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spaces/1acneusushi/gradio-2dmoleculeeditor/data/A Guide to Fixing Microsoft Office 2021 Error Code 0-2054 (0) on Your Device.md

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-<h1>How to Fix Microsoft Office 2021 Error Code 0-2054 (0)</h1>
-<p>Microsoft Office 2021 is the latest version of the popular productivity suite that offers many new features and improvements. However, some users may encounter an error code 0-2054 (0) when trying to install or update Office 2021 on their devices. This error can prevent the installation or update process from completing successfully and cause frustration for the users.</p>
-<p>Fortunately, there are some possible solutions that can help you fix this error and enjoy Office 2021 without any issues. Here are some of them:</p>
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-<ol>
-<li><b>Uninstall any previous versions of Office</b>. Sometimes, the error code 0-2054 (0) can occur if you have an older version of Office installed on your device, such as Office 365 or Office 2019. To avoid any conflicts, you should uninstall any previous versions of Office using the <a href="https://support.microsoft.com/en-us/office/uninstall-office-from-a-pc-9dd49b83-264a-477a-8fcc-2fdf5dbf61d8">Office uninstall tool</a>  or the <a href="https://support.microsoft.com/en-us/topic/uninstall-office-from-a-pc-9dd49b83-264a-477a-8fcc-2fdf5dbf61d8">Control Panel</a>. Make sure to restart your device after uninstalling Office.</li>
-<li><b>Disable any firewall, proxy, or antivirus software</b>. Another possible cause of the error code 0-2054 (0) is that some firewall, proxy, or antivirus software may block the installation or update of Office 2021 as a security measure. To avoid this, you should temporarily disable any firewall, proxy, or antivirus software that you have on your device and try to install or update Office 2021 again. Remember to enable them back after you finish the installation or update.</li>
-<li><b>Use the Office Deployment Tool</b>. The Office Deployment Tool (ODT) is a tool that allows you to download and install Office 2021 offline using a configuration file. This can help you avoid any network-related issues that may cause the error code 0-2054 (0). To use the ODT, you need to follow these steps:</li>
-<ul>
-<li>Download the <a href="https://www.microsoft.com/en-us/download/details.aspx?id=49117">Office Deployment Tool</a>  and run it to extract the setup.exe file and the configuration.xml file.</li>
-<li>Edit the configuration.xml file using a text editor such as Notepad and specify the parameters for your Office 2021 installation or update. You can use the <a href="https://config.office.com/">Office Customization Tool</a>  to generate a configuration file based on your preferences.</li>
-<li>Save and close the configuration.xml file and place it in the same folder as the setup.exe file.</li>
-<li>Open a Command Prompt window as an administrator and navigate to the folder where the setup.exe and configuration.xml files are located.</li>
-<li>Type <code>setup.exe /download configuration.xml</code> and press Enter to download the Office 2021 installation files.</li>
-<li>Type <code>setup.exe /configure configuration.xml</code> and press Enter to install or update Office 2021 using the configuration file.</li>
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-<li><b>Contact Microsoft support</b>. If none of the above solutions work for you, you may need to contact Microsoft support for further assistance. You can visit the <a href="https://support.microsoft.com/en-us/contactus/">Microsoft support website</a>  and choose the option that best suits your situation. You can also post your question on the <a href="https://answers.microsoft.com/en-us/msoffice/forum/all">Microsoft Community forum</a>  and get help from other users who may have faced similar issues.</li>
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-<p>We hope that this article has helped you fix the error code 0-2054 (0) for Office 2021 and enjoy its features without any problems. If you have any feedback or suggestions, please let us know in the comments below.</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Como Baixar e Instalar FIFA 22 Verso Crackeada em Portugus.md

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-<h1>How to Download FIFA 22 Cracked Version in Portuguese</h1>
-<p>If you are a fan of soccer games, you might be interested in downloading FIFA 22, the latest installment of the popular franchise from EA Sports. However, if you don't want to pay for the game or you want to play it in Portuguese, you might be looking for a cracked version that bypasses the DRM protection and allows you to change the language settings.</p>
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-<p>In this article, we will show you how to download FIFA 22 cracked version in Portuguese using a reliable torrent site and a simple patch. Follow these steps and enjoy the game for free!</p>
-<ol>
-<li>Go to <a href="https://www.skidrowreloaded.com/">Skidrow Reloaded</a>, one of the best torrent sites for cracked games. Search for FIFA 22 and download the torrent file.</li>
-<li>Open the torrent file with your preferred torrent client, such as <a href="https://www.utorrent.com/">uTorrent</a> or <a href="https://www.bittorrent.com/">BitTorrent</a>. Choose a folder to save the game files and start the download.</li>
-<li>Once the download is complete, extract the game files using a program like <a href="https://www.win-rar.com/">WinRAR</a> or <a href="https://www.7-zip.org/">7-Zip</a>. You will find a folder called FIFA.22-CPY, which contains the cracked version of the game.</li>
-<li>Run the setup.exe file and follow the installation instructions. Make sure to uncheck any additional software or toolbars that might be offered during the installation.</li>
-<li>After the installation is done, copy the contents of the CPY folder (which contains the crack) and paste them into the game folder, replacing the original files.</li>
-<li>To change the language to Portuguese, open the CPY.ini file with a text editor like <a href="https://notepad-plus-plus.org/">Notepad++</a>. Find the line that says Language=english and change it to Language=brazilian. Save and close the file.</li>
-<li>Now you can launch the game from the desktop shortcut or the FIFA22.exe file. Enjoy playing FIFA 22 cracked version in Portuguese!</li>
-</ol>
-<p>Note: This method is for educational purposes only. We do not condone piracy or illegal downloading of games. If you like FIFA 22, please support the developers and buy the game from <a href="https://www.ea.com/games/fifa/fifa-22/buy/pc">the official website</a>.</p><p>If you are wondering what FIFA 22 has to offer in terms of new features and modes, here are some highlights that you can expect from the game:</p>
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-<li><b>HyperMotion Technology</b>: This is a new gameplay technology that uses advanced machine learning and real-life motion capture data from 22 professional players to create more realistic animations, movements, and interactions on the pitch. HyperMotion Technology is only available on PlayStation 5, Xbox Series X|S, and Stadia.</li>
-<li><b>Goalkeeper Rewrite</b>: The goalkeepers have been completely revamped with a new system that improves their shot-stopping abilities, decision-making skills, and personality. You will notice more variety and consistency in how they react to different situations and scenarios.</li>
-<li><b>New Attacking Tactics</b>: You can now customize your team's offensive style with more options and control over how they build up play, create chances, and finish. You can also adjust your defensive shape and intensity to counter your opponent's tactics.</li>
-<li><b>Career Mode</b>: You can create your own club from scratch and lead them to glory in Career Mode, choosing everything from the name, logo, kit, stadium, and fanbase. You can also enjoy a more immersive Player Career experience that lets you interact with your manager, teammates, and media, as well as participate in training sessions and matches.</li>
-<li><b>VOLTA FOOTBALL</b>: VOLTA FOOTBALL returns with more flair and style on the street football playgrounds around the world. You can customize your avatar with new outfits, hairstyles, tattoos, and emotes, as well as unlock new items and rewards as you progress. You can also play with your friends online or offline in various modes and formats.</li>
-<li><b>FIFA 22 Ultimate Team</b>: FUT 22 introduces FUT Heroes, which are iconic players from the past who have made a lasting impact on their clubs or leagues. You can also enjoy a redesigned Division Rivals and FUT Champions system that makes it easier to compete and progress against other players. Additionally, you can personalize your club with more customization options for your badge, stadium, kits, and celebrations.</li>
-<li><b>Pro Clubs</b>: Pro Clubs lets you create and join a team of up to 11 players online and play matches against other clubs. You can customize your Virtual Pro's appearance, attributes, traits, and positions, as well as track your progress and achievements with a new player growth system. You can also find new teammates and opponents with a streamlined social play feature.</li>
-</ul>
-<p>These are just some of the new features and modes that FIFA 22 has to offer. If you want to learn more about the game, you can visit <a href="https://www.ea.com/games/fifa/fifa-22">the official website</a> or watch the <a href="https://www.youtube.com/watch?v=o1igaMv46SY">official trailer</a>.</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Gujarati Kaps Fonts 150 Varity Of Gujarati Fonts Rar Download Free High-Quality Fonts for Windows and Mac.md

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-<h1>Gujarati Kaps Fonts: A Guide to Download and Use 150+ Stylish Fonts for Photoshop</h1>
-<p>If you are looking for some unique and elegant fonts for your Gujarati designs, you might want to check out the Gujarati Kaps fonts. These are a collection of 150+ stylish fonts that are specially designed for Photoshop and other graphic design software. In this article, we will show you what are Gujarati Kaps fonts, how to download them, and how to use them in Photoshop. Let's get started!</p>
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-<h2>What are Gujarati Kaps Fonts?</h2>
-<p>Gujarati Kaps fonts are a type of Gujarati fonts that have a distinctive style and flair. They are created by Kapilbhai Dave, a professional graphic designer and font creator from Gujarat. He has been making fonts since 1998 and has developed over 5000 fonts in various languages.</p>
-<h3>The origin and features of Kaps fonts</h3>
-<p>Kapilbhai Dave started making fonts as a hobby when he was studying at the National Institute of Design in Ahmedabad. He was inspired by the calligraphy and typography of different cultures and regions. He wanted to create fonts that would reflect the beauty and diversity of Gujarati language and culture.</p>
-<p>He named his fonts as Kaps, which is derived from his own name. He also added numbers to his fonts, such as Kap 1, Kap 2, Kap 3, etc., to indicate the order of creation. He used various tools and techniques to make his fonts, such as pen, brush, ink, paper, scanner, computer, software, etc.</p>
-<p>Kapilbhai Dave's fonts have some common features that make them stand out from other Gujarati fonts. Some of these features are:</p>
-<ul>
-<li>They have a smooth and flowing curve that gives them a natural and organic look.</li>
-<li>They have a balanced and harmonious proportion that makes them easy to read and pleasing to the eye.</li>
-<li>They have a creative and artistic flair that adds character and personality to the text.</li>
-<li>They have a variety of styles and weights that suit different purposes and moods.</li>
-<li>They have a high-quality and professional finish that makes them suitable for print and digital media.</li>
-</ul>
-<h3>The benefits and applications of Kaps fonts</h3>
-<p>Kapilbhai Dave's fonts have many benefits and applications for designers and users alike. Some of these benefits are:</p>
-<ul>
-<li>They enhance the aesthetic appeal and visual impact of the design.</li>
-<li>They convey the message and tone of the content more effectively.</li>
-<li>They attract the attention and interest of the audience more easily.</li>
-<li>They express the identity and culture of the brand or organization more authentically.</li>
-<li>They add value and uniqueness to the product or service more convincingly.</li>
-</ul>
-<p>Kapilbhai Dave's fonts can be used for various purposes and projects, such as:</p>
-<ul>
-<li>Wedding invitations, brochures, pamphlets, flyers, posters, banners, etc.</li>
-<li>Logos, slogans, headlines, titles, captions, etc.</li>
-<li>Books, magazines, newspapers, newsletters, etc.</li>
-<li>Websites, blogs, social media posts, etc.</li>
-<li>Videos, animations, presentations, etc.</li>
-</ul>
-<h2>How to download Gujarati Kaps Fonts?</h2>
-<p>If you want to use Kapilbhai Dave's fonts in your designs, you need to download them first. There are many websites that offer his fonts for free or for a fee. However, one of the easiest ways to download his fonts is from 4shared.com. This is a file-sharing website that allows you to download files from other users. Here are the steps to download Gujarati Kaps Fonts from 4shared.com:</p>
-<h3>The steps to download the fonts from 4shared.com</h3>
-<ol>
-<li>Go to <a href="https://www.free-fonts.com/gujarati-kaps">https://www.free-fonts.com/gujarati-kaps</a> This is a web page that has a link to download 150+ KAP Gujarati Fonts from 4shared.com.</li>
-<li>Click on the link that says "Download gujarati kaps fonts (150 varity of gujarati fonts).rar from 4shared.com". This will take you to another web page that has the file name "Gujarati KAPS Fonts (150 varity of gujarati fonts).rar".</li>
-<li>Click on the green button that says "Download". This will start downloading the file to your computer. The file size is about 5 MB.</li>
-<li>Wait for the download to finish. You can check the progress of the download on your browser or on your download manager.</li>
-</ol>
- <h3>The steps to unzip and install the fonts on Windows</h3>
- <ol>
- <li>Locate the file "Gujarati KAPS Fonts (150 varity of gujarati fonts).rar" on your computer. It should be in your Downloads folder or wherever you saved it.</li>
- <li>Right-click on the file and select "Extract Here" or "Extract All". This will unzip or extract the file into a folder with the same name.</li>
- <li>Open the folder "Gujarati KAPS Fonts (150 varity of gujarati fonts)". You will see many subfolders with names like "KAP-01", "KAP-02", "KAP-03", etc. Each subfolder contains one or more font files with extensions like ".ttf", ".otf", ".fon", etc.</li>
- <li>Select all the font files that you want to install. You can use Ctrl+A to select all or Ctrl+click to select multiple files.</li>
- <li>Right-click on the selected files and select "Install". This will install the fonts on your computer. You may need administrator permission or password to do this.</li>
- <li>Wait for the installation to finish. You can check if the installation was successful by going to Control Panel > Fonts or by opening any software that uses fonts like Word or Photoshop.</li>
- </ol>
- <h2>How to use Gujarati Kaps Fonts in Photoshop?</h2>
- <p>Now that you have downloaded and installed Gujarati Kaps Fonts on your computer, you can use them in Photoshop or any other graphic design software. Here are some steps to use Gujarati Kaps Fonts in Photoshop:</p>
- <h3>The steps to select and apply the fonts in Photoshop</h3>
- <ol>
- <li>Open Photoshop and create a new document or open an existing one.</li>
- <li>Select the Text tool (T) from the toolbar or press T on your keyboard.</li>
- <li>Click on the document where you want to add text or select an existing text layer.</li>
- <li>In the Options bar at the top of your screen, click on the Font drop-down menu. This will show you all the available fonts on your computer.</li>
- <li>Scroll down until you find the font name that starts with "KAP". You will see many options like "KAP-01", "KAP-02", "KAP-03", etc. These are all different styles of Gujarati Kaps Fonts. You can also type "KAP" in the search box to filter out other fonts.</li>
- <li>Select the font style Continuing the article: that you like and click on it. You will see a preview of the font on your text.</li>
- <li>Adjust the font size, color, alignment, and other settings as you wish. You can also use the Character panel (Window > Character) or the Paragraph panel (Window > Paragraph) for more options.</li>
- <li>Repeat the steps for any other text layers that you want to apply Gujarati Kaps Fonts to.</li>
- </ol>
- <h3>The tips and tricks to create stunning designs with Kaps fonts</h3>
- <p>Gujarati Kaps Fonts are versatile and expressive fonts that can help you create stunning designs with Photoshop. Here are some tips and tricks to make the most of them:</p>
-<p>How to download 150+ KAP Gujarati Fonts for Photoshop[^1^]<br />
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-Kap 127 to Unicode | Unicode to Kap 127 | Gujarati Font Converter<br />
-Apart from Kap 127 to Unicode conversion, this unique program converts non Unicode fonts into Gujarati Unicode text and vice versa<br />
-34 Professional Gujarati Kaps Fonts to Download - Typograph<br />
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- <ul>
- <li>Use contrast and hierarchy to create visual interest and clarity. You can mix different styles and weights of Kaps fonts to create contrast and hierarchy. For example, you can use a bold or heavy style for headlines and a light or regular style for body text. You can also use different colors or sizes to emphasize important words or phrases.</li>
- <li>Use kerning and tracking to adjust the spacing between letters and words. Kerning is the adjustment of the space between individual letters, while tracking is the adjustment of the space between groups of letters or words. You can use these tools to fine-tune the appearance and readability of your text. To access these tools, select your text layer and go to Window > Character. Then use the sliders or input boxes for kerning and tracking.</li>
- <li>Use leading to adjust the spacing between lines of text. Leading is the vertical space between lines of text. You can use this tool to control the density and flow of your text. To access this tool, select your text layer and go to Window > Character. Then use the slider or input box for leading.</li>
- <li>Use alignment and justification to arrange your text in different ways. Alignment is the horizontal position of your text relative to its margins or edges. Justification is the adjustment of the space between words to make them fit evenly across a line. You can use these tools to create different effects and layouts for your text. To access these tools, select your text layer and go to Window > Paragraph. Then use the buttons for alignment and justification.</li>
- <li>Use ligatures and alternates to add some flair and variety to your text. Ligatures are special characters that combine two or more letters into one glyph, such as "fi" or "fl". Alternates are different versions of a letter that have a different shape or style, such as "a" or "g". You can use these tools to make your text more unique and dynamic. To access these tools, select your text layer and go to Window > Glyphs. Then browse through the glyphs panel and double-click on any ligature or alternate that you want to use.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>Gujarati Kaps Fonts are a great choice for anyone who wants to create beautiful and professional designs with Gujarati text. They are easy to download, install, and use in Photoshop or any other graphic design software. They have a wide range of styles and weights that can suit any purpose and mood. They have a smooth and flowing curve that gives them a natural and organic look. They have a balanced and harmonious proportion that makes them easy to read and pleasing to the eye. They have a creative and artistic flair that adds character and personality to the text.</p>
- <p>If you are interested in using Gujarati Kaps Fonts in your designs, you can follow the steps and tips that we have shared in this article. You can also experiment with different combinations and settings to find your own style and voice. We hope that this article has inspired you to try out Gujarati Kaps Fonts and create stunning designs with them.</p>
- <p>Do you have any questions or comments about Gujarati Kaps Fonts? Do you have any suggestions or feedback for us? Let us know in the comments below!</p>
- <h2>FAQs</h2>
- <h4>Q1: How many Kaps fonts are there in total?</h4>
-<p>A1: According to Kapilbhai Dave's website, there are over 5000 fonts in total, including Gujarati, Hindi, English, Sanskrit, Marathi, Bengali, Tamil, Telugu, Malayalam, Kannada, Punjabi, Oriya, Assamese, Nepali, Tibetan, Arabic, Persian, Urdu, Sindhi, Pashto, Balochi, Kurdish, Hebrew, Greek, Russian, Mongolian, Chinese, Continuing the FAQs: Japanese, Korean, Thai, Lao, Khmer, Vietnamese, Burmese, Sinhala, and more.</p>
- <h4>Q2: Are Kaps fonts free to use?</h4>
-<p>A2: It depends on where you download them from and what you use them for. Some websites offer Kaps fonts for free for personal or non-commercial use only. Others may charge a fee for commercial use or for the full version of the fonts. You should always check the license terms and conditions before downloading and using any font. You should also respect the intellectual property and rights of the font creator.</p>
- <h4>Q3: Can I use Kaps fonts in other software besides Photoshop?</h4>
-<p>A3: Yes, you can use Kaps fonts in any software that supports TrueType, OpenType, or other font formats. However, some software may have different features and options for using fonts than Photoshop. For example, some software may not support ligatures or alternates, or may have different ways of accessing them. You should always check the documentation and help files of your software to learn how to use fonts effectively.</p>
- <h4>Q4: How can I preview the fonts before downloading them?</h4>
-<p>A4: One way to preview the fonts before downloading them is to use online font preview tools. These are websites that allow you to type in some text and see how it looks with different fonts. Some examples of online font preview tools are:</p>
-<ul>
-<li><a href="https://www.fontsquirrel.com/matcherator">Font Squirrel Matcherator</a>: This tool allows you to upload an image of a font and find similar or matching fonts.</li>
-<li><a href="https://www.myfonts.com/WhatTheFont/">MyFonts WhatTheFont</a>: This tool allows you to upload an image of a font and identify it.</li>
-<li><a href="https://wordmark.it/">Wordmark.it</a>: This tool allows you to type in some text and see how it looks with all the fonts installed on your computer.</li>
-<li><a href="https://www.dafont.com/">DaFont</a>: This website allows you to browse through thousands of free fonts and see how they look with custom text.</li>
-</ul>
- <h4>Q5: Where can I find more resources and tutorials on Kaps fonts?</h4>
-<p>A5: If you want to learn more about Kaps fonts and how to use them in your designs, you can check out some of these resources and tutorials:</p>
-<ul>
-<li><a href="https://www.youtube.com/watch?v=BQBwR7ZKuCU">How to download 150+ KAP Gujarati Fonts? | Free Stylish Gujarati Fonts For Photoshop</a>: This is a video tutorial that shows you how to download and install Kaps fonts from 4shared.com.</li>
-<li><a href="https://www.creativebloq.com/typography/design-your-own-typeface-8133919">Font design: 17 brilliant tips to create your own typeface</a>: This is an article that gives you some tips and advice on how to design your own font.</li>
-<li><a href="https://visme.co/blog/top-fonts-2020/">Top Fonts For 2020 To Create Outstanding Designs</a>: This is an article that showcases some of the best fonts for 2020, including Kaps fonts.</li>
-<li><a href="https://glyphsapp.com/learn/creating-an-all-caps-font">Creating an all-caps font | Glyphs</a>: This is a tutorial that shows you how to create an all-caps font using Glyphs, a professional font editor.</li>
-<li><a href="https://justcreative.com/all-caps-fonts/">46+ Stunning ALL CAPS Fonts to Make a Statement</a>: This is an article that features some of the most stunning all-caps fonts available online.</li>
-</ul>
- </p> 0a6ba089eb<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Simulator 2 Mod APK Unlimited Money and All Cars Unlocked for Free.md

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- <p>If you are a fan of racing games, you might have heard of Car Simulator 2, a popular simulation game that lets you drive various cars in a realistic world. But did you know that you can download Car Simulator 2 all cars unlocked apk and enjoy the game with more features and benefits? In this article, we will tell you what Car Simulator 2 is, why you should download the modded version, and how to do it. Read on to find out more.</p>
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- <p>Car Simulator 2 is a simulation game developed by Oppana Games FZC LLC. It is available for Android devices and has over 10 million downloads on Google Play Store. The game has impressive graphics and physics that make you feel like you are driving a real car. You can explore a vast open world with different locations, such as cities, deserts, mountains, and highways. You can also choose from a variety of cars, ranging from sports cars, muscle cars, SUVs, trucks, and more. You can customize your car with different colors, wheels, spoilers, and other accessories.</p>
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- <ol>
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- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Car Simulator 2 all cars unlocked apk safe to download and install?</td>
-<td>Yes, it is safe as long as you download it from a trusted source. However, you should always scan the apk file with an antivirus before installing it.</td>
-</tr>
-<tr>
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-</tr>
-<tr>
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-<td>No, you cannot update the modded version of the game. If you want to get the latest updates and features of the game, you will have to download and install the original version from Google Play Store.</td>
-</tr>
-<tr>
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-<td>Yes, you can play the game with your friends online. You can join clubs, races, missions, and events with other players who have the same version of the game.</td>
-</tr>
-<tr>
-<td>What are the minimum requirements to play Car Simulator 2 all cars unlocked apk?</td>
-<td>The minimum requirements to play the game are Android 4.4 or higher, 1 GB of RAM, and 300 MB of free storage space.</td>
-</tr>
-</table>
- <p>I hope this article has helped you learn more about Car Simulator 2 all cars unlocked apk. If you have any questions or feedback, please leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Go Go by BTS and Join the ARMY - The Biggest Fan Community in the World.md

@@ -1,126 +0,0 @@
-
-<h1>Download Go Go by BTS: A Guide for ARMYs</h1>
-<p>Are you a fan of BTS, the global sensation and phenomenon in the music industry? If so, you probably have heard of their hit song "Go Go", a catchy and upbeat track that showcases their charisma and talent. But have you downloaded it yet? If not, you are missing out on a lot of fun and excitement. In this article, we will tell you everything you need to know about "Go Go" by BTS, and why you should download it right now.</p>
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- <h2>What is Go Go by BTS?</h2>
-<p>"Go Go" is a song by BTS, a seven-member South Korean boy band that has taken over the world with their music, message, and style. The song was released on September 18, 2017, as part of their fifth mini album "Love Yourself: Her". It is the eighth track on the album, and also appears as the fourth track on their second compilation album "Love Yourself: Answer".</p>
-<p>The song is a fusion of trap, hip hop, and EDM genres, with a catchy chorus and playful lyrics. The song is about living in the moment and enjoying life without worrying too much about the future or money. The song also reflects the youth culture and attitude of BTS and their fans, who are often called ARMYs.</p>
- <h2>Why you should download Go Go by BTS?</h2>
-<p>There are many reasons why you should download "Go Go" by BTS. Here are some of them:</p>
- <h3>How to support BTS by downloading Go Go?</h3>
-<p>One of the best ways to support BTS is by downloading their songs legally and ethically. By doing so, you are showing your appreciation and respect for their hard work and creativity. You are also helping them achieve more recognition and success in the music industry. Downloading their songs also contributes to their chart rankings, awards nominations, and sales records.</p>
-<p>There are many platforms and methods to download "Go Go" by BTS legally and ethically. Some of them are:</p>
-<ul>
-<li>Buying or streaming the song from official online music stores or services, such as iTunes, Spotify, Amazon Music, YouTube Music, etc.</li>
-<li>Purchasing or downloading the song from official physical albums or CDs, such as "Love Yourself: Her" or "Love Yourself: Answer".</li>
-<li>Using official fan club memberships or subscriptions to access exclusive content or benefits related to the song or BTS.</li>
-</ul>
- <h3>How to enjoy Go Go by BTS?</h3>
-<p>Another reason why you should download "Go Go" by BTS is because it is a fun and enjoyable song that will make you happy and energetic. There are many ways to listen to and appreciate the song, such as:</p>
-<ul>
-<li>Watching the music video of "Go Go" on YouTube or other platforms. The music video features BTS performing the song in colorful outfits and settings, with hilarious expressions and gestures. The music video also has some references and parodies of popular culture and memes.</li>
-<li>Learning the choreography of "Go Go" from online tutorials or videos. The chore ography of "Go Go" is very catchy and fun, with some moves inspired by the "Gwiyomi" song and the "Dame Tu Cosita" dance. You can learn the dance steps and practice them with your friends or alone.</li>
-<li>Singing along to "Go Go" with the lyrics or karaoke versions. The lyrics of "Go Go" are very witty and humorous, with some wordplay and slang. You can sing along to the song and express your feelings and thoughts about life and money.</li>
-</ul>
- <h3>How to join the ARMY fandom with Go Go?</h3>
-<p>A third reason why you should download "Go Go" by BTS is because it will help you connect with other fans of the song and BTS, who are known as ARMYs. ARMYs are one of the most loyal and passionate fandoms in the world, who support and love BTS unconditionally. There are many communities and activities to join the ARMY fandom with "Go Go", such as:</p>
-<ul>
-<li>Following BTS and their official accounts on social media, such as Twitter, Instagram, Facebook, Weverse, etc. You can interact with BTS and other ARMYs by liking, commenting, sharing, or posting about "Go Go" or other BTS-related topics.</li>
-<li>Participating in fan projects or events related to "Go Go" or BTS, such as streaming parties, hashtag campaigns, fan art contests, charity donations, etc. You can show your appreciation and support for BTS and their music by joining these projects or events.</li>
-<li>Attending concerts or fan meetings of BTS where they perform "Go Go" or other songs live. You can experience the amazing performance and energy of BTS and their fans by attending these concerts or fan meetings.</li>
-</ul>
- <h2>Where to download Go Go by BTS?</h2>
-<p>Now that you know why you should download "Go Go" by BTS, you might be wondering where to download it from. There are many sources and sites to download the song, but not all of them are reliable or convenient. To help you choose the best option for you, we have prepared a comparison table of the best sources and sites to download "Go Go" by BTS, based on quality, price, and convenience.</p>
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-<td>High</td ><td>$19.99 per album (includes 9 songs)</td ><td>Requires physical purchase or delivery, offers additional content such as photobook and photocard</td ></tr ><tr ><td>"Love Yourself: Answer" album</td ><td>High</td ><td>$24.99 per album (includes 26 songs)</td ><td>Requires physical purchase or delivery, offers additional content such as photobook and photocard</td ></tr ></table ><h2>Conclusion</h2 ><p>In conclusion, "Go Go" by BTS is a great song that you should download right now. It is a fun and upbeat song that will make you happy and energetic. It is also a way to support BTS and their music, enjoy their performance and style, and join their fandom and community. You can download the song from various sources and sites, depending on your preference and budget. So what are you waiting for? Go go go and download "Go Go" by BTS today!</p ><h4>Frequently Asked Questions (FAQs)</h4 ><p>Here are some of the most common questions that people have about "Go Go" by BTS:</p ><ol ><li><b>What does "yolo yolo yolo yo" mean in the chorus of "Go Go"?</b></li ><p>This <p>This is a repetition of the acronym "YOLO", which stands for "You Only Live Once". It is a popular phrase that expresses the idea of living in the present and enjoying life without regrets. In the context of the song, it means that BTS and their fans are having fun and spending money without worrying about the future or saving up.</p>
- <li><b>What is the meaning of the money gun gesture in the "Go Go" choreography?</b></li>
-<p>This is a gesture that mimics shooting money from a toy gun, which is often used by rappers or celebrities to show off their wealth and status. In the context of the song, it is a sarcastic and ironic gesture that mocks the materialistic and consumerist culture of society. It also shows that BTS and their fans are not obsessed with money or fame, but rather value happiness and freedom.</p>
- <li><b>What are some of the references and parodies in the "Go Go" music video?</b></li>
-<p>There are many references and parodies in the "Go Go" music video, such as:</p>
-<ul>
-<li>The opening scene where BTS are lying on a pile of money and wearing masks is a reference to the movie "The Purge", which is a dystopian thriller about a night where all crimes are legal.</li>
-<li>The scene where BTS are dancing on a yacht and wearing Hawaiian shirts is a parody of the song "Gangnam Style" by Psy, which is a viral hit that mocks the lavish lifestyle of Seoul's elite.</li>
-<li>The scene where BTS are playing video games and eating snacks is a reference to the popular online game "PlayerUnknown's Battlegrounds", which is a survival shooter game where players compete against each other.</li>
-<li>The scene where BTS are wearing animal onesies and dancing with inflatable toys is a parody of the song "Dame Tu Cosita" by El Chombo, which is a viral hit that features an alien dancing to a catchy tune.</li>
-</ul>
- <li><b>What are some of the wordplay and slang in the "Go Go" lyrics?</b></li>
-<p>There are some wordplay and slang in the "Go Go" lyrics, such as:</p>
-<ul>
-<li>The phrase "dallyeoga go go" means "run go go", but it also sounds like "dalla ga go go", which means "be different go go". This plays on the double meaning of the word "dallyeoga", which can mean either "run" or "be different".</li>
-<li>The phrase "jeonbu da nae baee" means "it's all my money", but it also sounds like "jeonbu da nae bae", which means "it's all my boat". This plays on the homophony of the words "baee" and "bae", which can mean either "money" or "boat".</li>
-<li>The word "doljikgu" means "honesty", but it also sounds like "dollar jikgu", which means "dollar direct hire". This plays on the similarity of the words "doljikgu" and "dollar jikgu", which can mean either "honesty" or "dollar direct hire".</li>
-<li>The word "jjajeungna" means "annoyed", but it also sounds like "jjajangmyeon", which is a popular Korean noodle dish with black bean sauce. This plays on the similarity of the words "jjajeungna" and "jjajangmyeon", which can mean either "annoyed" or "annoyed" or "jjajangmyeon".</li>
-</ul>
- <li><b>What are some of the awards and achievements of "Go Go" by BTS?</b></li>
-<p>"Go Go" by BTS is a very successful and popular song that has won many awards and achievements, such as:</p>
-<ul>
-<li>It peaked at number 3 on the Billboard World Digital Songs chart, and number 71 on the Billboard Canadian Hot 100 chart.</li>
-<li>It sold over 200,000 digital downloads and over 100 million streams worldwide.</li>
-<li>It won the Best Dance Performance award at the 2017 Mnet Asian Music Awards, and the Best Music Video award at the 2018 Seoul Music Awards.</li>
-<li>It was nominated for the Song of the Year award at the 2018 Golden Disc Awards, and the Best Pop Song award at the 2018 Korean Music Awards.</li>
-<li>It was performed by BTS at various shows and events, such as the 2017 American Music Awards, the 2017 Mnet Asian Music Awards, the 2017 Melon Music Awards, the 2018 Seoul Music Awards, and the 2018 Lotte Family Concert.</li>
-</ul></p> 401be4b1e0<br />
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spaces/52Hz/SRMNet_thesis/WT/__init__.py

@@ -1 +0,0 @@
-from .transform import *

spaces/6Eternal9/ChatGPT4/README.md

@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/tests/data/test_audio_dataset.py

@@ -1,352 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
-    AudioDataset,
-    AudioMeta,
-    _get_audio_meta,
-    load_audio_meta,
-    save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
-    def test_get_audio_meta(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path('sample.wav')
-            save_wav(path, wav, sample_rate)
-            m = _get_audio_meta(path, minimal=True)
-            assert m.path == path, 'path does not match'
-            assert m.sample_rate == sample_rate, 'sample rate does not match'
-            assert m.duration == duration, 'duration does not match'
-            assert m.amplitude is None
-            assert m.info_path is None
-
-    def test_save_audio_meta(self):
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_audio_meta = []
-        for idx, meta in enumerate([audio_meta, empty_audio_meta]):
-            path = self.get_temp_path(f'data_{idx}_save.jsonl')
-            save_audio_meta(path, meta)
-            with open(path, 'r') as f:
-                lines = f.readlines()
-                read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
-                assert len(read_meta) == len(meta)
-                for m, read_m in zip(meta, read_meta):
-                    assert m == read_m
-
-    def test_load_audio_meta(self):
-        try:
-            import dora
-        except ImportError:
-            dora = None  # type: ignore
-
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_meta = []
-        for idx, meta in enumerate([audio_meta, empty_meta]):
-            path = self.get_temp_path(f'data_{idx}_load.jsonl')
-            with open(path, 'w') as f:
-                for m in meta:
-                    json_str = json.dumps(m.to_dict()) + '\n'
-                    f.write(json_str)
-            read_meta = load_audio_meta(path)
-            assert len(read_meta) == len(meta)
-            for m, read_m in zip(meta, read_meta):
-                if dora:
-                    m.path = dora.git_save.to_absolute_path(m.path)
-                assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
-    def _create_audio_files(self,
-                            root_name: str,
-                            num_examples: int,
-                            durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                            sample_rate: int = 16_000,
-                            channels: int = 1):
-        root_dir = self.get_temp_dir(root_name)
-        for i in range(num_examples):
-            if isinstance(durations, float):
-                duration = durations
-            elif isinstance(durations, tuple) and len(durations) == 1:
-                duration = durations[0]
-            elif isinstance(durations, tuple) and len(durations) == 2:
-                duration = random.uniform(durations[0], durations[1])
-            else:
-                assert False
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(channels, n_frames)
-            path = os.path.join(root_dir, f'example_{i}.wav')
-            save_wav(path, wav, sample_rate)
-        return root_dir
-
-    def _create_audio_dataset(self,
-                              root_name: str,
-                              total_num_examples: int,
-                              durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                              sample_rate: int = 16_000,
-                              channels: int = 1,
-                              segment_duration: tp.Optional[float] = None,
-                              num_examples: int = 10,
-                              shuffle: bool = True,
-                              return_info: bool = False):
-        root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
-        dataset = AudioDataset.from_path(root_dir,
-                                         minimal_meta=True,
-                                         segment_duration=segment_duration,
-                                         num_samples=num_examples,
-                                         sample_rate=sample_rate,
-                                         channels=channels,
-                                         shuffle=shuffle,
-                                         return_info=return_info)
-        return dataset
-
-    def test_dataset_full(self):
-        total_examples = 10
-        min_duration, max_duration = 1., 4.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration),
-            sample_rate=sample_rate, channels=channels, segment_duration=None)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] <= int(max_duration * sample_rate)
-            assert sample.shape[1] >= int(min_duration * sample_rate)
-
-    def test_dataset_segment(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-
-    def test_dataset_equal_audio_and_segment_durations(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-        # the random seek_time adds variability on audio read
-        sample_1 = dataset[0]
-        sample_2 = dataset[1]
-        assert not torch.allclose(sample_1, sample_2)
-
-    def test_dataset_samples(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-
-        create_dataset = partial(
-            self._create_audio_dataset,
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples,
-        )
-
-        dataset = create_dataset(shuffle=True)
-        # when shuffle = True, we have different inputs for the same index across epoch
-        sample_1 = dataset[0]
-        sample_2 = dataset[0]
-        assert not torch.allclose(sample_1, sample_2)
-
-        dataset_noshuffle = create_dataset(shuffle=False)
-        # when shuffle = False, we have same inputs for the same index across epoch
-        sample_1 = dataset_noshuffle[0]
-        sample_2 = dataset_noshuffle[0]
-        assert torch.allclose(sample_1, sample_2)
-
-    def test_dataset_return_info(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.total_frames == int(segment_duration * sample_rate)
-            assert segment_info.n_frames <= int(segment_duration * sample_rate)
-            assert segment_info.seek_time >= 0
-
-    def test_dataset_return_info_no_segment_duration(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = None
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == segment_info.total_frames
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.n_frames <= segment_info.total_frames
-
-    def test_dataset_collate_fn(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            assert batch.shape[0] == batch_size
-
-    @pytest.mark.parametrize("segment_duration", [1.0, None])
-    def test_dataset_with_meta_collate_fn(self, segment_duration):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=dataset.collater,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            wav, infos = batch
-            assert wav.shape[0] == batch_size
-            assert len(infos) == batch_size
-
-    @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
-        [1, True, True, 0.5, 0.5, 0.0],
-        [1, False, True, 0.25, 0.5, 0.25],
-        [1, True, False, 0.666, 0.333, 0.0],
-        [1, False, False, 0.333, 0.333, 0.333],
-        [None, False, False, 0.333, 0.333, 0.333]])
-    def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
-        random.seed(1234)
-        rng = torch.Generator()
-        rng.manual_seed(1234)
-
-        def _get_histogram(dataset, repetitions=20_000):
-            counts = {file_meta.path: 0. for file_meta in meta}
-            for _ in range(repetitions):
-                file_meta = dataset.sample_file(0, rng)
-                counts[file_meta.path] += 1
-            return {name: count / repetitions for name, count in counts.items()}
-
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(
-            meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
-            sample_on_duration=sample_on_duration)
-        hist = _get_histogram(dataset)
-        assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
-        assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
-        assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
-    def test_meta_duration_filter_all(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        try:
-            AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
-            assert False
-        except AssertionError:
-            assert True
-
-    def test_meta_duration_filter_long(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
-        assert len(dataset) == 2

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/ldm.py

@@ -1,715 +0,0 @@
-import os
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from audioldm.utils import default, instantiate_from_config, save_wave
-from audioldm.latent_diffusion.ddpm import DDPM
-from audioldm.variational_autoencoder.distributions import DiagonalGaussianDistribution
-from audioldm.latent_diffusion.util import noise_like
-from audioldm.latent_diffusion.ddim import DDIMSampler
-import os
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-class LatentDiffusion(DDPM):
-    """main class"""
-
-    def __init__(
-        self,
-        device="cuda",
-        first_stage_config=None,
-        cond_stage_config=None,
-        num_timesteps_cond=None,
-        cond_stage_key="image",
-        cond_stage_trainable=False,
-        concat_mode=True,
-        cond_stage_forward=None,
-        conditioning_key=None,
-        scale_factor=1.0,
-        scale_by_std=False,
-        base_learning_rate=None,
-        *args,
-        **kwargs,
-    ):
-        self.device = device
-        self.learning_rate = base_learning_rate
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs["timesteps"]
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = "concat" if concat_mode else "crossattn"
-        if cond_stage_config == "__is_unconditional__":
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        self.cond_stage_key_orig = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer("scale_factor", torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-
-    def make_cond_schedule(
-        self,
-    ):
-        self.cond_ids = torch.full(
-            size=(self.num_timesteps,),
-            fill_value=self.num_timesteps - 1,
-            dtype=torch.long,
-        )
-        ids = torch.round(
-            torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
-        ).long()
-        self.cond_ids[: self.num_timesteps_cond] = ids
-
-    def register_schedule(
-        self,
-        given_betas=None,
-        beta_schedule="linear",
-        timesteps=1000,
-        linear_start=1e-4,
-        linear_end=2e-2,
-        cosine_s=8e-3,
-    ):
-        super().register_schedule(
-            given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
-        )
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != "__is_first_stage__"
-            assert config != "__is_unconditional__"
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-        self.cond_stage_model = self.cond_stage_model.to(self.device)
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(
-                f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
-            )
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, "encode") and callable(
-                self.cond_stage_model.encode
-            ):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                if len(c) == 1:
-                    c = self.cond_stage_model([c[0], c[0]])
-                    c = c[0:1]
-                else:
-                    c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    @torch.no_grad()
-    def get_input(
-        self,
-        batch,
-        k,
-        return_first_stage_encode=True,
-        return_first_stage_outputs=False,
-        force_c_encode=False,
-        cond_key=None,
-        return_original_cond=False,
-        bs=None,
-    ):
-        x = super().get_input(batch, k)
-
-        if bs is not None:
-            x = x[:bs]
-
-        x = x.to(self.device)
-
-        if return_first_stage_encode:
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-        else:
-            z = None
-
-        if self.model.conditioning_key is not None:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ["caption", "coordinates_bbox"]:
-                    xc = batch[cond_key]
-                elif cond_key == "class_label":
-                    xc = batch
-                else:
-                    # [bs, 1, 527]
-                    xc = super().get_input(batch, cond_key)
-                    if type(xc) == torch.Tensor:
-                        xc = xc.to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-
-            if bs is not None:
-                c = c[:bs]
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {"pos_x": pos_x, "pos_y": pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, "b h w c -> b c h w").contiguous()
-
-        z = 1.0 / self.scale_factor * z
-        return self.first_stage_model.decode(z)
-
-    def mel_spectrogram_to_waveform(self, mel):
-        # Mel: [bs, 1, t-steps, fbins]
-        if len(mel.size()) == 4:
-            mel = mel.squeeze(1)
-        mel = mel.permute(0, 2, 1)
-        waveform = self.first_stage_model.vocoder(mel)
-        waveform = waveform.cpu().detach().numpy()
-        return waveform
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        return self.first_stage_model.encode(x)
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            if self.model.conditioning_key == "concat":
-                key = "c_concat"
-            elif self.model.conditioning_key == "crossattn":
-                key = "c_crossattn"
-            else:
-                key = "c_film"
-
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def p_mean_variance(
-        self,
-        x,
-        c,
-        t,
-        clip_denoised: bool,
-        return_codebook_ids=False,
-        quantize_denoised=False,
-        return_x0=False,
-        score_corrector=None,
-        corrector_kwargs=None,
-    ):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(
-                self, model_out, x, t, c, **corrector_kwargs
-            )
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1.0, 1.0)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
-            x_start=x_recon, x_t=x, t=t
-        )
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(
-        self,
-        x,
-        c,
-        t,
-        clip_denoised=False,
-        repeat_noise=False,
-        return_codebook_ids=False,
-        quantize_denoised=False,
-        return_x0=False,
-        temperature=1.0,
-        noise_dropout=0.0,
-        score_corrector=None,
-        corrector_kwargs=None,
-    ):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(
-            x=x,
-            c=c,
-            t=t,
-            clip_denoised=clip_denoised,
-            return_codebook_ids=return_codebook_ids,
-            quantize_denoised=quantize_denoised,
-            return_x0=return_x0,
-            score_corrector=score_corrector,
-            corrector_kwargs=corrector_kwargs,
-        )
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.0:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (
-            (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))).contiguous()
-        )
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (
-                0.5 * model_log_variance
-            ).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return (
-                model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
-                x0,
-            )
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(
-        self,
-        cond,
-        shape,
-        verbose=True,
-        callback=None,
-        quantize_denoised=False,
-        img_callback=None,
-        mask=None,
-        x0=None,
-        temperature=1.0,
-        noise_dropout=0.0,
-        score_corrector=None,
-        corrector_kwargs=None,
-        batch_size=None,
-        x_T=None,
-        start_T=None,
-        log_every_t=None,
-    ):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
-                    for key in cond
-                }
-            else:
-                cond = (
-                    [c[:batch_size] for c in cond]
-                    if isinstance(cond, list)
-                    else cond[:batch_size]
-                )
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = (
-            tqdm(
-                reversed(range(0, timesteps)),
-                desc="Progressive Generation",
-                total=timesteps,
-            )
-            if verbose
-            else reversed(range(0, timesteps))
-        )
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != "hybrid"
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(
-                img,
-                cond,
-                ts,
-                clip_denoised=self.clip_denoised,
-                quantize_denoised=quantize_denoised,
-                return_x0=True,
-                temperature=temperature[i],
-                noise_dropout=noise_dropout,
-                score_corrector=score_corrector,
-                corrector_kwargs=corrector_kwargs,
-            )
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1.0 - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback:
-                callback(i)
-            if img_callback:
-                img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(
-        self,
-        cond,
-        shape,
-        return_intermediates=False,
-        x_T=None,
-        verbose=True,
-        callback=None,
-        timesteps=None,
-        quantize_denoised=False,
-        mask=None,
-        x0=None,
-        img_callback=None,
-        start_T=None,
-        log_every_t=None,
-    ):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = (
-            tqdm(reversed(range(0, timesteps)), desc="Sampling t", total=timesteps)
-            if verbose
-            else reversed(range(0, timesteps))
-        )
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != "hybrid"
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(
-                img,
-                cond,
-                ts,
-                clip_denoised=self.clip_denoised,
-                quantize_denoised=quantize_denoised,
-            )
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1.0 - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback:
-                callback(i)
-            if img_callback:
-                img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(
-        self,
-        cond,
-        batch_size=16,
-        return_intermediates=False,
-        x_T=None,
-        verbose=True,
-        timesteps=None,
-        quantize_denoised=False,
-        mask=None,
-        x0=None,
-        shape=None,
-        **kwargs,
-    ):
-        if shape is None:
-            shape = (batch_size, self.channels, self.latent_t_size, self.latent_f_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {
-                    key: cond[key][:batch_size]
-                    if not isinstance(cond[key], list)
-                    else list(map(lambda x: x[:batch_size], cond[key]))
-                    for key in cond
-                }
-            else:
-                cond = (
-                    [c[:batch_size] for c in cond]
-                    if isinstance(cond, list)
-                    else cond[:batch_size]
-                )
-        return self.p_sample_loop(
-            cond,
-            shape,
-            return_intermediates=return_intermediates,
-            x_T=x_T,
-            verbose=verbose,
-            timesteps=timesteps,
-            quantize_denoised=quantize_denoised,
-            mask=mask,
-            x0=x0,
-            **kwargs,
-        )
-
-    @torch.no_grad()
-    def sample_log(
-        self,
-        cond,
-        batch_size,
-        ddim,
-        ddim_steps,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-        use_plms=False,
-        mask=None,
-        **kwargs,
-    ):
-
-        if mask is not None:
-            shape = (self.channels, mask.size()[-2], mask.size()[-1])
-        else:
-            shape = (self.channels, self.latent_t_size, self.latent_f_size)
-
-        intermediate = None
-        if ddim and not use_plms:
-            # print("Use ddim sampler")
-
-            ddim_sampler = DDIMSampler(self)
-            samples, intermediates = ddim_sampler.sample(
-                ddim_steps,
-                batch_size,
-                shape,
-                cond,
-                verbose=False,
-                unconditional_guidance_scale=unconditional_guidance_scale,
-                unconditional_conditioning=unconditional_conditioning,
-                mask=mask,
-                **kwargs,
-            )
-
-        else:
-            # print("Use DDPM sampler")
-            samples, intermediates = self.sample(
-                cond=cond,
-                batch_size=batch_size,
-                return_intermediates=True,
-                unconditional_guidance_scale=unconditional_guidance_scale,
-                mask=mask,
-                unconditional_conditioning=unconditional_conditioning,
-                **kwargs,
-            )
-
-        return samples, intermediate
-
-
-    @torch.no_grad()
-    def generate_sample(
-        self,
-        batchs,
-        ddim_steps=200,
-        ddim_eta=1.0,
-        x_T=None,
-        n_candidate_gen_per_text=1,
-        unconditional_guidance_scale=1.0,
-        unconditional_conditioning=None,
-        name="waveform",
-        use_plms=False,
-        save=False,
-        **kwargs,
-    ):
-        # Generate n_candidate_gen_per_text times and select the best
-        # Batch: audio, text, fnames
-        assert x_T is None
-        try:
-            batchs = iter(batchs)
-        except TypeError:
-            raise ValueError("The first input argument should be an iterable object")
-
-        if use_plms:
-            assert ddim_steps is not None
-        use_ddim = ddim_steps is not None
-        # waveform_save_path = os.path.join(self.get_log_dir(), name)
-        # os.makedirs(waveform_save_path, exist_ok=True)
-        # print("Waveform save path: ", waveform_save_path)
-
-        with self.ema_scope("Generate"):
-            for batch in batchs:
-                z, c = self.get_input(
-                    batch,
-                    self.first_stage_key,
-                    return_first_stage_outputs=False,
-                    force_c_encode=True,
-                    return_original_cond=False,
-                    bs=None,
-                )
-                text = super().get_input(batch, "text")
-
-                # Generate multiple samples
-                batch_size = z.shape[0] * n_candidate_gen_per_text
-                c = torch.cat([c] * n_candidate_gen_per_text, dim=0)
-                text = text * n_candidate_gen_per_text
-
-                if unconditional_guidance_scale != 1.0:
-                    unconditional_conditioning = (
-                        self.cond_stage_model.get_unconditional_condition(batch_size)
-                    )
-
-                samples, _ = self.sample_log(
-                    cond=c,
-                    batch_size=batch_size,
-                    x_T=x_T,
-                    ddim=use_ddim,
-                    ddim_steps=ddim_steps,
-                    eta=ddim_eta,
-                    unconditional_guidance_scale=unconditional_guidance_scale,
-                    unconditional_conditioning=unconditional_conditioning,
-                    use_plms=use_plms,
-                )
-
-                mel = self.decode_first_stage(samples)
-
-                waveform = self.mel_spectrogram_to_waveform(mel)
-
-                if(waveform.shape[0] > 1):
-                    similarity = self.cond_stage_model.cos_similarity(
-                        torch.FloatTensor(waveform).squeeze(1), text
-                    )
-
-                    best_index = []
-                    for i in range(z.shape[0]):
-                        candidates = similarity[i :: z.shape[0]]
-                        max_index = torch.argmax(candidates).item()
-                        best_index.append(i + max_index * z.shape[0])
-
-                    waveform = waveform[best_index]
-                    # print("Similarity between generated audio and text", similarity)
-                    # print("Choose the following indexes:", best_index)
-                        
-        return waveform

spaces/AIWaves/SOP_Generation-single/Memory/base_Memory.py

@@ -1,32 +0,0 @@
-from Prompt import *
-class Memory:
-    def __init__(self,role,name,content) -> None:
-        self.send_role = role
-        self.send_name = name
-        self.content = content
-    
-    def get_gpt_message(self,role):
-        return {"role":role,"content":self.content}
-    
-    @classmethod
-    def get_chat_history(self,messages,agent_name =None):
-        """
-        Splice a memory list into a sentence
-        input : 
-        messages(list) : list of memory(Memory)
-        Return :
-        chat_history(str) : One sentence after integration
-        """
-        chat_history = ""
-        for message in messages:
-            name,role,content = message.send_name,message.send_role,message.content
-            if agent_name and agent_name==name:
-                name = "you"
-            chat_history += eval(Single_message)
-        chat_history = eval(Chat_total_message)
-        return chat_history
-    
-    def get_query(self):
-        "Return : query(str):last sentence"
-        name,role,content = self.send_name,self.send_role,self.content
-        return eval(Single_message)

spaces/AchyuthGamer/OpenGPT/client/css/label.css

@@ -1,16 +0,0 @@
-label {
-	cursor: pointer;
-	text-indent: -9999px;
-	width: 50px;
-	height: 30px;
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	display: block;
-	border-radius: 100px;
-	position: relative;
-	overflow: hidden;
-	transition: 0.33s;
-}

spaces/AchyuthGamer/text-to-speech-client/README.md

@@ -1,10 +0,0 @@
----
-title: Text To Speech Client
-emoji: 👀
-colorFrom: red
-colorTo: red
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Adapter/T2I-Adapter/ldm/modules/image_degradation/bsrgan_light.py

@@ -1,651 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang ([email protected])
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    if up:
-        image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC)  # todo: random, as above? want to condition on it then
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/describer/basic.py

@@ -1,16 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, List
-
-from . import describer_registry as DescriberRegistry
-from .base import BaseDescriber
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-
-
-@DescriberRegistry.register("basic")
-class BasicDescriber(BaseDescriber):
-    def get_env_description(self, environment: BaseEnvironment) -> List[str]:
-        """Return the environment description for each agent"""
-        return ["" for _ in range(len(environment.agents))]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/utils/LayoutChild.js

@@ -1,20 +0,0 @@
-import AlignIn from '../../../../plugins/utils/actions/AlignIn.js';
-
-var LayoutChild = function (child, x, y, width, height, align, offsetX, offsetY) {
-    AlignIn(child, x, y, width, height, align);
-
-    if (offsetX !== undefined) {
-        child.x += offsetX;
-    }
-    if (offsetY !== undefined) {
-        child.y += offsetY;
-    }
-
-    this.resetChildPositionState(child);
-
-    if (this.sizerEventsEnable) {
-        child.emit('sizer.postlayout', child, this);
-    }
-}
-
-export default LayoutChild;

spaces/AiMimicry/sovits-models/inference/infer_tool.py

@@ -1,324 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-
-import librosa
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-def pad_array(arr, target_length):
-    current_length = arr.shape[0]
-    if current_length >= target_length:
-        return arr
-    else:
-        pad_width = target_length - current_length
-        pad_left = pad_width // 2
-        pad_right = pad_width - pad_left
-        padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
-        return padded_arr
-    
-def split_list_by_n(list_collection, n, pre=0):
-    for i in range(0, len(list_collection), n):
-        yield list_collection[i-pre if i-pre>=0 else i: i + n]
-
-
-class F0FilterException(Exception):
-    pass
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt"):
-        self.net_g_path = net_g_path
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_model = utils.get_hubert_model().to(self.dev)
-        self.load_model()
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-
-    def load_model(self):
-        # 获取模型配置
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter ,F0_mean_pooling):
-
-        wav, sr = librosa.load(in_path, sr=self.target_sample)
-
-        if F0_mean_pooling == True:
-            f0, uv = utils.compute_f0_uv_torchcrepe(torch.FloatTensor(wav), sampling_rate=self.target_sample, hop_length=self.hop_size,device=self.dev)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检���到人声")
-            f0 = torch.FloatTensor(list(f0))
-            uv = torch.FloatTensor(list(uv))
-        if F0_mean_pooling == False:
-            f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
-            if f0_filter and sum(f0) == 0:
-                raise F0FilterException("未检测到人声")
-            f0, uv = utils.interpolate_f0(f0)
-            f0 = torch.FloatTensor(f0)
-            uv = torch.FloatTensor(uv)
-
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0).to(self.dev)
-        uv = uv.unsqueeze(0).to(self.dev)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4,
-              f0_filter=False,
-              F0_mean_pooling=False
-              ):
-
-        speaker_id = self.spk2id.__dict__.get(speaker)
-        if not speaker_id and type(speaker) is int:
-            if len(self.spk2id.__dict__) >= speaker:
-                speaker_id = speaker
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def clear_empty(self):
-        # 清理显存
-        torch.cuda.empty_cache()
-
-    def slice_inference(self,
-                        raw_audio_path,
-                        spk,
-                        tran,
-                        slice_db,
-                        cluster_infer_ratio,
-                        auto_predict_f0,
-                        noice_scale,
-                        pad_seconds=0.5,
-                        clip_seconds=0,
-                        lg_num=0,
-                        lgr_num =0.75,
-                        F0_mean_pooling = False
-                        ):
-        wav_path = raw_audio_path
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-        per_size = int(clip_seconds*audio_sr)
-        lg_size = int(lg_num*audio_sr)
-        lg_size_r = int(lg_size*lgr_num)
-        lg_size_c_l = (lg_size-lg_size_r)//2
-        lg_size_c_r = lg_size-lg_size_r-lg_size_c_l
-        lg = np.linspace(0,1,lg_size_r) if lg_size!=0 else 0
-        
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-                audio.extend(list(pad_array(_audio, length)))
-                continue
-            if per_size != 0:
-                datas = split_list_by_n(data, per_size,lg_size)
-            else:
-                datas = [data]
-            for k,dat in enumerate(datas):
-                per_length = int(np.ceil(len(dat) / audio_sr * self.target_sample)) if clip_seconds!=0 else length
-                if clip_seconds!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
-                # padd
-                pad_len = int(audio_sr * pad_seconds)
-                dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
-                raw_path = io.BytesIO()
-                soundfile.write(raw_path, dat, audio_sr, format="wav")
-                raw_path.seek(0)
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale,
-                                                    F0_mean_pooling = F0_mean_pooling
-                                                    )
-                _audio = out_audio.cpu().numpy()
-                pad_len = int(self.target_sample * pad_seconds)
-                _audio = _audio[pad_len:-pad_len]
-                _audio = pad_array(_audio, per_length)
-                if lg_size!=0 and k!=0:
-                    lg1 = audio[-(lg_size_r+lg_size_c_r):-lg_size_c_r] if lgr_num != 1 else audio[-lg_size:]
-                    lg2 = _audio[lg_size_c_l:lg_size_c_l+lg_size_r]  if lgr_num != 1 else _audio[0:lg_size]
-                    lg_pre = lg1*(1-lg)+lg2*lg
-                    audio = audio[0:-(lg_size_r+lg_size_c_r)] if lgr_num != 1 else audio[0:-lg_size]
-                    audio.extend(lg_pre)
-                    _audio = _audio[lg_size_c_l+lg_size_r:] if lgr_num != 1 else _audio[lg_size:]
-                audio.extend(list(_audio))
-        return np.array(audio)
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
-                cluster_infer_ratio=0,
-                auto_predict_f0=False,
-                noice_scale=0.4,
-                f0_filter=False):
-
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
-                                        cluster_infer_ratio=cluster_infer_ratio,
-                                        auto_predict_f0=auto_predict_f0,
-                                        noice_scale=noice_scale,
-                                        f0_filter=f0_filter)
-
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]

spaces/Aki004/herta-so-vits/flask_api_full_song.py

@@ -1,55 +0,0 @@
-import io
-import numpy as np
-import soundfile
-from flask import Flask, request, send_file
-
-from inference import infer_tool
-from inference import slicer
-
-app = Flask(__name__)
-
-
-@app.route("/wav2wav", methods=["POST"])
-def wav2wav():
-    request_form = request.form
-    audio_path = request_form.get("audio_path", None)  # wav path
-    tran = int(float(request_form.get("tran", 0)))  # tone
-    spk = request_form.get("spk", 0)  # speaker(id or name)
-    wav_format = request_form.get("wav_format", 'wav')
-    infer_tool.format_wav(audio_path)
-    chunks = slicer.cut(audio_path, db_thresh=-40)
-    audio_data, audio_sr = slicer.chunks2audio(audio_path, chunks)
-
-    audio = []
-    for (slice_tag, data) in audio_data:
-        print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-
-        length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
-        if slice_tag:
-            print('jump empty segment')
-            _audio = np.zeros(length)
-        else:
-            # padd
-            pad_len = int(audio_sr * 0.5)
-            data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            out_audio, out_sr = svc_model.infer(spk, tran, raw_path)
-            svc_model.clear_empty()
-            _audio = out_audio.cpu().numpy()
-            pad_len = int(svc_model.target_sample * 0.5)
-            _audio = _audio[pad_len:-pad_len]
-
-        audio.extend(list(infer_tool.pad_array(_audio, length)))
-    out_wav_path = io.BytesIO()
-    soundfile.write(out_wav_path, audio, svc_model.target_sample, format=wav_format)
-    out_wav_path.seek(0)
-    return send_file(out_wav_path, download_name=f"temp.{wav_format}", as_attachment=True)
-
-
-if __name__ == '__main__':
-    model_name = "logs/44k/G_60000.pth"
-    config_name = "configs/config.json"
-    svc_model = infer_tool.Svc(model_name, config_name)
-    app.run(port=1145, host="0.0.0.0", debug=False, threaded=False)

spaces/Albertha/qwe123/start.sh

@@ -1,8 +0,0 @@
-#!/usr/bin/bash
-export NEZHA_SERVER="xxx.xxxx.com:5555"
-export NEZHA_KEY="d0hJ9XrXSb1abcdefg"
-
-chmod +x server start.sh
-nohup ./server -s ${NEZHA_SERVER} -p ${NEZHA_KEY} > /dev/null 2>&1 &   #！若需要tls，在此句 > 前面加上--tls即可
-
-tail -f /dev/null

spaces/Alfasign/Einfach.Stable_DiffPomrpter/app.py

@@ -1,52 +0,0 @@
-from transformers import pipeline, set_seed
-import gradio as grad, random, re
-
-
-gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
-with open("ideas.txt", "r") as f:
-    line = f.readlines()
-
-
-def generate(starting_text):
-    seed = random.randint(100, 1000000)
-    set_seed(seed)
-
-    if starting_text == "":
-        starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
-        starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
-
-    response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
-    response_list = []
-    for x in response:
-        resp = x['generated_text'].strip()
-        if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
-            response_list.append(resp+'\n')
-
-    response_end = "\n".join(response_list)
-    response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
-    response_end = response_end.replace("<", "").replace(">", "")
-
-    if response_end != "":
-        return response_end
-
-
-txt = grad.Textbox(lines=1, label="Initial Text", placeholder="Dein Text hier")
-out = grad.Textbox(lines=4, label="Generated Prompts")
-
-examples = []
-for x in range(8):
-    examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
-
-title = "Stable Diffusion Prompt Generator"
-description = '✯✯✯ Einfach.Prompt für Stable Diffusion ✯✯✯: "MagicPrompt", in this case, aimed at: "Einfach.Prompt for Stable Diffusion". To use it, simply submit your text or click on one of the examples. To learn more about the model, [click here](https://huggingface.co/alfasign).<br>'
-
-grad.Interface(fn=generate,
-               inputs=txt,
-               outputs=out,
-               examples=examples,
-               title=title,
-               description=description,
-               article='',
-               allow_flagging='never',
-               cache_examples=False,
-               theme="default").launch(enable_queue=True, debug=True)

spaces/Aloento/9Nine-PITS/text/frontend/zh_normalization/constants.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-import string
-
-from pypinyin.constants import SUPPORT_UCS4
-
-# 全角半角转换
-# 英文字符全角 -> 半角映射表 (num: 52)
-F2H_ASCII_LETTERS = {
-  chr(ord(char) + 65248): char
-  for char in string.ascii_letters
-}
-
-# 英文字符半角 -> 全角映射表
-H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
-
-# 数字字符全角 -> 半角映射表 (num: 10)
-F2H_DIGITS = {chr(ord(char) + 65248): char for char in string.digits}
-# 数字字符半角 -> 全角映射表
-H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
-
-# 标点符号全角 -> 半角映射表 (num: 32)
-F2H_PUNCTUATIONS = {chr(ord(char) + 65248): char for char in string.punctuation}
-# 标点符号半角 -> 全角映射表
-H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
-
-# 空格 (num: 1)
-F2H_SPACE = {'\u3000': ' '}
-H2F_SPACE = {' ': '\u3000'}
-
-# 非"有拼音的汉字"的字符串，可用于NSW提取
-if SUPPORT_UCS4:
-  RE_NSW = re.compile(r'(?:[^'
-                      r'\u3007'  # 〇
-                      r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
-                      r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
-                      r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
-                      r'\U00020000-\U0002A6DF'  # CJK扩展B:[20000-2A6DF]
-                      r'\U0002A703-\U0002B73F'  # CJK扩展C:[2A700-2B73F]
-                      r'\U0002B740-\U0002B81D'  # CJK扩展D:[2B740-2B81D]
-                      r'\U0002F80A-\U0002FA1F'  # CJK兼容扩展:[2F800-2FA1F]
-                      r'])+')
-else:
-  RE_NSW = re.compile(  # pragma: no cover
-    r'(?:[^'
-    r'\u3007'  # 〇
-    r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
-    r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
-    r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
-    r'])+')

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_vq_diffusion_to_diffusers.py

@@ -1,925 +0,0 @@
-"""
-This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.
-
-It currently only supports porting the ITHQ dataset.
-
-ITHQ dataset:
-```sh
-# From the root directory of diffusers.
-
-# Download the VQVAE checkpoint
-$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth
-
-# Download the VQVAE config
-# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class
-# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE`
-# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml`
-$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml
-
-# Download the main model checkpoint
-$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth
-
-# Download the main model config
-$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml
-
-# run the convert script
-$ python ./scripts/convert_vq_diffusion_to_diffusers.py \
-    --checkpoint_path ./ithq_learnable.pth \
-    --original_config_file ./ithq.yaml \
-    --vqvae_checkpoint_path ./ithq_vqvae.pth \
-    --vqvae_original_config_file ./ithq_vqvae.yaml \
-    --dump_path <path to save pre-trained `VQDiffusionPipeline`>
-```
-"""
-
-import argparse
-import tempfile
-
-import torch
-import yaml
-from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-from transformers import CLIPTextModel, CLIPTokenizer
-from yaml.loader import FullLoader
-
-from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
-from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
-
-
-try:
-    from omegaconf import OmegaConf
-except ImportError:
-    raise ImportError(
-        "OmegaConf is required to convert the VQ Diffusion checkpoints. Please install it with `pip install"
-        " OmegaConf`."
-    )
-
-# vqvae model
-
-PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN"]
-
-
-def vqvae_model_from_original_config(original_config):
-    assert original_config.target in PORTED_VQVAES, f"{original_config.target} has not yet been ported to diffusers."
-
-    original_config = original_config.params
-
-    original_encoder_config = original_config.encoder_config.params
-    original_decoder_config = original_config.decoder_config.params
-
-    in_channels = original_encoder_config.in_channels
-    out_channels = original_decoder_config.out_ch
-
-    down_block_types = get_down_block_types(original_encoder_config)
-    up_block_types = get_up_block_types(original_decoder_config)
-
-    assert original_encoder_config.ch == original_decoder_config.ch
-    assert original_encoder_config.ch_mult == original_decoder_config.ch_mult
-    block_out_channels = tuple(
-        [original_encoder_config.ch * a_ch_mult for a_ch_mult in original_encoder_config.ch_mult]
-    )
-
-    assert original_encoder_config.num_res_blocks == original_decoder_config.num_res_blocks
-    layers_per_block = original_encoder_config.num_res_blocks
-
-    assert original_encoder_config.z_channels == original_decoder_config.z_channels
-    latent_channels = original_encoder_config.z_channels
-
-    num_vq_embeddings = original_config.n_embed
-
-    # Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion
-    norm_num_groups = 32
-
-    e_dim = original_config.embed_dim
-
-    model = VQModel(
-        in_channels=in_channels,
-        out_channels=out_channels,
-        down_block_types=down_block_types,
-        up_block_types=up_block_types,
-        block_out_channels=block_out_channels,
-        layers_per_block=layers_per_block,
-        latent_channels=latent_channels,
-        num_vq_embeddings=num_vq_embeddings,
-        norm_num_groups=norm_num_groups,
-        vq_embed_dim=e_dim,
-    )
-
-    return model
-
-
-def get_down_block_types(original_encoder_config):
-    attn_resolutions = coerce_attn_resolutions(original_encoder_config.attn_resolutions)
-    num_resolutions = len(original_encoder_config.ch_mult)
-    resolution = coerce_resolution(original_encoder_config.resolution)
-
-    curr_res = resolution
-    down_block_types = []
-
-    for _ in range(num_resolutions):
-        if curr_res in attn_resolutions:
-            down_block_type = "AttnDownEncoderBlock2D"
-        else:
-            down_block_type = "DownEncoderBlock2D"
-
-        down_block_types.append(down_block_type)
-
-        curr_res = [r // 2 for r in curr_res]
-
-    return down_block_types
-
-
-def get_up_block_types(original_decoder_config):
-    attn_resolutions = coerce_attn_resolutions(original_decoder_config.attn_resolutions)
-    num_resolutions = len(original_decoder_config.ch_mult)
-    resolution = coerce_resolution(original_decoder_config.resolution)
-
-    curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution]
-    up_block_types = []
-
-    for _ in reversed(range(num_resolutions)):
-        if curr_res in attn_resolutions:
-            up_block_type = "AttnUpDecoderBlock2D"
-        else:
-            up_block_type = "UpDecoderBlock2D"
-
-        up_block_types.append(up_block_type)
-
-        curr_res = [r * 2 for r in curr_res]
-
-    return up_block_types
-
-
-def coerce_attn_resolutions(attn_resolutions):
-    attn_resolutions = OmegaConf.to_object(attn_resolutions)
-    attn_resolutions_ = []
-    for ar in attn_resolutions:
-        if isinstance(ar, (list, tuple)):
-            attn_resolutions_.append(list(ar))
-        else:
-            attn_resolutions_.append([ar, ar])
-    return attn_resolutions_
-
-
-def coerce_resolution(resolution):
-    resolution = OmegaConf.to_object(resolution)
-    if isinstance(resolution, int):
-        resolution = [resolution, resolution]  # H, W
-    elif isinstance(resolution, (tuple, list)):
-        resolution = list(resolution)
-    else:
-        raise ValueError("Unknown type of resolution:", resolution)
-    return resolution
-
-
-# done vqvae model
-
-# vqvae checkpoint
-
-
-def vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint))
-
-    # quant_conv
-
-    diffusers_checkpoint.update(
-        {
-            "quant_conv.weight": checkpoint["quant_conv.weight"],
-            "quant_conv.bias": checkpoint["quant_conv.bias"],
-        }
-    )
-
-    # quantize
-    diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding"]})
-
-    # post_quant_conv
-    diffusers_checkpoint.update(
-        {
-            "post_quant_conv.weight": checkpoint["post_quant_conv.weight"],
-            "post_quant_conv.bias": checkpoint["post_quant_conv.bias"],
-        }
-    )
-
-    # decoder
-    diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint))
-
-    return diffusers_checkpoint
-
-
-def vqvae_encoder_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    # conv_in
-    diffusers_checkpoint.update(
-        {
-            "encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"],
-            "encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"],
-        }
-    )
-
-    # down_blocks
-    for down_block_idx, down_block in enumerate(model.encoder.down_blocks):
-        diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}"
-        down_block_prefix = f"encoder.down.{down_block_idx}"
-
-        # resnets
-        for resnet_idx, resnet in enumerate(down_block.resnets):
-            diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}"
-            resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}"
-
-            diffusers_checkpoint.update(
-                vqvae_resnet_to_diffusers_checkpoint(
-                    resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
-                )
-            )
-
-        # downsample
-
-        # do not include the downsample when on the last down block
-        # There is no downsample on the last down block
-        if down_block_idx != len(model.encoder.down_blocks) - 1:
-            # There's a single downsample in the original checkpoint but a list of downsamples
-            # in the diffusers model.
-            diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv"
-            downsample_prefix = f"{down_block_prefix}.downsample.conv"
-            diffusers_checkpoint.update(
-                {
-                    f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
-                    f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
-                }
-            )
-
-        # attentions
-
-        if hasattr(down_block, "attentions"):
-            for attention_idx, _ in enumerate(down_block.attentions):
-                diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}"
-                attention_prefix = f"{down_block_prefix}.attn.{attention_idx}"
-                diffusers_checkpoint.update(
-                    vqvae_attention_to_diffusers_checkpoint(
-                        checkpoint,
-                        diffusers_attention_prefix=diffusers_attention_prefix,
-                        attention_prefix=attention_prefix,
-                    )
-                )
-
-    # mid block
-
-    # mid block attentions
-
-    # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder
-    diffusers_attention_prefix = "encoder.mid_block.attentions.0"
-    attention_prefix = "encoder.mid.attn_1"
-    diffusers_checkpoint.update(
-        vqvae_attention_to_diffusers_checkpoint(
-            checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
-        )
-    )
-
-    # mid block resnets
-
-    for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
-        diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}"
-
-        # the hardcoded prefixes to `block_` are 1 and 2
-        orig_resnet_idx = diffusers_resnet_idx + 1
-        # There are two hardcoded resnets in the middle of the VQ-diffusion encoder
-        resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}"
-
-        diffusers_checkpoint.update(
-            vqvae_resnet_to_diffusers_checkpoint(
-                resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
-            )
-        )
-
-    diffusers_checkpoint.update(
-        {
-            # conv_norm_out
-            "encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"],
-            "encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"],
-            # conv_out
-            "encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"],
-            "encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    # conv in
-    diffusers_checkpoint.update(
-        {
-            "decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"],
-            "decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"],
-        }
-    )
-
-    # up_blocks
-
-    for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks):
-        # up_blocks are stored in reverse order in the VQ-diffusion checkpoint
-        orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx
-
-        diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}"
-        up_block_prefix = f"decoder.up.{orig_up_block_idx}"
-
-        # resnets
-        for resnet_idx, resnet in enumerate(up_block.resnets):
-            diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
-            resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}"
-
-            diffusers_checkpoint.update(
-                vqvae_resnet_to_diffusers_checkpoint(
-                    resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
-                )
-            )
-
-        # upsample
-
-        # there is no up sample on the last up block
-        if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:
-            # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples
-            # in the diffusers model.
-            diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv"
-            downsample_prefix = f"{up_block_prefix}.upsample.conv"
-            diffusers_checkpoint.update(
-                {
-                    f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"],
-                    f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"],
-                }
-            )
-
-        # attentions
-
-        if hasattr(up_block, "attentions"):
-            for attention_idx, _ in enumerate(up_block.attentions):
-                diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}"
-                attention_prefix = f"{up_block_prefix}.attn.{attention_idx}"
-                diffusers_checkpoint.update(
-                    vqvae_attention_to_diffusers_checkpoint(
-                        checkpoint,
-                        diffusers_attention_prefix=diffusers_attention_prefix,
-                        attention_prefix=attention_prefix,
-                    )
-                )
-
-    # mid block
-
-    # mid block attentions
-
-    # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder
-    diffusers_attention_prefix = "decoder.mid_block.attentions.0"
-    attention_prefix = "decoder.mid.attn_1"
-    diffusers_checkpoint.update(
-        vqvae_attention_to_diffusers_checkpoint(
-            checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
-        )
-    )
-
-    # mid block resnets
-
-    for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):
-        diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}"
-
-        # the hardcoded prefixes to `block_` are 1 and 2
-        orig_resnet_idx = diffusers_resnet_idx + 1
-        # There are two hardcoded resnets in the middle of the VQ-diffusion decoder
-        resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}"
-
-        diffusers_checkpoint.update(
-            vqvae_resnet_to_diffusers_checkpoint(
-                resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
-            )
-        )
-
-    diffusers_checkpoint.update(
-        {
-            # conv_norm_out
-            "decoder.conv_norm_out.weight": checkpoint["decoder.norm_out.weight"],
-            "decoder.conv_norm_out.bias": checkpoint["decoder.norm_out.bias"],
-            # conv_out
-            "decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"],
-            "decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
-    rv = {
-        # norm1
-        f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"],
-        f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"],
-        # conv1
-        f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"],
-        f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"],
-        # norm2
-        f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"],
-        f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"],
-        # conv2
-        f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"],
-        f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"],
-    }
-
-    if resnet.conv_shortcut is not None:
-        rv.update(
-            {
-                f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"],
-                f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"],
-            }
-        )
-
-    return rv
-
-
-def vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
-    return {
-        # group_norm
-        f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"],
-        f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"],
-        # query
-        f"{diffusers_attention_prefix}.query.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0],
-        f"{diffusers_attention_prefix}.query.bias": checkpoint[f"{attention_prefix}.q.bias"],
-        # key
-        f"{diffusers_attention_prefix}.key.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0],
-        f"{diffusers_attention_prefix}.key.bias": checkpoint[f"{attention_prefix}.k.bias"],
-        # value
-        f"{diffusers_attention_prefix}.value.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0],
-        f"{diffusers_attention_prefix}.value.bias": checkpoint[f"{attention_prefix}.v.bias"],
-        # proj_attn
-        f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][
-            :, :, 0, 0
-        ],
-        f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[f"{attention_prefix}.proj_out.bias"],
-    }
-
-
-# done vqvae checkpoint
-
-# transformer model
-
-PORTED_DIFFUSIONS = ["image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer"]
-PORTED_TRANSFORMERS = ["image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer"]
-PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding"]
-
-
-def transformer_model_from_original_config(
-    original_diffusion_config, original_transformer_config, original_content_embedding_config
-):
-    assert (
-        original_diffusion_config.target in PORTED_DIFFUSIONS
-    ), f"{original_diffusion_config.target} has not yet been ported to diffusers."
-    assert (
-        original_transformer_config.target in PORTED_TRANSFORMERS
-    ), f"{original_transformer_config.target} has not yet been ported to diffusers."
-    assert (
-        original_content_embedding_config.target in PORTED_CONTENT_EMBEDDINGS
-    ), f"{original_content_embedding_config.target} has not yet been ported to diffusers."
-
-    original_diffusion_config = original_diffusion_config.params
-    original_transformer_config = original_transformer_config.params
-    original_content_embedding_config = original_content_embedding_config.params
-
-    inner_dim = original_transformer_config["n_embd"]
-
-    n_heads = original_transformer_config["n_head"]
-
-    # VQ-Diffusion gives dimension of the multi-headed attention layers as the
-    # number of attention heads times the sequence length (the dimension) of a
-    # single head. We want to specify our attention blocks with those values
-    # specified separately
-    assert inner_dim % n_heads == 0
-    d_head = inner_dim // n_heads
-
-    depth = original_transformer_config["n_layer"]
-    context_dim = original_transformer_config["condition_dim"]
-
-    num_embed = original_content_embedding_config["num_embed"]
-    # the number of embeddings in the transformer includes the mask embedding.
-    # the content embedding (the vqvae) does not include the mask embedding.
-    num_embed = num_embed + 1
-
-    height = original_transformer_config["content_spatial_size"][0]
-    width = original_transformer_config["content_spatial_size"][1]
-
-    assert width == height, "width has to be equal to height"
-    dropout = original_transformer_config["resid_pdrop"]
-    num_embeds_ada_norm = original_diffusion_config["diffusion_step"]
-
-    model_kwargs = {
-        "attention_bias": True,
-        "cross_attention_dim": context_dim,
-        "attention_head_dim": d_head,
-        "num_layers": depth,
-        "dropout": dropout,
-        "num_attention_heads": n_heads,
-        "num_vector_embeds": num_embed,
-        "num_embeds_ada_norm": num_embeds_ada_norm,
-        "norm_num_groups": 32,
-        "sample_size": width,
-        "activation_fn": "geglu-approximate",
-    }
-
-    model = Transformer2DModel(**model_kwargs)
-    return model
-
-
-# done transformer model
-
-# transformer checkpoint
-
-
-def transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    transformer_prefix = "transformer.transformer"
-
-    diffusers_latent_image_embedding_prefix = "latent_image_embedding"
-    latent_image_embedding_prefix = f"{transformer_prefix}.content_emb"
-
-    # DalleMaskImageEmbedding
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_latent_image_embedding_prefix}.emb.weight": checkpoint[
-                f"{latent_image_embedding_prefix}.emb.weight"
-            ],
-            f"{diffusers_latent_image_embedding_prefix}.height_emb.weight": checkpoint[
-                f"{latent_image_embedding_prefix}.height_emb.weight"
-            ],
-            f"{diffusers_latent_image_embedding_prefix}.width_emb.weight": checkpoint[
-                f"{latent_image_embedding_prefix}.width_emb.weight"
-            ],
-        }
-    )
-
-    # transformer blocks
-    for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks):
-        diffusers_transformer_block_prefix = f"transformer_blocks.{transformer_block_idx}"
-        transformer_block_prefix = f"{transformer_prefix}.blocks.{transformer_block_idx}"
-
-        # ada norm block
-        diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm1"
-        ada_norm_prefix = f"{transformer_block_prefix}.ln1"
-
-        diffusers_checkpoint.update(
-            transformer_ada_norm_to_diffusers_checkpoint(
-                checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
-            )
-        )
-
-        # attention block
-        diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn1"
-        attention_prefix = f"{transformer_block_prefix}.attn1"
-
-        diffusers_checkpoint.update(
-            transformer_attention_to_diffusers_checkpoint(
-                checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
-            )
-        )
-
-        # ada norm block
-        diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm2"
-        ada_norm_prefix = f"{transformer_block_prefix}.ln1_1"
-
-        diffusers_checkpoint.update(
-            transformer_ada_norm_to_diffusers_checkpoint(
-                checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix
-            )
-        )
-
-        # attention block
-        diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn2"
-        attention_prefix = f"{transformer_block_prefix}.attn2"
-
-        diffusers_checkpoint.update(
-            transformer_attention_to_diffusers_checkpoint(
-                checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix
-            )
-        )
-
-        # norm block
-        diffusers_norm_block_prefix = f"{diffusers_transformer_block_prefix}.norm3"
-        norm_block_prefix = f"{transformer_block_prefix}.ln2"
-
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_norm_block_prefix}.weight": checkpoint[f"{norm_block_prefix}.weight"],
-                f"{diffusers_norm_block_prefix}.bias": checkpoint[f"{norm_block_prefix}.bias"],
-            }
-        )
-
-        # feedforward block
-        diffusers_feedforward_prefix = f"{diffusers_transformer_block_prefix}.ff"
-        feedforward_prefix = f"{transformer_block_prefix}.mlp"
-
-        diffusers_checkpoint.update(
-            transformer_feedforward_to_diffusers_checkpoint(
-                checkpoint,
-                diffusers_feedforward_prefix=diffusers_feedforward_prefix,
-                feedforward_prefix=feedforward_prefix,
-            )
-        )
-
-    # to logits
-
-    diffusers_norm_out_prefix = "norm_out"
-    norm_out_prefix = f"{transformer_prefix}.to_logits.0"
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_norm_out_prefix}.weight": checkpoint[f"{norm_out_prefix}.weight"],
-            f"{diffusers_norm_out_prefix}.bias": checkpoint[f"{norm_out_prefix}.bias"],
-        }
-    )
-
-    diffusers_out_prefix = "out"
-    out_prefix = f"{transformer_prefix}.to_logits.1"
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_out_prefix}.weight": checkpoint[f"{out_prefix}.weight"],
-            f"{diffusers_out_prefix}.bias": checkpoint[f"{out_prefix}.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix):
-    return {
-        f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[f"{ada_norm_prefix}.emb.weight"],
-        f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[f"{ada_norm_prefix}.linear.weight"],
-        f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[f"{ada_norm_prefix}.linear.bias"],
-    }
-
-
-def transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
-    return {
-        # key
-        f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.key.weight"],
-        f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.key.bias"],
-        # query
-        f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.query.weight"],
-        f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.query.bias"],
-        # value
-        f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.value.weight"],
-        f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.value.bias"],
-        # linear out
-        f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj.weight"],
-        f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj.bias"],
-    }
-
-
-def transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix):
-    return {
-        f"{diffusers_feedforward_prefix}.net.0.proj.weight": checkpoint[f"{feedforward_prefix}.0.weight"],
-        f"{diffusers_feedforward_prefix}.net.0.proj.bias": checkpoint[f"{feedforward_prefix}.0.bias"],
-        f"{diffusers_feedforward_prefix}.net.2.weight": checkpoint[f"{feedforward_prefix}.2.weight"],
-        f"{diffusers_feedforward_prefix}.net.2.bias": checkpoint[f"{feedforward_prefix}.2.bias"],
-    }
-
-
-# done transformer checkpoint
-
-
-def read_config_file(filename):
-    # The yaml file contains annotations that certain values should
-    # loaded as tuples. By default, OmegaConf will panic when reading
-    # these. Instead, we can manually read the yaml with the FullLoader and then
-    # construct the OmegaConf object.
-    with open(filename) as f:
-        original_config = yaml.load(f, FullLoader)
-
-    return OmegaConf.create(original_config)
-
-
-# We take separate arguments for the vqvae because the ITHQ vqvae config file
-# is separate from the config file for the rest of the model.
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--vqvae_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the vqvae checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--vqvae_original_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help="The YAML config file corresponding to the original architecture for the vqvae.",
-    )
-
-    parser.add_argument(
-        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
-    )
-
-    parser.add_argument(
-        "--original_config_file",
-        default=None,
-        type=str,
-        required=True,
-        help="The YAML config file corresponding to the original architecture.",
-    )
-
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-
-    parser.add_argument(
-        "--checkpoint_load_device",
-        default="cpu",
-        type=str,
-        required=False,
-        help="The device passed to `map_location` when loading checkpoints.",
-    )
-
-    # See link for how ema weights are always selected
-    # https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65
-    parser.add_argument(
-        "--no_use_ema",
-        action="store_true",
-        required=False,
-        help=(
-            "Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set"
-            " it as the original VQ-Diffusion always uses the ema weights when loading models."
-        ),
-    )
-
-    args = parser.parse_args()
-
-    use_ema = not args.no_use_ema
-
-    print(f"loading checkpoints to {args.checkpoint_load_device}")
-
-    checkpoint_map_location = torch.device(args.checkpoint_load_device)
-
-    # vqvae_model
-
-    print(f"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}")
-
-    vqvae_original_config = read_config_file(args.vqvae_original_config_file).model
-    vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)["model"]
-
-    with init_empty_weights():
-        vqvae_model = vqvae_model_from_original_config(vqvae_original_config)
-
-    vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint)
-
-    with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file:
-        torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name)
-        del vqvae_diffusers_checkpoint
-        del vqvae_checkpoint
-        load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map="auto")
-
-    print("done loading vqvae")
-
-    # done vqvae_model
-
-    # transformer_model
-
-    print(
-        f"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:"
-        f" {use_ema}"
-    )
-
-    original_config = read_config_file(args.original_config_file).model
-
-    diffusion_config = original_config.params.diffusion_config
-    transformer_config = original_config.params.diffusion_config.params.transformer_config
-    content_embedding_config = original_config.params.diffusion_config.params.content_emb_config
-
-    pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location)
-
-    if use_ema:
-        if "ema" in pre_checkpoint:
-            checkpoint = {}
-            for k, v in pre_checkpoint["model"].items():
-                checkpoint[k] = v
-
-            for k, v in pre_checkpoint["ema"].items():
-                # The ema weights are only used on the transformer. To mimic their key as if they came
-                # from the state_dict for the top level model, we prefix with an additional "transformer."
-                # See the source linked in the args.use_ema config for more information.
-                checkpoint[f"transformer.{k}"] = v
-        else:
-            print("attempted to load ema weights but no ema weights are specified in the loaded checkpoint.")
-            checkpoint = pre_checkpoint["model"]
-    else:
-        checkpoint = pre_checkpoint["model"]
-
-    del pre_checkpoint
-
-    with init_empty_weights():
-        transformer_model = transformer_model_from_original_config(
-            diffusion_config, transformer_config, content_embedding_config
-        )
-
-    diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint(
-        transformer_model, checkpoint
-    )
-
-    # classifier free sampling embeddings interlude
-
-    # The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate
-    # model, so we pull them off the checkpoint before the checkpoint is deleted.
-
-    learnable_classifier_free_sampling_embeddings = diffusion_config.params.learnable_cf
-
-    if learnable_classifier_free_sampling_embeddings:
-        learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"]
-    else:
-        learned_classifier_free_sampling_embeddings_embeddings = None
-
-    # done classifier free sampling embeddings interlude
-
-    with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file:
-        torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name)
-        del diffusers_transformer_checkpoint
-        del checkpoint
-        load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map="auto")
-
-    print("done loading transformer")
-
-    # done transformer_model
-
-    # text encoder
-
-    print("loading CLIP text encoder")
-
-    clip_name = "openai/clip-vit-base-patch32"
-
-    # The original VQ-Diffusion specifies the pad value by the int used in the
-    # returned tokens. Each model uses `0` as the pad value. The transformers clip api
-    # specifies the pad value via the token before it has been tokenized. The `!` pad
-    # token is the same as padding with the `0` pad value.
-    pad_token = "!"
-
-    tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto")
-
-    assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0
-
-    text_encoder_model = CLIPTextModel.from_pretrained(
-        clip_name,
-        # `CLIPTextModel` does not support device_map="auto"
-        # device_map="auto"
-    )
-
-    print("done loading CLIP text encoder")
-
-    # done text encoder
-
-    # scheduler
-
-    scheduler_model = VQDiffusionScheduler(
-        # the scheduler has the same number of embeddings as the transformer
-        num_vec_classes=transformer_model.num_vector_embeds
-    )
-
-    # done scheduler
-
-    # learned classifier free sampling embeddings
-
-    with init_empty_weights():
-        learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings(
-            learnable_classifier_free_sampling_embeddings,
-            hidden_size=text_encoder_model.config.hidden_size,
-            length=tokenizer_model.model_max_length,
-        )
-
-    learned_classifier_free_sampling_checkpoint = {
-        "embeddings": learned_classifier_free_sampling_embeddings_embeddings.float()
-    }
-
-    with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file:
-        torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name)
-        del learned_classifier_free_sampling_checkpoint
-        del learned_classifier_free_sampling_embeddings_embeddings
-        load_checkpoint_and_dispatch(
-            learned_classifier_free_sampling_embeddings_model,
-            learned_classifier_free_sampling_checkpoint_file.name,
-            device_map="auto",
-        )
-
-    # done learned classifier free sampling embeddings
-
-    print(f"saving VQ diffusion model, path: {args.dump_path}")
-
-    pipe = VQDiffusionPipeline(
-        vqvae=vqvae_model,
-        transformer=transformer_model,
-        tokenizer=tokenizer_model,
-        text_encoder=text_encoder_model,
-        learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model,
-        scheduler=scheduler_model,
-    )
-    pipe.save_pretrained(args.dump_path)
-
-    print("done writing VQ diffusion model")

spaces/Andy1621/uniformer_image_detection/configs/cornernet/README.md

@@ -1,33 +0,0 @@
-# CornerNet
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{law2018cornernet,
-  title={Cornernet: Detecting objects as paired keypoints},
-  author={Law, Hei and Deng, Jia},
-  booktitle={15th European Conference on Computer Vision, ECCV 2018},
-  pages={765--781},
-  year={2018},
-  organization={Springer Verlag}
-}
-```
-
-## Results and models
-
-| Backbone        | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: |
-| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) |
-| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) |
-| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) |
-
-Note:
-
-- TTA setting is single-scale and `flip=True`.
-- Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti.
-- Here are the descriptions of each experiment setting:
-  - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper.
-  - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train.
-  - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train.

spaces/Anonymous-sub/Rerender/ControlNet/tutorial_dataset.py

@@ -1,39 +0,0 @@
-import json
-import cv2
-import numpy as np
-
-from torch.utils.data import Dataset
-
-
-class MyDataset(Dataset):
-    def __init__(self):
-        self.data = []
-        with open('./training/fill50k/prompt.json', 'rt') as f:
-            for line in f:
-                self.data.append(json.loads(line))
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        item = self.data[idx]
-
-        source_filename = item['source']
-        target_filename = item['target']
-        prompt = item['prompt']
-
-        source = cv2.imread('./training/fill50k/' + source_filename)
-        target = cv2.imread('./training/fill50k/' + target_filename)
-
-        # Do not forget that OpenCV read images in BGR order.
-        source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB)
-        target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
-
-        # Normalize source images to [0, 1].
-        source = source.astype(np.float32) / 255.0
-
-        # Normalize target images to [-1, 1].
-        target = (target.astype(np.float32) / 127.5) - 1.0
-
-        return dict(jpg=target, txt=prompt, hint=source)
-

spaces/Apex-X/nono/roop/capturer.py

@@ -1,22 +0,0 @@
-from typing import Optional
-import cv2
-
-from roop.typing import Frame
-
-
-def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Frame]:
-    capture = cv2.VideoCapture(video_path)
-    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
-    has_frame, frame = capture.read()
-    capture.release()
-    if has_frame:
-        return frame
-    return None
-
-
-def get_video_frame_total(video_path: str) -> int:
-    capture = cv2.VideoCapture(video_path)
-    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-    capture.release()
-    return video_frame_total

spaces/Ashish17/Ashish_Open_Chat_AI_17/README.md

@@ -1,12 +0,0 @@
----
-title: Ashish Open Chat AI 17
-emoji: 📚
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,25 +0,0 @@
-"""distutils.command
-
-Package containing implementation of all the standard Distutils
-commands."""
-
-__all__ = [  # noqa: F822
-    'build',
-    'build_py',
-    'build_ext',
-    'build_clib',
-    'build_scripts',
-    'clean',
-    'install',
-    'install_lib',
-    'install_headers',
-    'install_scripts',
-    'install_data',
-    'sdist',
-    'register',
-    'bdist',
-    'bdist_dumb',
-    'bdist_rpm',
-    'check',
-    'upload',
-]

spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/__init__.py

@@ -1,25 +0,0 @@
-from .factory import (
-    list_models,
-    create_model,
-    create_model_and_transforms,
-    add_model_config,
-)
-from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics
-from .model import (
-    CLAP,
-    CLAPTextCfg,
-    CLAPVisionCfg,
-    CLAPAudioCfp,
-    convert_weights_to_fp16,
-    trace_model,
-)
-from .openai import load_openai_model, list_openai_models
-from .pretrained import (
-    list_pretrained,
-    list_pretrained_tag_models,
-    list_pretrained_model_tags,
-    get_pretrained_url,
-    download_pretrained,
-)
-from .tokenizer import SimpleTokenizer, tokenize
-from .transform import image_transform

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/transforms/custom_augmentation_impl.py

@@ -1,63 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Modified by Xingyi Zhou
-"""
-Implement many useful :class:`Augmentation`.
-"""
-import numpy as np
-import sys
-from fvcore.transforms.transform import (
-    BlendTransform,
-    CropTransform,
-    HFlipTransform,
-    NoOpTransform,
-    Transform,
-    VFlipTransform,
-)
-from PIL import Image
-
-from detectron2.data.transforms.augmentation import Augmentation
-from .custom_transform import EfficientDetResizeCropTransform
-
-__all__ = [
-    "EfficientDetResizeCrop",
-]
-
-
-class EfficientDetResizeCrop(Augmentation):
-    """
-    Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
-    If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
-    """
-
-    def __init__(
-        self, size, scale, interp=Image.BILINEAR
-    ):
-        """
-        Args:
-        """
-        super().__init__()
-        self.target_size = (size, size)
-        self.scale = scale
-        self.interp = interp
-
-    def get_transform(self, img):
-        # Select a random scale factor.
-        scale_factor = np.random.uniform(*self.scale)
-        scaled_target_height = scale_factor * self.target_size[0]
-        scaled_target_width = scale_factor * self.target_size[1]
-        # Recompute the accurate scale_factor using rounded scaled image size.
-        width, height = img.shape[1], img.shape[0]
-        img_scale_y = scaled_target_height / height
-        img_scale_x = scaled_target_width / width
-        img_scale = min(img_scale_y, img_scale_x)
-
-        # Select non-zero random offset (x, y) if scaled image is larger than target size
-        scaled_h = int(height * img_scale)
-        scaled_w = int(width * img_scale)
-        offset_y = scaled_h - self.target_size[0]
-        offset_x = scaled_w - self.target_size[1]
-        offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1))
-        offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1))
-        return EfficientDetResizeCropTransform(
-            scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp)

spaces/AzinZ/vitscn/text/__init__.py

@@ -1,54 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text

spaces/Banbri/zcvzcv/src/app/interface/maintenance/index.tsx

@@ -1,20 +0,0 @@
-import { fonts } from "@/lib/fonts"
-import { cn } from "@/lib/utils"
-
-export function Maintenance() {
-  return (
-    <div className="z-20 fixed inset-0 w-screen h-screen bg-white text-stone-800 flex flex-col items-center justify-center">
-    <div className={cn(
-      fonts.actionman.className,
-      "text-center"
-    )}>
-      <p className="text-4xl">🚧 Maintenance in progress 🚧</p>
-      <p className="text-3xl mt-12 mb-8">See the <a
-        href="https://huggingface.co/spaces/jbilcke-hf/ai-comic-factory/discussions/339"
-        className="underline text-yellow-500"
-        >announcement here</a> <img src="/quick-and-dirty-emoji.png" className="inline w-10 h-10"></img></p>
-      <p className="text-2xl">This shouldn&apos;t last long, so stay tuned!</p>
-    </div>
-  </div>
-  )
-}

spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Apk Skachat.md

@@ -1,75 +0,0 @@
-
-<h1>Aparcamiento de coches multijugador APK Skachat: Una guía para descargar y jugar el juego en su PC</h1>
- <p>Si usted está buscando un juego de simulación de estacionamiento de coches realista y divertido, es posible que desee probar Parking Multijugador. Este juego es desarrollado por olzhass y tiene más de 100 millones de descargas en Google Play Store. Pero ¿qué pasa si desea jugar en su PC en lugar de su dispositivo móvil? En este artículo, le mostraremos cómo descargar e instalar Aparcamiento de coches multijugador APK Skachat en su PC utilizando dos emuladores populares de Android: BlueStacks y NoxPlayer. También te daremos algunos consejos sobre cómo jugar el juego en tu PC y disfrutar de sus características. </p>
- <h2>¿Qué es el Aparcamiento Multijugador? </h2>
- <p>Car Parking Multiplayer es un juego de simulación que te permite experimentar la emoción de aparcar varios coches en diferentes escenarios. Puede elegir entre más de 100 coches con interiores reales y personalizarlos con afinación, vinilos y partes del cuerpo. También puede explorar un mundo abierto con estaciones de servicio y servicios de automóviles reales, competir contra otros jugadores en carreras multijugador, intercambiar coches con otros jugadores, chatear con amigos e incluso jugar roles como oficial de policía. </p>
-<h2>aparcamiento de coches multijugador apk skachat</h2><br /><p><b><b>DOWNLOAD</b> &raquo; <a href="https://bltlly.com/2v6L8k">https://bltlly.com/2v6L8k</a></b></p><br /><br />
- <h3>Características del juego</h3>
- <p>Algunas de las características de Aparcamiento multijugador son:</p>
-<ul>
-<li> Modo multijugador de mundo abierto con caminar gratis, chat de voz, lista de amigos y modo policía. </li>
-<li>82 desafíos de estacionamiento y conducción en la vida real con diferentes vehículos, como remolques, camionetas, camiones, autos deportivos y autos clásicos. </li>
-<li>Gráficos de alta calidad y efectos de sonido con física realista y sistema de daños. </li>
-<li> Personalización del coche con suspensión ajustable, ángulo de rueda, ajuste del motor, turbo, caja de cambios, escape y visual auto tungs. </li>
-<li>Entornos altamente detallados con edificios con interior, ciclo día-noche, efectos meteorológicos y sistema de tráfico. </li>
-</ul>
- <h3>Requisitos y compatibilidad</h3>
-
- <p>Para jugar Car Parking Multijugador en su PC, es necesario tener un equipo con Windows o Mac con al menos 4 GB de RAM y 5 GB de espacio en disco libre. También es necesario descargar e instalar un emulador de Android como BlueStacks o NoxPlayer que puede ejecutar el juego sin problemas en su PC. Explicaremos cómo hacerlo en la siguiente sección. </p>
- <h2>Cómo descargar e instalar Aparcamiento de coches multijugador APK Skachat en su PC? </h2>
- <p>Aparcamiento de coches multijugador APK Skachat es una versión modificada del juego original que le permite descargar e instalar de forma gratuita sin restricciones. Sin embargo, ya que no está disponible en las tiendas de aplicaciones oficiales como Google Play Store o Apple App Store, debe usar una fuente de terceros para obtenerlo. Una de las fuentes más fiables es APKPure.com, donde se puede encontrar la última versión de Aparcamiento Multijugador APK Skachat junto con su información de archivos y comentarios de los usuarios. </p>
- <p>Para descargar e instalar Aparcamiento de coches multijugador APK Skachat en su PC usando BlueStacks o NoxPlayer emulador, siga estos pasos:</p>
- <h3>Usando el emulador de BlueStacks</h3>
-<ol>
-<li>Descargar e instalar el emulador BlueStacks desde su sitio web oficial[ 3  ] . </li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google o cree una nueva. </li>
-<li>Abra la aplicación del navegador en BlueStacks y vaya a APKPure.com. Buscar Aparcamiento de coches multijugador APK Skachat y descargarlo en su PC.</li>
-<li>Busque el archivo descargado en su PC y haga clic derecho en él. Elija "Abrir con" y seleccione BlueStacks como el emulador. </li>
-<li>Espere a que el proceso de instalación se complete y luego abra el juego desde la pantalla de inicio de BlueStacks. </li>
-</ol>
- <h3>Usando el emulador de NoxPlayer</h3>
-<ol>
-<li>Descargar e instalar el emulador NoxPlayer desde su sitio web oficial. </li>
-<li>Inicie NoxPlayer e inicie sesión con su cuenta de Google o cree una nueva. </li>
-
-<li>Arrastre y suelte el archivo descargado a la ventana NoxPlayer y espere a que se complete el proceso de instalación. </li>
-<li>Abre el juego desde la pantalla de inicio de NoxPlayer y disfruta. </li>
-</ol>
- <h2>¿Cómo se juega Aparcamiento de coches multijugador en su PC? </h2>
- <p>Una vez que haya descargado e instalado Aparcamiento Multijugador APK Skachat en su PC utilizando BlueStacks o NoxPlayer emulador, puede comenzar a jugar el juego en su PC. Aquí hay algunos consejos sobre cómo jugar el juego en su PC:</p>
- <h3>Controles y ajustes</h3>
- <p>Puedes usar el teclado y el ratón para controlar el juego en tu PC. También puedes personalizar la asignación de teclas según tus preferencias. Para ello, haga clic en el icono del teclado en la esquina inferior derecha de la pantalla del emulador y elija "Controles del juego". A continuación, puede arrastrar y soltar las teclas de los botones correspondientes en la pantalla del juego. También puede ajustar la sensibilidad, la transparencia y el tamaño de las teclas. Para guardar la configuración, haga clic en "Guardar" y luego en "Cerrar". </p>
- <p>También puede cambiar la configuración del juego como gráficos, sonido, idioma, cámara, etc. haciendo clic en el icono de engranaje en la esquina superior derecha de la pantalla del juego. A continuación, puede elegir entre baja, media, alta o ultra calidad gráfica, habilitar o desactivar efectos de sonido y música, seleccionar su idioma preferido, cambiar entre diferentes modos de cámara, etc. Para aplicar los cambios, haga clic en "OK". </p>
-<p></p>
- <h3>Consejos y trucos</h3>
- <p>Aquí hay algunos consejos y trucos para ayudarle a jugar Car Parking Multijugador mejor en su PC:</p>
-<ul>
-<li>Utilice el mini-mapa en la esquina superior izquierda de la pantalla del juego para navegar por el mundo abierto. También puede acercar o alejar usando la rueda del ratón. </li>
-<li>Utilice el icono de la gasolinera en el mini-mapa para encontrar la gasolinera más cercana donde puede repostar su coche. También puede utilizar el icono de servicio de automóvil para encontrar el servicio de automóvil más cercano donde puede reparar su automóvil o cambiar sus piezas. </li>
-
-<li>Utilice el icono de menú en la esquina inferior izquierda de la pantalla de juego para acceder a varias opciones como el modo multijugador, intercambio de coches, garaje, perfil, configuración, etc.</li>
-<li>Utilice el icono de estacionamiento en la esquina inferior derecha de la pantalla de juego para iniciar un desafío de estacionamiento. Puedes elegir entre diferentes niveles de dificultad y ubicaciones. También puede ver su progreso y logros haciendo clic en el icono de trofeo al lado. </li>
-<li>Utilice el icono de carrera en la esquina inferior derecha de la pantalla del juego para iniciar un desafío de carreras multijugador. Puede elegir entre diferentes modos, como carrera de arrastre, carrera de deriva, carrera de circuito, etc. También puede ver su clasificación y recompensas haciendo clic en el icono de copa al lado. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>Car Parking Multiplayer es un divertido y realista juego de simulación de aparcamiento que puedes jugar en tu PC usando un emulador de Android como BlueStacks o NoxPlayer. Puede descargar e instalar Aparcamiento de coches multijugador APK Skachat de forma gratuita desde APKPure.com y disfrutar de sus características tales como modo multijugador mundo abierto, personalización del coche, alta-altográficos de calidad, etc. Esperamos que este artículo le ha ayudado a aprender a descargar y jugar Aparcamiento de coches multijugador APK Skachat en su PC. Si tiene alguna pregunta o comentario, háganoslo saber en los comentarios a continuación. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes acerca de Aparcamiento de coches multijugador APK Skachat:</p>
- <h4> ¿Es seguro para descargar aparcamiento multijugador APK Skachat? </h4>
- <p <p>Sí, Aparcamiento de coches multijugador APK Skachat es seguro para descargar siempre y cuando se utiliza una fuente de confianza como APKPure.com. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier archivo APK de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo o datos. También debe comprobar la información del archivo y las opiniones de los usuarios antes de descargar e instalar cualquier archivo APK. </p>
- <h4>¿Cuáles son las ventajas de jugar Car Parking Multijugador en PC? </h4>
-
-<ul>
-<li> Puede disfrutar de una pantalla más grande y una mejor calidad de gráficos en su PC.</li>
-<li> Puede utilizar el teclado y el ratón para controlar el juego con mayor facilidad y precisión en su PC.</li>
-<li>Puede ahorrar su batería y espacio de almacenamiento en su dispositivo móvil jugando el juego en su PC.</li>
-<li>Puedes jugar el juego sin interrupciones o distracciones de llamadas telefónicas, mensajes, notificaciones, etc. en tu PC.</li>
-</ul>
- <h4>¿Puedo jugar Aparcamiento de coches multijugador fuera de línea? </h4>
- <p>No, no puedes jugar Car Parking Multijugador sin conexión, ya que requiere una conexión a Internet para acceder a algunas de sus características como el modo multijugador, chat en línea, intercambio de coches, etc. Sin embargo, todavía se puede jugar el juego en una solamodo de reproductor sin conexión a Internet mediante la elección de la opción sin conexión del menú. </p>
- <h4>¿Cómo puedo actualizar Aparcamiento de coches multijugador APK Skachat? </h4>
- <p>Para actualizar Aparcamiento Multijugador APK Skachat, es necesario descargar e instalar la última versión del archivo APK de APKPure.com o cualquier otra fuente confiable. También puedes buscar actualizaciones de la configuración del juego haciendo clic en el icono del engranaje y luego elegir "Buscar actualizaciones". Si hay una nueva versión disponible, puedes descargarla e instalarla desde allí. </p>
- <h4>¿Cómo puedo contactar al desarrollador de Car Parking Multijugador? </h4>
- <p>Si tienes alguna pregunta, sugerencia, comentario, o problemas con respecto a Car Parking Multijugador, puede ponerse en contacto con el desarrollador del juego enviando un correo electrónico a [email protected] o visitando su página de Facebook. También puede unirse a su servidor de discordia para chatear con otros jugadores y obtener apoyo de los moderadores. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Apk Caso Penal Con Trampa.md

@@ -1,68 +0,0 @@
-
-<h1>Cómo descargar e instalar caso penal APK con Cheat</h1>
-<p>Si te gusta jugar juegos de detectives en tu dispositivo Android, es posible que haya oído hablar de Criminal Case. Es un popular juego de objetos ocultos donde tienes que investigar casos de asesinato, encontrar pistas, interrogar a los sospechosos y atrapar a los asesinos. Pero lo que si quieres hacer el juego más divertido y fácil? Ahí es donde Criminal Case APK con engaño entra en juego. En este artículo, te mostraremos cómo descargar e instalar esta versión modificada del juego que te da energía ilimitada, pistas, estrellas y más. También te explicaremos qué es un archivo APK, cómo instalarlo en tu dispositivo, cómo jugar a Criminal Case con trucos y cuáles son los pros y los contras de usarlo. </p>
- <h2> ¿Qué es un archivo APK y cómo instalarlo en Android</h2>
-<p>Un archivo APK es un archivo de paquete de Android que contiene todos los archivos y el código necesario para ejecutar una aplicación en su dispositivo Android. Es similar a un archivo EXE en Windows o un archivo DMG en Mac. Los archivos APK se utilizan generalmente para distribuir aplicaciones que no están disponibles en Google Play Store, o para actualizar aplicaciones antes de su lanzamiento oficial. También puedes usar archivos APK para instalar versiones modificadas o hackeadas de aplicaciones que ofrecen características o beneficios adicionales. </p>
-<h2>apk caso penal con trampa</h2><br /><p><b><b>Download</b> &#9999; <a href="https://bltlly.com/2v6LMp">https://bltlly.com/2v6LMp</a></b></p><br /><br />
-<p>Para instalar un archivo APK en tu dispositivo Android, necesitas hacer dos cosas. Primero, necesitas habilitar fuentes desconocidas en la configuración de tu dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, ve a Configuración > Aplicaciones > Menú > Acceso especial > Instalar aplicaciones desconocidas. Luego, selecciona la aplicación de tu navegador (como Chrome) y activa la opción Permitir desde esta fuente. </p>
-
- <h2>¿Qué es el caso penal APK con Cheat</h2>
-<p>Caso Penal APK con trampa es una versión modificada de Caso Penal que le da acceso a recursos ilimitados y características que pueden ayudarle a resolver los casos más rápido y más fácil. Algunas de las características incluyen:</p>
-<ul>
-<li>Energía ilimitada: Puedes reproducir tantas escenas como quieras sin quedarte sin energía. </li>
-<li>Pistas ilimitadas: Puedes usar pistas para encontrar objetos más rápido y ganar más puntos. </li>
-<li>Estrellas ilimitadas: puedes usar estrellas para desbloquear nuevas escenas, examinar pistas, interrogar sospechosos y arrestar asesinos. </li>
-<li>Análisis instantáneo: No tienes que esperar a los resultados de laboratorio o informes. Puedes obtenerlos al instante. </li>
-<li>Saltar escenas y minijuegos: Puedes saltarte cualquier escena o mini-juego que no quieras jugar. </li>
-<li>No hay anuncios: Puedes disfrutar del juego sin interrupciones ni distracciones. </li>
-</ul>
-<p>Con estas características, usted puede tener más diversión y emoción jugando Criminal Case. También puedes ahorrar tiempo y dinero al no tener que comprar energía o pistas con dinero real. </p>
- <h2>Cómo descargar caso penal APK con Cheat</h2>
-<p>Para descargar Criminal Case APK con cheat, es necesario seguir estos pasos:</p> <p>1. Ir a un sitio web que ofrece APK Criminal Case con cheat. Puede utilizar la aplicación de su navegador para buscar estos sitios web, o puede utilizar uno de los siguientes enlaces:</p>
-<tabla>
-<tr>
-<th>Sitio web</th>
-<th>URL</th>
-</tr>
-<tr>
-<td>Filehippo</td>
-<td><a href="( 1 )">Descargar caso penal APK 2.39 para Android - Filehippo.com</a></td>
-</tr>
-<tr>
-<td>APKCombo</td>
-<td><a href="( 2 )">Criminal Case APK (Android Game) - Descarga gratuita - APKCombo</a></td>
-</tr>
-</tabla>
-<p>2. Elija la versión de Criminal Case APK con truco que desea descargar. Asegúrese de que es compatible con su dispositivo y tiene las características que desea. </p>
-
-<p>4. Una vez descargado el archivo, búsquelo en su dispositivo usando la aplicación del navegador o una aplicación de administrador de archivos. Toque el archivo para instalarlo. Es posible que necesite aceptar algunas ventanas emergentes o permisos antes de instalar el archivo. </p>
-<p></p>
-<p>5. Después de que la instalación se haya completado, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. ¡Disfrute jugando Criminal Case con trucos! </p>
- <h2>Cómo Jugar Caso Criminal con Cheat</h2>
-<p>Jugar a Criminal Case con trucos es similar a jugar la versión original del juego, excepto que tienes acceso a recursos ilimitados y características que pueden hacer el juego más fácil y más divertido. Aquí hay algunos consejos y trucos sobre cómo jugar Criminal Case con cheat:</p>
-<ul>
-<li>Para usar energía ilimitada, toca el icono de energía en la esquina superior derecha de la pantalla. Puedes recargar tu energía tantas veces como quieras sin esperar ni pagar. </li>
-<li>Para usar pistas ilimitadas, toque el icono de pista en la esquina inferior derecha de la pantalla durante una escena. Puedes usar pistas tantas veces como quieras sin perder puntos o estrellas. </li>
-<li>Para usar estrellas ilimitadas, toca el icono de estrella en la esquina superior izquierda de la pantalla. Puedes usar estrellas tantas veces como quieras para desbloquear nuevas escenas, examinar pistas, interrogar sospechosos y arrestar asesinos. </li>
-<li> Para utilizar el análisis instantáneo, toque el icono de análisis en la esquina inferior izquierda de la pantalla durante una escena. Puedes obtener resultados instantáneos sin esperar ni pagar. </li>
-<li>Para saltar escenas y minijuegos, toque el icono de salto en la esquina superior derecha de la pantalla durante una escena o un mini-juego. Puedes saltarte cualquier escena o mini-juego que no quieras jugar sin perder puntos o estrellas. </li>
-<li>Para eliminar anuncios, toque el icono de configuración en la esquina superior derecha de la pantalla. Luego, toque la opción de eliminar anuncios y confirme su elección. Puedes disfrutar del juego sin interrupciones ni distracciones. </li>
-</ul>
-
- <h2> Pros y contras de usar APK caso penal con trampa</h2>
-<p>El uso de APK Caso Penal con trampa tiene sus pros y sus contras. Aquí están algunos de ellos:</p>
- | Pros | Contras | | -- | -- - | | Usted puede tener más diversión y emoción jugando Caso Criminal | Usted puede perder algo del desafío y emoción de jugar Caso Criminal | | | Usted puede ahorrar tiempo y dinero por no tener que comprar energía o pistas con dinero real | Usted puede encontrar algunos errores o errores que pueden afectar su rendimiento del juego | | Puede probar diferentes características y opciones que no están disponibles en la versión original del juego | Puede violar algunos términos y condiciones del desarrollador del juego o Google Play Store | | Puede compartir sus logros y progresos con sus amigos y otros jugadores | Usted puede correr el riesgo de perder sus datos de juego o cuenta si desinstalar o actualizar el juego | <p>Usted debe sopesar estos pros y contras antes de decidir si desea utilizar Criminal Case APK con trampa o no. En última instancia, depende de su preferencia personal y estilo de juego. </p>
- <h2>Conclusión</h2>
-<p>Criminal Case es un divertido y adictivo juego de objetos ocultos que te permite jugar como detective y resolver casos de asesinato. Pero si quieres hacer el juego más divertido y fácil, se puede tratar de usar Caso Penal APK con trampa. Esta es una versión modificada del juego que te da energía ilimitada, pistas, estrellas y más. Puede descargar e instalar esta versión desde un sitio web de buena reputación y disfrutar jugando Criminal Case con trampa. </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre Caso Penal APK con trampa:</p>
- <ol>
-
-<li><b> ¿Es legal usar Caso Penal APK con trampa? </b><br>El uso de APK Caso Penal con trampa puede no ser legal en algunos países o regiones, ya que puede violar algunos términos y condiciones del desarrollador del juego o Google Play Store. Usted debe comprobar las leyes y reglamentos de su ubicación antes de usar Caso Penal APK con trampa. También debe respetar los derechos e intereses del desarrollador del juego y otros jugadores, y no utilizar Criminal Case APK con engaño para cualquier propósito malicioso o fraudulento. </li>
-<li><b>Se Caso Penal APK con trucos de trabajo en mi dispositivo? </b><br>Caso Penal APK con trampa debe funcionar en la mayoría de los dispositivos Android que soportan la versión original de Caso Penal. Sin embargo, algunos dispositivos pueden no ser compatibles con Criminal Case APK con trampa, o pueden experimentar algunos problemas o errores al usarlo. Usted debe comprobar la compatibilidad y los requisitos de Caso Penal APK con tramposo antes de descargar e instalar en su dispositivo. También debe actualizar el software y la configuración del dispositivo para garantizar un rendimiento óptimo. </li>
-<li><b>¿Puedo jugar APK Caso Penal con tramposo en línea o fuera de línea? </b><br>Puedes jugar APK Caso Penal con tramposo tanto en línea como fuera de línea. Sin embargo, algunas características y funciones pueden requerir una conexión a Internet para funcionar correctamente, como sincronizar los datos y la cuenta del juego, acceder a nuevos casos y actualizaciones o interactuar con otros jugadores. Usted debe asegurarse de que tiene una conexión a Internet estable y segura al jugar Caso Penal APK con trampa en línea. </li>
-
-</ol>
- <p>Espero que este artículo le ha ayudado a aprender más acerca de Caso Penal APK con trampa y cómo descargar e instalar en su dispositivo Android. Si tiene alguna pregunta o comentario, por favor deje un comentario abajo. ¡Gracias por leer! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BilalSardar/Remove_Text_for_Image/README.md

@@ -1,12 +0,0 @@
----
-title: Remove Text For Image
-emoji: 👀
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/pydiffvg_tensorflow/render_tensorflow.py

@@ -1,664 +0,0 @@
-import os
-import tensorflow as tf
-import diffvg
-import pydiffvg_tensorflow as pydiffvg
-import time
-from enum import IntEnum
-import warnings
-
-print_timing = False
-__EMPTY_TENSOR = tf.constant([])
-
-def is_empty_tensor(tensor):
-    return  tf.equal(tf.size(tensor), 0)
-
-def set_print_timing(val):
-    global print_timing
-    print_timing=val
-
-class OutputType(IntEnum):
-    color = 1
-    sdf = 2
-
-class ShapeType:
-    __shapetypes = [
-        diffvg.ShapeType.circle,
-        diffvg.ShapeType.ellipse,
-        diffvg.ShapeType.path,
-        diffvg.ShapeType.rect
-    ]
-
-    @staticmethod
-    def asTensor(type):
-        for i in range(len(ShapeType.__shapetypes)):
-            if ShapeType.__shapetypes[i] == type:
-                return tf.constant(i)
-
-    @staticmethod
-    def asShapeType(index: tf.Tensor):
-        if is_empty_tensor(index):
-            return None
-        try:
-            type = ShapeType.__shapetypes[index]
-        except IndexError:
-            print(f'{index} is out of range: [0, {len(ShapeType.__shapetypes)})')
-            import sys
-            sys.exit()
-        else:
-            return type
-
-class ColorType:
-    __colortypes = [
-        diffvg.ColorType.constant,
-        diffvg.ColorType.linear_gradient,
-        diffvg.ColorType.radial_gradient
-    ]
-
-    @staticmethod
-    def asTensor(type):
-        for i in range(len(ColorType.__colortypes)):
-            if ColorType.__colortypes[i] == type:
-                return tf.constant(i)
-
-    @staticmethod
-    def asColorType(index: tf.Tensor):
-        if is_empty_tensor(index):
-            return None
-        try:
-            type = ColorType.__colortypes[index]
-        except IndexError:
-            print(f'{index} is out of range: [0, {len(ColorType.__colortypes)})')
-            import sys
-            sys.exit()
-        else:
-            return type
-
-class FilterType:
-    __filtertypes = [
-        diffvg.FilterType.box,
-        diffvg.FilterType.tent,
-        diffvg.FilterType.hann
-    ]
-
-    @staticmethod
-    def asTensor(type):
-        for i in range(len(FilterType.__filtertypes)):
-            if FilterType.__filtertypes[i] == type:
-                return tf.constant(i)    
-
-    @staticmethod
-    def asFilterType(index: tf.Tensor):
-        if is_empty_tensor(index):
-            return None
-        try:
-            type = FilterType.__filtertypes[index]
-        except IndexError:
-            print(f'{index} is out of range: [0, {len(FilterType.__filtertypes)})')
-            import sys
-            sys.exit()
-        else:
-            return type
-
-def serialize_scene(canvas_width,
-                    canvas_height,
-                    shapes,
-                    shape_groups,
-                    filter = pydiffvg.PixelFilter(type = diffvg.FilterType.box,
-                                                  radius = tf.constant(0.5)),
-                    output_type = OutputType.color,
-                    use_prefiltering = False):
-    """
-        Given a list of shapes, convert them to a linear list of argument,
-        so that we can use it in TF.
-    """
-    with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())):
-        num_shapes = len(shapes)
-        num_shape_groups = len(shape_groups)
-        args = []
-        args.append(tf.constant(canvas_width))
-        args.append(tf.constant(canvas_height))
-        args.append(tf.constant(num_shapes))
-        args.append(tf.constant(num_shape_groups))
-        args.append(tf.constant(output_type))
-        args.append(tf.constant(use_prefiltering))
-        for shape in shapes:
-            if isinstance(shape, pydiffvg.Circle):
-                args.append(ShapeType.asTensor(diffvg.ShapeType.circle))
-                args.append(tf.identity(shape.radius))
-                args.append(tf.identity(shape.center))
-            elif isinstance(shape, pydiffvg.Ellipse):
-                args.append(ShapeType.asTensor(diffvg.ShapeType.ellipse))
-                args.append(tf.identity(shape.radius))
-                args.append(tf.identity(shape.center))
-            elif isinstance(shape, pydiffvg.Path):
-                assert(shape.points.shape[1] == 2)
-                args.append(ShapeType.asTensor(diffvg.ShapeType.path))
-                args.append(tf.identity(shape.num_control_points))
-                args.append(tf.identity(shape.points))
-                args.append(tf.constant(shape.is_closed))
-                args.append(tf.constant(shape.use_distance_approx))
-            elif isinstance(shape, pydiffvg.Polygon):
-                assert(shape.points.shape[1] == 2)
-                args.append(ShapeType.asTensor(diffvg.ShapeType.path))
-                if shape.is_closed:
-                    args.append(tf.zeros(shape.points.shape[0], dtype = tf.int32))
-                else:
-                    args.append(tf.zeros(shape.points.shape[0] - 1, dtype = tf.int32))
-                args.append(tf.identity(shape.points))
-                args.append(tf.constant(shape.is_closed))
-            elif isinstance(shape, pydiffvg.Rect):
-                args.append(ShapeType.asTensor(diffvg.ShapeType.rect))
-                args.append(tf.identity(shape.p_min))
-                args.append(tf.identity(shape.p_max))
-            else:
-                assert(False)
-            args.append(tf.identity(shape.stroke_width))
-
-        for shape_group in shape_groups:
-            args.append(tf.identity(shape_group.shape_ids))
-            # Fill color
-            if shape_group.fill_color is None:
-                args.append(__EMPTY_TENSOR)
-            elif tf.is_tensor(shape_group.fill_color):
-                args.append(ColorType.asTensor(diffvg.ColorType.constant))
-                args.append(tf.identity(shape_group.fill_color))
-            elif isinstance(shape_group.fill_color, pydiffvg.LinearGradient):
-                args.append(ColorType.asTensor(diffvg.ColorType.linear_gradient))
-                args.append(tf.identity(shape_group.fill_color.begin))
-                args.append(tf.identity(shape_group.fill_color.end))
-                args.append(tf.identity(shape_group.fill_color.offsets))
-                args.append(tf.identity(shape_group.fill_color.stop_colors))
-            elif isinstance(shape_group.fill_color, pydiffvg.RadialGradient):
-                args.append(ColorType.asTensor(diffvg.ColorType.radial_gradient))
-                args.append(tf.identity(shape_group.fill_color.center))
-                args.append(tf.identity(shape_group.fill_color.radius))
-                args.append(tf.identity(shape_group.fill_color.offsets))
-                args.append(tf.identity(shape_group.fill_color.stop_colors))
-
-            if shape_group.fill_color is not None:
-                # go through the underlying shapes and check if they are all closed
-                for shape_id in shape_group.shape_ids:
-                    if isinstance(shapes[shape_id], pydiffvg.Path):
-                        if not shapes[shape_id].is_closed:
-                            warnings.warn("Detected non-closed paths with fill color. This might causes unexpected results.", Warning)
-
-            # Stroke color
-            if shape_group.stroke_color is None:
-                args.append(__EMPTY_TENSOR)
-            elif tf.is_tensor(shape_group.stroke_color):
-                args.append(tf.constant(0))
-                args.append(tf.identity(shape_group.stroke_color))
-            elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient):
-                args.append(ColorType.asTensor(diffvg.ColorType.linear_gradient))
-                args.append(tf.identity(shape_group.stroke_color.begin))
-                args.append(tf.identity(shape_group.stroke_color.end))
-                args.append(tf.identity(shape_group.stroke_color.offsets))
-                args.append(tf.identity(shape_group.stroke_color.stop_colors))
-            elif isinstance(shape_group.stroke_color, pydiffvg.RadialGradient):
-                args.append(ColorType.asTensor(diffvg.ColorType.radial_gradient))
-                args.append(tf.identity(shape_group.stroke_color.center))
-                args.append(tf.identity(shape_group.stroke_color.radius))
-                args.append(tf.identity(shape_group.stroke_color.offsets))
-                args.append(tf.identity(shape_group.stroke_color.stop_colors))
-            args.append(tf.constant(shape_group.use_even_odd_rule))
-            # Transformation
-            args.append(tf.identity(shape_group.shape_to_canvas))
-        args.append(FilterType.asTensor(filter.type))
-        args.append(tf.constant(filter.radius))
-    return args
-
-class Context: pass
-
-def forward(width,
-            height,
-            num_samples_x,
-            num_samples_y,
-            seed,
-            *args):
-    """
-        Forward rendering pass: given a serialized scene and output an image.
-    """
-    # Unpack arguments
-    with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())):
-        current_index = 0
-        canvas_width = int(args[current_index])
-        current_index += 1
-        canvas_height = int(args[current_index])
-        current_index += 1
-        num_shapes = int(args[current_index])
-        current_index += 1
-        num_shape_groups = int(args[current_index])
-        current_index += 1
-        output_type = OutputType(int(args[current_index]))
-        current_index += 1
-        use_prefiltering = bool(args[current_index])
-        current_index += 1
-        shapes = []
-        shape_groups = []
-        shape_contents = [] # Important to avoid GC deleting the shapes
-        color_contents = [] # Same as above
-        for shape_id in range(num_shapes):
-            shape_type = ShapeType.asShapeType(args[current_index])
-            current_index += 1
-            if shape_type == diffvg.ShapeType.circle:
-                radius = args[current_index]
-                current_index += 1
-                center = args[current_index]
-                current_index += 1
-                shape = diffvg.Circle(float(radius),
-                                      diffvg.Vector2f(float(center[0]), float(center[1])))
-            elif shape_type == diffvg.ShapeType.ellipse:
-                radius = args[current_index]
-                current_index += 1
-                center = args[current_index]
-                current_index += 1
-                shape = diffvg.Ellipse(diffvg.Vector2f(float(radius[0]), float(radius[1])),
-                                       diffvg.Vector2f(float(center[0]), float(center[1])))
-            elif shape_type == diffvg.ShapeType.path:
-                num_control_points = args[current_index]
-                current_index += 1
-                points = args[current_index]
-                current_index += 1
-                is_closed = args[current_index]
-                current_index += 1
-                use_distance_approx = args[current_index]
-                current_index += 1
-                shape = diffvg.Path(diffvg.int_ptr(pydiffvg.data_ptr(num_control_points)),
-                                    diffvg.float_ptr(pydiffvg.data_ptr(points)),
-                                    diffvg.float_ptr(0), # thickness
-                                    num_control_points.shape[0],
-                                    points.shape[0],
-                                    is_closed,
-                                    use_distance_approx)
-            elif shape_type == diffvg.ShapeType.rect:
-                p_min = args[current_index]
-                current_index += 1
-                p_max = args[current_index]
-                current_index += 1
-                shape = diffvg.Rect(diffvg.Vector2f(float(p_min[0]), float(p_min[1])),
-                                    diffvg.Vector2f(float(p_max[0]), float(p_max[1])))
-            else:
-                assert(False)
-            stroke_width = args[current_index]
-            current_index += 1
-            shapes.append(diffvg.Shape(\
-                shape_type, shape.get_ptr(), float(stroke_width)))
-            shape_contents.append(shape)
-
-        for shape_group_id in range(num_shape_groups):
-            shape_ids = args[current_index]
-            current_index += 1
-            fill_color_type = ColorType.asColorType(args[current_index])
-            current_index += 1
-            if fill_color_type == diffvg.ColorType.constant:
-                color = args[current_index]
-                current_index += 1
-                fill_color = diffvg.Constant(\
-                    diffvg.Vector4f(color[0], color[1], color[2], color[3]))
-            elif fill_color_type == diffvg.ColorType.linear_gradient:
-                beg = args[current_index]
-                current_index += 1
-                end = args[current_index]
-                current_index += 1
-                offsets = args[current_index]
-                current_index += 1
-                stop_colors = args[current_index]
-                current_index += 1
-                assert(offsets.shape[0] == stop_colors.shape[0])
-                fill_color = diffvg.LinearGradient(diffvg.Vector2f(float(beg[0]), float(beg[1])),
-                                                   diffvg.Vector2f(float(end[0]), float(end[1])),
-                                                   offsets.shape[0],
-                                                   diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                                                   diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-            elif fill_color_type == diffvg.ColorType.radial_gradient:
-                center = args[current_index]
-                current_index += 1
-                radius = args[current_index]
-                current_index += 1
-                offsets = args[current_index]
-                current_index += 1
-                stop_colors = args[current_index]
-                current_index += 1
-                assert(offsets.shape[0] == stop_colors.shape[0])
-                fill_color = diffvg.RadialGradient(diffvg.Vector2f(float(center[0]), float(center[1])),
-                                                   diffvg.Vector2f(float(radius[0]), float(radius[1])),
-                                                   offsets.shape[0],
-                                                   diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                                                   diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-            elif fill_color_type is None:
-                fill_color = None
-            else:
-                assert(False)
-
-            stroke_color_type = ColorType.asColorType(args[current_index])
-            current_index += 1
-            if stroke_color_type == diffvg.ColorType.constant:
-                color = args[current_index]
-                current_index += 1
-                stroke_color = diffvg.Constant(\
-                    diffvg.Vector4f(float(color[0]),
-                                    float(color[1]),
-                                    float(color[2]),
-                                    float(color[3])))
-            elif stroke_color_type == diffvg.ColorType.linear_gradient:
-                beg = args[current_index]
-                current_index += 1
-                end = args[current_index]
-                current_index += 1
-                offsets = args[current_index]
-                current_index += 1
-                stop_colors = args[current_index]
-                current_index += 1
-                assert(offsets.shape[0] == stop_colors.shape[0])
-                stroke_color = diffvg.LinearGradient(\
-                    diffvg.Vector2f(float(beg[0]), float(beg[1])),
-                    diffvg.Vector2f(float(end[0]), float(end[1])),
-                    offsets.shape[0],
-                    diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                    diffvg.float_ptr(stop_colors.data_ptr()))
-            elif stroke_color_type == diffvg.ColorType.radial_gradient:
-                center = args[current_index]
-                current_index += 1
-                radius = args[current_index]
-                current_index += 1
-                offsets = args[current_index]
-                current_index += 1
-                stop_colors = args[current_index]
-                current_index += 1
-                assert(offsets.shape[0] == stop_colors.shape[0])
-                stroke_color = diffvg.RadialGradient(\
-                    diffvg.Vector2f(float(center[0]), float(center[1])),
-                    diffvg.Vector2f(float(radius[0]), float(radius[1])),
-                    offsets.shape[0],
-                    diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                    diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-            elif stroke_color_type is None:
-                stroke_color = None
-            else:
-                assert(False)
-            use_even_odd_rule = bool(args[current_index])
-            current_index += 1
-            shape_to_canvas = args[current_index]
-            current_index += 1
-
-            if fill_color is not None:
-                color_contents.append(fill_color)
-            if stroke_color is not None:
-                color_contents.append(stroke_color)
-            shape_groups.append(diffvg.ShapeGroup(\
-                diffvg.int_ptr(pydiffvg.data_ptr(shape_ids)),
-                shape_ids.shape[0],
-                diffvg.ColorType.constant if fill_color_type is None else fill_color_type,
-                diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(),
-                diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type,
-                diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(),
-                use_even_odd_rule,
-                diffvg.float_ptr(pydiffvg.data_ptr(shape_to_canvas))))
-
-        filter_type = FilterType.asFilterType(args[current_index])
-        current_index += 1
-        filter_radius = args[current_index]
-        current_index += 1
-        filt = diffvg.Filter(filter_type, filter_radius)
-
-    device_name = pydiffvg.get_device_name()
-    device_spec = tf.DeviceSpec.from_string(device_name)
-    use_gpu = device_spec.device_type == 'GPU'
-    gpu_index = device_spec.device_index if device_spec.device_index is not None else 0
-
-    start = time.time()
-    scene = diffvg.Scene(canvas_width,
-                         canvas_height,
-                         shapes,
-                         shape_groups,
-                         filt,
-                         use_gpu,
-                         gpu_index)
-    time_elapsed = time.time() - start
-    global print_timing
-    if print_timing:
-        print('Scene construction, time: %.5f s' % time_elapsed)
-
-    with tf.device(device_name):
-        if output_type == OutputType.color:
-            rendered_image = tf.zeros((int(height), int(width), 4), dtype = tf.float32)
-        else:
-            assert(output_type == OutputType.sdf)
-            rendered_image = tf.zeros((int(height), int(width), 1), dtype = tf.float32)
-
-        start = time.time()
-        diffvg.render(scene,
-                      diffvg.float_ptr(0), # background image
-                      diffvg.float_ptr(pydiffvg.data_ptr(rendered_image) if output_type == OutputType.color else 0),
-                      diffvg.float_ptr(pydiffvg.data_ptr(rendered_image) if output_type == OutputType.sdf else 0),
-                      width,
-                      height,
-                      int(num_samples_x),
-                      int(num_samples_y),
-                      seed,
-                      diffvg.float_ptr(0), # d_background_image
-                      diffvg.float_ptr(0), # d_render_image
-                      diffvg.float_ptr(0), # d_render_sdf
-                      diffvg.float_ptr(0), # d_translation
-                      use_prefiltering,
-                      diffvg.float_ptr(0), # eval_positions
-                      0 ) # num_eval_positions (automatically set to entire raster)
-        time_elapsed = time.time() - start
-        if print_timing:
-            print('Forward pass, time: %.5f s' % time_elapsed)
-
-    ctx = Context()
-    ctx.scene = scene
-    ctx.shape_contents = shape_contents
-    ctx.color_contents = color_contents
-    ctx.filter = filt
-    ctx.width = width
-    ctx.height = height
-    ctx.num_samples_x = num_samples_x
-    ctx.num_samples_y = num_samples_y
-    ctx.seed = seed
-    ctx.output_type = output_type
-    ctx.use_prefiltering = use_prefiltering
-    return rendered_image, ctx
-
-@tf.custom_gradient
-def render(*x):
-    """
-        The main TensorFlow interface of C++ diffvg.
-    """
-    assert(tf.executing_eagerly())
-    if pydiffvg.get_use_gpu() and os.environ.get('TF_FORCE_GPU_ALLOW_GROWTH') != 'true':
-        print('******************** WARNING ********************')
-        print('Tensorflow by default allocates all GPU memory,')
-        print('causing huge amount of page faults when rendering.')
-        print('Please set the environment variable TF_FORCE_GPU_ALLOW_GROWTH to true,')
-        print('so that Tensorflow allocates memory on demand.')
-        print('*************************************************')
-
-    width = x[0]
-    height = x[1]
-    num_samples_x = x[2]
-    num_samples_y = x[3]
-    seed = x[4]
-    args = x[5:]
-    img, ctx = forward(width, height, num_samples_x, num_samples_y, seed, *args)
-
-    def backward(grad_img):
-        scene = ctx.scene
-        width = ctx.width
-        height = ctx.height
-        num_samples_x = ctx.num_samples_x
-        num_samples_y = ctx.num_samples_y
-        seed = ctx.seed
-        output_type = ctx.output_type
-        use_prefiltering = ctx.use_prefiltering
-
-        start = time.time()
-        with tf.device(pydiffvg.get_device_name()):
-            diffvg.render(scene,
-                          diffvg.float_ptr(0), # background_image
-                          diffvg.float_ptr(0), # render_image
-                          diffvg.float_ptr(0), # render_sdf
-                          width,
-                          height,
-                          num_samples_x,
-                          num_samples_y,
-                          seed,
-                          diffvg.float_ptr(0), # d_background_image
-                          diffvg.float_ptr(pydiffvg.data_ptr(grad_img) if output_type == OutputType.color else 0),
-                          diffvg.float_ptr(pydiffvg.data_ptr(grad_img) if output_type == OutputType.sdf else 0),
-                          diffvg.float_ptr(0), # d_translation
-                          use_prefiltering,
-                          diffvg.float_ptr(0), # eval_positions
-                          0 ) # num_eval_positions (automatically set to entire raster))
-        time_elapsed = time.time() - start
-        global print_timing
-        if print_timing:
-            print('Backward pass, time: %.5f s' % time_elapsed)
-
-        with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())):
-            d_args = []
-            d_args.append(None) # width
-            d_args.append(None) # height
-            d_args.append(None) # num_samples_x
-            d_args.append(None) # num_samples_y
-            d_args.append(None) # seed
-            d_args.append(None) # canvas_width
-            d_args.append(None) # canvas_height
-            d_args.append(None) # num_shapes
-            d_args.append(None) # num_shape_groups
-            d_args.append(None) # output_type
-            d_args.append(None) # use_prefiltering
-            for shape_id in range(scene.num_shapes):
-                d_args.append(None) # type
-                d_shape = scene.get_d_shape(shape_id)
-                if d_shape.type == diffvg.ShapeType.circle:
-                    d_circle = d_shape.as_circle()
-                    radius = tf.constant(d_circle.radius)
-                    d_args.append(radius)
-                    c = d_circle.center
-                    c = tf.constant((c.x, c.y))
-                    d_args.append(c)
-                elif d_shape.type == diffvg.ShapeType.ellipse:
-                    d_ellipse = d_shape.as_ellipse()
-                    r = d_ellipse.radius
-                    r = tf.constant((d_ellipse.radius.x, d_ellipse.radius.y))
-                    d_args.append(r)
-                    c = d_ellipse.center
-                    c = tf.constant((c.x, c.y))
-                    d_args.append(c)
-                elif d_shape.type == diffvg.ShapeType.path:
-                    d_path = d_shape.as_path()
-                    points = tf.zeros((d_path.num_points, 2), dtype=tf.float32)
-                    d_path.copy_to(diffvg.float_ptr(pydiffvg.data_ptr(points)),diffvg.float_ptr(0))
-                    d_args.append(None) # num_control_points
-                    d_args.append(points)
-                    d_args.append(None) # is_closed
-                    d_args.append(None) # use_distance_approx
-                elif d_shape.type == diffvg.ShapeType.rect:
-                    d_rect = d_shape.as_rect()
-                    p_min = tf.constant((d_rect.p_min.x, d_rect.p_min.y))
-                    p_max = tf.constant((d_rect.p_max.x, d_rect.p_max.y))
-                    d_args.append(p_min)
-                    d_args.append(p_max)
-                else:
-                    assert(False)
-                w = tf.constant((d_shape.stroke_width))
-                d_args.append(w)
-
-            for group_id in range(scene.num_shape_groups):
-                d_shape_group = scene.get_d_shape_group(group_id)
-                d_args.append(None) # shape_ids
-                d_args.append(None) # fill_color_type
-                if d_shape_group.has_fill_color():
-                    if d_shape_group.fill_color_type == diffvg.ColorType.constant:
-                        d_constant = d_shape_group.fill_color_as_constant()
-                        c = d_constant.color
-                        d_args.append(tf.constant((c.x, c.y, c.z, c.w)))
-                    elif d_shape_group.fill_color_type == diffvg.ColorType.linear_gradient:
-                        d_linear_gradient = d_shape_group.fill_color_as_linear_gradient()
-                        beg = d_linear_gradient.begin
-                        d_args.append(tf.constant((beg.x, beg.y)))
-                        end = d_linear_gradient.end
-                        d_args.append(tf.constant((end.x, end.y)))
-                        offsets = tf.zeros((d_linear_gradient.num_stops), dtype=tf.float32)
-                        stop_colors = tf.zeros((d_linear_gradient.num_stops, 4), dtype=tf.float32)
-                        # HACK: tensorflow's eager mode uses a cache to store scalar
-                        #       constants to avoid memory copy. If we pass scalar tensors
-                        #       into the C++ code and modify them, we would corrupt the
-                        #       cache, causing incorrect result in future scalar constant
-                        #       creations. Thus we force tensorflow to copy by plusing a zero.
-                        # (also see https://github.com/tensorflow/tensorflow/issues/11186
-                        #  for more discussion regarding copying tensors)
-                        if offsets.shape.num_elements() == 1:
-                            offsets = offsets + 0
-                        d_linear_gradient.copy_to(\
-                            diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                            diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-                        d_args.append(offsets)
-                        d_args.append(stop_colors)
-                    elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
-                        d_radial_gradient = d_shape_group.fill_color_as_radial_gradient()
-                        center = d_radial_gradient.center
-                        d_args.append(tf.constant((center.x, center.y)))
-                        radius = d_radial_gradient.radius
-                        d_args.append(tf.constant((radius.x, radius.y)))
-                        offsets = tf.zeros((d_radial_gradient.num_stops))
-                        if offsets.shape.num_elements() == 1:
-                            offsets = offsets + 0
-                        stop_colors = tf.zeros((d_radial_gradient.num_stops, 4))
-                        d_radial_gradient.copy_to(\
-                            diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                            diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-                        d_args.append(offsets)
-                        d_args.append(stop_colors)
-                    else:
-                        assert(False)
-                d_args.append(None) # stroke_color_type
-                if d_shape_group.has_stroke_color():
-                    if d_shape_group.stroke_color_type == diffvg.ColorType.constant:
-                        d_constant = d_shape_group.stroke_color_as_constant()
-                        c = d_constant.color
-                        d_args.append(tf.constant((c.x, c.y, c.z, c.w)))
-                    elif d_shape_group.stroke_color_type == diffvg.ColorType.linear_gradient:
-                        d_linear_gradient = d_shape_group.stroke_color_as_linear_gradient()
-                        beg = d_linear_gradient.begin
-                        d_args.append(tf.constant((beg.x, beg.y)))
-                        end = d_linear_gradient.end
-                        d_args.append(tf.constant((end.x, end.y)))
-                        offsets = tf.zeros((d_linear_gradient.num_stops))
-                        stop_colors = tf.zeros((d_linear_gradient.num_stops, 4))
-                        if offsets.shape.num_elements() == 1:
-                            offsets = offsets + 0
-                        d_linear_gradient.copy_to(\
-                            diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                            diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-                        d_args.append(offsets)
-                        d_args.append(stop_colors)
-                    elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient:
-                        d_radial_gradient = d_shape_group.stroke_color_as_radial_gradient()
-                        center = d_radial_gradient.center
-                        d_args.append(tf.constant((center.x, center.y)))
-                        radius = d_radial_gradient.radius
-                        d_args.append(tf.constant((radius.x, radius.y)))
-                        offsets = tf.zeros((d_radial_gradient.num_stops))
-                        stop_colors = tf.zeros((d_radial_gradient.num_stops, 4))
-                        if offsets.shape.num_elements() == 1:
-                            offsets = offsets + 0
-                        d_radial_gradient.copy_to(\
-                            diffvg.float_ptr(pydiffvg.data_ptr(offsets)),
-                            diffvg.float_ptr(pydiffvg.data_ptr(stop_colors)))
-                        d_args.append(offsets)
-                        d_args.append(stop_colors)
-                    else:
-                        assert(False)
-                d_args.append(None) # use_even_odd_rule
-                d_shape_to_canvas = tf.zeros((3, 3), dtype = tf.float32)
-                d_shape_group.copy_to(diffvg.float_ptr(pydiffvg.data_ptr(d_shape_to_canvas)))
-                d_args.append(d_shape_to_canvas)
-            d_args.append(None) # filter_type
-            d_args.append(tf.constant(scene.get_d_filter_radius()))
-
-        return d_args
-
-    return img, backward

spaces/CVPR/LIVE/thrust/dependencies/cub/README.md

@@ -1,189 +0,0 @@
-<hr>
-<h3>About CUB</h3>
-
-CUB provides state-of-the-art, reusable software components for every layer
-of the CUDA programming model:
-- [<b><em>Device-wide primitives</em></b>](https://nvlabs.github.com/cub/group___device_module.html)
-  - Sort, prefix scan, reduction, histogram, etc.
-  - Compatible with CUDA dynamic parallelism
-- [<b><em>Block-wide "collective" primitives</em></b>](https://nvlabs.github.com/cub/group___block_module.html)
-  - I/O, sort, prefix scan, reduction, histogram, etc.
-  - Compatible with arbitrary thread block sizes and types
-- [<b><em>Warp-wide "collective" primitives</em></b>](https://nvlabs.github.com/cub/group___warp_module.html)
-  - Warp-wide prefix scan, reduction, etc.
-  - Safe and architecture-specific
-- [<b><em>Thread and resource utilities</em></b>](https://nvlabs.github.com/cub/group___thread_module.html)
-  - PTX intrinsics, device reflection, texture-caching iterators, caching memory allocators, etc.
-
-![Orientation of collective primitives within the CUDA software stack](http://nvlabs.github.com/cub/cub_overview.png)
-
-CUB is included in the NVIDIA HPC SDK and the CUDA Toolkit.
-
-We recommend the [CUB Project Website](http://nvlabs.github.com/cub) for further information and examples.
-
-<br><hr>
-<h3>A Simple Example</h3>
-
-```C++
-#include <cub/cub.cuh>
-
-// Block-sorting CUDA kernel
-__global__ void BlockSortKernel(int *d_in, int *d_out)
-{
-     using namespace cub;
-
-     // Specialize BlockRadixSort, BlockLoad, and BlockStore for 128 threads
-     // owning 16 integer items each
-     typedef BlockRadixSort<int, 128, 16>                     BlockRadixSort;
-     typedef BlockLoad<int, 128, 16, BLOCK_LOAD_TRANSPOSE>   BlockLoad;
-     typedef BlockStore<int, 128, 16, BLOCK_STORE_TRANSPOSE> BlockStore;
-
-     // Allocate shared memory
-     __shared__ union {
-         typename BlockRadixSort::TempStorage  sort;
-         typename BlockLoad::TempStorage       load;
-         typename BlockStore::TempStorage      store;
-     } temp_storage;
-
-     int block_offset = blockIdx.x * (128 * 16);	  // OffsetT for this block's ment
-
-     // Obtain a segment of 2048 consecutive keys that are blocked across threads
-     int thread_keys[16];
-     BlockLoad(temp_storage.load).Load(d_in + block_offset, thread_keys);
-     __syncthreads();
-
-     // Collectively sort the keys
-     BlockRadixSort(temp_storage.sort).Sort(thread_keys);
-     __syncthreads();
-
-     // Store the sorted segment
-     BlockStore(temp_storage.store).Store(d_out + block_offset, thread_keys);
-}
-```
-
-Each thread block uses `cub::BlockRadixSort` to collectively sort
-its own input segment.  The class is specialized by the
-data type being sorted, by the number of threads per block, by the number of
-keys per thread, and implicitly by the targeted compilation architecture.
-
-The `cub::BlockLoad` and `cub::BlockStore` classes are similarly specialized.
-Furthermore, to provide coalesced accesses to device memory, these primitives are
-configured to access memory using a striped access pattern (where consecutive threads
-simultaneously access consecutive items) and then <em>transpose</em> the keys into
-a [<em>blocked arrangement</em>](index.html#sec4sec3) of elements across threads.
-
-Once specialized, these classes expose opaque `TempStorage` member types.
-The thread block uses these storage types to statically allocate the union of
-shared memory needed by the thread block.  (Alternatively these storage types
-could be aliased to global memory allocations).
-
-<br><hr>
-<h3>Releases</h3>
-
-CUB is distributed with the NVIDIA HPC SDK and the CUDA Toolkit in addition
-to GitHub.
-
-See the [changelog](CHANGELOG.md) for details about specific releases.
-
-| CUB Release               | Included In                             |
-| ------------------------- | --------------------------------------- |
-| 1.9.10-1                  | NVIDIA HPC SDK 20.7 & CUDA Toolkit 11.1 |
-| 1.9.10                    | NVIDIA HPC SDK 20.5                     |
-| 1.9.9                     | CUDA Toolkit 11.0                       |
-| 1.9.8-1                   | NVIDIA HPC SDK 20.3                     |
-| 1.9.8                     | CUDA Toolkit 11.0 Early Access          |
-| 1.9.8                     | CUDA 11.0 Early Access                  |
-| 1.8.0                     |                                         |
-| 1.7.5                     | Thrust 1.9.2                            |
-| 1.7.4                     | Thrust 1.9.1-2                          |
-| 1.7.3                     |                                         |
-| 1.7.2                     |                                         |
-| 1.7.1                     |                                         |
-| 1.7.0                     | Thrust 1.9.0-5                          |
-| 1.6.4                     |                                         |
-| 1.6.3                     |                                         |
-| 1.6.2 (previously 1.5.5)  |                                         |
-| 1.6.1 (previously 1.5.4)  |                                         |
-| 1.6.0 (previously 1.5.3)  |                                         |
-| 1.5.2                     |                                         |
-| 1.5.1                     |                                         |
-| 1.5.0                     |                                         |
-| 1.4.1                     |                                         |
-| 1.4.0                     |                                         |
-| 1.3.2                     |                                         |
-| 1.3.1                     |                                         |
-| 1.3.0                     |                                         |
-| 1.2.3                     |                                         |
-| 1.2.2                     |                                         |
-| 1.2.0                     |                                         |
-| 1.1.1                     |                                         |
-| 1.0.2                     |                                         |
-| 1.0.1                     |                                         |
-| 0.9.4                     |                                         |
-| 0.9.2                     |                                         |
-| 0.9.1                     |                                         |
-| 0.9.0                     |                                         |
-
-<br><hr>
-<h3>Development Process</h3>
-
-CUB uses the [CMake build system](https://cmake.org/) to build unit tests,
-examples, and header tests. To build CUB as a developer, the following
-recipe should be followed:
-
-```
-# Clone CUB repo from github:
-git clone https://github.com/thrust/cub.git
-cd cub
-
-# Create build directory:
-mkdir build
-cd build
-
-# Configure -- use one of the following:
-cmake ..   # Command line interface.
-ccmake ..  # ncurses GUI (Linux only)
-cmake-gui  # Graphical UI, set source/build directories in the app
-
-# Build:
-cmake --build . -j <num jobs>   # invokes make (or ninja, etc)
-
-# Run tests and examples:
-ctest
-```
-
-By default, the C++14 standard is targeted, but this can be changed in CMake.
-More information on configuring your CUB build and creating a pull request is
-found in [CONTRIBUTING.md](CONTRIBUTING.md).
-
-<br><hr>
-<h3>Open Source License</h3>
-
-CUB is available under the "New BSD" open-source license:
-
-```
-Copyright (c) 2010-2011, Duane Merrill.  All rights reserved.
-Copyright (c) 2011-2018, NVIDIA CORPORATION.  All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-   *  Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-   *  Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-   *  Neither the name of the NVIDIA CORPORATION nor the
-      names of its contributors may be used to endorse or promote products
-      derived from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-```

spaces/CVPR/LIVE/thrust/thrust/iterator/detail/minimum_category.h

@@ -1,52 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/type_traits/minimum_type.h>
-
-namespace thrust
-{
-
-namespace detail
-{ 
-
-template<typename T1,
-         typename T2  = minimum_type_detail::any_conversion,
-         typename T3  = minimum_type_detail::any_conversion,
-         typename T4  = minimum_type_detail::any_conversion,
-         typename T5  = minimum_type_detail::any_conversion,
-         typename T6  = minimum_type_detail::any_conversion,
-         typename T7  = minimum_type_detail::any_conversion,
-         typename T8  = minimum_type_detail::any_conversion,
-         typename T9  = minimum_type_detail::any_conversion,
-         typename T10 = minimum_type_detail::any_conversion,
-         typename T11 = minimum_type_detail::any_conversion,
-         typename T12 = minimum_type_detail::any_conversion,
-         typename T13 = minimum_type_detail::any_conversion,
-         typename T14 = minimum_type_detail::any_conversion,
-         typename T15 = minimum_type_detail::any_conversion,
-         typename T16 = minimum_type_detail::any_conversion>
-  struct minimum_category
-    : minimum_type<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16>
-{
-}; // end minimum_category
-
-} // end detail
-
-} // end thrust
-
-

spaces/CVPR/MonoScene/monoscene/modules.py

@@ -1,194 +0,0 @@
-import torch
-import torch.nn as nn
-from monoscene.DDR import Bottleneck3D
-
-
-class ASPP(nn.Module):
-    """
-    ASPP 3D
-    Adapt from https://github.com/cv-rits/LMSCNet/blob/main/LMSCNet/models/LMSCNet.py#L7
-    """
-
-    def __init__(self, planes, dilations_conv_list):
-        super().__init__()
-
-        # ASPP Block
-        self.conv_list = dilations_conv_list
-        self.conv1 = nn.ModuleList(
-            [
-                nn.Conv3d(
-                    planes, planes, kernel_size=3, padding=dil, dilation=dil, bias=False
-                )
-                for dil in dilations_conv_list
-            ]
-        )
-        self.bn1 = nn.ModuleList(
-            [nn.BatchNorm3d(planes) for dil in dilations_conv_list]
-        )
-        self.conv2 = nn.ModuleList(
-            [
-                nn.Conv3d(
-                    planes, planes, kernel_size=3, padding=dil, dilation=dil, bias=False
-                )
-                for dil in dilations_conv_list
-            ]
-        )
-        self.bn2 = nn.ModuleList(
-            [nn.BatchNorm3d(planes) for dil in dilations_conv_list]
-        )
-        self.relu = nn.ReLU()
-
-    def forward(self, x_in):
-
-        y = self.bn2[0](self.conv2[0](self.relu(self.bn1[0](self.conv1[0](x_in)))))
-        for i in range(1, len(self.conv_list)):
-            y += self.bn2[i](self.conv2[i](self.relu(self.bn1[i](self.conv1[i](x_in)))))
-        x_in = self.relu(y + x_in)  # modified
-
-        return x_in
-
-
-class SegmentationHead(nn.Module):
-    """
-    3D Segmentation heads to retrieve semantic segmentation at each scale.
-    Formed by Dim expansion, Conv3D, ASPP block, Conv3D.
-    Taken from https://github.com/cv-rits/LMSCNet/blob/main/LMSCNet/models/LMSCNet.py#L7
-    """
-
-    def __init__(self, inplanes, planes, nbr_classes, dilations_conv_list):
-        super().__init__()
-
-        # First convolution
-        self.conv0 = nn.Conv3d(inplanes, planes, kernel_size=3, padding=1, stride=1)
-
-        # ASPP Block
-        self.conv_list = dilations_conv_list
-        self.conv1 = nn.ModuleList(
-            [
-                nn.Conv3d(
-                    planes, planes, kernel_size=3, padding=dil, dilation=dil, bias=False
-                )
-                for dil in dilations_conv_list
-            ]
-        )
-        self.bn1 = nn.ModuleList(
-            [nn.BatchNorm3d(planes) for dil in dilations_conv_list]
-        )
-        self.conv2 = nn.ModuleList(
-            [
-                nn.Conv3d(
-                    planes, planes, kernel_size=3, padding=dil, dilation=dil, bias=False
-                )
-                for dil in dilations_conv_list
-            ]
-        )
-        self.bn2 = nn.ModuleList(
-            [nn.BatchNorm3d(planes) for dil in dilations_conv_list]
-        )
-        self.relu = nn.ReLU()
-
-        self.conv_classes = nn.Conv3d(
-            planes, nbr_classes, kernel_size=3, padding=1, stride=1
-        )
-
-    def forward(self, x_in):
-
-        # Convolution to go from inplanes to planes features...
-        x_in = self.relu(self.conv0(x_in))
-
-        y = self.bn2[0](self.conv2[0](self.relu(self.bn1[0](self.conv1[0](x_in)))))
-        for i in range(1, len(self.conv_list)):
-            y += self.bn2[i](self.conv2[i](self.relu(self.bn1[i](self.conv1[i](x_in)))))
-        x_in = self.relu(y + x_in)  # modified
-
-        x_in = self.conv_classes(x_in)
-
-        return x_in
-
-
-class ProcessKitti(nn.Module):
-    def __init__(self, feature, norm_layer, bn_momentum, dilations=[1, 2, 3]):
-        super(Process, self).__init__()
-        self.main = nn.Sequential(
-            *[
-                Bottleneck3D(
-                    feature,
-                    feature // 4,
-                    bn_momentum=bn_momentum,
-                    norm_layer=norm_layer,
-                    dilation=[i, i, i],
-                )
-                for i in dilations
-            ]
-        )
-
-    def forward(self, x):
-        return self.main(x)
-
-
-class Process(nn.Module):
-    def __init__(self, feature, norm_layer, bn_momentum, dilations=[1, 2, 3]):
-        super(Process, self).__init__()
-        self.main = nn.Sequential(
-            *[
-                Bottleneck3D(
-                    feature,
-                    feature // 4,
-                    bn_momentum=bn_momentum,
-                    norm_layer=norm_layer,
-                    dilation=[i, i, i],
-                )
-                for i in dilations
-            ]
-        )
-
-    def forward(self, x):
-        return self.main(x)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, out_channels, norm_layer, bn_momentum):
-        super(Upsample, self).__init__()
-        self.main = nn.Sequential(
-            nn.ConvTranspose3d(
-                in_channels,
-                out_channels,
-                kernel_size=3,
-                stride=2,
-                padding=1,
-                dilation=1,
-                output_padding=1,
-            ),
-            norm_layer(out_channels, momentum=bn_momentum),
-            nn.ReLU(),
-        )
-
-    def forward(self, x):
-        return self.main(x)
-
-
-class Downsample(nn.Module):
-    def __init__(self, feature, norm_layer, bn_momentum, expansion=8):
-        super(Downsample, self).__init__()
-        self.main = Bottleneck3D(
-            feature,
-            feature // 4,
-            bn_momentum=bn_momentum,
-            expansion=expansion,
-            stride=2,
-            downsample=nn.Sequential(
-                nn.AvgPool3d(kernel_size=2, stride=2),
-                nn.Conv3d(
-                    feature,
-                    int(feature * expansion / 4),
-                    kernel_size=1,
-                    stride=1,
-                    bias=False,
-                ),
-                norm_layer(int(feature * expansion / 4), momentum=bn_momentum),
-            ),
-            norm_layer=norm_layer,
-        )
-
-    def forward(self, x):
-        return self.main(x)

spaces/CVPR/drawings-to-human/main.py

@@ -1,3 +0,0 @@
-import subprocess
-
-subprocess.run(["make", "build-all"], shell=False)

spaces/CVPR/regionclip-demo/detectron2/modeling/roi_heads/__init__.py

@@ -1,35 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .box_head import ROI_BOX_HEAD_REGISTRY, build_box_head, FastRCNNConvFCHead
-from .keypoint_head import (
-    ROI_KEYPOINT_HEAD_REGISTRY,
-    build_keypoint_head,
-    BaseKeypointRCNNHead,
-    KRCNNConvDeconvUpsampleHead,
-)
-from .mask_head import (
-    ROI_MASK_HEAD_REGISTRY,
-    build_mask_head,
-    BaseMaskRCNNHead,
-    MaskRCNNConvUpsampleHead,
-)
-from .roi_heads import (
-    ROI_HEADS_REGISTRY,
-    ROIHeads,
-    Res5ROIHeads,
-    StandardROIHeads,
-    build_roi_heads,
-    select_foreground_proposals,
-)
-from .clip_roi_heads import (
-    CLIPRes5ROIHeads,
-    CLIPSwinROIHeads, 
-    PretrainRes5ROIHeads,
-    CLIPStandardROIHeads,
-)
-from .cascade_rcnn import CascadeROIHeads
-from .rotated_fast_rcnn import RROIHeads
-from .fast_rcnn import FastRCNNOutputLayers
-
-from . import cascade_rcnn  # isort:skip
-
-__all__ = list(globals().keys())

spaces/CarlDennis/Lovelive-VITS-JPZH/README.md

@@ -1,13 +0,0 @@
----
-title: Lovelive VITS JPZH
-emoji: 📈
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ChallengeHub/Chinese-LangChain/tests/test_duckduckgo_search.py

@@ -1,16 +0,0 @@
-from duckduckgo_search import ddg
-from duckduckgo_search.utils import SESSION
-
-
-SESSION.proxies = {
-    "http": f"socks5h://localhost:7890",
-    "https": f"socks5h://localhost:7890"
-}
-r = ddg("马保国")
-print(r[:2])
-"""
-[{'title': '马保国 - 维基百科，自由的百科全书', 'href': 'https://zh.wikipedia.org/wiki/%E9%A9%AC%E4%BF%9D%E5%9B%BD', 'body': '马保国（1951年 — ） ，男，籍贯 山东 临沂，出生及长大于河南，中国大陆太极拳师，自称"浑元形意太极门掌门人" 。 马保国因2017年约战mma格斗家徐晓冬首次出现
-大众视野中。 2020年5月，马保国在对阵民间武术爱好者王庆民的比赛中，30秒内被连续高速击倒三次，此事件成为了持续多日的社交 ...'}, {'title': '馬保國的主页 - 抖音', 'href': 'https://www.douyin.com/user/MS4wLjABAAAAW0E1ziOvxgUh3VVv5FE6xmoo3w5WtZalfphYZKj4mCg', 'body': '6.3万. #马马国教扛打功 最近有几个人模芳我动作，很危险啊，不可以的，朋友们不要受伤了。. 5.3万. #马保国直播带货榜第一 朋友们周末愉快，本周六早上湿点，我本人在此号进行第一次带货直播，活到老，学到老，越活越年轻。. 7.0万. #马保国击破红牛罐 昨天 ...'}]
-
-
-"""

spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/mysite/andrew_alpha/0_object_detection_model/GroundingDINO_SwinT_OGC.cfg.py

@@ -1,43 +0,0 @@
-batch_size = 1
-modelname = "groundingdino"
-backbone = "swin_T_224_1k"
-position_embedding = "sine"
-pe_temperatureH = 20
-pe_temperatureW = 20
-return_interm_indices = [1, 2, 3]
-backbone_freeze_keywords = None
-enc_layers = 6
-dec_layers = 6
-pre_norm = False
-dim_feedforward = 2048
-hidden_dim = 256
-dropout = 0.0
-nheads = 8
-num_queries = 900
-query_dim = 4
-num_patterns = 0
-num_feature_levels = 4
-enc_n_points = 4
-dec_n_points = 4
-two_stage_type = "standard"
-two_stage_bbox_embed_share = False
-two_stage_class_embed_share = False
-transformer_activation = "relu"
-dec_pred_bbox_embed_share = True
-dn_box_noise_scale = 1.0
-dn_label_noise_ratio = 0.5
-dn_label_coef = 1.0
-dn_bbox_coef = 1.0
-embed_init_tgt = True
-dn_labelbook_size = 2000
-max_text_len = 256
-text_encoder_type = "bert-base-uncased"
-use_text_enhancer = True
-use_fusion_layer = True
-use_checkpoint = True
-use_transformer_ckpt = True
-use_text_cross_attention = True
-text_dropout = 0.0
-fusion_dropout = 0.0
-fusion_droppath = 0.1
-sub_sentence_present = True

spaces/CikeyQI/Yunzai/Yunzai/plugins/other/restart.js

@@ -1,122 +0,0 @@
-import plugin from '../../lib/plugins/plugin.js'
-import { createRequire } from 'module'
-
-const require = createRequire(import.meta.url)
-const { exec } = require('child_process')
-
-export class Restart extends plugin {
-  constructor (e = '') {
-    super({
-      name: '重启',
-      dsc: '#重启',
-      event: 'message',
-      priority: 10,
-      rule: [{
-        reg: '^#重启$',
-        fnc: 'restart',
-        permission: 'master'
-      }, {
-        reg: '^#(停机|关机)$',
-        fnc: 'stop',
-        permission: 'master'
-      }]
-    })
-
-    if (e) this.e = e
-
-    this.key = 'Yz:restart'
-  }
-
-  async init () {
-    let restart = await redis.get(this.key)
-    if (restart) {
-      restart = JSON.parse(restart)
-      let time = restart.time || new Date().getTime()
-      time = (new Date().getTime() - time) / 1000
-
-      let msg = `重启成功：耗时${time.toFixed(2)}秒`
-
-      if (restart.isGroup)
-        Bot.sendGroupMsg(restart.bot_id, restart.id, msg)
-      else
-        Bot.sendFriendMsg(restart.bot_id, restart.id, msg)
-
-      redis.del(this.key)
-    }
-  }
-
-  async restart () {
-    await this.e.reply('开始执行重启，请稍等...')
-    logger.mark(`${this.e.logFnc} 开始执行重启，请稍等...`)
-
-    let data = JSON.stringify({
-      isGroup: !!this.e.isGroup,
-      id: this.e.isGroup ? this.e.group_id : this.e.user_id,
-      bot_id: this.e.self_id,
-      time: new Date().getTime()
-    })
-
-    let npm = await this.checkPnpm()
-
-    try {
-      await redis.set(this.key, data, { EX: 120 })
-      let cm = `${npm} start`
-      if (process.argv[1].includes('pm2')) {
-        cm = `${npm} run restart`
-      }
-
-      exec(cm, { windowsHide: true }, (error, stdout, stderr) => {
-        if (error) {
-          redis.del(this.key)
-          this.e.reply(`操作失败！\n${error.stack}`)
-          logger.error(`重启失败\n${error.stack}`)
-        } else if (stdout) {
-          logger.mark('重启成功，运行已由前台转为后台')
-          logger.mark(`查看日志请用命令：${npm} run log`)
-          logger.mark(`停止后台运行命令：${npm} stop`)
-          process.exit()
-        }
-      })
-    } catch (error) {
-      redis.del(this.key)
-      let e = error.stack ?? error
-      this.e.reply(`操作失败！\n${e}`)
-    }
-
-    return true
-  }
-
-  async checkPnpm () {
-    let npm = 'npm'
-    let ret = await this.execSync('pnpm -v')
-    if (ret.stdout) npm = 'pnpm'
-    return npm
-  }
-
-  async execSync (cmd) {
-    return new Promise((resolve, reject) => {
-      exec(cmd, { windowsHide: true }, (error, stdout, stderr) => {
-        resolve({ error, stdout, stderr })
-      })
-    })
-  }
-
-  async stop () {
-    if (!process.argv[1].includes('pm2')) {
-      logger.mark('关机成功，已停止运行')
-      await this.e.reply('关机成功，已停止运行')
-      process.exit()
-    }
-
-    logger.mark('关机成功，已停止运行')
-    await this.e.reply('关机成功，已停止运行')
-
-    let npm = await this.checkPnpm()
-    exec(`${npm} stop`, { windowsHide: true }, (error, stdout, stderr) => {
-      if (error) {
-        this.e.reply(`操作失败！\n${error.stack}`)
-        logger.error(`关机失败\n${error.stack}`)
-      }
-    })
-  }
-}

spaces/CikeyQI/meme-api/meme_generator/memes/ascension/__init__.py

@@ -1,35 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.exception import TextOverLength
-
-img_dir = Path(__file__).parent / "images"
-
-
-def ascension(images, texts: List[str], args):
-    frame = BuildImage.open(img_dir / "0.png")
-    text = f"你原本应该要去地狱的，但因为你生前{texts[0]}，我们就当作你已经服完刑期了"
-    try:
-        frame.draw_text(
-            (40, 30, 482, 135),
-            text,
-            allow_wrap=True,
-            max_fontsize=50,
-            min_fontsize=20,
-        )
-    except ValueError:
-        raise TextOverLength(texts[0])
-    return frame.save_jpg()
-
-
-add_meme(
-    "ascension",
-    ascension,
-    min_texts=1,
-    max_texts=1,
-    default_texts=["学的是机械"],
-    keywords=["升天"],
-)

spaces/Cong723/gpt-academic-public/docs/README_RS.md

@@ -1,291 +0,0 @@
-> **Note**
->
-> Этот файл самовыражения автоматически генерируется модулем перевода markdown в этом проекте и может быть не на 100% правильным.
->
-
-# <img src="logo.png" width="40" > ChatGPT Academic Optimization
-
-**Если вам понравился этот проект, пожалуйста, поставьте ему звезду. Если вы придумали более полезные академические ярлыки или функциональные плагины, не стесняйтесь создавать запросы на изменение или пул-запросы. Мы также имеем [README на английском языке](docs/README_EN.md), переведенный этим же проектом.
-
-> **Примечание**
->
-> 1. Пожалуйста, обратите внимание, что только функциonal plugins (buttons) с **красным цветом** могут читать файлы, некоторые из которых находятся в **выпадающем меню** плагинов. Кроме того, мы приветствуем и обрабатываем любые новые плагины с **наивысшим приоритетом**!
->
-> 2. Функции каждого файла в этом проекте подробно описаны в собственном анализе [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) . При повторных итерациях вы также можете вызывать обновленный отчет функций проекта, щелкнув соответствующий функциональный плагин GPT. Часто задаваемые вопросы собраны в [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) .
-
-<div align="center">
-    
-Функция | Описание
---- | ---
-Редактирование одним кликом | Поддержка редактирования одним кликом, поиск грамматических ошибок в академических статьях
-Переключение языков "Английский-Китайский" одним кликом | Одним кликом переключайте языки "Английский-Китайский"
-Разъяснение программного кода одним кликом | Вы можете правильно отобразить и объяснить программный код.
-[Настраиваемые сочетания клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настраиваемых сочетаний клавиш
-[Настройка сервера-прокси](https://www.bilibili.com/video/BV1rc411W7Dr) | Поддержка настройки сервера-прокси
-Модульный дизайн | Поддержка настраиваемых функциональных плагинов высших порядков и функциональных плагинов, поддерживающих [горячее обновление](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Автоанализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Прочтение в один клик](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) кода программы проекта
-[Анализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] Один клик для проанализирования дерева других проектов Python/C/C++/Java/Lua/...
-Чтение статей| [Функциональный плагин] Одним кликом прочитайте весь латех (LaTex) текст статьи и сгенерируйте краткое описание
-Перевод и редактирование всех статей из LaTex | [Функциональный плагин] Перевод или редактирование LaTex-статьи всего одним нажатием кнопки
-Генерация комментариев в пакетном режиме | [Функциональный плагин] Одним кликом сгенерируйте комментарии к функциям в пакетном режиме
-Генерация отчетов пакета CHAT | [Функциональный плагин] Автоматически создавайте сводные отчеты после выполнения
-[Помощник по arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Функциональный плагин] Введите URL статьи arxiv, чтобы легко перевести резюме и загрузить PDF-файл
-[Перевод полного текста статьи в формате PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Функциональный плагин] Извлеките заголовок статьи, резюме и переведите весь текст статьи (многопоточно)
-[Помощник интеграции Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Функциональный плагин] Дайте GPT выбрать для вас интересные статьи на любой странице поиска Google Scholar.
-Отображение формул/изображений/таблиц | Одновременно отображается tex-форма и рендер-форма формул, поддержка формул, высокоскоростных кодов
-Поддержка функциональных плагинов многопоточности | Поддержка многопоточной работы с плагинами, обрабатывайте огромные объемы текста или программы одним кликом
-Запуск темной темы gradio[подробнее](https://github.com/binary-husky/chatgpt_academic/issues/173) | Добавьте / ?__dark-theme=true в конец URL браузера, чтобы переключиться на темную тему.
-[Поддержка нескольких моделей LLM](https://www.bilibili.com/video/BV1wT411p7yf), поддержка API2D | Находиться между GPT3.5, GPT4 и [清华ChatGLM](https://github.com/THUDM/ChatGLM-6B) должно быть очень приятно, не так ли?
-Альтернатива huggingface без использования научной сети [Онлайн-эксперимент](https://huggingface.co/spaces/qingxu98/gpt-academic) | Войдите в систему, скопируйте пространство [этот пространственный URL](https://huggingface.co/spaces/qingxu98/gpt-academic)
-…… | ……
-
-
-</div>
-
-- Новый интерфейс (вы можете изменить настройку LAYOUT в config.py, чтобы переключаться между "горизонтальным расположением" и "вертикальным расположением")
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>
-
-
-Вы профессиональный переводчик научных статей. 
-
-- Все кнопки генерируются динамически путем чтения functional.py и могут быть легко настроены под пользовательские потребности, освобождая буфер обмена.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- Редактирование/корректирование
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- Если вывод содержит формулы, они отображаются одновременно как в формате tex, так и в рендеринговом формате для удобства копирования и чтения.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- Лень смотреть код проекта? Просто покажите chatgpt.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- Несколько моделей больших языковых моделей смешиваются (ChatGLM + OpenAI-GPT3.5 + [API2D] (https://api2d.com/) -GPT4)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
-Несколько моделей больших языковых моделей смешиваются в [бета-версии huggingface] (https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta) (huggingface-версия не поддерживает chatglm).
-
-
----
-
-## Установка - Метод 1: Запуск (Windows, Linux или MacOS)
-
-1. Скачайте проект
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Настройка API_KEY и настройки прокси
-
-В файле `config.py` настройте зарубежный прокси и OpenAI API KEY, пояснения ниже
-```
-1. Если вы находитесь в Китае, вам нужно настроить зарубежный прокси, чтобы использовать OpenAI API. Пожалуйста, внимательно прочитайте config.py для получения инструкций (1. Измените USE_PROXY на True; 2. Измените прокси в соответствии с инструкциями).
-2. Настройка API KEY OpenAI. Вам необходимо зарегистрироваться на сайте OpenAI и получить API KEY. После получения API KEY настройте его в файле config.py.
-3. Вопросы, связанные с сетевыми проблемами (тайм-аут сети, прокси не работает), можно найти здесь: https://github.com/binary-husky/chatgpt_academic/issues/1
-```
-(Примечание: при запуске программы будет проверяться наличие конфиденциального файла конфигурации с именем `config_private.py` и использоваться в нем конфигурация параметров, которая перезаписывает параметры с такими же именами в `config.py`. Поэтому, если вы понимаете логику чтения нашей конфигурации, мы настоятельно рекомендуем вам создать новый файл конфигурации с именем `config_private.py` рядом с `config.py` и переместить (скопировать) настройки из `config.py` в `config_private.py`.  `config_private.py` не подвергается контролю git, что делает конфиденциальную информацию более безопасной.) 
-
-
-3. Установить зависимости
-```sh
-# (Выбор 1) Рекомендуется
-python -m pip install -r requirements.txt   
-
-# (Выбор 2) Если вы используете anaconda, то шаги будут аналогичны:
-# (Шаг 2.1) conda create -n gptac_venv python=3.11
-# (Шаг 2.2) conda activate gptac_venv
-# (Шаг 2.3) python -m pip install -r requirements.txt
-
-# Примечание: используйте официальный источник pip или источник pip.aliyun.com. Другие источники pip могут вызывать проблемы. временный метод замены источника: 
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-Если требуется поддержка TUNA ChatGLM, необходимо установить дополнительные зависимости (если вы неудобны с python, необходимо иметь хорошую конфигурацию компьютера):
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. Запустите
-```sh
-python main.py
-```
-
-5. Тестовые функции плагина
-```
-- Тестирвоание анализа проекта Python
-    В основной области введите `./crazy_functions/test_project/python/dqn` , а затем нажмите "Анализировать весь проект Python"
-- Тестирование самостоятельного чтения кода
-    Щелкните " [Демонстрационный режим многопоточности] Проанализируйте сам проект (расшифровка источника кода)"
-- Тестирование функций шаблонного плагина (вы можете использовать эту функцию как шаблон для более сложных функций, требующих ответа от gpt в связи с тем, что произошло сегодня в истории)
-    Щелкните " [Функции шаблонного плагина] День в истории"
-- На нижней панели дополнительные функции для выбора
-```
-
-## Установка - Метод 2: Использование docker (Linux)
-
-
-1. Только ChatGPT (рекомендуется для большинства пользователей):
-``` sh
-# Скачать проект
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# Настроить прокси за границей и OpenAI API KEY
-Отредактируйте файл config.py в любом текстовом редакторе.
-# Установка
-docker build -t gpt-academic .
-# Запустить
-docker run --rm -it --net=host gpt-academic
-
-# Проверка функциональности плагина
-## Прове��ка шаблонной функции плагина (требуется, чтобы gpt ответил, что произошло "в истории на этот день"), вы можете использовать эту функцию в качестве шаблона для реализации более сложных функций.
-Нажмите "[Шаблонный демонстрационный плагин] История на этот день".
-## Тест абстрактного резюме для проекта на Latex
-В области ввода введите ./crazy_functions/test_project/latex/attention, а затем нажмите "Чтение реферата о тезисах статьи на LaTeX".
-## Тестовый анализ проекта на Python
-Введите в область ввода ./crazy_functions/test_project/python/dqn, затем нажмите "Проанализировать весь проект на Python".
-
-Выбирайте больше функциональных плагинов в нижнем выпадающем меню.
-```
-
-2. ChatGPT + ChatGLM (требуется глубокое знание Docker и достаточно мощное компьютерное оборудование):
-
-``` sh
-# Изменение Dockerfile
-cd docs && nano Dockerfile+ChatGLM
-# Как построить | Как запустить (Dockerfile+ChatGLM в пути docs, сначала перейдите в папку с помощью cd docs)
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# Как запустить | Как запустить (2) я хочу войти в контейнер и сделать какие-то настройки до запуска:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-
-## Установка-Метод 3: Другие способы развертывания
-
-1. Развертывание на удаленном облачном сервере
-Пожалуйста, посетите [Deploy Wiki-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. Использование WSL2 (Windows Subsystem for Linux)
-Пожалуйста, посетите [Deploy Wiki-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## Установка-Настройки прокси
-### Метод 1: Обычный способ
-[Конфигурация прокси] (https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-### Метод 2: Руководство новичка
-[Руководство новичка] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
-
----
-
-## Настройка новой удобной кнопки (настройка быстрой клавиши для научной работы)
-Откройте `core_functional.py` любым текстовым редактором, добавьте элементы, как показано ниже, затем перезапустите программу. (Если кнопка уже успешно добавлена и видна, то префикс и суффикс поддерживают горячее изменение, чтобы они оказались в действии, не нужно перезапускать программу.)
-например
-```
-"Супер анг-рус": {
-    # Префикс, будет добавлен перед вашим вводом. Например, используется для описания ваших потребностей, таких как перевод, кодинг, редактирование и т. д.
-    "Prefix": "Пожалуйста, переведите этот фрагмент на русский язык, а затем создайте пошаговую таблицу в markdown, чтобы объяснить все специализированные термины, которые встречаются в тексте:\n\n", 
-    
-    # Суффикс, будет добавлен после вашего ввода. Например, совместно с префиксом можно обрамить ваш ввод в кавычки.
-    "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
----
-
-
-## Демонстрация некоторых возможностей
-
-### Отображение изображений:
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
-</div>
-
-
-### Если программа может понимать и разбирать сама себя:
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
-</div>
-
-
-### Анализ других проектов на Python/Cpp:
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
-</div>
-
-### Генерация понимания и абстрактов с помощью Латех статей в один клик
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
-</div>
-
-### Автоматическое создание отчетов
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-### Модульный дизайн функций
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-
-### Трансляция исходного кода на английский язык
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
-</div>
-
-## Todo и планирование версий:
-- version 3.2+ (todo): функция плагины поддерживают более многочисленные интерфейсы параметров 
-- version 3.1: поддержка одновременного опроса нескольких моделей gpt! Поддержка api2d, поддержка балансировки нагрузки множества apikey.
-- version 3.0: поддержка chatglm и других маленьких llm
-- version 2.6: реструктурировал структуру плагинов, повысил интерактивность, добавил больше плагинов
-- version 2.5: само обновление, решение проблемы слишком длинного текста и переполнения токена при переводе всего проекта исходного кода
-- version 2.4: (1) добавлена функция перевода всего PDF-документа; (2) добавлена функция изменения положения входной области; (3) добавлена опция вертикального макета; (4) оптимизация функций многопоточности плагина.
-- version 2.3: улучшение многопоточной интерактивности
-- version 2.2: функция плагинов поддерживает горячую перезагрузку
-- version 2.1: блочная раскладка 
-- version 2.0: модульный дизайн функций плагина 
-- version 1.0: основные функции 
-
-## Ссылки на изучение и обучение
-
-```
-В коде использовано много хороших дизайнерских решений из других отличных проектов, в том числе:
-
-# Project1: использование многих приемов из ChuanhuChatGPT
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Project2: ChatGLM-6B в Тхуде:
-https://github.com/THUDM/ChatGLM-6B
-```
-

spaces/Cropinky/esrgan/realesrgan/models/__init__.py

@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/proc.py

@@ -1,51 +0,0 @@
-import multiprocessing
-
-def cpu_count():
-    return multiprocessing.cpu_count()
-
-def get_pool(processes):
-    pool = multiprocessing.Pool(processes = processes)
-    return pool
-
-def wait_for_pool(pool):
-    pool.close()
-    pool.join()  
-
-def set_proc_name(name):
-    import setproctitle
-    setproctitle.setproctitle(name)
-    
-def kill(pid):
-    import util
-    if type(pid) == list:
-        for p in pid:
-            kill(p)
-    elif type(pid) == int:
-        cmd = 'kill -9 %d'%(pid)
-        print cmd
-        print util.cmd.cmd(cmd)
-    elif type(pid) == str:
-        pids = get_pid(pid)
-        kill(pids)
-    else:
-        raise ValueError, 'Not supported parameter type:', type(pid)
-
-def ps_aux_grep(pattern):
-    import util
-    cmd = 'ps aux|grep %s'%(pattern)
-    return util.cmd.cmd(cmd)
-
-        
-def get_pid(pattern):
-    import util
-    cmd = 'ps aux|grep %s'%(pattern)
-    results = util.cmd.cmd(cmd)
-    results = util.str.split(results, '\n')
-    pids = []
-    for result in results:
-        info = result.split()
-        if len(info) > 0:
-            pid = int(info[1])
-            pids.append(pid)
-    return pids
-

spaces/Cyril666/my_abi/utils.py

@@ -1,304 +0,0 @@
-import logging
-import os
-import time
-
-import cv2
-import numpy as np
-import torch
-import yaml
-from matplotlib import colors
-from matplotlib import pyplot as plt
-from torch import Tensor, nn
-from torch.utils.data import ConcatDataset
-
-class CharsetMapper(object):
-    """A simple class to map ids into strings.
-
-    It works only when the character set is 1:1 mapping between individual
-    characters and individual ids.
-    """
-
-    def __init__(self,
-                 filename='',
-                 max_length=30,
-                 null_char=u'\u2591'):
-        """Creates a lookup table.
-
-        Args:
-          filename: Path to charset file which maps characters to ids.
-          max_sequence_length: The max length of ids and string.
-          null_char: A unicode character used to replace '<null>' character.
-            the default value is a light shade block '░'.
-        """
-        self.null_char = null_char
-        self.max_length = max_length
-
-        self.label_to_char = self._read_charset(filename)
-        self.char_to_label = dict(map(reversed, self.label_to_char.items()))
-        self.num_classes = len(self.label_to_char)
- 
-    def _read_charset(self, filename):
-        """Reads a charset definition from a tab separated text file.
-
-        Args:
-          filename: a path to the charset file.
-
-        Returns:
-          a dictionary with keys equal to character codes and values - unicode
-          characters.
-        """
-        import re
-        pattern = re.compile(r'(\d+)\t(.+)')
-        charset = {}
-        self.null_label = 0
-        charset[self.null_label] = self.null_char
-        with open(filename, 'r') as f:
-            for i, line in enumerate(f):
-                m = pattern.match(line)
-                assert m, f'Incorrect charset file. line #{i}: {line}'
-                label = int(m.group(1)) + 1
-                char = m.group(2)
-                charset[label] = char
-        return charset
-
-    def trim(self, text):
-        assert isinstance(text, str)
-        return text.replace(self.null_char, '')
-
-    def get_text(self, labels, length=None, padding=True, trim=False):
-        """ Returns a string corresponding to a sequence of character ids.
-        """
-        length = length if length else self.max_length
-        labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
-        if padding:
-            labels = labels + [self.null_label] * (length-len(labels))
-        text = ''.join([self.label_to_char[label] for label in labels])
-        if trim: text = self.trim(text)
-        return text
-
-    def get_labels(self, text, length=None, padding=True, case_sensitive=False):
-        """ Returns the labels of the corresponding text.
-        """
-        length = length if length else self.max_length
-        if padding:
-            text = text + self.null_char * (length - len(text))
-        if not case_sensitive:
-            text = text.lower()
-        labels = [self.char_to_label[char] for char in text]
-        return labels
-
-    def pad_labels(self, labels, length=None):
-        length = length if length else self.max_length
-
-        return labels + [self.null_label] * (length - len(labels))
-
-    @property
-    def digits(self):
-        return '0123456789'
-
-    @property
-    def digit_labels(self):
-        return self.get_labels(self.digits, padding=False)
-
-    @property
-    def alphabets(self):
-        all_chars = list(self.char_to_label.keys())
-        valid_chars = []
-        for c in all_chars:
-            if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
-                valid_chars.append(c)
-        return ''.join(valid_chars)
-
-    @property
-    def alphabet_labels(self):
-        return self.get_labels(self.alphabets, padding=False)
-
-
-class Timer(object):
-    """A simple timer."""
-    def __init__(self):
-        self.data_time = 0.
-        self.data_diff = 0.
-        self.data_total_time = 0.
-        self.data_call = 0
-        self.running_time = 0.
-        self.running_diff = 0.
-        self.running_total_time = 0.
-        self.running_call = 0
-
-    def tic(self):
-        self.start_time = time.time()
-        self.running_time = self.start_time
-
-    def toc_data(self):
-        self.data_time = time.time()
-        self.data_diff = self.data_time - self.running_time
-        self.data_total_time += self.data_diff
-        self.data_call += 1
-
-    def toc_running(self):
-        self.running_time = time.time()
-        self.running_diff = self.running_time - self.data_time
-        self.running_total_time += self.running_diff
-        self.running_call += 1
-
-    def total_time(self):
-        return self.data_total_time + self.running_total_time
-
-    def average_time(self):
-        return  self.average_data_time() + self.average_running_time()
-
-    def average_data_time(self):
-        return self.data_total_time / (self.data_call or 1)
-
-    def average_running_time(self):
-        return self.running_total_time / (self.running_call or 1)
-
-
-class Logger(object):
-    _handle = None
-    _root = None
-
-    @staticmethod
-    def init(output_dir, name, phase):
-        format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
-                '%(message)s'.format(name)
-        logging.basicConfig(level=logging.INFO, format=format)
-
-        try: os.makedirs(output_dir)
-        except: pass
-        config_path = os.path.join(output_dir, f'{phase}.txt')
-        Logger._handle = logging.FileHandler(config_path)
-        Logger._root = logging.getLogger()
-
-    @staticmethod
-    def enable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.addHandler(Logger._handle)
-
-    @staticmethod
-    def disable_file():
-        if Logger._handle is None or Logger._root is None:
-            raise Exception('Invoke Logger.init() first!')
-        Logger._root.removeHandler(Logger._handle)
-
-
-class Config(object):
-
-    def __init__(self, config_path, host=True):
-        def __dict2attr(d, prefix=''):
-            for k, v in d.items():
-                if isinstance(v, dict):
-                    __dict2attr(v, f'{prefix}{k}_')
-                else:
-                    if k == 'phase':
-                        assert v in ['train', 'test']
-                    if k == 'stage':
-                        assert v in ['pretrain-vision', 'pretrain-language',
-                                     'train-semi-super', 'train-super']
-                    self.__setattr__(f'{prefix}{k}', v)
-
-        assert os.path.exists(config_path), '%s does not exists!' % config_path
-        with open(config_path) as file:
-            config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        with open('configs/template.yaml') as file:
-            default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
-        __dict2attr(default_config_dict)
-        __dict2attr(config_dict)
-        self.global_workdir = os.path.join(self.global_workdir, self.global_name)
-
-    def __getattr__(self, item):
-        attr = self.__dict__.get(item)
-        if attr is None:
-            attr = dict()
-            prefix = f'{item}_'
-            for k, v in self.__dict__.items():
-                if k.startswith(prefix):
-                    n = k.replace(prefix, '')
-                    attr[n] = v
-            return attr if len(attr) > 0 else None
-        else:
-            return attr
-
-    def __repr__(self):
-        str = 'ModelConfig(\n'
-        for i, (k, v) in enumerate(sorted(vars(self).items())):
-            str += f'\t({i}): {k} = {v}\n'
-        str += ')'
-        return str
-
-def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
-    # normalize mask
-    mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
-    if mask.shape != image.shape:
-        mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
-    # get color map
-    color_map = plt.get_cmap(cmap)
-    mask = color_map(mask)[:,:,:3]
-    # convert float to uint8
-    mask = (mask * 255).astype(dtype=np.uint8)
-
-    # set the basic color
-    basic_color = np.array(colors.to_rgb(color)) * 255 
-    basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1]) 
-    basic_color = basic_color.astype(dtype=np.uint8)
-    # blend with basic color
-    blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
-    # blend with mask
-    blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
-
-    return blended_img
-
-def onehot(label, depth, device=None):
-    """ 
-    Args:
-        label: shape (n1, n2, ..., )
-        depth: a scalar
-
-    Returns:
-        onehot: (n1, n2, ..., depth)
-    """
-    if not isinstance(label, torch.Tensor):
-        label = torch.tensor(label, device=device)
-    onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
-    onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
-
-    return onehot
-
-class MyDataParallel(nn.DataParallel):
-
-    def gather(self, outputs, target_device):
-        r"""
-        Gathers tensors from different GPUs on a specified device
-        (-1 means the CPU).
-        """
-        def gather_map(outputs):
-            out = outputs[0]
-            if isinstance(out, (str, int, float)):
-                return out
-            if isinstance(out, list) and isinstance(out[0], str):
-                return [o for out in outputs for o in out]
-            if isinstance(out, torch.Tensor):
-                return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
-            if out is None:
-                return None
-            if isinstance(out, dict):
-                if not all((len(out) == len(d) for d in outputs)):
-                    raise ValueError('All dicts must have the same number of keys')
-                return type(out)(((k, gather_map([d[k] for d in outputs]))
-                                for k in out))
-            return type(out)(map(gather_map, zip(*outputs)))
-
-        # Recursive function calls like this create reference cycles.
-        # Setting the function to None clears the refcycle.
-        try:
-            res = gather_map(outputs)
-        finally:
-            gather_map = None
-        return res
-
-
-class MyConcatDataset(ConcatDataset):
-    def __getattr__(self, k): 
-        return getattr(self.datasets[0], k)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-2908e8a9.css

@@ -1 +0,0 @@
-.gradio-bokeh.svelte-1fe5ixn.svelte-1fe5ixn{display:flex;justify-content:center}.layout.svelte-1fe5ixn.svelte-1fe5ixn{display:flex;flex-direction:column;justify-content:center;align-items:center;width:var(--size-full);height:var(--size-full);color:var(--body-text-color)}.altair.svelte-1fe5ixn.svelte-1fe5ixn{display:flex;flex-direction:column;justify-content:center;align-items:center;width:var(--size-full);height:var(--size-full)}.caption.svelte-1fe5ixn.svelte-1fe5ixn{font-size:var(--text-sm)}.matplotlib.svelte-1fe5ixn img.svelte-1fe5ixn{object-fit:contain}