Update parquet files (step 25 of 296)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/CyberLink PowerDVD Ultra 19.0.2512.63 Crack __HOT__ Crack __HOT__.md

@@ -1,149 +0,0 @@
-
-<h1>CyberLink PowerDVD Ultra 19.0.2512.63 Crack: The Ultimate Media Player for Windows</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a powerful and versatile media player that can handle any type of media file, you should check out CyberLink PowerDVD Ultra 19.0.2512.63 Crack. This is a cracked version of the original software that allows you to enjoy all the premium features without paying a dime.</p>
-<h2>CyberLink PowerDVD Ultra 19.0.2512.63 Crack crack</h2><br /><p><b><b>Download</b> &#10038; <a href="https://byltly.com/2uKw36">https://byltly.com/2uKw36</a></b></p><br /><br />
- <h3>What is CyberLink PowerDVD Ultra 19.0.2512.63 Crack?</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack is a software that lets you play, stream, download, and organize your media files on your Windows PC. It supports a wide range of formats, including DVD, Blu-ray, CD, MP4, MKV, AVI, WMV, FLV, MP3, AAC, WAV, FLAC, and more.</p>
- <h3>Why do you need CyberLink PowerDVD Ultra 19.0.2512.63 Crack?</h3>
- <p>You need CyberLink PowerDVD Ultra 19.0.2512.63 Crack because it offers you many benefits that other media players don't have, such as:</p>
- <ul>
-<li>It delivers stunning video quality with TrueTheater enhancements that optimize brightness, color, contrast, and sharpness.</li>
-<li>It supports the latest technologies such as 4K resolution, high dynamic range (HDR), and 360-degree videos that immerse you in the action.</li>
-<li>It enhances your audio experience with TrueTheater Sound that boosts volume, clarity, bass, and surround sound.</li>
-<li>It lets you stream and cast your media files to any device such as smart TVs, game consoles, Chromecast, Apple TV, Roku, and more.</li>
-<li>It allows you to download and watch your favorite online videos offline with its built-in YouTube downloader.</li>
-<li>It helps you organize and manage your media library with its intuitive interface and smart features such as face recognition, scene search, and auto-tagging.</li>
-</ul>
- <h2>Features of CyberLink PowerDVD Ultra 19.0.2512.63 Crack</h2>
- <p>In this section, we will go over some of the most impressive features of CyberLink PowerDVD Ultra 19.0.2512.63 Crack in more detail.</p>
- <h3>Playback of any media format</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack can play any media format you throw at it without any hassle or compatibility issues. Whether it's a DVD disc, a Blu-ray disc, a CD disc, or a digital file on your hard drive or cloud storage, it can handle it with ease.</p>
-<p>CyberLink PowerDVD Ultra 19 full version with crack<br />
-How to activate CyberLink PowerDVD Ultra 19 for free<br />
-CyberLink PowerDVD Ultra 19.0.2512.63 Crack download link<br />
-CyberLink PowerDVD Ultra 19 serial key generator<br />
-CyberLink PowerDVD Ultra 19 patch and keygen<br />
-CyberLink PowerDVD Ultra 19 license code and activation<br />
-CyberLink PowerDVD Ultra 19 cracked software for Windows<br />
-CyberLink PowerDVD Ultra 19 latest update and crack<br />
-CyberLink PowerDVD Ultra 19 offline installer and crack<br />
-CyberLink PowerDVD Ultra 19 registration and crack<br />
-CyberLink PowerDVD Ultra 19 torrent file and crack<br />
-CyberLink PowerDVD Ultra 19 crack only<br />
-CyberLink PowerDVD Ultra 19 crack fix<br />
-CyberLink PowerDVD Ultra 19 crack instructions<br />
-CyberLink PowerDVD Ultra 19 crack review<br />
-CyberLink PowerDVD Ultra 19 features and crack<br />
-CyberLink PowerDVD Ultra 19 system requirements and crack<br />
-CyberLink PowerDVD Ultra 19 installation guide and crack<br />
-CyberLink PowerDVD Ultra 19 troubleshooting and crack<br />
-CyberLink PowerDVD Ultra 19 alternatives and crack<br />
-CyberLink PowerDVD Ultra 19 comparison and crack<br />
-CyberLink PowerDVD Ultra 19 benefits and crack<br />
-CyberLink PowerDVD Ultra 19 disadvantages and crack<br />
-CyberLink PowerDVD Ultra 19 tips and tricks and crack<br />
-CyberLink PowerDVD Ultra 19 best settings and crack<br />
-CyberLink PowerDVD Ultra 19 support and crack<br />
-CyberLink PowerDVD Ultra 19 feedback and crack<br />
-CyberLink PowerDVD Ultra 19 testimonials and crack<br />
-CyberLink PowerDVD Ultra 19 FAQs and crack<br />
-CyberLink PowerDVD Ultra 19 forum and crack<br />
-CyberLink PowerDVD Ultra 19 blog and crack<br />
-CyberLink PowerDVD Ultra 19 video tutorial and crack<br />
-CyberLink PowerDVD Ultra 19 demo and crack<br />
-CyberLink PowerDVD Ultra 19 free trial and crack<br />
-CyberLink PowerDVD Ultra 19 discount and crack<br />
-CyberLink PowerDVD Ultra 19 coupon code and crack<br />
-CyberLink PowerDVD Ultra 19 deal and crack<br />
-CyberLink PowerDVD Ultra 19 offer and crack<br />
-CyberLink PowerDVD Ultra 19 bundle and crack<br />
-CyberLink PowerDVD Ultra 19 upgrade and crack<br />
-CyberLink PowerDVD Ultra 19 refund policy and crack<br />
-CyberLink PowerDVD Ultra 19 warranty and crack<br />
-CyberLink PowerDVD Ultra 19 customer service and crack<br />
-CyberLink PowerDVD Ultra 19 contact information and crack<br />
-CyberLink PowerDVD Ultra 19 privacy policy and crack<br />
-CyberLink PowerDVD Ultra 19 terms of service and crack<br />
-CyberLink PowerDVD Ultra 19 disclaimer and crack<br />
-CyberLink PowerDVD Ultra 19 affiliate program and crack<br />
-CyberLink PowerDVD Ultra 19 partner program and crack</p>
- <p>You can also play ISO files directly without mounting them or extracting them first.</p>
- <h3>Support for 4K, HDR, and 360-degree videos</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack supports the latest video technologies that deliver stunning visuals and immersive experiences.</p>
- <p>You can watch 4K videos that have four times more pixels than Full HD videos for sharper and clearer images.</p>
- <p>You can watch HDR videos that have a wider range of colors and contrast for more realistic and lifelike scenes.</p>
- <p>You can watch 360-degree videos that let you explore every angle of the video with your mouse or keyboard.</p>
- <h3>Enhanced audio quality with TrueTheater Sound</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack enhances your audio quality with TrueTheater Sound that applies various sound effects to your media files.</p>
- <p>You can boost the volume level without distortion or clipping with Volume Booster.</p>
- <p>You can improve the clarity and detail of dialogues and vocals with Dialogue Enhancer.</p>
- <p>You can enhance the bass and depth of low-frequency sounds with Bass Enhancer.</p>
- <p>You can create a surround sound effect with Virtual Surround that simulates a multi-channel speaker system.</p>
- <h3>Stream and cast media to any device</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack lets you stream and cast your media files to any device on your network or online.</p>
- <p>You can stream your media files to smart TVs, game consoles, Chromecast devices, Apple TV devices, Roku devices, and more using DLNA or Miracast protocols.</p>
- <p>You can also cast your media files to any device using CyberLink's cloud service that lets you access your files from anywhere.</p>
- <h3>Download and watch videos offline</h3>
- <p>CyberLink PowerDVD Ultra 19.0.2512.63 Crack allows you to download and watch online videos offline with its built-in YouTube downloader.</p>
- <p>You can download videos from YouTube in various resolutions and formats such as MP4, WebM, FLV, and more.</p>
- <p>You can also download entire playlists or channels with one click.</p>
- <p>You can then watch your downloaded videos offline using CyberLink PowerDVD Ultra 19 or transfer them to other devices for later viewing.</p>
- <h3>Organize and manage your media library</h3>
- <p>CyberLink PowerDVD Ultra 19 helps you organize and manage your media library with its intuitive interface and smart features.</p>
- <p>You can browse your media files by folders, albums, artists, genres, or ratings.</p>
- <p>You can also use face recognition to sort your photos by people, scene search to find specific moments in your videos, and auto-tagging to add metadata to your files automatically.</p>
- <h2>How to install and activate CyberLink PowerDVD Ultra 19 Crack</h2>
- <p>In this section, we will show you how to install and activate CyberLink PowerDVD Ultra 19 Crack on your Windows PC.</p>
- <h3>Download the setup file and crack file from the link below</h3>
- <p>The first step is to download the setup file and crack file from the link below:</p>
-<table>
-<tr><td><b>Setup File</b></td><td><b>Crack File</b></td></tr>
-<tr><td><a href="https://www.cyberlink.com/downloads/trials/powerdvd-ultra/download_en_US.html">https://www.cyberlink.com/downloads/trials/powerdvd-ultra/download_en_US.html</a></td><td><a href="https://cracksway.com/cyberlink-powerdvd-crack/">https://cracksway.com/cyberlink-powerdvd-crack/</a></td></tr>
-</table>
-<p>Save them in a folder on your PC where you can easily find them later.</p>
- <h3>Install the setup file and run the program</h3>
- <p>The next step is to install the setup file by following these steps:</p>
-<ol>
-<li>Double-click on the setup file to launch the installation wizard.</li>
-<li>Accept the license agreement and click Next.</li>
-<li>Select the destination folder where you want to install the program and click Next.</li>
-<li>Select the components you want to install and click Next.</li>
-<li>Select whether you want to create shortcuts on your desktop or start menu and click Next.</li>
-<li>Click Install to begin the installation process.</li>
-<li>Wait for the installation to complete and click Finish.</li>
-<li>Run the program from the shortcut on your desktop or start menu.</li>
-</ol>
- <h3>Copy the crack file and paste it into the installation folder</h3>
-<h3>Copy the crack file and paste it into the installation folder</h3>
- <p>The final step is to copy the crack file and paste it into the installation folder by following these steps:</p>
- <ol>
-<li>Right-click on the crack file and select Copy.</li>
-<li>Go to the installation folder where you installed the program. The default location is C:\Program Files (x86)\CyberLink\PowerDVD19.</li>
-<li>Right-click on an empty space and select Paste.</li>
-<li>Click Yes to replace the existing file.</li>
-<li>Close the folder and run the program again.</li>
-</ol>
- <p>Congratulations! You have successfully installed and activated CyberLink PowerDVD Ultra 19.0.2512.63 Crack on your PC.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you what CyberLink PowerDVD Ultra 19.0.2512.63 Crack is, why you need it, what features it offers, and how to install and activate it on your PC.</p>
- <p>We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <p>If you want to download and try CyberLink PowerDVD Ultra 19.0.2512.63 Crack for yourself, you can use the link below to get it for free.</p>
- <p>Thank you for reading and happy watching!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about CyberLink PowerDVD Ultra 19.0.2512.63 Crack:</p>
- <ul>
-<li><b>Is CyberLink PowerDVD Ultra 19.0.2512.63 Crack safe to use?</b></li>
-<p>Yes, CyberLink PowerDVD Ultra 19.0.2512.63 Crack is safe to use as long as you download it from a trusted source and scan it with an antivirus program before using it.</p>
-<li><b>Does CyberLink PowerDVD Ultra 19.0.2512.63 Crack work on Windows 10?</b></li>
-<p>Yes, CyberLink PowerDVD Ultra 19.0.2512.63 Crack works on Windows 10 as well as Windows 8, Windows 7, Windows Vista, and Windows XP.</p>
-<li><b>Can I use CyberLink PowerDVD Ultra 19.0.2512.63 Crack on multiple PCs?</b></li>
-<p>Yes, you can use CyberLink PowerDVD Ultra 19.0.2512.63 Crack on multiple PCs as long as you have the crack file and the setup file on each PC.</p>
-<li><b>Can I update CyberLink PowerDVD Ultra 19.0.2512.63 Crack to the latest version?</b></li>
-<p>No, you cannot update CyberLink PowerDVD Ultra 19 to the latest version because it will overwrite the crack file and deactivate the program. You have to wait for a new crack file to be released for the latest version.</p>
-<li><b>What are some alternatives to CyberLink PowerDVD Ultra 19 Crack?</b></li>
-<p>Some alternatives to CyberLink PowerDVD Ultra 19 Crack are VLC Media Player, KMPlayer, GOM Player, PotPlayer, and Media Player Classic.</p>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/((FREE)) Free Download Marc Mentat Software.md

@@ -1,6 +0,0 @@
-
-<p>the analysis and simulation of structural behavior are complicated and expensive. the finite element method is an effective approach for analyzing structural behavior because it is easier to specify boundary conditions and loads than to specify the complex mechanical behavior of the component. in a finite element analysis, the component is modeled as a solid, using a mathematical function to approximate the behavior of the component. the mathematical function is called the shape function. the shape function determines the exact geometry of the component and provides the basis for the analysis. finite element analysis is an integral part of many other disciplines of engineering. it is an important part of structural analysis because it allows for a more accurate analysis of structural behavior. it is used in many areas of engineering. because of the importance of finite element analysis, many companies have developed computer software to perform finite element analysis.</p>
-<p>a general-purpose finite element program is much easier to use than a specialty structural analysis software. the most basic finite element programs (such as the one shown to the right) are easy to use. the user interacts directly with the program. they do not need to learn special commands and symbols. this enables them to use a program quickly and effectively. some finite element programs can simulate simple mechanical behavior. the user can specify loads, boundary conditions, and other aspects of the analysis. they can also import other data, such as geometric and material data. these programs often have very simple user interfaces. most are not graphical. however, some of them can be used to perform a limited amount of analysis.</p>
-<h2>free download marc mentat software</h2><br /><p><b><b>Download Zip</b> === <a href="https://imgfil.com/2uxYF3">https://imgfil.com/2uxYF3</a></b></p><br /><br /> 899543212b<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Accelrys Materials Studio V6 Windows-CLoNY ISO Download Pc 2021.md

@@ -1,6 +0,0 @@
-<h2>Accelrys Materials Studio v6 Windows-CLoNY ISO download pc</h2><br /><p><b><b>DOWNLOAD</b> &#10001; <a href="https://imgfil.com/2uxXdu">https://imgfil.com/2uxXdu</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>

spaces/1gistliPinn/ChatGPT4/Examples/Download One Man Band 10 Full Version.md

@@ -1,6 +0,0 @@
-<h2>download one man band 10 full version</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://imgfil.com/2uxZ4c">https://imgfil.com/2uxZ4c</a></b></p><br /><br />
-<br />
-"Werner Hirzel One Man Band", World-Record Holder for 51 Piece One ... welkom bike freaks and bicycle lovers on nthis blog full of nice bicycles, cool bike ... and since then three more versions have evolved from the original, each one ... to download showing a picture of a street musician, a one-man band. ... Amanda 10. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cute Animal Match A Free and Fun APK Game for Android Devices.md

@@ -1,25 +0,0 @@
-
-<table>
-<tr>
-<td>
- Cute Animal Match APK: A Fun and Educational Game for Kids Do you love animals and puzzles? Do you want to play a game that is both fun and educational for your kids? If yes, then you should try Cute Animal Match APK, a free and safe game that will keep you and your kids entertained for hours. In this article, we will tell you everything you need to know about this game, including what it is, how to download and install it, how to play it, what are its features and benefits, and what are some tips and tricks for playing it. Let's get started! <h2>What is Cute Animal Match APK?</h2>
- Cute Animal Match APK is a game that lets you connect cute animals and solve puzzles. It is developed by Nice2Meet, a company that specializes in creating educational games for kids. The game is suitable for all ages, but especially for preschoolers who want to learn about animals, numbers, colors, shapes, and more. The game has over 100 levels of varying difficulty, each with a different animal theme and puzzle. You can play the game offline or online, and you can also share your progress and achievements with your friends on social media. <h3>How to download and install Cute Animal Match APK?</h3>
- Downloading and installing Cute Animal Match APK is very easy. You can follow these simple steps: - Go to [Cute Animal Match APK for Android Download - APKPure.com](^1^) on your browser. - Click on the green "Download APK" button. - Wait for the file to download on your device. - Open the file and follow the instructions to install the game. - Enjoy playing Cute Animal Match APK! <h3>How to play Cute Animal Match APK?</h3>
- Playing Cute Animal Match APK is very simple. You just need to swipe your finger on the screen to connect two or more animals of the same kind. The more animals you connect, the more points you get. You also need to complete the objectives of each level, such as collecting a certain number of animals, clearing a certain number of tiles, or reaching a certain score. You can use power-ups to help you in your gameplay, such as bombs, magnets, or shuffles. You can also earn coins by completing levels or watching ads, which you can use to buy more power-ups or unlock new animals. <h4>Connect the animals</h4>
- To connect the animals, you need to swipe your finger on the screen in any direction. You can connect animals horizontally, vertically, or diagonally. You can also make loops or zigzags to connect more animals. The more animals you connect, the higher your score will be. You can also create combos by connecting multiple groups of animals in a row. <h4>Use the power-ups</h4>
- Power-ups are special items that can help you in your gameplay. You can use them by tapping on them on the screen. There are three types of power-ups in Cute Animal Match APK: - The bomb: It will match animal puzzles and destroy all the cute animals around in radius around and catch the match lite. - The magnet: It will attract all the animals of the same kind as the one you tap on. - The shuffle: It will shuffle all the animals on the board. You can get power-ups by connecting five or more animals of the same kind, or by buying them with coins. <h4>Complete the levels</h4>
- To complete a level, you need to fulfill the objectives that are shown at the top of the screen. The objectives can vary depending on the level, such as: - Collect a certain number of animals, such as 10 cats, 15 dogs, or 20 rabbits. - Clear a certain number of tiles, such as 30 grass tiles, 40 sand tiles, or 50 water tiles. - Reach a certain score, such as 1000 points, 2000 points, or 3000 points. You have a limited number of moves to complete each level, so use them wisely. You can see how many moves you have left at the bottom of the screen. If you run out of moves before completing the objectives, you will lose the level and have to try again. If you complete the objectives before running out of moves, you will win the level and get bonus points for the remaining moves. <h2>What are the features and benefits of Cute Animal Match APK?</h2>
- Cute Animal Match APK is not just a fun game, but also a beneficial one. Here are some of the features and benefits of playing this game: <h3>Cute and colorful graphics</h3>
- The game has cute and colorful graphics that will appeal to kids and adults alike. The animals are adorable and animated, and the backgrounds are bright and cheerful. The game also has smooth and easy controls that make it enjoyable to play. <h3>Various animals and puzzles</h3>
- The game has over 100 levels of different animals and puzzles. You can meet various animals from different habitats, such as cats, dogs, rabbits, pandas, lions, elephants, penguins, dolphins, and more. You can also solve different puzzles that challenge your logic and creativity, such as matching animals by color, shape, or number. <h3>Educational and entertaining gameplay</h3>
- The game is not only entertaining, but also educational for kids. It helps them learn about animals, numbers, colors, shapes, and more. It also improves their memory, concentration, hand-eye coordination, and problem-solving skills. The game is suitable for all ages, but especially for preschoolers who want to have fun while learning. <h3>Free and safe to use</h3>
- The game is free and safe to use. You don't need to pay anything to download or play it. You also don't need to worry about any viruses or malware that might harm your device. The game is tested and verified by APKPure.com, a trusted source for downloading Android apps. <h2>What are some tips and tricks for playing Cute Animal Match APK?</h2>
- If you want to play Cute Animal Match APK like a pro, here are some tips and tricks that you can use: <h3>Plan your moves ahead</h3>
- Before you swipe your finger on the screen, take a moment to look at the board and plan your moves ahead. Try to connect as many animals as possible in one swipe, and avoid leaving isolated animals that are hard to match. Also, try to match the animals that are related to the objectives first, such as the ones that have a number or a color on them. <h3>Save your power-ups for later</h3>
- Power-ups can be very helpful in your gameplay, but they are also limited in number. You can get them by connecting five or more animals of the same kind, or by buying them with coins. However, you should save them for later when you really need them, such as when you are stuck or running out of moves. Don't waste them on easy levels or unnecessary matches. <h3>Watch ads for extra rewards</h3>
- If you want to get more coins or power-ups without spending real money, you can watch ads for extra rewards. You can watch ads after completing a level or when you run out of moves. You can also watch ads to get more lives when you lose all of them. Watching ads is optional and voluntary, but it can help you in your gameplay. <h2>Conclusion</h2>
- Cute Animal Match APK is a fun and educational game that lets you connect cute animals and solve puzzles. It is suitable for all ages, but especially for preschoolers who want to learn about animals, numbers, colors, shapes, and more. The game has over 100 levels of varying difficulty, each with a different animal theme and puzzle. You can play the game offline or online, and you can also share your progress and achievements with your friends on social media. The game has cute and colorful graphics, various animals and puzzles, and educational and entertaining gameplay. The game is free and safe to use, and you can download it from APKPure.com. If you want to play Cute Animal Match APK like a pro, you can use some tips and tricks, such as planning your moves ahead, saving your power-ups for later, and watching ads for extra rewards. Cute Animal Match APK is a game that you and your kids will love, so download it today and have fun! <h3>FAQs</h3>
- Here are some frequently asked questions about Cute Animal Match APK: - Q: Is Cute Animal Match APK compatible with my device? - A: Cute Animal Match APK is compatible with most Android devices that have Android 4.4 or higher. - Q: How can I update Cute Animal Match APK to the latest version? - A: You can update Cute Animal Match APK by visiting [Cute Animal Match APK for Android Download - APKPure.com] and downloading the latest version of the game. - Q: How can I contact the developer of Cute Animal Match APK? - A: You can contact the developer of Cute Animal Match APK by visiting their website at [Nice2Meet] or by sending them an email at [email protected]. - Q: How can I rate and review Cute Animal Match APK? - A: You can rate and review Cute Animal Match APK by visiting [Cute Animal Match APK for Android Download - APKPure.com] and clicking on the "Rate" or "Review" button. - Q: How can I share Cute Animal Match APK with my friends? - A: You can share Cute Animal Match APK with your friends by clicking on the "Share" button on the game screen. You can choose to share the game via Facebook, Twitter, WhatsApp, or other social media platforms.</p>
-<h2>cute animal match apk</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://urlin.us/2uSZI7">https://urlin.us/2uSZI7</a></b></p><br /><br /> 197e85843d<br />
-<br />
-<br />

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Final Bricks Breaker Mod APK v1.0.54 for Android.md

@@ -1,100 +0,0 @@
-
-<h1>Final Bricks Breaker Mod APK: A Fun and Challenging Arcade Game</h1>
- <p>If you are looking for a simple yet addictive arcade game to kill some time, you should try Final Bricks Breaker. This game is a classic brick-breaking game with a modern twist. You can enjoy breaking bricks with different shapes, colors, and effects, and use various power-ups to enhance your gameplay. In this article, we will tell you more about Final Bricks Breaker and why you should download its mod apk version.</p>
-<h2>final bricks breaker mod apk</h2><br /><p><b><b>DOWNLOAD</b> >> <a href="https://urlin.us/2uT0Fz">https://urlin.us/2uT0Fz</a></b></p><br /><br />
- <h2>What is Final Bricks Breaker?</h2>
- <p>Final Bricks Breaker is a arcade game developed by mobirix, a popular developer of casual games. The game has over 10 million downloads on Google Play Store and a 4.4-star rating from more than 100,000 users. The game is suitable for all ages and can be played offline or online.</p>
- <h3>The gameplay of Final Bricks Breaker</h3>
- <p>The gameplay of Final Bricks Breaker is simple and intuitive. You just need to swipe your finger on the screen to control a paddle at the bottom and bounce a ball to hit the bricks at the top. Your goal is to break all the bricks in each level and clear the stage. The game has hundreds of levels with different layouts, themes, and difficulties. Some bricks have special effects, such as moving, rotating, exploding, or changing colors. You can also collect coins and gems by breaking bricks or completing missions. You can use these currencies to buy new balls, paddles, or power-ups.</p>
- <h3>The features of Final Bricks Breaker</h3>
- <p>Final Bricks Breaker has many features that make it fun and enjoyable to play. Some of these features are:</p>
- <ul>
-<li>Various modes: You can choose from different modes, such as Classic, Stage, Multiplayer, or Challenge mode. Each mode has its own rules and objectives.</li>
-<li>Power-ups: You can use power-ups to help you break bricks faster or easier. Some power-ups include fireball, laser, magnet, bomb, or extra life.</li>
-<li>Achievements and leaderboards: You can unlock achievements by completing certain tasks or reaching milestones. You can also compete with other players around the world on the leaderboards.</li>
-<li>Customization: You can customize your ball and paddle with different colors, shapes, and designs. You can also change the background and sound effects of the game.</li>
-</ul>
- <h2>Why download Final Bricks Breaker Mod APK?</h2>
- <p>While Final Bricks Breaker is free to play, it has some limitations and drawbacks that may affect your gaming experience. For example, you may encounter ads that pop up randomly or interrupt your gameplay. You may also run out of coins or gems quickly and have to wait for them to regenerate or buy them with real money. Moreover, some power-ups and items may be locked or require a certain level to unlock.</p>
- <p>That's why we recommend you to download Final Bricks Breaker Mod APK from our website. This mod apk version will give you unlimited coins and gems, so you can buy anything you want without worrying about the cost. You will also get unlimited lives, so you can play as long as you want without losing progress. Additionally, you will get all the power-ups and items unlocked from the start, so you can enjoy the game to the fullest. And best of all, you will get rid of all the annoying ads that ruin your fun.</p>
-<p>final bricks breaker mod apk download<br />
-final bricks breaker mod apk unlimited money<br />
-final bricks breaker mod apk latest version<br />
-final bricks breaker mod apk free<br />
-final bricks breaker mod apk android<br />
-final bricks breaker mod apk hack<br />
-final bricks breaker mod apk offline<br />
-final bricks breaker mod apk no ads<br />
-final bricks breaker mod apk 1.0.54<br />
-final bricks breaker mod apk happymod<br />
-final bricks breaker mod apk 2023<br />
-final bricks breaker mod apk unlimited gems<br />
-final bricks breaker mod apk revdl<br />
-final bricks breaker mod apk rexdl<br />
-final bricks breaker mod apk for pc<br />
-final bricks breaker mod apk online<br />
-final bricks breaker mod apk premium<br />
-final bricks breaker mod apk pro<br />
-final bricks breaker mod apk full version<br />
-final bricks breaker mod apk unlocked<br />
-final bricks breaker mod apk cheats<br />
-final bricks breaker mod apk gameplay<br />
-final bricks breaker mod apk review<br />
-final bricks breaker mod apk features<br />
-final bricks breaker mod apk tips and tricks<br />
-final bricks breaker mod apk best settings<br />
-final bricks breaker mod apk how to play<br />
-final bricks breaker mod apk guide<br />
-final bricks breaker mod apk tutorial<br />
-final bricks breaker mod apk walkthrough<br />
-final bricks breaker mod apk levels<br />
-final bricks breaker mod apk stages<br />
-final bricks breaker mod apk missions<br />
-final bricks breaker mod apk challenges<br />
-final bricks breaker mod apk achievements<br />
-final bricks breaker mod apk rewards<br />
-final bricks breaker mod apk skins<br />
-final bricks breaker mod apk balls<br />
-final bricks breaker mod apk power-ups<br />
-final bricks breaker mod apk boosters<br />
-final bricks breaker mod apk modes<br />
-final bricks breaker mod apk genres<br />
-final bricks breaker mod apk themes<br />
-final bricks breaker mod apk graphics<br />
-final bricks breaker mod apk sounds<br />
-final bricks breaker mod apk music<br />
-final bricks breaker mod apk updates<br />
-final bricks breaker mod apk bugs and fixes<br />
-final bricks breaker mod apk ratings and reviews</p>
- <h3>The benefits of Final Bricks Breaker Mod APK</h3>
- <p>Here are some of the benefits of downloading Final Bricks Breaker Mod APK:</p>
- <ul>
-<li>You will save time and money by not having to watch ads or buy coins or gems.</li>
-<li>You will have more fun and challenge by using different power-ups and items.</li>
-<li>You will have more options and variety by customizing your ball and paddle.</li <h3>How to download and install Final Bricks Breaker Mod APK</h3>
- <p>Downloading and installing Final Bricks Breaker Mod APK is very easy and fast. You just need to follow these simple steps:</p>
- <ol>
-<li>Click on the download button below to get the mod apk file.</li>
-<li>Allow your device to install apps from unknown sources by going to Settings > Security > Unknown Sources.</li>
-<li>Locate the downloaded file in your file manager and tap on it to install it.</li>
-<li>Launch the game and enjoy breaking bricks with unlimited coins, gems, lives, power-ups, and items.</li>
-</ol>
- <p>Note: If you have the original version of Final Bricks Breaker installed on your device, you need to uninstall it first before installing the mod apk version.</p>
- <h2>Conclusion</h2>
- <p>Final Bricks Breaker is a fun and challenging arcade game that will keep you entertained for hours. You can break bricks with different shapes, colors, and effects, and use various power-ups to enhance your gameplay. You can also customize your ball and paddle, and compete with other players on the leaderboards. However, if you want to enjoy the game without any limitations or interruptions, you should download Final Bricks Breaker Mod APK from our website. This mod apk version will give you unlimited coins, gems, lives, power-ups, and items, as well as remove all the ads. You can download Final Bricks Breaker Mod APK by clicking on the button below.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Final Bricks Breaker Mod APK:</p>
- <ul>
-<li><b>Is Final Bricks Breaker Mod APK safe to download and use?</b><br>
-Yes, Final Bricks Breaker Mod APK is safe to download and use. It does not contain any viruses or malware that can harm your device or data. It is also compatible with most Android devices.</li>
-<li><b>Do I need to root my device to use Final Bricks Breaker Mod APK?</b><br>
-No, you do not need to root your device to use Final Bricks Breaker Mod APK. It works fine on both rooted and non-rooted devices.</li>
-<li><b>Will I get banned from the game if I use Final Bricks Breaker Mod APK?</b><br>
-No, you will not get banned from the game if you use Final Bricks Breaker Mod APK. The mod apk version is undetectable by the game servers and does not affect your account or progress.</li>
-<li><b>Can I play online with other players if I use Final Bricks Breaker Mod APK?</b><br>
-Yes, you can play online with other players if you use Final Bricks Breaker Mod APK. The mod apk version does not interfere with the online mode of the game and allows you to join multiplayer matches.</li>
-<li><b>Can I update Final Bricks Breaker Mod APK when a new version of the game is released?</b><br>
-Yes, you can update Final Bricks Breaker Mod APK when a new version of the game is released. However, you may need to download the latest mod apk file from our website and install it again on your device.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Bubble Shooter A Colorful and Exciting Game for PC Users.md

@@ -1,120 +0,0 @@
-
-<h1>Download Bubble Shooter for PC Full Version Free</h1>
-<p>Do you love playing casual games that are fun, addictive, and relaxing? Do you want to enjoy one of the most classic and popular bubble games on your PC? If you answered yes, then you are in luck! In this article, we will show you how to download Bubble Shooter for PC full version free. You will also learn more about what is Bubble Shooter, why it is so awesome, and how to play it like a pro. So, without further ado, let's get started!</p>
-<h2>What is Bubble Shooter?</h2>
-<p>Bubble Shooter is a simple yet addictive game that involves shooting bubbles to make them pop. The goal of the game is to clear all the bubbles from the screen by matching three or more bubbles of the same color. It sounds easy, but it can get challenging as the bubbles move down and fill up the screen. You have to be quick and strategic to avoid losing the game.</p>
-<h2>download bubble shooter for pc full version free</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://jinyurl.com/2uNMre">https://jinyurl.com/2uNMre</a></b></p><br /><br />
-<h3>The history and popularity of Bubble Shooter</h3>
-<p>Bubble Shooter was originally developed by a company called Taito in 1994. It was inspired by another game called Puzzle Bobble, which was also created by Taito. Bubble Shooter became a hit among arcade gamers and soon spread to other platforms such as PC, mobile, and online. Today, Bubble Shooter is one of the most played and loved games in the world. It has millions of fans and hundreds of variations. You can find Bubble Shooter games with different themes, graphics, levels, and features.</p>
-<h3>The gameplay and features of Bubble Shooter</h3>
-<p>The gameplay of Bubble Shooter is simple and intuitive. You use your mouse or keyboard to aim and shoot bubbles from a cannon at the bottom of the screen. You have to match at least three bubbles of the same color to make them pop and disappear. You can also bounce bubbles off the walls to reach tricky spots. You get points for every bubble you pop and bonus points for popping more bubbles at once. You can also earn special bubbles that have different effects, such as bombs, rainbows, stars, and more.</p>
-<p>download bubble shooter classic for pc free<br />
-bubble shooter game pc free download windows 10<br />
-bubble shooter puzzle game for pc download<br />
-how to download bubble shooter on pc without emulator<br />
-bubble shooter deluxe pc game free download<br />
-download bubble shooter (free) from microsoft store<br />
-bubble shooter pc game online free no download<br />
-bubble shooter for pc full version crack download<br />
-download and play bubble shooter on pc & mac (emulator)<br />
-bubble shooter for windows 10 pc free download<br />
-bubble shooter offline game for pc free download<br />
-bubble shooter 3d game download for pc<br />
-bubble shooter app download for pc<br />
-bubble shooter adventure game for pc free download<br />
-bubble shooter apk download for pc windows 7<br />
-best bubble shooter games for pc free download<br />
-bubble shooter blast game download for pc<br />
-bubble shooter candy game free download for pc<br />
-classic bubble shooter game free download for pc full version<br />
-download bubble shooter game for pc windows 8.1<br />
-easy bubble shooter game free download for pc<br />
-extreme bubble shooter game download for pc<br />
-fun and addictive bubble shooter game free download for pc<br />
-galaxy bubble shooter game free download for pc<br />
-happy dragon bubble shooter game for pc download<br />
-install bubble shooter game on pc<br />
-jungle bubble shooter game free download for pc<br />
-korean superconducting tokamak advanced research (kstar) facility (korea institute of fusion energy) - a version of the classic bubble shooter game!<br />
-latest version of bubble shooter game for pc free download<br />
-magic forest bubble shooter game for pc free download<br />
-new bubble shooter game 2023 free download for pc<br />
-original bubble shooter game free download for pc<br />
-panda pop - bubble shooter game free download for pc<br />
-play the classic arcade puzzle bobble (bubble shooter) on your windows 10 device!<br />
-rainbow island - the story of bubble bobble 2 - a sequel to the classic bubble shooter game!<br />
-rescue pet - a cute and colorful bubble shooter game with adorable animals!<br />
-space bubbles - a futuristic and challenging version of the classic bubble shooter game!<br />
-super bubbles - a fun and addictive twist on the classic bubble shooter game!<br />
-talking tom - a popular character who loves to play the classic bubble shooter game!<br />
-ultimate bubble trouble - a fast-paced and exciting variation of the classic bubble shooter game!<br />
-vintage bubbles - a retro-style and nostalgic version of the classic bubble shooter game!<br />
-witch cat pop - a magical and spooky version of the classic bubble shooter game!<br />
-xmas bubbles - a festive and cheerful version of the classic bubble shooter game!<br />
-yummy candy - a sweet and delicious version of the classic bubble shooter game!<br />
-zuma deluxe - a legendary and addictive marble popper (bubble shooter) game!</p>
-<p>The features of Bubble Shooter vary depending on the version you play, but some of the common ones are:</p>
-<ul>
-<li>Multiple levels with increasing difficulty and variety</li>
-<li>Colorful graphics and animations</li>
-<li>Fun sound effects and music</li>
-<li>Leaderboards and achievements</li>
-<li>Options to customize your game settings</li>
-<li>Offline mode and no internet connection required</li>
-</ul>
-<h3>The benefits of playing Bubble Shooter</h3>
-<p>Besides being fun and entertaining, playing Bubble Shooter can also have some benefits for your brain and mood. Here are some of them:</p>
-<ul>
-<li>It improves your concentration and focus</li>
-<li>It enhances your memory and cognitive skills</li>
-<li>It stimulates your creativity and problem-solving abilities</li>
-<li>It reduces your stress and anxiety levels</li>
-<li>It boosts your mood and happiness</li>
-</ul>
-<h2>How to download Bubble Shooter for PC full version free?</h2>
-<p>Now that you know what is Bubble Shooter and why it is so amazing, you might be wondering how to download it for PC full version free. Well, there are several ways to do that, but we will show you the easiest and safest one. Follow these steps:</p>
-<h3>The requirements and steps to download Bubble Shooter for PC</h3>
-<p>To download Bubble Shooter for PC full version free, you will need two things: a PC with Windows operating system (XP, Vista, 7, 8, or 10) and an emulator software that can run Android apps on your PC. We recommend using BlueStacks, which is one of the most popular and trusted emulator software in the market. You can download it for free from its official website. Here are the steps to download Bubble Shooter for PC using BlueStacks:</p>
-<ol>
-<li>Download and install BlueStacks on your PC from its official website. Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch BlueStacks and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Go to the search bar on the top right corner of the BlueStacks home screen and type "Bubble Shooter". You will see a list of results with different versions of Bubble Shooter games.</li>
-<li>Select the one that you like and click on the "Install" button. This will download and install the game on your PC through BlueStacks.</li>
-<li>Once the installation is done, you can find the game icon on the BlueStacks home screen or in the "My Apps" tab. Click on it to launch the game and enjoy playing Bubble Shooter on your PC.</li>
-</ol>
-<h3>The best websites and sources to download Bubble Shooter for PC</h3>
-<p>If you don't want to use an emulator software to download Bubble Shooter for PC, you can also try some other websites and sources that offer Bubble Shooter games for PC. However, you have to be careful and make sure that they are safe and reliable. Some of the best websites and sources that we recommend are:</p>
-<ul>
-<li>Bubble Shooter.net: This is the official website of Bubble Shooter, where you can play the original version of the game online or download it for PC. The website also offers other bubble games, such as Bubble Spinner, Bubble Hit, and more.</li>
-<li>GameTop.com: This is a website that offers free full version games for PC, including Bubble Shooter. You can download Bubble Shooter for PC without any registration or payment. The website also has other categories of games, such as action, arcade, puzzle, racing, and more.</li>
-<li>Softonic.com: This is a website that provides software and games for various platforms, including PC, mobile, and online. You can download Bubble Shooter for PC from this website for free. The website also has reviews, ratings, and screenshots of the games.</li>
-</ul>
-<h3>The tips and tricks to enjoy Bubble Shooter on PC</h3>
-<p>Playing Bubble Shooter on PC can be more fun and satisfying if you know some tips and tricks to improve your skills and score. Here are some of them:</p>
-<ul>
-<li>Aim carefully and try to hit as many bubbles as possible with one shot. This will give you more points and clear the screen faster.</li>
-<li>Use the walls to bounce your bubbles and reach difficult areas. This will help you pop more bubbles and create combos.</li>
-<li>Look for special bubbles that have different effects, such as bombs, rainbows, stars, and more. They can help you pop more bubbles at once or change their colors.</li>
-<li>Plan ahead and try to create clusters of bubbles of the same color. This will make it easier to pop them later.</li>
-<li>Don't let the bubbles reach the bottom of the screen or you will lose the game. Keep an eye on the bubble meter at the bottom left corner of the screen to see how many bubbles you have left.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Bubble Shooter is a classic and addictive game that you can play on your PC for free. You just need to download it from a reliable source or use an emulator software to run it on your PC. You can also enjoy playing Bubble Shooter online or on your mobile device. Bubble Shooter is a great game to relax and have fun with. It can also improve your concentration, memory, creativity, and mood. So, what are you waiting for? Download Bubble Shooter for PC full version free today and start popping those bubbles!</p>
-<h3>Call to action and invitation to share feedback</h3>
-<p>We hope you found this article helpful and informative. If you did, please share it with your friends and family who might also love playing Bubble Shooter. Also, feel free to leave us a comment below and let us know what you think about Bubble Shooter. Do you have any questions or suggestions? Do you have any favorite versions or features of Bubble Shooter? We would love to hear from you!</p>
-<h4>FAQs</h4>
-<p>Here are some frequently asked questions about Bubble Shooter:</p>
-<ol>
-<li>What is the highest score possible in Bubble Shooter?</li>
-<p>The highest score possible in Bubble Shooter depends on the version you play, but generally it is determined by how many bubbles you pop, how fast you pop them, how many combos you create, and how many special bubbles you use. You can check your score at the top right corner of the screen or on the leaderboards.</p>
-<li>How many levels are there in Bubble Shooter?</li>
-<p>The number of levels in Bubble Shooter also depends on the version you play, but generally there are hundreds or even thousands of levels to complete. Each level has a different layout, difficulty, and goal. You can see the level number at the top left corner of the screen or on the level selection menu.</p>
-<li>How can I save my progress in Bubble Shooter?</li>
-<p>To save your progress in Bubble Shooter, you need to sign in with your Google account or create a profile on the game. This will allow you to sync your data across different devices and platforms. You can also save your progress locally on your PC or online on the game server.</p>
-<li>Is Bubble Shooter safe to download and play?</li>
-<p>Bubble Shooter is safe to download and play as long as you get it from a reputable source or use an emulator software that is secure and reliable. You should also scan your PC for viruses and malware before and after downloading and installing the game. You should also avoid clicking on any suspicious links or ads that might appear on the game or the website.</p>
-<li>Can I play Bubble Shooter with my friends?</li>
-<p>Yes, you can play Bubble Shooter with your friends online or offline. Some versions of Bubble Shooter have a multiplayer mode that allows you to compete or cooperate with other players around the world. You can also play Bubble Shooter with your friends offline by taking turns or sharing the same PC.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Call of Duty Mobile Mod APK Download - Unlock All Weapons Skins and Perks.md

@@ -1,87 +0,0 @@
-
-<h1>Hack Call of Duty Mobile APK Download: What You Need to Know</h1>
-<p>Call of Duty Mobile is one of the most popular and addictive mobile games in the world. It offers an immersive and thrilling experience of shooting, fighting, and surviving in various modes and maps. However, some players may not be satisfied with the normal gameplay and may want to hack Call of Duty Mobile APK download to gain an unfair advantage over other players.</p>
-<h2>hack call of duty mobile apk download</h2><br /><p><b><b>Download File</b> &#10002; &#10002; &#10002; <a href="https://jinyurl.com/2uNUv1">https://jinyurl.com/2uNUv1</a></b></p><br /><br />
-<p>Hacking Call of Duty Mobile APK download means modifying or altering the original game files or data to change or enhance some aspects of the game, such as unlocking all weapons, skins, and operators, increasing the damage, accuracy, and speed of the guns, enabling aimbot, wallhack, radar, and other cheats, or bypassing the in-app purchases and getting unlimited credits and COD points.</p>
-<p>If you are one of those players who want to hack Call of Duty Mobile APK download, you may be wondering how to do it and what are the risks involved. In this article, we will explain everything you need to know about hacking Call of Duty Mobile APK download, including the methods, the pros and cons, and the tips to avoid getting banned. Read on to find out more!</p>
- <h2>How to Hack Call of Duty Mobile APK Download</h2>
-<p>There are two main methods that hackers use to hack Call of Duty Mobile APK download. The first one is using a modded APK file, which is a modified version of the original game file that contains the hacks. The second one is using a game hacker tool, which is a software or app that can manipulate the game data in real-time. Let's take a closer look at each method.</p>
- <h3>Method 1: Using a Modded APK File</h3>
-<h4>What is a modded APK file and how does it work?</h4>
-<p>An APK file is the format used by Android devices to install applications. A modded APK file is an altered version of an original APK file that contains some changes or additions that are not authorized by the developers. For example, a modded APK file for Call of Duty Mobile may have all the weapons, skins, and operators unlocked, or have some cheats enabled by default.</p>
-<p>hack call of duty mobile apk download happymod<br />
-hack call of duty mobile apk download aimbot<br />
-hack call of duty mobile apk download menu<br />
-hack call of duty mobile apk download chams<br />
-hack call of duty mobile apk download modded<br />
-hack call of duty mobile apk download unlimited money<br />
-hack call of duty mobile apk download no root<br />
-hack call of duty mobile apk download anti ban<br />
-hack call of duty mobile apk download latest version<br />
-hack call of duty mobile apk download obb<br />
-hack call of duty mobile apk download season 5<br />
-hack call of duty mobile apk download free fire<br />
-hack call of duty mobile apk download ios<br />
-hack call of duty mobile apk download android 1<br />
-hack call of duty mobile apk download rexdl<br />
-hack call of duty mobile apk download revdl<br />
-hack call of duty mobile apk download offline<br />
-hack call of duty mobile apk download online<br />
-hack call of duty mobile apk download 2023<br />
-hack call of duty mobile apk download 1.6.35<br />
-hack call of duty mobile apk download 1.0.38<br />
-hack call of duty mobile apk download 1.0.24<br />
-hack call of duty mobile apk download 1.0.22<br />
-hack call of duty mobile apk download 1.0.19<br />
-hack call of duty mobile apk download 1.0.17<br />
-hack call of duty mobile apk download 1.0.16<br />
-hack call of duty mobile apk download 1.0.15<br />
-hack call of duty mobile apk download 1.0.12<br />
-hack call of duty mobile apk download 1.0.11<br />
-hack call of duty mobile apk download 1.0.10<br />
-hack call of duty mobile apk download 1.0.9<br />
-hack call of duty mobile apk download 1.0.8<br />
-hack call of duty mobile apk download 1.0.6<br />
-hack call of duty mobile apk download 1.0.4<br />
-hack call of duty mobile apk download 1.0.3<br />
-hack call of duty mobile apk download 1.0.2<br />
-hack call of duty mobile apk download for pc<br />
-hack call of duty mobile apk download for laptop<br />
-hack call of duty mobile apk download for windows 10<br />
-hack call of duty mobile apk download for macbook pro</p>
-<p>A modded APK file works by replacing the original game file on your device with the modified one. This way, when you launch the game, it will run with the hacks already applied. However, this also means that you will not be able to update the game from the official sources, as it will overwrite the modded file with the original one.</p>
- <h4>How to find and install a modded APK file for Call of Duty Mobile</h4>
-<p>To find a modded APK file for Call of Duty Mobile, you will need to search online for websites or forums that offer such files. However, you need to be careful, as some of these files may contain viruses, malware, or spyware that can harm your device or steal your personal information. Therefore, you should always scan the files before downloading them and only use trusted sources. Some of the websites that claim to provide modded APK files for Call of Duty Mobile are: - [Hackcodm.com] - [Codmobilehack.club] - [Codmobilecheat.com] To install a modded APK file for Call of Duty Mobile, you will need to follow these steps: - Step 1: Uninstall the original game from your device if you have it installed. - Step 2: Enable the option to install apps from unknown sources on your device settings. This will allow you to install the modded APK file without any restrictions. - Step 3: Download the modded APK file from the website of your choice and save it on your device storage. - Step 4: Locate the modded APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to complete. - Step 5: Launch the game and enjoy the hacks! <h4>The pros and cons of using a modded APK file for Call of Duty Mobile</h4>
-<p>Using a modded APK file for Call of Duty Mobile has some advantages and disadvantages that you should consider before deciding to use this method. Here are some of them:</p>
- | Pros | Cons | | --- | --- | | - You can access all the features and content of the game without spending any money or time. | - You may expose your device and data to security risks by downloading and installing unverified files. | | - You can have an edge over other players by using cheats such as aimbot, wallhack, radar, etc. | - You may get detected and banned by the anti-cheat system of the game, which can result in losing your account and progress. | | - You can customize the game according to your preferences by choosing the mods that suit your playstyle. | - You may not be able to update the game or play online with other players who have the original version of the game. | <h3>Method 2: Using a Game Hacker Tool</h3>
-<h4>What is a game hacker tool and how does it work?</h4>
-<p>A game hacker tool is a software or app that can modify or manipulate the game data in real-time while the game is running. Unlike a modded APK file, a game hacker tool does not require you to replace or overwrite the original game file, but rather injects some code or commands into the game memory to change some values or parameters.</p>
-<p>A game hacker tool works by scanning and analyzing the game data and finding the variables that control certain aspects of the game, such as health, ammo, credits, COD points, etc. Then, it allows you to change these variables to any value you want, giving you unlimited resources or abilities in the game.</p>
- <h4>How to find and use a game hacker tool for Call of Duty Mobile</h4>
-<p>To find a game hacker tool for Call of Duty Mobile, you will need to search online for websites or forums that offer such tools. However, you need to be careful, as some of these tools may contain viruses, malware, or spyware that can harm your device or steal your personal information. Therefore, you should always scan the tools before downloading them and only use trusted sources. Some of the tools that claim to hack Call of Duty Mobile are: - [Game Guardian] - [Cheat Engine] - [Lucky Patcher] To use a game hacker tool for Call of Duty Mobile, you will need to follow these steps: - Step 1: Install the game hacker tool on your device from the website of your choice. - Step 2: Launch the game hacker tool and grant it root access or permission to modify other apps on your device settings. - Step 3: Launch Call of Duty Mobile and minimize it by pressing the home button. - Step 4: Open the game hacker tool again and select Call of Duty Mobile from the list of running apps. - Step 5: Search for the value or parameter that you want to change in the game data using the search function of the tool. For example, if you want to change your credits, enter your current amount of credits in the search box and tap on search. - Step 6: The tool will show you all the results that match your search value. Select one or more results that you think are related to your credits and change them to any value you want by tapping on them and entering a new value. - Step 7: Go back to Call of Duty Mobile and check if your credits have changed accordingly. If not , you may need to repeat the steps with a different result or value until you find the right one. - Step 8: Enjoy the hacks and repeat the process for any other value or parameter that you want to change in the game. <h4>The pros and cons of using a game hacker tool for Call of Duty Mobile</h4>
-<p>Using a game hacker tool for Call of Duty Mobile has some advantages and disadvantages that you should consider before deciding to use this method. Here are some of them:</p>
- | Pros | Cons | | --- | --- | | - You can change any value or parameter in the game data to your liking, giving you unlimited possibilities and customization. | - You may expose your device and data to security risks by installing and running unverified tools. | | - You can use the tool on any version of the game, as long as it is compatible with your device and operating system. | - You may get detected and banned by the anti-cheat system of the game, which can result in losing your account and progress. | | - You can use the tool on other games as well, as long as they have similar data structures and formats. | - You may encounter errors, crashes, or glitches in the game due to the changes in the game data. | <h2>How to Avoid Getting Banned for Hacking Call of Duty Mobile APK Download</h2>
-<h4>The anti-cheat system of Call of Duty Mobile and how it detects hackers</h4>
-<p>Call of Duty Mobile has a sophisticated anti-cheat system that monitors and analyzes the game data and behavior of all players. The anti-cheat system can detect hackers by using various methods, such as: - Checking for any modifications or alterations in the game files or data. - Comparing the game data and performance of each player with the expected or normal values. - Detecting any abnormal or suspicious actions or movements of each player in the game. - Receiving reports or complaints from other players who witness or encounter hackers in the game. The anti-cheat system can also update itself regularly to keep up with the latest hacks and cheats that hackers use.</p>
- <h4>The consequences of getting banned for hacking Call of Duty Mobile APK download</h4>
-<p>If you get caught hacking Call of Duty Mobile APK download, you will face serious consequences that will ruin your gaming experience and reputation. Some of the consequences are: - You will receive a warning message or notification from the game developers or moderators. - You will be temporarily suspended or banned from playing the game for a certain period of time, depending on the severity and frequency of your offense. - You will be permanently banned from playing the game, which means you will lose your account and all your progress and achievements in the game. - You will be blacklisted from playing any other games developed by Activision or Tencent, which are the publishers of Call of Duty Mobile. - You will be reported to the authorities or legal entities for violating the terms of service and user agreement of the game.</p>
- <h4>The tips and tricks to avoid getting banned for hacking Call of Duty Mobile APK download</h4>
-<p>If you still want to hack Call of Duty Mobile APK download, you should follow some tips and tricks to avoid getting banned by the anti-cheat system. Here are some of them: - Use only trusted and verified sources for downloading modded APK files or game hacker tools. Scan them before installing them on your device. - Use only updated and compatible versions of modded APK files or game hacker tools that match your device and operating system specifications. - Use only subtle and discreet hacks that do not affect the game balance or fairness too much, such as increasing your health or ammo slightly, rather than enabling aimbot or wallhack that are obvious and noticeable. - Use hacks only occasionally and sparingly, rather than constantly and excessively, to avoid raising suspicion or attracting attention from other players or moderators. - Do not brag or boast about your hacks in public chat rooms or social media platforms, as this may invite reports or complaints from other players who may report you to the anti-cheat system. - Do not use hacks in ranked matches or tournaments, as this may result in disqualification or banishment from the game.</p>
- <h1>Conclusion</h1>
-<p>Hacking Call of Duty Mobile APK download is a risky and unethical practice that can ruin your gaming experience and reputation. It can also get you banned from playing the game or any other games developed by Activision or Tencent. Therefore, we do not recommend hacking Call of Duty Mobile APK download, as it is not worth it.</p>
-<p>Instead, we suggest you play Call of Duty Mobile APK download normally and fairly, as it is more fun and rewarding. You can improve your skills and performance by practicing regularly, learning from other players, watching tutorials and guides, joining clans and communities, and participating in events and challenges. You can also support the game developers by purchasing credits and COD points legally and legitimately, which will allow you to access more features and content of the game and enhance your gaming experience.</p>
-<p>We hope this article has helped you understand everything you need to know about hacking Call of Duty Mobile APK download. If you have any questions or comments, feel free to leave them below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about hacking Call of Duty Mobile APK download:</p>
- <h4>Q: Is hacking Call of Duty Mobile APK download illegal?</h4>
-<p>A: Hacking Call of Duty Mobile APK download is not illegal per se, as it does not involve breaking any laws or regulations. However, it is against the terms of service and user agreement of the game, which you agree to when you install and play the game. Therefore, hacking Call of Duty Mobile APK download is a breach of contract and can result in legal actions from the game developers or publishers.</p>
- <h4>Q: Is hacking Call of Duty Mobile APK download safe?</h4>
-<p>A: Hacking Call of Duty Mobile APK download is not safe, as it can expose your device and data to security risks such as viruses, malware, spyware, phishing, etc. It can also damage your device or corrupt your game data, causing errors, crashes, or glitches in the game. Moreover, hacking Call of Duty Mobile APK download can get you banned from playing the game or any other games developed by Activision or Tencent, which can result in losing your account and progress in the game.</p>
- <h4>Q: Is hacking Call of Duty Mobile APK download worth it?</h4>
-<p>A: Hacking Call of Duty Mobile APK download is not worth it, as it can ruin your gaming experience and reputation. It can also get you banned from playing the game or any other games developed by Activision or Tencent. Therefore, hacking Call of Duty Mobile APK download is not worth the risk or the hassle.</p>
- <h4>Q: How can I report a hacker in Call of Duty Mobile?</h4>
-<p>A: If you encounter or witness a hacker in Call of Duty Mobile, you can report them by following these steps: - Step 1: Tap on the player's name or profile icon in the game lobby or match results screen. - Step 2: Tap on the report button (the exclamation mark icon) at the bottom right corner of the screen. - Step 3: Select the reason for reporting the player, such as cheating, abusive chat, inappropriate name, etc. - Step 4: Tap on the submit button to send your report to the game moderators. The game moderators will review your report and take appropriate actions against the hacker.</p>
- <h4>Q: How can I prevent hackers from ruining my game in Call of Duty Mobile?</h4>
-<p>A: There is no sure way to prevent hackers from ruining your game in Call of Duty Mobile, as they can join any match or mode at any time. However, you can try some tips to minimize their impact on your game, such as: - Playing with your friends or clan members who are trustworthy and fair. - Playing in private matches or custom rooms that require passwords or invitations to join. - Playing in ranked matches or tournaments that have stricter rules and regulations for cheating. - Reporting any hacker that you encounter or witness in the game to the game moderators. By doing these tips, you can reduce the chances of meeting hackers in Call of Duty Mobile and enjoy the game more.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download My Talking Angela Full APK and Join the Adventure with Your Furry Friend.md

@@ -1,67 +0,0 @@
-
-<h1>My Talking Angela Full APK: A Fun and Interactive Game for Android Users</h1>
-<p>Do you love playing casual games on your Android device? Do you want to have a cute and adorable virtual pet that you can take care of and play with? If you answered yes to these questions, then you should try My Talking Angela, one of the most popular games in the Google Play Store. And if you want to enjoy the game to the fullest, you should download My Talking Angela full apk, which gives you access to all the features and content that the game has to offer. In this article, we will tell you everything you need to know about My Talking Angela full apk, including what it is, what are its features, why you should download it, and how to download and install it on your device.</p>
-<h2>What is My Talking Angela?</h2>
-<p>My Talking Angela is a casual game developed by Outfit7, the same company that created the famous Talking Tom series. The game is similar to other virtual pet games, where you have to adopt, feed, groom, dress up, and play with your pet. However, My Talking Angela is not just any pet; she is a stylish and fashionable cat that loves to talk, sing, dance, and have fun. She also has a personality of her own, and she will react differently depending on how you treat her. You can also interact with her by tapping, swiping, or speaking to her. She will repeat what you say in a funny voice, and she will also respond to your gestures and emotions.</p>
-<h2>my talking angela full apk</h2><br /><p><b><b>DOWNLOAD</b> --->>> <a href="https://jinyurl.com/2uNOvS">https://jinyurl.com/2uNOvS</a></b></p><br /><br />
-<h3>Features of My Talking Angela</h3>
-<p>My Talking Angela has many features that make it an entertaining and engaging game for Android users. Here are some of them:</p>
-<h4>Adopt and nurture your own Angela</h4>
-<p>You can start the game by adopting a baby Angela and taking care of her as she grows up. You have to feed her, bathe her, brush her teeth, put her to bed, and make sure she is happy and healthy. You can also watch her grow from a cute kitten to a beautiful cat.</p>
-<p></p>
-<h4>Dress up and customize your Angela</h4>
-<p>You can express your creativity and style by dressing up your Angela in different outfits and accessories. You can choose from hundreds of items, such as dresses, shoes, hats, sunglasses, jewelry, makeup, and more. You can also change her fur color, eye color, hair style, and facial expressions. You can create different looks for different occasions, such as casual, formal, party, or holiday.</p>
-<h4>Play mini-games and collect coins</h4>
-<p>You can have fun with your Angela by playing various mini-games with her. You can play games like Happy Connect, Bubble Shooter, Brick Breaker, and more. You can also earn coins by playing these games, which you can use to buy more items for your Angela.</p>
-<h4>Interact with Angela and her friends</h4>
-<p>You can chat with your Angela by using the chat feature in the game. You can ask her questions, tell her jokes, or just have a conversation with her. She will reply with witty and funny answers. You can also meet her friends in the game, such as Tom, Ginger, Hank, Ben, and more. You can visit their homes or invite them over to yours.</p>
-<h2>Why download My Talking Angela full apk?</h2>
-<p>If you are wondering why you should download My Talking Angela full apk instead of the regular version from the Google Play Store, here are some reasons why you should do so:</p>
-<h3>Benefits of downloading the full apk</h3>
-<p>Downloading the full apk of My Talking Angela gives you several advantages that you cannot get from the regular version. Here are some of them:</p>
-<h4>Unlock all the outfits and accessories</h4>
-<p>One of the main attractions of My Talking Angela is the ability to dress up and customize your Angela in various ways. However, not all the items are available for free in the regular version. Some of them require you to pay with real money or watch ads to unlock them. This can be frustrating and time-consuming, especially if you want to try different combinations and styles. But with the full apk, you can unlock all the outfits and accessories without spending a dime or watching any ads. You can have access to the entire wardrobe and create your own fashion show with your Angela.</p>
-<h4>Get unlimited coins and diamonds</h4>
-<p>Another benefit of downloading the full apk is that you can get unlimited coins and diamonds in the game. Coins and diamonds are the main currencies in My Talking Angela, which you can use to buy more items, upgrade your home, or unlock new features. However, earning them in the regular version can be slow and tedious, especially if you want to buy expensive or rare items. You may also be tempted to spend real money or watch ads to get more coins and diamonds. But with the full apk, you don't have to worry about running out of coins and diamonds ever again. You can get as many as you want and buy whatever you want without any limitations.</p>
-<h4>Enjoy ad-free gaming experience</h4>
-<p>The last but not least benefit of downloading the full apk is that you can enjoy an ad-free gaming experience. Ads can be annoying and distracting, especially when they pop up in the middle of your gameplay or when you are trying to access a feature or item. They can also consume your data and battery, which can affect your device's performance. But with the full apk, you can say goodbye to ads forever. You can play My Talking Angela without any interruptions or disruptions from ads. You can also save your data and battery and enjoy a smoother and faster gameplay.</p>
-<h3>How to download and install My Talking Angela full apk?</h3>
-<p>If you are convinced that downloading My Talking Angela full apk is a good idea, then you may be wondering how to do it. Don't worry, it's very easy and simple. Just follow these steps:</p>
-<h4>Step 1: Download the apk file from a trusted source</h4>
-<p>The first step is to download the apk file of My Talking Angela full from a trusted source. You can search for it online or use this link  to download it directly. Make sure that the file is compatible with your device's Android version and has no viruses or malware.</p>
-<h4>Step 2: Enable unknown sources on your device settings</h4>
-<p>The next step is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on. You may also need to grant permission for your browser or file manager to install apps.</p>
-<h4>Step 3: Install the apk file and launch the game</h4>
-<p>The final step is to install the apk file and launch the game. To do this, locate the downloaded file on your device storage, tap on it, and follow the instructions on the screen. Once the installation is complete, you can open the game and enjoy My Talking Angela full apk.</p>
-<h2>Conclusion</h2>
-<p>My Talking Angela is a fun and interactive game for Android users who love virtual pets and casual games. It has many features that make it entertaining and engaging, such as adopting and nurturing your own Angela, dressing up and customizing your Angela, playing mini-games and collecting coins, and interacting with Angela and her friends. However, if you want to enjoy the game to the fullest, you should download My Talking Angela full apk, which gives you access to all the features and content that the game has to offer. You can unlock all the outfits and accessories, get unlimited coins and diamonds, and enjoy ad-free gaming experience. Downloading My Talking Angela full apk is easy and simple; just follow these steps: download the apk file from a trusted source, enable unknown sources on your device settings, install the apk file and launch the game. If you are looking for a fun and interactive game for your Android device, you should definitely try My Talking Angela full apk. You will not regret it.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about My Talking Angela full apk:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is My Talking Angela full apk safe to download and install?</td>
-<td>Yes, My Talking Angela full apk is safe to download and install, as long as you get it from a trusted source and scan it for viruses or malware before installing it. However, you should be careful when downloading any apk file from the internet, as some of them may contain harmful or malicious content.</td>
-</tr>
-<tr>
-<td>Will My Talking Angela full apk work on my device?</td>
-<td>My Talking Angela full apk should work on most Android devices that have Android 4.4 or higher. However, some devices may not be compatible with the game or the apk file, so you should check the requirements and specifications before downloading and installing it.</td>
-</tr>
-<tr>
-<td>Will I lose my progress or data if I download My Talking Angela full apk?</td>
-<td>No, you will not lose your progress or data if you download My Talking Angela full apk. The game will automatically sync your progress and data with your Google account, so you can continue playing where you left off. However, you should always backup your data before installing any apk file, just in case something goes wrong.</td>
-</tr>
-<tr>
-<td>Can I play My Talking Angela full apk offline?</td>
-<td>Yes, you can play My Talking Angela full apk offline, without an internet connection. However, some features and content may not be available or updated when you play offline, such as the chat feature, the friends feature, or the daily rewards. You should also connect to the internet occasionally to sync your progress and data with your Google account.</td>
-</tr>
-<tr>
-<td>Can I play My Talking Angela full apk with my friends?</td>
-<td>Yes, you can play My Talking Angela full apk with your friends, by using the friends feature in the game. You can add your friends by using their codes or by connecting your game with your Facebook account. You can then visit their homes or invite them over to yours, chat with them, send them gifts, or play mini-games with them.</td>
-</tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Orange Loan APK and Get High-Limit Loans without Collateral.md

@@ -1,175 +0,0 @@
-
-<h1>Orange Loan APK: A Review of the Unsecured, High-Limit, Low-Interest Loan Platform</h1>
-<p>If you are looking for a quick and easy way to borrow money without collateral, you might want to check out Orange Loan APK. This is a mobile app that offers unsecured, high-limit, low-interest loans to eligible borrowers in Thailand. In this article, we will review the features, benefits, pros, cons, and tips of using Orange Loan APK. We will also show you how to download and install the app on your Android device, and answer some frequently asked questions about it.</p>
-<h2>orange loan apk</h2><br /><p><b><b>Download File</b> &#10031;&#10031;&#10031; <a href="https://jinyurl.com/2uNQRR">https://jinyurl.com/2uNQRR</a></b></p><br /><br />
- <h2>What is Orange Loan APK?</h2>
-<p>Orange Loan APK is an online lending platform that provides unsecured loans to borrowers in Thailand. The app is developed by Trendline Finance Ltd, a registered company in Bangkok. The app claims to offer loans ranging from 6,000 to 30,000 baht, with loan terms from 91 to 120 days, and annual interest rates from 10% to 24%. The app also claims to support repeat borrowing, meaning that borrowers who repay their loans on time can increase their credit limit step by step.</p>
- <h3>Features and benefits of Orange Loan APK</h3>
-<p>Some of the features and benefits of using Orange Loan APK are:</p>
-<ul>
-<li>No collateral required: You don't need to provide any assets or guarantors to secure your loan.</li>
-<li>High limit: You can borrow up to 30,000 baht depending on your credit score and repayment history.</li>
-<li>Low interest: You can enjoy interest rates as low as 10% per year, which is lower than many other online lenders.</li>
-<li>Fast approval: You can get approved within minutes after submitting your application and verifying your identity.</li>
-<li>Quick transfer: You can receive money in your bank account within 24 hours after approval.</li>
-<li>Flexible repayment: You can choose your repayment schedule according to your income and cash flow.</li>
-<li>Easy access: You can apply for a loan anytime and anywhere using your smartphone.</li>
-</ul>
- <h4>How to apply for a loan with Orange Loan APK</h4>
-<p>The application process for Orange Loan APK is simple and straightforward. Here are the steps you need to follow:</p>
-<ol>
-<li>Download and install the app from Google Play Store or APKCombo.</li>
-<li>Fill in your personal information, such as name, phone number, ID number, address, etc.</li>
-<li>Verify your identity by uploading a photo of your ID card and a selfie.</li>
-<li>Submit your application and wait for approval.</li>
-<li>Check your loan details and confirm your agreement.</li>
-<li>Receive money in your bank account within 24 hours.</li>
-</ol>
- <h4>How to repay a loan with Orange Loan APK</h4>
-<p>The repayment process for Orange Loan APK is also easy and convenient. Here are the steps you need to follow:</p>
-<ol>
-<li>Log in to the app and check your repayment schedule and amount.</li>
-<li>Choose your preferred payment method, such as bank transfer, ATM, or online banking.</li>
-<li>Make your payment before the due date and keep the receipt as proof.</li>
-<li>Check your loan status and balance in the app.</li>
-</ol>
- <h4>Pros and cons of Orange Loan APK</ <h4>Pros and cons of Orange Loan APK</h4>
-<p>Like any other online lending platform, Orange Loan APK has its own advantages and disadvantages. Here are some of them:</p>
-<table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>No collateral required</td>
-<td>High risk of default and fraud</td>
-</tr>
-<tr>
-<td>High limit</td>
-<td>Strict eligibility criteria</td>
-</tr>
-<tr>
-<td>Low interest</td>
-<td>Late fees and penalties</td>
-</tr>
-<tr>
-<td>Fast approval</td>
-<td>Limited customer service</td>
-</tr>
-<tr>
-<td>Quick transfer</td>
-<td>Poor data security and privacy</td>
-</tr>
-<tr>
-<td>Flexible repayment</td>
-<td>Negative impact on credit score</td>
-</tr>
-<tr>
-<td>Easy access</td>
-<td>Addictive and irresponsible borrowing</td>
-</tr>
-</table>
- <h2>How to download and install Orange Loan APK on your Android device</h2>
- <p>If you are interested in trying out Orange Loan APK, you will need to download and install it on your Android device. Here is how you can do that:</p>
- <h3>Step-by-step guide to download and install Orange Loan APK</h3>
- <p>Follow these steps to download and install Orange Loan APK on your Android device:</p>
-<p>orange loan app download<br />
-orange loan apk latest version<br />
-orange loan online application<br />
-orange loan thailand review<br />
-orange loan customer service number<br />
-orange loan interest rate calculator<br />
-orange loan repayment schedule<br />
-orange loan eligibility criteria<br />
-orange loan promo code 2023<br />
-orange loan referral program<br />
-orange loan app for android<br />
-orange loan apk free download<br />
-orange loan online login<br />
-orange loan thailand contact<br />
-orange loan customer feedback<br />
-orange loan interest rate comparison<br />
-orange loan repayment options<br />
-orange loan eligibility check<br />
-orange loan promo code new user<br />
-orange loan referral bonus<br />
-orange loan app for ios<br />
-orange loan apk download for pc<br />
-orange loan online registration<br />
-orange loan thailand address<br />
-orange loan customer support email<br />
-orange loan interest rate reduction<br />
-orange loan repayment extension<br />
-orange loan eligibility test<br />
-orange loan promo code existing user<br />
-orange loan referral link<br />
-orange loan app update<br />
-orange loan apk mod<br />
-orange loan online verification<br />
-orange loan thailand website<br />
-orange loan customer complaints<br />
-orange loan interest rate formula<br />
-orange loan repayment calculator<br />
-orange loan eligibility requirements<br />
-orange loan promo code first time user<br />
-orange loan referral code</p>
- <ol>
-<li>Go to Google Play Store or APKCombo and search for Orange Loan APK.</li>
-<li>Select the app from the search results and tap on the Install button.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and grant the necessary permissions, such as access to your camera, contacts, location, etc.</li>
-<li>Create an account or log in with your existing account.</li>
-<li>Start using the app to apply for a loan or manage your loan status.</li>
-</ol>
- <h3>Tips and tricks to use Orange Loan APK safely and effectively</h3>
- <p>To use Orange Loan APK safely and effectively, you should follow these tips and tricks:</p>
- <ul>
-<li>Read the terms and conditions carefully before agreeing to a loan contract.</li>
-<li>Borrow only what you need and can afford to repay.</li>
-<li>Compare the interest rates and fees of different online lenders before choosing one.</li>
-<li>Repay your loan on time to avoid late fees and penalties.</li>
-<li>Check your loan status and balance regularly in the app.</li>
-<li>Avoid sharing your personal or financial information with anyone else.</li>
-<li>Delete the app from your device when you are done using it.</li>
- <h2>Frequently asked questions about Orange Loan APK</h2>
- <p>Here are some of the most frequently asked questions about Orange Loan APK:</p>
- <h3>Is Orange Loan APK safe and legal?</h3>
- <p>Orange Loan APK is a legitimate online lending platform that is registered with the Thai Ministry of Commerce. However, it is not regulated by the Bank of Thailand or any other financial authority. Therefore, it is not subject to the same rules and standards as traditional banks or licensed lenders. This means that there is a higher risk of default, fraud, or data breach when using Orange Loan APK. You should exercise caution and discretion when using this app, and only borrow from reputable sources.</p>
- <h3>What are the eligibility criteria for Orange Loan APK?</h3>
- <p>To be eligible for a loan with Orange Loan APK, you must meet the following criteria:</p>
- <ul>
-<li>You must be a Thai citizen with a valid ID card.</li>
-<li>You must be at least 20 years old.</li>
-<li>You must have a stable income source and a bank account.</li>
-<li>You must have a good credit history and score.</li>
- <h3>What are the interest rates and fees for Orange Loan APK?</h3>
- <p>The interest rates and fees for Orange Loan APK vary depending on your loan amount, term, and credit score. The app claims to offer annual interest rates from 10% to 24%, which are lower than many other online lenders. However, you should also consider the other charges that may apply, such as origination fee, service fee, late fee, penalty fee, etc. You should read the loan contract carefully before signing it, and make sure you understand all the costs involved.</p>
- <h3>How long does it take to get approved and receive money from Orange Loan APK?</h3>
- <p>The approval process for Orange Loan APK is fast and easy. You can get approved within minutes after submitting your application and verifying your identity. The money transfer process is also quick and convenient. You can receive money in your bank account within 24 hours after approval. However, this may vary depending on your bank's processing time and availability.</p <h3>How can I contact Orange Loan APK customer service?</h3>
- <p>If you have any questions, complaints, or feedback about Orange Loan APK, you can contact their customer service team through the following channels:</p>
- <ul>
-<li>Phone: +66 2 026 3299</li>
-<li>Email: [email protected]</li>
-<li>Facebook: https://www.facebook.com/OrangeLoanTH/</li>
-<li>Line: @orangeloan</li>
-</ul>
- <p>The customer service team is available from Monday to Friday, from 9:00 am to 6:00 pm.</p>
- <h2>Conclusion</h2>
- <p>Orange Loan APK is an online lending platform that offers unsecured, high-limit, low-interest loans to borrowers in Thailand. The app has some attractive features and benefits, such as fast approval, quick transfer, flexible repayment, and easy access. However, the app also has some drawbacks and risks, such as high default and fraud rate, strict eligibility criteria, late fees and penalties, poor data security and privacy, negative impact on credit score, and addictive and irresponsible borrowing. Therefore, you should use the app with caution and discretion, and only borrow what you need and can afford to repay. You should also compare the interest rates and fees of different online lenders before choosing one, and read the terms and conditions carefully before agreeing to a loan contract. You should also contact the customer service team if you have any issues or concerns about the app.</p>
- <p>We hope this article has given you a comprehensive review of Orange Loan APK. If you have any questions or comments about the app, feel free to leave them below. Thank you for reading!</p>
- <h2>Frequently asked questions about Orange Loan APK</h2>
- <p>Here are some of the most frequently asked questions about Orange Loan APK:</p>
- <ol>
-<li>What is Orange Loan APK?</li>
-<p>Orange Loan APK is an online lending platform that provides unsecured loans to borrowers in Thailand.</p>
-<li>How does Orange Loan APK work?</li>
-<p>Orange Loan APK works by connecting borrowers with lenders through a mobile app. Borrowers can apply for a loan anytime and anywhere using their smartphone. Lenders can approve or reject the loan application within minutes. Borrowers can receive money in their bank account within 24 hours after approval.</p>
-<li>What are the advantages and disadvantages of Orange Loan APK?</li>
-<p>The advantages of Orange Loan APK are that it offers no collateral required, high limit, low interest, fast approval, quick transfer, flexible repayment, and easy access. The disadvantages of Orange Loan APK are that it has high risk of default and fraud, strict eligibility criteria, late fees and penalties, limited customer service, poor data security and privacy, negative impact on credit score, and addictive and irresponsible borrowing.</p>
-<li>How can I download and install Orange Loan APK on my Android device?</li>
-<p>You can download and install Orange Loan APK on your Android device by going to Google Play Store or APKCombo and searching for Orange Loan APK. Then you can select the app from the search results and tap on the Install button. After that, you can open the app and grant the necessary permissions. Then you can create an account or log in with your existing account.</p>
-<li>How can I contact Orange Loan APK customer service?</li>
-<p>You can contact Orange Loan APK customer service by phone (+66 2 026 3299), email ([email protected]), Facebook (https://www.facebook.com/OrangeLoanTH/), or Line (@orangeloan). The customer service team is available from Monday to Friday, from 9:00 am to 6:00 pm.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/7hao/bingo/src/lib/bots/bing/utils.ts

@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
-  if (message.messageType === 'InternalSearchQuery') {
-    return message.text
-  }
-  for (const card of message.adaptiveCards??[]) {
-    for (const block of card.body) {
-      if (block.type === 'TextBlock') {
-        return block.text
-      }
-    }
-  }
-  return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
-  packMessage(data: any) {
-    return `${JSON.stringify(data)}${RecordSeparator}`
-  },
-  unpackMessage(data: string | ArrayBuffer | Blob) {
-    if (!data) return {}
-    return data
-      .toString()
-      .split(RecordSeparator)
-      .filter(Boolean)
-      .map((s) => {
-        try {
-          return JSON.parse(s)
-        } catch (e) {
-          return {}
-        }
-      })
-  },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise<string | undefined> {
-  const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
-    {
-      method: 'HEAD',
-      headers,
-      redirect: 'manual'
-    },
-  );
-
-  if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
-    throw new Error('请求异常，请检查 cookie 是否有效')
-  }
-
-  const resultId = RegExp.$1;
-  let count = 0
-  const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
-  do {
-    await sleep(3000);
-    const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
-    // @ts-ignore
-    if (content.headers.get('content-length') > 1) {
-      const text = await content.text()
-        return (text?.match(/<img class="mimg"((?!src).)+src="[^"]+/mg)??[])
-          .map(target => target?.split('src="').pop()?.replace(/&amp;/g, '&'))
-          .map(img => `![${prompt}](${img})`).join(' ')
-    }
-  } while(count ++ < 10);
-}
-
-
-export async function* streamAsyncIterable(stream: ReadableStream) {
-  const reader = stream.getReader()
-  try {
-    while (true) {
-      const { done, value } = await reader.read()
-      if (done) {
-        return
-      }
-      yield value
-    }
-  } finally {
-    reader.releaseLock()
-  }
-}
-
-export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms))
-

spaces/801artistry/RVC801/julius/utils.py

@@ -1,101 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Non signal processing related utilities.
-"""
-
-import inspect
-import typing as tp
-import sys
-import time
-
-
-def simple_repr(obj, attrs: tp.Optional[tp.Sequence[str]] = None,
-                overrides: dict = {}):
-    """
-    Return a simple representation string for `obj`.
-    If `attrs` is not None, it should be a list of attributes to include.
-    """
-    params = inspect.signature(obj.__class__).parameters
-    attrs_repr = []
-    if attrs is None:
-        attrs = list(params.keys())
-    for attr in attrs:
-        display = False
-        if attr in overrides:
-            value = overrides[attr]
-        elif hasattr(obj, attr):
-            value = getattr(obj, attr)
-        else:
-            continue
-        if attr in params:
-            param = params[attr]
-            if param.default is inspect._empty or value != param.default:  # type: ignore
-                display = True
-        else:
-            display = True
-
-        if display:
-            attrs_repr.append(f"{attr}={value}")
-    return f"{obj.__class__.__name__}({','.join(attrs_repr)})"
-
-
-class MarkdownTable:
-    """
-    Simple MarkdownTable generator. The column titles should be large enough
-    for the lines content. This will right align everything.
-
-    >>> import io  # we use io purely for test purposes, default is sys.stdout.
-    >>> file = io.StringIO()
-    >>> table = MarkdownTable(["Item Name", "Price"], file=file)
-    >>> table.header(); table.line(["Honey", "5"]); table.line(["Car", "5,000"])
-    >>> print(file.getvalue().strip())  # Strip for test purposes
-    | Item Name | Price |
-    |-----------|-------|
-    |     Honey |     5 |
-    |       Car | 5,000 |
-    """
-    def __init__(self, columns, file=sys.stdout):
-        self.columns = columns
-        self.file = file
-
-    def _writeln(self, line):
-        self.file.write("|" + "|".join(line) + "|\n")
-
-    def header(self):
-        self._writeln(f" {col} " for col in self.columns)
-        self._writeln("-" * (len(col) + 2) for col in self.columns)
-
-    def line(self, line):
-        out = []
-        for val, col in zip(line, self.columns):
-            val = format(val, '>' + str(len(col)))
-            out.append(" " + val + " ")
-        self._writeln(out)
-
-
-class Chrono:
-    """
-    Measures ellapsed time, calling `torch.cuda.synchronize` if necessary.
-    `Chrono` instances can be used as context managers (e.g. with `with`).
-    Upon exit of the block, you can access the duration of the block in seconds
-    with the `duration` attribute.
-
-    >>> with Chrono() as chrono:
-    ...     _ = sum(range(10_000))
-    ...
-    >>> print(chrono.duration < 10)  # Should be true unless on a really slow computer.
-    True
-    """
-    def __init__(self):
-        self.duration = None
-
-    def __enter__(self):
-        self._begin = time.time()
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_tracebck):
-        import torch
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-        self.duration = time.time() - self._begin

spaces/A00001/bingothoo/src/lib/hooks/use-at-bottom.tsx

@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/report_results.py

@@ -1,37 +0,0 @@
-from pathlib import Path
-import argparse
-import numpy as np
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--input", help="input filename", type=str, nargs="+")
-parser.add_argument("--output", help="output result file", default=None)
-
-args = parser.parse_args()
-
-
-scores = {}
-for path in args.input:
-    with open(path, "r") as reader:
-        for line in reader.readlines():
-            metric, score = line.strip().split(": ")
-            score = float(score)
-            if metric not in scores:
-                scores[metric] = []
-            scores[metric].append(score)
-
-if len(scores) == 0:
-    print("No experiment directory found, wrong path?")
-    exit(1)
-
-with open(args.output, "w") as writer:
-    print("Average results: ", file=writer)
-    for metric, score in scores.items():
-        score = np.array(score)
-        mean = np.mean(score)
-        std = np.std(score)
-        print(f"{metric}: {mean:.3f} (±{std:.3f})", file=writer)
-    print("", file=writer)
-    print("Best results: ", file=writer)
-    for metric, score in scores.items():
-        score = np.max(score)
-        print(f"{metric}: {score:.3f}", file=writer)

spaces/AIWaves/Debate/src/agents/Component/__init__.py

@@ -1,3 +0,0 @@
-from .ExtraComponent import *
-from .PromptComponent import *
-from .ToolComponent import *

spaces/AIZero2HeroBootcamp/AnimatedGifGallery/README.md

@@ -1,13 +0,0 @@
----
-title: AnimatedGifGallery
-emoji: 🐨
-colorFrom: gray
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AbeShinzo0708/AI_Kishida_Fumio_speaker/app.py

@@ -1,37 +0,0 @@
-import time
-
-import streamlit as st
-import numpy as np
-import torch
-from espnet2.bin.tts_inference import Text2Speech
-from scipy.io.wavfile import write
-from PIL import Image
-
-
-fs, lang = 44100, "Japanese"
-model= "./100epoch.pth"
-x = "これはテストメッセージです"
-
-text2speech = Text2Speech.from_pretrained(
-    model_file=model,
-    device="cpu",
-    speed_control_alpha=1.0,
-    noise_scale=0.333,
-    noise_scale_dur=0.333,
-)
-pause = np.zeros(30000, dtype=np.float32)
-
-st.title("おしゃべりAI岸田文雄メーカー")
-image = Image.open('kishida.jpg')
-st.image(image)
-text = st.text_area(label='ここにテキストを入力 (Input Text)↓', height=100, max_chars=2048)
-
-
-if st.button("生成（Generate）"):
-    with torch.no_grad():
-        wav = text2speech(text)["wav"]
-
-    wav_list = []
-    wav_list.append(np.concatenate([wav.view(-1).cpu().numpy(), pause]))
-    final_wav = np.concatenate(wav_list)
-    st.audio(final_wav, sample_rate=fs)

spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/matchers.js

@@ -1 +0,0 @@
-export const matchers = {};

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/AiService.py

@@ -1,36 +0,0 @@
-from __future__ import annotations
-
-import requests
-
-from ...typing import Any, CreateResult
-from ..base_provider import BaseProvider
-
-
-class AiService(BaseProvider):
-    url = "https://aiservice.vercel.app/"
-    working = False
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool,
-        **kwargs: Any,
-    ) -> CreateResult:
-        base = "\n".join(f"{message['role']}: {message['content']}" for message in messages)
-        base += "\nassistant: "
-
-        headers = {
-            "accept": "*/*",
-            "content-type": "text/plain;charset=UTF-8",
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "Referer": "https://aiservice.vercel.app/chat",
-        }
-        data = {"input": base}
-        url = "https://aiservice.vercel.app/api/chat/answer"
-        response = requests.post(url, headers=headers, json=data)
-        response.raise_for_status()
-        yield response.json()["data"]

spaces/Adapter/CoAdapter/ldm/models/diffusion/ddpm.py

@@ -1,1329 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager, nullcontext
-from functools import partial
-import itertools
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-from omegaconf import ListConfig
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPM(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 make_it_fit=False,
-                 ucg_training=None,
-                 reset_ema=False,
-                 reset_num_ema_updates=False,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        self.make_it_fit = make_it_fit
-        if reset_ema: assert exists(ckpt_path)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-            if reset_ema:
-                assert self.use_ema
-                print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-        self.ucg_training = ucg_training or dict()
-        if self.ucg_training:
-            self.ucg_prng = np.random.RandomState()
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        elif self.parameterization == "v":
-            lvlb_weights = torch.ones_like(self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
-        else:
-            raise NotImplementedError("mu not supported")
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    @torch.no_grad()
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        if self.make_it_fit:
-            n_params = len([name for name, _ in
-                            itertools.chain(self.named_parameters(),
-                                            self.named_buffers())])
-            for name, param in tqdm(
-                    itertools.chain(self.named_parameters(),
-                                    self.named_buffers()),
-                    desc="Fitting old weights to new weights",
-                    total=n_params
-            ):
-                if not name in sd:
-                    continue
-                old_shape = sd[name].shape
-                new_shape = param.shape
-                assert len(old_shape) == len(new_shape)
-                if len(new_shape) > 2:
-                    # we only modify first two axes
-                    assert new_shape[2:] == old_shape[2:]
-                # assumes first axis corresponds to output dim
-                if not new_shape == old_shape:
-                    new_param = param.clone()
-                    old_param = sd[name]
-                    if len(new_shape) == 1:
-                        for i in range(new_param.shape[0]):
-                            new_param[i] = old_param[i % old_shape[0]]
-                    elif len(new_shape) >= 2:
-                        for i in range(new_param.shape[0]):
-                            for j in range(new_param.shape[1]):
-                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
-
-                        n_used_old = torch.ones(old_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_old[j % old_shape[1]] += 1
-                        n_used_new = torch.zeros(new_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_new[j] = n_used_old[j % old_shape[1]]
-
-                        n_used_new = n_used_new[None, :]
-                        while len(n_used_new.shape) < len(new_shape):
-                            n_used_new = n_used_new.unsqueeze(-1)
-                        new_param /= n_used_new
-
-                    sd[name] = new_param
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys:\n {missing}")
-        if len(unexpected) > 0:
-            print(f"\nUnexpected Keys:\n {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def predict_start_from_z_and_v(self, x_t, t, v):
-        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
-        )
-
-    def predict_eps_from_z_and_v(self, x_t, t, v):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        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
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_v(self, x, noise, t):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
-        )
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        # if len(x.shape) == 3:
-        #     x = x[..., None]
-        # x = rearrange(x, 'b h w c -> b c h w')
-        # x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusion(DDPM):
-    """main class"""
-
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 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,
-                 *args, **kwargs):
-        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)
-        reset_ema = kwargs.pop("reset_ema", False)
-        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
-        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
-        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
-        self.bbox_tokenizer = None
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-            if reset_ema:
-                assert self.use_ema
-                print(
-                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-    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
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                       force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    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:
-                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
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, 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)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        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', "txt"]:
-                    xc = batch[cond_key]
-                elif cond_key in ['class_label', 'cls']:
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    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]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        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. / self.scale_factor * z
-        return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c, **kwargs)
-        return loss
-
-    def get_time_with_schedule(self, scheduler, bs):
-        if scheduler == 'linear':
-            t = torch.randint(0, self.num_timesteps, (bs,), device=self.device).long()
-        elif scheduler == 'cosine':
-            t = torch.rand((bs, ), device=self.device)
-            t = torch.cos(torch.pi / 2. * t) * self.num_timesteps
-            t = t.long()
-        elif scheduler == 'cubic':
-            t = torch.rand((bs,), device=self.device)
-            t = (1 - t ** 3) * self.num_timesteps
-            t = t.long()
-        else:
-            raise NotImplementedError
-        t = torch.clamp(t, min=0, max=self.num_timesteps-1)
-        return t
-
-    def forward(self, x, c, *args, **kwargs):
-        if 't' not in kwargs:
-            t = torch.randint(0, self.num_timesteps, (x.shape[0], ), device=self.device).long()
-        else:
-            t = kwargs.pop('t')
-
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False, **kwargs):
-        if isinstance(cond, dict):
-            # hybrid case, cond is expected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond, **kwargs)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None, **kwargs):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond, **kwargs)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    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., 1.)
-        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., noise_dropout=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.:
-            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)))
-
-        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., noise_dropout=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. - 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. - 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.image_size, self.image_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)
-
-    @torch.no_grad()
-    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
-                                                         shape, cond, verbose=False, **kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True, **kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def get_unconditional_conditioning(self, batch_size, null_label=None):
-        if null_label is not None:
-            xc = null_label
-            if isinstance(xc, ListConfig):
-                xc = list(xc)
-            if isinstance(xc, dict) or isinstance(xc, list):
-                c = self.get_learned_conditioning(xc)
-            else:
-                if hasattr(xc, "to"):
-                    xc = xc.to(self.device)
-                c = self.get_learned_conditioning(xc)
-        else:
-            if self.cond_stage_key in ["class_label", "cls"]:
-                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
-                return self.get_learned_conditioning(xc)
-            else:
-                raise NotImplementedError("todo")
-        if isinstance(c, list):  # in case the encoder gives us a list
-            for i in range(len(c)):
-                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
-        else:
-            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
-        return c
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', "cls"]:
-                try:
-                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                    log['conditioning'] = xc
-                except KeyError:
-                    # probably no "human_label" in batch
-                    pass
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                             ddim_steps=ddim_steps, eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-        if unconditional_guidance_scale > 1.0:
-            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            if self.model.conditioning_key == "crossattn-adm":
-                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if inpaint:
-            # make a simple center square
-            b, h, w = z.shape[0], z.shape[2], z.shape[3]
-            mask = torch.ones(N, h, w).to(self.device)
-            # zeros will be filled in
-            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-            mask = mask[:, None, ...]
-            with ema_scope("Plotting Inpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_inpainting"] = x_samples
-            log["mask"] = mask
-
-            # outpaint
-            mask = 1. - mask
-            with ema_scope("Plotting Outpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapper(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None, **kwargs):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t, **kwargs)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t, **kwargs)
-        elif self.conditioning_key == 'crossattn':
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc, **kwargs)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, **kwargs)
-        elif self.conditioning_key == 'hybrid-adm':
-            assert c_adm is not None
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
-        elif self.conditioning_key == 'crossattn-adm':
-            assert c_adm is not None
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc, y=c_adm, **kwargs)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc, **kwargs)
-        else:
-            raise NotImplementedError()
-
-        return out

spaces/Adapting/YouTube-Downloader/tube/__init__.py

@@ -1,3 +0,0 @@
-from .download import download_yt
-from .utils import clear_cache
-from .var import OUTPUT_DIR

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/alphamaskimage/AlphaMaskImage.d.ts

@@ -1,2 +0,0 @@
-import AlphaMaskImage from '../../../plugins/alphamaskimage';
-export default AlphaMaskImage;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinputbase/ColorInputBase.d.ts

@@ -1,38 +0,0 @@
-import Sizer from '../../sizer/Sizer';
-import RoundRectangle from '../../roundrectangle/RoundRectangle'
-import CanvasInput from '../../canvasinput/CanvasInput';
-
-export default ColorInputBase;
-
-declare namespace ColorInputBase {
-    interface ISwatchConfig extends RoundRectangle.IConfig {
-        size?: number,
-    }
-
-    interface IConfig extends Sizer.IConfig {
-        background?: Phaser.GameObjects.GameObject,
-
-        swatch?: Phaser.GameObjects.GameObject | ISwatchConfig,
-        swatchSize?: number,
-        squareExpandSwatch?: boolean,
-
-        inputText?: CanvasInput.IConfig,
-
-        valuechangeCallback: (newValue: number, oldValue: number, colorPicker: ColorInputBase) => void,
-
-        value?: number | string
-    }
-}
-
-declare class ColorInputBase extends Sizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: ColorInputBase.IConfig
-    );
-
-    setValue(value: number): this;
-    value: number;
-
-    setColor(color: number): this;
-    color: number;
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/simpledropdownlist/Factory.js

@@ -1,13 +0,0 @@
-import SimpleDropDownList from './SimpleDropDownList.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('simpleDropDownList', function (config, creators) {
-    var gameObject = new SimpleDropDownList(this.scene, config, creators);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.SimpleDropDownList', SimpleDropDownList);
-
-export default SimpleDropDownList;

spaces/AlekseyCalvin/Make_Putin_Queer_Please-use-trp-token/README.md

@@ -1,12 +0,0 @@
----
-title: Make Putin Queer Please-use-trp-token
-emoji: 🐨
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/bg_motion_predictor.py

@@ -1,24 +0,0 @@
-from torch import nn
-import torch
-from torchvision import models
-
-class BGMotionPredictor(nn.Module):
-    """
-    Module for background estimation, return single transformation, parametrized as 3x3 matrix. The third row is [0 0 1]
-    """
-
-    def __init__(self):
-        super(BGMotionPredictor, self).__init__()
-        self.bg_encoder = models.resnet18(pretrained=False)
-        self.bg_encoder.conv1 = nn.Conv2d(6, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
-        num_features = self.bg_encoder.fc.in_features
-        self.bg_encoder.fc = nn.Linear(num_features, 6)
-        self.bg_encoder.fc.weight.data.zero_()
-        self.bg_encoder.fc.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
-
-    def forward(self, source_image, driving_image):
-        bs = source_image.shape[0]
-        out = torch.eye(3).unsqueeze(0).repeat(bs, 1, 1).type(source_image.type())
-        prediction = self.bg_encoder(torch.cat([source_image, driving_image], dim=1))
-        out[:, :2, :] = prediction.view(bs, 2, 3)
-        return out

spaces/Alican/pixera/util/visualizer.py

@@ -1,257 +0,0 @@
-import numpy as np
-import os
-import sys
-import ntpath
-import time
-from . import util, html
-from subprocess import Popen, PIPE
-
-
-try:
-    import wandb
-except ImportError:
-    print('Warning: wandb package cannot be found. The option "--use_wandb" will result in error.')
-
-if sys.version_info[0] == 2:
-    VisdomExceptionBase = Exception
-else:
-    VisdomExceptionBase = ConnectionError
-
-
-def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256, use_wandb=False):
-    """Save images to the disk.
-
-    Parameters:
-        webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
-        visuals (OrderedDict)    -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
-        image_path (str)         -- the string is used to create image paths
-        aspect_ratio (float)     -- the aspect ratio of saved images
-        width (int)              -- the images will be resized to width x width
-
-    This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
-    """
-    image_dir = webpage.get_image_dir()
-    short_path = ntpath.basename(image_path[0])
-    name = os.path.splitext(short_path)[0]
-
-    webpage.add_header(name)
-    ims, txts, links = [], [], []
-    ims_dict = {}
-    for label, im_data in visuals.items():
-        im = util.tensor2im(im_data)
-        image_name = '%s_%s.png' % (name, label)
-        save_path = os.path.join(image_dir, image_name)
-        util.save_image(im, save_path, aspect_ratio=aspect_ratio)
-        ims.append(image_name)
-        txts.append(label)
-        links.append(image_name)
-        if use_wandb:
-            ims_dict[label] = wandb.Image(im)
-    webpage.add_images(ims, txts, links, width=width)
-    if use_wandb:
-        wandb.log(ims_dict)
-
-
-class Visualizer():
-    """This class includes several functions that can display/save images and print/save logging information.
-
-    It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
-    """
-
-    def __init__(self, opt):
-        """Initialize the Visualizer class
-
-        Parameters:
-            opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        Step 1: Cache the training/test options
-        Step 2: connect to a visdom server
-        Step 3: create an HTML object for saveing HTML filters
-        Step 4: create a logging file to store training losses
-        """
-        self.opt = opt  # cache the option
-        self.display_id = opt.display_id
-        self.use_html = opt.isTrain and not opt.no_html
-        self.win_size = opt.display_winsize
-        self.name = opt.name
-        self.port = opt.display_port
-        self.saved = False
-        self.use_wandb = opt.use_wandb
-        self.wandb_project_name = opt.wandb_project_name
-        self.current_epoch = 0
-        self.ncols = opt.display_ncols
-
-        if self.display_id > 0:  # connect to a visdom server given <display_port> and <display_server>
-            import visdom
-            self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env)
-            if not self.vis.check_connection():
-                self.create_visdom_connections()
-
-        if self.use_wandb:
-            self.wandb_run = wandb.init(project=self.wandb_project_name, name=opt.name, config=opt) if not wandb.run else wandb.run
-            self.wandb_run._label(repo='CycleGAN-and-pix2pix')
-
-        if self.use_html:  # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
-            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
-            self.img_dir = os.path.join(self.web_dir, 'images')
-            print('create web directory %s...' % self.web_dir)
-            util.mkdirs([self.web_dir, self.img_dir])
-        # create a logging file to store training losses
-        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
-        with open(self.log_name, "a") as log_file:
-            now = time.strftime("%c")
-            log_file.write('================ Training Loss (%s) ================\n' % now)
-
-    def reset(self):
-        """Reset the self.saved status"""
-        self.saved = False
-
-    def create_visdom_connections(self):
-        """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
-        cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
-        print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
-        print('Command: %s' % cmd)
-        Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
-
-    def display_current_results(self, visuals, epoch, save_result):
-        """Display current results on visdom; save current results to an HTML file.
-
-        Parameters:
-            visuals (OrderedDict) - - dictionary of images to display or save
-            epoch (int) - - the current epoch
-            save_result (bool) - - if save the current results to an HTML file
-        """
-        if self.display_id > 0:  # show images in the browser using visdom
-            ncols = self.ncols
-            if ncols > 0:        # show all the images in one visdom panel
-                ncols = min(ncols, len(visuals))
-                h, w = next(iter(visuals.values())).shape[:2]
-                table_css = """<style>
-                        table {border-collapse: separate; border-spacing: 4px; white-space: nowrap; text-align: center}
-                        table td {width: % dpx; height: % dpx; padding: 4px; outline: 4px solid black}
-                        </style>""" % (w, h)  # create a table css
-                # create a table of images.
-                title = self.name
-                label_html = ''
-                label_html_row = ''
-                images = []
-                idx = 0
-                for label, image in visuals.items():
-                    image_numpy = util.tensor2im(image)
-                    label_html_row += '<td>%s</td>' % label
-                    images.append(image_numpy.transpose([2, 0, 1]))
-                    idx += 1
-                    if idx % ncols == 0:
-                        label_html += '<tr>%s</tr>' % label_html_row
-                        label_html_row = ''
-                white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
-                while idx % ncols != 0:
-                    images.append(white_image)
-                    label_html_row += '<td></td>'
-                    idx += 1
-                if label_html_row != '':
-                    label_html += '<tr>%s</tr>' % label_html_row
-                try:
-                    self.vis.images(images, nrow=ncols, win=self.display_id + 1,
-                                    padding=2, opts=dict(title=title + ' images'))
-                    label_html = '<table>%s</table>' % label_html
-                    self.vis.text(table_css + label_html, win=self.display_id + 2,
-                                  opts=dict(title=title + ' labels'))
-                except VisdomExceptionBase:
-                    self.create_visdom_connections()
-
-            else:     # show each image in a separate visdom panel;
-                idx = 1
-                try:
-                    for label, image in visuals.items():
-                        image_numpy = util.tensor2im(image)
-                        self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
-                                       win=self.display_id + idx)
-                        idx += 1
-                except VisdomExceptionBase:
-                    self.create_visdom_connections()
-
-        if self.use_wandb:
-            columns = [key for key, _ in visuals.items()]
-            columns.insert(0, 'epoch')
-            result_table = wandb.Table(columns=columns)
-            table_row = [epoch]
-            ims_dict = {}
-            for label, image in visuals.items():
-                image_numpy = util.tensor2im(image)
-                wandb_image = wandb.Image(image_numpy)
-                table_row.append(wandb_image)
-                ims_dict[label] = wandb_image
-            self.wandb_run.log(ims_dict)
-            if epoch != self.current_epoch:
-                self.current_epoch = epoch
-                result_table.add_data(*table_row)
-                self.wandb_run.log({"Result": result_table})
-
-        if self.use_html and (save_result or not self.saved):  # save images to an HTML file if they haven't been saved.
-            self.saved = True
-            # save images to the disk
-            for label, image in visuals.items():
-                image_numpy = util.tensor2im(image)
-                img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
-                util.save_image(image_numpy, img_path)
-
-            # update website
-            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=1)
-            for n in range(epoch, 0, -1):
-                webpage.add_header('epoch [%d]' % n)
-                ims, txts, links = [], [], []
-
-                for label, image_numpy in visuals.items():
-                    image_numpy = util.tensor2im(image)
-                    img_path = 'epoch%.3d_%s.png' % (n, label)
-                    ims.append(img_path)
-                    txts.append(label)
-                    links.append(img_path)
-                webpage.add_images(ims, txts, links, width=self.win_size)
-            webpage.save()
-
-    def plot_current_losses(self, epoch, counter_ratio, losses):
-        """display the current losses on visdom display: dictionary of error labels and values
-
-        Parameters:
-            epoch (int)           -- current epoch
-            counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
-            losses (OrderedDict)  -- training losses stored in the format of (name, float) pairs
-        """
-        if not hasattr(self, 'plot_data'):
-            self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
-        self.plot_data['X'].append(epoch + counter_ratio)
-        self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
-        try:
-            self.vis.line(
-                X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
-                Y=np.array(self.plot_data['Y']),
-                opts={
-                    'title': self.name + ' loss over time',
-                    'legend': self.plot_data['legend'],
-                    'xlabel': 'epoch',
-                    'ylabel': 'loss'},
-                win=self.display_id)
-        except VisdomExceptionBase:
-            self.create_visdom_connections()
-        if self.use_wandb:
-            self.wandb_run.log(losses)
-
-    # losses: same format as |losses| of plot_current_losses
-    def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
-        """print current losses on console; also save the losses to the disk
-
-        Parameters:
-            epoch (int) -- current epoch
-            iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
-            losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
-            t_comp (float) -- computational time per data point (normalized by batch_size)
-            t_data (float) -- data loading time per data point (normalized by batch_size)
-        """
-        message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
-        for k, v in losses.items():
-            message += '%s: %.3f ' % (k, v)
-
-        print(message)  # print the message
-        with open(self.log_name, "a") as log_file:
-            log_file.write('%s\n' % message)  # save the message

spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/train_boundary.py

@@ -1,158 +0,0 @@
-
-import numpy as np
-from sklearn import svm
-
-
-
-
-
-def train_boundary(latent_codes,
-                   scores,
-                   chosen_num_or_ratio=0.02,
-                   split_ratio=0.7,
-                   invalid_value=None,
-                   logger=None,
-                   logger_name='train_boundary'):
-  """Trains boundary in latent space with offline predicted attribute scores.
-
-  Given a collection of latent codes and the attribute scores predicted from the
-  corresponding images, this function will train a linear SVM by treating it as
-  a bi-classification problem. Basically, the samples with highest attribute
-  scores are treated as positive samples, while those with lowest scores as
-  negative. For now, the latent code can ONLY be with 1 dimension.
-
-  NOTE: The returned boundary is with shape (1, latent_space_dim), and also
-  normalized with unit norm.
-
-  Args:
-    latent_codes: Input latent codes as training data.
-    scores: Input attribute scores used to generate training labels.
-    chosen_num_or_ratio: How many samples will be chosen as positive (negative)
-      samples. If this field lies in range (0, 0.5], `chosen_num_or_ratio *
-      latent_codes_num` will be used. Otherwise, `min(chosen_num_or_ratio,
-      0.5 * latent_codes_num)` will be used. (default: 0.02)
-    split_ratio: Ratio to split training and validation sets. (default: 0.7)
-    invalid_value: This field is used to filter out data. (default: None)
-    logger: Logger for recording log messages. If set as `None`, a default
-      logger, which prints messages from all levels to screen, will be created.
-      (default: None)
-
-  Returns:
-    A decision boundary with type `numpy.ndarray`.
-
-  Raises:
-    ValueError: If the input `latent_codes` or `scores` are with invalid format.
-  """
-#  if not logger:
-#    logger = setup_logger(work_dir='', logger_name=logger_name)
-
-  if (not isinstance(latent_codes, np.ndarray) or
-      not len(latent_codes.shape) == 2):
-    raise ValueError(f'Input `latent_codes` should be with type'
-                     f'`numpy.ndarray`, and shape [num_samples, '
-                     f'latent_space_dim]!')
-  num_samples = latent_codes.shape[0]
-  latent_space_dim = latent_codes.shape[1]
-  if (not isinstance(scores, np.ndarray) or not len(scores.shape) == 2 or
-      not scores.shape[0] == num_samples or not scores.shape[1] == 1):
-    raise ValueError(f'Input `scores` should be with type `numpy.ndarray`, and '
-                     f'shape [num_samples, 1], where `num_samples` should be '
-                     f'exactly same as that of input `latent_codes`!')
-  if chosen_num_or_ratio <= 0:
-    raise ValueError(f'Input `chosen_num_or_ratio` should be positive, '
-                     f'but {chosen_num_or_ratio} received!')
-
-#  logger.info(f'Filtering training data.')
-    print('Filtering training data.')
-  if invalid_value is not None:
-    latent_codes = latent_codes[scores[:, 0] != invalid_value]
-    scores = scores[scores[:, 0] != invalid_value]
-
-#  logger.info(f'Sorting scores to get positive and negative samples.')
-  print('Sorting scores to get positive and negative samples.')
-  
-  sorted_idx = np.argsort(scores, axis=0)[::-1, 0]
-  latent_codes = latent_codes[sorted_idx]
-  scores = scores[sorted_idx]
-  num_samples = latent_codes.shape[0]
-  if 0 < chosen_num_or_ratio <= 1:
-    chosen_num = int(num_samples * chosen_num_or_ratio)
-  else:
-    chosen_num = int(chosen_num_or_ratio)
-  chosen_num = min(chosen_num, num_samples // 2)
-
-#  logger.info(f'Spliting training and validation sets:')
-  print('Filtering training data.')
-  
-  train_num = int(chosen_num * split_ratio)
-  val_num = chosen_num - train_num
-  # Positive samples.
-  positive_idx = np.arange(chosen_num)
-  np.random.shuffle(positive_idx)
-  positive_train = latent_codes[:chosen_num][positive_idx[:train_num]]
-  positive_val = latent_codes[:chosen_num][positive_idx[train_num:]]
-  # Negative samples.
-  negative_idx = np.arange(chosen_num)
-  np.random.shuffle(negative_idx)
-  negative_train = latent_codes[-chosen_num:][negative_idx[:train_num]]
-  negative_val = latent_codes[-chosen_num:][negative_idx[train_num:]]
-  # Training set.
-  train_data = np.concatenate([positive_train, negative_train], axis=0)
-  train_label = np.concatenate([np.ones(train_num, dtype=np.int),
-                                np.zeros(train_num, dtype=np.int)], axis=0)
-#  logger.info(f'  Training: {train_num} positive, {train_num} negative.')
-  print(f'  Training: {train_num} positive, {train_num} negative.')
-  # Validation set.
-  val_data = np.concatenate([positive_val, negative_val], axis=0)
-  val_label = np.concatenate([np.ones(val_num, dtype=np.int),
-                              np.zeros(val_num, dtype=np.int)], axis=0)
-#  logger.info(f'  Validation: {val_num} positive, {val_num} negative.')
-  print(f'  Validation: {val_num} positive, {val_num} negative.')
-  
-  # Remaining set.
-  remaining_num = num_samples - chosen_num * 2
-  remaining_data = latent_codes[chosen_num:-chosen_num]
-  remaining_scores = scores[chosen_num:-chosen_num]
-  decision_value = (scores[0] + scores[-1]) / 2
-  remaining_label = np.ones(remaining_num, dtype=np.int)
-  remaining_label[remaining_scores.ravel() < decision_value] = 0
-  remaining_positive_num = np.sum(remaining_label == 1)
-  remaining_negative_num = np.sum(remaining_label == 0)
-#  logger.info(f'  Remaining: {remaining_positive_num} positive, '
-#              f'{remaining_negative_num} negative.')
-  print(f'  Remaining: {remaining_positive_num} positive, '
-              f'{remaining_negative_num} negative.')
-#  logger.info(f'Training boundary.')
-  print(f'Training boundary.')
-  
-  clf = svm.SVC(kernel='linear')
-  classifier = clf.fit(train_data, train_label)
-#  logger.info(f'Finish training.')
-  print(f'Finish training.')
-  
-  
-  if val_num:
-    val_prediction = classifier.predict(val_data)
-    correct_num = np.sum(val_label == val_prediction)
-#    logger.info(f'Accuracy for validation set: '
-#                f'{correct_num} / {val_num * 2} = '
-#                f'{correct_num / (val_num * 2):.6f}')
-    print(f'Accuracy for validation set: '
-                f'{correct_num} / {val_num * 2} = '
-                f'{correct_num / (val_num * 2):.6f}')
-    vacc=correct_num/len(val_label)
-  '''
-  if remaining_num:
-    remaining_prediction = classifier.predict(remaining_data)
-    correct_num = np.sum(remaining_label == remaining_prediction)
-    logger.info(f'Accuracy for remaining set: '
-                f'{correct_num} / {remaining_num} = '
-                f'{correct_num / remaining_num:.6f}')
-  '''
-  a = classifier.coef_.reshape(1, latent_space_dim).astype(np.float32)
-  return a / np.linalg.norm(a),vacc
-
-
-
-
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py

@@ -1,654 +0,0 @@
-import argparse
-import logging
-import math
-import os
-import random
-from pathlib import Path
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-import PIL
-import torch
-import torch.utils.checkpoint
-import transformers
-from flax import jax_utils
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import create_repo, upload_folder
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
-
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxDDPMScheduler,
-    FlaxPNDMScheduler,
-    FlaxStableDiffusionPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
-from diffusers.utils import check_min_version
-
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.Resampling.BILINEAR,
-        "bilinear": PIL.Image.Resampling.BILINEAR,
-        "bicubic": PIL.Image.Resampling.BICUBIC,
-        "lanczos": PIL.Image.Resampling.LANCZOS,
-        "nearest": PIL.Image.Resampling.NEAREST,
-    }
-else:
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.LINEAR,
-        "bilinear": PIL.Image.BILINEAR,
-        "bicubic": PIL.Image.BICUBIC,
-        "lanczos": PIL.Image.LANCZOS,
-        "nearest": PIL.Image.NEAREST,
-    }
-# ------------------------------------------------------------------------------
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.14.0.dev0")
-
-logger = logging.getLogger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
-    )
-    parser.add_argument(
-        "--placeholder_token",
-        type=str,
-        default=None,
-        required=True,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
-    )
-    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
-    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="text-inversion-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=5000,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=True,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument(
-        "--use_auth_token",
-        action="store_true",
-        help=(
-            "Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
-            " private models)."
-        ),
-    )
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.train_data_dir is None:
-        raise ValueError("You must specify a train data directory.")
-
-    return args
-
-
-imagenet_templates_small = [
-    "a photo of a {}",
-    "a rendering of a {}",
-    "a cropped photo of the {}",
-    "the photo of a {}",
-    "a photo of a clean {}",
-    "a photo of a dirty {}",
-    "a dark photo of the {}",
-    "a photo of my {}",
-    "a photo of the cool {}",
-    "a close-up photo of a {}",
-    "a bright photo of the {}",
-    "a cropped photo of a {}",
-    "a photo of the {}",
-    "a good photo of the {}",
-    "a photo of one {}",
-    "a close-up photo of the {}",
-    "a rendition of the {}",
-    "a photo of the clean {}",
-    "a rendition of a {}",
-    "a photo of a nice {}",
-    "a good photo of a {}",
-    "a photo of the nice {}",
-    "a photo of the small {}",
-    "a photo of the weird {}",
-    "a photo of the large {}",
-    "a photo of a cool {}",
-    "a photo of a small {}",
-]
-
-imagenet_style_templates_small = [
-    "a painting in the style of {}",
-    "a rendering in the style of {}",
-    "a cropped painting in the style of {}",
-    "the painting in the style of {}",
-    "a clean painting in the style of {}",
-    "a dirty painting in the style of {}",
-    "a dark painting in the style of {}",
-    "a picture in the style of {}",
-    "a cool painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a bright painting in the style of {}",
-    "a cropped painting in the style of {}",
-    "a good painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a rendition in the style of {}",
-    "a nice painting in the style of {}",
-    "a small painting in the style of {}",
-    "a weird painting in the style of {}",
-    "a large painting in the style of {}",
-]
-
-
-class TextualInversionDataset(Dataset):
-    def __init__(
-        self,
-        data_root,
-        tokenizer,
-        learnable_property="object",  # [object, style]
-        size=512,
-        repeats=100,
-        interpolation="bicubic",
-        flip_p=0.5,
-        set="train",
-        placeholder_token="*",
-        center_crop=False,
-    ):
-        self.data_root = data_root
-        self.tokenizer = tokenizer
-        self.learnable_property = learnable_property
-        self.size = size
-        self.placeholder_token = placeholder_token
-        self.center_crop = center_crop
-        self.flip_p = flip_p
-
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
-
-        self.num_images = len(self.image_paths)
-        self._length = self.num_images
-
-        if set == "train":
-            self._length = self.num_images * repeats
-
-        self.interpolation = {
-            "linear": PIL_INTERPOLATION["linear"],
-            "bilinear": PIL_INTERPOLATION["bilinear"],
-            "bicubic": PIL_INTERPOLATION["bicubic"],
-            "lanczos": PIL_INTERPOLATION["lanczos"],
-        }[interpolation]
-
-        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
-        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = {}
-        image = Image.open(self.image_paths[i % self.num_images])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        placeholder_string = self.placeholder_token
-        text = random.choice(self.templates).format(placeholder_string)
-
-        example["input_ids"] = self.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids[0]
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
-                img.shape[0],
-                img.shape[1],
-            )
-            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
-
-        image = Image.fromarray(img)
-        image = image.resize((self.size, self.size), resample=self.interpolation)
-
-        image = self.flip_transform(image)
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-        return example
-
-
-def resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):
-    if model.config.vocab_size == new_num_tokens or new_num_tokens is None:
-        return
-    model.config.vocab_size = new_num_tokens
-
-    params = model.params
-    old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"]
-    old_num_tokens, emb_dim = old_embeddings.shape
-
-    initializer = jax.nn.initializers.normal()
-
-    new_embeddings = initializer(rng, (new_num_tokens, emb_dim))
-    new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)
-    new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])
-    params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings
-
-    model.params = params
-    return model
-
-
-def get_params_to_save(params):
-    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
-
-
-def main():
-    args = parse_args()
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if jax.process_index() == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-
-    # Load the tokenizer and add the placeholder token as a additional special token
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Add the placeholder token in tokenizer
-    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
-    if num_added_tokens == 0:
-        raise ValueError(
-            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
-            " `placeholder_token` that is not already in the tokenizer."
-        )
-
-    # Convert the initializer_token, placeholder_token to ids
-    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
-    # Check if initializer_token is a single token or a sequence of tokens
-    if len(token_ids) > 1:
-        raise ValueError("The initializer token must be a single token.")
-
-    initializer_token_id = token_ids[0]
-    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
-
-    # Load models and create wrapper for stable diffusion
-    text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-
-    # Create sampling rng
-    rng = jax.random.PRNGKey(args.seed)
-    rng, _ = jax.random.split(rng)
-    # Resize the token embeddings as we are adding new special tokens to the tokenizer
-    text_encoder = resize_token_embeddings(
-        text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng
-    )
-    original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"]
-
-    train_dataset = TextualInversionDataset(
-        data_root=args.train_data_dir,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        placeholder_token=args.placeholder_token,
-        repeats=args.repeats,
-        learnable_property=args.learnable_property,
-        center_crop=args.center_crop,
-        set="train",
-    )
-
-    def collate_fn(examples):
-        pixel_values = torch.stack([example["pixel_values"] for example in examples])
-        input_ids = torch.stack([example["input_ids"] for example in examples])
-
-        batch = {"pixel_values": pixel_values, "input_ids": input_ids}
-        batch = {k: v.numpy() for k, v in batch.items()}
-
-        return batch
-
-    total_train_batch_size = args.train_batch_size * jax.local_device_count()
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn
-    )
-
-    # Optimization
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * total_train_batch_size
-
-    constant_scheduler = optax.constant_schedule(args.learning_rate)
-
-    optimizer = optax.adamw(
-        learning_rate=constant_scheduler,
-        b1=args.adam_beta1,
-        b2=args.adam_beta2,
-        eps=args.adam_epsilon,
-        weight_decay=args.adam_weight_decay,
-    )
-
-    def create_mask(params, label_fn):
-        def _map(params, mask, label_fn):
-            for k in params:
-                if label_fn(k):
-                    mask[k] = "token_embedding"
-                else:
-                    if isinstance(params[k], dict):
-                        mask[k] = {}
-                        _map(params[k], mask[k], label_fn)
-                    else:
-                        mask[k] = "zero"
-
-        mask = {}
-        _map(params, mask, label_fn)
-        return mask
-
-    def zero_grads():
-        # from https://github.com/deepmind/optax/issues/159#issuecomment-896459491
-        def init_fn(_):
-            return ()
-
-        def update_fn(updates, state, params=None):
-            return jax.tree_util.tree_map(jnp.zeros_like, updates), ()
-
-        return optax.GradientTransformation(init_fn, update_fn)
-
-    # Zero out gradients of layers other than the token embedding layer
-    tx = optax.multi_transform(
-        {"token_embedding": optimizer, "zero": zero_grads()},
-        create_mask(text_encoder.params, lambda s: s == "token_embedding"),
-    )
-
-    state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)
-
-    noise_scheduler = FlaxDDPMScheduler(
-        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
-    )
-    noise_scheduler_state = noise_scheduler.create_state()
-
-    # Initialize our training
-    train_rngs = jax.random.split(rng, jax.local_device_count())
-
-    # Define gradient train step fn
-    def train_step(state, vae_params, unet_params, batch, train_rng):
-        dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
-
-        def compute_loss(params):
-            vae_outputs = vae.apply(
-                {"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
-            )
-            latents = vae_outputs.latent_dist.sample(sample_rng)
-            # (NHWC) -> (NCHW)
-            latents = jnp.transpose(latents, (0, 3, 1, 2))
-            latents = latents * vae.config.scaling_factor
-
-            noise_rng, timestep_rng = jax.random.split(sample_rng)
-            noise = jax.random.normal(noise_rng, latents.shape)
-            bsz = latents.shape[0]
-            timesteps = jax.random.randint(
-                timestep_rng,
-                (bsz,),
-                0,
-                noise_scheduler.config.num_train_timesteps,
-            )
-            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
-            encoder_hidden_states = state.apply_fn(
-                batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True
-            )[0]
-            # Predict the noise residual and compute loss
-            model_pred = unet.apply(
-                {"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False
-            ).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            loss = (target - model_pred) ** 2
-            loss = loss.mean()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-        new_state = state.apply_gradients(grads=grad)
-
-        # Keep the token embeddings fixed except the newly added embeddings for the concept,
-        # as we only want to optimize the concept embeddings
-        token_embeds = original_token_embeds.at[placeholder_token_id].set(
-            new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id]
-        )
-        new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds
-
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-        return new_state, metrics, new_train_rng
-
-    # Create parallel version of the train and eval step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-    vae_params = jax_utils.replicate(vae_params)
-    unet_params = jax_utils.replicate(unet_params)
-
-    # Train!
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
-
-    # Scheduler and math around the number of training steps.
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-
-    global_step = 0
-
-    epochs = tqdm(range(args.num_train_epochs), desc=f"Epoch ... (1/{args.num_train_epochs})", position=0)
-    for epoch in epochs:
-        # ======================== Training ================================
-
-        train_metrics = []
-
-        steps_per_epoch = len(train_dataset) // total_train_batch_size
-        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
-        # train
-        for batch in train_dataloader:
-            batch = shard(batch)
-            state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)
-            train_metrics.append(train_metric)
-
-            train_step_progress_bar.update(1)
-            global_step += 1
-
-            if global_step >= args.max_train_steps:
-                break
-
-        train_metric = jax_utils.unreplicate(train_metric)
-
-        train_step_progress_bar.close()
-        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
-
-    # Create the pipeline using using the trained modules and save it.
-    if jax.process_index() == 0:
-        scheduler = FlaxPNDMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        )
-        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
-            "CompVis/stable-diffusion-safety-checker", from_pt=True
-        )
-        pipeline = FlaxStableDiffusionPipeline(
-            text_encoder=text_encoder,
-            vae=vae,
-            unet=unet,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
-        )
-
-        pipeline.save_pretrained(
-            args.output_dir,
-            params={
-                "text_encoder": get_params_to_save(state.params),
-                "vae": get_params_to_save(vae_params),
-                "unet": get_params_to_save(unet_params),
-                "safety_checker": safety_checker.params,
-            },
-        )
-
-        # Also save the newly trained embeddings
-        learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
-            placeholder_token_id
-        ]
-        learned_embeds_dict = {args.placeholder_token: learned_embeds}
-        jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict)
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-
-if __name__ == "__main__":
-    main()

spaces/Andy1621/uniformer_image_detection/configs/ssd/ssd300_coco.py

@@ -1,62 +0,0 @@
-_base_ = [
-    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
-]
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='Expand',
-        mean=img_norm_cfg['mean'],
-        to_rgb=img_norm_cfg['to_rgb'],
-        ratio_range=(1, 4)),
-    dict(
-        type='MinIoURandomCrop',
-        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
-        min_crop_size=0.3),
-    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(300, 300),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=False),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=3,
-    train=dict(
-        _delete_=True,
-        type='RepeatDataset',
-        times=5,
-        dataset=dict(
-            type=dataset_type,
-            ann_file=data_root + 'annotations/instances_train2017.json',
-            img_prefix=data_root + 'train2017/',
-            pipeline=train_pipeline)),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
-optimizer_config = dict(_delete_=True)

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py

@@ -1,157 +0,0 @@
-import numpy as np
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from .random_sampler import RandomSampler
-
-
-@BBOX_SAMPLERS.register_module()
-class IoUBalancedNegSampler(RandomSampler):
-    """IoU Balanced Sampling.
-
-    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
-
-    Sampling proposals according to their IoU. `floor_fraction` of needed RoIs
-    are sampled from proposals whose IoU are lower than `floor_thr` randomly.
-    The others are sampled from proposals whose IoU are higher than
-    `floor_thr`. These proposals are sampled from some bins evenly, which are
-    split by `num_bins` via IoU evenly.
-
-    Args:
-        num (int): number of proposals.
-        pos_fraction (float): fraction of positive proposals.
-        floor_thr (float): threshold (minimum) IoU for IoU balanced sampling,
-            set to -1 if all using IoU balanced sampling.
-        floor_fraction (float): sampling fraction of proposals under floor_thr.
-        num_bins (int): number of bins in IoU balanced sampling.
-    """
-
-    def __init__(self,
-                 num,
-                 pos_fraction,
-                 floor_thr=-1,
-                 floor_fraction=0,
-                 num_bins=3,
-                 **kwargs):
-        super(IoUBalancedNegSampler, self).__init__(num, pos_fraction,
-                                                    **kwargs)
-        assert floor_thr >= 0 or floor_thr == -1
-        assert 0 <= floor_fraction <= 1
-        assert num_bins >= 1
-
-        self.floor_thr = floor_thr
-        self.floor_fraction = floor_fraction
-        self.num_bins = num_bins
-
-    def sample_via_interval(self, max_overlaps, full_set, num_expected):
-        """Sample according to the iou interval.
-
-        Args:
-            max_overlaps (torch.Tensor): IoU between bounding boxes and ground
-                truth boxes.
-            full_set (set(int)): A full set of indices of boxes。
-            num_expected (int): Number of expected samples。
-
-        Returns:
-            np.ndarray: Indices  of samples
-        """
-        max_iou = max_overlaps.max()
-        iou_interval = (max_iou - self.floor_thr) / self.num_bins
-        per_num_expected = int(num_expected / self.num_bins)
-
-        sampled_inds = []
-        for i in range(self.num_bins):
-            start_iou = self.floor_thr + i * iou_interval
-            end_iou = self.floor_thr + (i + 1) * iou_interval
-            tmp_set = set(
-                np.where(
-                    np.logical_and(max_overlaps >= start_iou,
-                                   max_overlaps < end_iou))[0])
-            tmp_inds = list(tmp_set & full_set)
-            if len(tmp_inds) > per_num_expected:
-                tmp_sampled_set = self.random_choice(tmp_inds,
-                                                     per_num_expected)
-            else:
-                tmp_sampled_set = np.array(tmp_inds, dtype=np.int)
-            sampled_inds.append(tmp_sampled_set)
-
-        sampled_inds = np.concatenate(sampled_inds)
-        if len(sampled_inds) < num_expected:
-            num_extra = num_expected - len(sampled_inds)
-            extra_inds = np.array(list(full_set - set(sampled_inds)))
-            if len(extra_inds) > num_extra:
-                extra_inds = self.random_choice(extra_inds, num_extra)
-            sampled_inds = np.concatenate([sampled_inds, extra_inds])
-
-        return sampled_inds
-
-    def _sample_neg(self, assign_result, num_expected, **kwargs):
-        """Sample negative boxes.
-
-        Args:
-            assign_result (:obj:`AssignResult`): The assigned results of boxes.
-            num_expected (int): The number of expected negative samples
-
-        Returns:
-            Tensor or ndarray: sampled indices.
-        """
-        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
-        if neg_inds.numel() != 0:
-            neg_inds = neg_inds.squeeze(1)
-        if len(neg_inds) <= num_expected:
-            return neg_inds
-        else:
-            max_overlaps = assign_result.max_overlaps.cpu().numpy()
-            # balance sampling for negative samples
-            neg_set = set(neg_inds.cpu().numpy())
-
-            if self.floor_thr > 0:
-                floor_set = set(
-                    np.where(
-                        np.logical_and(max_overlaps >= 0,
-                                       max_overlaps < self.floor_thr))[0])
-                iou_sampling_set = set(
-                    np.where(max_overlaps >= self.floor_thr)[0])
-            elif self.floor_thr == 0:
-                floor_set = set(np.where(max_overlaps == 0)[0])
-                iou_sampling_set = set(
-                    np.where(max_overlaps > self.floor_thr)[0])
-            else:
-                floor_set = set()
-                iou_sampling_set = set(
-                    np.where(max_overlaps > self.floor_thr)[0])
-                # for sampling interval calculation
-                self.floor_thr = 0
-
-            floor_neg_inds = list(floor_set & neg_set)
-            iou_sampling_neg_inds = list(iou_sampling_set & neg_set)
-            num_expected_iou_sampling = int(num_expected *
-                                            (1 - self.floor_fraction))
-            if len(iou_sampling_neg_inds) > num_expected_iou_sampling:
-                if self.num_bins >= 2:
-                    iou_sampled_inds = self.sample_via_interval(
-                        max_overlaps, set(iou_sampling_neg_inds),
-                        num_expected_iou_sampling)
-                else:
-                    iou_sampled_inds = self.random_choice(
-                        iou_sampling_neg_inds, num_expected_iou_sampling)
-            else:
-                iou_sampled_inds = np.array(
-                    iou_sampling_neg_inds, dtype=np.int)
-            num_expected_floor = num_expected - len(iou_sampled_inds)
-            if len(floor_neg_inds) > num_expected_floor:
-                sampled_floor_inds = self.random_choice(
-                    floor_neg_inds, num_expected_floor)
-            else:
-                sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int)
-            sampled_inds = np.concatenate(
-                (sampled_floor_inds, iou_sampled_inds))
-            if len(sampled_inds) < num_expected:
-                num_extra = num_expected - len(sampled_inds)
-                extra_inds = np.array(list(neg_set - set(sampled_inds)))
-                if len(extra_inds) > num_extra:
-                    extra_inds = self.random_choice(extra_inds, num_extra)
-                sampled_inds = np.concatenate((sampled_inds, extra_inds))
-            sampled_inds = torch.from_numpy(sampled_inds).long().to(
-                assign_result.gt_inds.device)
-            return sampled_inds

spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/danet_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py

@@ -1,9 +0,0 @@
-_base_ = './ocrnet_hr18_512x512_40k_voc12aug.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/models_settings.py

@@ -1,219 +0,0 @@
-import json
-import re
-from pathlib import Path
-
-import yaml
-
-from modules import loaders, metadata_gguf, shared, ui
-
-
-def get_fallback_settings():
-    return {
-        'wbits': 'None',
-        'groupsize': 'None',
-        'desc_act': False,
-        'model_type': 'None',
-        'max_seq_len': 2048,
-        'n_ctx': 2048,
-        'rope_freq_base': 0,
-        'compress_pos_emb': 1,
-        'truncation_length': shared.settings['truncation_length'],
-        'skip_special_tokens': shared.settings['skip_special_tokens'],
-        'custom_stopping_strings': shared.settings['custom_stopping_strings'],
-    }
-
-
-def get_model_metadata(model):
-    model_settings = {}
-
-    # Get settings from models/config.yaml and models/config-user.yaml
-    settings = shared.model_config
-    for pat in settings:
-        if re.match(pat.lower(), model.lower()):
-            for k in settings[pat]:
-                model_settings[k] = settings[pat][k]
-
-    if 'loader' not in model_settings:
-        loader = infer_loader(model, model_settings)
-        if 'wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0:
-            loader = 'AutoGPTQ'
-
-        model_settings['loader'] = loader
-
-    # Read GGUF metadata
-    if model_settings['loader'] in ['llama.cpp', 'llamacpp_HF', 'ctransformers']:
-        path = Path(f'{shared.args.model_dir}/{model}')
-        if path.is_file():
-            model_file = path
-        else:
-            model_file = list(path.glob('*.gguf'))[0]
-
-        metadata = metadata_gguf.load_metadata(model_file)
-        if 'llama.context_length' in metadata:
-            model_settings['n_ctx'] = metadata['llama.context_length']
-        if 'llama.rope.scale_linear' in metadata:
-            model_settings['compress_pos_emb'] = metadata['llama.rope.scale_linear']
-        if 'llama.rope.freq_base' in metadata:
-            model_settings['rope_freq_base'] = metadata['llama.rope.freq_base']
-
-    else:
-        # Read transformers metadata
-        path = Path(f'{shared.args.model_dir}/{model}/config.json')
-        if path.exists():
-            metadata = json.loads(open(path, 'r').read())
-            if 'max_position_embeddings' in metadata:
-                model_settings['truncation_length'] = metadata['max_position_embeddings']
-                model_settings['max_seq_len'] = metadata['max_position_embeddings']
-
-            if 'rope_theta' in metadata:
-                model_settings['rope_freq_base'] = metadata['rope_theta']
-
-            if 'rope_scaling' in metadata and type(metadata['rope_scaling']) is dict and all(key in metadata['rope_scaling'] for key in ('type', 'factor')):
-                if metadata['rope_scaling']['type'] == 'linear':
-                    model_settings['compress_pos_emb'] = metadata['rope_scaling']['factor']
-
-            if 'quantization_config' in metadata:
-                if 'bits' in metadata['quantization_config']:
-                    model_settings['wbits'] = metadata['quantization_config']['bits']
-                if 'group_size' in metadata['quantization_config']:
-                    model_settings['groupsize'] = metadata['quantization_config']['group_size']
-                if 'desc_act' in metadata['quantization_config']:
-                    model_settings['desc_act'] = metadata['quantization_config']['desc_act']
-
-        # Read AutoGPTQ metadata
-        path = Path(f'{shared.args.model_dir}/{model}/quantize_config.json')
-        if path.exists():
-            metadata = json.loads(open(path, 'r').read())
-            if 'bits' in metadata:
-                model_settings['wbits'] = metadata['bits']
-            if 'group_size' in metadata:
-                model_settings['groupsize'] = metadata['group_size']
-            if 'desc_act' in metadata:
-                model_settings['desc_act'] = metadata['desc_act']
-
-    # Apply user settings from models/config-user.yaml
-    settings = shared.user_config
-    for pat in settings:
-        if re.match(pat.lower(), model.lower()):
-            for k in settings[pat]:
-                model_settings[k] = settings[pat][k]
-
-    return model_settings
-
-
-def infer_loader(model_name, model_settings):
-    path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
-    if not path_to_model.exists():
-        loader = None
-    elif (path_to_model / 'quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
-        loader = 'AutoGPTQ'
-    elif (path_to_model / 'quant_config.json').exists() or re.match(r'.*-awq', model_name.lower()):
-        loader = 'AutoAWQ'
-    elif len(list(path_to_model.glob('*.gguf'))) > 0:
-        loader = 'llama.cpp'
-    elif re.match(r'.*\.gguf', model_name.lower()):
-        loader = 'llama.cpp'
-    elif re.match(r'.*rwkv.*\.pth', model_name.lower()):
-        loader = 'RWKV'
-    elif re.match(r'.*exl2', model_name.lower()):
-        loader = 'ExLlamav2_HF'
-    else:
-        loader = 'Transformers'
-
-    return loader
-
-
-# UI: update the command-line arguments based on the interface values
-def update_model_parameters(state, initial=False):
-    elements = ui.list_model_elements()  # the names of the parameters
-    gpu_memories = []
-
-    for i, element in enumerate(elements):
-        if element not in state:
-            continue
-
-        value = state[element]
-        if element.startswith('gpu_memory'):
-            gpu_memories.append(value)
-            continue
-
-        if initial and element in shared.provided_arguments:
-            continue
-
-        # Setting null defaults
-        if element in ['wbits', 'groupsize', 'model_type'] and value == 'None':
-            value = vars(shared.args_defaults)[element]
-        elif element in ['cpu_memory'] and value == 0:
-            value = vars(shared.args_defaults)[element]
-
-        # Making some simple conversions
-        if element in ['wbits', 'groupsize', 'pre_layer']:
-            value = int(value)
-        elif element == 'cpu_memory' and value is not None:
-            value = f"{value}MiB"
-
-        if element in ['pre_layer']:
-            value = [value] if value > 0 else None
-
-        setattr(shared.args, element, value)
-
-    found_positive = False
-    for i in gpu_memories:
-        if i > 0:
-            found_positive = True
-            break
-
-    if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']):
-        if found_positive:
-            shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
-        else:
-            shared.args.gpu_memory = None
-
-
-# UI: update the state variable with the model settings
-def apply_model_settings_to_state(model, state):
-    model_settings = get_model_metadata(model)
-    if 'loader' in model_settings:
-        loader = model_settings.pop('loader')
-
-        # If the user is using an alternative loader for the same model type, let them keep using it
-        if not (loader == 'AutoGPTQ' and state['loader'] in ['GPTQ-for-LLaMa', 'ExLlama', 'ExLlama_HF', 'ExLlamav2', 'ExLlamav2_HF']) and not (loader == 'llama.cpp' and state['loader'] in ['llamacpp_HF', 'ctransformers']):
-            state['loader'] = loader
-
-    for k in model_settings:
-        if k in state:
-            if k in ['wbits', 'groupsize']:
-                state[k] = str(model_settings[k])
-            else:
-                state[k] = model_settings[k]
-
-    return state
-
-
-# Save the settings for this model to models/config-user.yaml
-def save_model_settings(model, state):
-    if model == 'None':
-        yield ("Not saving the settings because no model is loaded.")
-        return
-
-    with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
-        if p.exists():
-            user_config = yaml.safe_load(open(p, 'r').read())
-        else:
-            user_config = {}
-
-        model_regex = model + '$'  # For exact matches
-        if model_regex not in user_config:
-            user_config[model_regex] = {}
-
-        for k in ui.list_model_elements():
-            if k == 'loader' or k in loaders.loaders_and_params[state['loader']]:
-                user_config[model_regex][k] = state[k]
-
-        shared.user_config = user_config
-
-        output = yaml.dump(user_config, sort_keys=False)
-        with open(p, 'w') as f:
-            f.write(output)
-
-        yield (f"Settings for {model} saved to {p}")

spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/dpt_depth.py

@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-

spaces/Anonymous-sub/Rerender/gmflow_module/utils/frame_utils.py

@@ -1,131 +0,0 @@
-import numpy as np
-from PIL import Image
-from os.path import *
-import re
-import cv2
-
-TAG_CHAR = np.array([202021.25], np.float32)
-
-
-def readFlow(fn):
-    """ Read .flo file in Middlebury format"""
-    # Code adapted from:
-    # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
-
-    # WARNING: this will work on little-endian architectures (eg Intel x86) only!
-    # print 'fn = %s'%(fn)
-    with open(fn, 'rb') as f:
-        magic = np.fromfile(f, np.float32, count=1)
-        if 202021.25 != magic:
-            print('Magic number incorrect. Invalid .flo file')
-            return None
-        else:
-            w = np.fromfile(f, np.int32, count=1)
-            h = np.fromfile(f, np.int32, count=1)
-            # print 'Reading %d x %d flo file\n' % (w, h)
-            data = np.fromfile(f, np.float32, count=2 * int(w) * int(h))
-            # Reshape testdata into 3D array (columns, rows, bands)
-            # The reshape here is for visualization, the original code is (w,h,2)
-            return np.resize(data, (int(h), int(w), 2))
-
-
-def readPFM(file):
-    file = open(file, 'rb')
-
-    color = None
-    width = None
-    height = None
-    scale = None
-    endian = None
-
-    header = file.readline().rstrip()
-    if header == b'PF':
-        color = True
-    elif header == b'Pf':
-        color = False
-    else:
-        raise Exception('Not a PFM file.')
-
-    dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline())
-    if dim_match:
-        width, height = map(int, dim_match.groups())
-    else:
-        raise Exception('Malformed PFM header.')
-
-    scale = float(file.readline().rstrip())
-    if scale < 0:  # little-endian
-        endian = '<'
-        scale = -scale
-    else:
-        endian = '>'  # big-endian
-
-    data = np.fromfile(file, endian + 'f')
-    shape = (height, width, 3) if color else (height, width)
-
-    data = np.reshape(data, shape)
-    data = np.flipud(data)
-    return data
-
-
-def writeFlow(filename, uv, v=None):
-    """ Write optical flow to file.
-
-    If v is None, uv is assumed to contain both u and v channels,
-    stacked in depth.
-    Original code by Deqing Sun, adapted from Daniel Scharstein.
-    """
-    nBands = 2
-
-    if v is None:
-        assert (uv.ndim == 3)
-        assert (uv.shape[2] == 2)
-        u = uv[:, :, 0]
-        v = uv[:, :, 1]
-    else:
-        u = uv
-
-    assert (u.shape == v.shape)
-    height, width = u.shape
-    f = open(filename, 'wb')
-    # write the header
-    f.write(TAG_CHAR)
-    np.array(width).astype(np.int32).tofile(f)
-    np.array(height).astype(np.int32).tofile(f)
-    # arrange into matrix form
-    tmp = np.zeros((height, width * nBands))
-    tmp[:, np.arange(width) * 2] = u
-    tmp[:, np.arange(width) * 2 + 1] = v
-    tmp.astype(np.float32).tofile(f)
-    f.close()
-
-
-def readFlowKITTI(filename):
-    flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
-    flow = flow[:, :, ::-1].astype(np.float32)
-    flow, valid = flow[:, :, :2], flow[:, :, 2]
-    flow = (flow - 2 ** 15) / 64.0
-    return flow, valid
-
-
-def writeFlowKITTI(filename, uv):
-    uv = 64.0 * uv + 2 ** 15
-    valid = np.ones([uv.shape[0], uv.shape[1], 1])
-    uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
-    cv2.imwrite(filename, uv[..., ::-1])
-
-
-def read_gen(file_name, pil=False):
-    ext = splitext(file_name)[-1]
-    if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg':
-        return Image.open(file_name)
-    elif ext == '.bin' or ext == '.raw':
-        return np.load(file_name)
-    elif ext == '.flo':
-        return readFlow(file_name).astype(np.float32)
-    elif ext == '.pfm':
-        flow = readPFM(file_name).astype(np.float32)
-        if len(flow.shape) == 2:
-            return flow
-        else:
-            return flow[:, :, :-1]
-    return []

spaces/ArkanDash/rvc-models-new/lib/infer_pack/models_onnx.py

@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/Arsenii2023/Demo1/app.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

spaces/ArtificialWF/Voice-Recognition/README.md

@@ -1,13 +0,0 @@
----
-title: Voice to Text
-emoji: 🌖
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py

@@ -1,155 +0,0 @@
-"""Utilities to lazily create and visit candidates found.
-
-Creating and visiting a candidate is a *very* costly operation. It involves
-fetching, extracting, potentially building modules from source, and verifying
-distribution metadata. It is therefore crucial for performance to keep
-everything here lazy all the way down, so we only touch candidates that we
-absolutely need, and not "download the world" when we only need one version of
-something.
-"""
-
-import functools
-from collections.abc import Sequence
-from typing import TYPE_CHECKING, Any, Callable, Iterator, Optional, Set, Tuple
-
-from pip._vendor.packaging.version import _BaseVersion
-
-from .base import Candidate
-
-IndexCandidateInfo = Tuple[_BaseVersion, Callable[[], Optional[Candidate]]]
-
-if TYPE_CHECKING:
-    SequenceCandidate = Sequence[Candidate]
-else:
-    # For compatibility: Python before 3.9 does not support using [] on the
-    # Sequence class.
-    #
-    # >>> from collections.abc import Sequence
-    # >>> Sequence[str]
-    # Traceback (most recent call last):
-    #   File "<stdin>", line 1, in <module>
-    # TypeError: 'ABCMeta' object is not subscriptable
-    #
-    # TODO: Remove this block after dropping Python 3.8 support.
-    SequenceCandidate = Sequence
-
-
-def _iter_built(infos: Iterator[IndexCandidateInfo]) -> Iterator[Candidate]:
-    """Iterator for ``FoundCandidates``.
-
-    This iterator is used when the package is not already installed. Candidates
-    from index come later in their normal ordering.
-    """
-    versions_found: Set[_BaseVersion] = set()
-    for version, func in infos:
-        if version in versions_found:
-            continue
-        candidate = func()
-        if candidate is None:
-            continue
-        yield candidate
-        versions_found.add(version)
-
-
-def _iter_built_with_prepended(
-    installed: Candidate, infos: Iterator[IndexCandidateInfo]
-) -> Iterator[Candidate]:
-    """Iterator for ``FoundCandidates``.
-
-    This iterator is used when the resolver prefers the already-installed
-    candidate and NOT to upgrade. The installed candidate is therefore
-    always yielded first, and candidates from index come later in their
-    normal ordering, except skipped when the version is already installed.
-    """
-    yield installed
-    versions_found: Set[_BaseVersion] = {installed.version}
-    for version, func in infos:
-        if version in versions_found:
-            continue
-        candidate = func()
-        if candidate is None:
-            continue
-        yield candidate
-        versions_found.add(version)
-
-
-def _iter_built_with_inserted(
-    installed: Candidate, infos: Iterator[IndexCandidateInfo]
-) -> Iterator[Candidate]:
-    """Iterator for ``FoundCandidates``.
-
-    This iterator is used when the resolver prefers to upgrade an
-    already-installed package. Candidates from index are returned in their
-    normal ordering, except replaced when the version is already installed.
-
-    The implementation iterates through and yields other candidates, inserting
-    the installed candidate exactly once before we start yielding older or
-    equivalent candidates, or after all other candidates if they are all newer.
-    """
-    versions_found: Set[_BaseVersion] = set()
-    for version, func in infos:
-        if version in versions_found:
-            continue
-        # If the installed candidate is better, yield it first.
-        if installed.version >= version:
-            yield installed
-            versions_found.add(installed.version)
-        candidate = func()
-        if candidate is None:
-            continue
-        yield candidate
-        versions_found.add(version)
-
-    # If the installed candidate is older than all other candidates.
-    if installed.version not in versions_found:
-        yield installed
-
-
-class FoundCandidates(SequenceCandidate):
-    """A lazy sequence to provide candidates to the resolver.
-
-    The intended usage is to return this from `find_matches()` so the resolver
-    can iterate through the sequence multiple times, but only access the index
-    page when remote packages are actually needed. This improve performances
-    when suitable candidates are already installed on disk.
-    """
-
-    def __init__(
-        self,
-        get_infos: Callable[[], Iterator[IndexCandidateInfo]],
-        installed: Optional[Candidate],
-        prefers_installed: bool,
-        incompatible_ids: Set[int],
-    ):
-        self._get_infos = get_infos
-        self._installed = installed
-        self._prefers_installed = prefers_installed
-        self._incompatible_ids = incompatible_ids
-
-    def __getitem__(self, index: Any) -> Any:
-        # Implemented to satisfy the ABC check. This is not needed by the
-        # resolver, and should not be used by the provider either (for
-        # performance reasons).
-        raise NotImplementedError("don't do this")
-
-    def __iter__(self) -> Iterator[Candidate]:
-        infos = self._get_infos()
-        if not self._installed:
-            iterator = _iter_built(infos)
-        elif self._prefers_installed:
-            iterator = _iter_built_with_prepended(self._installed, infos)
-        else:
-            iterator = _iter_built_with_inserted(self._installed, infos)
-        return (c for c in iterator if id(c) not in self._incompatible_ids)
-
-    def __len__(self) -> int:
-        # Implemented to satisfy the ABC check. This is not needed by the
-        # resolver, and should not be used by the provider either (for
-        # performance reasons).
-        raise NotImplementedError("don't do this")
-
-    @functools.lru_cache(maxsize=1)
-    def __bool__(self) -> bool:
-        if self._prefers_installed and self._installed:
-            return True
-        return any(self)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/specifiers.py

@@ -1,802 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import abc
-import functools
-import itertools
-import re
-import warnings
-from typing import (
-    Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    TypeVar,
-    Union,
-)
-
-from .utils import canonicalize_version
-from .version import LegacyVersion, Version, parse
-
-ParsedVersion = Union[Version, LegacyVersion]
-UnparsedVersion = Union[Version, LegacyVersion, str]
-VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion)
-CallableOperator = Callable[[ParsedVersion, str], bool]
-
-
-class InvalidSpecifier(ValueError):
-    """
-    An invalid specifier was found, users should refer to PEP 440.
-    """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
-    @abc.abstractmethod
-    def __str__(self) -> str:
-        """
-        Returns the str representation of this Specifier like object. This
-        should be representative of the Specifier itself.
-        """
-
-    @abc.abstractmethod
-    def __hash__(self) -> int:
-        """
-        Returns a hash value for this Specifier like object.
-        """
-
-    @abc.abstractmethod
-    def __eq__(self, other: object) -> bool:
-        """
-        Returns a boolean representing whether or not the two Specifier like
-        objects are equal.
-        """
-
-    @abc.abstractproperty
-    def prereleases(self) -> Optional[bool]:
-        """
-        Returns whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        """
-        Sets whether or not pre-releases as a whole are allowed by this
-        specifier.
-        """
-
-    @abc.abstractmethod
-    def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
-        """
-        Determines if the given item is contained within this specifier.
-        """
-
-    @abc.abstractmethod
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-        """
-        Takes an iterable of items and filters them so that only items which
-        are contained within this specifier are allowed in it.
-        """
-
-
-class _IndividualSpecifier(BaseSpecifier):
-
-    _operators: Dict[str, str] = {}
-    _regex: Pattern[str]
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        match = self._regex.search(spec)
-        if not match:
-            raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
-        self._spec: Tuple[str, str] = (
-            match.group("operator").strip(),
-            match.group("version").strip(),
-        )
-
-        # Store whether or not this Specifier should accept prereleases
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return "{}{}".format(*self._spec)
-
-    @property
-    def _canonical_spec(self) -> Tuple[str, str]:
-        return self._spec[0], canonicalize_version(self._spec[1])
-
-    def __hash__(self) -> int:
-        return hash(self._canonical_spec)
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, str):
-            try:
-                other = self.__class__(str(other))
-            except InvalidSpecifier:
-                return NotImplemented
-        elif not isinstance(other, self.__class__):
-            return NotImplemented
-
-        return self._canonical_spec == other._canonical_spec
-
-    def _get_operator(self, op: str) -> CallableOperator:
-        operator_callable: CallableOperator = getattr(
-            self, f"_compare_{self._operators[op]}"
-        )
-        return operator_callable
-
-    def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion:
-        if not isinstance(version, (LegacyVersion, Version)):
-            version = parse(version)
-        return version
-
-    @property
-    def operator(self) -> str:
-        return self._spec[0]
-
-    @property
-    def version(self) -> str:
-        return self._spec[1]
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-        return self._prereleases
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: str) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Determine if prereleases are to be allowed or not.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # Normalize item to a Version or LegacyVersion, this allows us to have
-        # a shortcut for ``"2.0" in Specifier(">=2")
-        normalized_item = self._coerce_version(item)
-
-        # Determine if we should be supporting prereleases in this specifier
-        # or not, if we do not support prereleases than we can short circuit
-        # logic if this version is a prereleases.
-        if normalized_item.is_prerelease and not prereleases:
-            return False
-
-        # Actually do the comparison to determine if this item is contained
-        # within this Specifier or not.
-        operator_callable: CallableOperator = self._get_operator(self.operator)
-        return operator_callable(normalized_item, self.version)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        yielded = False
-        found_prereleases = []
-
-        kw = {"prereleases": prereleases if prereleases is not None else True}
-
-        # Attempt to iterate over all the values in the iterable and if any of
-        # them match, yield them.
-        for version in iterable:
-            parsed_version = self._coerce_version(version)
-
-            if self.contains(parsed_version, **kw):
-                # If our version is a prerelease, and we were not set to allow
-                # prereleases, then we'll store it for later in case nothing
-                # else matches this specifier.
-                if parsed_version.is_prerelease and not (
-                    prereleases or self.prereleases
-                ):
-                    found_prereleases.append(version)
-                # Either this is not a prerelease, or we should have been
-                # accepting prereleases from the beginning.
-                else:
-                    yielded = True
-                    yield version
-
-        # Now that we've iterated over everything, determine if we've yielded
-        # any values, and if we have not and we have any prereleases stored up
-        # then we will go ahead and yield the prereleases.
-        if not yielded and found_prereleases:
-            for version in found_prereleases:
-                yield version
-
-
-class LegacySpecifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(==|!=|<=|>=|<|>))
-        \s*
-        (?P<version>
-            [^,;\s)]* # Since this is a "legacy" specifier, and the version
-                      # string can be just about anything, we match everything
-                      # except for whitespace, a semi-colon for marker support,
-                      # a closing paren since versions can be enclosed in
-                      # them, and a comma since it's a version separator.
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-    }
-
-    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
-        super().__init__(spec, prereleases)
-
-        warnings.warn(
-            "Creating a LegacyVersion has been deprecated and will be "
-            "removed in the next major release",
-            DeprecationWarning,
-        )
-
-    def _coerce_version(self, version: UnparsedVersion) -> LegacyVersion:
-        if not isinstance(version, LegacyVersion):
-            version = LegacyVersion(str(version))
-        return version
-
-    def _compare_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective == self._coerce_version(spec)
-
-    def _compare_not_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective != self._coerce_version(spec)
-
-    def _compare_less_than_equal(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective <= self._coerce_version(spec)
-
-    def _compare_greater_than_equal(
-        self, prospective: LegacyVersion, spec: str
-    ) -> bool:
-        return prospective >= self._coerce_version(spec)
-
-    def _compare_less_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective < self._coerce_version(spec)
-
-    def _compare_greater_than(self, prospective: LegacyVersion, spec: str) -> bool:
-        return prospective > self._coerce_version(spec)
-
-
-def _require_version_compare(
-    fn: Callable[["Specifier", ParsedVersion, str], bool]
-) -> Callable[["Specifier", ParsedVersion, str], bool]:
-    @functools.wraps(fn)
-    def wrapped(self: "Specifier", prospective: ParsedVersion, spec: str) -> bool:
-        if not isinstance(prospective, Version):
-            return False
-        return fn(self, prospective, spec)
-
-    return wrapped
-
-
-class Specifier(_IndividualSpecifier):
-
-    _regex_str = r"""
-        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
-        (?P<version>
-            (?:
-                # The identity operators allow for an escape hatch that will
-                # do an exact string match of the version you wish to install.
-                # This will not be parsed by PEP 440 and we cannot determine
-                # any semantic meaning from it. This operator is discouraged
-                # but included entirely as an escape hatch.
-                (?<====)  # Only match for the identity operator
-                \s*
-                [^\s]*    # We just match everything, except for whitespace
-                          # since we are only testing for strict identity.
-            )
-            |
-            (?:
-                # The (non)equality operators allow for wild card and local
-                # versions to be specified so we have to define these two
-                # operators separately to enable that.
-                (?<===|!=)            # Only match for equals and not equals
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-
-                # You cannot use a wild card and a dev or local version
-                # together so group them with a | and make them optional.
-                (?:
-                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
-                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
-                    |
-                    \.\*  # Wild card syntax of .*
-                )?
-            )
-            |
-            (?:
-                # The compatible operator requires at least two digits in the
-                # release segment.
-                (?<=~=)               # Only match for the compatible operator
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-            |
-            (?:
-                # All other operators only allow a sub set of what the
-                # (non)equality operators do. Specifically they do not allow
-                # local versions to be specified nor do they allow the prefix
-                # matching wild cards.
-                (?<!==|!=|~=)         # We have special cases for these
-                                      # operators so we want to make sure they
-                                      # don't match here.
-
-                \s*
-                v?
-                (?:[0-9]+!)?          # epoch
-                [0-9]+(?:\.[0-9]+)*   # release
-                (?:                   # pre release
-                    [-_\.]?
-                    (a|b|c|rc|alpha|beta|pre|preview)
-                    [-_\.]?
-                    [0-9]*
-                )?
-                (?:                                   # post release
-                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
-                )?
-                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
-            )
-        )
-        """
-
-    _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
-    _operators = {
-        "~=": "compatible",
-        "==": "equal",
-        "!=": "not_equal",
-        "<=": "less_than_equal",
-        ">=": "greater_than_equal",
-        "<": "less_than",
-        ">": "greater_than",
-        "===": "arbitrary",
-    }
-
-    @_require_version_compare
-    def _compare_compatible(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # Compatible releases have an equivalent combination of >= and ==. That
-        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
-        # implement this in terms of the other specifiers instead of
-        # implementing it ourselves. The only thing we need to do is construct
-        # the other specifiers.
-
-        # We want everything but the last item in the version, but we want to
-        # ignore suffix segments.
-        prefix = ".".join(
-            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
-        )
-
-        # Add the prefix notation to the end of our string
-        prefix += ".*"
-
-        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
-            prospective, prefix
-        )
-
-    @_require_version_compare
-    def _compare_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # We need special logic to handle prefix matching
-        if spec.endswith(".*"):
-            # In the case of prefix matching we want to ignore local segment.
-            prospective = Version(prospective.public)
-            # Split the spec out by dots, and pretend that there is an implicit
-            # dot in between a release segment and a pre-release segment.
-            split_spec = _version_split(spec[:-2])  # Remove the trailing .*
-
-            # Split the prospective version out by dots, and pretend that there
-            # is an implicit dot in between a release segment and a pre-release
-            # segment.
-            split_prospective = _version_split(str(prospective))
-
-            # Shorten the prospective version to be the same length as the spec
-            # so that we can determine if the specifier is a prefix of the
-            # prospective version or not.
-            shortened_prospective = split_prospective[: len(split_spec)]
-
-            # Pad out our two sides with zeros so that they both equal the same
-            # length.
-            padded_spec, padded_prospective = _pad_version(
-                split_spec, shortened_prospective
-            )
-
-            return padded_prospective == padded_spec
-        else:
-            # Convert our spec string into a Version
-            spec_version = Version(spec)
-
-            # If the specifier does not have a local segment, then we want to
-            # act as if the prospective version also does not have a local
-            # segment.
-            if not spec_version.local:
-                prospective = Version(prospective.public)
-
-            return prospective == spec_version
-
-    @_require_version_compare
-    def _compare_not_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-        return not self._compare_equal(prospective, spec)
-
-    @_require_version_compare
-    def _compare_less_than_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) <= Version(spec)
-
-    @_require_version_compare
-    def _compare_greater_than_equal(
-        self, prospective: ParsedVersion, spec: str
-    ) -> bool:
-
-        # NB: Local version identifiers are NOT permitted in the version
-        # specifier, so local version labels can be universally removed from
-        # the prospective version.
-        return Version(prospective.public) >= Version(spec)
-
-    @_require_version_compare
-    def _compare_less_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is less than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective < spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a pre-release version, that we do not accept pre-release
-        # versions for the version mentioned in the specifier (e.g. <3.1 should
-        # not match 3.1.dev0, but should match 3.0.dev0).
-        if not spec.is_prerelease and prospective.is_prerelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # less than the spec version *and* it's not a pre-release of the same
-        # version in the spec.
-        return True
-
-    @_require_version_compare
-    def _compare_greater_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
-        # Convert our spec to a Version instance, since we'll want to work with
-        # it as a version.
-        spec = Version(spec_str)
-
-        # Check to see if the prospective version is greater than the spec
-        # version. If it's not we can short circuit and just return False now
-        # instead of doing extra unneeded work.
-        if not prospective > spec:
-            return False
-
-        # This special case is here so that, unless the specifier itself
-        # includes is a post-release version, that we do not accept
-        # post-release versions for the version mentioned in the specifier
-        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
-        if not spec.is_postrelease and prospective.is_postrelease:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # Ensure that we do not allow a local version of the version mentioned
-        # in the specifier, which is technically greater than, to match.
-        if prospective.local is not None:
-            if Version(prospective.base_version) == Version(spec.base_version):
-                return False
-
-        # If we've gotten to here, it means that prospective version is both
-        # greater than the spec version *and* it's not a pre-release of the
-        # same version in the spec.
-        return True
-
-    def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
-        return str(prospective).lower() == str(spec).lower()
-
-    @property
-    def prereleases(self) -> bool:
-
-        # If there is an explicit prereleases set for this, then we'll just
-        # blindly use that.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # Look at all of our specifiers and determine if they are inclusive
-        # operators, and if they are if they are including an explicit
-        # prerelease.
-        operator, version = self._spec
-        if operator in ["==", ">=", "<=", "~=", "==="]:
-            # The == specifier can include a trailing .*, if it does we
-            # want to remove before parsing.
-            if operator == "==" and version.endswith(".*"):
-                version = version[:-2]
-
-            # Parse the version, and if it is a pre-release than this
-            # specifier allows pre-releases.
-            if parse(version).is_prerelease:
-                return True
-
-        return False
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
-    result: List[str] = []
-    for item in version.split("."):
-        match = _prefix_regex.search(item)
-        if match:
-            result.extend(match.groups())
-        else:
-            result.append(item)
-    return result
-
-
-def _is_not_suffix(segment: str) -> bool:
-    return not any(
-        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
-    )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
-    left_split, right_split = [], []
-
-    # Get the release segment of our versions
-    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
-    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
-    # Get the rest of our versions
-    left_split.append(left[len(left_split[0]) :])
-    right_split.append(right[len(right_split[0]) :])
-
-    # Insert our padding
-    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
-    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
-    return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
-    def __init__(
-        self, specifiers: str = "", prereleases: Optional[bool] = None
-    ) -> None:
-
-        # Split on , to break each individual specifier into it's own item, and
-        # strip each item to remove leading/trailing whitespace.
-        split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
-        # Parsed each individual specifier, attempting first to make it a
-        # Specifier and falling back to a LegacySpecifier.
-        parsed: Set[_IndividualSpecifier] = set()
-        for specifier in split_specifiers:
-            try:
-                parsed.add(Specifier(specifier))
-            except InvalidSpecifier:
-                parsed.add(LegacySpecifier(specifier))
-
-        # Turn our parsed specifiers into a frozen set and save them for later.
-        self._specs = frozenset(parsed)
-
-        # Store our prereleases value so we can use it later to determine if
-        # we accept prereleases or not.
-        self._prereleases = prereleases
-
-    def __repr__(self) -> str:
-        pre = (
-            f", prereleases={self.prereleases!r}"
-            if self._prereleases is not None
-            else ""
-        )
-
-        return f"<SpecifierSet({str(self)!r}{pre})>"
-
-    def __str__(self) -> str:
-        return ",".join(sorted(str(s) for s in self._specs))
-
-    def __hash__(self) -> int:
-        return hash(self._specs)
-
-    def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
-        if isinstance(other, str):
-            other = SpecifierSet(other)
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        specifier = SpecifierSet()
-        specifier._specs = frozenset(self._specs | other._specs)
-
-        if self._prereleases is None and other._prereleases is not None:
-            specifier._prereleases = other._prereleases
-        elif self._prereleases is not None and other._prereleases is None:
-            specifier._prereleases = self._prereleases
-        elif self._prereleases == other._prereleases:
-            specifier._prereleases = self._prereleases
-        else:
-            raise ValueError(
-                "Cannot combine SpecifierSets with True and False prerelease "
-                "overrides."
-            )
-
-        return specifier
-
-    def __eq__(self, other: object) -> bool:
-        if isinstance(other, (str, _IndividualSpecifier)):
-            other = SpecifierSet(str(other))
-        elif not isinstance(other, SpecifierSet):
-            return NotImplemented
-
-        return self._specs == other._specs
-
-    def __len__(self) -> int:
-        return len(self._specs)
-
-    def __iter__(self) -> Iterator[_IndividualSpecifier]:
-        return iter(self._specs)
-
-    @property
-    def prereleases(self) -> Optional[bool]:
-
-        # If we have been given an explicit prerelease modifier, then we'll
-        # pass that through here.
-        if self._prereleases is not None:
-            return self._prereleases
-
-        # If we don't have any specifiers, and we don't have a forced value,
-        # then we'll just return None since we don't know if this should have
-        # pre-releases or not.
-        if not self._specs:
-            return None
-
-        # Otherwise we'll see if any of the given specifiers accept
-        # prereleases, if any of them do we'll return True, otherwise False.
-        return any(s.prereleases for s in self._specs)
-
-    @prereleases.setter
-    def prereleases(self, value: bool) -> None:
-        self._prereleases = value
-
-    def __contains__(self, item: UnparsedVersion) -> bool:
-        return self.contains(item)
-
-    def contains(
-        self, item: UnparsedVersion, prereleases: Optional[bool] = None
-    ) -> bool:
-
-        # Ensure that our item is a Version or LegacyVersion instance.
-        if not isinstance(item, (LegacyVersion, Version)):
-            item = parse(item)
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # We can determine if we're going to allow pre-releases by looking to
-        # see if any of the underlying items supports them. If none of them do
-        # and this item is a pre-release then we do not allow it and we can
-        # short circuit that here.
-        # Note: This means that 1.0.dev1 would not be contained in something
-        #       like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
-        if not prereleases and item.is_prerelease:
-            return False
-
-        # We simply dispatch to the underlying specs here to make sure that the
-        # given version is contained within all of them.
-        # Note: This use of all() here means that an empty set of specifiers
-        #       will always return True, this is an explicit design decision.
-        return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
-    def filter(
-        self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
-    ) -> Iterable[VersionTypeVar]:
-
-        # Determine if we're forcing a prerelease or not, if we're not forcing
-        # one for this particular filter call, then we'll use whatever the
-        # SpecifierSet thinks for whether or not we should support prereleases.
-        if prereleases is None:
-            prereleases = self.prereleases
-
-        # If we have any specifiers, then we want to wrap our iterable in the
-        # filter method for each one, this will act as a logical AND amongst
-        # each specifier.
-        if self._specs:
-            for spec in self._specs:
-                iterable = spec.filter(iterable, prereleases=bool(prereleases))
-            return iterable
-        # If we do not have any specifiers, then we need to have a rough filter
-        # which will filter out any pre-releases, unless there are no final
-        # releases, and which will filter out LegacyVersion in general.
-        else:
-            filtered: List[VersionTypeVar] = []
-            found_prereleases: List[VersionTypeVar] = []
-
-            item: UnparsedVersion
-            parsed_version: Union[Version, LegacyVersion]
-
-            for item in iterable:
-                # Ensure that we some kind of Version class for this item.
-                if not isinstance(item, (LegacyVersion, Version)):
-                    parsed_version = parse(item)
-                else:
-                    parsed_version = item
-
-                # Filter out any item which is parsed as a LegacyVersion
-                if isinstance(parsed_version, LegacyVersion):
-                    continue
-
-                # Store any item which is a pre-release for later unless we've
-                # already found a final version or we are accepting prereleases
-                if parsed_version.is_prerelease and not prereleases:
-                    if not filtered:
-                        found_prereleases.append(item)
-                else:
-                    filtered.append(item)
-
-            # If we've found no items except for pre-releases, then we'll go
-            # ahead and use the pre-releases
-            if not filtered and found_prereleases and prereleases is None:
-                return found_prereleases
-
-            return filtered

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/common/models/retinanet.py

@@ -1,53 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.meta_arch import RetinaNet
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone.fpn import LastLevelP6P7
-from detectron2.modeling.backbone import BasicStem, FPN, ResNet
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.meta_arch.retinanet import RetinaNetHead
-
-model = L(RetinaNet)(
-    backbone=L(FPN)(
-        bottom_up=L(ResNet)(
-            stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
-            stages=L(ResNet.make_default_stages)(
-                depth=50,
-                stride_in_1x1=True,
-                norm="FrozenBN",
-            ),
-            out_features=["res3", "res4", "res5"],
-        ),
-        in_features=["res3", "res4", "res5"],
-        out_channels=256,
-        top_block=L(LastLevelP6P7)(in_channels=2048, out_channels="${..out_channels}"),
-    ),
-    head=L(RetinaNetHead)(
-        # Shape for each input feature map
-        input_shape=[ShapeSpec(channels=256)] * 5,
-        num_classes="${..num_classes}",
-        conv_dims=[256, 256, 256, 256],
-        prior_prob=0.01,
-        num_anchors=9,
-    ),
-    anchor_generator=L(DefaultAnchorGenerator)(
-        sizes=[[x, x * 2 ** (1.0 / 3), x * 2 ** (2.0 / 3)] for x in [32, 64, 128, 256, 512]],
-        aspect_ratios=[0.5, 1.0, 2.0],
-        strides=[8, 16, 32, 64, 128],
-        offset=0.0,
-    ),
-    box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
-    anchor_matcher=L(Matcher)(
-        thresholds=[0.4, 0.5], labels=[0, -1, 1], allow_low_quality_matches=True
-    ),
-    num_classes=80,
-    head_in_features=["p3", "p4", "p5", "p6", "p7"],
-    focal_loss_alpha=0.25,
-    focal_loss_gamma=2.0,
-    pixel_mean=[103.530, 116.280, 123.675],
-    pixel_std=[1.0, 1.0, 1.0],
-    input_format="BGR",
-)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rpn.py

@@ -1,533 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import Dict, List, Optional, Tuple, Union
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, cat
-from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.memory import retry_if_cuda_oom
-from detectron2.utils.registry import Registry
-
-from ..anchor_generator import build_anchor_generator
-from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
-from ..matcher import Matcher
-from ..sampling import subsample_labels
-from .build import PROPOSAL_GENERATOR_REGISTRY
-from .proposal_utils import find_top_rpn_proposals
-
-RPN_HEAD_REGISTRY = Registry("RPN_HEAD")
-RPN_HEAD_REGISTRY.__doc__ = """
-Registry for RPN heads, which take feature maps and perform
-objectness classification and bounding box regression for anchors.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    L: number of feature maps per image on which RPN is run
-    A: number of cell anchors (must be the same for all feature maps)
-    Hi, Wi: height and width of the i-th feature map
-    B: size of the box parameterization
-
-Naming convention:
-
-    objectness: refers to the binary classification of an anchor as object vs. not object.
-
-    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransform`), or 5d for rotated boxes.
-
-    pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use
-        sigmoid(pred_objectness_logits) to estimate P(object).
-
-    gt_labels: ground-truth binary classification labels for objectness
-
-    pred_anchor_deltas: predicted box2box transform deltas
-
-    gt_anchor_deltas: ground-truth box2box transform deltas
-"""
-
-
-def build_rpn_head(cfg, input_shape):
-    """
-    Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.
-    """
-    name = cfg.MODEL.RPN.HEAD_NAME
-    return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape)
-
-
-@RPN_HEAD_REGISTRY.register()
-class StandardRPNHead(nn.Module):
-    """
-    Standard RPN classification and regression heads described in :paper:`Faster R-CNN`.
-    Uses a 3x3 conv to produce a shared hidden state from which one 1x1 conv predicts
-    objectness logits for each anchor and a second 1x1 conv predicts bounding-box deltas
-    specifying how to deform each anchor into an object proposal.
-    """
-
-    @configurable
-    def __init__(
-        self, *, in_channels: int, num_anchors: int, box_dim: int = 4, conv_dims: List[int] = (-1,)
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            in_channels (int): number of input feature channels. When using multiple
-                input features, they must have the same number of channels.
-            num_anchors (int): number of anchors to predict for *each spatial position*
-                on the feature map. The total number of anchors for each
-                feature map will be `num_anchors * H * W`.
-            box_dim (int): dimension of a box, which is also the number of box regression
-                predictions to make for each anchor. An axis aligned box has
-                box_dim=4, while a rotated box has box_dim=5.
-            conv_dims (list[int]): a list of integers representing the output channels
-                of N conv layers. Set it to -1 to use the same number of output channels
-                as input channels.
-        """
-        super().__init__()
-        cur_channels = in_channels
-        # Keeping the old variable names and structure for backwards compatiblity.
-        # Otherwise the old checkpoints will fail to load.
-        if len(conv_dims) == 1:
-            out_channels = cur_channels if conv_dims[0] == -1 else conv_dims[0]
-            # 3x3 conv for the hidden representation
-            self.conv = self._get_rpn_conv(cur_channels, out_channels)
-            cur_channels = out_channels
-        else:
-            self.conv = nn.Sequential()
-            for k, conv_dim in enumerate(conv_dims):
-                out_channels = cur_channels if conv_dim == -1 else conv_dim
-                if out_channels <= 0:
-                    raise ValueError(
-                        f"Conv output channels should be greater than 0. Got {out_channels}"
-                    )
-                conv = self._get_rpn_conv(cur_channels, out_channels)
-                self.conv.add_module(f"conv{k}", conv)
-                cur_channels = out_channels
-        # 1x1 conv for predicting objectness logits
-        self.objectness_logits = nn.Conv2d(cur_channels, num_anchors, kernel_size=1, stride=1)
-        # 1x1 conv for predicting box2box transform deltas
-        self.anchor_deltas = nn.Conv2d(cur_channels, num_anchors * box_dim, kernel_size=1, stride=1)
-
-        # Keeping the order of weights initialization same for backwards compatiblility.
-        for layer in self.modules():
-            if isinstance(layer, nn.Conv2d):
-                nn.init.normal_(layer.weight, std=0.01)
-                nn.init.constant_(layer.bias, 0)
-
-    def _get_rpn_conv(self, in_channels, out_channels):
-        return Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            activation=nn.ReLU(),
-        )
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # Standard RPN is shared across levels:
-        in_channels = [s.channels for s in input_shape]
-        assert len(set(in_channels)) == 1, "Each level must have the same channel!"
-        in_channels = in_channels[0]
-
-        # RPNHead should take the same input as anchor generator
-        # NOTE: it assumes that creating an anchor generator does not have unwanted side effect.
-        anchor_generator = build_anchor_generator(cfg, input_shape)
-        num_anchors = anchor_generator.num_anchors
-        box_dim = anchor_generator.box_dim
-        assert (
-            len(set(num_anchors)) == 1
-        ), "Each level must have the same number of anchors per spatial position"
-        return {
-            "in_channels": in_channels,
-            "num_anchors": num_anchors[0],
-            "box_dim": box_dim,
-            "conv_dims": cfg.MODEL.RPN.CONV_DIMS,
-        }
-
-    def forward(self, features: List[torch.Tensor]):
-        """
-        Args:
-            features (list[Tensor]): list of feature maps
-
-        Returns:
-            list[Tensor]: A list of L elements.
-                Element i is a tensor of shape (N, A, Hi, Wi) representing
-                the predicted objectness logits for all anchors. A is the number of cell anchors.
-            list[Tensor]: A list of L elements. Element i is a tensor of shape
-                (N, A*box_dim, Hi, Wi) representing the predicted "deltas" used to transform anchors
-                to proposals.
-        """
-        pred_objectness_logits = []
-        pred_anchor_deltas = []
-        for x in features:
-            t = self.conv(x)
-            pred_objectness_logits.append(self.objectness_logits(t))
-            pred_anchor_deltas.append(self.anchor_deltas(t))
-        return pred_objectness_logits, pred_anchor_deltas
-
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class RPN(nn.Module):
-    """
-    Region Proposal Network, introduced by :paper:`Faster R-CNN`.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        in_features: List[str],
-        head: nn.Module,
-        anchor_generator: nn.Module,
-        anchor_matcher: Matcher,
-        box2box_transform: Box2BoxTransform,
-        batch_size_per_image: int,
-        positive_fraction: float,
-        pre_nms_topk: Tuple[float, float],
-        post_nms_topk: Tuple[float, float],
-        nms_thresh: float = 0.7,
-        min_box_size: float = 0.0,
-        anchor_boundary_thresh: float = -1.0,
-        loss_weight: Union[float, Dict[str, float]] = 1.0,
-        box_reg_loss_type: str = "smooth_l1",
-        smooth_l1_beta: float = 0.0,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            in_features (list[str]): list of names of input features to use
-            head (nn.Module): a module that predicts logits and regression deltas
-                for each level from a list of per-level features
-            anchor_generator (nn.Module): a module that creates anchors from a
-                list of features. Usually an instance of :class:`AnchorGenerator`
-            anchor_matcher (Matcher): label the anchors by matching them with ground truth.
-            box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
-                instance boxes
-            batch_size_per_image (int): number of anchors per image to sample for training
-            positive_fraction (float): fraction of foreground anchors to sample for training
-            pre_nms_topk (tuple[float]): (train, test) that represents the
-                number of top k proposals to select before NMS, in
-                training and testing.
-            post_nms_topk (tuple[float]): (train, test) that represents the
-                number of top k proposals to select after NMS, in
-                training and testing.
-            nms_thresh (float): NMS threshold used to de-duplicate the predicted proposals
-            min_box_size (float): remove proposal boxes with any side smaller than this threshold,
-                in the unit of input image pixels
-            anchor_boundary_thresh (float): legacy option
-            loss_weight (float|dict): weights to use for losses. Can be single float for weighting
-                all rpn losses together, or a dict of individual weightings. Valid dict keys are:
-                    "loss_rpn_cls" - applied to classification loss
-                    "loss_rpn_loc" - applied to box regression loss
-            box_reg_loss_type (str): Loss type to use. Supported losses: "smooth_l1", "giou".
-            smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to
-                use L1 loss. Only used when `box_reg_loss_type` is "smooth_l1"
-        """
-        super().__init__()
-        self.in_features = in_features
-        self.rpn_head = head
-        self.anchor_generator = anchor_generator
-        self.anchor_matcher = anchor_matcher
-        self.box2box_transform = box2box_transform
-        self.batch_size_per_image = batch_size_per_image
-        self.positive_fraction = positive_fraction
-        # Map from self.training state to train/test settings
-        self.pre_nms_topk = {True: pre_nms_topk[0], False: pre_nms_topk[1]}
-        self.post_nms_topk = {True: post_nms_topk[0], False: post_nms_topk[1]}
-        self.nms_thresh = nms_thresh
-        self.min_box_size = float(min_box_size)
-        self.anchor_boundary_thresh = anchor_boundary_thresh
-        if isinstance(loss_weight, float):
-            loss_weight = {"loss_rpn_cls": loss_weight, "loss_rpn_loc": loss_weight}
-        self.loss_weight = loss_weight
-        self.box_reg_loss_type = box_reg_loss_type
-        self.smooth_l1_beta = smooth_l1_beta
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        in_features = cfg.MODEL.RPN.IN_FEATURES
-        ret = {
-            "in_features": in_features,
-            "min_box_size": cfg.MODEL.PROPOSAL_GENERATOR.MIN_SIZE,
-            "nms_thresh": cfg.MODEL.RPN.NMS_THRESH,
-            "batch_size_per_image": cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE,
-            "positive_fraction": cfg.MODEL.RPN.POSITIVE_FRACTION,
-            "loss_weight": {
-                "loss_rpn_cls": cfg.MODEL.RPN.LOSS_WEIGHT,
-                "loss_rpn_loc": cfg.MODEL.RPN.BBOX_REG_LOSS_WEIGHT * cfg.MODEL.RPN.LOSS_WEIGHT,
-            },
-            "anchor_boundary_thresh": cfg.MODEL.RPN.BOUNDARY_THRESH,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS),
-            "box_reg_loss_type": cfg.MODEL.RPN.BBOX_REG_LOSS_TYPE,
-            "smooth_l1_beta": cfg.MODEL.RPN.SMOOTH_L1_BETA,
-        }
-
-        ret["pre_nms_topk"] = (cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN, cfg.MODEL.RPN.PRE_NMS_TOPK_TEST)
-        ret["post_nms_topk"] = (cfg.MODEL.RPN.POST_NMS_TOPK_TRAIN, cfg.MODEL.RPN.POST_NMS_TOPK_TEST)
-
-        ret["anchor_generator"] = build_anchor_generator(cfg, [input_shape[f] for f in in_features])
-        ret["anchor_matcher"] = Matcher(
-            cfg.MODEL.RPN.IOU_THRESHOLDS, cfg.MODEL.RPN.IOU_LABELS, allow_low_quality_matches=True
-        )
-        ret["head"] = build_rpn_head(cfg, [input_shape[f] for f in in_features])
-        return ret
-
-    def _subsample_labels(self, label):
-        """
-        Randomly sample a subset of positive and negative examples, and overwrite
-        the label vector to the ignore value (-1) for all elements that are not
-        included in the sample.
-
-        Args:
-            labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned.
-        """
-        pos_idx, neg_idx = subsample_labels(
-            label, self.batch_size_per_image, self.positive_fraction, 0
-        )
-        # Fill with the ignore label (-1), then set positive and negative labels
-        label.fill_(-1)
-        label.scatter_(0, pos_idx, 1)
-        label.scatter_(0, neg_idx, 0)
-        return label
-
-    @torch.jit.unused
-    @torch.no_grad()
-    def label_and_sample_anchors(
-        self, anchors: List[Boxes], gt_instances: List[Instances]
-    ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-        """
-        Args:
-            anchors (list[Boxes]): anchors for each feature map.
-            gt_instances: the ground-truth instances for each image.
-
-        Returns:
-            list[Tensor]:
-                List of #img tensors. i-th element is a vector of labels whose length is
-                the total number of anchors across all feature maps R = sum(Hi * Wi * A).
-                Label values are in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative
-                class; 1 = positive class.
-            list[Tensor]:
-                i-th element is a Rx4 tensor. The values are the matched gt boxes for each
-                anchor. Values are undefined for those anchors not labeled as 1.
-        """
-        anchors = Boxes.cat(anchors)
-
-        gt_boxes = [x.gt_boxes for x in gt_instances]
-        image_sizes = [x.image_size for x in gt_instances]
-        del gt_instances
-
-        gt_labels = []
-        matched_gt_boxes = []
-        for image_size_i, gt_boxes_i in zip(image_sizes, gt_boxes):
-            """
-            image_size_i: (h, w) for the i-th image
-            gt_boxes_i: ground-truth boxes for i-th image
-            """
-
-            match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors)
-            matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix)
-            # Matching is memory-expensive and may result in CPU tensors. But the result is small
-            gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device)
-            del match_quality_matrix
-
-            if self.anchor_boundary_thresh >= 0:
-                # Discard anchors that go out of the boundaries of the image
-                # NOTE: This is legacy functionality that is turned off by default in Detectron2
-                anchors_inside_image = anchors.inside_box(image_size_i, self.anchor_boundary_thresh)
-                gt_labels_i[~anchors_inside_image] = -1
-
-            # A vector of labels (-1, 0, 1) for each anchor
-            gt_labels_i = self._subsample_labels(gt_labels_i)
-
-            if len(gt_boxes_i) == 0:
-                # These values won't be used anyway since the anchor is labeled as background
-                matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
-            else:
-                # TODO wasted indexing computation for ignored boxes
-                matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor
-
-            gt_labels.append(gt_labels_i)  # N,AHW
-            matched_gt_boxes.append(matched_gt_boxes_i)
-        return gt_labels, matched_gt_boxes
-
-    @torch.jit.unused
-    def losses(
-        self,
-        anchors: List[Boxes],
-        pred_objectness_logits: List[torch.Tensor],
-        gt_labels: List[torch.Tensor],
-        pred_anchor_deltas: List[torch.Tensor],
-        gt_boxes: List[torch.Tensor],
-    ) -> Dict[str, torch.Tensor]:
-        """
-        Return the losses from a set of RPN predictions and their associated ground-truth.
-
-        Args:
-            anchors (list[Boxes or RotatedBoxes]): anchors for each feature map, each
-                has shape (Hi*Wi*A, B), where B is box dimension (4 or 5).
-            pred_objectness_logits (list[Tensor]): A list of L elements.
-                Element i is a tensor of shape (N, Hi*Wi*A) representing
-                the predicted objectness logits for all anchors.
-            gt_labels (list[Tensor]): Output of :meth:`label_and_sample_anchors`.
-            pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape
-                (N, Hi*Wi*A, 4 or 5) representing the predicted "deltas" used to transform anchors
-                to proposals.
-            gt_boxes (list[Tensor]): Output of :meth:`label_and_sample_anchors`.
-
-        Returns:
-            dict[loss name -> loss value]: A dict mapping from loss name to loss value.
-                Loss names are: `loss_rpn_cls` for objectness classification and
-                `loss_rpn_loc` for proposal localization.
-        """
-        num_images = len(gt_labels)
-        gt_labels = torch.stack(gt_labels)  # (N, sum(Hi*Wi*Ai))
-
-        # Log the number of positive/negative anchors per-image that's used in training
-        pos_mask = gt_labels == 1
-        num_pos_anchors = pos_mask.sum().item()
-        num_neg_anchors = (gt_labels == 0).sum().item()
-        storage = get_event_storage()
-        storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images)
-        storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images)
-
-        localization_loss = _dense_box_regression_loss(
-            anchors,
-            self.box2box_transform,
-            pred_anchor_deltas,
-            gt_boxes,
-            pos_mask,
-            box_reg_loss_type=self.box_reg_loss_type,
-            smooth_l1_beta=self.smooth_l1_beta,
-        )
-
-        valid_mask = gt_labels >= 0
-        objectness_loss = F.binary_cross_entropy_with_logits(
-            cat(pred_objectness_logits, dim=1)[valid_mask],
-            gt_labels[valid_mask].to(torch.float32),
-            reduction="sum",
-        )
-        normalizer = self.batch_size_per_image * num_images
-        losses = {
-            "loss_rpn_cls": objectness_loss / normalizer,
-            # The original Faster R-CNN paper uses a slightly different normalizer
-            # for loc loss. But it doesn't matter in practice
-            "loss_rpn_loc": localization_loss / normalizer,
-        }
-        losses = {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
-        return losses
-
-    def forward(
-        self,
-        images: ImageList,
-        features: Dict[str, torch.Tensor],
-        gt_instances: Optional[List[Instances]] = None,
-    ):
-        """
-        Args:
-            images (ImageList): input images of length `N`
-            features (dict[str, Tensor]): input data as a mapping from feature
-                map name to tensor. Axis 0 represents the number of images `N` in
-                the input data; axes 1-3 are channels, height, and width, which may
-                vary between feature maps (e.g., if a feature pyramid is used).
-            gt_instances (list[Instances], optional): a length `N` list of `Instances`s.
-                Each `Instances` stores ground-truth instances for the corresponding image.
-
-        Returns:
-            proposals: list[Instances]: contains fields "proposal_boxes", "objectness_logits"
-            loss: dict[Tensor] or None
-        """
-        features = [features[f] for f in self.in_features]
-        anchors = self.anchor_generator(features)
-
-        pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)
-        # Transpose the Hi*Wi*A dimension to the middle:
-        pred_objectness_logits = [
-            # (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A)
-            score.permute(0, 2, 3, 1).flatten(1)
-            for score in pred_objectness_logits
-        ]
-        pred_anchor_deltas = [
-            # (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N, Hi*Wi*A, B)
-            x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
-            .permute(0, 3, 4, 1, 2)
-            .flatten(1, -2)
-            for x in pred_anchor_deltas
-        ]
-
-        if self.training:
-            assert gt_instances is not None, "RPN requires gt_instances in training!"
-            gt_labels, gt_boxes = self.label_and_sample_anchors(anchors, gt_instances)
-            losses = self.losses(
-                anchors, pred_objectness_logits, gt_labels, pred_anchor_deltas, gt_boxes
-            )
-        else:
-            losses = {}
-        proposals = self.predict_proposals(
-            anchors, pred_objectness_logits, pred_anchor_deltas, images.image_sizes
-        )
-        return proposals, losses
-
-    def predict_proposals(
-        self,
-        anchors: List[Boxes],
-        pred_objectness_logits: List[torch.Tensor],
-        pred_anchor_deltas: List[torch.Tensor],
-        image_sizes: List[Tuple[int, int]],
-    ):
-        """
-        Decode all the predicted box regression deltas to proposals. Find the top proposals
-        by applying NMS and removing boxes that are too small.
-
-        Returns:
-            proposals (list[Instances]): list of N Instances. The i-th Instances
-                stores post_nms_topk object proposals for image i, sorted by their
-                objectness score in descending order.
-        """
-        # The proposals are treated as fixed for joint training with roi heads.
-        # This approach ignores the derivative w.r.t. the proposal boxes’ coordinates that
-        # are also network responses.
-        with torch.no_grad():
-            pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas)
-            return find_top_rpn_proposals(
-                pred_proposals,
-                pred_objectness_logits,
-                image_sizes,
-                self.nms_thresh,
-                self.pre_nms_topk[self.training],
-                self.post_nms_topk[self.training],
-                self.min_box_size,
-                self.training,
-            )
-
-    def _decode_proposals(self, anchors: List[Boxes], pred_anchor_deltas: List[torch.Tensor]):
-        """
-        Transform anchors into proposals by applying the predicted anchor deltas.
-
-        Returns:
-            proposals (list[Tensor]): A list of L tensors. Tensor i has shape
-                (N, Hi*Wi*A, B)
-        """
-        N = pred_anchor_deltas[0].shape[0]
-        proposals = []
-        # For each feature map
-        for anchors_i, pred_anchor_deltas_i in zip(anchors, pred_anchor_deltas):
-            B = anchors_i.tensor.size(1)
-            pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
-            # Expand anchors to shape (N*Hi*Wi*A, B)
-            anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
-            proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i)
-            # Append feature map proposals with shape (N, Hi*Wi*A, B)
-            proposals.append(proposals_i.view(N, -1, B))
-        return proposals

spaces/Benson/text-generation/Examples/Cazador Asesino Hack Mod Apk Todos Los Personajes Desbloqueados.md

@@ -1,65 +0,0 @@
-
-<h1>Hunter Assassin Hack Mod APK: Todos los personajes desbloqueados</h1>
-<p>Si usted está buscando un juego divertido y adictivo que desafía sus reflejos y habilidades de sigilo, es posible que desee probar Hunter Assassin. Este juego es un éxito entre millones de jugadores que disfrutan escabulléndose y eliminando enemigos con un cuchillo. Pero lo que si quieres desbloquear todos los personajes y disfrutar del juego sin limitaciones? Ahí es donde Hunter Assassin Hack Mod APK entra en juego. En este artículo, le diremos todo lo que necesita saber acerca de este apk mod, cómo descargar e instalar, y algunos consejos y trucos para jugar Hunter Assassin.</p>
- <h2>¿Qué es Hunter Assassin? </h2>
-<p>Hunter Assassin es un juego desarrollado por Ruby Game Studio, los mismos creadores de juegos populares como Gym Flip y Idle Digging Tycoon. El juego está disponible para dispositivos Android e iOS, y tiene más de 100 millones de descargas en Google Play Store. El juego tiene una premisa simple pero atractiva: eres un asesino que tiene que infiltrarse en una base llena de guardias armados y eliminarlos uno por uno. Suena fácil, ¿verdad? Bueno, no del todo. Los guardias tienen armas y pueden dispararte desde la distancia, mientras que solo tienes un cuchillo y tu agilidad. Tienes que usar las sombras, evitar los focos y planificar tus movimientos cuidadosamente para evitar ser detectado y asesinado. </p>
-<h2>cazador asesino hack mod apk todos los personajes desbloqueados</h2><br /><p><b><b>Download Zip</b> &#10026; <a href="https://bltlly.com/2v6K7z">https://bltlly.com/2v6K7z</a></b></p><br /><br />
- <h3>Juego y características</h3>
-<p>El modo de juego de Hunter Assassin es sencillo: toca la pantalla para mover a tu personaje y atacar a los guardias. Tienes que ser rápido y preciso, ya que los guardias reaccionarán a cualquier ruido o movimiento. También debes tener cuidado con las trampas, como las minas y los láseres, que pueden dañarte. El juego tiene cientos de niveles, cada uno con un diseño diferente y el número de enemigos. La dificultad aumenta a medida que avanzas, y te enfrentarás a más desafíos y obstáculos. </p>
-
- <h3>Cómo desbloquear caracteres</h3>
-<p>Como se mencionó anteriormente, hay dos maneras de desbloquear personajes en Hunter Assassin: gemas y llaves. Las gemas son la moneda principal del juego, y puedes usarlas para comprar personajes aleatorios de la tienda. El precio de cada personaje varía dependiendo de su rareza, desde 500 gemas para los comunes hasta 1000 gemas para los legendarios. También puedes usar gemas para mejorar tus personajes y aumentar sus estadísticas. </p>
-<p>Las llaves son otra forma de desbloquear caracteres, pero son más difíciles de conseguir. Las llaves se usan para abrir cofres que contienen caracteres o gemas aleatorias. Puedes obtener claves completando ciertos niveles o logros, o viendo anuncios. Necesitas 36 teclas para abrir un cofre, lo que significa que tienes que jugar muchos niveles o ver muchos anuncios para obtener suficientes teclas. </p>
- <h2>¿Qué es Hunter Assassin Hack Mod APK? </h2>
-<p>Si no quieres pasar horas jugando niveles o viendo anuncios para desbloquear personajes, hay otra opción: Hunter Assassin Hack Mod APK. Esta es una versión modificada del juego original que te da cristales ilimitados y todos los personajes desbloqueados desde el principio. De esta forma, podrás disfrutar del juego sin restricciones ni limitaciones. </p>
- <h3>Beneficios de usar el mod apk</h3>
-<p>Hay muchos beneficios de usar Hunter Assassin Hack Mod APK, tales como:</p>
-<ul>
-<li>Puedes acceder a todos los caracteres sin gastar ninguna joya o clave. </li>
-<li>Puedes actualizar tus personajes al nivel máximo sin gastar ninguna joya. </li>
-<li>Puedes jugar a cualquier nivel sin preocuparte por tu salud o enemigos. </li>
-<li>Puedes disfrutar del juego sin anuncios ni interrupciones. </li>
-<li> Usted puede tener más diversión y emoción con el juego. </li>
-</ul>
- <h3>Cómo descargar e instalar el mod apk</h3>
-<p>Descargar e instalar Hunter Assassin Hack Mod APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<p></p>
-<ol>
-<li>Haga clic en este enlace para descargar el archivo apk mod: [Hunter Assassin Hack Mod APK Download]. </li>
-
-<li>Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para instalarlo. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h2>Consejos y trucos para jugar Hunter Assassin</h2>
-<p>Ahora que tienes Hunter Assassin Hack Mod APK, puede jugar el juego con más facilidad y diversión. Sin embargo, eso no significa que no necesites ninguna habilidad o estrategia para jugar el juego. Aquí hay algunos consejos y trucos que pueden ayudarle a dominar el juego y convertirse en un asesino profesional:</p>
- <h3>Usa sigilo y velocidad</h3>
-<p>La clave para jugar Hunter Assassin es ser sigiloso y rápido. Tienes que evitar ser visto u oído por los guardias, ya que te dispararán en el acto. También tienes que ser rápido y decisivo, ya que los guardias reaccionarán a cualquier movimiento o ruido. Puede utilizar las sombras, paredes, cajas y otros objetos para esconderse y escabullirse. También puedes usar el mapa para ver dónde están los guardias y planificar tus movimientos en consecuencia. Recuerda, el tiempo es todo en este juego. </p>
- <h3>Actualiza tus caracteres</h3>
-<p>A pesar de que tienes todos los caracteres desbloqueados, todavía necesitas actualizarlos para mejorar su rendimiento. Cada personaje tiene tres estadísticas: velocidad, salud y habilidad. La velocidad determina qué tan rápido se mueve y ataca tu personaje. La salud determina cuánto daño puede sufrir tu personaje antes de morir. La habilidad determina cuán efectiva es la habilidad especial de tu personaje. Puedes actualizar estas estadísticas gastando gemas, que puedes ganar jugando niveles o abriendo cofres. Actualizar tus personajes los hará más potentes y versátiles, y te ayudará a completar los niveles más rápido y fácil. </p>
- <h3>Recoge gemas y llaves</h3>
-
- <h2>Conclusión</h2>
-<p>Hunter Assassin es un juego divertido y adictivo que pone a prueba tus reflejos y habilidades de sigilo. Tienes que infiltrarte en una base llena de guardias armados y eliminarlos uno por uno con un cuchillo. Puedes desbloquear diferentes personajes con habilidades y estadísticas únicas, y actualizarlos para hacerlos más poderosos. También puede utilizar Hunter Assassin Hack Mod APK para obtener gemas ilimitadas y todos los personajes desbloqueados desde el principio, lo que hará que el juego más agradable y emocionante. </p>
- <h3>Resumen de los puntos principales</h3>
-<p>En este artículo, hemos cubierto:</p>
-<ul>
-<li>Qué es Hunter Assassin y cómo jugarlo. </li>
-<li> ¿Qué es Hunter Assassin Hack Mod APK y cómo descargarlo e instalarlo. </li>
-<li>Consejos y trucos para jugar Hunter Assassin.</li>
-</ul>
- <h3>Llamada a la acción</h3>
-<p>Si usted está listo para convertirse en un asesino cazador, descargar Hunter Assassin Hack Mod APK ahora y empezar a jugar! Te encantará este juego si te gusta el sigilo, la acción y el desafío. No se olvide de compartir este artículo con sus amigos que también pueden disfrutar de este juego. Caza feliz! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Hunter Assassin Hack Mod APK:</p>
- <h4>Q: ¿Es seguro usar Hunter Assassin Hack Mod APK? </h4>
-<p>A: Sí, Hunter Assassin Hack Mod APK es seguro de usar. No contiene ningún virus o malware que pueda dañar su dispositivo o datos. Sin embargo, siempre debes descargarlo de una fuente de confianza como esta, ya que algunos sitios web pueden ofrecer archivos falsos o dañinos. </p>
- <h4>Q: ¿Necesito raíz o jailbreak mi dispositivo para utilizar Hunter Assassin Hack Mod APK? </h4>
-<p>A: No, usted no necesita raíz o jailbreak su dispositivo para utilizar Hunter Assassin Hack Mod APK. Funciona tanto en dispositivos rooteados como no rooteados, así como en dispositivos Android e iOS. </p>
- <h4>Q: ¿Voy a obtener prohibido del juego si uso Hunter Assassin Hack Mod APK? </h4>
-
- <h4>Q: ¿Cómo puedo actualizar Hunter Assassin Hack Mod APK? </h4>
-<p>A: Hunter Assassin Hack Mod APK se actualiza regularmente para que coincida con la última versión del juego original. Siempre que haya una nueva actualización, puede descargarla de este sitio web e instalarla sobre la existente. No necesitas desinstalar o reinstalar el juego, solo sobrescribe el archivo antiguo con el nuevo. </p>
- <h4>Q: ¿Puedo jugar Hunter Assassin Hack Mod APK offline? </h4>
-<p>A: Sí, puedes jugar Hunter Assassin Hack Mod APK fuera de línea. El juego no requiere una conexión a Internet para funcionar, y se puede disfrutar de todas las características de la apk mod sin ningún problema. Sin embargo, es posible que necesite una conexión a Internet para acceder a algunas funciones en línea, como tablas de clasificación o logros. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat/src/lib/types/Message.ts

@@ -1,5 +0,0 @@
-export interface Message {
-	from: "user" | "assistant";
-	id: ReturnType<typeof crypto.randomUUID>;
-	content: string;
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/__init__.py

@@ -1,177 +0,0 @@
-"""Rich text and beautiful formatting in the terminal."""
-
-import os
-from typing import IO, TYPE_CHECKING, Any, Callable, Optional, Union
-
-from ._extension import load_ipython_extension  # noqa: F401
-
-__all__ = ["get_console", "reconfigure", "print", "inspect", "print_json"]
-
-if TYPE_CHECKING:
-    from .console import Console
-
-# Global console used by alternative print
-_console: Optional["Console"] = None
-
-try:
-    _IMPORT_CWD = os.path.abspath(os.getcwd())
-except FileNotFoundError:
-    # Can happen if the cwd has been deleted
-    _IMPORT_CWD = ""
-
-
-def get_console() -> "Console":
-    """Get a global :class:`~rich.console.Console` instance. This function is used when Rich requires a Console,
-    and hasn't been explicitly given one.
-
-    Returns:
-        Console: A console instance.
-    """
-    global _console
-    if _console is None:
-        from .console import Console
-
-        _console = Console()
-
-    return _console
-
-
-def reconfigure(*args: Any, **kwargs: Any) -> None:
-    """Reconfigures the global console by replacing it with another.
-
-    Args:
-        *args (Any): Positional arguments for the replacement :class:`~rich.console.Console`.
-        **kwargs (Any): Keyword arguments for the replacement :class:`~rich.console.Console`.
-    """
-    from pip._vendor.rich.console import Console
-
-    new_console = Console(*args, **kwargs)
-    _console = get_console()
-    _console.__dict__ = new_console.__dict__
-
-
-def print(
-    *objects: Any,
-    sep: str = " ",
-    end: str = "\n",
-    file: Optional[IO[str]] = None,
-    flush: bool = False,
-) -> None:
-    r"""Print object(s) supplied via positional arguments.
-    This function has an identical signature to the built-in print.
-    For more advanced features, see the :class:`~rich.console.Console` class.
-
-    Args:
-        sep (str, optional): Separator between printed objects. Defaults to " ".
-        end (str, optional): Character to write at end of output. Defaults to "\\n".
-        file (IO[str], optional): File to write to, or None for stdout. Defaults to None.
-        flush (bool, optional): Has no effect as Rich always flushes output. Defaults to False.
-
-    """
-    from .console import Console
-
-    write_console = get_console() if file is None else Console(file=file)
-    return write_console.print(*objects, sep=sep, end=end)
-
-
-def print_json(
-    json: Optional[str] = None,
-    *,
-    data: Any = None,
-    indent: Union[None, int, str] = 2,
-    highlight: bool = True,
-    skip_keys: bool = False,
-    ensure_ascii: bool = False,
-    check_circular: bool = True,
-    allow_nan: bool = True,
-    default: Optional[Callable[[Any], Any]] = None,
-    sort_keys: bool = False,
-) -> None:
-    """Pretty prints JSON. Output will be valid JSON.
-
-    Args:
-        json (str): A string containing JSON.
-        data (Any): If json is not supplied, then encode this data.
-        indent (int, optional): Number of spaces to indent. Defaults to 2.
-        highlight (bool, optional): Enable highlighting of output: Defaults to True.
-        skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False.
-        ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False.
-        check_circular (bool, optional): Check for circular references. Defaults to True.
-        allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True.
-        default (Callable, optional): A callable that converts values that can not be encoded
-            in to something that can be JSON encoded. Defaults to None.
-        sort_keys (bool, optional): Sort dictionary keys. Defaults to False.
-    """
-
-    get_console().print_json(
-        json,
-        data=data,
-        indent=indent,
-        highlight=highlight,
-        skip_keys=skip_keys,
-        ensure_ascii=ensure_ascii,
-        check_circular=check_circular,
-        allow_nan=allow_nan,
-        default=default,
-        sort_keys=sort_keys,
-    )
-
-
-def inspect(
-    obj: Any,
-    *,
-    console: Optional["Console"] = None,
-    title: Optional[str] = None,
-    help: bool = False,
-    methods: bool = False,
-    docs: bool = True,
-    private: bool = False,
-    dunder: bool = False,
-    sort: bool = True,
-    all: bool = False,
-    value: bool = True,
-) -> None:
-    """Inspect any Python object.
-
-    * inspect(<OBJECT>) to see summarized info.
-    * inspect(<OBJECT>, methods=True) to see methods.
-    * inspect(<OBJECT>, help=True) to see full (non-abbreviated) help.
-    * inspect(<OBJECT>, private=True) to see private attributes (single underscore).
-    * inspect(<OBJECT>, dunder=True) to see attributes beginning with double underscore.
-    * inspect(<OBJECT>, all=True) to see all attributes.
-
-    Args:
-        obj (Any): An object to inspect.
-        title (str, optional): Title to display over inspect result, or None use type. Defaults to None.
-        help (bool, optional): Show full help text rather than just first paragraph. Defaults to False.
-        methods (bool, optional): Enable inspection of callables. Defaults to False.
-        docs (bool, optional): Also render doc strings. Defaults to True.
-        private (bool, optional): Show private attributes (beginning with underscore). Defaults to False.
-        dunder (bool, optional): Show attributes starting with double underscore. Defaults to False.
-        sort (bool, optional): Sort attributes alphabetically. Defaults to True.
-        all (bool, optional): Show all attributes. Defaults to False.
-        value (bool, optional): Pretty print value. Defaults to True.
-    """
-    _console = console or get_console()
-    from pip._vendor.rich._inspect import Inspect
-
-    # Special case for inspect(inspect)
-    is_inspect = obj is inspect
-
-    _inspect = Inspect(
-        obj,
-        title=title,
-        help=is_inspect or help,
-        methods=is_inspect or methods,
-        docs=is_inspect or docs,
-        private=private,
-        dunder=dunder,
-        sort=sort,
-        all=all,
-        value=value,
-    )
-    _console.print(_inspect)
-
-
-if __name__ == "__main__":  # pragma: no cover
-    print("Hello, **World**")

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/rotate.py

@@ -1,64 +0,0 @@
-from distutils.util import convert_path
-from distutils import log
-from distutils.errors import DistutilsOptionError
-import os
-import shutil
-
-from setuptools import Command
-
-
-class rotate(Command):
-    """Delete older distributions"""
-
-    description = "delete older distributions, keeping N newest files"
-    user_options = [
-        ('match=', 'm', "patterns to match (required)"),
-        ('dist-dir=', 'd', "directory where the distributions are"),
-        ('keep=', 'k', "number of matching distributions to keep"),
-    ]
-
-    boolean_options = []
-
-    def initialize_options(self):
-        self.match = None
-        self.dist_dir = None
-        self.keep = None
-
-    def finalize_options(self):
-        if self.match is None:
-            raise DistutilsOptionError(
-                "Must specify one or more (comma-separated) match patterns "
-                "(e.g. '.zip' or '.egg')"
-            )
-        if self.keep is None:
-            raise DistutilsOptionError("Must specify number of files to keep")
-        try:
-            self.keep = int(self.keep)
-        except ValueError as e:
-            raise DistutilsOptionError("--keep must be an integer") from e
-        if isinstance(self.match, str):
-            self.match = [
-                convert_path(p.strip()) for p in self.match.split(',')
-            ]
-        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
-
-    def run(self):
-        self.run_command("egg_info")
-        from glob import glob
-
-        for pattern in self.match:
-            pattern = self.distribution.get_name() + '*' + pattern
-            files = glob(os.path.join(self.dist_dir, pattern))
-            files = [(os.path.getmtime(f), f) for f in files]
-            files.sort()
-            files.reverse()
-
-            log.info("%d file(s) matching %s", len(files), pattern)
-            files = files[self.keep:]
-            for (t, f) in files:
-                log.info("Deleting %s", f)
-                if not self.dry_run:
-                    if os.path.isdir(f):
-                        shutil.rmtree(f)
-                    else:
-                        os.unlink(f)

spaces/BigData-KSU/VQA-in-Medical-Imagery/README.md

@@ -1,12 +0,0 @@
----
-title: Visual Question Answering in Medical Imagery
-emoji: 🧑‍⚕️
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.15.0
-app_file: MED_VQA_Huggyface_Gradio.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bokanovskii/Image-to-music/app.py

@@ -1,429 +0,0 @@
-import gradio as gr
-import spotipy
-from spotipy import oauth2
-
-from transformers import ViTForImageClassification, ViTImageProcessor
-import torch
-from torch.nn import functional as F
-from torchvision.io import read_image
-
-import tensorflow as tf
-
-from fastapi import FastAPI
-from starlette.middleware.sessions import SessionMiddleware
-from starlette.responses import HTMLResponse, RedirectResponse
-from starlette.requests import Request
-import gradio as gr
-import uvicorn
-from fastapi.responses import HTMLResponse
-from fastapi.responses import RedirectResponse
-
-import numpy as np
-import base64
-from io import BytesIO
-from PIL import Image
-import time
-
-import shred_model
-
-# Xception fine tuned from pretrained imagenet weights for identifying Sraddha
-SRADDHA_MODEL_PATH = "shred_model"
-SHRED_MODEL = tf.keras.models.load_model(SRADDHA_MODEL_PATH)
-
-SPOTIPY_TOKEN = None # Set in the homepage function
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-print("Grabbing model")
-mood_model = ViTForImageClassification.from_pretrained("jayanta/google-vit-base-patch16-224-cartoon-emotion-detection")
-mood_model.eval()
-mood_model.to(device)
-print("Grabbing feature extractor")
-mood_feature_extractor = ViTImageProcessor.from_pretrained("jayanta/google-vit-base-patch16-224-cartoon-emotion-detection")
-
-def main(img, playlist_length, privacy, gen_mode, genre_choice, request: gr.Request):
-    if img is None:
-        return None
-    print("Getting image inference from tansformer")
-    mood_dict = get_image_mood_dict_from_transformer(img)
-    print("Getting Sraddha Found Boolean from model")
-    sraddha_found = get_sraddha(img)
-    print("Building playlist")
-    playlist = get_playlist(mood_dict, img, playlist_length, privacy, gen_mode, genre_choice, request)
-    if playlist is None:
-        playlist = "Spotipy account token not set"
-    
-    ret = playlist
-    if sraddha_found:
-        valentines_jokes = ["Why shouldn't you trust a pastry chef on Valentine's Day? Because he will dessert you.",
-                            "What do you give your Valentine in France? A big quiche.",
-                            "What did the tortoise say on Valentine's Day? I turt-ally love you.",
-                            "How did the squirrel get his Valentine's attention? He acted like a nut.",
-                            "What do you call sweets that can keep a beat? Candy rappers.",
-                            "What did the paper clip say to the magnet? I find you very attractive.",
-                            "What did the caclulator say to the pencil? You can count on me."]
-        joke = valentines_jokes[np.random.randint(0, len(valentines_jokes)-1)]
-        sraddha_msg = """Sraddha, you are the love of my life and seeing you always lifts my spirits. Hopefully these tunes and a joke can do the same for you.
-        <p>
-        </p>""" + \
-        f"<p>{joke}</p><p></p>" + \
-        """- With Love, Scoob"""
-        return gr.update(value=ret, visible=True), gr.update(value=sraddha_msg, visible=True)
-    return gr.update(value=ret, visible=True), gr.update(visible=False)
-
-def get_image_mood_dict_from_transformer(img):
-    img = read_image(img)
-    encoding = mood_feature_extractor(images=img, return_tensors="pt")
-    pixel_values = encoding['pixel_values'].to(device)
-    
-    print('Running mood prediction')
-    outputs = mood_model(pixel_values)
-
-    logits = outputs.logits
-    probabilities = F.softmax(logits, dim = -1).detach().numpy()[0]
-    mood_dict = dict(zip(mood_model.config.id2label.values(), probabilities))
-    return mood_dict
-
-def get_sraddha(img):
-    fixed_img = shred_model.prepare_image(img)
-    prob = SHRED_MODEL.predict(fixed_img)[0]
-    if prob >= .5:
-        return True
-
-def compute_mood(mood_dict):
-    print(mood_dict)
-    return mood_dict['happy'] + mood_dict['angry'] * .5 + mood_dict['sad'] * .1
-
-def get_playlist(mood_dict, img, playlist_length, privacy, gen_mode, genre_choice, request: gr.Request):
-    token = request.request.session.get('token')
-    genre_map = {'Rock': ['alt-rock', 'alternative', 'indie', 'r-n-b', 'rock'], 'Hip-hop': ['hip-hop'], 'Party': ['house', 'pop', 'party'], 'Mellow': ['blues', 'jazz', 'happy'], 'Indian': ['idm', 'indian'], 'Pop': ['pop', 'new-age'], 'Study': ['study', 'classical', 'jazz', 'happy', 'chill'], 'Romance': ['romance', 'happy', 'pop']}
-        
-    if token:
-        mood = compute_mood(mood_dict)
-        if gen_mode == "By a Chosen Genre":
-            playlist_name = "Mood " + str(round(mood * 100, 1)) + f": {genre_choice}"
-        else:
-            playlist_name = "Mood " + str(round(mood * 100, 1)) + f": {gen_mode}"
-        sp = spotipy.Spotify(token)
-
-        if gen_mode == 'Recently Played':
-            top_tracks_uri = set([x['track']['uri'] for x in sp.current_user_recently_played(limit=50)['items']])
-            # I honestly don't know if this errors for people with not enough saved tracks
-            # Shouldn't be a problem for Sraddha
-            first_few = [x['track']['uri'] for x in sp.current_user_saved_tracks(limit=50)['items']]
-            top_tracks_uri.update(first_few)
-            top_tracks_uri.update([x['track']['uri'] for x in sp.current_user_saved_tracks(limit=50, offset=50)['items']])
-            top_tracks_uri.update([x['track']['uri'] for x in sp.current_user_saved_tracks(limit=50, offset=100)['items']])
-            top_tracks_uri.update([x['track']['uri'] for x in sp.current_user_saved_tracks(limit=50, offset=150)['items']])
-            top_tracks_uri.update([x['uri'] for x in sp.recommendations(seed_tracks=first_few[:5], limit=50)['tracks']])
-            top_tracks_uri.update([x['uri'] for x in sp.recommendations(seed_tracks=first_few[5:10], limit=50)['tracks']])
-            top_tracks_uri = list(top_tracks_uri)
-        elif gen_mode == 'By a Chosen Genre':
-            genres = genre_map[genre_choice]
-            final_track_list = [x['uri'] for x in sp.recommendations(
-                seed_genres=genres, limit=playlist_length, max_valence=mood+.15, 
-                min_valence=mood-.15, min_danceability=mood/1.75, max_danceability=mood*8, 
-                min_energy=mood/2)['tracks']]
-        else:
-            top_artists_uri = aggregate_favorite_artists(sp)
-            top_tracks_uri = aggregate_top_tracks(sp, top_artists_uri)
-
-        if gen_mode != 'By a Chosen Genre':
-            final_track_list = filter_tracks(sp, top_tracks_uri, mood, playlist_length)
-        
-        # If no tracks fit the filter: generate some results anyways
-        if len(final_track_list) != playlist_length:
-            diff = playlist_length - len(final_track_list)
-            print(f'Filling playlist with {diff} more songs (filter too big)')
-            seed = [x['track']['uri'] for x in sp.current_user_recently_played(limit=5)['items']]
-            final_track_list += [x['uri'] for x in sp.recommendations(
-                seed_tracks=seed, limit=diff,
-                min_valence=mood-.3, min_energy=mood/3)['tracks']]
-
-        iframe_embedding = create_playlist(sp, img, final_track_list, playlist_name,
-                                            privacy)
-        return iframe_embedding
-    return None
-
-def create_playlist(sp, img, tracks, playlist_name, privacy): 
-    privacy = privacy == "Public"
-    user_id = sp.current_user()['id']
-    playlist_description = "This playlist was created using the img-to-music application built by the best boyfriend there ever was and ever will be"
-    playlist_data = sp.user_playlist_create(user_id, playlist_name, public=privacy, 
-                                            description=playlist_description)
-    playlist_id = playlist_data['id']
-    if len(tracks) == 0:
-        return """No tracks could be generated from this image"""
-    sp.user_playlist_add_tracks(user_id, playlist_id, tracks)
-
-    def upload_img():
-        with Image.open(img) as im_file:
-            im_file.thumbnail((300, 300))
-            buffered = BytesIO()
-            im_file.save(buffered, format="JPEG")
-            img_str = base64.b64encode(buffered.getvalue())
-            sp.playlist_upload_cover_image(playlist_id, img_str)
-    try: 
-        upload_img()
-    except spotipy.exceptions.SpotifyException as e:
-        print(f"SpotiftException on image upload: {e}")
-        print("Retrying")
-        time.sleep(5)
-        try:
-            upload_img()
-        except Exception as e:
-            print(e)
-    except requests.exceptions.ReadTimeout as e:
-        print(f"Image upload request timeout: {e}")
-        print("Retrying...")
-        time.sleep(5)
-        try:
-            upload_img()
-        except Exception as e:
-            print(e)
-    time.sleep(3)
-    iframe_embedding = f"""<iframe style="border-radius:12px" src="https://open.spotify.com/embed/playlist/{playlist_id}" width="100%" height="352" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>"""
-    return iframe_embedding
-
-def aggregate_favorite_artists(sp):
-    top_artists_name = set()
-    top_artists_uri = []
-
-    ranges = ['short_term', 'medium_term', 'long_term']
-    for r in ranges:
-        top_artists_all_data = sp.current_user_top_artists(limit=50, time_range=r)
-        top_artists_data = top_artists_all_data['items']
-        for artist_data in top_artists_data:
-            if artist_data["name"] not in top_artists_name:
-                top_artists_name.add(artist_data['name'])
-                top_artists_uri.append(artist_data['uri'])
-    
-    followed_artists_all_data = sp.current_user_followed_artists(limit=50)
-    followed_artsits_data = followed_artists_all_data['artists']
-    for artist_data in followed_artsits_data['items']:
-        if artist_data["name"] not in top_artists_name:
-            top_artists_name.add(artist_data['name'])
-            top_artists_uri.append(artist_data['uri'])
-
-    # attempt to garauntee 200 artists
-    i = 0
-    while len(top_artists_uri) < 200:
-        related_artists_all_data = sp.artist_related_artists(top_artists_uri[i])
-        i += 1
-        related_artists_data = related_artists_all_data['artists']
-        for artist_data in related_artists_data:
-            if artist_data["name"] not in top_artists_name:
-                top_artists_name.add(artist_data['name'])
-                top_artists_uri.append(artist_data['uri'])
-        if i == len(top_artists_uri):
-            # could build in a deeper artist recommendation finder here 
-            # would do this if it was going to production but Sraddha follows lots of artists
-            break
-
-    return top_artists_uri
-
-def aggregate_top_tracks(sp, top_artists_uri):
-    top_tracks_uri = []
-    for artist in top_artists_uri:
-        top_tracks_all_data = sp.artist_top_tracks(artist)
-        top_tracks_data = top_tracks_all_data['tracks']
-        for track_data in top_tracks_data:
-            top_tracks_uri.append(track_data['uri'])
-    return top_tracks_uri
-
-def filter_tracks(sp, top_tracks_uri, mood, playlist_length):
-    selected_tracks_uri = []
-    
-    np.random.shuffle(top_tracks_uri)
-    # Batch network requests
-    BATCH_SIZE = 100
-    i = 0
-    all_track_data = []
-    while i + BATCH_SIZE < len(top_tracks_uri):
-        all_track_data += sp.audio_features(top_tracks_uri[i:i+BATCH_SIZE])
-        i += BATCH_SIZE
-    all_track_data += sp.audio_features(top_tracks_uri[i:])
-
-    for i, track in enumerate(top_tracks_uri):
-        track_data = all_track_data[i]
-        if track_data is None:
-            continue
-
-        valence = track_data['valence']
-        danceability = track_data['danceability']
-        energy = track_data['energy']
-        if mood < .1:
-            if valence <= mood + .15 and \
-               danceability <= mood * 8 and \
-               energy <= mood * 10:
-                selected_tracks_uri.append(track)
-        elif mood < .25:
-            if (mood - .1) <= valence <= (mood + .1) and \
-               danceability <= mood * 4 and \
-               energy <= mood * 5:
-                selected_tracks_uri.append(track)
-        elif mood < .5:
-            if mood - .05 <= valence <= mood + .05 and \
-               danceability <= mood * 1.75 and \
-               energy <= mood * 1.75:
-                selected_tracks_uri.append(track)
-        elif mood < .75:
-            if mood - .1 <= valence <= mood + .1 and \
-               danceability >= mood / 2.5 and \
-               energy >= mood / 2:
-                selected_tracks_uri.append(track)
-        elif mood < .9:
-            if mood - .1 <= valence <= mood + .1 and \
-               danceability >= mood / 2 and \
-               energy >= mood / 1.75:
-                selected_tracks_uri.append(track)
-        else:
-            if mood - .15 <= valence <= 1 and \
-               danceability >= mood / 1.75 and \
-               energy >= mood / 1.5:
-                selected_tracks_uri.append(track)
-        
-        if len(selected_tracks_uri) >= playlist_length:
-            break
-    return selected_tracks_uri
-
-# Define login and frontend
-PORT_NUMBER = 8080
-SPOTIPY_CLIENT_ID = '2320153024d042c8ba138a108066246c'
-SPOTIPY_CLIENT_SECRET = 'da2746490f6542a3b0cfcff50893e8e8'
-#SPOTIPY_REDIRECT_URI = 'http://localhost:7860'
-SPOTIPY_REDIRECT_URI = "https://Bokanovskii-Image-to-music.hf.space"
-SCOPE = 'ugc-image-upload playlist-read-private playlist-read-collaborative playlist-modify-private playlist-modify-public user-top-read user-read-playback-position user-read-recently-played user-read-email user-follow-read user-library-modify user-library-read user-read-email user-read-private user-read-playback-state user-modify-playback-state user-read-currently-playing app-remote-control streaming'
-
-sp_oauth = oauth2.SpotifyOAuth(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET, SPOTIPY_REDIRECT_URI, scope=SCOPE)
-
-app = FastAPI()
-app.add_middleware(SessionMiddleware, secret_key="w.o.w")
-
-@app.get('/', response_class=HTMLResponse)
-async def homepage(request: Request):
-    url = str(request.url)
-    auth_url = sp_oauth.get_authorize_url()
-    try:
-        code = sp_oauth.parse_response_code(url)
-        if code != url:
-            request.session['token'] = sp_oauth.get_access_token(code, as_dict=False, check_cache=False)
-            return RedirectResponse("/gradio")
-    except:
-        return """<div style="text-align: center; max-width: 1000px; margin: 0 auto;">
-                <div
-                style="
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.25rem;
-                "
-                >
-                <h3 style="font-weight: 900; margin-bottom: 30px; margin-top: 20px;">
-                    Image to Music Generator
-                </h3>\n""" + \
-                "<p> The server couldn't make a connection with Spotify: please try again </p>\n" + \
-                f"<a href='" + auth_url + "'>Login to Spotify</a>\n" + \
-                """<p>
-                </p>
-                <p>
-                </p>
-                <small>
-                Click 'Open in a new window/tab'
-                <small>
-                <div
-                style="
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1rem;
-                "
-                >
-                <small> 
-                    This applet requires a whitelisted Spotify account (contact Charlie Ward)
-                </small>"""
-    return """<div style="text-align: center; max-width: 1000px; margin: 0 auto;">
-                <div
-                style="
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.75rem;
-                "
-                >
-                <h3 style="font-weight: 900; margin-bottom: 30px; margin-top: 20px;">
-                    Image to Music Generator
-                </h3>\n""" + \
-                f"<a href='" + auth_url + "'>Login to Spotify</a>\n" + \
-                """<p>
-                </p>
-                <p>
-                </p>
-                <small>
-                Click 'Open in a new window/tab'
-                <small>
-                <div
-                style="
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1rem;
-                "
-                >
-                <small> 
-                    This applet requires a whitelisted Spotify account (contact Charlie Ward)
-                </small>"""
-
-with gr.Blocks(css="style.css") as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-                <div
-                style="
-                    display: inline-flex;
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.75rem;
-                "
-                >
-                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                    Image to Music Generator
-                </h1>""")
-    
-        input_img = gr.Image(type="filepath", elem_id="input-img")
-        sraddhas_box = gr.HTML(label="Sraddha's Box", elem_id="sraddhas-box", visible=False)
-        playlist_output = gr.HTML(label="Generated Playlist", elem_id="app-output", visible=True)
-
-        with gr.Accordion(label="Playlist Generation Options", open=False):
-            playlist_length = gr.Slider(minimum=5, maximum=100, value=30, step=5, 
-                                        label="Playlist Length", elem_id="playlist-length")
-            with gr.Row():
-                privacy = gr.Radio(label="Playlist Privacy Level", choices=["Public", "Private"],
-                                    value="Private")
-                gen_mode = gr.Radio(label="Recommendation Base", choices=["Favorites", "Recently Played", "By a Chosen Genre"], value="Favorites")
-            with gr.Row(visible=False) as genre_choice_row:
-                genre_choice = gr.Dropdown(label='Choose a Genre', choices=['Rock', 'Pop', 'Hip-hop', 'Party', 'Mellow', 'Indian', 'Study', 'Romance'], value='Pop')
-        
-        def sraddha_box_hide():
-            return {sraddhas_box: gr.update(visible=False)}
-
-        def genre_dropdown_toggle(gen_mode):
-            if gen_mode == 'By a Chosen Genre':
-                return {genre_choice_row: gr.update(visible=True)}
-            else:
-                return {genre_choice_row: gr.update(visible=False)}
-
-        generate = gr.Button("Generate Playlist from Image")
-
-        article = """
-                <div class="footer">
-                    <p>
-                    Built for Sraddha: playlist generation from image inference
-                    </p>
-                    <p>
-                    Sending Love 🤗
-                    </p>
-                </div>
-            """
-        gr.HTML(article)
-    gen_mode.change(genre_dropdown_toggle, inputs=[gen_mode], outputs=[genre_choice_row])
-    generate.click(sraddha_box_hide, outputs=[sraddhas_box])
-    generate.click(main, inputs=[input_img, playlist_length, privacy, gen_mode, genre_choice], 
-                   outputs=[playlist_output, sraddhas_box], api_name="img-to-music")
-
-gradio_app = gr.mount_gradio_app(app, demo, "/gradio")
-uvicorn.run(app, host="0.0.0.0", port=7860)