the restriction."
|
| 200 |
-
)
|
| 201 |
-
elif command_name == "execute_shell_popen":
|
| 202 |
-
if CFG.execute_local_commands:
|
| 203 |
-
return execute_shell_popen(arguments["command_line"])
|
| 204 |
-
else:
|
| 205 |
-
return (
|
| 206 |
-
"You are not allowed to run local shell commands. To execute"
|
| 207 |
-
" shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
|
| 208 |
-
"in your config. Do not attempt to bypass the restriction."
|
| 209 |
-
)
|
| 210 |
-
elif command_name == "read_audio_from_file":
|
| 211 |
-
return read_audio_from_file(arguments["file"])
|
| 212 |
-
elif command_name == "generate_image":
|
| 213 |
-
return generate_image(arguments["prompt"])
|
| 214 |
-
elif command_name == "send_tweet":
|
| 215 |
-
return send_tweet(arguments["text"])
|
| 216 |
-
elif command_name == "do_nothing":
|
| 217 |
-
return "No action performed."
|
| 218 |
-
elif command_name == "task_complete":
|
| 219 |
-
shutdown()
|
| 220 |
-
else:
|
| 221 |
-
return (
|
| 222 |
-
f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
|
| 223 |
-
" list for available commands and only respond in the specified JSON"
|
| 224 |
-
" format."
|
| 225 |
-
)
|
| 226 |
-
except Exception as e:
|
| 227 |
-
return f"Error: {str(e)}"
|
| 228 |
-
|
| 229 |
-
|
| 230 |
-
def get_text_summary(url: str, question: str) -> str:
|
| 231 |
-
"""Return the results of a Google search
|
| 232 |
-
|
| 233 |
-
Args:
|
| 234 |
-
url (str): The url to scrape
|
| 235 |
-
question (str): The question to summarize the text for
|
| 236 |
-
|
| 237 |
-
Returns:
|
| 238 |
-
str: The summary of the text
|
| 239 |
-
"""
|
| 240 |
-
text = scrape_text(url)
|
| 241 |
-
summary = summarize_text(url, text, question)
|
| 242 |
-
return f""" "Result" : {summary}"""
|
| 243 |
-
|
| 244 |
-
|
| 245 |
-
def get_hyperlinks(url: str) -> Union[str, List[str]]:
|
| 246 |
-
"""Return the results of a Google search
|
| 247 |
-
|
| 248 |
-
Args:
|
| 249 |
-
url (str): The url to scrape
|
| 250 |
-
|
| 251 |
-
Returns:
|
| 252 |
-
str or list: The hyperlinks on the page
|
| 253 |
-
"""
|
| 254 |
-
return scrape_links(url)
|
| 255 |
-
|
| 256 |
-
|
| 257 |
-
def shutdown() -> NoReturn:
|
| 258 |
-
"""Shut down the program"""
|
| 259 |
-
print("Shutting down...")
|
| 260 |
-
quit()
|
| 261 |
-
|
| 262 |
-
|
| 263 |
-
def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
|
| 264 |
-
"""Start an agent with a given name, task, and prompt
|
| 265 |
-
|
| 266 |
-
Args:
|
| 267 |
-
name (str): The name of the agent
|
| 268 |
-
task (str): The task of the agent
|
| 269 |
-
prompt (str): The prompt for the agent
|
| 270 |
-
model (str): The model to use for the agent
|
| 271 |
-
|
| 272 |
-
Returns:
|
| 273 |
-
str: The response of the agent
|
| 274 |
-
"""
|
| 275 |
-
# Remove underscores from name
|
| 276 |
-
voice_name = name.replace("_", " ")
|
| 277 |
-
|
| 278 |
-
first_message = f"""You are {name}. Respond with: "Acknowledged"."""
|
| 279 |
-
agent_intro = f"{voice_name} here, Reporting for duty!"
|
| 280 |
-
|
| 281 |
-
# Create agent
|
| 282 |
-
if CFG.speak_mode:
|
| 283 |
-
say_text(agent_intro, 1)
|
| 284 |
-
key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
|
| 285 |
-
|
| 286 |
-
if CFG.speak_mode:
|
| 287 |
-
say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
|
| 288 |
-
|
| 289 |
-
# Assign task (prompt), get response
|
| 290 |
-
agent_response = AGENT_MANAGER.message_agent(key, prompt)
|
| 291 |
-
|
| 292 |
-
return f"Agent {name} created with key {key}. First response: {agent_response}"
|
| 293 |
-
|
| 294 |
-
|
| 295 |
-
def message_agent(key: str, message: str) -> str:
|
| 296 |
-
"""Message an agent with a given key and message"""
|
| 297 |
-
# Check if the key is a valid integer
|
| 298 |
-
if is_valid_int(key):
|
| 299 |
-
agent_response = AGENT_MANAGER.message_agent(int(key), message)
|
| 300 |
-
else:
|
| 301 |
-
return "Invalid key, must be an integer."
|
| 302 |
-
|
| 303 |
-
# Speak response
|
| 304 |
-
if CFG.speak_mode:
|
| 305 |
-
say_text(agent_response, 1)
|
| 306 |
-
return agent_response
|
| 307 |
-
|
| 308 |
-
|
| 309 |
-
def list_agents():
|
| 310 |
-
"""List all agents
|
| 311 |
-
|
| 312 |
-
Returns:
|
| 313 |
-
str: A list of all agents
|
| 314 |
-
"""
|
| 315 |
-
return "List of agents:\n" + "\n".join(
|
| 316 |
-
[str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
|
| 317 |
-
)
|
| 318 |
-
|
| 319 |
-
|
| 320 |
-
def delete_agent(key: str) -> str:
|
| 321 |
-
"""Delete an agent with a given key
|
| 322 |
-
|
| 323 |
-
Args:
|
| 324 |
-
key (str): The key of the agent to delete
|
| 325 |
-
|
| 326 |
-
Returns:
|
| 327 |
-
str: A message indicating whether the agent was deleted or not
|
| 328 |
-
"""
|
| 329 |
-
result = AGENT_MANAGER.delete_agent(key)
|
| 330 |
-
return f"Agent {key} deleted." if result else f"Agent {key} does not exist."
|
|
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|
spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Call of Duty Mobile - The Ultimate FPS Experience on Mobile Devices - Download Now and Join the Action.md
DELETED
|
@@ -1,97 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>How to Download the Game Call of Duty Mobile</h1>
|
| 3 |
-
<p>If you are a fan of first-person shooter (FPS) games, you might have heard of Call of Duty, one of the most popular and successful franchises in the gaming industry. But did you know that you can also enjoy the thrill of Call of Duty on your mobile device? That's right, Call of Duty Mobile is a free-to-play game that brings you the best of Call of Duty on the go. In this article, we will show you how to download the game call of duty mobile, what are the requirements for downloading it, and what are the benefits of playing it. So, let's get started!</p>
|
| 4 |
-
<h2>Requirements for Downloading Call of Duty Mobile</h2>
|
| 5 |
-
<p>Before you download the game, you need to make sure that your device meets the minimum requirements for running it. Here are some of the things you need to check:</p>
|
| 6 |
-
<h2>download the game call of duty mobile</h2><br /><p><b><b>DOWNLOAD</b> ✔ <a href="https://urlin.us/2uT08d">https://urlin.us/2uT08d</a></b></p><br /><br />
|
| 7 |
-
<h3>Device compatibility</h3>
|
| 8 |
-
<p>Call of Duty Mobile is compatible with both Android and iOS devices, but not all models can run it smoothly. For Android devices, you need at least Android version 5.1.1 or higher, and at least 2 GB of RAM. For iOS devices, you need at least iOS version 9.0 or higher, and an iPhone 6 or newer model. You can check your device's specifications in the settings menu.</p>
|
| 9 |
-
<h3>Storage space</h3>
|
| 10 |
-
<p>Call of Duty Mobile is a large game that requires a lot of storage space on your device. The initial app download size is about 2 GB, but you will also need additional space for optional features such as HD resources, maps, weapons, and operators. You can choose what to download based on your preferences, but we recommend having at least 4 GB of free space on your device. You can check your device's storage space in the settings menu.</p>
|
| 11 |
-
<h3>Internet connection</h3>
|
| 12 |
-
<p>Call of Duty Mobile is an online game that requires a stable internet connection to play. You can use either Wi-Fi or mobile data, but make sure that your connection is fast and reliable enough to avoid lagging or disconnecting during gameplay. You can check your internet speed using online tools such as Speedtest.net.</p>
|
| 13 |
-
<h2>Steps for Downloading Call of Duty Mobile</h2>
|
| 14 |
-
<p>Now that you have checked your device's compatibility, storage space, and internet connection, you are ready to download the game call of duty mobile. Here are the steps you need to follow:</p>
|
| 15 |
-
<h3>Step 1: Go to the official website or app store of your device</h3>
|
| 16 |
-
<p>The easiest way to download the game is to visit the official website of Call of Duty Mobile at <a href="">https://www.callofduty.com/mobile</a>. There, you will find the links to download the game from the Google Play Store for Android devices, or the App Store for iOS devices. Alternatively, you can also go directly to the app store of your device and search for Call of Duty Mobile.</p>
|
| 17 |
-
<h3>Step 2: Search for Call of Duty Mobile and tap on the download button</h3>
|
| 18 |
-
<p>Once you have found the game on the app store, tap on the download button to start downloading it. You might need to accept some permissions and terms of service before proceeding. The download time will vary depending on your internet speed and device performance, but it should not take more than a few minutes.</p>
|
| 19 |
-
<h3>Step 3: Wait for the download to finish and launch the game</h3>
|
| 20 |
-
<p>After the download is complete, you will see a notification on your device that the game is ready to play. Tap on the notification or find the game icon on your home screen and launch the game. You might need to wait for some additional files to load before you can access the game menu.</p>
|
| 21 |
-
<h3>Step 4: Create an account or log in with your existing one</h3>
|
| 22 |
-
<p>The first time you launch the game, you will be asked to create an account or log in with your existing one. You can use your Facebook, Google, Apple, or Activision account to sign in, or create a new account with your email address. Creating an account will allow you to save your progress, access your loadouts, and play with friends across different devices.</p>
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| 23 |
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<h3>Step 5: Choose your game mode and start playing</h3>
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<p>Congratulations, you have successfully downloaded the game call of duty mobile! Now, you can choose from various game modes and maps that suit your preference and skill level. You can play solo or team up with other players in multiplayer mode, or test your survival skills in battle royale mode. You can also customize your loadouts and operators, and unlock new weapons and items as you level up. Have fun!</p>
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<p>Downloading Call of Duty Mobile is not only easy and free, but also rewarding and enjoyable. Here are some of the benefits of playing this game:</p>
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<h3>High-quality graphics and sound effects that immerse you in the action</h3>
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<p>Call of Duty Mobile delivers stunning graphics and realistic sound effects that make you feel like you are in the middle of a war zone. You can experience different environments and weather conditions, such as snow, rain, fog, and night. You can also hear the gunfire, explosions, footsteps, and voices of your enemies and allies. The game also supports high frame rates and 3D touch controls for a smoother and more responsive gameplay.</p>
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<h3>Multiple game modes and maps that offer variety and challenge</h3>
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<p>Call of Duty Mobile features several game modes and maps that cater to different tastes and preferences. You can choose from classic modes such as Team Deathmatch, Domination, Search and Destroy, Hardpoint, and Free for All, or try new modes such as Gunfight, Kill Confirmed, Cranked, Rapid Fire, and Attack of the Undead. You can also explore iconic maps from previous Call of Duty games, such as Nuketown, Crash, Crossfire, Firing Range, Hijacked, Summit, Standoff, Raid, and more. Each mode and map has its own rules and strategies that will keep you on your toes.</p>
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<h3>Customizable loadouts and operators that let you play your way</h3>
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<p>Call of Duty Mobile allows you to customize your loadouts and operators according to your play style and preferences. You can choose from a wide range of weapons, such as assault rifles, sniper rifles, shotguns, SMGs, LMGs, pistols, launchers, and melee weapons. You can also equip different attachments, perks, grenades, and skills to enhance your performance. Moreover, you can select from various operators that have their own unique abilities and outfits. You can unlock new weapons and operators as you progress through the game and complete challenges.</p>
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<h3>Competitive and social features that allow you to connect and play with friends and other players</h3>
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<p>Call of Duty Mobile is not only a game, but also a community. You can connect and play with your friends and other players from around the world using the in-game chat and voice chat features. You can also join or create clans, invite or join friends in private matches, and participate in clan wars and tournaments. You can also compare your stats and achievements with other players on the leaderboards and earn rewards for your performance.</p>
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<p>Call of Duty Mobile is constantly updated with new content and rewards that keep you engaged and entertained. Every season, you can enjoy new themes, events, missions, modes, maps, weapons, operators, skins, and more. You can also earn seasonal rewards by completing seasonal challenges and ranking up in the battle pass. The game also features special events such as Halloween, Christmas, Lunar New Year, Valentine's Day, and more that offer exclusive items and bonuses.</p>
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<p>Call of Duty Mobile is a game that you don't want to miss if you love FPS games. It offers you an amazing gaming experience on your mobile device that rivals console and PC games. It has high-quality graphics and sound effects, multiple game modes and maps, customizable loadouts and operators, competitive and social features, and seasonal content and rewards. It is easy and free to download and play, and it will keep you hooked for hours. So what are you waiting for? Download the game call of duty mobile now and join the action!</p>
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<p>Here are some of the frequently asked questions about Call of Duty Mobile:</p>
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<h3>Q1: Is Call of Duty Mobile free to play?</h3>
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<p>A1: Yes, Call of Duty Mobile is free to play. You can download it from the app store of your device without paying anything. However, the game also offers optional in-app purchases that can enhance your gameplay or unlock premium items. You can choose whether to buy them or not according to your preference.</p>
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<h3>Q2: How can I update Call of Duty Mobile?</h3>
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<p>A2: Call of Duty Mobile is regularly updated with new content and features. You can update the game by going to the app store of your device and tapping on the update button. Alternatively, you can also enable automatic updates in the settings menu of your device or the game. Make sure that you have enough storage space and internet connection before updating the game.</p>
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<p>A3: If you have any issues or questions about the game, you can contact the support team by going to the settings menu of the game and tapping on the help button. There, you will find a FAQ section that might answer your queries. If not, you can also submit a ticket or chat with a live agent who will assist you.</p>
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<p>A4: Joining or creating a clan in Call of Duty Mobile is a great way to connect with other players and enjoy clan benefits. To join or create a clan, you need to go to the clan menu of the game and tap on the clan button. There, you will see a list of clans that you can join or apply for. You can also create your own clan by tapping on the create button and filling out the clan details. You need to be at least level 5 to join or create a clan.</p>
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<h3>Q5: How can I participate in the World Championship 2023 in Call of Duty Mobile?</h3>
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<p>A5: The World Championship 2023 is a global tournament that showcases the best Call of Duty Mobile players in the world. To participate in it, you need to register for it in the game menu when it is available. You also need to be at least level 10 and have a verified email address. You will then need to compete in online qualifiers and regional finals to earn a spot in the global finals where you can win prizes and glory.</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Crafting and Building 2.4.19.66 APK Learn How to Build Your House in a Variety of Environments.md
DELETED
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<h1>Crafting and Building 2.4.19.66 APK: A Fun and Creative Adventure Game for Android</h1>
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<p>Do you love sandbox games where you can create your own world, explore different environments, and interact with other players? If yes, then you should try <strong>Crafting and Building</strong>, a popular adventure game for Android devices that lets you do all that and more.</p>
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<p>Crafting and Building is a game that gives you the freedom to express your creativity and imagination in a virtual world. You can build anything you want, from houses and castles to farms and cities, using various blocks and materials. You can also craft tools, weapons, armor, and other items to help you survive and thrive.</p>
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<p>The game also supports online multiplayer mode, where you can join or create servers and play with other people from around the world. You can chat with them, make friends, form teams, trade items, or compete with them in mini-games. You can also invite your friends to your private server and show them your creations.</p>
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<h3>A game with different modes and maps</h3>
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<p>The game offers two main modes: survival mode and creative mode. In survival mode, you have to gather resources, craft items, fight enemies, and manage your hunger and health. In creative mode, you have unlimited resources and no enemies, so you can focus on building whatever you want.</p>
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<p>The game also has different maps that you can choose from or create your own using the map editor. Some of the maps are based on popular movies, games, or books, such as Harry Potter, Star Wars, Jurassic Park, Minecraft, etc.</p>
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<h2>What is new in Crafting and Building 2.4.19.66 APK?</h2>
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<p>The latest version of Crafting and Building is 2.4.19.66 APK, which was released on October 12th 2022. This version has some new features and improvements that make the game more enjoyable.</p>
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<h3>Faster extraction of resources</h3>
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<p>One of the new features is that the extraction of resources is faster than before. This means that you can collect more blocks and materials in less time, which is useful for both survival mode and creative mode.</p>
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<h3>Vietnam translation added</h3>
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<p>Another new feature is that the game now supports Vietnam language translation. This makes the game more accessible for players who speak Vietnamese or want to learn it.</p>
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<h3>New maps to explore</h3>
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<p>The last new feature is that the game has added some new maps to its collection. These maps are based on different themes and genres, such as horror, fantasy, sci-fi, etc. Some of the new maps are:</p>
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<table>
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<tr>
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<th </th>
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<td>1</td>
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<td>The Haunted House</td>
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<h3>Download the APK file from a trusted source</h3>
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<p>Crafting and Building 2.4.19.66 APK is a fun and creative adventure game for Android devices that lets you create your own world, explore different environments, and interact with other players. The game has no specific goals or missions, so you can play it however you like. The game also supports online multiplayer mode, where you can join or create servers and play with other people from around the world.</p>
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<p>The latest version of Crafting and Building is 2.4.19.66 APK, which was released on October 12th 2022. This version has some new features and improvements that make the game more enjoyable, such as faster extraction of resources, Vietnam translation added, and new maps to explore.</p>
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<p>If you want to play Crafting and Building 2.4.19.66 APK on your Android device, you need to download and install the APK file from a trusted source. You also need to enable unknown sources on your device and follow the instructions on the screen.</p>
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<p>Here are some of the frequently asked questions about Crafting and Building 2.4.19.66 APK:</p>
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<h3>Is Crafting and Building 2.4.19.66 APK safe to download and install?</h3>
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<h1>8 Ball Pool 5.12.2 Mod APK: The Ultimate Guide</h1>
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<p>If you are a fan of pool games, you must have heard of <strong>8 Ball Pool</strong>, the most popular and addictive online pool game in the world. But did you know that there is a way to make the game even more fun and exciting? Yes, we are talking about <strong>8 Ball Pool 5.12.2 mod apk</strong>, the latest version of the modified game that gives you unlimited access to all the features and resources of the game. In this article, we will tell you everything you need to know about this amazing mod apk, including what it is, what it offers, and how to download and install it on your Android device. So, without further ado, let's dive in!</p>
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<p>8 Ball Pool is an online multiplayer pool game developed by Miniclip, a Swiss-based company that specializes in web and mobile games. The game was released in 2010 and has since become one of the most downloaded and played games on both Android and iOS platforms. The game allows you to play pool with players from all over the world, or with your friends, in various modes and tournaments. You can also customize your cue, table, and avatar, and collect various rewards and trophies as you level up.</p>
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<p>The gameplay of 8 Ball Pool is simple and intuitive. You just need to swipe your finger on the screen to aim your cue, and then release it to hit the cue ball. The goal is to pocket all your balls (either solids or stripes) before your opponent does, and then pocket the black 8 ball to win the game. You can also use various tricks and strategies to outsmart your opponent, such as applying spin, power, or angle to your shots.</p>
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<h4>Step 2: Enable unknown sources</h4>
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<p>The next step is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
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<p>The third step is to locate the downloaded mod apk file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to complete.</p>
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<h4>Step 4: Launch the game and enjoy</h4>
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<p>The final step is to launch the game from your app drawer and enjoy playing 8 Ball Pool with unlimited coins, cash, long lines, and anti-ban system. You can now challenge anyone in any mode or tournament, buy any cue or table you want, and unlock any feature or item you need.</p>
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<p>We hope this article has helped you learn everything you need to know about this amazing mod apk, including what it is, what it offers, and how to download and install it on your device. If you have any questions or feedback, feel free to leave a comment below. And don't forget to share this article with your friends who love playing pool games too!</p>
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<li>No, you cannot update this mod apk as it may cause some problems or errors on your device. If there is a new version of the game available, you will have to download and install it again from a new source.</li>
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spaces/1phancelerku/anime-remove-background/Blur PC Game Experience Realistic Racing and Combat.md
DELETED
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<br />
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<p>If you are looking for a car racing game that combines realism and fun, then you should check out Blur. Blur is an arcade racing video game that was released in 2010 for Microsoft Windows, PlayStation 3 and Xbox 360. It was developed by Bizarre Creations and published by Activision. In this article, we will tell you what Blur is, what features it has, and how to download it for PC.</p>
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| 66 |
-
<p>Blur offers several multiplayer modes that let you race with or against other players online or offline. You can play split-screen with up to four players on the same console or PC. You can also play online with up to 20 players in different modes, such as racing, team racing, destruction derby, capture the flag, checkpoint race, and more. You can also create your own custom races with your own rules and settings.</p>
|
| 67 |
-
<h2>How to Download Blur for PC?</h2>
|
| 68 |
-
<p>If you want to play Blur on your PC, you will need to download it first. Here are the requirements and steps to download Blur for PC:</p>
|
| 69 |
-
<h3>Requirements for Blur</h3>
|
| 70 |
-
<p>Before you download Blur for PC, you need to make sure that your PC meets the minimum or recommended requirements for the game. Here are the requirements for Blur:</p>
|
| 71 |
-
<h4>Minimum requirements</h4>
|
| 72 |
-
<ul>
|
| 73 |
-
<li>OS: Windows XP/Vista/7</li>
|
| 74 |
-
<li>CPU: Intel Pentium D Dual Core 3.4GHz or AMD Athlon 64 X2 3800+</li>
|
| 75 |
-
<li>RAM: 1 GB (XP) or 2 GB (Vista/7)</li>
|
| 76 |
-
<li>GPU: 256 MB NVIDIA GeForce 6600 GT or ATI Radeon 1600XT</li>
|
| 77 |
-
<li>Storage: 14 GB available space</li>
|
| 78 |
-
<li>Sound: DirectX Compatible Sound Card</li>
|
| 79 |
-
</ul>
|
| 80 |
-
<h4>Recommended requirements</h4>
|
| 81 |
-
<ul>
|
| 82 |
-
<li>OS: Windows XP/Vista/7</li>
|
| 83 |
-
<li>CPU: Intel Core 2 Duo E6400 or AMD Athlon 64 X2 4200+</li>
|
| 84 |
-
<li>RAM: 2 GB (XP) or 3 GB (Vista/7)</li>
|
| 85 |
-
<li>GPU: 512 MB NVIDIA GeForce 8800 GT or ATI Radeon HD 3870</li>
|
| 86 |
-
<li>Storage: 14 GB available space</li>
|
| 87 |
-
<li>Sound: DirectX Compatible Sound Card</li>
|
| 88 |
-
</ul>
|
| 89 |
-
<h3>Steps to Download Blur for PC</h3>
|
| 90 |
-
<p>There are two ways to download Blur for PC: from the official website or from a third-party website. Here are the steps for both methods:</p>
|
| 91 |
-
<h4>Download from official website</h4>
|
| 92 |
-
<ol>
|
| 93 |
-
<li>Go to the official website of Blur at <a href="">https://www.blurthegame.com/</a></li>
|
| 94 |
-
<li>Click on the "Buy Now" button and choose your preferred platform (PC, PS3, or Xbox 360)</li>
|
| 95 |
-
<li>Select your preferred retailer and follow the instructions to purchase and download the game</li>
|
| 96 |
-
<li>You will receive a confirmation email with a download link and a product key for the game</li>
|
| 97 |
-
<li>Click on the download link and save the game file to your PC</li>
|
| 98 |
-
<li>Enter the product key when prompted and follow the instructions to install and run the game</li>
|
| 99 |
-
</ol>
|
| 100 |
-
<h4>Download from third-party website</h4>
|
| 101 |
-
<ol>
|
| 102 |
-
<li>Go to a third-party website that offers Blur for PC, such as <a href="">https://oceanofgames.com/blur-pc-game-free-download/</a></li>
|
| 103 |
-
<li>Click on the "Download Now" button and wait for the game file to be downloaded to your PC</li>
|
| 104 |
-
<li>Extract the game file using a software like WinRAR or 7-Zip</li>
|
| 105 |
-
<li>Open the extracted folder and run the setup.exe file as administrator</li>
|
| 106 |
-
<li>Follow the instructions to install and run the game</li>
|
| 107 |
-
<li>You may need to crack the game or use a patch to bypass the activation process</li>
|
| 108 |
-
</ol>
|
| 109 |
-
<h2>Conclusion</h2>
|
| 110 |
-
<p>Blur is a car racing game that combines realism and fun. It lets you drive real world cars and race in real world locations, while also using vehicular combat and power-ups to spice up the gameplay. It offers a variety of game modes, including career mode, single race, split-screen, and online multiplayer. You can download Blur for PC from the official website or from a third-party website, depending on your preference. If you are looking for a car racing game that has a twist, then you should give Blur a try.</p>
|
| 111 |
-
<h3>Frequently Asked Questions (FAQs)</h3>
|
| 112 |
-
<ul>
|
| 113 |
-
<li><b>Q: Is Blur still playable online?</b></li>
|
| 114 |
-
<li>A: Yes, Blur is still playable online, but you may need to use a third-party service like Tunngle or Hamachi to connect with other players.</li>
|
| 115 |
-
<li><b>Q: How many cars and tracks are there in Blur?</b></li>
|
| 116 |
-
<li>A: Blur features over 50 cars and 14 tracks, plus additional cars and tracks that can be unlocked by earning fans.</li>
|
| 117 |
-
<li><b>Q: Can I play Blur with a controller or a steering wheel?</b></li>
|
| 118 |
-
<li>A: Yes, Blur supports both controllers and steering wheels, as well as keyboard and mouse.</li>
|
| 119 |
-
<li><b>Q: What is the difference between Blur and Split/Second?</b></li>
|
| 120 |
-
<li>A: Blur and Split/Second are both arcade racing games that were released in 2010, but they have different styles. Blur focuses on vehicular combat and power-ups, while Split/Second focuses on environmental destruction and triggers.</li>
|
| 121 |
-
<li><b>Q: Will there be a sequel to Blur?</b></li>
|
| 122 |
-
<li>A: Unfortunately, there is no official confirmation of a sequel to Blur. The developer Bizarre Creations was shut down by Activision in 2011, and the rights to the game are unclear.</li>
|
| 123 |
-
</ul></p> 401be4b1e0<br />
|
| 124 |
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|
| 125 |
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spaces/1phancelerku/anime-remove-background/Cargo Simulator 2021 Trkiyede Trclk Keyfi APK ndir.md
DELETED
|
@@ -1,168 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cargo Simulator 2021 Türkiye Apk Dayı: A Truck Driving Simulation Game with a Realistic Turkey Map</h1>
|
| 3 |
-
<p>If you are a fan of truck driving simulation games, you might want to check out Cargo Simulator 2021 Türkiye Apk Dayı, a new game that offers a realistic and immersive experience of driving a truck across Turkey. In this article, we will tell you what this game is, how to download and install it on your Android device, why you should play it, how to play it, and some frequently asked questions about it.</p>
|
| 4 |
-
<h2>cargo simulator 2021 türkiye apk dayı</h2><br /><p><b><b>Download File</b> • <a href="https://jinyurl.com/2uNKgu">https://jinyurl.com/2uNKgu</a></b></p><br /><br />
|
| 5 |
-
<h2>What is Cargo Simulator 2021 Türkiye Apk Dayı?</h2>
|
| 6 |
-
<h3>A brief introduction to the game and its features</h3>
|
| 7 |
-
<p>Cargo Simulator 2021 Türkiye Apk Dayı is a truck driving simulation game that contains a scaled Turkey map with all the cities and more than 300 districts. The game features a Real-time Multiplayer Mode where you can play and chat with your friends on the same map, as well as a Single Player Mode where you can complete various missions and tasks. You can choose from different types of trucks and trailers, such as excavators, loaders, dozers, cement, construction materials, food, and fuel tanks. You can also customize your truck with various accessories at the modification centers along the road. You can start your own company in any city or district and expand your business by buying new garages and trucks. You can also enjoy the realistic graphics, physics, sounds, and weather effects of the game.</p>
|
| 8 |
-
<h3>How to download and install the game on your Android device</h3>
|
| 9 |
-
<p>To download and install Cargo Simulator 2021 Türkiye Apk Dayı on your Android device, you need to follow these steps:</p>
|
| 10 |
-
<ol>
|
| 11 |
-
<li>Go to [this link](^1^) and click on the "Download APK" button.</li>
|
| 12 |
-
<li>Wait for the download to finish and then open the APK file.</li>
|
| 13 |
-
<li>Allow the installation of unknown sources if prompted by your device.</li>
|
| 14 |
-
<li>Follow the instructions on the screen to complete the installation.</li>
|
| 15 |
-
<li>Launch the game and enjoy!</li>
|
| 16 |
-
</ol>
|
| 17 |
-
<h2>Why should you play Cargo Simulator 2021 Türkiye Apk Dayı?</h2>
|
| 18 |
-
<h3>The benefits of playing a truck driving simulation game</h3>
|
| 19 |
-
<p>Playing a truck driving simulation game can have many benefits for you, such as:</p>
|
| 20 |
-
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|
| 65 |
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cargo simulator 2021 türkiye oyun delisi</p>
|
| 66 |
-
<ul>
|
| 67 |
-
<li>It can improve your concentration, coordination, reflexes, and spatial awareness.</li>
|
| 68 |
-
<li>It can enhance your creativity, imagination, and problem-solving skills.</li>
|
| 69 |
-
<li>It can provide you with entertainment, relaxation, and fun.</li>
|
| 70 |
-
<li>It can teach you about different aspects of <h3>The unique features of Cargo Simulator 2021 Türkiye Apk Dayı that make it stand out from other similar games</h3>
|
| 71 |
-
<p>Cargo Simulator 2021 Türkiye Apk Dayı is not just another truck driving simulation game. It has some unique features that make it different and better than other similar games, such as:</p>
|
| 72 |
-
<ul>
|
| 73 |
-
<li>It has a realistic and detailed Turkey map with all the cities and more than 300 districts, which you can explore and discover.</li>
|
| 74 |
-
<li>It has a Real-time Multiplayer Mode where you can play and chat with your friends on the same map, as well as a Single Player Mode where you can complete various missions and tasks.</li>
|
| 75 |
-
<li>It has a dynamic economy system where you can buy and sell goods, start your own company, and expand your business.</li>
|
| 76 |
-
<li>It has a realistic traffic system with traffic lights, signs, speed limits, police, and accidents.</li>
|
| 77 |
-
<li>It has a realistic damage system where you can repair your truck at the service stations or call for roadside assistance.</li>
|
| 78 |
-
<li>It has a realistic weather system with day and night cycles, rain, snow, fog, and wind.</li>
|
| 79 |
-
<li>It has a realistic sound system with engine sounds, horn sounds, radio sounds, and ambient sounds.</li>
|
| 80 |
-
<li>It has a realistic physics system with suspension, brakes, steering, weight, and traction.</li>
|
| 81 |
-
<li>It has a realistic graphics system with high-quality textures, shadows, lighting, and reflections.</li>
|
| 82 |
-
</ul>
|
| 83 |
-
<h2>How to play Cargo Simulator 2021 Türkiye Apk Dayı?</h2>
|
| 84 |
-
<h3>The basic gameplay and controls of the game</h3>
|
| 85 |
-
<p>The basic gameplay of Cargo Simulator 2021 Türkiye Apk Dayı is to drive your truck across Turkey and deliver various cargoes to different destinations. You can use the following controls to play the game:</p>
|
| 86 |
-
<table>
|
| 87 |
-
<tr>
|
| 88 |
-
<th>Control</th>
|
| 89 |
-
<th>Function</th>
|
| 90 |
-
</tr>
|
| 91 |
-
<tr>
|
| 92 |
-
<td>Steering wheel</td>
|
| 93 |
-
<td>To steer your truck left or right</td>
|
| 94 |
-
</tr>
|
| 95 |
-
<tr>
|
| 96 |
-
<td>Pedals</td>
|
| 97 |
-
<td>To accelerate or brake your truck</td>
|
| 98 |
-
</tr>
|
| 99 |
-
<tr>
|
| 100 |
-
<td>Gearbox</td>
|
| 101 |
-
<td>To change the gears of your truck (automatic or manual)</td>
|
| 102 |
-
</tr>
|
| 103 |
-
<tr>
|
| 104 |
-
<td>Horn</td>
|
| 105 |
-
<td>To honk your horn</td>
|
| 106 |
-
</tr>
|
| 107 |
-
<tr>
|
| 108 |
-
<td>Lights</td>
|
| 109 |
-
<td>To turn on or off your headlights, indicators, or hazard lights</td>
|
| 110 |
-
</tr>
|
| 111 |
-
<tr>
|
| 112 |
-
<td>Wipers</td>
|
| 113 |
-
<td>To turn on or off your windshield wipers</td>
|
| 114 |
-
</tr>
|
| 115 |
-
<tr>
|
| 116 |
-
<td>Mirrors</td>
|
| 117 |
-
<td>To view your rearview or side mirrors</td>
|
| 118 |
-
</tr>
|
| 119 |
-
<tr>
|
| 120 |
-
<td>Camera</td>
|
| 121 |
-
<td>To change the camera angle (interior or exterior)</td>
|
| 122 |
-
</tr>
|
| 123 |
-
<tr>
|
| 124 |
-
<td>Map</td>
|
| 125 |
-
<td>To view the map of Turkey and your current location</td>
|
| 126 |
-
</tr>
|
| 127 |
-
<tr>
|
| 128 |
-
<td>Menu</td>
|
| 129 |
-
<td>To access the game settings, options, or modes</td>
|
| 130 |
-
</tr>
|
| 131 |
-
<h3>The different modes and missions of the game</h3>
|
| 132 |
-
<p>Cargo Simulator 2021 Türkiye Apk Dayı has two main modes: Real-time Multiplayer Mode and Single Player Mode. In Real-time Multiplayer Mode, you can play and chat with your friends on the same map. You can join or create a room with up to 16 players. You can also join or create a convoy with up to 4 players. You can choose any cargo and destination you want. You can also interact with other players on the road by honking, flashing lights, or chatting. In Single Player Mode, you can complete various missions and tasks. You can choose from different types of cargoes and trailers. You can also choose from different difficulty levels: easy, medium, or hard. You can earn money and experience points by completing the missions. You can use the money to buy new trucks or trailers, or to customize your truck. You can use the experience points to unlock new features or skills.</p>
|
| 133 |
-
<h3>The tips and tricks to improve your performance and enjoy the game more</h3>
|
| 134 |
-
<p>If you want to improve your performance and enjoy the game more, you can follow these tips and tricks:</p>
|
| 135 |
-
<ul>
|
| 136 |
-
<li>Follow the traffic rules and regulations to avoid fines or accidents.</li>
|
| 137 |
-
<li>Drive carefully and smoothly to avoid damaging your cargo or truck.</li>
|
| 138 |
-
<li>Use the map and GPS to find the best route to your destination.</li>
|
| 139 |
-
<li>Use the radio to listen to music or news while driving.</li>
|
| 140 |
-
<li>Use the chat feature to communicate with other players or ask for help.</li>
|
| 141 |
-
<li>Use the modification centers to upgrade or customize your truck.</li>
|
| 142 |
-
<li>Use the service stations to ref uel your truck or repair your damage.</li>
|
| 143 |
-
<li>Use the roadside assistance feature to call for help if you get stuck or break down.</li>
|
| 144 |
-
<li>Use the company feature to start your own business and expand your fleet.</li>
|
| 145 |
-
<li>Use the leaderboard feature to compare your score and rank with other players.</li>
|
| 146 |
-
<li>Use the settings feature to adjust the game options according to your preference.</li>
|
| 147 |
-
</ul>
|
| 148 |
-
<h2>Conclusion</h2>
|
| 149 |
-
<p>Cargo Simulator 2021 Türkiye Apk Dayı is a truck driving simulation game that offers a realistic and immersive experience of driving a truck across Turkey. You can play the game in Real-time Multiplayer Mode or Single Player Mode. You can choose from different types of trucks and trailers, and customize your truck with various accessories. You can also start your own company and expand your business. You can enjoy the realistic graphics, physics, sounds, and weather effects of the game. You can also interact with other players on the road or chat with your friends. If you are looking for a fun and challenging truck driving simulation game, you should definitely try Cargo Simulator 2021 Türkiye Apk Dayı. You can download and install the game on your Android device by following the steps mentioned above. Have fun and drive safely!</p>
|
| 150 |
-
<h2>FAQs</h2>
|
| 151 |
-
<h3>Q1: Is Cargo Simulator 2021 Türkiye Apk Dayı free to play?</h3>
|
| 152 |
-
<p>A1: Yes, Cargo Simulator 2021 Türkiye Apk Dayı is free to play. However, you can also purchase some in-game items with real money if you want to support the developers or enhance your gameplay.</p>
|
| 153 |
-
<h3>Q2: Can I play Cargo Simulator 2021 Türkiye Apk Dayı with my friends online?</h3>
|
| 154 |
-
<p>A2: Yes, you can play Cargo Simulator 2021 Türkiye Apk Dayı with your friends online. You can join or create a room with up to 16 players in Real-time Multiplayer Mode. You can also join or create a convoy with up to 4 players. You can chat with your friends or other players on the same map.</p>
|
| 155 |
-
<h3>Q3: What are the minimum requirements to run Cargo Simulator 2021 Türkiye Apk Dayı on my Android device?</h3>
|
| 156 |
-
<p>A3: The minimum requirements to run Cargo Simulator 2021 Türkiye Apk Dayı on your Android device are:</p>
|
| 157 |
-
<ul>
|
| 158 |
-
<li>Android version: 5.0 or higher</li>
|
| 159 |
-
<li>RAM: 2 GB or higher</li>
|
| 160 |
-
<li>Storage: 500 MB or higher</li>
|
| 161 |
-
<li>Internet connection: Required for Real-time Multiplayer Mode</li>
|
| 162 |
-
</ul>
|
| 163 |
-
<h3>Q4: How can I customize my truck in Cargo Simulator 2021 Türkiye Apk Dayı?</h3>
|
| 164 |
-
<p>A4: You can customize your truck in Cargo Simulator 2021 Türkiye Apk Dayı by visiting the modification centers along the road. You can change the color, wheels, lights, horns, exhausts, spoilers, bumpers, mirrors, and stickers of your truck. You can also add some accessories such as flags, antennas, plates, and mascots to your truck.</p>
|
| 165 |
-
<h3>Q5: Where can I find more information and support for Cargo Simulator 2021 Türkiye Apk Dayı?</h3>
|
| 166 |
-
<p>A5: You can find more information and support for Cargo Simulator 2021 Türkiye Apk Dayı by visiting the official website of the game [here]. You can also follow the official social media accounts of the game [here] and [here]. You can also contact the developers of the game by sending an email to [this address].</p> 401be4b1e0<br />
|
| 167 |
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| 168 |
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|
spaces/A00001/bingothoo/src/components/tone-selector.tsx
DELETED
|
@@ -1,43 +0,0 @@
|
|
| 1 |
-
import React from 'react'
|
| 2 |
-
import { BingConversationStyle } from '@/lib/bots/bing/types'
|
| 3 |
-
import { cn } from '@/lib/utils'
|
| 4 |
-
|
| 5 |
-
type ToneItem = {
|
| 6 |
-
type: BingConversationStyle,
|
| 7 |
-
name: string
|
| 8 |
-
}
|
| 9 |
-
|
| 10 |
-
const ToneList: ToneItem[] = [
|
| 11 |
-
{ name: '有创造力', type: BingConversationStyle.Creative },
|
| 12 |
-
{ name: '更平衡', type: BingConversationStyle.Balanced },
|
| 13 |
-
{ name: '更精确', type: BingConversationStyle.Precise }
|
| 14 |
-
]
|
| 15 |
-
|
| 16 |
-
interface ToneSelectorProps {
|
| 17 |
-
type: BingConversationStyle | ''
|
| 18 |
-
onChange?: (type: BingConversationStyle) => void
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
export function ToneSelector({ type, onChange }: ToneSelectorProps) {
|
| 22 |
-
return (
|
| 23 |
-
<div className="fieldset">
|
| 24 |
-
<div className="legend">
|
| 25 |
-
选择对话样式
|
| 26 |
-
</div>
|
| 27 |
-
<div className="options-list-container">
|
| 28 |
-
<ul id="tone-options" className="options">
|
| 29 |
-
{
|
| 30 |
-
ToneList.map(tone => (
|
| 31 |
-
<li className="option" key={tone.name} onClick={() => onChange?.(tone.type)}>
|
| 32 |
-
<button className={cn(`tone-${type.toLowerCase()}`, { selected: tone.type === type}) } aria-pressed="true" >
|
| 33 |
-
<span className="caption-2-strong label-modifier">更</span>
|
| 34 |
-
<span className="body-1-strong label">{tone.name}</span>
|
| 35 |
-
</button>
|
| 36 |
-
</li>
|
| 37 |
-
))
|
| 38 |
-
}
|
| 39 |
-
</ul>
|
| 40 |
-
</div>
|
| 41 |
-
</div>
|
| 42 |
-
)
|
| 43 |
-
}
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|
spaces/ADOPLE/AdopleAI-Website-DocumentQA/style.css
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
#col-container {
|
| 2 |
-
max-width: 700px;
|
| 3 |
-
margin-left: auto;
|
| 4 |
-
margin-right: auto;
|
| 5 |
-
}
|
| 6 |
-
#row-flex {
|
| 7 |
-
display: flex;
|
| 8 |
-
align-items: center;
|
| 9 |
-
justify-content: center;
|
| 10 |
-
}
|
| 11 |
-
.filenameshow{
|
| 12 |
-
height:85px;
|
| 13 |
-
}
|
| 14 |
-
.spaceH{
|
| 15 |
-
padding-top:45px;
|
| 16 |
-
}
|
| 17 |
-
.leftimage .rightimage{
|
| 18 |
-
float:left;
|
| 19 |
-
}
|
| 20 |
-
.leftimage{
|
| 21 |
-
padding-top:26px;
|
| 22 |
-
margin-left:380px;
|
| 23 |
-
}
|
| 24 |
-
.rightimage{
|
| 25 |
-
margin-right:380px;
|
| 26 |
-
margin-top:15px;
|
| 27 |
-
}
|
| 28 |
-
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|
spaces/AI-Hobbyist/Hoyo-RVC/uvr5_pack/lib_v5/nets_33966KB.py
DELETED
|
@@ -1,122 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from uvr5_pack.lib_v5 import layers_33966KB as layers
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class BaseASPPNet(nn.Module):
|
| 9 |
-
def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
|
| 10 |
-
super(BaseASPPNet, self).__init__()
|
| 11 |
-
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
| 12 |
-
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
| 13 |
-
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
| 14 |
-
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
| 15 |
-
|
| 16 |
-
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
|
| 17 |
-
|
| 18 |
-
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
| 19 |
-
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
| 20 |
-
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
| 21 |
-
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
| 22 |
-
|
| 23 |
-
def __call__(self, x):
|
| 24 |
-
h, e1 = self.enc1(x)
|
| 25 |
-
h, e2 = self.enc2(h)
|
| 26 |
-
h, e3 = self.enc3(h)
|
| 27 |
-
h, e4 = self.enc4(h)
|
| 28 |
-
|
| 29 |
-
h = self.aspp(h)
|
| 30 |
-
|
| 31 |
-
h = self.dec4(h, e4)
|
| 32 |
-
h = self.dec3(h, e3)
|
| 33 |
-
h = self.dec2(h, e2)
|
| 34 |
-
h = self.dec1(h, e1)
|
| 35 |
-
|
| 36 |
-
return h
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class CascadedASPPNet(nn.Module):
|
| 40 |
-
def __init__(self, n_fft):
|
| 41 |
-
super(CascadedASPPNet, self).__init__()
|
| 42 |
-
self.stg1_low_band_net = BaseASPPNet(2, 16)
|
| 43 |
-
self.stg1_high_band_net = BaseASPPNet(2, 16)
|
| 44 |
-
|
| 45 |
-
self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
|
| 46 |
-
self.stg2_full_band_net = BaseASPPNet(8, 16)
|
| 47 |
-
|
| 48 |
-
self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
|
| 49 |
-
self.stg3_full_band_net = BaseASPPNet(16, 32)
|
| 50 |
-
|
| 51 |
-
self.out = nn.Conv2d(32, 2, 1, bias=False)
|
| 52 |
-
self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
|
| 53 |
-
self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
|
| 54 |
-
|
| 55 |
-
self.max_bin = n_fft // 2
|
| 56 |
-
self.output_bin = n_fft // 2 + 1
|
| 57 |
-
|
| 58 |
-
self.offset = 128
|
| 59 |
-
|
| 60 |
-
def forward(self, x, aggressiveness=None):
|
| 61 |
-
mix = x.detach()
|
| 62 |
-
x = x.clone()
|
| 63 |
-
|
| 64 |
-
x = x[:, :, : self.max_bin]
|
| 65 |
-
|
| 66 |
-
bandw = x.size()[2] // 2
|
| 67 |
-
aux1 = torch.cat(
|
| 68 |
-
[
|
| 69 |
-
self.stg1_low_band_net(x[:, :, :bandw]),
|
| 70 |
-
self.stg1_high_band_net(x[:, :, bandw:]),
|
| 71 |
-
],
|
| 72 |
-
dim=2,
|
| 73 |
-
)
|
| 74 |
-
|
| 75 |
-
h = torch.cat([x, aux1], dim=1)
|
| 76 |
-
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
| 77 |
-
|
| 78 |
-
h = torch.cat([x, aux1, aux2], dim=1)
|
| 79 |
-
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
| 80 |
-
|
| 81 |
-
mask = torch.sigmoid(self.out(h))
|
| 82 |
-
mask = F.pad(
|
| 83 |
-
input=mask,
|
| 84 |
-
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
| 85 |
-
mode="replicate",
|
| 86 |
-
)
|
| 87 |
-
|
| 88 |
-
if self.training:
|
| 89 |
-
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
| 90 |
-
aux1 = F.pad(
|
| 91 |
-
input=aux1,
|
| 92 |
-
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
| 93 |
-
mode="replicate",
|
| 94 |
-
)
|
| 95 |
-
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
| 96 |
-
aux2 = F.pad(
|
| 97 |
-
input=aux2,
|
| 98 |
-
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
| 99 |
-
mode="replicate",
|
| 100 |
-
)
|
| 101 |
-
return mask * mix, aux1 * mix, aux2 * mix
|
| 102 |
-
else:
|
| 103 |
-
if aggressiveness:
|
| 104 |
-
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
|
| 105 |
-
mask[:, :, : aggressiveness["split_bin"]],
|
| 106 |
-
1 + aggressiveness["value"] / 3,
|
| 107 |
-
)
|
| 108 |
-
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
|
| 109 |
-
mask[:, :, aggressiveness["split_bin"] :],
|
| 110 |
-
1 + aggressiveness["value"],
|
| 111 |
-
)
|
| 112 |
-
|
| 113 |
-
return mask * mix
|
| 114 |
-
|
| 115 |
-
def predict(self, x_mag, aggressiveness=None):
|
| 116 |
-
h = self.forward(x_mag, aggressiveness)
|
| 117 |
-
|
| 118 |
-
if self.offset > 0:
|
| 119 |
-
h = h[:, :, :, self.offset : -self.offset]
|
| 120 |
-
assert h.size()[3] > 0
|
| 121 |
-
|
| 122 |
-
return h
|
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spaces/AIConsultant/MusicGen/tests/modules/test_conv.py
DELETED
|
@@ -1,203 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
from itertools import product
|
| 8 |
-
import math
|
| 9 |
-
import random
|
| 10 |
-
|
| 11 |
-
import pytest
|
| 12 |
-
import torch
|
| 13 |
-
from torch import nn
|
| 14 |
-
|
| 15 |
-
from audiocraft.modules import (
|
| 16 |
-
NormConv1d,
|
| 17 |
-
NormConvTranspose1d,
|
| 18 |
-
StreamableConv1d,
|
| 19 |
-
StreamableConvTranspose1d,
|
| 20 |
-
pad1d,
|
| 21 |
-
unpad1d,
|
| 22 |
-
)
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def test_get_extra_padding_for_conv1d():
|
| 26 |
-
# TODO: Implement me!
|
| 27 |
-
pass
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def test_pad1d_zeros():
|
| 31 |
-
x = torch.randn(1, 1, 20)
|
| 32 |
-
|
| 33 |
-
xp1 = pad1d(x, (0, 5), mode='constant', value=0.)
|
| 34 |
-
assert xp1.shape[-1] == 25
|
| 35 |
-
xp2 = pad1d(x, (5, 5), mode='constant', value=0.)
|
| 36 |
-
assert xp2.shape[-1] == 30
|
| 37 |
-
xp3 = pad1d(x, (0, 0), mode='constant', value=0.)
|
| 38 |
-
assert xp3.shape[-1] == 20
|
| 39 |
-
xp4 = pad1d(x, (10, 30), mode='constant', value=0.)
|
| 40 |
-
assert xp4.shape[-1] == 60
|
| 41 |
-
|
| 42 |
-
with pytest.raises(AssertionError):
|
| 43 |
-
pad1d(x, (-1, 0), mode='constant', value=0.)
|
| 44 |
-
|
| 45 |
-
with pytest.raises(AssertionError):
|
| 46 |
-
pad1d(x, (0, -1), mode='constant', value=0.)
|
| 47 |
-
|
| 48 |
-
with pytest.raises(AssertionError):
|
| 49 |
-
pad1d(x, (-1, -1), mode='constant', value=0.)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def test_pad1d_reflect():
|
| 53 |
-
x = torch.randn(1, 1, 20)
|
| 54 |
-
|
| 55 |
-
xp1 = pad1d(x, (0, 5), mode='reflect', value=0.)
|
| 56 |
-
assert xp1.shape[-1] == 25
|
| 57 |
-
xp2 = pad1d(x, (5, 5), mode='reflect', value=0.)
|
| 58 |
-
assert xp2.shape[-1] == 30
|
| 59 |
-
xp3 = pad1d(x, (0, 0), mode='reflect', value=0.)
|
| 60 |
-
assert xp3.shape[-1] == 20
|
| 61 |
-
xp4 = pad1d(x, (10, 30), mode='reflect', value=0.)
|
| 62 |
-
assert xp4.shape[-1] == 60
|
| 63 |
-
|
| 64 |
-
with pytest.raises(AssertionError):
|
| 65 |
-
pad1d(x, (-1, 0), mode='reflect', value=0.)
|
| 66 |
-
|
| 67 |
-
with pytest.raises(AssertionError):
|
| 68 |
-
pad1d(x, (0, -1), mode='reflect', value=0.)
|
| 69 |
-
|
| 70 |
-
with pytest.raises(AssertionError):
|
| 71 |
-
pad1d(x, (-1, -1), mode='reflect', value=0.)
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
def test_unpad1d():
|
| 75 |
-
x = torch.randn(1, 1, 20)
|
| 76 |
-
|
| 77 |
-
u1 = unpad1d(x, (5, 5))
|
| 78 |
-
assert u1.shape[-1] == 10
|
| 79 |
-
u2 = unpad1d(x, (0, 5))
|
| 80 |
-
assert u2.shape[-1] == 15
|
| 81 |
-
u3 = unpad1d(x, (5, 0))
|
| 82 |
-
assert u3.shape[-1] == 15
|
| 83 |
-
u4 = unpad1d(x, (0, 0))
|
| 84 |
-
assert u4.shape[-1] == x.shape[-1]
|
| 85 |
-
|
| 86 |
-
with pytest.raises(AssertionError):
|
| 87 |
-
unpad1d(x, (-1, 0))
|
| 88 |
-
|
| 89 |
-
with pytest.raises(AssertionError):
|
| 90 |
-
unpad1d(x, (0, -1))
|
| 91 |
-
|
| 92 |
-
with pytest.raises(AssertionError):
|
| 93 |
-
unpad1d(x, (-1, -1))
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
class TestNormConv1d:
|
| 97 |
-
|
| 98 |
-
def test_norm_conv1d_modules(self):
|
| 99 |
-
N, C, T = 2, 2, random.randrange(1, 100_000)
|
| 100 |
-
t0 = torch.randn(N, C, T)
|
| 101 |
-
|
| 102 |
-
C_out, kernel_size, stride = 1, 4, 1
|
| 103 |
-
expected_out_length = int((T - kernel_size) / stride + 1)
|
| 104 |
-
wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm')
|
| 105 |
-
gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm')
|
| 106 |
-
nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none')
|
| 107 |
-
|
| 108 |
-
assert isinstance(wn_conv.norm, nn.Identity)
|
| 109 |
-
assert isinstance(wn_conv.conv, nn.Conv1d)
|
| 110 |
-
|
| 111 |
-
assert isinstance(gn_conv.norm, nn.GroupNorm)
|
| 112 |
-
assert isinstance(gn_conv.conv, nn.Conv1d)
|
| 113 |
-
|
| 114 |
-
assert isinstance(nn_conv.norm, nn.Identity)
|
| 115 |
-
assert isinstance(nn_conv.conv, nn.Conv1d)
|
| 116 |
-
|
| 117 |
-
for conv_layer in [wn_conv, gn_conv, nn_conv]:
|
| 118 |
-
out = conv_layer(t0)
|
| 119 |
-
assert isinstance(out, torch.Tensor)
|
| 120 |
-
assert list(out.shape) == [N, C_out, expected_out_length]
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
class TestNormConvTranspose1d:
|
| 124 |
-
|
| 125 |
-
def test_normalizations(self):
|
| 126 |
-
N, C, T = 2, 2, random.randrange(1, 100_000)
|
| 127 |
-
t0 = torch.randn(N, C, T)
|
| 128 |
-
|
| 129 |
-
C_out, kernel_size, stride = 1, 4, 1
|
| 130 |
-
expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1
|
| 131 |
-
|
| 132 |
-
wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm')
|
| 133 |
-
gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm')
|
| 134 |
-
nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none')
|
| 135 |
-
|
| 136 |
-
assert isinstance(wn_convtr.norm, nn.Identity)
|
| 137 |
-
assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d)
|
| 138 |
-
|
| 139 |
-
assert isinstance(gn_convtr.norm, nn.GroupNorm)
|
| 140 |
-
assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d)
|
| 141 |
-
|
| 142 |
-
assert isinstance(nn_convtr.norm, nn.Identity)
|
| 143 |
-
assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d)
|
| 144 |
-
|
| 145 |
-
for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]:
|
| 146 |
-
out = convtr_layer(t0)
|
| 147 |
-
assert isinstance(out, torch.Tensor)
|
| 148 |
-
assert list(out.shape) == [N, C_out, expected_out_length]
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
class TestStreamableConv1d:
|
| 152 |
-
|
| 153 |
-
def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation):
|
| 154 |
-
# StreamableConv1d internally pads to make sure that the last window is full
|
| 155 |
-
padding_total = (kernel_size - 1) * dilation - (stride - 1)
|
| 156 |
-
n_frames = (length - kernel_size + padding_total) / stride + 1
|
| 157 |
-
ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
|
| 158 |
-
return ideal_length // stride
|
| 159 |
-
|
| 160 |
-
def test_streamable_conv1d(self):
|
| 161 |
-
N, C, T = 2, 2, random.randrange(1, 100_000)
|
| 162 |
-
t0 = torch.randn(N, C, T)
|
| 163 |
-
C_out = 1
|
| 164 |
-
|
| 165 |
-
# conv params are [(kernel_size, stride, dilation)]
|
| 166 |
-
conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)]
|
| 167 |
-
for causal, (kernel_size, stride, dilation) in product([False, True], conv_params):
|
| 168 |
-
expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation)
|
| 169 |
-
sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal)
|
| 170 |
-
out = sconv(t0)
|
| 171 |
-
assert isinstance(out, torch.Tensor)
|
| 172 |
-
print(list(out.shape), [N, C_out, expected_out_length])
|
| 173 |
-
assert list(out.shape) == [N, C_out, expected_out_length]
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
class TestStreamableConvTranspose1d:
|
| 177 |
-
|
| 178 |
-
def get_streamable_convtr1d_output_length(self, length, kernel_size, stride):
|
| 179 |
-
padding_total = (kernel_size - stride)
|
| 180 |
-
return (length - 1) * stride - padding_total + (kernel_size - 1) + 1
|
| 181 |
-
|
| 182 |
-
def test_streamable_convtr1d(self):
|
| 183 |
-
N, C, T = 2, 2, random.randrange(1, 100_000)
|
| 184 |
-
t0 = torch.randn(N, C, T)
|
| 185 |
-
|
| 186 |
-
C_out = 1
|
| 187 |
-
|
| 188 |
-
with pytest.raises(AssertionError):
|
| 189 |
-
StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5)
|
| 190 |
-
StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.)
|
| 191 |
-
StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2)
|
| 192 |
-
|
| 193 |
-
# causal params are [(causal, trim_right)]
|
| 194 |
-
causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)]
|
| 195 |
-
# conv params are [(kernel_size, stride)]
|
| 196 |
-
conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)]
|
| 197 |
-
for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params):
|
| 198 |
-
expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride)
|
| 199 |
-
sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride,
|
| 200 |
-
causal=causal, trim_right_ratio=trim_right_ratio)
|
| 201 |
-
out = sconvtr(t0)
|
| 202 |
-
assert isinstance(out, torch.Tensor)
|
| 203 |
-
assert list(out.shape) == [N, C_out, expected_out_length]
|
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spaces/AIFILMS/StyleGANEX/models/stylegan2/op_ori/upfirdn2d.py
DELETED
|
@@ -1,184 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
from torch.autograd import Function
|
| 5 |
-
from torch.utils.cpp_extension import load
|
| 6 |
-
|
| 7 |
-
module_path = os.path.dirname(__file__)
|
| 8 |
-
upfirdn2d_op = load(
|
| 9 |
-
'upfirdn2d',
|
| 10 |
-
sources=[
|
| 11 |
-
os.path.join(module_path, 'upfirdn2d.cpp'),
|
| 12 |
-
os.path.join(module_path, 'upfirdn2d_kernel.cu'),
|
| 13 |
-
],
|
| 14 |
-
)
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
class UpFirDn2dBackward(Function):
|
| 18 |
-
@staticmethod
|
| 19 |
-
def forward(
|
| 20 |
-
ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
|
| 21 |
-
):
|
| 22 |
-
up_x, up_y = up
|
| 23 |
-
down_x, down_y = down
|
| 24 |
-
g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
|
| 25 |
-
|
| 26 |
-
grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
|
| 27 |
-
|
| 28 |
-
grad_input = upfirdn2d_op.upfirdn2d(
|
| 29 |
-
grad_output,
|
| 30 |
-
grad_kernel,
|
| 31 |
-
down_x,
|
| 32 |
-
down_y,
|
| 33 |
-
up_x,
|
| 34 |
-
up_y,
|
| 35 |
-
g_pad_x0,
|
| 36 |
-
g_pad_x1,
|
| 37 |
-
g_pad_y0,
|
| 38 |
-
g_pad_y1,
|
| 39 |
-
)
|
| 40 |
-
grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
|
| 41 |
-
|
| 42 |
-
ctx.save_for_backward(kernel)
|
| 43 |
-
|
| 44 |
-
pad_x0, pad_x1, pad_y0, pad_y1 = pad
|
| 45 |
-
|
| 46 |
-
ctx.up_x = up_x
|
| 47 |
-
ctx.up_y = up_y
|
| 48 |
-
ctx.down_x = down_x
|
| 49 |
-
ctx.down_y = down_y
|
| 50 |
-
ctx.pad_x0 = pad_x0
|
| 51 |
-
ctx.pad_x1 = pad_x1
|
| 52 |
-
ctx.pad_y0 = pad_y0
|
| 53 |
-
ctx.pad_y1 = pad_y1
|
| 54 |
-
ctx.in_size = in_size
|
| 55 |
-
ctx.out_size = out_size
|
| 56 |
-
|
| 57 |
-
return grad_input
|
| 58 |
-
|
| 59 |
-
@staticmethod
|
| 60 |
-
def backward(ctx, gradgrad_input):
|
| 61 |
-
kernel, = ctx.saved_tensors
|
| 62 |
-
|
| 63 |
-
gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
|
| 64 |
-
|
| 65 |
-
gradgrad_out = upfirdn2d_op.upfirdn2d(
|
| 66 |
-
gradgrad_input,
|
| 67 |
-
kernel,
|
| 68 |
-
ctx.up_x,
|
| 69 |
-
ctx.up_y,
|
| 70 |
-
ctx.down_x,
|
| 71 |
-
ctx.down_y,
|
| 72 |
-
ctx.pad_x0,
|
| 73 |
-
ctx.pad_x1,
|
| 74 |
-
ctx.pad_y0,
|
| 75 |
-
ctx.pad_y1,
|
| 76 |
-
)
|
| 77 |
-
# gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
|
| 78 |
-
gradgrad_out = gradgrad_out.view(
|
| 79 |
-
ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
|
| 80 |
-
)
|
| 81 |
-
|
| 82 |
-
return gradgrad_out, None, None, None, None, None, None, None, None
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
class UpFirDn2d(Function):
|
| 86 |
-
@staticmethod
|
| 87 |
-
def forward(ctx, input, kernel, up, down, pad):
|
| 88 |
-
up_x, up_y = up
|
| 89 |
-
down_x, down_y = down
|
| 90 |
-
pad_x0, pad_x1, pad_y0, pad_y1 = pad
|
| 91 |
-
|
| 92 |
-
kernel_h, kernel_w = kernel.shape
|
| 93 |
-
batch, channel, in_h, in_w = input.shape
|
| 94 |
-
ctx.in_size = input.shape
|
| 95 |
-
|
| 96 |
-
input = input.reshape(-1, in_h, in_w, 1)
|
| 97 |
-
|
| 98 |
-
ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
|
| 99 |
-
|
| 100 |
-
out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
|
| 101 |
-
out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
|
| 102 |
-
ctx.out_size = (out_h, out_w)
|
| 103 |
-
|
| 104 |
-
ctx.up = (up_x, up_y)
|
| 105 |
-
ctx.down = (down_x, down_y)
|
| 106 |
-
ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
|
| 107 |
-
|
| 108 |
-
g_pad_x0 = kernel_w - pad_x0 - 1
|
| 109 |
-
g_pad_y0 = kernel_h - pad_y0 - 1
|
| 110 |
-
g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
|
| 111 |
-
g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
|
| 112 |
-
|
| 113 |
-
ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
|
| 114 |
-
|
| 115 |
-
out = upfirdn2d_op.upfirdn2d(
|
| 116 |
-
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
|
| 117 |
-
)
|
| 118 |
-
# out = out.view(major, out_h, out_w, minor)
|
| 119 |
-
out = out.view(-1, channel, out_h, out_w)
|
| 120 |
-
|
| 121 |
-
return out
|
| 122 |
-
|
| 123 |
-
@staticmethod
|
| 124 |
-
def backward(ctx, grad_output):
|
| 125 |
-
kernel, grad_kernel = ctx.saved_tensors
|
| 126 |
-
|
| 127 |
-
grad_input = UpFirDn2dBackward.apply(
|
| 128 |
-
grad_output,
|
| 129 |
-
kernel,
|
| 130 |
-
grad_kernel,
|
| 131 |
-
ctx.up,
|
| 132 |
-
ctx.down,
|
| 133 |
-
ctx.pad,
|
| 134 |
-
ctx.g_pad,
|
| 135 |
-
ctx.in_size,
|
| 136 |
-
ctx.out_size,
|
| 137 |
-
)
|
| 138 |
-
|
| 139 |
-
return grad_input, None, None, None, None
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
|
| 143 |
-
out = UpFirDn2d.apply(
|
| 144 |
-
input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
|
| 145 |
-
)
|
| 146 |
-
|
| 147 |
-
return out
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
def upfirdn2d_native(
|
| 151 |
-
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
|
| 152 |
-
):
|
| 153 |
-
_, in_h, in_w, minor = input.shape
|
| 154 |
-
kernel_h, kernel_w = kernel.shape
|
| 155 |
-
|
| 156 |
-
out = input.view(-1, in_h, 1, in_w, 1, minor)
|
| 157 |
-
out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
|
| 158 |
-
out = out.view(-1, in_h * up_y, in_w * up_x, minor)
|
| 159 |
-
|
| 160 |
-
out = F.pad(
|
| 161 |
-
out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
|
| 162 |
-
)
|
| 163 |
-
out = out[
|
| 164 |
-
:,
|
| 165 |
-
max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0),
|
| 166 |
-
max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0),
|
| 167 |
-
:,
|
| 168 |
-
]
|
| 169 |
-
|
| 170 |
-
out = out.permute(0, 3, 1, 2)
|
| 171 |
-
out = out.reshape(
|
| 172 |
-
[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
|
| 173 |
-
)
|
| 174 |
-
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
|
| 175 |
-
out = F.conv2d(out, w)
|
| 176 |
-
out = out.reshape(
|
| 177 |
-
-1,
|
| 178 |
-
minor,
|
| 179 |
-
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
|
| 180 |
-
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
|
| 181 |
-
)
|
| 182 |
-
out = out.permute(0, 2, 3, 1)
|
| 183 |
-
|
| 184 |
-
return out[:, ::down_y, ::down_x, :]
|
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|
spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/dataset.py
DELETED
|
@@ -1,224 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import argparse
|
| 3 |
-
import csv
|
| 4 |
-
import os
|
| 5 |
-
import glob
|
| 6 |
-
import datetime
|
| 7 |
-
import time
|
| 8 |
-
import logging
|
| 9 |
-
import h5py
|
| 10 |
-
import librosa
|
| 11 |
-
|
| 12 |
-
from utilities import (create_folder, get_filename, create_logging,
|
| 13 |
-
float32_to_int16, pad_or_truncate, read_metadata)
|
| 14 |
-
import config
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def split_unbalanced_csv_to_partial_csvs(args):
|
| 18 |
-
"""Split unbalanced csv to part csvs. Each part csv contains up to 50000 ids.
|
| 19 |
-
"""
|
| 20 |
-
|
| 21 |
-
unbalanced_csv_path = args.unbalanced_csv
|
| 22 |
-
unbalanced_partial_csvs_dir = args.unbalanced_partial_csvs_dir
|
| 23 |
-
|
| 24 |
-
create_folder(unbalanced_partial_csvs_dir)
|
| 25 |
-
|
| 26 |
-
with open(unbalanced_csv_path, 'r') as f:
|
| 27 |
-
lines = f.readlines()
|
| 28 |
-
|
| 29 |
-
lines = lines[3:] # Remove head info
|
| 30 |
-
audios_num_per_file = 50000
|
| 31 |
-
|
| 32 |
-
files_num = int(np.ceil(len(lines) / float(audios_num_per_file)))
|
| 33 |
-
|
| 34 |
-
for r in range(files_num):
|
| 35 |
-
lines_per_file = lines[r * audios_num_per_file :
|
| 36 |
-
(r + 1) * audios_num_per_file]
|
| 37 |
-
|
| 38 |
-
out_csv_path = os.path.join(unbalanced_partial_csvs_dir,
|
| 39 |
-
'unbalanced_train_segments_part{:02d}.csv'.format(r))
|
| 40 |
-
|
| 41 |
-
with open(out_csv_path, 'w') as f:
|
| 42 |
-
f.write('empty\n')
|
| 43 |
-
f.write('empty\n')
|
| 44 |
-
f.write('empty\n')
|
| 45 |
-
for line in lines_per_file:
|
| 46 |
-
f.write(line)
|
| 47 |
-
|
| 48 |
-
print('Write out csv to {}'.format(out_csv_path))
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def download_wavs(args):
|
| 52 |
-
"""Download videos and extract audio in wav format.
|
| 53 |
-
"""
|
| 54 |
-
|
| 55 |
-
# Paths
|
| 56 |
-
csv_path = args.csv_path
|
| 57 |
-
audios_dir = args.audios_dir
|
| 58 |
-
mini_data = args.mini_data
|
| 59 |
-
|
| 60 |
-
if mini_data:
|
| 61 |
-
logs_dir = '_logs/download_dataset/{}'.format(get_filename(csv_path))
|
| 62 |
-
else:
|
| 63 |
-
logs_dir = '_logs/download_dataset_minidata/{}'.format(get_filename(csv_path))
|
| 64 |
-
|
| 65 |
-
create_folder(audios_dir)
|
| 66 |
-
create_folder(logs_dir)
|
| 67 |
-
create_logging(logs_dir, filemode='w')
|
| 68 |
-
logging.info('Download log is saved to {}'.format(logs_dir))
|
| 69 |
-
|
| 70 |
-
# Read csv
|
| 71 |
-
with open(csv_path, 'r') as f:
|
| 72 |
-
lines = f.readlines()
|
| 73 |
-
|
| 74 |
-
lines = lines[3:] # Remove csv head info
|
| 75 |
-
|
| 76 |
-
if mini_data:
|
| 77 |
-
lines = lines[0 : 10] # Download partial data for debug
|
| 78 |
-
|
| 79 |
-
download_time = time.time()
|
| 80 |
-
|
| 81 |
-
# Download
|
| 82 |
-
for (n, line) in enumerate(lines):
|
| 83 |
-
|
| 84 |
-
items = line.split(', ')
|
| 85 |
-
audio_id = items[0]
|
| 86 |
-
start_time = float(items[1])
|
| 87 |
-
end_time = float(items[2])
|
| 88 |
-
duration = end_time - start_time
|
| 89 |
-
|
| 90 |
-
logging.info('{} {} start_time: {:.1f}, end_time: {:.1f}'.format(
|
| 91 |
-
n, audio_id, start_time, end_time))
|
| 92 |
-
|
| 93 |
-
# Download full video of whatever format
|
| 94 |
-
video_name = os.path.join(audios_dir, '_Y{}.%(ext)s'.format(audio_id))
|
| 95 |
-
os.system("youtube-dl --quiet -o '{}' -x https://www.youtube.com/watch?v={}"\
|
| 96 |
-
.format(video_name, audio_id))
|
| 97 |
-
|
| 98 |
-
video_paths = glob.glob(os.path.join(audios_dir, '_Y' + audio_id + '.*'))
|
| 99 |
-
|
| 100 |
-
# If download successful
|
| 101 |
-
if len(video_paths) > 0:
|
| 102 |
-
video_path = video_paths[0] # Choose one video
|
| 103 |
-
|
| 104 |
-
# Add 'Y' to the head because some video ids are started with '-'
|
| 105 |
-
# which will cause problem
|
| 106 |
-
audio_path = os.path.join(audios_dir, 'Y' + audio_id + '.wav')
|
| 107 |
-
|
| 108 |
-
# Extract audio in wav format
|
| 109 |
-
os.system("ffmpeg -loglevel panic -i {} -ac 1 -ar 32000 -ss {} -t 00:00:{} {} "\
|
| 110 |
-
.format(video_path,
|
| 111 |
-
str(datetime.timedelta(seconds=start_time)), duration,
|
| 112 |
-
audio_path))
|
| 113 |
-
|
| 114 |
-
# Remove downloaded video
|
| 115 |
-
os.system("rm {}".format(video_path))
|
| 116 |
-
|
| 117 |
-
logging.info("Download and convert to {}".format(audio_path))
|
| 118 |
-
|
| 119 |
-
logging.info('Download finished! Time spent: {:.3f} s'.format(
|
| 120 |
-
time.time() - download_time))
|
| 121 |
-
|
| 122 |
-
logging.info('Logs can be viewed in {}'.format(logs_dir))
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
def pack_waveforms_to_hdf5(args):
|
| 126 |
-
"""Pack waveform and target of several audio clips to a single hdf5 file.
|
| 127 |
-
This can speed up loading and training.
|
| 128 |
-
"""
|
| 129 |
-
|
| 130 |
-
# Arguments & parameters
|
| 131 |
-
audios_dir = args.audios_dir
|
| 132 |
-
csv_path = args.csv_path
|
| 133 |
-
waveforms_hdf5_path = args.waveforms_hdf5_path
|
| 134 |
-
mini_data = args.mini_data
|
| 135 |
-
|
| 136 |
-
clip_samples = config.clip_samples
|
| 137 |
-
classes_num = config.classes_num
|
| 138 |
-
sample_rate = config.sample_rate
|
| 139 |
-
id_to_ix = config.id_to_ix
|
| 140 |
-
|
| 141 |
-
# Paths
|
| 142 |
-
if mini_data:
|
| 143 |
-
prefix = 'mini_'
|
| 144 |
-
waveforms_hdf5_path += '.mini'
|
| 145 |
-
else:
|
| 146 |
-
prefix = ''
|
| 147 |
-
|
| 148 |
-
create_folder(os.path.dirname(waveforms_hdf5_path))
|
| 149 |
-
|
| 150 |
-
logs_dir = '_logs/pack_waveforms_to_hdf5/{}{}'.format(prefix, get_filename(csv_path))
|
| 151 |
-
create_folder(logs_dir)
|
| 152 |
-
create_logging(logs_dir, filemode='w')
|
| 153 |
-
logging.info('Write logs to {}'.format(logs_dir))
|
| 154 |
-
|
| 155 |
-
# Read csv file
|
| 156 |
-
meta_dict = read_metadata(csv_path, classes_num, id_to_ix)
|
| 157 |
-
|
| 158 |
-
if mini_data:
|
| 159 |
-
mini_num = 10
|
| 160 |
-
for key in meta_dict.keys():
|
| 161 |
-
meta_dict[key] = meta_dict[key][0 : mini_num]
|
| 162 |
-
|
| 163 |
-
audios_num = len(meta_dict['audio_name'])
|
| 164 |
-
|
| 165 |
-
# Pack waveform to hdf5
|
| 166 |
-
total_time = time.time()
|
| 167 |
-
|
| 168 |
-
with h5py.File(waveforms_hdf5_path, 'w') as hf:
|
| 169 |
-
hf.create_dataset('audio_name', shape=((audios_num,)), dtype='S20')
|
| 170 |
-
hf.create_dataset('waveform', shape=((audios_num, clip_samples)), dtype=np.int16)
|
| 171 |
-
hf.create_dataset('target', shape=((audios_num, classes_num)), dtype=np.bool)
|
| 172 |
-
hf.attrs.create('sample_rate', data=sample_rate, dtype=np.int32)
|
| 173 |
-
|
| 174 |
-
# Pack waveform & target of several audio clips to a single hdf5 file
|
| 175 |
-
for n in range(audios_num):
|
| 176 |
-
audio_path = os.path.join(audios_dir, meta_dict['audio_name'][n])
|
| 177 |
-
|
| 178 |
-
if os.path.isfile(audio_path):
|
| 179 |
-
logging.info('{} {}'.format(n, audio_path))
|
| 180 |
-
(audio, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
|
| 181 |
-
audio = pad_or_truncate(audio, clip_samples)
|
| 182 |
-
|
| 183 |
-
hf['audio_name'][n] = meta_dict['audio_name'][n].encode()
|
| 184 |
-
hf['waveform'][n] = float32_to_int16(audio)
|
| 185 |
-
hf['target'][n] = meta_dict['target'][n]
|
| 186 |
-
else:
|
| 187 |
-
logging.info('{} File does not exist! {}'.format(n, audio_path))
|
| 188 |
-
|
| 189 |
-
logging.info('Write to {}'.format(waveforms_hdf5_path))
|
| 190 |
-
logging.info('Pack hdf5 time: {:.3f}'.format(time.time() - total_time))
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
if __name__ == '__main__':
|
| 194 |
-
parser = argparse.ArgumentParser()
|
| 195 |
-
subparsers = parser.add_subparsers(dest='mode')
|
| 196 |
-
|
| 197 |
-
parser_split = subparsers.add_parser('split_unbalanced_csv_to_partial_csvs')
|
| 198 |
-
parser_split.add_argument('--unbalanced_csv', type=str, required=True, help='Path of unbalanced_csv file to read.')
|
| 199 |
-
parser_split.add_argument('--unbalanced_partial_csvs_dir', type=str, required=True, help='Directory to save out split unbalanced partial csv.')
|
| 200 |
-
|
| 201 |
-
parser_download_wavs = subparsers.add_parser('download_wavs')
|
| 202 |
-
parser_download_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
|
| 203 |
-
parser_download_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
|
| 204 |
-
parser_download_wavs.add_argument('--mini_data', action='store_true', default=True, help='Set true to only download 10 audios for debugging.')
|
| 205 |
-
|
| 206 |
-
parser_pack_wavs = subparsers.add_parser('pack_waveforms_to_hdf5')
|
| 207 |
-
parser_pack_wavs.add_argument('--csv_path', type=str, required=True, help='Path of csv file containing audio info to be downloaded.')
|
| 208 |
-
parser_pack_wavs.add_argument('--audios_dir', type=str, required=True, help='Directory to save out downloaded audio.')
|
| 209 |
-
parser_pack_wavs.add_argument('--waveforms_hdf5_path', type=str, required=True, help='Path to save out packed hdf5.')
|
| 210 |
-
parser_pack_wavs.add_argument('--mini_data', action='store_true', default=False, help='Set true to only download 10 audios for debugging.')
|
| 211 |
-
|
| 212 |
-
args = parser.parse_args()
|
| 213 |
-
|
| 214 |
-
if args.mode == 'split_unbalanced_csv_to_partial_csvs':
|
| 215 |
-
split_unbalanced_csv_to_partial_csvs(args)
|
| 216 |
-
|
| 217 |
-
elif args.mode == 'download_wavs':
|
| 218 |
-
download_wavs(args)
|
| 219 |
-
|
| 220 |
-
elif args.mode == 'pack_waveforms_to_hdf5':
|
| 221 |
-
pack_waveforms_to_hdf5(args)
|
| 222 |
-
|
| 223 |
-
else:
|
| 224 |
-
raise Exception('Incorrect arguments!')
|
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spaces/AIGC-Audio/Make_An_Audio/ldm/modules/distributions/distributions.py
DELETED
|
@@ -1,92 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import numpy as np
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class AbstractDistribution:
|
| 6 |
-
def sample(self):
|
| 7 |
-
raise NotImplementedError()
|
| 8 |
-
|
| 9 |
-
def mode(self):
|
| 10 |
-
raise NotImplementedError()
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class DiracDistribution(AbstractDistribution):
|
| 14 |
-
def __init__(self, value):
|
| 15 |
-
self.value = value
|
| 16 |
-
|
| 17 |
-
def sample(self):
|
| 18 |
-
return self.value
|
| 19 |
-
|
| 20 |
-
def mode(self):
|
| 21 |
-
return self.value
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class DiagonalGaussianDistribution(object):
|
| 25 |
-
def __init__(self, parameters, deterministic=False):
|
| 26 |
-
self.parameters = parameters
|
| 27 |
-
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
|
| 28 |
-
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
| 29 |
-
self.deterministic = deterministic
|
| 30 |
-
self.std = torch.exp(0.5 * self.logvar)
|
| 31 |
-
self.var = torch.exp(self.logvar)
|
| 32 |
-
if self.deterministic:
|
| 33 |
-
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
|
| 34 |
-
|
| 35 |
-
def sample(self):
|
| 36 |
-
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
|
| 37 |
-
return x
|
| 38 |
-
|
| 39 |
-
def kl(self, other=None):
|
| 40 |
-
if self.deterministic:
|
| 41 |
-
return torch.Tensor([0.])
|
| 42 |
-
else:
|
| 43 |
-
if other is None:
|
| 44 |
-
return 0.5 * torch.sum(torch.pow(self.mean, 2)
|
| 45 |
-
+ self.var - 1.0 - self.logvar,
|
| 46 |
-
dim=[1, 2, 3])
|
| 47 |
-
else:
|
| 48 |
-
return 0.5 * torch.sum(
|
| 49 |
-
torch.pow(self.mean - other.mean, 2) / other.var
|
| 50 |
-
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
|
| 51 |
-
dim=[1, 2, 3])
|
| 52 |
-
|
| 53 |
-
def nll(self, sample, dims=[1,2,3]):
|
| 54 |
-
if self.deterministic:
|
| 55 |
-
return torch.Tensor([0.])
|
| 56 |
-
logtwopi = np.log(2.0 * np.pi)
|
| 57 |
-
return 0.5 * torch.sum(
|
| 58 |
-
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
|
| 59 |
-
dim=dims)
|
| 60 |
-
|
| 61 |
-
def mode(self):
|
| 62 |
-
return self.mean
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
def normal_kl(mean1, logvar1, mean2, logvar2):
|
| 66 |
-
"""
|
| 67 |
-
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
|
| 68 |
-
Compute the KL divergence between two gaussians.
|
| 69 |
-
Shapes are automatically broadcasted, so batches can be compared to
|
| 70 |
-
scalars, among other use cases.
|
| 71 |
-
"""
|
| 72 |
-
tensor = None
|
| 73 |
-
for obj in (mean1, logvar1, mean2, logvar2):
|
| 74 |
-
if isinstance(obj, torch.Tensor):
|
| 75 |
-
tensor = obj
|
| 76 |
-
break
|
| 77 |
-
assert tensor is not None, "at least one argument must be a Tensor"
|
| 78 |
-
|
| 79 |
-
# Force variances to be Tensors. Broadcasting helps convert scalars to
|
| 80 |
-
# Tensors, but it does not work for torch.exp().
|
| 81 |
-
logvar1, logvar2 = [
|
| 82 |
-
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
|
| 83 |
-
for x in (logvar1, logvar2)
|
| 84 |
-
]
|
| 85 |
-
|
| 86 |
-
return 0.5 * (
|
| 87 |
-
-1.0
|
| 88 |
-
+ logvar2
|
| 89 |
-
- logvar1
|
| 90 |
-
+ torch.exp(logvar1 - logvar2)
|
| 91 |
-
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
|
| 92 |
-
)
|
|
|
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|
spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/ema.py
DELETED
|
@@ -1,76 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch import nn
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class LitEma(nn.Module):
|
| 6 |
-
def __init__(self, model, decay=0.9999, use_num_upates=True):
|
| 7 |
-
super().__init__()
|
| 8 |
-
if decay < 0.0 or decay > 1.0:
|
| 9 |
-
raise ValueError('Decay must be between 0 and 1')
|
| 10 |
-
|
| 11 |
-
self.m_name2s_name = {}
|
| 12 |
-
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
|
| 13 |
-
self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
|
| 14 |
-
else torch.tensor(-1,dtype=torch.int))
|
| 15 |
-
|
| 16 |
-
for name, p in model.named_parameters():
|
| 17 |
-
if p.requires_grad:
|
| 18 |
-
#remove as '.'-character is not allowed in buffers
|
| 19 |
-
s_name = name.replace('.','')
|
| 20 |
-
self.m_name2s_name.update({name:s_name})
|
| 21 |
-
self.register_buffer(s_name,p.clone().detach().data)
|
| 22 |
-
|
| 23 |
-
self.collected_params = []
|
| 24 |
-
|
| 25 |
-
def forward(self,model):
|
| 26 |
-
decay = self.decay
|
| 27 |
-
|
| 28 |
-
if self.num_updates >= 0:
|
| 29 |
-
self.num_updates += 1
|
| 30 |
-
decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
|
| 31 |
-
|
| 32 |
-
one_minus_decay = 1.0 - decay
|
| 33 |
-
|
| 34 |
-
with torch.no_grad():
|
| 35 |
-
m_param = dict(model.named_parameters())
|
| 36 |
-
shadow_params = dict(self.named_buffers())
|
| 37 |
-
|
| 38 |
-
for key in m_param:
|
| 39 |
-
if m_param[key].requires_grad:
|
| 40 |
-
sname = self.m_name2s_name[key]
|
| 41 |
-
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
|
| 42 |
-
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
|
| 43 |
-
else:
|
| 44 |
-
assert not key in self.m_name2s_name
|
| 45 |
-
|
| 46 |
-
def copy_to(self, model):
|
| 47 |
-
m_param = dict(model.named_parameters())
|
| 48 |
-
shadow_params = dict(self.named_buffers())
|
| 49 |
-
for key in m_param:
|
| 50 |
-
if m_param[key].requires_grad:
|
| 51 |
-
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
|
| 52 |
-
else:
|
| 53 |
-
assert not key in self.m_name2s_name
|
| 54 |
-
|
| 55 |
-
def store(self, parameters):
|
| 56 |
-
"""
|
| 57 |
-
Save the current parameters for restoring later.
|
| 58 |
-
Args:
|
| 59 |
-
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
| 60 |
-
temporarily stored.
|
| 61 |
-
"""
|
| 62 |
-
self.collected_params = [param.clone() for param in parameters]
|
| 63 |
-
|
| 64 |
-
def restore(self, parameters):
|
| 65 |
-
"""
|
| 66 |
-
Restore the parameters stored with the `store` method.
|
| 67 |
-
Useful to validate the model with EMA parameters without affecting the
|
| 68 |
-
original optimization process. Store the parameters before the
|
| 69 |
-
`copy_to` method. After validation (or model saving), use this to
|
| 70 |
-
restore the former parameters.
|
| 71 |
-
Args:
|
| 72 |
-
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
| 73 |
-
updated with the stored parameters.
|
| 74 |
-
"""
|
| 75 |
-
for c_param, param in zip(self.collected_params, parameters):
|
| 76 |
-
param.data.copy_(c_param.data)
|
|
|
|
|
|
|
|
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|
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spaces/AILab-CVC/SEED-Bench_Leaderboard/src/utils_display.py
DELETED
|
@@ -1,99 +0,0 @@
|
|
| 1 |
-
from dataclasses import dataclass
|
| 2 |
-
|
| 3 |
-
# These classes are for user facing column names, to avoid having to change them
|
| 4 |
-
# all around the code when a modif is needed
|
| 5 |
-
@dataclass
|
| 6 |
-
class ColumnContent:
|
| 7 |
-
name: str
|
| 8 |
-
type: str
|
| 9 |
-
displayed_by_default: bool
|
| 10 |
-
hidden: bool = False
|
| 11 |
-
|
| 12 |
-
def fields(raw_class):
|
| 13 |
-
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
|
| 14 |
-
|
| 15 |
-
@dataclass(frozen=True)
|
| 16 |
-
class AutoEvalColumn: # Auto evals column
|
| 17 |
-
model_type_symbol = ColumnContent("T", "str", True)
|
| 18 |
-
model = ColumnContent("Model", "markdown", True)
|
| 19 |
-
average = ColumnContent("Average ⬆️", "number", True)
|
| 20 |
-
arc = ColumnContent("ARC", "number", True)
|
| 21 |
-
hellaswag = ColumnContent("HellaSwag", "number", True)
|
| 22 |
-
mmlu = ColumnContent("MMLU", "number", True)
|
| 23 |
-
truthfulqa = ColumnContent("TruthfulQA", "number", True)
|
| 24 |
-
model_type = ColumnContent("Type", "str", False)
|
| 25 |
-
precision = ColumnContent("Precision", "str", False, True)
|
| 26 |
-
license = ColumnContent("Hub License", "str", False)
|
| 27 |
-
params = ColumnContent("#Params (B)", "number", False)
|
| 28 |
-
likes = ColumnContent("Hub ❤️", "number", False)
|
| 29 |
-
revision = ColumnContent("Model sha", "str", False, False)
|
| 30 |
-
dummy = ColumnContent("model_name_for_query", "str", True) # dummy col to implement search bar (hidden by custom CSS)
|
| 31 |
-
|
| 32 |
-
@dataclass(frozen=True)
|
| 33 |
-
class EloEvalColumn: # Elo evals column
|
| 34 |
-
model = ColumnContent("Model", "markdown", True)
|
| 35 |
-
gpt4 = ColumnContent("GPT-4 (all)", "number", True)
|
| 36 |
-
human_all = ColumnContent("Human (all)", "number", True)
|
| 37 |
-
human_instruct = ColumnContent("Human (instruct)", "number", True)
|
| 38 |
-
human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
@dataclass(frozen=True)
|
| 42 |
-
class EvalQueueColumn: # Queue column
|
| 43 |
-
model = ColumnContent("model", "markdown", True)
|
| 44 |
-
revision = ColumnContent("revision", "str", True)
|
| 45 |
-
private = ColumnContent("private", "bool", True)
|
| 46 |
-
precision = ColumnContent("precision", "bool", True)
|
| 47 |
-
weight_type = ColumnContent("weight_type", "str", "Original")
|
| 48 |
-
status = ColumnContent("status", "str", True)
|
| 49 |
-
|
| 50 |
-
LLAMAS = ["huggingface/llama-7b", "huggingface/llama-13b", "huggingface/llama-30b", "huggingface/llama-65b"]
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
|
| 54 |
-
VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
|
| 55 |
-
OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
|
| 56 |
-
DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b"
|
| 57 |
-
MODEL_PAGE = "https://huggingface.co/models"
|
| 58 |
-
LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/"
|
| 59 |
-
VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta"
|
| 60 |
-
ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html"
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
def model_hyperlink(link, model_name):
|
| 64 |
-
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
def make_clickable_model(model_name):
|
| 68 |
-
link = f"https://huggingface.co/{model_name}"
|
| 69 |
-
|
| 70 |
-
if model_name in LLAMAS:
|
| 71 |
-
link = LLAMA_LINK
|
| 72 |
-
model_name = model_name.split("/")[1]
|
| 73 |
-
elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904":
|
| 74 |
-
link = VICUNA_LINK
|
| 75 |
-
model_name = "stable-vicuna-13b"
|
| 76 |
-
elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca":
|
| 77 |
-
link = ALPACA_LINK
|
| 78 |
-
model_name = "alpaca-13b"
|
| 79 |
-
if model_name == "dolly-12b":
|
| 80 |
-
link = DOLLY_LINK
|
| 81 |
-
elif model_name == "vicuna-13b":
|
| 82 |
-
link = VICUNA_LINK
|
| 83 |
-
elif model_name == "koala-13b":
|
| 84 |
-
link = KOALA_LINK
|
| 85 |
-
elif model_name == "oasst-12b":
|
| 86 |
-
link = OASST_LINK
|
| 87 |
-
#else:
|
| 88 |
-
# link = MODEL_PAGE
|
| 89 |
-
|
| 90 |
-
return model_hyperlink(link, model_name)
|
| 91 |
-
|
| 92 |
-
def styled_error(error):
|
| 93 |
-
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
|
| 94 |
-
|
| 95 |
-
def styled_warning(warn):
|
| 96 |
-
return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
|
| 97 |
-
|
| 98 |
-
def styled_message(message):
|
| 99 |
-
return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
|
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnext101_4xb32_2048e_4channel.py
DELETED
|
@@ -1,107 +0,0 @@
|
|
| 1 |
-
_base_ = [ # 此配置文件将继承所有 `_base_` 中的配置
|
| 2 |
-
'../configs/_base_/schedules/custom_schedule.py', # 训练策略配置
|
| 3 |
-
'../configs/_base_/default_runtime.py' # 默认运行设置
|
| 4 |
-
]
|
| 5 |
-
|
| 6 |
-
default_hooks = dict(
|
| 7 |
-
# print log every 50 iterations.
|
| 8 |
-
logger=dict(type='LoggerHook', interval=10),
|
| 9 |
-
# save checkpoint per 8 epochs.
|
| 10 |
-
checkpoint=dict(save_best='auto', interval=16)
|
| 11 |
-
)
|
| 12 |
-
|
| 13 |
-
visualizer = dict(
|
| 14 |
-
vis_backends=[dict(type='LocalVisBackend'),
|
| 15 |
-
dict(type='WandbVisBackend')])
|
| 16 |
-
|
| 17 |
-
dataset_type = 'CustomDataset'
|
| 18 |
-
|
| 19 |
-
# config of pipline
|
| 20 |
-
train_pipeline = [
|
| 21 |
-
dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像
|
| 22 |
-
dict(type='RandomResizedCrop', scale=224), # 随机放缩裁剪
|
| 23 |
-
dict(type='RandomFlip', prob=0.5, direction='horizontal'), # 随机水平翻转
|
| 24 |
-
dict(type='PackInputs'), # 准备图像以及标签
|
| 25 |
-
]
|
| 26 |
-
|
| 27 |
-
test_pipeline = [
|
| 28 |
-
dict(type='LoadImageFromFile', imdecode_backend='pillow', color_type='unchanged'), # 读取图像
|
| 29 |
-
dict(type='ResizeEdge', scale=256, edge='short'), # 缩放短边尺寸至 256px
|
| 30 |
-
dict(type='CenterCrop', crop_size=224), # 中心裁剪
|
| 31 |
-
dict(type='PackInputs'), # 准备图像以及标签
|
| 32 |
-
]
|
| 33 |
-
|
| 34 |
-
# config of dataloader
|
| 35 |
-
train_dataloader = dict(
|
| 36 |
-
batch_size=32, # 每张 GPU 的 batchsize
|
| 37 |
-
num_workers=5, # 每个 GPU 的线程数
|
| 38 |
-
dataset=dict( # 训练数据集
|
| 39 |
-
type=dataset_type,
|
| 40 |
-
data_root='../2_preprocess_data_3000',
|
| 41 |
-
with_label=True,
|
| 42 |
-
ann_file='',
|
| 43 |
-
data_prefix='train',
|
| 44 |
-
pipeline=train_pipeline),
|
| 45 |
-
sampler=dict(type='DefaultSampler', shuffle=True), # 默认采样器
|
| 46 |
-
persistent_workers=True, # 是否保持进程，可以缩短每个 epoch 的准备时间
|
| 47 |
-
)
|
| 48 |
-
|
| 49 |
-
# 构造验证集 dataloader
|
| 50 |
-
val_dataloader = dict(
|
| 51 |
-
batch_size=32,
|
| 52 |
-
num_workers=5,
|
| 53 |
-
dataset=dict(
|
| 54 |
-
type=dataset_type,
|
| 55 |
-
data_root='../2_preprocess_data_3000',
|
| 56 |
-
with_label=True,
|
| 57 |
-
ann_file='',
|
| 58 |
-
data_prefix='val',
|
| 59 |
-
pipeline=test_pipeline),
|
| 60 |
-
sampler=dict(type='DefaultSampler', shuffle=False),
|
| 61 |
-
persistent_workers=True,
|
| 62 |
-
)
|
| 63 |
-
|
| 64 |
-
# set evaluator of validation dataset. Here uses top1 and top3 accuracy
|
| 65 |
-
val_evaluator = dict(type='Accuracy', topk=(1, 3))
|
| 66 |
-
|
| 67 |
-
test_dataloader = val_dataloader
|
| 68 |
-
test_evaluator = val_evaluator
|
| 69 |
-
|
| 70 |
-
model = dict(
|
| 71 |
-
type='ImageClassifier', # 主模型类型（对于图像分类任务，使用 `ImageClassifier`）
|
| 72 |
-
backbone=dict(
|
| 73 |
-
type='ResNeXt', # 主干网络类型
|
| 74 |
-
depth=101,
|
| 75 |
-
in_channels=4, # 输入通道数
|
| 76 |
-
),
|
| 77 |
-
neck=dict(type='GlobalAveragePooling'), # 颈网络类型
|
| 78 |
-
head=dict(
|
| 79 |
-
type='LinearClsHead', # 分类颈网络类型
|
| 80 |
-
# 除了 `type` 之外的所有字段都来自 `LinearClsHead` 类的 __init__ 方法
|
| 81 |
-
# 可查阅 https://mmpretrain.readthedocs.io/zh_CN/latest/api/generated/mmpretrain.models.heads.LinearClsHead.html
|
| 82 |
-
num_classes=7, # 分类类别数
|
| 83 |
-
in_channels=2048,
|
| 84 |
-
loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # 损失函数配置信息
|
| 85 |
-
topk=(1, 3), # 评估指标，Top-k 准确率
|
| 86 |
-
))
|
| 87 |
-
|
| 88 |
-
optim_wrapper = dict(
|
| 89 |
-
accumulative_counts=8
|
| 90 |
-
)
|
| 91 |
-
|
| 92 |
-
param_scheduler = [
|
| 93 |
-
# 在前10轮迭代中，逐迭代次数，线性预热
|
| 94 |
-
dict(type='LinearLR',
|
| 95 |
-
start_factor=0.00001,
|
| 96 |
-
by_epoch=True,
|
| 97 |
-
end=10,
|
| 98 |
-
convert_to_iter_based=True, # 逐迭代次数更新学习率.
|
| 99 |
-
),
|
| 100 |
-
# 在 10 轮次后，通过余弦退火衰减
|
| 101 |
-
dict(type='MultiStepLR',
|
| 102 |
-
by_epoch=True, # 按轮次更新学习率
|
| 103 |
-
milestones=[30, 210, 390, 570, 750, 930, 1110, 1290, 1470, 1650, 1830],
|
| 104 |
-
gamma=0.9)
|
| 105 |
-
]
|
| 106 |
-
|
| 107 |
-
train_cfg = dict(by_epoch=True, max_epochs=2048, val_interval=16)
|
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|
spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/sde_team_given_tests.py
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
import logging
|
| 4 |
-
import re
|
| 5 |
-
import random
|
| 6 |
-
from typing import TYPE_CHECKING, Any, List, Optional
|
| 7 |
-
|
| 8 |
-
from . import order_registry as OrderRegistry
|
| 9 |
-
from .base import BaseOrder
|
| 10 |
-
|
| 11 |
-
if TYPE_CHECKING:
|
| 12 |
-
from agentverse.environments import BaseEnvironment
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
@OrderRegistry.register("sde_team_given_tests")
|
| 16 |
-
class SdeTeamGivenTestsOrder(BaseOrder):
|
| 17 |
-
"""The order for a code problem solving given unit tests
|
| 18 |
-
0 - code writer
|
| 19 |
-
1 - code tester
|
| 20 |
-
2 - code reviewer
|
| 21 |
-
"""
|
| 22 |
-
next_agent_idx: int = 0
|
| 23 |
-
|
| 24 |
-
def get_next_agent_idx(self, environment: BaseEnvironment) -> List[int]:
|
| 25 |
-
if self.next_agent_idx == 0:
|
| 26 |
-
self.next_agent_idx = 1
|
| 27 |
-
return [0]
|
| 28 |
-
elif self.next_agent_idx == 1:
|
| 29 |
-
self.next_agent_idx = 2
|
| 30 |
-
return [1]
|
| 31 |
-
elif self.next_agent_idx == 2:
|
| 32 |
-
self.next_agent_idx = 0
|
| 33 |
-
return [2]
|
| 34 |
-
else:
|
| 35 |
-
raise ValueError("Invalid next_agent_idx: {}".format(self.next_agent_idx))
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spaces/Aloento/9Nine-VITS/text/cleaners.py
DELETED
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@@ -1,36 +0,0 @@
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|
| 1 |
-
import re
|
| 2 |
-
|
| 3 |
-
import pyopenjtalk
|
| 4 |
-
from unidecode import unidecode
|
| 5 |
-
|
| 6 |
-
_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
| 7 |
-
_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def japanese_cleaner(text):
|
| 11 |
-
'''Pipeline for dividing Japanese text into phrases.'''
|
| 12 |
-
sentences = re.split(_japanese_marks, text)
|
| 13 |
-
marks = re.findall(_japanese_marks, text)
|
| 14 |
-
text = ''
|
| 15 |
-
for i, sentence in enumerate(sentences):
|
| 16 |
-
if re.match(_japanese_characters, sentence):
|
| 17 |
-
labels = pyopenjtalk.extract_fullcontext(sentence)
|
| 18 |
-
for n, label in enumerate(labels):
|
| 19 |
-
phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
|
| 20 |
-
if phoneme not in ['sil', 'pau']:
|
| 21 |
-
text += phoneme.replace('ch', 'ʧ').replace('sh', 'ʃ').replace('cl', 'Q').replace('ts', 'ʦ')
|
| 22 |
-
else:
|
| 23 |
-
continue
|
| 24 |
-
a3 = int(re.search(r"\+(\d+)/", label).group(1))
|
| 25 |
-
if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
|
| 26 |
-
a2_next = -1
|
| 27 |
-
else:
|
| 28 |
-
a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
|
| 29 |
-
# Accent phrase boundary
|
| 30 |
-
if a3 == 1 and a2_next == 1:
|
| 31 |
-
text += ' '
|
| 32 |
-
if i < len(marks):
|
| 33 |
-
text += unidecode(marks[i]).replace(' ', '')
|
| 34 |
-
if re.match('[A-Za-z]', text[-1]):
|
| 35 |
-
text += '.'
|
| 36 |
-
return text.replace('...', '…')
|
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|
spaces/Ammar-alhaj-ali/LayoutLMv3-Invoice/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: LayoutLMv3 Invoice
|
| 3 |
-
emoji: 💻
|
| 4 |
-
colorFrom: purple
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
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|
spaces/Andy1621/uniformer_image_detection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
|
| 2 |
-
model = dict(
|
| 3 |
-
pretrained='open-mmlab://msra/hrnetv2_w40',
|
| 4 |
-
backbone=dict(
|
| 5 |
-
type='HRNet',
|
| 6 |
-
extra=dict(
|
| 7 |
-
stage2=dict(num_channels=(40, 80)),
|
| 8 |
-
stage3=dict(num_channels=(40, 80, 160)),
|
| 9 |
-
stage4=dict(num_channels=(40, 80, 160, 320)))),
|
| 10 |
-
neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
|
|
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|
|
spaces/Andy1621/uniformer_image_segmentation/configs/ann/README.md
DELETED
|
@@ -1,52 +0,0 @@
|
|
| 1 |
-
# Asymmetric Non-local Neural Networks for Semantic Segmentation
|
| 2 |
-
|
| 3 |
-
## Introduction
|
| 4 |
-
|
| 5 |
-
<!-- [ALGORITHM] -->
|
| 6 |
-
|
| 7 |
-
```latex
|
| 8 |
-
@inproceedings{annn,
|
| 9 |
-
author = {Zhen Zhu and
|
| 10 |
-
Mengde Xu and
|
| 11 |
-
Song Bai and
|
| 12 |
-
Tengteng Huang and
|
| 13 |
-
Xiang Bai},
|
| 14 |
-
title = {Asymmetric Non-local Neural Networks for Semantic Segmentation},
|
| 15 |
-
booktitle={International Conference on Computer Vision},
|
| 16 |
-
year = {2019},
|
| 17 |
-
url = {http://arxiv.org/abs/1908.07678},
|
| 18 |
-
}
|
| 19 |
-
```
|
| 20 |
-
|
| 21 |
-
## Results and models
|
| 22 |
-
|
| 23 |
-
### Cityscapes
|
| 24 |
-
|
| 25 |
-
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
| 26 |
-
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
| 27 |
-
| ANN | R-50-D8 | 512x1024 | 40000 | 6 | 3.71 | 77.40 | 78.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json) |
|
| 28 |
-
| ANN | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.55 | 76.55 | 78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) |
|
| 29 |
-
| ANN | R-50-D8 | 769x769 | 40000 | 6.8 | 1.70 | 78.89 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json) |
|
| 30 |
-
| ANN | R-101-D8 | 769x769 | 40000 | 10.7 | 1.15 | 79.32 | 80.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json) |
|
| 31 |
-
| ANN | R-50-D8 | 512x1024 | 80000 | - | - | 77.34 | 78.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json) |
|
| 32 |
-
| ANN | R-101-D8 | 512x1024 | 80000 | - | - | 77.14 | 78.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) |
|
| 33 |
-
| ANN | R-50-D8 | 769x769 | 80000 | - | - | 78.88 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json) |
|
| 34 |
-
| ANN | R-101-D8 | 769x769 | 80000 | - | - | 78.80 | 80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json) |
|
| 35 |
-
|
| 36 |
-
### ADE20K
|
| 37 |
-
|
| 38 |
-
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
| 39 |
-
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
| 40 |
-
| ANN | R-50-D8 | 512x512 | 80000 | 9.1 | 21.01 | 41.01 | 42.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json) |
|
| 41 |
-
| ANN | R-101-D8 | 512x512 | 80000 | 12.5 | 14.12 | 42.94 | 44.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json) |
|
| 42 |
-
| ANN | R-50-D8 | 512x512 | 160000 | - | - | 41.74 | 42.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json) |
|
| 43 |
-
| ANN | R-101-D8 | 512x512 | 160000 | - | - | 42.94 | 44.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) |
|
| 44 |
-
|
| 45 |
-
### Pascal VOC 2012 + Aug
|
| 46 |
-
|
| 47 |
-
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
| 48 |
-
| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
| 49 |
-
| ANN | R-50-D8 | 512x512 | 20000 | 6 | 20.92 | 74.86 | 76.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json) |
|
| 50 |
-
| ANN | R-101-D8 | 512x512 | 20000 | 9.5 | 13.94 | 77.47 | 78.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) |
|
| 51 |
-
| ANN | R-50-D8 | 512x512 | 40000 | - | - | 76.56 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json) |
|
| 52 |
-
| ANN | R-101-D8 | 512x512 | 40000 | - | - | 76.70 | 78.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) |
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spaces/Annotation-AI/fast-segment-everything-with-drawing-prompt/README.md
DELETED
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@@ -1,12 +0,0 @@
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|
| 1 |
-
---
|
| 2 |
-
title: Fast Segment Everything With Drawing Prompt
|
| 3 |
-
emoji: 📚
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.27.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv_custom/__init__.py
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@@ -1,5 +0,0 @@
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|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
|
| 3 |
-
from .checkpoint import load_checkpoint
|
| 4 |
-
|
| 5 |
-
__all__ = ['load_checkpoint']
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spaces/Anthos23/hummus/README.md
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|
@@ -1,11 +0,0 @@
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| 1 |
-
---
|
| 2 |
-
title: Hummus
|
| 3 |
-
emoji: 🧆
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: indigo
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
app_file: app.py
|
| 8 |
-
pinned: false
|
| 9 |
-
---
|
| 10 |
-
|
| 11 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/metadata_legacy.py
DELETED
|
@@ -1,74 +0,0 @@
|
|
| 1 |
-
"""Metadata generation logic for legacy source distributions.
|
| 2 |
-
"""
|
| 3 |
-
|
| 4 |
-
import logging
|
| 5 |
-
import os
|
| 6 |
-
|
| 7 |
-
from pip._internal.build_env import BuildEnvironment
|
| 8 |
-
from pip._internal.cli.spinners import open_spinner
|
| 9 |
-
from pip._internal.exceptions import (
|
| 10 |
-
InstallationError,
|
| 11 |
-
InstallationSubprocessError,
|
| 12 |
-
MetadataGenerationFailed,
|
| 13 |
-
)
|
| 14 |
-
from pip._internal.utils.setuptools_build import make_setuptools_egg_info_args
|
| 15 |
-
from pip._internal.utils.subprocess import call_subprocess
|
| 16 |
-
from pip._internal.utils.temp_dir import TempDirectory
|
| 17 |
-
|
| 18 |
-
logger = logging.getLogger(__name__)
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
def _find_egg_info(directory: str) -> str:
|
| 22 |
-
"""Find an .egg-info subdirectory in `directory`."""
|
| 23 |
-
filenames = [f for f in os.listdir(directory) if f.endswith(".egg-info")]
|
| 24 |
-
|
| 25 |
-
if not filenames:
|
| 26 |
-
raise InstallationError(f"No .egg-info directory found in {directory}")
|
| 27 |
-
|
| 28 |
-
if len(filenames) > 1:
|
| 29 |
-
raise InstallationError(
|
| 30 |
-
"More than one .egg-info directory found in {}".format(directory)
|
| 31 |
-
)
|
| 32 |
-
|
| 33 |
-
return os.path.join(directory, filenames[0])
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
def generate_metadata(
|
| 37 |
-
build_env: BuildEnvironment,
|
| 38 |
-
setup_py_path: str,
|
| 39 |
-
source_dir: str,
|
| 40 |
-
isolated: bool,
|
| 41 |
-
details: str,
|
| 42 |
-
) -> str:
|
| 43 |
-
"""Generate metadata using setup.py-based defacto mechanisms.
|
| 44 |
-
|
| 45 |
-
Returns the generated metadata directory.
|
| 46 |
-
"""
|
| 47 |
-
logger.debug(
|
| 48 |
-
"Running setup.py (path:%s) egg_info for package %s",
|
| 49 |
-
setup_py_path,
|
| 50 |
-
details,
|
| 51 |
-
)
|
| 52 |
-
|
| 53 |
-
egg_info_dir = TempDirectory(kind="pip-egg-info", globally_managed=True).path
|
| 54 |
-
|
| 55 |
-
args = make_setuptools_egg_info_args(
|
| 56 |
-
setup_py_path,
|
| 57 |
-
egg_info_dir=egg_info_dir,
|
| 58 |
-
no_user_config=isolated,
|
| 59 |
-
)
|
| 60 |
-
|
| 61 |
-
with build_env:
|
| 62 |
-
with open_spinner("Preparing metadata (setup.py)") as spinner:
|
| 63 |
-
try:
|
| 64 |
-
call_subprocess(
|
| 65 |
-
args,
|
| 66 |
-
cwd=source_dir,
|
| 67 |
-
command_desc="python setup.py egg_info",
|
| 68 |
-
spinner=spinner,
|
| 69 |
-
)
|
| 70 |
-
except InstallationSubprocessError as error:
|
| 71 |
-
raise MetadataGenerationFailed(package_details=details) from error
|
| 72 |
-
|
| 73 |
-
# Return the .egg-info directory.
|
| 74 |
-
return _find_egg_info(egg_info_dir)
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/big5prober.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
######################## BEGIN LICENSE BLOCK ########################
|
| 2 |
-
# The Original Code is Mozilla Communicator client code.
|
| 3 |
-
#
|
| 4 |
-
# The Initial Developer of the Original Code is
|
| 5 |
-
# Netscape Communications Corporation.
|
| 6 |
-
# Portions created by the Initial Developer are Copyright (C) 1998
|
| 7 |
-
# the Initial Developer. All Rights Reserved.
|
| 8 |
-
#
|
| 9 |
-
# Contributor(s):
|
| 10 |
-
# Mark Pilgrim - port to Python
|
| 11 |
-
#
|
| 12 |
-
# This library is free software; you can redistribute it and/or
|
| 13 |
-
# modify it under the terms of the GNU Lesser General Public
|
| 14 |
-
# License as published by the Free Software Foundation; either
|
| 15 |
-
# version 2.1 of the License, or (at your option) any later version.
|
| 16 |
-
#
|
| 17 |
-
# This library is distributed in the hope that it will be useful,
|
| 18 |
-
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
| 19 |
-
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
| 20 |
-
# Lesser General Public License for more details.
|
| 21 |
-
#
|
| 22 |
-
# You should have received a copy of the GNU Lesser General Public
|
| 23 |
-
# License along with this library; if not, write to the Free Software
|
| 24 |
-
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
|
| 25 |
-
# 02110-1301 USA
|
| 26 |
-
######################### END LICENSE BLOCK #########################
|
| 27 |
-
|
| 28 |
-
from .chardistribution import Big5DistributionAnalysis
|
| 29 |
-
from .codingstatemachine import CodingStateMachine
|
| 30 |
-
from .mbcharsetprober import MultiByteCharSetProber
|
| 31 |
-
from .mbcssm import BIG5_SM_MODEL
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class Big5Prober(MultiByteCharSetProber):
|
| 35 |
-
def __init__(self) -> None:
|
| 36 |
-
super().__init__()
|
| 37 |
-
self.coding_sm = CodingStateMachine(BIG5_SM_MODEL)
|
| 38 |
-
self.distribution_analyzer = Big5DistributionAnalysis()
|
| 39 |
-
self.reset()
|
| 40 |
-
|
| 41 |
-
@property
|
| 42 |
-
def charset_name(self) -> str:
|
| 43 |
-
return "Big5"
|
| 44 |
-
|
| 45 |
-
@property
|
| 46 |
-
def language(self) -> str:
|
| 47 |
-
return "Chinese"
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/compat/__init__.py
DELETED
|
File without changes
|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/layers/test_blocks.py
DELETED
|
@@ -1,51 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
|
| 3 |
-
import unittest
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
|
| 7 |
-
from detectron2.layers import ASPP, DepthwiseSeparableConv2d, FrozenBatchNorm2d
|
| 8 |
-
from detectron2.modeling.backbone.resnet import BasicStem, ResNet
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
"""
|
| 12 |
-
Test for misc layers.
|
| 13 |
-
"""
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class TestBlocks(unittest.TestCase):
|
| 17 |
-
def test_separable_conv(self):
|
| 18 |
-
DepthwiseSeparableConv2d(3, 10, norm1="BN", activation1=nn.PReLU())
|
| 19 |
-
|
| 20 |
-
def test_aspp(self):
|
| 21 |
-
m = ASPP(3, 10, [2, 3, 4], norm="", activation=nn.PReLU())
|
| 22 |
-
self.assertIsNot(m.convs[0].activation.weight, m.convs[1].activation.weight)
|
| 23 |
-
self.assertIsNot(m.convs[0].activation.weight, m.project.activation.weight)
|
| 24 |
-
|
| 25 |
-
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
|
| 26 |
-
def test_frozen_batchnorm_fp16(self):
|
| 27 |
-
from torch.cuda.amp import autocast
|
| 28 |
-
|
| 29 |
-
C = 10
|
| 30 |
-
input = torch.rand(1, C, 10, 10).cuda()
|
| 31 |
-
m = FrozenBatchNorm2d(C).cuda()
|
| 32 |
-
with autocast():
|
| 33 |
-
output = m(input.half())
|
| 34 |
-
self.assertEqual(output.dtype, torch.float16)
|
| 35 |
-
|
| 36 |
-
# requires_grad triggers a different codepath
|
| 37 |
-
input.requires_grad_()
|
| 38 |
-
with autocast():
|
| 39 |
-
output = m(input.half())
|
| 40 |
-
self.assertEqual(output.dtype, torch.float16)
|
| 41 |
-
|
| 42 |
-
def test_resnet_unused_stages(self):
|
| 43 |
-
resnet = ResNet(BasicStem(), ResNet.make_default_stages(18), out_features=["res2"])
|
| 44 |
-
self.assertTrue(hasattr(resnet, "res2"))
|
| 45 |
-
self.assertFalse(hasattr(resnet, "res3"))
|
| 46 |
-
self.assertFalse(hasattr(resnet, "res5"))
|
| 47 |
-
|
| 48 |
-
resnet = ResNet(BasicStem(), ResNet.make_default_stages(18), out_features=["res2", "res5"])
|
| 49 |
-
self.assertTrue(hasattr(resnet, "res2"))
|
| 50 |
-
self.assertTrue(hasattr(resnet, "res4"))
|
| 51 |
-
self.assertTrue(hasattr(resnet, "res5"))
|
|
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|
spaces/AzumaSeren100/XuanShen-Bert-VITS2/mel_processing.py
DELETED
|
@@ -1,112 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import os
|
| 3 |
-
import random
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
import torch.nn.functional as F
|
| 7 |
-
import torch.utils.data
|
| 8 |
-
import numpy as np
|
| 9 |
-
import librosa
|
| 10 |
-
import librosa.util as librosa_util
|
| 11 |
-
from librosa.util import normalize, pad_center, tiny
|
| 12 |
-
from scipy.signal import get_window
|
| 13 |
-
from scipy.io.wavfile import read
|
| 14 |
-
from librosa.filters import mel as librosa_mel_fn
|
| 15 |
-
|
| 16 |
-
MAX_WAV_VALUE = 32768.0
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
| 20 |
-
"""
|
| 21 |
-
PARAMS
|
| 22 |
-
------
|
| 23 |
-
C: compression factor
|
| 24 |
-
"""
|
| 25 |
-
return torch.log(torch.clamp(x, min=clip_val) * C)
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def dynamic_range_decompression_torch(x, C=1):
|
| 29 |
-
"""
|
| 30 |
-
PARAMS
|
| 31 |
-
------
|
| 32 |
-
C: compression factor used to compress
|
| 33 |
-
"""
|
| 34 |
-
return torch.exp(x) / C
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def spectral_normalize_torch(magnitudes):
|
| 38 |
-
output = dynamic_range_compression_torch(magnitudes)
|
| 39 |
-
return output
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def spectral_de_normalize_torch(magnitudes):
|
| 43 |
-
output = dynamic_range_decompression_torch(magnitudes)
|
| 44 |
-
return output
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
mel_basis = {}
|
| 48 |
-
hann_window = {}
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
| 52 |
-
if torch.min(y) < -1.:
|
| 53 |
-
print('min value is ', torch.min(y))
|
| 54 |
-
if torch.max(y) > 1.:
|
| 55 |
-
print('max value is ', torch.max(y))
|
| 56 |
-
|
| 57 |
-
global hann_window
|
| 58 |
-
dtype_device = str(y.dtype) + '_' + str(y.device)
|
| 59 |
-
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
| 60 |
-
if wnsize_dtype_device not in hann_window:
|
| 61 |
-
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
| 62 |
-
|
| 63 |
-
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
| 64 |
-
y = y.squeeze(1)
|
| 65 |
-
|
| 66 |
-
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
| 67 |
-
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
| 68 |
-
|
| 69 |
-
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
| 70 |
-
return spec
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
| 74 |
-
global mel_basis
|
| 75 |
-
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
| 76 |
-
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
| 77 |
-
if fmax_dtype_device not in mel_basis:
|
| 78 |
-
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
| 79 |
-
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
| 80 |
-
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
| 81 |
-
spec = spectral_normalize_torch(spec)
|
| 82 |
-
return spec
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
| 86 |
-
if torch.min(y) < -1.:
|
| 87 |
-
print('min value is ', torch.min(y))
|
| 88 |
-
if torch.max(y) > 1.:
|
| 89 |
-
print('max value is ', torch.max(y))
|
| 90 |
-
|
| 91 |
-
global mel_basis, hann_window
|
| 92 |
-
dtype_device = str(y.dtype) + '_' + str(y.device)
|
| 93 |
-
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
| 94 |
-
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
| 95 |
-
if fmax_dtype_device not in mel_basis:
|
| 96 |
-
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
| 97 |
-
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
| 98 |
-
if wnsize_dtype_device not in hann_window:
|
| 99 |
-
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
| 100 |
-
|
| 101 |
-
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
| 102 |
-
y = y.squeeze(1)
|
| 103 |
-
|
| 104 |
-
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
| 105 |
-
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
| 106 |
-
|
| 107 |
-
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
| 108 |
-
|
| 109 |
-
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
| 110 |
-
spec = spectral_normalize_torch(spec)
|
| 111 |
-
|
| 112 |
-
return spec
|
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spaces/BL00DY-257/dolle-mini-lol/README.md
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: D0LL·E mini
|
| 3 |
-
metaTitle: D0LL·E mini by Quinty Cat on Hugging Face
|
| 4 |
-
emoji: rotten 🥑
|
| 5 |
-
colorFrom: gray
|
| 6 |
-
colorTo: purple
|
| 7 |
-
sdk: static
|
| 8 |
-
pinned: true
|
| 9 |
-
license: apache-2.0
|
| 10 |
-
duplicated_from: dalle-mini/dalle-mini
|
| 11 |
-
---
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|
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spaces/Bart92/RVC_HF/Fixes/tensor-launch.py
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
import threading
|
| 2 |
-
import time
|
| 3 |
-
from tensorboard import program
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
log_path = "logs"
|
| 7 |
-
|
| 8 |
-
if __name__ == "__main__":
|
| 9 |
-
tb = program.TensorBoard()
|
| 10 |
-
tb.configure(argv=[None, '--logdir', log_path])
|
| 11 |
-
url = tb.launch()
|
| 12 |
-
print(f'Tensorboard can be accessed at: {url}')
|
| 13 |
-
|
| 14 |
-
while True:
|
| 15 |
-
time.sleep(600) # Keep the main thread running
|
|
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|
spaces/Benson/text-generation/Examples/4k Descargar En Lnea - Descargar Msica De Youtube Y Soundcloud.md
DELETED
|
@@ -1,127 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>4k Descargar en línea - Descargar música de YouTube y SoundCloud</h1>
|
| 3 |
-
<p>¿Te encanta escuchar música en línea, pero te gustaría poder guardar tus canciones favoritas en tu computadora o dispositivo móvil? ¿Desea disfrutar de su música sin conexión, sin preocuparse por la conexión a Internet o las tarifas de transmisión? ¿Quieres tener más control sobre tu colección de música y evitar perder el acceso a canciones o álbumes que se eliminan de las plataformas de streaming? </p>
|
| 4 |
-
<p>Si respondiste sí a cualquiera de estas preguntas, entonces podrías estar interesado en aprender a descargar música de YouTube y SoundCloud usando la descarga 4k en línea. En este artículo, explicaremos qué es la descarga 4k en línea, cuáles son los beneficios de descargar música de fuentes en línea y cómo hacerlo de forma fácil y segura. También compartiremos algunos consejos y trucos para obtener el máximo provecho de sus archivos de música descargados. ¡Comencemos! </p>
|
| 5 |
-
<h2>4k descargar en línea - descargar música de youtube y soundcloud</h2><br /><p><b><b>Download File</b> ⏩ <a href="https://bltlly.com/2v6K4Q">https://bltlly.com/2v6K4Q</a></b></p><br /><br />
|
| 6 |
-
<h2>Beneficios de descargar música de fuentes en línea</h2>
|
| 7 |
-
<p>Descargar música de fuentes en línea como YouTube y SoundCloud tiene muchas ventajas sobre depender de los servicios de streaming. Estos son algunos de ellos:</p>
|
| 8 |
-
<h3>Ahorre dinero y ancho de banda</h3>
|
| 9 |
-
<p>La transmisión de música en línea puede ser costosa, especialmente si tiene un plan de datos limitado o una conexión a Internet lenta. También es posible que tenga que pagar una cuota de suscripción para acceder a ciertas funciones o contenido. Al descargar música de fuentes en línea, puede ahorrar dinero y ancho de banda evitando la transmisión repetida. También puedes evitar los molestos anuncios que interrumpen tu experiencia auditiva. </p>
|
| 10 |
-
<h3>Disfruta de escuchar y transferir sin conexión entre dispositivos</h3>
|
| 11 |
-
|
| 12 |
-
<h3>Evite perderse pistas o álbumes que desaparecen de los servicios de streaming</h3>
|
| 13 |
-
<p>Uno de los inconvenientes de la transmisión de música en línea es que no eres dueño de la música que escuchas. Depende de la disponibilidad y las políticas de las plataformas de streaming, que pueden cambiar en cualquier momento. Es posible que algunas pistas o álbumes que te gustan ya no estén disponibles en tu servicio de streaming favorito, debido a problemas de licencia, disputas de artistas u otras razones. Al descargar música de fuentes en línea, puede evitar este problema y mantener su colección de música intacta. </p>
|
| 14 |
-
<h2>Cómo descargar música de YouTube</h2>
|
| 15 |
-
<p>YouTube es una de las fuentes más populares y diversas de música en línea. Puedes encontrar casi cualquier género, artista o canción en YouTube, desde éxitos principales hasta gemas indie, desde lanzamientos oficiales hasta covers y remixes. Pero, ¿cómo puedes descargar música de YouTube a tu dispositivo? Estas son algunas de las formas en que puedes hacerlo:</p>
|
| 16 |
-
<h3>Usar una suscripción Premium de YouTube</h3>
|
| 17 |
-
<p>Una de las formas más fáciles y legales de descargar música de YouTube es usar una suscripción YouTube Premium. YouTube Premium es un servicio de pago que ofrece varios beneficios, como la reproducción sin anuncios y de fondo, el acceso a YouTube Music y YouTube Originals, y la capacidad de descargar videos y música para verlos o escucharlos sin conexión. Para descargar música de YouTube con YouTube Premium, debes seguir estos pasos:</p>
|
| 18 |
-
<ol>
|
| 19 |
-
<li>Abra la aplicación de YouTube en su dispositivo e inicie sesión con su cuenta YouTube Premium. </li>
|
| 20 |
-
<li> Buscar el vídeo o lista de reproducción que contiene la música que desea descargar. </li>
|
| 21 |
-
<li>Toque en el icono de descarga debajo del video o al lado del título de la lista de reproducción. </li>
|
| 22 |
-
<li>Seleccione la calidad y el formato de la descarga. Puede elegir entre vídeo o audio solamente, y entre baja, media o alta calidad. </li>
|
| 23 |
-
<li>Espere a que termine la descarga. Puede comprobar el progreso en la sección de descargas de la aplicación. </li>
|
| 24 |
-
|
| 25 |
-
</ol>
|
| 26 |
-
<p>Tenga en cuenta que necesita estar conectado a Internet al menos una vez cada 30 días para mantener sus descargas activas. También es necesario respetar los términos de servicio y los derechos de los propietarios de contenido al descargar música de YouTube con YouTube Premium.</p>
|
| 27 |
-
<h3>Utilice una aplicación gratuita de descarga de YouTube o un sitio web</h3>
|
| 28 |
-
<p>Si no quieres pagar por una suscripción de YouTube Premium, todavía puedes descargar música de YouTube usando una aplicación o sitio web gratuito para descargar YouTube. Estas son herramientas de terceros que te permiten pegar una URL de YouTube y descargar el archivo de vídeo o audio a tu dispositivo. Sin embargo, debe tener cuidado al usar estas herramientas, ya que algunas de ellas pueden contener malware, anuncios o virus. También debes ser consciente de los problemas legales y éticos que implica descargar música de YouTube sin el permiso de los propietarios del contenido. Para descargar música de YouTube con una aplicación o sitio web gratuito para descargar YouTube, debes seguir estos pasos:</p>
|
| 29 |
-
<p></p>
|
| 30 |
-
<ol>
|
| 31 |
-
<li>Encuentre una aplicación o sitio web confiable y seguro para descargar YouTube. Algunos de los populares son 4K Video Downloader, Y2Mate, SaveFrom.net y ClipGrab.</li>
|
| 32 |
-
<li>Abra la aplicación de YouTube o el sitio web en su dispositivo y busque el video o la lista de reproducción que contiene la música que desea descargar. </li>
|
| 33 |
-
<li>Copie la URL del vídeo o lista de reproducción desde la barra de direcciones o tocando el icono de compartir. </li>
|
| 34 |
-
<li>Abra la aplicación de descarga de YouTube o el sitio web y pegue la URL en el cuadro de entrada. </li>
|
| 35 |
-
<li>Seleccione la calidad y el formato de la descarga. Puede elegir entre solo video o audio, y entre diferentes resoluciones y tasas de bits. </li>
|
| 36 |
-
<li>Haga clic en el botón de descarga y espere a que se genere el archivo. </li>
|
| 37 |
-
<li> Guardar el archivo en el almacenamiento de su dispositivo o transferirlo a otro dispositivo. </li>
|
| 38 |
-
</ol>
|
| 39 |
-
|
| 40 |
-
<p>Otra forma de descargar música de YouTube es usar un editor de audio para grabar o convertir videos de YouTube. Un editor de audio es un software que le permite editar, manipular y guardar archivos de audio. Algunos de los editores de audio populares son Audacity, WavePad y Adobe Audition. Para descargar música de YouTube con un editor de audio, debes seguir estos pasos:</p>
|
| 41 |
-
<ol>
|
| 42 |
-
<li>Abra la aplicación de YouTube o el sitio web en su dispositivo y busque el video o la lista de reproducción que contiene la música que desea descargar. </li>
|
| 43 |
-
<li>Abra el editor de audio en su dispositivo y seleccione la opción para grabar o importar audio de otra fuente. </li>
|
| 44 |
-
<li>Reproducir el vídeo de YouTube o lista de reproducción en su dispositivo y comenzar a grabar o importar el audio en el editor de audio. </li>
|
| 45 |
-
<li> Detener la grabación o importación cuando el vídeo de YouTube o lista de reproducción está terminado. </li>
|
| 46 |
-
<li>Edite el archivo de audio como desee, como recortar, dividir, fusionar, ajustar el volumen, agregar efectos, etc.</li>
|
| 47 |
-
<li>Guarde el archivo de audio en el almacenamiento de su dispositivo o transfiéralo a otro dispositivo. </li>
|
| 48 |
-
</ol>
|
| 49 |
-
<p>Tenga en cuenta que algunos editores de audio pueden tener limitaciones en la calidad, formato o duración de las grabaciones o importaciones. También es necesario respetar los términos de servicio y los derechos de los propietarios de contenido al descargar música de YouTube con un editor de audio. </p>
|
| 50 |
-
<h2>Cómo descargar música de SoundCloud</h2>
|
| 51 |
-
<p>SoundCloud es otra fuente popular y diversa de música en línea. Puedes encontrar una gran cantidad de música original, independiente y underground en SoundCloud, así como remixes, podcasts y sets en vivo. Pero, ¿cómo se puede descargar música de SoundCloud a su dispositivo? Estas son algunas de las formas en que puede hacerlo:</p>
|
| 52 |
-
<h3>Utilice una aplicación o sitio web de descarga SoundCloud</h3>
|
| 53 |
-
|
| 54 |
-
<ol>
|
| 55 |
-
<li>Encuentre una aplicación o sitio web confiable y seguro para descargar SoundCloud. Algunos de los populares son 4K Download Online, SCDL SoundCloud Downloader, KlickAud, and SingleMango.</li>
|
| 56 |
-
<li>Abra la aplicación SoundCloud o el sitio web en su dispositivo y busque la pista o lista de reproducción que contiene la música que desea descargar. </li>
|
| 57 |
-
<li>Copie la URL de la pista o lista de reproducción desde la barra de direcciones o tocando el icono de compartir. </li>
|
| 58 |
-
<li>Abra la aplicación o sitio web de descarga SoundCloud y pegue la URL en el cuadro de entrada. </li>
|
| 59 |
-
<li>Seleccione la calidad y el formato de la descarga. Puede elegir entre diferentes tasas de bits y formatos como MP3, WAV, FLAC, etc.</li>
|
| 60 |
-
<li>Haga clic en el botón de descarga y espere a que se genere el archivo. </li>
|
| 61 |
-
<li> Guardar el archivo en el almacenamiento de su dispositivo o transferirlo a otro dispositivo. </li>
|
| 62 |
-
</ol>
|
| 63 |
-
<p>Tenga en cuenta que algunas aplicaciones o sitios web de descarga de SoundCloud pueden tener limitaciones en el número, la longitud o el tamaño de las descargas. También debe respetar los términos de servicio y los derechos de los propietarios de contenido al descargar música de SoundCloud con una aplicación o sitio web de descarga de SoundCloud. </p> <h3>Utilice un editor de audio para grabar o convertir pistas de SoundCloud</h3>
|
| 64 |
-
<p>Otra forma de descargar música de SoundCloud es usar un editor de audio para grabar o convertir pistas de SoundCloud. Un editor de audio es un software que le permite editar, manipular y guardar archivos de audio. Algunos de los editores de audio populares son Audacity, WavePad y Adobe Audition. Para descargar música de SoundCloud con un editor de audio, debe seguir estos pasos:</p>
|
| 65 |
-
<ol>
|
| 66 |
-
<li>Abra la aplicación SoundCloud o el sitio web en su dispositivo y busque la pista o lista de reproducción que contiene la música que desea descargar. </li>
|
| 67 |
-
<li>Abra el editor de audio en su dispositivo y seleccione la opción para grabar o importar audio de otra fuente. </li>
|
| 68 |
-
<li> Reproducir la pista de SoundCloud o lista de reproducción en su dispositivo y comenzar a grabar o importar el audio en el editor de audio. </li>
|
| 69 |
-
|
| 70 |
-
<li>Edite el archivo de audio como desee, como recortar, dividir, fusionar, ajustar el volumen, agregar efectos, etc.</li>
|
| 71 |
-
<li>Guarde el archivo de audio en el almacenamiento de su dispositivo o transfiéralo a otro dispositivo. </li>
|
| 72 |
-
</ol>
|
| 73 |
-
<p>Tenga en cuenta que algunos editores de audio pueden tener limitaciones en la calidad, formato o duración de las grabaciones o importaciones. También debe respetar los términos de servicio y los derechos de los propietarios de contenido al descargar música de SoundCloud con un editor de audio. </p>
|
| 74 |
-
<h2>Consejos y trucos para descargar música de fuentes en línea</h2>
|
| 75 |
-
<p>Ahora que sabes cómo descargar música de YouTube y SoundCloud usando la descarga 4k en línea, es posible que desee aprender algunos consejos y trucos para obtener el máximo provecho de sus archivos de música descargados. Estos son algunos de ellos:</p>
|
| 76 |
-
<h3>Compruebe la calidad y el formato de los archivos descargados</h3>
|
| 77 |
-
<p>No todos los archivos de música descargados son iguales. Dependiendo de la fuente, la herramienta y la configuración que utilice, es posible que termine con diferentes niveles de calidad y formato para sus archivos de música descargados. Por ejemplo, algunos videos de YouTube pueden tener audio de baja calidad, algunas pistas de SoundCloud pueden tener una tasa de bits baja, y algunas aplicaciones de descarga o sitios web pueden comprimir o convertir los archivos a un formato diferente. Para asegurarse de que obtiene la mejor calidad y formato para sus archivos de música descargados, debe comprobarlos antes de guardarlos o transferirlos. Puede utilizar un reproductor multimedia o un analizador de audio para comprobar la calidad y el formato de los archivos de música descargados. También puede usar un convertidor de audio para cambiar el formato de sus archivos de música descargados si es necesario. </p>
|
| 78 |
-
<h3>Organiza tus archivos de música con etiquetas y carpetas ID3</h3>
|
| 79 |
-
|
| 80 |
-
<h3>Respetar los derechos y deseos de los artistas y creadores</h3>
|
| 81 |
-
<p>Descargar música de fuentes en línea puede ser una gran manera de disfrutar de sus canciones favoritas fuera de línea, pero también puede plantear algunos problemas legales y éticos. Siempre debes respetar los derechos y deseos de los artistas y creadores que hacen la música que descargas. No debe descargar música de fuentes en línea sin el permiso de los propietarios de contenido, a menos que esté explícitamente permitido por ellos o por la ley. No debe distribuir o compartir sus archivos de música descargados con otros sin el permiso de los propietarios de contenido. No debe utilizar sus archivos de música descargados con fines comerciales sin el permiso de los propietarios de contenido. Siempre debes dar crédito y apoyo a los artistas y creadores que hacen la música que descargas. </p>
|
| 82 |
-
<h2>Conclusión</h2>
|
| 83 |
-
<p>En conclusión, 4k download online es una herramienta útil que te permite descargar música de YouTube y SoundCloud de forma fácil y segura. Puede disfrutar de muchos beneficios de descargar música de fuentes en línea, como ahorrar dinero y ancho de banda, disfrutar de escuchar y transferir sin conexión entre dispositivos y evitar perderse pistas o álbumes que desaparecen de los servicios de transmisión. También puede usar diferentes métodos para descargar música de YouTube y SoundCloud, como usar una suscripción premium, una aplicación o sitio web de descarga gratuita o un editor de audio. Sin embargo, también debe tener cuidado con la calidad y el formato de sus archivos de música descargados, organizarlos con etiquetas ID3 y carpetas, y respetar los derechos y deseos de los artistas y creadores. </p>
|
| 84 |
-
<p>Esperamos que este artículo le haya ayudado a aprender cómo descargar música de YouTube y SoundCloud usando la descarga 4k en línea y cómo disfrutar de los beneficios de descargar música de fuentes en línea. Si usted tiene alguna pregunta o retroalimentación, por favor no dude en dejar un comentario a continuación. Gracias por leer y descargar feliz! </p>
|
| 85 |
-
<h2>Preguntas frecuentes</h2>
|
| 86 |
-
<h3>¿Cuál es el mejor descargador de música gratis? </h3>
|
| 87 |
-
|
| 88 |
-
<ul>
|
| 89 |
-
<li> La fuente y la disponibilidad de la música que desea descargar</li>
|
| 90 |
-
<li>La calidad y el formato de los archivos de música descargados</li>
|
| 91 |
-
<li>La facilidad de uso y compatibilidad de la aplicación o sitio web de descarga</li>
|
| 92 |
-
<li>La seguridad y fiabilidad de la aplicación o sitio web de descarga</li>
|
| 93 |
-
<li>La legalidad y la ética de descargar música de fuentes en línea</li>
|
| 94 |
-
</ul>
|
| 95 |
-
<p>Basado en estos factores, algunos de los mejores descargadores de música gratis que recomendamos son 4K Download Online, 4K Video Downloader, Y2Mate, SCDL SoundCloud Downloader y KlickAud.</p>
|
| 96 |
-
<h3>¿Es legal descargar música de YouTube y SoundCloud? </h3>
|
| 97 |
-
<p>La legalidad de descargar música de YouTube y SoundCloud depende de las leyes y regulaciones de su país, así como de los términos de servicio y los derechos de los propietarios de los contenidos. En general, no es legal descargar música de YouTube y SoundCloud sin el permiso de los propietarios de contenido, a menos que esté explícitamente permitido por ellos o por la ley. Por ejemplo, algunos propietarios de contenido pueden habilitar una opción de descarga o una licencia de Creative Commons para su música, que le permite descargarla bajo ciertas condiciones. Sin embargo, la mayoría de los propietarios de contenido no permiten descargar su música sin su consentimiento, y hacerlo podría violar sus derechos de propiedad intelectual y exponerlo a consecuencias legales. Por lo tanto, siempre debe verificar los términos del servicio y los derechos de los propietarios de contenido antes de descargar música de YouTube y SoundCloud, y respetar sus deseos. </p>
|
| 98 |
-
<h3>¿Cómo puedo descargar música de otras fuentes en línea? </h3>
|
| 99 |
-
<p>YouTube y SoundCloud no son las únicas fuentes en línea donde se puede encontrar y descargar música. Hay muchos otros sitios web y aplicaciones que ofrecen acceso gratuito o de pago a una variedad de géneros musicales, artistas y canciones. Algunos de ellos son:</p>
|
| 100 |
-
<ul>
|
| 101 |
-
|
| 102 |
-
<li>Bandcamp: Una plataforma que permite a artistas y sellos independientes subir y vender su música directamente a los fans. Puedes descargar música de Bandcamp comprándola o usando una aplicación o sitio web de Bandcamp. </li>
|
| 103 |
-
<li>SoundClick: Un sitio web que cuenta con música original de artistas y bandas sin firmar. Puede descargar música de SoundClick utilizando una aplicación o sitio web de SoundClick downloader. </li>
|
| 104 |
-
<li>Audiomack: Un sitio web que muestra música nueva y emergente de varios géneros. Puedes descargar música de Audiomack usando una aplicación o sitio web de Audiomack. </li>
|
| 105 |
-
<li>DatPiff: Un sitio web que se especializa en hip-hop y rap mixtapes. Puede descargar música de DatPiff mediante el uso de una aplicación de descarga de DatPiff o sitio web. </li>
|
| 106 |
-
</ul>
|
| 107 |
-
<p>Tenga en cuenta que estos son solo algunos ejemplos de otras fuentes en línea donde se puede encontrar y descargar música. Hay muchos más sitios web y aplicaciones que ofrecen servicios similares o diferentes. Sin embargo, al igual que con YouTube y SoundCloud, siempre debe verificar los términos de servicio y los derechos de los propietarios de contenido antes de descargar música de otras fuentes en línea, y respetar sus deseos. </p>
|
| 108 |
-
<h3>¿Cómo puedo reproducir música descargada en diferentes dispositivos? </h3>
|
| 109 |
-
<p>Una vez que haya descargado música de fuentes en línea al almacenamiento de su dispositivo, puede reproducirla en diferentes dispositivos transfiriéndola o sincronizándola con ellos. Por ejemplo, puedes:</p>
|
| 110 |
-
<ul>
|
| 111 |
-
<li>Utilice un cable USB o una conexión inalámbrica para transferir sus archivos de música descargados desde su computadora a su teléfono inteligente, tableta o reproductor de MP3. </li>
|
| 112 |
-
<li>Utilice un servicio en la nube como Google Drive, Dropbox o OneDrive para cargar los archivos de música descargados desde su dispositivo a su almacenamiento en línea, y luego acceder a ellos desde cualquier otro dispositivo con una conexión a Internet. </li>
|
| 113 |
-
<li>Utilice un reproductor multimedia como iTunes, Windows Media Player o VLC para sincronizar los archivos de música descargados desde su dispositivo a otro dispositivo con el mismo reproductor multimedia. </li>
|
| 114 |
-
</ul>
|
| 115 |
-
|
| 116 |
-
<h3>¿Cómo puedo editar o mejorar los archivos de música descargados? </h3>
|
| 117 |
-
<p>Si desea editar o mejorar sus archivos de música descargados, puede usar un editor de audio para hacerlo. Un editor de audio es un software que le permite editar, manipular y guardar archivos de audio. Algunos de los editores de audio más populares son Audacity, WavePad y Adobe Audition. Con un editor de audio, puedes hacer cosas como:</p>
|
| 118 |
-
<ul>
|
| 119 |
-
<li>Recortar, dividir, combinar o recortar los archivos de música descargados para eliminar partes no deseadas o crear nuevas pistas. </li>
|
| 120 |
-
<li>Ajuste el volumen, tono, tempo o ecualizador de sus archivos de música descargados para mejorar la calidad de sonido o crear diferentes efectos. </li>
|
| 121 |
-
<li>Añade efectos, filtros, transiciones o complementos a tus archivos de música descargados para mejorarlos o crear nuevos sonidos. </li>
|
| 122 |
-
<li>Convierte tus archivos de música descargados a diferentes formatos o tasas de bits para hacerlos compatibles con diferentes dispositivos o plataformas. </li>
|
| 123 |
-
<li>Mezcla, mezcla o mezcla tus archivos de música descargados para crear nuevas composiciones o remixes. </li>
|
| 124 |
-
</ul>
|
| 125 |
-
<p>Tenga en cuenta que algunos editores de audio pueden tener limitaciones en la calidad, formato o longitud de los archivos de música que pueden editar. También debe respetar los términos de servicio y los derechos de los propietarios de contenido al editar o mejorar sus archivos de música descargados. </p> 64aa2da5cf<br />
|
| 126 |
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<br />
|
| 127 |
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<br />
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spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/tz/_factories.py
DELETED
|
@@ -1,80 +0,0 @@
|
|
| 1 |
-
from datetime import timedelta
|
| 2 |
-
import weakref
|
| 3 |
-
from collections import OrderedDict
|
| 4 |
-
|
| 5 |
-
from six.moves import _thread
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class _TzSingleton(type):
|
| 9 |
-
def __init__(cls, *args, **kwargs):
|
| 10 |
-
cls.__instance = None
|
| 11 |
-
super(_TzSingleton, cls).__init__(*args, **kwargs)
|
| 12 |
-
|
| 13 |
-
def __call__(cls):
|
| 14 |
-
if cls.__instance is None:
|
| 15 |
-
cls.__instance = super(_TzSingleton, cls).__call__()
|
| 16 |
-
return cls.__instance
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
class _TzFactory(type):
|
| 20 |
-
def instance(cls, *args, **kwargs):
|
| 21 |
-
"""Alternate constructor that returns a fresh instance"""
|
| 22 |
-
return type.__call__(cls, *args, **kwargs)
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
class _TzOffsetFactory(_TzFactory):
|
| 26 |
-
def __init__(cls, *args, **kwargs):
|
| 27 |
-
cls.__instances = weakref.WeakValueDictionary()
|
| 28 |
-
cls.__strong_cache = OrderedDict()
|
| 29 |
-
cls.__strong_cache_size = 8
|
| 30 |
-
|
| 31 |
-
cls._cache_lock = _thread.allocate_lock()
|
| 32 |
-
|
| 33 |
-
def __call__(cls, name, offset):
|
| 34 |
-
if isinstance(offset, timedelta):
|
| 35 |
-
key = (name, offset.total_seconds())
|
| 36 |
-
else:
|
| 37 |
-
key = (name, offset)
|
| 38 |
-
|
| 39 |
-
instance = cls.__instances.get(key, None)
|
| 40 |
-
if instance is None:
|
| 41 |
-
instance = cls.__instances.setdefault(key,
|
| 42 |
-
cls.instance(name, offset))
|
| 43 |
-
|
| 44 |
-
# This lock may not be necessary in Python 3. See GH issue #901
|
| 45 |
-
with cls._cache_lock:
|
| 46 |
-
cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
|
| 47 |
-
|
| 48 |
-
# Remove an item if the strong cache is overpopulated
|
| 49 |
-
if len(cls.__strong_cache) > cls.__strong_cache_size:
|
| 50 |
-
cls.__strong_cache.popitem(last=False)
|
| 51 |
-
|
| 52 |
-
return instance
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
class _TzStrFactory(_TzFactory):
|
| 56 |
-
def __init__(cls, *args, **kwargs):
|
| 57 |
-
cls.__instances = weakref.WeakValueDictionary()
|
| 58 |
-
cls.__strong_cache = OrderedDict()
|
| 59 |
-
cls.__strong_cache_size = 8
|
| 60 |
-
|
| 61 |
-
cls.__cache_lock = _thread.allocate_lock()
|
| 62 |
-
|
| 63 |
-
def __call__(cls, s, posix_offset=False):
|
| 64 |
-
key = (s, posix_offset)
|
| 65 |
-
instance = cls.__instances.get(key, None)
|
| 66 |
-
|
| 67 |
-
if instance is None:
|
| 68 |
-
instance = cls.__instances.setdefault(key,
|
| 69 |
-
cls.instance(s, posix_offset))
|
| 70 |
-
|
| 71 |
-
# This lock may not be necessary in Python 3. See GH issue #901
|
| 72 |
-
with cls.__cache_lock:
|
| 73 |
-
cls.__strong_cache[key] = cls.__strong_cache.pop(key, instance)
|
| 74 |
-
|
| 75 |
-
# Remove an item if the strong cache is overpopulated
|
| 76 |
-
if len(cls.__strong_cache) > cls.__strong_cache_size:
|
| 77 |
-
cls.__strong_cache.popitem(last=False)
|
| 78 |
-
|
| 79 |
-
return instance
|
| 80 |
-
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_text.py
DELETED
|
@@ -1,99 +0,0 @@
|
|
| 1 |
-
import re
|
| 2 |
-
|
| 3 |
-
from ._functools import method_cache
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
# from jaraco.text 3.5
|
| 7 |
-
class FoldedCase(str):
|
| 8 |
-
"""
|
| 9 |
-
A case insensitive string class; behaves just like str
|
| 10 |
-
except compares equal when the only variation is case.
|
| 11 |
-
|
| 12 |
-
>>> s = FoldedCase('hello world')
|
| 13 |
-
|
| 14 |
-
>>> s == 'Hello World'
|
| 15 |
-
True
|
| 16 |
-
|
| 17 |
-
>>> 'Hello World' == s
|
| 18 |
-
True
|
| 19 |
-
|
| 20 |
-
>>> s != 'Hello World'
|
| 21 |
-
False
|
| 22 |
-
|
| 23 |
-
>>> s.index('O')
|
| 24 |
-
4
|
| 25 |
-
|
| 26 |
-
>>> s.split('O')
|
| 27 |
-
['hell', ' w', 'rld']
|
| 28 |
-
|
| 29 |
-
>>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
|
| 30 |
-
['alpha', 'Beta', 'GAMMA']
|
| 31 |
-
|
| 32 |
-
Sequence membership is straightforward.
|
| 33 |
-
|
| 34 |
-
>>> "Hello World" in [s]
|
| 35 |
-
True
|
| 36 |
-
>>> s in ["Hello World"]
|
| 37 |
-
True
|
| 38 |
-
|
| 39 |
-
You may test for set inclusion, but candidate and elements
|
| 40 |
-
must both be folded.
|
| 41 |
-
|
| 42 |
-
>>> FoldedCase("Hello World") in {s}
|
| 43 |
-
True
|
| 44 |
-
>>> s in {FoldedCase("Hello World")}
|
| 45 |
-
True
|
| 46 |
-
|
| 47 |
-
String inclusion works as long as the FoldedCase object
|
| 48 |
-
is on the right.
|
| 49 |
-
|
| 50 |
-
>>> "hello" in FoldedCase("Hello World")
|
| 51 |
-
True
|
| 52 |
-
|
| 53 |
-
But not if the FoldedCase object is on the left:
|
| 54 |
-
|
| 55 |
-
>>> FoldedCase('hello') in 'Hello World'
|
| 56 |
-
False
|
| 57 |
-
|
| 58 |
-
In that case, use in_:
|
| 59 |
-
|
| 60 |
-
>>> FoldedCase('hello').in_('Hello World')
|
| 61 |
-
True
|
| 62 |
-
|
| 63 |
-
>>> FoldedCase('hello') > FoldedCase('Hello')
|
| 64 |
-
False
|
| 65 |
-
"""
|
| 66 |
-
|
| 67 |
-
def __lt__(self, other):
|
| 68 |
-
return self.lower() < other.lower()
|
| 69 |
-
|
| 70 |
-
def __gt__(self, other):
|
| 71 |
-
return self.lower() > other.lower()
|
| 72 |
-
|
| 73 |
-
def __eq__(self, other):
|
| 74 |
-
return self.lower() == other.lower()
|
| 75 |
-
|
| 76 |
-
def __ne__(self, other):
|
| 77 |
-
return self.lower() != other.lower()
|
| 78 |
-
|
| 79 |
-
def __hash__(self):
|
| 80 |
-
return hash(self.lower())
|
| 81 |
-
|
| 82 |
-
def __contains__(self, other):
|
| 83 |
-
return super().lower().__contains__(other.lower())
|
| 84 |
-
|
| 85 |
-
def in_(self, other):
|
| 86 |
-
"Does self appear in other?"
|
| 87 |
-
return self in FoldedCase(other)
|
| 88 |
-
|
| 89 |
-
# cache lower since it's likely to be called frequently.
|
| 90 |
-
@method_cache
|
| 91 |
-
def lower(self):
|
| 92 |
-
return super().lower()
|
| 93 |
-
|
| 94 |
-
def index(self, sub):
|
| 95 |
-
return self.lower().index(sub.lower())
|
| 96 |
-
|
| 97 |
-
def split(self, splitter=' ', maxsplit=0):
|
| 98 |
-
pattern = re.compile(re.escape(splitter), re.I)
|
| 99 |
-
return pattern.split(self, maxsplit)
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/build_py.py
DELETED
|
@@ -1,368 +0,0 @@
|
|
| 1 |
-
from functools import partial
|
| 2 |
-
from glob import glob
|
| 3 |
-
from distutils.util import convert_path
|
| 4 |
-
import distutils.command.build_py as orig
|
| 5 |
-
import os
|
| 6 |
-
import fnmatch
|
| 7 |
-
import textwrap
|
| 8 |
-
import io
|
| 9 |
-
import distutils.errors
|
| 10 |
-
import itertools
|
| 11 |
-
import stat
|
| 12 |
-
import warnings
|
| 13 |
-
from pathlib import Path
|
| 14 |
-
from typing import Dict, Iterable, Iterator, List, Optional, Tuple
|
| 15 |
-
|
| 16 |
-
from setuptools._deprecation_warning import SetuptoolsDeprecationWarning
|
| 17 |
-
from setuptools.extern.more_itertools import unique_everseen
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def make_writable(target):
|
| 21 |
-
os.chmod(target, os.stat(target).st_mode | stat.S_IWRITE)
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class build_py(orig.build_py):
|
| 25 |
-
"""Enhanced 'build_py' command that includes data files with packages
|
| 26 |
-
|
| 27 |
-
The data files are specified via a 'package_data' argument to 'setup()'.
|
| 28 |
-
See 'setuptools.dist.Distribution' for more details.
|
| 29 |
-
|
| 30 |
-
Also, this version of the 'build_py' command allows you to specify both
|
| 31 |
-
'py_modules' and 'packages' in the same setup operation.
|
| 32 |
-
"""
|
| 33 |
-
editable_mode: bool = False
|
| 34 |
-
existing_egg_info_dir: Optional[str] = None #: Private API, internal use only.
|
| 35 |
-
|
| 36 |
-
def finalize_options(self):
|
| 37 |
-
orig.build_py.finalize_options(self)
|
| 38 |
-
self.package_data = self.distribution.package_data
|
| 39 |
-
self.exclude_package_data = self.distribution.exclude_package_data or {}
|
| 40 |
-
if 'data_files' in self.__dict__:
|
| 41 |
-
del self.__dict__['data_files']
|
| 42 |
-
self.__updated_files = []
|
| 43 |
-
|
| 44 |
-
def copy_file(self, infile, outfile, preserve_mode=1, preserve_times=1,
|
| 45 |
-
link=None, level=1):
|
| 46 |
-
# Overwrite base class to allow using links
|
| 47 |
-
if link:
|
| 48 |
-
infile = str(Path(infile).resolve())
|
| 49 |
-
outfile = str(Path(outfile).resolve())
|
| 50 |
-
return super().copy_file(infile, outfile, preserve_mode, preserve_times,
|
| 51 |
-
link, level)
|
| 52 |
-
|
| 53 |
-
def run(self):
|
| 54 |
-
"""Build modules, packages, and copy data files to build directory"""
|
| 55 |
-
if not (self.py_modules or self.packages) or self.editable_mode:
|
| 56 |
-
return
|
| 57 |
-
|
| 58 |
-
if self.py_modules:
|
| 59 |
-
self.build_modules()
|
| 60 |
-
|
| 61 |
-
if self.packages:
|
| 62 |
-
self.build_packages()
|
| 63 |
-
self.build_package_data()
|
| 64 |
-
|
| 65 |
-
# Only compile actual .py files, using our base class' idea of what our
|
| 66 |
-
# output files are.
|
| 67 |
-
self.byte_compile(orig.build_py.get_outputs(self, include_bytecode=0))
|
| 68 |
-
|
| 69 |
-
def __getattr__(self, attr):
|
| 70 |
-
"lazily compute data files"
|
| 71 |
-
if attr == 'data_files':
|
| 72 |
-
self.data_files = self._get_data_files()
|
| 73 |
-
return self.data_files
|
| 74 |
-
return orig.build_py.__getattr__(self, attr)
|
| 75 |
-
|
| 76 |
-
def build_module(self, module, module_file, package):
|
| 77 |
-
outfile, copied = orig.build_py.build_module(self, module, module_file, package)
|
| 78 |
-
if copied:
|
| 79 |
-
self.__updated_files.append(outfile)
|
| 80 |
-
return outfile, copied
|
| 81 |
-
|
| 82 |
-
def _get_data_files(self):
|
| 83 |
-
"""Generate list of '(package,src_dir,build_dir,filenames)' tuples"""
|
| 84 |
-
self.analyze_manifest()
|
| 85 |
-
return list(map(self._get_pkg_data_files, self.packages or ()))
|
| 86 |
-
|
| 87 |
-
def get_data_files_without_manifest(self):
|
| 88 |
-
"""
|
| 89 |
-
Generate list of ``(package,src_dir,build_dir,filenames)`` tuples,
|
| 90 |
-
but without triggering any attempt to analyze or build the manifest.
|
| 91 |
-
"""
|
| 92 |
-
# Prevent eventual errors from unset `manifest_files`
|
| 93 |
-
# (that would otherwise be set by `analyze_manifest`)
|
| 94 |
-
self.__dict__.setdefault('manifest_files', {})
|
| 95 |
-
return list(map(self._get_pkg_data_files, self.packages or ()))
|
| 96 |
-
|
| 97 |
-
def _get_pkg_data_files(self, package):
|
| 98 |
-
# Locate package source directory
|
| 99 |
-
src_dir = self.get_package_dir(package)
|
| 100 |
-
|
| 101 |
-
# Compute package build directory
|
| 102 |
-
build_dir = os.path.join(*([self.build_lib] + package.split('.')))
|
| 103 |
-
|
| 104 |
-
# Strip directory from globbed filenames
|
| 105 |
-
filenames = [
|
| 106 |
-
os.path.relpath(file, src_dir)
|
| 107 |
-
for file in self.find_data_files(package, src_dir)
|
| 108 |
-
]
|
| 109 |
-
return package, src_dir, build_dir, filenames
|
| 110 |
-
|
| 111 |
-
def find_data_files(self, package, src_dir):
|
| 112 |
-
"""Return filenames for package's data files in 'src_dir'"""
|
| 113 |
-
patterns = self._get_platform_patterns(
|
| 114 |
-
self.package_data,
|
| 115 |
-
package,
|
| 116 |
-
src_dir,
|
| 117 |
-
)
|
| 118 |
-
globs_expanded = map(partial(glob, recursive=True), patterns)
|
| 119 |
-
# flatten the expanded globs into an iterable of matches
|
| 120 |
-
globs_matches = itertools.chain.from_iterable(globs_expanded)
|
| 121 |
-
glob_files = filter(os.path.isfile, globs_matches)
|
| 122 |
-
files = itertools.chain(
|
| 123 |
-
self.manifest_files.get(package, []),
|
| 124 |
-
glob_files,
|
| 125 |
-
)
|
| 126 |
-
return self.exclude_data_files(package, src_dir, files)
|
| 127 |
-
|
| 128 |
-
def get_outputs(self, include_bytecode=1) -> List[str]:
|
| 129 |
-
"""See :class:`setuptools.commands.build.SubCommand`"""
|
| 130 |
-
if self.editable_mode:
|
| 131 |
-
return list(self.get_output_mapping().keys())
|
| 132 |
-
return super().get_outputs(include_bytecode)
|
| 133 |
-
|
| 134 |
-
def get_output_mapping(self) -> Dict[str, str]:
|
| 135 |
-
"""See :class:`setuptools.commands.build.SubCommand`"""
|
| 136 |
-
mapping = itertools.chain(
|
| 137 |
-
self._get_package_data_output_mapping(),
|
| 138 |
-
self._get_module_mapping(),
|
| 139 |
-
)
|
| 140 |
-
return dict(sorted(mapping, key=lambda x: x[0]))
|
| 141 |
-
|
| 142 |
-
def _get_module_mapping(self) -> Iterator[Tuple[str, str]]:
|
| 143 |
-
"""Iterate over all modules producing (dest, src) pairs."""
|
| 144 |
-
for (package, module, module_file) in self.find_all_modules():
|
| 145 |
-
package = package.split('.')
|
| 146 |
-
filename = self.get_module_outfile(self.build_lib, package, module)
|
| 147 |
-
yield (filename, module_file)
|
| 148 |
-
|
| 149 |
-
def _get_package_data_output_mapping(self) -> Iterator[Tuple[str, str]]:
|
| 150 |
-
"""Iterate over package data producing (dest, src) pairs."""
|
| 151 |
-
for package, src_dir, build_dir, filenames in self.data_files:
|
| 152 |
-
for filename in filenames:
|
| 153 |
-
target = os.path.join(build_dir, filename)
|
| 154 |
-
srcfile = os.path.join(src_dir, filename)
|
| 155 |
-
yield (target, srcfile)
|
| 156 |
-
|
| 157 |
-
def build_package_data(self):
|
| 158 |
-
"""Copy data files into build directory"""
|
| 159 |
-
for target, srcfile in self._get_package_data_output_mapping():
|
| 160 |
-
self.mkpath(os.path.dirname(target))
|
| 161 |
-
_outf, _copied = self.copy_file(srcfile, target)
|
| 162 |
-
make_writable(target)
|
| 163 |
-
|
| 164 |
-
def analyze_manifest(self):
|
| 165 |
-
self.manifest_files = mf = {}
|
| 166 |
-
if not self.distribution.include_package_data:
|
| 167 |
-
return
|
| 168 |
-
src_dirs = {}
|
| 169 |
-
for package in self.packages or ():
|
| 170 |
-
# Locate package source directory
|
| 171 |
-
src_dirs[assert_relative(self.get_package_dir(package))] = package
|
| 172 |
-
|
| 173 |
-
if (
|
| 174 |
-
getattr(self, 'existing_egg_info_dir', None)
|
| 175 |
-
and Path(self.existing_egg_info_dir, "SOURCES.txt").exists()
|
| 176 |
-
):
|
| 177 |
-
egg_info_dir = self.existing_egg_info_dir
|
| 178 |
-
manifest = Path(egg_info_dir, "SOURCES.txt")
|
| 179 |
-
files = manifest.read_text(encoding="utf-8").splitlines()
|
| 180 |
-
else:
|
| 181 |
-
self.run_command('egg_info')
|
| 182 |
-
ei_cmd = self.get_finalized_command('egg_info')
|
| 183 |
-
egg_info_dir = ei_cmd.egg_info
|
| 184 |
-
files = ei_cmd.filelist.files
|
| 185 |
-
|
| 186 |
-
check = _IncludePackageDataAbuse()
|
| 187 |
-
for path in self._filter_build_files(files, egg_info_dir):
|
| 188 |
-
d, f = os.path.split(assert_relative(path))
|
| 189 |
-
prev = None
|
| 190 |
-
oldf = f
|
| 191 |
-
while d and d != prev and d not in src_dirs:
|
| 192 |
-
prev = d
|
| 193 |
-
d, df = os.path.split(d)
|
| 194 |
-
f = os.path.join(df, f)
|
| 195 |
-
if d in src_dirs:
|
| 196 |
-
if f == oldf:
|
| 197 |
-
if check.is_module(f):
|
| 198 |
-
continue # it's a module, not data
|
| 199 |
-
else:
|
| 200 |
-
importable = check.importable_subpackage(src_dirs[d], f)
|
| 201 |
-
if importable:
|
| 202 |
-
check.warn(importable)
|
| 203 |
-
mf.setdefault(src_dirs[d], []).append(path)
|
| 204 |
-
|
| 205 |
-
def _filter_build_files(self, files: Iterable[str], egg_info: str) -> Iterator[str]:
|
| 206 |
-
"""
|
| 207 |
-
``build_meta`` may try to create egg_info outside of the project directory,
|
| 208 |
-
and this can be problematic for certain plugins (reported in issue #3500).
|
| 209 |
-
|
| 210 |
-
Extensions might also include between their sources files created on the
|
| 211 |
-
``build_lib`` and ``build_temp`` directories.
|
| 212 |
-
|
| 213 |
-
This function should filter this case of invalid files out.
|
| 214 |
-
"""
|
| 215 |
-
build = self.get_finalized_command("build")
|
| 216 |
-
build_dirs = (egg_info, self.build_lib, build.build_temp, build.build_base)
|
| 217 |
-
norm_dirs = [os.path.normpath(p) for p in build_dirs if p]
|
| 218 |
-
|
| 219 |
-
for file in files:
|
| 220 |
-
norm_path = os.path.normpath(file)
|
| 221 |
-
if not os.path.isabs(file) or all(d not in norm_path for d in norm_dirs):
|
| 222 |
-
yield file
|
| 223 |
-
|
| 224 |
-
def get_data_files(self):
|
| 225 |
-
pass # Lazily compute data files in _get_data_files() function.
|
| 226 |
-
|
| 227 |
-
def check_package(self, package, package_dir):
|
| 228 |
-
"""Check namespace packages' __init__ for declare_namespace"""
|
| 229 |
-
try:
|
| 230 |
-
return self.packages_checked[package]
|
| 231 |
-
except KeyError:
|
| 232 |
-
pass
|
| 233 |
-
|
| 234 |
-
init_py = orig.build_py.check_package(self, package, package_dir)
|
| 235 |
-
self.packages_checked[package] = init_py
|
| 236 |
-
|
| 237 |
-
if not init_py or not self.distribution.namespace_packages:
|
| 238 |
-
return init_py
|
| 239 |
-
|
| 240 |
-
for pkg in self.distribution.namespace_packages:
|
| 241 |
-
if pkg == package or pkg.startswith(package + '.'):
|
| 242 |
-
break
|
| 243 |
-
else:
|
| 244 |
-
return init_py
|
| 245 |
-
|
| 246 |
-
with io.open(init_py, 'rb') as f:
|
| 247 |
-
contents = f.read()
|
| 248 |
-
if b'declare_namespace' not in contents:
|
| 249 |
-
raise distutils.errors.DistutilsError(
|
| 250 |
-
"Namespace package problem: %s is a namespace package, but "
|
| 251 |
-
"its\n__init__.py does not call declare_namespace()! Please "
|
| 252 |
-
'fix it.\n(See the setuptools manual under '
|
| 253 |
-
'"Namespace Packages" for details.)\n"' % (package,)
|
| 254 |
-
)
|
| 255 |
-
return init_py
|
| 256 |
-
|
| 257 |
-
def initialize_options(self):
|
| 258 |
-
self.packages_checked = {}
|
| 259 |
-
orig.build_py.initialize_options(self)
|
| 260 |
-
self.editable_mode = False
|
| 261 |
-
self.existing_egg_info_dir = None
|
| 262 |
-
|
| 263 |
-
def get_package_dir(self, package):
|
| 264 |
-
res = orig.build_py.get_package_dir(self, package)
|
| 265 |
-
if self.distribution.src_root is not None:
|
| 266 |
-
return os.path.join(self.distribution.src_root, res)
|
| 267 |
-
return res
|
| 268 |
-
|
| 269 |
-
def exclude_data_files(self, package, src_dir, files):
|
| 270 |
-
"""Filter filenames for package's data files in 'src_dir'"""
|
| 271 |
-
files = list(files)
|
| 272 |
-
patterns = self._get_platform_patterns(
|
| 273 |
-
self.exclude_package_data,
|
| 274 |
-
package,
|
| 275 |
-
src_dir,
|
| 276 |
-
)
|
| 277 |
-
match_groups = (fnmatch.filter(files, pattern) for pattern in patterns)
|
| 278 |
-
# flatten the groups of matches into an iterable of matches
|
| 279 |
-
matches = itertools.chain.from_iterable(match_groups)
|
| 280 |
-
bad = set(matches)
|
| 281 |
-
keepers = (fn for fn in files if fn not in bad)
|
| 282 |
-
# ditch dupes
|
| 283 |
-
return list(unique_everseen(keepers))
|
| 284 |
-
|
| 285 |
-
@staticmethod
|
| 286 |
-
def _get_platform_patterns(spec, package, src_dir):
|
| 287 |
-
"""
|
| 288 |
-
yield platform-specific path patterns (suitable for glob
|
| 289 |
-
or fn_match) from a glob-based spec (such as
|
| 290 |
-
self.package_data or self.exclude_package_data)
|
| 291 |
-
matching package in src_dir.
|
| 292 |
-
"""
|
| 293 |
-
raw_patterns = itertools.chain(
|
| 294 |
-
spec.get('', []),
|
| 295 |
-
spec.get(package, []),
|
| 296 |
-
)
|
| 297 |
-
return (
|
| 298 |
-
# Each pattern has to be converted to a platform-specific path
|
| 299 |
-
os.path.join(src_dir, convert_path(pattern))
|
| 300 |
-
for pattern in raw_patterns
|
| 301 |
-
)
|
| 302 |
-
|
| 303 |
-
|
| 304 |
-
def assert_relative(path):
|
| 305 |
-
if not os.path.isabs(path):
|
| 306 |
-
return path
|
| 307 |
-
from distutils.errors import DistutilsSetupError
|
| 308 |
-
|
| 309 |
-
msg = (
|
| 310 |
-
textwrap.dedent(
|
| 311 |
-
"""
|
| 312 |
-
Error: setup script specifies an absolute path:
|
| 313 |
-
|
| 314 |
-
%s
|
| 315 |
-
|
| 316 |
-
setup() arguments must *always* be /-separated paths relative to the
|
| 317 |
-
setup.py directory, *never* absolute paths.
|
| 318 |
-
"""
|
| 319 |
-
).lstrip()
|
| 320 |
-
% path
|
| 321 |
-
)
|
| 322 |
-
raise DistutilsSetupError(msg)
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
class _IncludePackageDataAbuse:
|
| 326 |
-
"""Inform users that package or module is included as 'data file'"""
|
| 327 |
-
|
| 328 |
-
MESSAGE = """\
|
| 329 |
-
Installing {importable!r} as data is deprecated, please list it in `packages`.
|
| 330 |
-
!!\n\n
|
| 331 |
-
############################
|
| 332 |
-
# Package would be ignored #
|
| 333 |
-
############################
|
| 334 |
-
Python recognizes {importable!r} as an importable package,
|
| 335 |
-
but it is not listed in the `packages` configuration of setuptools.
|
| 336 |
-
|
| 337 |
-
{importable!r} has been automatically added to the distribution only
|
| 338 |
-
because it may contain data files, but this behavior is likely to change
|
| 339 |
-
in future versions of setuptools (and therefore is considered deprecated).
|
| 340 |
-
|
| 341 |
-
Please make sure that {importable!r} is included as a package by using
|
| 342 |
-
the `packages` configuration field or the proper discovery methods
|
| 343 |
-
(for example by using `find_namespace_packages(...)`/`find_namespace:`
|
| 344 |
-
instead of `find_packages(...)`/`find:`).
|
| 345 |
-
|
| 346 |
-
You can read more about "package discovery" and "data files" on setuptools
|
| 347 |
-
documentation page.
|
| 348 |
-
\n\n!!
|
| 349 |
-
"""
|
| 350 |
-
|
| 351 |
-
def __init__(self):
|
| 352 |
-
self._already_warned = set()
|
| 353 |
-
|
| 354 |
-
def is_module(self, file):
|
| 355 |
-
return file.endswith(".py") and file[:-len(".py")].isidentifier()
|
| 356 |
-
|
| 357 |
-
def importable_subpackage(self, parent, file):
|
| 358 |
-
pkg = Path(file).parent
|
| 359 |
-
parts = list(itertools.takewhile(str.isidentifier, pkg.parts))
|
| 360 |
-
if parts:
|
| 361 |
-
return ".".join([parent, *parts])
|
| 362 |
-
return None
|
| 363 |
-
|
| 364 |
-
def warn(self, importable):
|
| 365 |
-
if importable not in self._already_warned:
|
| 366 |
-
msg = textwrap.dedent(self.MESSAGE).format(importable=importable)
|
| 367 |
-
warnings.warn(msg, SetuptoolsDeprecationWarning, stacklevel=2)
|
| 368 |
-
self._already_warned.add(importable)
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|
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/meta_arch/retinanet.py
DELETED
|
@@ -1,497 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 2 |
-
import logging
|
| 3 |
-
import math
|
| 4 |
-
import numpy as np
|
| 5 |
-
from typing import List
|
| 6 |
-
import torch
|
| 7 |
-
from fvcore.nn import sigmoid_focal_loss_jit, smooth_l1_loss
|
| 8 |
-
from torch import nn
|
| 9 |
-
|
| 10 |
-
from detectron2.layers import ShapeSpec, batched_nms, cat
|
| 11 |
-
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
|
| 12 |
-
from detectron2.utils.events import get_event_storage
|
| 13 |
-
from detectron2.utils.logger import log_first_n
|
| 14 |
-
|
| 15 |
-
from ..anchor_generator import build_anchor_generator
|
| 16 |
-
from ..backbone import build_backbone
|
| 17 |
-
from ..box_regression import Box2BoxTransform
|
| 18 |
-
from ..matcher import Matcher
|
| 19 |
-
from ..postprocessing import detector_postprocess
|
| 20 |
-
from .build import META_ARCH_REGISTRY
|
| 21 |
-
|
| 22 |
-
__all__ = ["RetinaNet"]
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def permute_to_N_HWA_K(tensor, K):
|
| 26 |
-
"""
|
| 27 |
-
Transpose/reshape a tensor from (N, (A x K), H, W) to (N, (HxWxA), K)
|
| 28 |
-
"""
|
| 29 |
-
assert tensor.dim() == 4, tensor.shape
|
| 30 |
-
N, _, H, W = tensor.shape
|
| 31 |
-
tensor = tensor.view(N, -1, K, H, W)
|
| 32 |
-
tensor = tensor.permute(0, 3, 4, 1, 2)
|
| 33 |
-
tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K)
|
| 34 |
-
return tensor
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def permute_all_cls_and_box_to_N_HWA_K_and_concat(box_cls, box_delta, num_classes=80):
|
| 38 |
-
"""
|
| 39 |
-
Rearrange the tensor layout from the network output, i.e.:
|
| 40 |
-
list[Tensor]: #lvl tensors of shape (N, A x K, Hi, Wi)
|
| 41 |
-
to per-image predictions, i.e.:
|
| 42 |
-
Tensor: of shape (N x sum(Hi x Wi x A), K)
|
| 43 |
-
"""
|
| 44 |
-
# for each feature level, permute the outputs to make them be in the
|
| 45 |
-
# same format as the labels. Note that the labels are computed for
|
| 46 |
-
# all feature levels concatenated, so we keep the same representation
|
| 47 |
-
# for the objectness and the box_delta
|
| 48 |
-
box_cls_flattened = [permute_to_N_HWA_K(x, num_classes) for x in box_cls]
|
| 49 |
-
box_delta_flattened = [permute_to_N_HWA_K(x, 4) for x in box_delta]
|
| 50 |
-
# concatenate on the first dimension (representing the feature levels), to
|
| 51 |
-
# take into account the way the labels were generated (with all feature maps
|
| 52 |
-
# being concatenated as well)
|
| 53 |
-
box_cls = cat(box_cls_flattened, dim=1).view(-1, num_classes)
|
| 54 |
-
box_delta = cat(box_delta_flattened, dim=1).view(-1, 4)
|
| 55 |
-
return box_cls, box_delta
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
@META_ARCH_REGISTRY.register()
|
| 59 |
-
class RetinaNet(nn.Module):
|
| 60 |
-
"""
|
| 61 |
-
Implement RetinaNet (https://arxiv.org/abs/1708.02002).
|
| 62 |
-
"""
|
| 63 |
-
|
| 64 |
-
def __init__(self, cfg):
|
| 65 |
-
super().__init__()
|
| 66 |
-
|
| 67 |
-
self.device = torch.device(cfg.MODEL.DEVICE)
|
| 68 |
-
|
| 69 |
-
# fmt: off
|
| 70 |
-
self.num_classes = cfg.MODEL.RETINANET.NUM_CLASSES
|
| 71 |
-
self.in_features = cfg.MODEL.RETINANET.IN_FEATURES
|
| 72 |
-
# Loss parameters:
|
| 73 |
-
self.focal_loss_alpha = cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA
|
| 74 |
-
self.focal_loss_gamma = cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA
|
| 75 |
-
self.smooth_l1_loss_beta = cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA
|
| 76 |
-
# Inference parameters:
|
| 77 |
-
self.score_threshold = cfg.MODEL.RETINANET.SCORE_THRESH_TEST
|
| 78 |
-
self.topk_candidates = cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST
|
| 79 |
-
self.nms_threshold = cfg.MODEL.RETINANET.NMS_THRESH_TEST
|
| 80 |
-
self.max_detections_per_image = cfg.TEST.DETECTIONS_PER_IMAGE
|
| 81 |
-
# Vis parameters
|
| 82 |
-
self.vis_period = cfg.VIS_PERIOD
|
| 83 |
-
self.input_format = cfg.INPUT.FORMAT
|
| 84 |
-
# fmt: on
|
| 85 |
-
|
| 86 |
-
self.backbone = build_backbone(cfg)
|
| 87 |
-
|
| 88 |
-
backbone_shape = self.backbone.output_shape()
|
| 89 |
-
feature_shapes = [backbone_shape[f] for f in self.in_features]
|
| 90 |
-
self.head = RetinaNetHead(cfg, feature_shapes)
|
| 91 |
-
self.anchor_generator = build_anchor_generator(cfg, feature_shapes)
|
| 92 |
-
|
| 93 |
-
# Matching and loss
|
| 94 |
-
self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
|
| 95 |
-
self.matcher = Matcher(
|
| 96 |
-
cfg.MODEL.RETINANET.IOU_THRESHOLDS,
|
| 97 |
-
cfg.MODEL.RETINANET.IOU_LABELS,
|
| 98 |
-
allow_low_quality_matches=True,
|
| 99 |
-
)
|
| 100 |
-
|
| 101 |
-
assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD)
|
| 102 |
-
num_channels = len(cfg.MODEL.PIXEL_MEAN)
|
| 103 |
-
pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(num_channels, 1, 1)
|
| 104 |
-
pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(num_channels, 1, 1)
|
| 105 |
-
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
|
| 106 |
-
self.to(self.device)
|
| 107 |
-
|
| 108 |
-
"""
|
| 109 |
-
In Detectron1, loss is normalized by number of foreground samples in the batch.
|
| 110 |
-
When batch size is 1 per GPU, #foreground has a large variance and
|
| 111 |
-
using it lead to lower performance. Here we maintain an EMA of #foreground to
|
| 112 |
-
stabilize the normalizer.
|
| 113 |
-
"""
|
| 114 |
-
self.loss_normalizer = 100 # initialize with any reasonable #fg that's not too small
|
| 115 |
-
self.loss_normalizer_momentum = 0.9
|
| 116 |
-
|
| 117 |
-
def visualize_training(self, batched_inputs, results):
|
| 118 |
-
"""
|
| 119 |
-
A function used to visualize ground truth images and final network predictions.
|
| 120 |
-
It shows ground truth bounding boxes on the original image and up to 20
|
| 121 |
-
predicted object bounding boxes on the original image.
|
| 122 |
-
|
| 123 |
-
Args:
|
| 124 |
-
batched_inputs (list): a list that contains input to the model.
|
| 125 |
-
results (List[Instances]): a list of #images elements.
|
| 126 |
-
"""
|
| 127 |
-
from detectron2.utils.visualizer import Visualizer
|
| 128 |
-
|
| 129 |
-
assert len(batched_inputs) == len(
|
| 130 |
-
results
|
| 131 |
-
), "Cannot visualize inputs and results of different sizes"
|
| 132 |
-
storage = get_event_storage()
|
| 133 |
-
max_boxes = 20
|
| 134 |
-
|
| 135 |
-
image_index = 0 # only visualize a single image
|
| 136 |
-
img = batched_inputs[image_index]["image"].cpu().numpy()
|
| 137 |
-
assert img.shape[0] == 3, "Images should have 3 channels."
|
| 138 |
-
if self.input_format == "BGR":
|
| 139 |
-
img = img[::-1, :, :]
|
| 140 |
-
img = img.transpose(1, 2, 0)
|
| 141 |
-
v_gt = Visualizer(img, None)
|
| 142 |
-
v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index]["instances"].gt_boxes)
|
| 143 |
-
anno_img = v_gt.get_image()
|
| 144 |
-
processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])
|
| 145 |
-
predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()
|
| 146 |
-
|
| 147 |
-
v_pred = Visualizer(img, None)
|
| 148 |
-
v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])
|
| 149 |
-
prop_img = v_pred.get_image()
|
| 150 |
-
vis_img = np.vstack((anno_img, prop_img))
|
| 151 |
-
vis_img = vis_img.transpose(2, 0, 1)
|
| 152 |
-
vis_name = f"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results"
|
| 153 |
-
storage.put_image(vis_name, vis_img)
|
| 154 |
-
|
| 155 |
-
def forward(self, batched_inputs):
|
| 156 |
-
"""
|
| 157 |
-
Args:
|
| 158 |
-
batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
|
| 159 |
-
Each item in the list contains the inputs for one image.
|
| 160 |
-
For now, each item in the list is a dict that contains:
|
| 161 |
-
|
| 162 |
-
* image: Tensor, image in (C, H, W) format.
|
| 163 |
-
* instances: Instances
|
| 164 |
-
|
| 165 |
-
Other information that's included in the original dicts, such as:
|
| 166 |
-
|
| 167 |
-
* "height", "width" (int): the output resolution of the model, used in inference.
|
| 168 |
-
See :meth:`postprocess` for details.
|
| 169 |
-
Returns:
|
| 170 |
-
dict[str: Tensor]:
|
| 171 |
-
mapping from a named loss to a tensor storing the loss. Used during training only.
|
| 172 |
-
"""
|
| 173 |
-
images = self.preprocess_image(batched_inputs)
|
| 174 |
-
if "instances" in batched_inputs[0]:
|
| 175 |
-
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
|
| 176 |
-
elif "targets" in batched_inputs[0]:
|
| 177 |
-
log_first_n(
|
| 178 |
-
logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10
|
| 179 |
-
)
|
| 180 |
-
gt_instances = [x["targets"].to(self.device) for x in batched_inputs]
|
| 181 |
-
else:
|
| 182 |
-
gt_instances = None
|
| 183 |
-
|
| 184 |
-
features = self.backbone(images.tensor)
|
| 185 |
-
features = [features[f] for f in self.in_features]
|
| 186 |
-
box_cls, box_delta = self.head(features)
|
| 187 |
-
anchors = self.anchor_generator(features)
|
| 188 |
-
|
| 189 |
-
if self.training:
|
| 190 |
-
gt_classes, gt_anchors_reg_deltas = self.get_ground_truth(anchors, gt_instances)
|
| 191 |
-
losses = self.losses(gt_classes, gt_anchors_reg_deltas, box_cls, box_delta)
|
| 192 |
-
|
| 193 |
-
if self.vis_period > 0:
|
| 194 |
-
storage = get_event_storage()
|
| 195 |
-
if storage.iter % self.vis_period == 0:
|
| 196 |
-
results = self.inference(box_cls, box_delta, anchors, images.image_sizes)
|
| 197 |
-
self.visualize_training(batched_inputs, results)
|
| 198 |
-
|
| 199 |
-
return losses
|
| 200 |
-
else:
|
| 201 |
-
results = self.inference(box_cls, box_delta, anchors, images.image_sizes)
|
| 202 |
-
processed_results = []
|
| 203 |
-
for results_per_image, input_per_image, image_size in zip(
|
| 204 |
-
results, batched_inputs, images.image_sizes
|
| 205 |
-
):
|
| 206 |
-
height = input_per_image.get("height", image_size[0])
|
| 207 |
-
width = input_per_image.get("width", image_size[1])
|
| 208 |
-
r = detector_postprocess(results_per_image, height, width)
|
| 209 |
-
processed_results.append({"instances": r})
|
| 210 |
-
return processed_results
|
| 211 |
-
|
| 212 |
-
def losses(self, gt_classes, gt_anchors_deltas, pred_class_logits, pred_anchor_deltas):
|
| 213 |
-
"""
|
| 214 |
-
Args:
|
| 215 |
-
For `gt_classes` and `gt_anchors_deltas` parameters, see
|
| 216 |
-
:meth:`RetinaNet.get_ground_truth`.
|
| 217 |
-
Their shapes are (N, R) and (N, R, 4), respectively, where R is
|
| 218 |
-
the total number of anchors across levels, i.e. sum(Hi x Wi x A)
|
| 219 |
-
For `pred_class_logits` and `pred_anchor_deltas`, see
|
| 220 |
-
:meth:`RetinaNetHead.forward`.
|
| 221 |
-
|
| 222 |
-
Returns:
|
| 223 |
-
dict[str: Tensor]:
|
| 224 |
-
mapping from a named loss to a scalar tensor
|
| 225 |
-
storing the loss. Used during training only. The dict keys are:
|
| 226 |
-
"loss_cls" and "loss_box_reg"
|
| 227 |
-
"""
|
| 228 |
-
pred_class_logits, pred_anchor_deltas = permute_all_cls_and_box_to_N_HWA_K_and_concat(
|
| 229 |
-
pred_class_logits, pred_anchor_deltas, self.num_classes
|
| 230 |
-
) # Shapes: (N x R, K) and (N x R, 4), respectively.
|
| 231 |
-
|
| 232 |
-
gt_classes = gt_classes.flatten()
|
| 233 |
-
gt_anchors_deltas = gt_anchors_deltas.view(-1, 4)
|
| 234 |
-
|
| 235 |
-
valid_idxs = gt_classes >= 0
|
| 236 |
-
foreground_idxs = (gt_classes >= 0) & (gt_classes != self.num_classes)
|
| 237 |
-
num_foreground = foreground_idxs.sum().item()
|
| 238 |
-
get_event_storage().put_scalar("num_foreground", num_foreground)
|
| 239 |
-
self.loss_normalizer = (
|
| 240 |
-
self.loss_normalizer_momentum * self.loss_normalizer
|
| 241 |
-
+ (1 - self.loss_normalizer_momentum) * num_foreground
|
| 242 |
-
)
|
| 243 |
-
|
| 244 |
-
gt_classes_target = torch.zeros_like(pred_class_logits)
|
| 245 |
-
gt_classes_target[foreground_idxs, gt_classes[foreground_idxs]] = 1
|
| 246 |
-
|
| 247 |
-
# logits loss
|
| 248 |
-
loss_cls = sigmoid_focal_loss_jit(
|
| 249 |
-
pred_class_logits[valid_idxs],
|
| 250 |
-
gt_classes_target[valid_idxs],
|
| 251 |
-
alpha=self.focal_loss_alpha,
|
| 252 |
-
gamma=self.focal_loss_gamma,
|
| 253 |
-
reduction="sum",
|
| 254 |
-
) / max(1, self.loss_normalizer)
|
| 255 |
-
|
| 256 |
-
# regression loss
|
| 257 |
-
loss_box_reg = smooth_l1_loss(
|
| 258 |
-
pred_anchor_deltas[foreground_idxs],
|
| 259 |
-
gt_anchors_deltas[foreground_idxs],
|
| 260 |
-
beta=self.smooth_l1_loss_beta,
|
| 261 |
-
reduction="sum",
|
| 262 |
-
) / max(1, self.loss_normalizer)
|
| 263 |
-
|
| 264 |
-
return {"loss_cls": loss_cls, "loss_box_reg": loss_box_reg}
|
| 265 |
-
|
| 266 |
-
@torch.no_grad()
|
| 267 |
-
def get_ground_truth(self, anchors, targets):
|
| 268 |
-
"""
|
| 269 |
-
Args:
|
| 270 |
-
anchors (list[list[Boxes]]): a list of N=#image elements. Each is a
|
| 271 |
-
list of #feature level Boxes. The Boxes contains anchors of
|
| 272 |
-
this image on the specific feature level.
|
| 273 |
-
targets (list[Instances]): a list of N `Instances`s. The i-th
|
| 274 |
-
`Instances` contains the ground-truth per-instance annotations
|
| 275 |
-
for the i-th input image. Specify `targets` during training only.
|
| 276 |
-
|
| 277 |
-
Returns:
|
| 278 |
-
gt_classes (Tensor):
|
| 279 |
-
An integer tensor of shape (N, R) storing ground-truth
|
| 280 |
-
labels for each anchor.
|
| 281 |
-
R is the total number of anchors, i.e. the sum of Hi x Wi x A for all levels.
|
| 282 |
-
Anchors with an IoU with some target higher than the foreground threshold
|
| 283 |
-
are assigned their corresponding label in the [0, K-1] range.
|
| 284 |
-
Anchors whose IoU are below the background threshold are assigned
|
| 285 |
-
the label "K". Anchors whose IoU are between the foreground and background
|
| 286 |
-
thresholds are assigned a label "-1", i.e. ignore.
|
| 287 |
-
gt_anchors_deltas (Tensor):
|
| 288 |
-
Shape (N, R, 4).
|
| 289 |
-
The last dimension represents ground-truth box2box transform
|
| 290 |
-
targets (dx, dy, dw, dh) that map each anchor to its matched ground-truth box.
|
| 291 |
-
The values in the tensor are meaningful only when the corresponding
|
| 292 |
-
anchor is labeled as foreground.
|
| 293 |
-
"""
|
| 294 |
-
gt_classes = []
|
| 295 |
-
gt_anchors_deltas = []
|
| 296 |
-
anchors = [Boxes.cat(anchors_i) for anchors_i in anchors]
|
| 297 |
-
# list[Tensor(R, 4)], one for each image
|
| 298 |
-
|
| 299 |
-
for anchors_per_image, targets_per_image in zip(anchors, targets):
|
| 300 |
-
match_quality_matrix = pairwise_iou(targets_per_image.gt_boxes, anchors_per_image)
|
| 301 |
-
gt_matched_idxs, anchor_labels = self.matcher(match_quality_matrix)
|
| 302 |
-
|
| 303 |
-
has_gt = len(targets_per_image) > 0
|
| 304 |
-
if has_gt:
|
| 305 |
-
# ground truth box regression
|
| 306 |
-
matched_gt_boxes = targets_per_image.gt_boxes[gt_matched_idxs]
|
| 307 |
-
gt_anchors_reg_deltas_i = self.box2box_transform.get_deltas(
|
| 308 |
-
anchors_per_image.tensor, matched_gt_boxes.tensor
|
| 309 |
-
)
|
| 310 |
-
|
| 311 |
-
gt_classes_i = targets_per_image.gt_classes[gt_matched_idxs]
|
| 312 |
-
# Anchors with label 0 are treated as background.
|
| 313 |
-
gt_classes_i[anchor_labels == 0] = self.num_classes
|
| 314 |
-
# Anchors with label -1 are ignored.
|
| 315 |
-
gt_classes_i[anchor_labels == -1] = -1
|
| 316 |
-
else:
|
| 317 |
-
gt_classes_i = torch.zeros_like(gt_matched_idxs) + self.num_classes
|
| 318 |
-
gt_anchors_reg_deltas_i = torch.zeros_like(anchors_per_image.tensor)
|
| 319 |
-
|
| 320 |
-
gt_classes.append(gt_classes_i)
|
| 321 |
-
gt_anchors_deltas.append(gt_anchors_reg_deltas_i)
|
| 322 |
-
|
| 323 |
-
return torch.stack(gt_classes), torch.stack(gt_anchors_deltas)
|
| 324 |
-
|
| 325 |
-
def inference(self, box_cls, box_delta, anchors, image_sizes):
|
| 326 |
-
"""
|
| 327 |
-
Arguments:
|
| 328 |
-
box_cls, box_delta: Same as the output of :meth:`RetinaNetHead.forward`
|
| 329 |
-
anchors (list[list[Boxes]]): a list of #images elements. Each is a
|
| 330 |
-
list of #feature level Boxes. The Boxes contain anchors of this
|
| 331 |
-
image on the specific feature level.
|
| 332 |
-
image_sizes (List[torch.Size]): the input image sizes
|
| 333 |
-
|
| 334 |
-
Returns:
|
| 335 |
-
results (List[Instances]): a list of #images elements.
|
| 336 |
-
"""
|
| 337 |
-
assert len(anchors) == len(image_sizes)
|
| 338 |
-
results = []
|
| 339 |
-
|
| 340 |
-
box_cls = [permute_to_N_HWA_K(x, self.num_classes) for x in box_cls]
|
| 341 |
-
box_delta = [permute_to_N_HWA_K(x, 4) for x in box_delta]
|
| 342 |
-
# list[Tensor], one per level, each has shape (N, Hi x Wi x A, K or 4)
|
| 343 |
-
|
| 344 |
-
for img_idx, anchors_per_image in enumerate(anchors):
|
| 345 |
-
image_size = image_sizes[img_idx]
|
| 346 |
-
box_cls_per_image = [box_cls_per_level[img_idx] for box_cls_per_level in box_cls]
|
| 347 |
-
box_reg_per_image = [box_reg_per_level[img_idx] for box_reg_per_level in box_delta]
|
| 348 |
-
results_per_image = self.inference_single_image(
|
| 349 |
-
box_cls_per_image, box_reg_per_image, anchors_per_image, tuple(image_size)
|
| 350 |
-
)
|
| 351 |
-
results.append(results_per_image)
|
| 352 |
-
return results
|
| 353 |
-
|
| 354 |
-
def inference_single_image(self, box_cls, box_delta, anchors, image_size):
|
| 355 |
-
"""
|
| 356 |
-
Single-image inference. Return bounding-box detection results by thresholding
|
| 357 |
-
on scores and applying non-maximum suppression (NMS).
|
| 358 |
-
|
| 359 |
-
Arguments:
|
| 360 |
-
box_cls (list[Tensor]): list of #feature levels. Each entry contains
|
| 361 |
-
tensor of size (H x W x A, K)
|
| 362 |
-
box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
|
| 363 |
-
anchors (list[Boxes]): list of #feature levels. Each entry contains
|
| 364 |
-
a Boxes object, which contains all the anchors for that
|
| 365 |
-
image in that feature level.
|
| 366 |
-
image_size (tuple(H, W)): a tuple of the image height and width.
|
| 367 |
-
|
| 368 |
-
Returns:
|
| 369 |
-
Same as `inference`, but for only one image.
|
| 370 |
-
"""
|
| 371 |
-
boxes_all = []
|
| 372 |
-
scores_all = []
|
| 373 |
-
class_idxs_all = []
|
| 374 |
-
|
| 375 |
-
# Iterate over every feature level
|
| 376 |
-
for box_cls_i, box_reg_i, anchors_i in zip(box_cls, box_delta, anchors):
|
| 377 |
-
# (HxWxAxK,)
|
| 378 |
-
box_cls_i = box_cls_i.flatten().sigmoid_()
|
| 379 |
-
|
| 380 |
-
# Keep top k top scoring indices only.
|
| 381 |
-
num_topk = min(self.topk_candidates, box_reg_i.size(0))
|
| 382 |
-
# torch.sort is actually faster than .topk (at least on GPUs)
|
| 383 |
-
predicted_prob, topk_idxs = box_cls_i.sort(descending=True)
|
| 384 |
-
predicted_prob = predicted_prob[:num_topk]
|
| 385 |
-
topk_idxs = topk_idxs[:num_topk]
|
| 386 |
-
|
| 387 |
-
# filter out the proposals with low confidence score
|
| 388 |
-
keep_idxs = predicted_prob > self.score_threshold
|
| 389 |
-
predicted_prob = predicted_prob[keep_idxs]
|
| 390 |
-
topk_idxs = topk_idxs[keep_idxs]
|
| 391 |
-
|
| 392 |
-
anchor_idxs = topk_idxs // self.num_classes
|
| 393 |
-
classes_idxs = topk_idxs % self.num_classes
|
| 394 |
-
|
| 395 |
-
box_reg_i = box_reg_i[anchor_idxs]
|
| 396 |
-
anchors_i = anchors_i[anchor_idxs]
|
| 397 |
-
# predict boxes
|
| 398 |
-
predicted_boxes = self.box2box_transform.apply_deltas(box_reg_i, anchors_i.tensor)
|
| 399 |
-
|
| 400 |
-
boxes_all.append(predicted_boxes)
|
| 401 |
-
scores_all.append(predicted_prob)
|
| 402 |
-
class_idxs_all.append(classes_idxs)
|
| 403 |
-
|
| 404 |
-
boxes_all, scores_all, class_idxs_all = [
|
| 405 |
-
cat(x) for x in [boxes_all, scores_all, class_idxs_all]
|
| 406 |
-
]
|
| 407 |
-
keep = batched_nms(boxes_all, scores_all, class_idxs_all, self.nms_threshold)
|
| 408 |
-
keep = keep[: self.max_detections_per_image]
|
| 409 |
-
|
| 410 |
-
result = Instances(image_size)
|
| 411 |
-
result.pred_boxes = Boxes(boxes_all[keep])
|
| 412 |
-
result.scores = scores_all[keep]
|
| 413 |
-
result.pred_classes = class_idxs_all[keep]
|
| 414 |
-
return result
|
| 415 |
-
|
| 416 |
-
def preprocess_image(self, batched_inputs):
|
| 417 |
-
"""
|
| 418 |
-
Normalize, pad and batch the input images.
|
| 419 |
-
"""
|
| 420 |
-
images = [x["image"].to(self.device) for x in batched_inputs]
|
| 421 |
-
images = [self.normalizer(x) for x in images]
|
| 422 |
-
images = ImageList.from_tensors(images, self.backbone.size_divisibility)
|
| 423 |
-
return images
|
| 424 |
-
|
| 425 |
-
|
| 426 |
-
class RetinaNetHead(nn.Module):
|
| 427 |
-
"""
|
| 428 |
-
The head used in RetinaNet for object classification and box regression.
|
| 429 |
-
It has two subnets for the two tasks, with a common structure but separate parameters.
|
| 430 |
-
"""
|
| 431 |
-
|
| 432 |
-
def __init__(self, cfg, input_shape: List[ShapeSpec]):
|
| 433 |
-
super().__init__()
|
| 434 |
-
# fmt: off
|
| 435 |
-
in_channels = input_shape[0].channels
|
| 436 |
-
num_classes = cfg.MODEL.RETINANET.NUM_CLASSES
|
| 437 |
-
num_convs = cfg.MODEL.RETINANET.NUM_CONVS
|
| 438 |
-
prior_prob = cfg.MODEL.RETINANET.PRIOR_PROB
|
| 439 |
-
num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors
|
| 440 |
-
# fmt: on
|
| 441 |
-
assert (
|
| 442 |
-
len(set(num_anchors)) == 1
|
| 443 |
-
), "Using different number of anchors between levels is not currently supported!"
|
| 444 |
-
num_anchors = num_anchors[0]
|
| 445 |
-
|
| 446 |
-
cls_subnet = []
|
| 447 |
-
bbox_subnet = []
|
| 448 |
-
for _ in range(num_convs):
|
| 449 |
-
cls_subnet.append(
|
| 450 |
-
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
|
| 451 |
-
)
|
| 452 |
-
cls_subnet.append(nn.ReLU())
|
| 453 |
-
bbox_subnet.append(
|
| 454 |
-
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
|
| 455 |
-
)
|
| 456 |
-
bbox_subnet.append(nn.ReLU())
|
| 457 |
-
|
| 458 |
-
self.cls_subnet = nn.Sequential(*cls_subnet)
|
| 459 |
-
self.bbox_subnet = nn.Sequential(*bbox_subnet)
|
| 460 |
-
self.cls_score = nn.Conv2d(
|
| 461 |
-
in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1
|
| 462 |
-
)
|
| 463 |
-
self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1)
|
| 464 |
-
|
| 465 |
-
# Initialization
|
| 466 |
-
for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]:
|
| 467 |
-
for layer in modules.modules():
|
| 468 |
-
if isinstance(layer, nn.Conv2d):
|
| 469 |
-
torch.nn.init.normal_(layer.weight, mean=0, std=0.01)
|
| 470 |
-
torch.nn.init.constant_(layer.bias, 0)
|
| 471 |
-
|
| 472 |
-
# Use prior in model initialization to improve stability
|
| 473 |
-
bias_value = -math.log((1 - prior_prob) / prior_prob)
|
| 474 |
-
torch.nn.init.constant_(self.cls_score.bias, bias_value)
|
| 475 |
-
|
| 476 |
-
def forward(self, features):
|
| 477 |
-
"""
|
| 478 |
-
Arguments:
|
| 479 |
-
features (list[Tensor]): FPN feature map tensors in high to low resolution.
|
| 480 |
-
Each tensor in the list correspond to different feature levels.
|
| 481 |
-
|
| 482 |
-
Returns:
|
| 483 |
-
logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi).
|
| 484 |
-
The tensor predicts the classification probability
|
| 485 |
-
at each spatial position for each of the A anchors and K object
|
| 486 |
-
classes.
|
| 487 |
-
bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi).
|
| 488 |
-
The tensor predicts 4-vector (dx,dy,dw,dh) box
|
| 489 |
-
regression values for every anchor. These values are the
|
| 490 |
-
relative offset between the anchor and the ground truth box.
|
| 491 |
-
"""
|
| 492 |
-
logits = []
|
| 493 |
-
bbox_reg = []
|
| 494 |
-
for feature in features:
|
| 495 |
-
logits.append(self.cls_score(self.cls_subnet(feature)))
|
| 496 |
-
bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature)))
|
| 497 |
-
return logits, bbox_reg
|
|
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spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tests/test_swap_align2nat.py
DELETED
|
@@ -1,32 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python3
|
| 2 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 3 |
-
|
| 4 |
-
import unittest
|
| 5 |
-
import torch
|
| 6 |
-
from torch.autograd import gradcheck
|
| 7 |
-
|
| 8 |
-
from tensormask.layers.swap_align2nat import SwapAlign2Nat
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class SwapAlign2NatTest(unittest.TestCase):
|
| 12 |
-
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
|
| 13 |
-
def test_swap_align2nat_gradcheck_cuda(self):
|
| 14 |
-
dtype = torch.float64
|
| 15 |
-
device = torch.device("cuda")
|
| 16 |
-
m = SwapAlign2Nat(2).to(dtype=dtype, device=device)
|
| 17 |
-
x = torch.rand(2, 4, 10, 10, dtype=dtype, device=device, requires_grad=True)
|
| 18 |
-
|
| 19 |
-
self.assertTrue(gradcheck(m, x), "gradcheck failed for SwapAlign2Nat CUDA")
|
| 20 |
-
|
| 21 |
-
def _swap_align2nat(self, tensor, lambda_val):
|
| 22 |
-
"""
|
| 23 |
-
The basic setup for testing Swap_Align
|
| 24 |
-
"""
|
| 25 |
-
op = SwapAlign2Nat(lambda_val, pad_val=0.0)
|
| 26 |
-
input = torch.from_numpy(tensor[None, :, :, :].astype("float32"))
|
| 27 |
-
output = op.forward(input.cuda()).cpu().numpy()
|
| 28 |
-
return output[0]
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
if __name__ == "__main__":
|
| 32 |
-
unittest.main()
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|
spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/find.h
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
/*! \file find.h
|
| 19 |
-
* \brief Sequential implementation of find_if.
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
#pragma once
|
| 23 |
-
|
| 24 |
-
#include <thrust/detail/config.h>
|
| 25 |
-
#include <thrust/detail/function.h>
|
| 26 |
-
#include <thrust/system/detail/sequential/execution_policy.h>
|
| 27 |
-
|
| 28 |
-
namespace thrust
|
| 29 |
-
{
|
| 30 |
-
namespace system
|
| 31 |
-
{
|
| 32 |
-
namespace detail
|
| 33 |
-
{
|
| 34 |
-
namespace sequential
|
| 35 |
-
{
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
__thrust_exec_check_disable__
|
| 39 |
-
template<typename DerivedPolicy,
|
| 40 |
-
typename InputIterator,
|
| 41 |
-
typename Predicate>
|
| 42 |
-
__host__ __device__
|
| 43 |
-
InputIterator find_if(execution_policy<DerivedPolicy> &,
|
| 44 |
-
InputIterator first,
|
| 45 |
-
InputIterator last,
|
| 46 |
-
Predicate pred)
|
| 47 |
-
{
|
| 48 |
-
// wrap pred
|
| 49 |
-
thrust::detail::wrapped_function<
|
| 50 |
-
Predicate,
|
| 51 |
-
bool
|
| 52 |
-
> wrapped_pred(pred);
|
| 53 |
-
|
| 54 |
-
while(first != last)
|
| 55 |
-
{
|
| 56 |
-
if (wrapped_pred(*first))
|
| 57 |
-
return first;
|
| 58 |
-
|
| 59 |
-
++first;
|
| 60 |
-
}
|
| 61 |
-
|
| 62 |
-
// return first so zip_iterator works correctly
|
| 63 |
-
return first;
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
} // end namespace sequential
|
| 68 |
-
} // end namespace detail
|
| 69 |
-
} // end namespace system
|
| 70 |
-
} // end namespace thrust
|
| 71 |
-
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|
spaces/CVPR/drawings-to-human/frontend/src/data.ts
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
import type { Color } from './types';
|
| 2 |
-
|
| 3 |
-
export const COLOR_LIST: Color[] = [
|
| 4 |
-
{ color: [0, 0, 0], label: 'background' },
|
| 5 |
-
{ color: [255, 140, 0], label: 'bag' },
|
| 6 |
-
{ color: [255, 255, 0], label: 'belt' },
|
| 7 |
-
{ color: [255, 250, 205], label: 'dress' },
|
| 8 |
-
{ color: [130, 165, 180], label: 'earrings' },
|
| 9 |
-
{ color: [0, 100, 0], label: 'eyeglass' },
|
| 10 |
-
{ color: [16, 78, 139], label: 'face' },
|
| 11 |
-
{ color: [245, 222, 179], label: 'footwear' },
|
| 12 |
-
{ color: [213, 140, 88], label: 'gloves' },
|
| 13 |
-
{ color: [255, 0, 0], label: 'hair' },
|
| 14 |
-
{ color: [127, 255, 212], label: 'headwear' },
|
| 15 |
-
{ color: [70, 130, 180], label: 'leggings' },
|
| 16 |
-
{ color: [90, 140, 90], label: 'necklace' },
|
| 17 |
-
{ color: [50, 205, 50], label: 'neckwear' },
|
| 18 |
-
{ color: [220, 220, 220], label: 'outer' },
|
| 19 |
-
{ color: [211, 211, 211], label: 'pants' },
|
| 20 |
-
{ color: [50, 205, 174], label: 'ring' },
|
| 21 |
-
{ color: [185, 210, 205], label: 'rompers' },
|
| 22 |
-
{ color: [144, 238, 144], label: 'skin' },
|
| 23 |
-
{ color: [250, 235, 215], label: 'skirt' },
|
| 24 |
-
{ color: [160, 140, 88], label: 'socks' },
|
| 25 |
-
{ color: [225, 141, 151], label: 'tie' },
|
| 26 |
-
{ color: [255, 250, 250], label: 'top' },
|
| 27 |
-
{ color: [50, 155, 250], label: 'wrist wearing' }
|
| 28 |
-
];
|
| 29 |
-
|
| 30 |
-
export const API = 'https://radames-text2human-api.hf.space';
|
| 31 |
-
// export const API = 'http://localhost:7860';
|
| 32 |
-
// export const API = 'https://hf.space/embed/CVPR/Text2Human';
|
| 33 |
-
// export const API = 'https://hf.space/embed/hysts/Text2Human';
|
| 34 |
-
//
|
| 35 |
-
export const IMAGES_LIST = [
|
| 36 |
-
'/samples/WOMEN-Skirts-id_00004406-02_7_additional_segm.png',
|
| 37 |
-
'/samples/MEN-Pants-id_00002565-02_1_front_segm.png',
|
| 38 |
-
'/samples/MEN-Pants-id_00005213-02_4_full_segm.png',
|
| 39 |
-
'/samples/WOMEN-Blouses_Shirts-id_00002356-02_4_full_segm.png',
|
| 40 |
-
'/samples/WOMEN-Blouses_Shirts-id_00004090-03_7_additional_segm.png',
|
| 41 |
-
'/samples/WOMEN-Cardigans-id_00000853-01_2_side_segm.png',
|
| 42 |
-
'/samples/WOMEN-Cardigans-id_00000899-02_1_front_segm.png',
|
| 43 |
-
'/samples/WOMEN-Cardigans-id_00006462-02_7_additional_segm.png',
|
| 44 |
-
'/samples/WOMEN-Dresses-id_00000021-05_1_front_segm.png',
|
| 45 |
-
'/samples/WOMEN-Dresses-id_00002430-04_1_front_segm.png',
|
| 46 |
-
'/samples/WOMEN-Dresses-id_00002966-01_7_additional_segm.png',
|
| 47 |
-
'/samples/WOMEN-Dresses-id_00007332-01_3_back_segm.png',
|
| 48 |
-
'/samples/WOMEN-Graphic_Tees-id_00007242-01_4_full_segm.png',
|
| 49 |
-
'/samples/WOMEN-Jackets_Coats-id_00005263-06_1_front_segm.png',
|
| 50 |
-
'/samples/WOMEN-Jackets_Coats-id_00006296-05_7_additional_segm.png',
|
| 51 |
-
'/samples/WOMEN-Rompers_Jumpsuits-id_00004575-02_1_front_segm.png',
|
| 52 |
-
'/samples/WOMEN-Sweaters-id_00004667-01_4_full_segm.png',
|
| 53 |
-
'/samples/WOMEN-Tees_Tanks-id_00001620-02_4_full_segm.png',
|
| 54 |
-
'/samples/WOMEN-Tees_Tanks-id_00005288-01_2_side_segm.png',
|
| 55 |
-
'/samples/WOMEN-Tees_Tanks-id_00006566-04_4_full_segm.png'
|
| 56 |
-
];
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
export const SECTIONS = [
|
| 60 |
-
"upper clothing texture",
|
| 61 |
-
"lower clothing texture",
|
| 62 |
-
"outer clothing texture"
|
| 63 |
-
];
|
| 64 |
-
|
| 65 |
-
export const TEXTURES = [
|
| 66 |
-
"pure color",
|
| 67 |
-
"stripe/spline",
|
| 68 |
-
"plaid/lattice",
|
| 69 |
-
"floral",
|
| 70 |
-
"denim"
|
| 71 |
-
];
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spaces/CactiStaccingCrane/OpenAssistant-oasst-sft-1-pythia-12b/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: OpenAssistant Oasst Sft 1 Pythia 12b
|
| 3 |
-
emoji: 🦀
|
| 4 |
-
colorFrom: blue
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.20.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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spaces/CikeyQI/meme-api/meme_generator/memes/anti_kidnap/__init__.py
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
from pathlib import Path
|
| 2 |
-
from typing import List
|
| 3 |
-
|
| 4 |
-
from pil_utils import BuildImage
|
| 5 |
-
|
| 6 |
-
from meme_generator import add_meme
|
| 7 |
-
|
| 8 |
-
img_dir = Path(__file__).parent / "images"
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def anti_kidnap(images: List[BuildImage], texts, args):
|
| 12 |
-
img = images[0].convert("RGBA").resize((450, 450), keep_ratio=True)
|
| 13 |
-
frame = BuildImage.open(img_dir / "0.png")
|
| 14 |
-
frame.paste(img, (30, 78), below=True)
|
| 15 |
-
return frame.save_jpg()
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
add_meme("anti_kidnap", anti_kidnap, min_images=1, max_images=1, keywords=["防诱拐"])
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spaces/Clatonh/moth_or_butterfly/app.py
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
|
| 2 |
-
|
| 3 |
-
# Cell
|
| 4 |
-
from fastai.vision.all import *
|
| 5 |
-
import gradio as gr
|
| 6 |
-
|
| 7 |
-
# Cell
|
| 8 |
-
title = 'Is it a Butterfly or Moth'
|
| 9 |
-
desc = '<p style="text-align: center;">Prediction model built using FastAI to predict if its a Butterfly or Moth. (other images will show wrong results, no promises <img src="https://html-online.com/editor/tiny4_9_11/plugins/emoticons/img/smiley-laughing.gif" alt="laughing" />) </p>'
|
| 10 |
-
|
| 11 |
-
# Cell
|
| 12 |
-
learn = load_learner('export.pkl')
|
| 13 |
-
|
| 14 |
-
# Cell
|
| 15 |
-
categories = learn.dls.vocab
|
| 16 |
-
|
| 17 |
-
def classify_image(img):
|
| 18 |
-
pred,idx,probs = learn.predict(img)
|
| 19 |
-
return dict(zip(categories, map(float,probs)))
|
| 20 |
-
|
| 21 |
-
# Cell
|
| 22 |
-
image = gr.inputs.Image(shape=(192, 192))
|
| 23 |
-
label = gr.outputs.Label()
|
| 24 |
-
examples = ['Butterfly.jpg','Moth.jpg']
|
| 25 |
-
|
| 26 |
-
# Cell
|
| 27 |
-
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples,title=title,description=desc)
|
| 28 |
-
intf.launch()
|
|
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|
spaces/DHEIVER/classificador_de_imagem_colonoscopia/app.py
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from transformers import ViTFeatureExtractor, ViTForImageClassification
|
| 3 |
-
import numpy as np
|
| 4 |
-
import datetime
|
| 5 |
-
|
| 6 |
-
# Mapeamento de classe ID para rótulo
|
| 7 |
-
id2label = {
|
| 8 |
-
"0": "dyed-lifted-polyps",
|
| 9 |
-
"1": "dyed-resection-margins",
|
| 10 |
-
"2": "esophagitis",
|
| 11 |
-
"3": "normal-cecum",
|
| 12 |
-
"4": "normal-pylorus",
|
| 13 |
-
"5": "normal-z-line",
|
| 14 |
-
"6": "polyps",
|
| 15 |
-
"7": "ulcerative-colitis"
|
| 16 |
-
}
|
| 17 |
-
|
| 18 |
-
# Carregue o modelo ViT
|
| 19 |
-
model_name = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy"
|
| 20 |
-
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
|
| 21 |
-
model = ViTForImageClassification.from_pretrained(model_name)
|
| 22 |
-
|
| 23 |
-
# Função para classificar a imagem
|
| 24 |
-
def classify_image(input_image):
|
| 25 |
-
# Pré-processar a imagem usando o extrator de características
|
| 26 |
-
inputs = feature_extractor(input_image, return_tensors="pt")
|
| 27 |
-
# Realizar inferência com o modelo
|
| 28 |
-
outputs = model(**inputs)
|
| 29 |
-
# Obter a classe prevista
|
| 30 |
-
predicted_class_id = np.argmax(outputs.logits[0].detach().numpy())
|
| 31 |
-
# Obter o rótulo da classe a partir do mapeamento id2label
|
| 32 |
-
predicted_class_label = id2label.get(str(predicted_class_id), "Desconhecido")
|
| 33 |
-
|
| 34 |
-
# Obter a data e hora atual
|
| 35 |
-
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
| 36 |
-
|
| 37 |
-
# Formatar a saída em HTML com rótulo da classe e data/hora
|
| 38 |
-
result_html = f"""
|
| 39 |
-
<h2>Resultado da Classificação</h2>
|
| 40 |
-
<p><strong>Rótulo da Classe:</strong> {predicted_class_label}</p>
|
| 41 |
-
<p><strong>Data e Hora:</strong> {current_time}</p>
|
| 42 |
-
"""
|
| 43 |
-
|
| 44 |
-
# Retornar o resultado formatado em HTML
|
| 45 |
-
return result_html
|
| 46 |
-
|
| 47 |
-
# Informações de como usar o aplicativo em HTML
|
| 48 |
-
instructions_html = """
|
| 49 |
-
<h2>Como Usar o Aplicativo</h2>
|
| 50 |
-
<ol>
|
| 51 |
-
<li>Clique no botão 'Escolher Arquivo' para fazer o upload de uma imagem colonoscópica.</li>
|
| 52 |
-
<li>Aguarde a classificação automática.</li>
|
| 53 |
-
<li>O resultado mostrará o rótulo da classe e a data e hora da classificação.</li>
|
| 54 |
-
</ol>
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
# Criar uma interface Gradio com informações de diagnóstico, HTML e instruções
|
| 58 |
-
interface = gr.Interface(
|
| 59 |
-
fn=classify_image,
|
| 60 |
-
inputs=gr.inputs.Image(type="numpy", label="Carregar uma imagem"),
|
| 61 |
-
outputs=gr.outputs.HTML(),
|
| 62 |
-
title="Classificador de Imagem ViT para Colonoscopia",
|
| 63 |
-
description="""
|
| 64 |
-
<h3>Classifique imagens colonoscópicas usando um modelo Vision Transformer (ViT).</h3>
|
| 65 |
-
<p>O modelo identificará a condição ou diagnóstico da imagem, como 'polyps', 'esophagitis', etc.</p>
|
| 66 |
-
""",
|
| 67 |
-
article=instructions_html
|
| 68 |
-
)
|
| 69 |
-
|
| 70 |
-
# Iniciar a aplicação Gradio
|
| 71 |
-
interface.launch(share=True) # Compartilhar a interface com um link público
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/base.py
DELETED
|
@@ -1,111 +0,0 @@
|
|
| 1 |
-
"""Various base classes."""
|
| 2 |
-
from types import coroutine
|
| 3 |
-
from collections.abc import Coroutine
|
| 4 |
-
from asyncio import get_running_loop
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class AsyncBase:
|
| 8 |
-
def __init__(self, file, loop, executor):
|
| 9 |
-
self._file = file
|
| 10 |
-
self._executor = executor
|
| 11 |
-
self._ref_loop = loop
|
| 12 |
-
|
| 13 |
-
@property
|
| 14 |
-
def _loop(self):
|
| 15 |
-
return self._ref_loop or get_running_loop()
|
| 16 |
-
|
| 17 |
-
def __aiter__(self):
|
| 18 |
-
"""We are our own iterator."""
|
| 19 |
-
return self
|
| 20 |
-
|
| 21 |
-
def __repr__(self):
|
| 22 |
-
return super().__repr__() + " wrapping " + repr(self._file)
|
| 23 |
-
|
| 24 |
-
async def __anext__(self):
|
| 25 |
-
"""Simulate normal file iteration."""
|
| 26 |
-
line = await self.readline()
|
| 27 |
-
if line:
|
| 28 |
-
return line
|
| 29 |
-
else:
|
| 30 |
-
raise StopAsyncIteration
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
class AsyncIndirectBase(AsyncBase):
|
| 34 |
-
def __init__(self, name, loop, executor, indirect):
|
| 35 |
-
self._indirect = indirect
|
| 36 |
-
self._name = name
|
| 37 |
-
super().__init__(None, loop, executor)
|
| 38 |
-
|
| 39 |
-
@property
|
| 40 |
-
def _file(self):
|
| 41 |
-
return self._indirect()
|
| 42 |
-
|
| 43 |
-
@_file.setter
|
| 44 |
-
def _file(self, v):
|
| 45 |
-
pass # discard writes
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
class _ContextManager(Coroutine):
|
| 49 |
-
__slots__ = ("_coro", "_obj")
|
| 50 |
-
|
| 51 |
-
def __init__(self, coro):
|
| 52 |
-
self._coro = coro
|
| 53 |
-
self._obj = None
|
| 54 |
-
|
| 55 |
-
def send(self, value):
|
| 56 |
-
return self._coro.send(value)
|
| 57 |
-
|
| 58 |
-
def throw(self, typ, val=None, tb=None):
|
| 59 |
-
if val is None:
|
| 60 |
-
return self._coro.throw(typ)
|
| 61 |
-
elif tb is None:
|
| 62 |
-
return self._coro.throw(typ, val)
|
| 63 |
-
else:
|
| 64 |
-
return self._coro.throw(typ, val, tb)
|
| 65 |
-
|
| 66 |
-
def close(self):
|
| 67 |
-
return self._coro.close()
|
| 68 |
-
|
| 69 |
-
@property
|
| 70 |
-
def gi_frame(self):
|
| 71 |
-
return self._coro.gi_frame
|
| 72 |
-
|
| 73 |
-
@property
|
| 74 |
-
def gi_running(self):
|
| 75 |
-
return self._coro.gi_running
|
| 76 |
-
|
| 77 |
-
@property
|
| 78 |
-
def gi_code(self):
|
| 79 |
-
return self._coro.gi_code
|
| 80 |
-
|
| 81 |
-
def __next__(self):
|
| 82 |
-
return self.send(None)
|
| 83 |
-
|
| 84 |
-
@coroutine
|
| 85 |
-
def __iter__(self):
|
| 86 |
-
resp = yield from self._coro
|
| 87 |
-
return resp
|
| 88 |
-
|
| 89 |
-
def __await__(self):
|
| 90 |
-
resp = yield from self._coro
|
| 91 |
-
return resp
|
| 92 |
-
|
| 93 |
-
async def __anext__(self):
|
| 94 |
-
resp = await self._coro
|
| 95 |
-
return resp
|
| 96 |
-
|
| 97 |
-
async def __aenter__(self):
|
| 98 |
-
self._obj = await self._coro
|
| 99 |
-
return self._obj
|
| 100 |
-
|
| 101 |
-
async def __aexit__(self, exc_type, exc, tb):
|
| 102 |
-
self._obj.close()
|
| 103 |
-
self._obj = None
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
class AiofilesContextManager(_ContextManager):
|
| 107 |
-
"""An adjusted async context manager for aiofiles."""
|
| 108 |
-
|
| 109 |
-
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
| 110 |
-
await self._obj.close()
|
| 111 |
-
self._obj = None
|
|
|
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/converters.py
DELETED
|
@@ -1,144 +0,0 @@
|
|
| 1 |
-
# SPDX-License-Identifier: MIT
|
| 2 |
-
|
| 3 |
-
"""
|
| 4 |
-
Commonly useful converters.
|
| 5 |
-
"""
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
import typing
|
| 9 |
-
|
| 10 |
-
from ._compat import _AnnotationExtractor
|
| 11 |
-
from ._make import NOTHING, Factory, pipe
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
__all__ = [
|
| 15 |
-
"default_if_none",
|
| 16 |
-
"optional",
|
| 17 |
-
"pipe",
|
| 18 |
-
"to_bool",
|
| 19 |
-
]
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def optional(converter):
|
| 23 |
-
"""
|
| 24 |
-
A converter that allows an attribute to be optional. An optional attribute
|
| 25 |
-
is one which can be set to ``None``.
|
| 26 |
-
|
| 27 |
-
Type annotations will be inferred from the wrapped converter's, if it
|
| 28 |
-
has any.
|
| 29 |
-
|
| 30 |
-
:param callable converter: the converter that is used for non-``None``
|
| 31 |
-
values.
|
| 32 |
-
|
| 33 |
-
.. versionadded:: 17.1.0
|
| 34 |
-
"""
|
| 35 |
-
|
| 36 |
-
def optional_converter(val):
|
| 37 |
-
if val is None:
|
| 38 |
-
return None
|
| 39 |
-
return converter(val)
|
| 40 |
-
|
| 41 |
-
xtr = _AnnotationExtractor(converter)
|
| 42 |
-
|
| 43 |
-
t = xtr.get_first_param_type()
|
| 44 |
-
if t:
|
| 45 |
-
optional_converter.__annotations__["val"] = typing.Optional[t]
|
| 46 |
-
|
| 47 |
-
rt = xtr.get_return_type()
|
| 48 |
-
if rt:
|
| 49 |
-
optional_converter.__annotations__["return"] = typing.Optional[rt]
|
| 50 |
-
|
| 51 |
-
return optional_converter
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def default_if_none(default=NOTHING, factory=None):
|
| 55 |
-
"""
|
| 56 |
-
A converter that allows to replace ``None`` values by *default* or the
|
| 57 |
-
result of *factory*.
|
| 58 |
-
|
| 59 |
-
:param default: Value to be used if ``None`` is passed. Passing an instance
|
| 60 |
-
of `attrs.Factory` is supported, however the ``takes_self`` option
|
| 61 |
-
is *not*.
|
| 62 |
-
:param callable factory: A callable that takes no parameters whose result
|
| 63 |
-
is used if ``None`` is passed.
|
| 64 |
-
|
| 65 |
-
:raises TypeError: If **neither** *default* or *factory* is passed.
|
| 66 |
-
:raises TypeError: If **both** *default* and *factory* are passed.
|
| 67 |
-
:raises ValueError: If an instance of `attrs.Factory` is passed with
|
| 68 |
-
``takes_self=True``.
|
| 69 |
-
|
| 70 |
-
.. versionadded:: 18.2.0
|
| 71 |
-
"""
|
| 72 |
-
if default is NOTHING and factory is None:
|
| 73 |
-
raise TypeError("Must pass either `default` or `factory`.")
|
| 74 |
-
|
| 75 |
-
if default is not NOTHING and factory is not None:
|
| 76 |
-
raise TypeError(
|
| 77 |
-
"Must pass either `default` or `factory` but not both."
|
| 78 |
-
)
|
| 79 |
-
|
| 80 |
-
if factory is not None:
|
| 81 |
-
default = Factory(factory)
|
| 82 |
-
|
| 83 |
-
if isinstance(default, Factory):
|
| 84 |
-
if default.takes_self:
|
| 85 |
-
raise ValueError(
|
| 86 |
-
"`takes_self` is not supported by default_if_none."
|
| 87 |
-
)
|
| 88 |
-
|
| 89 |
-
def default_if_none_converter(val):
|
| 90 |
-
if val is not None:
|
| 91 |
-
return val
|
| 92 |
-
|
| 93 |
-
return default.factory()
|
| 94 |
-
|
| 95 |
-
else:
|
| 96 |
-
|
| 97 |
-
def default_if_none_converter(val):
|
| 98 |
-
if val is not None:
|
| 99 |
-
return val
|
| 100 |
-
|
| 101 |
-
return default
|
| 102 |
-
|
| 103 |
-
return default_if_none_converter
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def to_bool(val):
|
| 107 |
-
"""
|
| 108 |
-
Convert "boolean" strings (e.g., from env. vars.) to real booleans.
|
| 109 |
-
|
| 110 |
-
Values mapping to :code:`True`:
|
| 111 |
-
|
| 112 |
-
- :code:`True`
|
| 113 |
-
- :code:`"true"` / :code:`"t"`
|
| 114 |
-
- :code:`"yes"` / :code:`"y"`
|
| 115 |
-
- :code:`"on"`
|
| 116 |
-
- :code:`"1"`
|
| 117 |
-
- :code:`1`
|
| 118 |
-
|
| 119 |
-
Values mapping to :code:`False`:
|
| 120 |
-
|
| 121 |
-
- :code:`False`
|
| 122 |
-
- :code:`"false"` / :code:`"f"`
|
| 123 |
-
- :code:`"no"` / :code:`"n"`
|
| 124 |
-
- :code:`"off"`
|
| 125 |
-
- :code:`"0"`
|
| 126 |
-
- :code:`0`
|
| 127 |
-
|
| 128 |
-
:raises ValueError: for any other value.
|
| 129 |
-
|
| 130 |
-
.. versionadded:: 21.3.0
|
| 131 |
-
"""
|
| 132 |
-
if isinstance(val, str):
|
| 133 |
-
val = val.lower()
|
| 134 |
-
truthy = {True, "true", "t", "yes", "y", "on", "1", 1}
|
| 135 |
-
falsy = {False, "false", "f", "no", "n", "off", "0", 0}
|
| 136 |
-
try:
|
| 137 |
-
if val in truthy:
|
| 138 |
-
return True
|
| 139 |
-
if val in falsy:
|
| 140 |
-
return False
|
| 141 |
-
except TypeError:
|
| 142 |
-
# Raised when "val" is not hashable (e.g., lists)
|
| 143 |
-
pass
|
| 144 |
-
raise ValueError(f"Cannot convert value to bool: {val}")
|
|
|
|
|
|
|
|
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|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/psOperators.py
DELETED
|
@@ -1,574 +0,0 @@
|
|
| 1 |
-
_accessstrings = {0: "", 1: "readonly", 2: "executeonly", 3: "noaccess"}
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
class ps_object(object):
|
| 5 |
-
|
| 6 |
-
literal = 1
|
| 7 |
-
access = 0
|
| 8 |
-
value = None
|
| 9 |
-
|
| 10 |
-
def __init__(self, value):
|
| 11 |
-
self.value = value
|
| 12 |
-
self.type = self.__class__.__name__[3:] + "type"
|
| 13 |
-
|
| 14 |
-
def __repr__(self):
|
| 15 |
-
return "<%s %s>" % (self.__class__.__name__[3:], repr(self.value))
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
class ps_operator(ps_object):
|
| 19 |
-
|
| 20 |
-
literal = 0
|
| 21 |
-
|
| 22 |
-
def __init__(self, name, function):
|
| 23 |
-
self.name = name
|
| 24 |
-
self.function = function
|
| 25 |
-
self.type = self.__class__.__name__[3:] + "type"
|
| 26 |
-
|
| 27 |
-
def __repr__(self):
|
| 28 |
-
return "<operator %s>" % self.name
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
class ps_procedure(ps_object):
|
| 32 |
-
literal = 0
|
| 33 |
-
|
| 34 |
-
def __repr__(self):
|
| 35 |
-
return "<procedure>"
|
| 36 |
-
|
| 37 |
-
def __str__(self):
|
| 38 |
-
psstring = "{"
|
| 39 |
-
for i in range(len(self.value)):
|
| 40 |
-
if i:
|
| 41 |
-
psstring = psstring + " " + str(self.value[i])
|
| 42 |
-
else:
|
| 43 |
-
psstring = psstring + str(self.value[i])
|
| 44 |
-
return psstring + "}"
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
class ps_name(ps_object):
|
| 48 |
-
literal = 0
|
| 49 |
-
|
| 50 |
-
def __str__(self):
|
| 51 |
-
if self.literal:
|
| 52 |
-
return "/" + self.value
|
| 53 |
-
else:
|
| 54 |
-
return self.value
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
class ps_literal(ps_object):
|
| 58 |
-
def __str__(self):
|
| 59 |
-
return "/" + self.value
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
class ps_array(ps_object):
|
| 63 |
-
def __str__(self):
|
| 64 |
-
psstring = "["
|
| 65 |
-
for i in range(len(self.value)):
|
| 66 |
-
item = self.value[i]
|
| 67 |
-
access = _accessstrings[item.access]
|
| 68 |
-
if access:
|
| 69 |
-
access = " " + access
|
| 70 |
-
if i:
|
| 71 |
-
psstring = psstring + " " + str(item) + access
|
| 72 |
-
else:
|
| 73 |
-
psstring = psstring + str(item) + access
|
| 74 |
-
return psstring + "]"
|
| 75 |
-
|
| 76 |
-
def __repr__(self):
|
| 77 |
-
return "<array>"
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
_type1_pre_eexec_order = [
|
| 81 |
-
"FontInfo",
|
| 82 |
-
"FontName",
|
| 83 |
-
"Encoding",
|
| 84 |
-
"PaintType",
|
| 85 |
-
"FontType",
|
| 86 |
-
"FontMatrix",
|
| 87 |
-
"FontBBox",
|
| 88 |
-
"UniqueID",
|
| 89 |
-
"Metrics",
|
| 90 |
-
"StrokeWidth",
|
| 91 |
-
]
|
| 92 |
-
|
| 93 |
-
_type1_fontinfo_order = [
|
| 94 |
-
"version",
|
| 95 |
-
"Notice",
|
| 96 |
-
"FullName",
|
| 97 |
-
"FamilyName",
|
| 98 |
-
"Weight",
|
| 99 |
-
"ItalicAngle",
|
| 100 |
-
"isFixedPitch",
|
| 101 |
-
"UnderlinePosition",
|
| 102 |
-
"UnderlineThickness",
|
| 103 |
-
]
|
| 104 |
-
|
| 105 |
-
_type1_post_eexec_order = ["Private", "CharStrings", "FID"]
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
def _type1_item_repr(key, value):
|
| 109 |
-
psstring = ""
|
| 110 |
-
access = _accessstrings[value.access]
|
| 111 |
-
if access:
|
| 112 |
-
access = access + " "
|
| 113 |
-
if key == "CharStrings":
|
| 114 |
-
psstring = psstring + "/%s %s def\n" % (
|
| 115 |
-
key,
|
| 116 |
-
_type1_CharString_repr(value.value),
|
| 117 |
-
)
|
| 118 |
-
elif key == "Encoding":
|
| 119 |
-
psstring = psstring + _type1_Encoding_repr(value, access)
|
| 120 |
-
else:
|
| 121 |
-
psstring = psstring + "/%s %s %sdef\n" % (str(key), str(value), access)
|
| 122 |
-
return psstring
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
def _type1_Encoding_repr(encoding, access):
|
| 126 |
-
encoding = encoding.value
|
| 127 |
-
psstring = "/Encoding 256 array\n0 1 255 {1 index exch /.notdef put} for\n"
|
| 128 |
-
for i in range(256):
|
| 129 |
-
name = encoding[i].value
|
| 130 |
-
if name != ".notdef":
|
| 131 |
-
psstring = psstring + "dup %d /%s put\n" % (i, name)
|
| 132 |
-
return psstring + access + "def\n"
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
def _type1_CharString_repr(charstrings):
|
| 136 |
-
items = sorted(charstrings.items())
|
| 137 |
-
return "xxx"
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
class ps_font(ps_object):
|
| 141 |
-
def __str__(self):
|
| 142 |
-
psstring = "%d dict dup begin\n" % len(self.value)
|
| 143 |
-
for key in _type1_pre_eexec_order:
|
| 144 |
-
try:
|
| 145 |
-
value = self.value[key]
|
| 146 |
-
except KeyError:
|
| 147 |
-
pass
|
| 148 |
-
else:
|
| 149 |
-
psstring = psstring + _type1_item_repr(key, value)
|
| 150 |
-
items = sorted(self.value.items())
|
| 151 |
-
for key, value in items:
|
| 152 |
-
if key not in _type1_pre_eexec_order + _type1_post_eexec_order:
|
| 153 |
-
psstring = psstring + _type1_item_repr(key, value)
|
| 154 |
-
psstring = psstring + "currentdict end\ncurrentfile eexec\ndup "
|
| 155 |
-
for key in _type1_post_eexec_order:
|
| 156 |
-
try:
|
| 157 |
-
value = self.value[key]
|
| 158 |
-
except KeyError:
|
| 159 |
-
pass
|
| 160 |
-
else:
|
| 161 |
-
psstring = psstring + _type1_item_repr(key, value)
|
| 162 |
-
return (
|
| 163 |
-
psstring
|
| 164 |
-
+ "dup/FontName get exch definefont pop\nmark currentfile closefile\n"
|
| 165 |
-
+ 8 * (64 * "0" + "\n")
|
| 166 |
-
+ "cleartomark"
|
| 167 |
-
+ "\n"
|
| 168 |
-
)
|
| 169 |
-
|
| 170 |
-
def __repr__(self):
|
| 171 |
-
return "<font>"
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
class ps_file(ps_object):
|
| 175 |
-
pass
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
class ps_dict(ps_object):
|
| 179 |
-
def __str__(self):
|
| 180 |
-
psstring = "%d dict dup begin\n" % len(self.value)
|
| 181 |
-
items = sorted(self.value.items())
|
| 182 |
-
for key, value in items:
|
| 183 |
-
access = _accessstrings[value.access]
|
| 184 |
-
if access:
|
| 185 |
-
access = access + " "
|
| 186 |
-
psstring = psstring + "/%s %s %sdef\n" % (str(key), str(value), access)
|
| 187 |
-
return psstring + "end "
|
| 188 |
-
|
| 189 |
-
def __repr__(self):
|
| 190 |
-
return "<dict>"
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
class ps_mark(ps_object):
|
| 194 |
-
def __init__(self):
|
| 195 |
-
self.value = "mark"
|
| 196 |
-
self.type = self.__class__.__name__[3:] + "type"
|
| 197 |
-
|
| 198 |
-
|
| 199 |
-
class ps_procmark(ps_object):
|
| 200 |
-
def __init__(self):
|
| 201 |
-
self.value = "procmark"
|
| 202 |
-
self.type = self.__class__.__name__[3:] + "type"
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
class ps_null(ps_object):
|
| 206 |
-
def __init__(self):
|
| 207 |
-
self.type = self.__class__.__name__[3:] + "type"
|
| 208 |
-
|
| 209 |
-
|
| 210 |
-
class ps_boolean(ps_object):
|
| 211 |
-
def __str__(self):
|
| 212 |
-
if self.value:
|
| 213 |
-
return "true"
|
| 214 |
-
else:
|
| 215 |
-
return "false"
|
| 216 |
-
|
| 217 |
-
|
| 218 |
-
class ps_string(ps_object):
|
| 219 |
-
def __str__(self):
|
| 220 |
-
return "(%s)" % repr(self.value)[1:-1]
|
| 221 |
-
|
| 222 |
-
|
| 223 |
-
class ps_integer(ps_object):
|
| 224 |
-
def __str__(self):
|
| 225 |
-
return repr(self.value)
|
| 226 |
-
|
| 227 |
-
|
| 228 |
-
class ps_real(ps_object):
|
| 229 |
-
def __str__(self):
|
| 230 |
-
return repr(self.value)
|
| 231 |
-
|
| 232 |
-
|
| 233 |
-
class PSOperators(object):
|
| 234 |
-
def ps_def(self):
|
| 235 |
-
obj = self.pop()
|
| 236 |
-
name = self.pop()
|
| 237 |
-
self.dictstack[-1][name.value] = obj
|
| 238 |
-
|
| 239 |
-
def ps_bind(self):
|
| 240 |
-
proc = self.pop("proceduretype")
|
| 241 |
-
self.proc_bind(proc)
|
| 242 |
-
self.push(proc)
|
| 243 |
-
|
| 244 |
-
def proc_bind(self, proc):
|
| 245 |
-
for i in range(len(proc.value)):
|
| 246 |
-
item = proc.value[i]
|
| 247 |
-
if item.type == "proceduretype":
|
| 248 |
-
self.proc_bind(item)
|
| 249 |
-
else:
|
| 250 |
-
if not item.literal:
|
| 251 |
-
try:
|
| 252 |
-
obj = self.resolve_name(item.value)
|
| 253 |
-
except:
|
| 254 |
-
pass
|
| 255 |
-
else:
|
| 256 |
-
if obj.type == "operatortype":
|
| 257 |
-
proc.value[i] = obj
|
| 258 |
-
|
| 259 |
-
def ps_exch(self):
|
| 260 |
-
if len(self.stack) < 2:
|
| 261 |
-
raise RuntimeError("stack underflow")
|
| 262 |
-
obj1 = self.pop()
|
| 263 |
-
obj2 = self.pop()
|
| 264 |
-
self.push(obj1)
|
| 265 |
-
self.push(obj2)
|
| 266 |
-
|
| 267 |
-
def ps_dup(self):
|
| 268 |
-
if not self.stack:
|
| 269 |
-
raise RuntimeError("stack underflow")
|
| 270 |
-
self.push(self.stack[-1])
|
| 271 |
-
|
| 272 |
-
def ps_exec(self):
|
| 273 |
-
obj = self.pop()
|
| 274 |
-
if obj.type == "proceduretype":
|
| 275 |
-
self.call_procedure(obj)
|
| 276 |
-
else:
|
| 277 |
-
self.handle_object(obj)
|
| 278 |
-
|
| 279 |
-
def ps_count(self):
|
| 280 |
-
self.push(ps_integer(len(self.stack)))
|
| 281 |
-
|
| 282 |
-
def ps_eq(self):
|
| 283 |
-
any1 = self.pop()
|
| 284 |
-
any2 = self.pop()
|
| 285 |
-
self.push(ps_boolean(any1.value == any2.value))
|
| 286 |
-
|
| 287 |
-
def ps_ne(self):
|
| 288 |
-
any1 = self.pop()
|
| 289 |
-
any2 = self.pop()
|
| 290 |
-
self.push(ps_boolean(any1.value != any2.value))
|
| 291 |
-
|
| 292 |
-
def ps_cvx(self):
|
| 293 |
-
obj = self.pop()
|
| 294 |
-
obj.literal = 0
|
| 295 |
-
self.push(obj)
|
| 296 |
-
|
| 297 |
-
def ps_matrix(self):
|
| 298 |
-
matrix = [
|
| 299 |
-
ps_real(1.0),
|
| 300 |
-
ps_integer(0),
|
| 301 |
-
ps_integer(0),
|
| 302 |
-
ps_real(1.0),
|
| 303 |
-
ps_integer(0),
|
| 304 |
-
ps_integer(0),
|
| 305 |
-
]
|
| 306 |
-
self.push(ps_array(matrix))
|
| 307 |
-
|
| 308 |
-
def ps_string(self):
|
| 309 |
-
num = self.pop("integertype").value
|
| 310 |
-
self.push(ps_string("\0" * num))
|
| 311 |
-
|
| 312 |
-
def ps_type(self):
|
| 313 |
-
obj = self.pop()
|
| 314 |
-
self.push(ps_string(obj.type))
|
| 315 |
-
|
| 316 |
-
def ps_store(self):
|
| 317 |
-
value = self.pop()
|
| 318 |
-
key = self.pop()
|
| 319 |
-
name = key.value
|
| 320 |
-
for i in range(len(self.dictstack) - 1, -1, -1):
|
| 321 |
-
if name in self.dictstack[i]:
|
| 322 |
-
self.dictstack[i][name] = value
|
| 323 |
-
break
|
| 324 |
-
self.dictstack[-1][name] = value
|
| 325 |
-
|
| 326 |
-
def ps_where(self):
|
| 327 |
-
name = self.pop()
|
| 328 |
-
# XXX
|
| 329 |
-
self.push(ps_boolean(0))
|
| 330 |
-
|
| 331 |
-
def ps_systemdict(self):
|
| 332 |
-
self.push(ps_dict(self.dictstack[0]))
|
| 333 |
-
|
| 334 |
-
def ps_userdict(self):
|
| 335 |
-
self.push(ps_dict(self.dictstack[1]))
|
| 336 |
-
|
| 337 |
-
def ps_currentdict(self):
|
| 338 |
-
self.push(ps_dict(self.dictstack[-1]))
|
| 339 |
-
|
| 340 |
-
def ps_currentfile(self):
|
| 341 |
-
self.push(ps_file(self.tokenizer))
|
| 342 |
-
|
| 343 |
-
def ps_eexec(self):
|
| 344 |
-
f = self.pop("filetype").value
|
| 345 |
-
f.starteexec()
|
| 346 |
-
|
| 347 |
-
def ps_closefile(self):
|
| 348 |
-
f = self.pop("filetype").value
|
| 349 |
-
f.skipwhite()
|
| 350 |
-
f.stopeexec()
|
| 351 |
-
|
| 352 |
-
def ps_cleartomark(self):
|
| 353 |
-
obj = self.pop()
|
| 354 |
-
while obj != self.mark:
|
| 355 |
-
obj = self.pop()
|
| 356 |
-
|
| 357 |
-
def ps_readstring(self, ps_boolean=ps_boolean, len=len):
|
| 358 |
-
s = self.pop("stringtype")
|
| 359 |
-
oldstr = s.value
|
| 360 |
-
f = self.pop("filetype")
|
| 361 |
-
# pad = file.value.read(1)
|
| 362 |
-
# for StringIO, this is faster
|
| 363 |
-
f.value.pos = f.value.pos + 1
|
| 364 |
-
newstr = f.value.read(len(oldstr))
|
| 365 |
-
s.value = newstr
|
| 366 |
-
self.push(s)
|
| 367 |
-
self.push(ps_boolean(len(oldstr) == len(newstr)))
|
| 368 |
-
|
| 369 |
-
def ps_known(self):
|
| 370 |
-
key = self.pop()
|
| 371 |
-
d = self.pop("dicttype", "fonttype")
|
| 372 |
-
self.push(ps_boolean(key.value in d.value))
|
| 373 |
-
|
| 374 |
-
def ps_if(self):
|
| 375 |
-
proc = self.pop("proceduretype")
|
| 376 |
-
if self.pop("booleantype").value:
|
| 377 |
-
self.call_procedure(proc)
|
| 378 |
-
|
| 379 |
-
def ps_ifelse(self):
|
| 380 |
-
proc2 = self.pop("proceduretype")
|
| 381 |
-
proc1 = self.pop("proceduretype")
|
| 382 |
-
if self.pop("booleantype").value:
|
| 383 |
-
self.call_procedure(proc1)
|
| 384 |
-
else:
|
| 385 |
-
self.call_procedure(proc2)
|
| 386 |
-
|
| 387 |
-
def ps_readonly(self):
|
| 388 |
-
obj = self.pop()
|
| 389 |
-
if obj.access < 1:
|
| 390 |
-
obj.access = 1
|
| 391 |
-
self.push(obj)
|
| 392 |
-
|
| 393 |
-
def ps_executeonly(self):
|
| 394 |
-
obj = self.pop()
|
| 395 |
-
if obj.access < 2:
|
| 396 |
-
obj.access = 2
|
| 397 |
-
self.push(obj)
|
| 398 |
-
|
| 399 |
-
def ps_noaccess(self):
|
| 400 |
-
obj = self.pop()
|
| 401 |
-
if obj.access < 3:
|
| 402 |
-
obj.access = 3
|
| 403 |
-
self.push(obj)
|
| 404 |
-
|
| 405 |
-
def ps_not(self):
|
| 406 |
-
obj = self.pop("booleantype", "integertype")
|
| 407 |
-
if obj.type == "booleantype":
|
| 408 |
-
self.push(ps_boolean(not obj.value))
|
| 409 |
-
else:
|
| 410 |
-
self.push(ps_integer(~obj.value))
|
| 411 |
-
|
| 412 |
-
def ps_print(self):
|
| 413 |
-
str = self.pop("stringtype")
|
| 414 |
-
print("PS output --->", str.value)
|
| 415 |
-
|
| 416 |
-
def ps_anchorsearch(self):
|
| 417 |
-
seek = self.pop("stringtype")
|
| 418 |
-
s = self.pop("stringtype")
|
| 419 |
-
seeklen = len(seek.value)
|
| 420 |
-
if s.value[:seeklen] == seek.value:
|
| 421 |
-
self.push(ps_string(s.value[seeklen:]))
|
| 422 |
-
self.push(seek)
|
| 423 |
-
self.push(ps_boolean(1))
|
| 424 |
-
else:
|
| 425 |
-
self.push(s)
|
| 426 |
-
self.push(ps_boolean(0))
|
| 427 |
-
|
| 428 |
-
def ps_array(self):
|
| 429 |
-
num = self.pop("integertype")
|
| 430 |
-
array = ps_array([None] * num.value)
|
| 431 |
-
self.push(array)
|
| 432 |
-
|
| 433 |
-
def ps_astore(self):
|
| 434 |
-
array = self.pop("arraytype")
|
| 435 |
-
for i in range(len(array.value) - 1, -1, -1):
|
| 436 |
-
array.value[i] = self.pop()
|
| 437 |
-
self.push(array)
|
| 438 |
-
|
| 439 |
-
def ps_load(self):
|
| 440 |
-
name = self.pop()
|
| 441 |
-
self.push(self.resolve_name(name.value))
|
| 442 |
-
|
| 443 |
-
def ps_put(self):
|
| 444 |
-
obj1 = self.pop()
|
| 445 |
-
obj2 = self.pop()
|
| 446 |
-
obj3 = self.pop("arraytype", "dicttype", "stringtype", "proceduretype")
|
| 447 |
-
tp = obj3.type
|
| 448 |
-
if tp == "arraytype" or tp == "proceduretype":
|
| 449 |
-
obj3.value[obj2.value] = obj1
|
| 450 |
-
elif tp == "dicttype":
|
| 451 |
-
obj3.value[obj2.value] = obj1
|
| 452 |
-
elif tp == "stringtype":
|
| 453 |
-
index = obj2.value
|
| 454 |
-
obj3.value = obj3.value[:index] + chr(obj1.value) + obj3.value[index + 1 :]
|
| 455 |
-
|
| 456 |
-
def ps_get(self):
|
| 457 |
-
obj1 = self.pop()
|
| 458 |
-
if obj1.value == "Encoding":
|
| 459 |
-
pass
|
| 460 |
-
obj2 = self.pop(
|
| 461 |
-
"arraytype", "dicttype", "stringtype", "proceduretype", "fonttype"
|
| 462 |
-
)
|
| 463 |
-
tp = obj2.type
|
| 464 |
-
if tp in ("arraytype", "proceduretype"):
|
| 465 |
-
self.push(obj2.value[obj1.value])
|
| 466 |
-
elif tp in ("dicttype", "fonttype"):
|
| 467 |
-
self.push(obj2.value[obj1.value])
|
| 468 |
-
elif tp == "stringtype":
|
| 469 |
-
self.push(ps_integer(ord(obj2.value[obj1.value])))
|
| 470 |
-
else:
|
| 471 |
-
assert False, "shouldn't get here"
|
| 472 |
-
|
| 473 |
-
def ps_getinterval(self):
|
| 474 |
-
obj1 = self.pop("integertype")
|
| 475 |
-
obj2 = self.pop("integertype")
|
| 476 |
-
obj3 = self.pop("arraytype", "stringtype")
|
| 477 |
-
tp = obj3.type
|
| 478 |
-
if tp == "arraytype":
|
| 479 |
-
self.push(ps_array(obj3.value[obj2.value : obj2.value + obj1.value]))
|
| 480 |
-
elif tp == "stringtype":
|
| 481 |
-
self.push(ps_string(obj3.value[obj2.value : obj2.value + obj1.value]))
|
| 482 |
-
|
| 483 |
-
def ps_putinterval(self):
|
| 484 |
-
obj1 = self.pop("arraytype", "stringtype")
|
| 485 |
-
obj2 = self.pop("integertype")
|
| 486 |
-
obj3 = self.pop("arraytype", "stringtype")
|
| 487 |
-
tp = obj3.type
|
| 488 |
-
if tp == "arraytype":
|
| 489 |
-
obj3.value[obj2.value : obj2.value + len(obj1.value)] = obj1.value
|
| 490 |
-
elif tp == "stringtype":
|
| 491 |
-
newstr = obj3.value[: obj2.value]
|
| 492 |
-
newstr = newstr + obj1.value
|
| 493 |
-
newstr = newstr + obj3.value[obj2.value + len(obj1.value) :]
|
| 494 |
-
obj3.value = newstr
|
| 495 |
-
|
| 496 |
-
def ps_cvn(self):
|
| 497 |
-
self.push(ps_name(self.pop("stringtype").value))
|
| 498 |
-
|
| 499 |
-
def ps_index(self):
|
| 500 |
-
n = self.pop("integertype").value
|
| 501 |
-
if n < 0:
|
| 502 |
-
raise RuntimeError("index may not be negative")
|
| 503 |
-
self.push(self.stack[-1 - n])
|
| 504 |
-
|
| 505 |
-
def ps_for(self):
|
| 506 |
-
proc = self.pop("proceduretype")
|
| 507 |
-
limit = self.pop("integertype", "realtype").value
|
| 508 |
-
increment = self.pop("integertype", "realtype").value
|
| 509 |
-
i = self.pop("integertype", "realtype").value
|
| 510 |
-
while 1:
|
| 511 |
-
if increment > 0:
|
| 512 |
-
if i > limit:
|
| 513 |
-
break
|
| 514 |
-
else:
|
| 515 |
-
if i < limit:
|
| 516 |
-
break
|
| 517 |
-
if type(i) == type(0.0):
|
| 518 |
-
self.push(ps_real(i))
|
| 519 |
-
else:
|
| 520 |
-
self.push(ps_integer(i))
|
| 521 |
-
self.call_procedure(proc)
|
| 522 |
-
i = i + increment
|
| 523 |
-
|
| 524 |
-
def ps_forall(self):
|
| 525 |
-
proc = self.pop("proceduretype")
|
| 526 |
-
obj = self.pop("arraytype", "stringtype", "dicttype")
|
| 527 |
-
tp = obj.type
|
| 528 |
-
if tp == "arraytype":
|
| 529 |
-
for item in obj.value:
|
| 530 |
-
self.push(item)
|
| 531 |
-
self.call_procedure(proc)
|
| 532 |
-
elif tp == "stringtype":
|
| 533 |
-
for item in obj.value:
|
| 534 |
-
self.push(ps_integer(ord(item)))
|
| 535 |
-
self.call_procedure(proc)
|
| 536 |
-
elif tp == "dicttype":
|
| 537 |
-
for key, value in obj.value.items():
|
| 538 |
-
self.push(ps_name(key))
|
| 539 |
-
self.push(value)
|
| 540 |
-
self.call_procedure(proc)
|
| 541 |
-
|
| 542 |
-
def ps_definefont(self):
|
| 543 |
-
font = self.pop("dicttype")
|
| 544 |
-
name = self.pop()
|
| 545 |
-
font = ps_font(font.value)
|
| 546 |
-
self.dictstack[0]["FontDirectory"].value[name.value] = font
|
| 547 |
-
self.push(font)
|
| 548 |
-
|
| 549 |
-
def ps_findfont(self):
|
| 550 |
-
name = self.pop()
|
| 551 |
-
font = self.dictstack[0]["FontDirectory"].value[name.value]
|
| 552 |
-
self.push(font)
|
| 553 |
-
|
| 554 |
-
def ps_pop(self):
|
| 555 |
-
self.pop()
|
| 556 |
-
|
| 557 |
-
def ps_dict(self):
|
| 558 |
-
self.pop("integertype")
|
| 559 |
-
self.push(ps_dict({}))
|
| 560 |
-
|
| 561 |
-
def ps_begin(self):
|
| 562 |
-
self.dictstack.append(self.pop("dicttype").value)
|
| 563 |
-
|
| 564 |
-
def ps_end(self):
|
| 565 |
-
if len(self.dictstack) > 2:
|
| 566 |
-
del self.dictstack[-1]
|
| 567 |
-
else:
|
| 568 |
-
raise RuntimeError("dictstack underflow")
|
| 569 |
-
|
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notdef = ".notdef"
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from fontTools.encodings.StandardEncoding import StandardEncoding
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ps_StandardEncoding = list(map(ps_name, StandardEncoding))
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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-9e912372.js
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
import{S as P,e as Q,s as R,N as I,O as U,P as G,K as k,U as z,p as j,M as C,Q as A,R as H,n as D,A as B,a1 as V,B as W,am as X,k as S,o as T,z as h,v,x as q,E as Y,ae as Z,h as F,j as K,q as p,r as y,u as x,y as $,t as M,F as N}from"./index-3370be2a.js";/* empty css */import{B as ee}from"./Button-89624748.js";import{I as te}from"./Info-5611e10f.js";function ae(n){let e,t,a,l,u,o,c;return{c(){e=I("label"),t=I("input"),a=U(),l=I("span"),u=G(n[2]),t.disabled=n[1],k(t,"type","checkbox"),k(t,"name","test"),k(t,"data-testid","checkbox"),k(t,"class","svelte-1ojmf70"),k(l,"class","ml-2 svelte-1ojmf70"),k(e,"class","svelte-1ojmf70"),z(e,"disabled",n[1])},m(_,d){j(_,e,d),C(e,t),t.checked=n[0],C(e,a),C(e,l),C(l,u),o||(c=[A(t,"change",n[5]),A(t,"input",n[6])],o=!0)},p(_,[d]){d&2&&(t.disabled=_[1]),d&1&&(t.checked=_[0]),d&4&&H(u,_[2]),d&2&&z(e,"disabled",_[1])},i:D,o:D,d(_){_&&B(e),o=!1,V(c)}}}function ne(n,e,t){let{value:a}=e,{value_is_output:l=!1}=e,{disabled:u=!1}=e,{label:o}=e;const c=W();function _(){c("change",a),l||c("input")}X(()=>{t(4,l=!1)});function d(){a=this.checked,t(0,a)}const f=m=>{t(0,a=m.currentTarget.checked),c("select",{index:0,value:o,selected:m.currentTarget.checked})};return n.$$set=m=>{"value"in m&&t(0,a=m.value),"value_is_output"in m&&t(4,l=m.value_is_output),"disabled"in m&&t(1,u=m.disabled),"label"in m&&t(2,o=m.label)},n.$$.update=()=>{n.$$.dirty&1&&_()},[a,u,o,c,l,d,f]}class le extends P{constructor(e){super(),Q(this,e,ne,ae,R,{value:0,value_is_output:4,disabled:1,label:2})}}function O(n){let e,t;return e=new te({props:{$$slots:{default:[se]},$$scope:{ctx:n}}}),{c(){S(e.$$.fragment)},m(a,l){T(e,a,l),t=!0},p(a,l){const u={};l&131136&&(u.$$scope={dirty:l,ctx:a}),e.$set(u)},i(a){t||(h(e.$$.fragment,a),t=!0)},o(a){v(e.$$.fragment,a),t=!1},d(a){q(e,a)}}}function se(n){let e;return{c(){e=G(n[6])},m(t,a){j(t,e,a)},p(t,a){a&64&&H(e,t[6])},d(t){t&&B(e)}}}function ie(n){let e,t,a,l,u,o,c;const _=[n[11]];let d={};for(let s=0;s<_.length;s+=1)d=Y(d,_[s]);e=new Z({props:d});let f=n[6]&&O(n);function m(s){n[12](s)}function w(s){n[13](s)}let g={label:n[5],disabled:n[7]==="static"};return n[0]!==void 0&&(g.value=n[0]),n[1]!==void 0&&(g.value_is_output=n[1]),l=new le({props:g}),F.push(()=>K(l,"value",m)),F.push(()=>K(l,"value_is_output",w)),l.$on("change",n[14]),l.$on("input",n[15]),l.$on("select",n[16]),{c(){S(e.$$.fragment),t=U(),f&&f.c(),a=U(),S(l.$$.fragment)},m(s,b){T(e,s,b),j(s,t,b),f&&f.m(s,b),j(s,a,b),T(l,s,b),c=!0},p(s,b){const E=b&2048?p(_,[y(s[11])]):{};e.$set(E),s[6]?f?(f.p(s,b),b&64&&h(f,1)):(f=O(s),f.c(),h(f,1),f.m(a.parentNode,a)):f&&(x(),v(f,1,1,()=>{f=null}),$());const r={};b&32&&(r.label=s[5]),b&128&&(r.disabled=s[7]==="static"),!u&&b&1&&(u=!0,r.value=s[0],M(()=>u=!1)),!o&&b&2&&(o=!0,r.value_is_output=s[1],M(()=>o=!1)),l.$set(r)},i(s){c||(h(e.$$.fragment,s),h(f),h(l.$$.fragment,s),c=!0)},o(s){v(e.$$.fragment,s),v(f),v(l.$$.fragment,s),c=!1},d(s){s&&(B(t),B(a)),q(e,s),f&&f.d(s),q(l,s)}}}function ue(n){let e,t;return e=new ee({props:{visible:n[4],elem_id:n[2],elem_classes:n[3],container:n[8],scale:n[9],min_width:n[10],$$slots:{default:[ie]},$$scope:{ctx:n}}}),{c(){S(e.$$.fragment)},m(a,l){T(e,a,l),t=!0},p(a,[l]){const u={};l&16&&(u.visible=a[4]),l&4&&(u.elem_id=a[2]),l&8&&(u.elem_classes=a[3]),l&256&&(u.container=a[8]),l&512&&(u.scale=a[9]),l&1024&&(u.min_width=a[10]),l&133347&&(u.$$scope={dirty:l,ctx:a}),e.$set(u)},i(a){t||(h(e.$$.fragment,a),t=!0)},o(a){v(e.$$.fragment,a),t=!1},d(a){q(e,a)}}}function fe(n,e,t){let{elem_id:a=""}=e,{elem_classes:l=[]}=e,{visible:u=!0}=e,{value:o=!1}=e,{value_is_output:c=!1}=e,{label:_="Checkbox"}=e,{info:d=void 0}=e,{mode:f}=e,{container:m=!0}=e,{scale:w=null}=e,{min_width:g=void 0}=e,{loading_status:s}=e;function b(i){o=i,t(0,o)}function E(i){c=i,t(1,c)}function r(i){N.call(this,n,i)}function J(i){N.call(this,n,i)}function L(i){N.call(this,n,i)}return n.$$set=i=>{"elem_id"in i&&t(2,a=i.elem_id),"elem_classes"in i&&t(3,l=i.elem_classes),"visible"in i&&t(4,u=i.visible),"value"in i&&t(0,o=i.value),"value_is_output"in i&&t(1,c=i.value_is_output),"label"in i&&t(5,_=i.label),"info"in i&&t(6,d=i.info),"mode"in i&&t(7,f=i.mode),"container"in i&&t(8,m=i.container),"scale"in i&&t(9,w=i.scale),"min_width"in i&&t(10,g=i.min_width),"loading_status"in i&&t(11,s=i.loading_status)},[o,c,a,l,u,_,d,f,m,w,g,s,b,E,r,J,L]}class ce extends P{constructor(e){super(),Q(this,e,fe,ue,R,{elem_id:2,elem_classes:3,visible:4,value:0,value_is_output:1,label:5,info:6,mode:7,container:8,scale:9,min_width:10,loading_status:11})}}const be=ce,re=["static","dynamic"],he=n=>({type:{payload:"boolean"},description:{payload:"checked status"},example_data:n.value});export{be as Component,he as document,re as modes};
|
| 2 |
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//# sourceMappingURL=index-9e912372.js.map
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