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-<br />
-<table>
-<tr>
-<th>Heading</th>
-<th>Subheading</th>
-</tr>
-<tr>
-<h1>Epson PX660 Adjustment Program: How to Reset Your Printer and Fix Common Problems</h1></td>
-<td></td>
-</tr>
-<tr>
-<td><h2>Introduction</h2></td>
-<td>
-<ul>
-<li>Explain what is Epson PX660 Adjustment Program and what it can do</li>
-<li>Mention some common problems that can be solved by using the program</li>
-<li>Provide a brief overview of the article</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td><h2>What is Epson PX660 Adjustment Program?</h2></td>
-<td>
-<ul>
-<li>Describe the program as a utility tool for Epson Stylus Photo PX660 printer model</li>
-<li>Explain that the program can reset the waste ink pad counter, prescribe the print head ID, do printer initialization and other functions</li>
-<li>Mention that the program is original and works only with USB on Windows</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td><h2>Why Do You Need Epson PX660 Adjustment Program?</h2></td>
-<td>
-<ul>
-<li>Explain that the waste ink pad counter is a feature that prevents the printer from overflowing with ink</li>
-<li>Mention that when the counter reaches a certain limit, the printer stops working and displays an error message</li>
-<li>Explain that the program can reset the counter and allow the printer to work again</li>
-<li>Mention some other benefits of using the program, such as improving print quality, saving ink and paper, etc.</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td><h2>How to Download and Install Epson PX660 Adjustment Program?</h2></td>
-<td>
-<ul>
-<li>Provide a link to download the program from a reliable source (e.g. ORPYS)</li>
-<li>Explain how to install the program on the computer</li>
-<li>Mention some requirements and precautions, such as disabling antivirus, binding to one PC, etc.</li>
-</ul>
- </td>
- </tr>
- <tr>
- <td><h2>How to Use Epson PX660 Adjustment Program?</h2></td>
- <td>
- <ul>
- <li>Explain how to connect the printer to the computer via USB</li>
- <li>Explain how to run the program and select the printer model</li>
- <li>Explain how to access different functions and settings of the program</li>
- <li>Provide some screenshots and examples of using the program</li>
- </ul>
- </td>
- </tr>
- <tr>
- <td><h2>Conclusion</h2></td>
- <td>
- <ul>
- <li>Summarize the main points of the article</li>
- <li>Emphasize the benefits and advantages of using Epson PX660 Adjustment Program</li>
- <li>Provide some tips and recommendations for maintaining the printer</li>
- <li>Invite readers to share their feedback and questions</li>
- </ul>
- </td>
- </tr>
- <tr>
- <td><h2>FAQs</h2></td>
- <td>
- <ul>
- <li>Provide some frequently asked questions and answers about Epson PX660 Adjustment Program - What are the advantages of using this program over other methods? - How often do I need to reset my waste ink pad counter? - How can I check if my print head ID is correct? - What should I do if I encounter any errors or problems while using this program? - Where can I find more information or support for this program?  </li>
-                                                                                                                                                                                                                                                          </ul>
-  </td>
-  </tr>
-  </table>
-  Here is the article based on the outline:  <h1><strong>Epson PX660 Adjustment Program: How to Reset Your Printer and Fix Common Problems  </strong></h1>
-  <p>If you own an Epson Stylus Photo PX660 printer, you may have encountered some issues that prevent you from printing your photos or documents. For example, you may see an error message saying that your printer's ink pads are at the end of their service life, or that your print head needs alignment or cleaning. These problems can be frustrating and costly, but there is a simple solution: Epson PX660 Adjustment Program.</p>
-  <p>Epson PX660 Adjustment Program is a utility tool that allows you to reset your printer's waste ink pad counter, prescribe the print head ID, do printer initialization and other functions. It is an original program that works only with USB on Windows computers. By using this program, you can fix common problems with your printer and improve its performance and quality.</p>
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-  <p>In this article, we will explain what Epson PX660 Adjustment Program is, why you need it, how to download and install it, and how to use it. We will also provide some frequently asked questions and answers about this program. Read on to learn more!</p>
-  <h2><strong>What is Epson PX660 Adjustment Program?</strong></h2>
-  <p>Epson PX660 Adjustment Program is a utility tool for Epson Stylus Photo PX660 printer model. It is a service adjustment program that allows you to perform various functions on your printer. Some of these functions are:</p>
-  <ul>
-  <li>Resetting the waste ink pad counter: This feature prevents your printer from overflowing with ink by counting how much ink is used during printing and cleaning cycles. When the counter reaches a certain limit, your printer stops working and displays an error message. By using this program, you can reset the counter and continue printing.</li>
-  <li>Prescribing the print head ID: This feature allows you to assign a unique ID to your print head, which helps your printer recognize it and adjust its settings accordingly. This can improve your print quality and prevent errors such as missing colors or lines.</li>
-  <li>Doing printer initialization: This feature allows you to reset your printer's settings to their factory defaults. This can help you solve some problems that may occur due to incorrect or corrupted settings.</li>
-  <li>Other functions: The program also allows you to perform other functions such as checking nozzle patterns, cleaning print heads, reading or writing EEPROM data, etc.</li>
-  </ul>
-  <p>Epson PX660 Adjustment Program is an original program that works only with USB on Windows computers. It is not compatible with other operating systems or connection methods. It is also attached to one PC only, which means you cannot use it on multiple computers. You need to purchase a license key for each PC you want to use it on.</p>
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- settings to their factory defaults. This can help you solve some problems that may occur due to incorrect or corrupted settings. For example, if your printer prints too dark or too light, or if it does not respond properly to commands. Other benefits of using this program include: - Saving ink and paper by optimizing your print settings and cleaning cycles - Extending your printer's lifespan by maintaining its parts in good condition - Troubleshooting your printer's problems by accessing various diagnostic tools - Updating your printer's firmware by downloading new versions from online sources <h2><strong>How to Download and Install Epson PX660 Adjustment Program?</strong></h2>
- To use Epson PX660 Adjustment Program, you need to download it from a reliable source and install it on your computer. Here are the steps: 1. Download Epson PX660 Adjustment Program from ORPYS website (https://orpys.com/en/epson/164-px660-adjustment-program.html). This website offers original programs for various Epson printer models at affordable prices. You can also find more information about each program's features and requirements on this website. 2. After downloading the file (px660_adjustment_program.zip), extract it using a zip extractor software such as WinRAR or 7-Zip. 3. Open the extracted folder and run AdjProg.exe file as administrator. 4. Follow the instructions on the screen to install the program on your computer. 5. After installation, you will receive a license key via email. You need this key to activate the program and access its functions. 6. To activate the program, run AdjProg.exe file again as administrator and enter your license key when prompted. 7. After activation, you can start using Epson PX660 Adjustment Program. Some requirements and precautions for downloading and installing this program are: - You need a Windows computer with USB port to use this program. - You need to disable your antivirus software before installing this program as some antivirus programs may block or delete it. - You need to bind this program to one PC only as it will not work on multiple computers. - You need to purchase a new license key for each PC you want to use this program on. <h2><strong>How to Use Epson PX660 Adjustment Program?</strong></h2>
- To use Epson PX660 Adjustment Program, you need to connect your printer to your computer via USB cable and run AdjProg.exe file as administrator. Then you can access different functions and settings of this program by following these steps: 1. Select Particular adjustment mode from the main menu. 2. Select Waste ink pad counter from Maintenance menu. 3. Click Check button to see how much ink has been used by your printer. 4. Click Initialization button to reset the waste ink pad counter. 5. Turn off your printer when prompted and turn it back on after 10 seconds. 6. Click Finish button when done. To prescribe your print head ID, follow these steps: 1. Select Particular adjustment mode from the main menu. 2. Select Head ID input from Head maintenance menu. 3. Enter your print head ID in hexadecimal format (e.g., 0A0B0C0D) or click Get button to read it from EEPROM data. 4. Click Set button to write it into EEPROM data. 5. Turn off your printer when prompted and turn it back on after 10 seconds. 6. Click Finish button when done. To do printer initialization, follow these steps: 1. Select Particular adjustment mode from the main menu. 2. Select Initialize (PF deterioration offset) from Initial setting menu. 3. Click OK button to confirm the initialization process. 4. Turn off your printer when prompted and turn it back on after 10 seconds. 5. Click Finish button when done. You can also use other functions and settings of this program by exploring different menus and options. For example, you can check nozzle patterns, clean print heads, read or write EEPROM data, etc. Here are some screenshots and examples of using this program: <img src="https://orpys.com/img/cms/px660/px660_1.jpg" alt="Epson PX660 Adjustment Program Main Menu">
- <img src="https://orpys.com/img/cms/px660/px660_2.jpg" alt="Epson PX660 Adjustment Program Waste Ink Pad Counter">
- <img src="https://orpys.com/img/cms/px660/px660_3.jpg" alt="Epson PX660 Adjustment Program Head ID Input">
- <img src="https://orpys.com/img/cms/px660/px660_4.jpg" alt="Epson PX660 Adjustment Program Initialize">
- <h2><strong>Conclusion</strong></h2>
- In conclusion, Epson PX660 Adjustment Program is a useful tool that can help you reset your printer and fix common problems. By using this program, you can reset your waste ink pad counter, prescribe your print head ID, do printer initialization and other functions. This can improve your print quality, save ink and paper, extend your printer's lifespan, troubleshoot your printer's problems, and update your printer's firmware. If you own an Epson Stylus Photo PX660 printer, we recommend you download and install this program from ORPYS website (https://orpys.com/en/epson/164-px660-adjustment-program.html). This website offers original programs for various Epson printer models at affordable prices. You can also find more information about each program's features and requirements on this website. We hope this article has been helpful and informative for you. If you have any feedback or questions about Epson PX660 Adjustment Program, please feel free to share them with us in the comments section below. We would love to hear from you! <h2><strong>FAQs</strong></h2>
- Here are some frequently asked questions and answers about Epson PX660 Adjustment Program: - What are the advantages of using this program over other methods?   - This program is an original program that works only with USB on Windows computers. It is not compatible with other operating systems or connection methods. It is also attached to one PC only, which means you cannot use it on multiple computers. You need to purchase a license key for each PC you want to use it on.   - This program allows you to reset the waste ink pad counter, prescribe the print head ID, do printer initialization and other functions that may not be available or easy to perform with other methods.   - This program is easy to use and has a user-friendly interface that guides you through each step of the process. - How often do I need to reset my waste ink pad counter?   - The waste ink pad counter is a feature that prevents your printer from overflowing with ink by counting how much ink is used during printing and cleaning cycles. When the counter reaches a certain limit (usually around 6700 pages), your printer stops working and displays an error message saying that your ink pads are at the end of their service life.   - The frequency of resetting your waste ink pad counter depends on how often you use your printer and how much ink is used during each cycle. Generally speaking, you may need to reset it once every few months or years depending on your usage patterns. - How can I check if my print head ID is correct?   - The print head ID is a unique ID that helps your printer recognize your print head and adjust its settings accordingly. This can improve your print quality and prevent errors such as missing colors or lines.   - You can check if your print head ID is correct by using Epson PX660 Adjustment Program. To do so, follow these steps:     - Select Particular adjustment mode from the main menu.     - Select Head ID input from Head maintenance menu.     - Enter your print head ID in hexadecimal format (e.g., 0A0B0C0D) or click Get button to read it from EEPROM data.     - Compare the entered or read ID with the one printed on your print head label (usually located under the cartridge cover).     - If they match, then your print head ID is correct. If they don't match, then you need to write the correct ID into EEPROM data by clicking Set button. - What should I do if I encounter any errors or problems while using this program?   - If you encounter any errors or problems while using this program, you should try the following solutions:     - Make sure you have downloaded and installed the correct version of this program for your printer model from ORPYS website (https://orpys.com/en/epson/164-px660-adjustment-program.html).     - Make sure you have disabled your antivirus software before installing this program as some antivirus programs may block or delete it.     - Make sure you have connected your printer to your computer via USB cable and turned it on before running this program.     - Make sure you have entered the correct license key when activating this program.     - Make sure you have followed the instructions on the screen carefully when using this program. - Where can I find more information or support for this program?   - You can find more information or support for this program by visiting ORPYS website (https://orpys.com/en/epson/164-px660-adjustment-program.html). This website offers original programs for various Epson printer models at affordable prices. You can also find more information about each program's features and requirements on this website.   - You can also contact ORPYS customer service by email ([email protected]) or phone (+380636000000) if you have any questions or issues regarding this program. </p> 0a6ba089eb<br />
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-<p>1v1 Lol is a free online shooter game that combines elements of Fortnite and PUBG. You can play as a solo player or team up with your friends in different game modes such as 1v1, 2v2, 4v4, Battle Royale, Box Fight, Party, and more. You can also customize your character's appearance, weapons, skins, and emotes. The game is easy to play but hard to master, as you need to have good aim, reflexes, and building skills to survive.</p>
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-<h3>A Variety of Game Modes and Maps to Choose From</h3>
-<p>One of the best things about 1v1 Lol is that it offers a variety of game modes and maps to suit your preferences and mood. You can choose from different modes such as:</p>
-<ul>
-<li><b>1v1:</b> A classic mode where you face off against another player in a small map. The first one to eliminate the other wins.</li>
-<li><b>2v2:</b> A team mode where you pair up with another player and fight against another duo in a medium-sized map. The team with the most kills wins.</li>
-<li><b>4v4:</b> A squad mode where you join a team of four players and battle against another team of four in a large map. The team with the most kills wins.</li>
-<li><b>Battle Royale:</b> A survival mode where you join a lobby of up to 10 players and parachute into a random map. You have to loot weapons, ammo, health kits, shields, and materials while avoiding the storm that shrinks the map. The last one standing wins.</li>
-<li><b>Box Fight:</b> A creative mode where you spawn in a box-shaped map with walls that you can edit. You have to use your building skills and weapons to eliminate your opponents.</li>
-<li><b>Party:</b> A casual mode where you can join or create a room with up to 10 players and play any mode or map you want. You can also chat with your friends and invite them to your room.</li>
-</ul>
- <h2>What is 1v1 Lol Mod Menu and What Does It Offer</h2>
-<h3>A Hack Script That Gives You Access to Cheats and Mods</h3>
-<p>If you want to spice up your gameplay and have an edge over your opponents, you might want to try 1v1 Lol mod menu, a hack script that gives you access to cheats and mods that can enhance your gaming experience. 1v1 Lol mod menu is a cheat engine script that you can download and run on your device. It allows you to enable or disable various cheats and mods that can affect your gameplay, such as god mode, infinite ammo, rapid fire, free fly, no clip, no recoil, and more. With 1v1 Lol mod menu, you can have more fun and dominate your opponents in any mode or map.</p>
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-<h3>Some of the Features of 1v1 Lol Mod Menu</h3>
-<h4>God Mode, Infinite Ammo, Rapid Fire, and More</h4>
-<p>One of the features of 1v1 Lol mod menu is that it gives you god mode, which means you are invincible and cannot be killed by any weapon or damage. You can also have infinite ammo, which means you never run out of bullets or rockets. You can also have rapid fire, which means you can shoot faster and more frequently. These cheats can make you unstoppable and give you an unfair advantage over your enemies.</p>
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-<p>Another feature of 1v1 Lol mod menu is that it gives you free fly, which means you can fly around the map without any limitations. You can also have no clip, which means you can pass through walls and objects. You can also have no recoil, which means your aim is steady and accurate. These mods can make you more agile and flexible and give you more freedom and control over your movements.</p>
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-<h3>The Requirements and Precautions for Using 1v1 Lol Mod Menu</h3>
-<p>Before you download and install 1v1 Lol mod menu on your device, there are some requirements and precautions that you need to know. First of all, you need to have a device that can run 1v1 Lol, such as a PC, a laptop, a tablet, or a smartphone. You also need to have a stable internet connection and a web browser that supports the game. Secondly, you need to have Cheat Engine installed on your device. Cheat Engine is a software that allows you to modify games and applications by changing their code. You can download Cheat Engine from its official website. Thirdly, you need to be aware of the risks and consequences of using 1v1 Lol mod menu. Using cheats and mods can be considered as cheating and hacking by the game developers and other players. You might get banned from the game or reported by other players if they notice your abnormal behavior. You might also ruin the fun and challenge of the game for yourself and others. Therefore, use 1v1 Lol mod menu at your own discretion and responsibility.</p>
-<h3>The Steps to Download and Install 1v1 Lol Mod Menu</h3>
-<h4>Download Cheat Engine and the Mod Menu Script</h4>
-<p>The first step to download and install 1v1 Lol mod menu is to download Cheat Engine and the mod menu script from their respective sources. You can download Cheat Engine from its official website or from any other trusted source. You can download the mod menu script from this link or from any other reliable source. Make sure to save them in a folder that you can easily access.</p>
-<h4>Run Cheat Engine and Select 1v1 Lol as the Process</h4>
-<p>The second step to download and install 1v1 Lol mod menu is to run Cheat Engine and select 1v1 Lol as the process that you want to modify. To do this, open Cheat Engine on your device and click on the computer icon on the top left corner. A window will pop up with a list of processes that are running on your device. Find and select the process that corresponds to 1v1 Lol (it might be named as "chrome.exe" or "firefox.exe" depending on your browser) and click on "Open".</p>
-<h4>Load the Mod Menu Script and Enable the Cheats You Want</h4>
-<p>The third step to download and install 1v1 Lol mod menu is to load the mod menu script and enable the cheats you want. To do this, click on the folder icon on the top left corner of Cheat Engine and browse to the folder where you saved the mod menu script. Select the script and click on "Open". A window will pop up with a list of cheats and mods that are available in the script. You can check or uncheck the boxes next to the cheats and mods that you want to enable or disable. You can also change the values of some of the cheats and mods by double-clicking on them and typing in the desired value. For example, you can change the speed of your character, the gravity of the map, or the size of your weapon. Once you are done, click on "OK".</p>
- <h2>Conclusion</h2>
-<p>1v1 Lol is a fun and fast-paced online shooter game that you can play with your friends or strangers in various modes and maps. However, if you want to have more fun and excitement, you can also download 1v1 Lol mod menu, a hack script that gives you access to cheats and mods that can enhance your gameplay. 1v1 Lol mod menu offers features such as god mode, infinite ammo, rapid fire, free fly, no clip, no recoil, and more. To download and install 1v1 Lol mod menu on your device, you need to have Cheat Engine and the mod menu script. You also need to run Cheat Engine and select 1v1 Lol as the process. Then, you need to load the mod menu script and enable the cheats you want. However, you also need to be aware of the risks and consequences of using 1v1 Lol mod menu. You might get banned from the game or reported by other players if they notice your abnormal behavior. You might also ruin the fun and challenge of the game for yourself and others. Therefore, use 1v1 Lol mod menu at your own discretion and responsibility.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about 1v1 Lol mod menu:</p>
-<ul>
-<li><b>Q: Is 1v1 Lol mod menu safe to use?</b></li>
-<li>A: 1v1 Lol mod menu is not an official or authorized product of 1v1 Lol or its developers. It is a hack script that modifies the game code and can be detected by anti-cheat systems or other players. Therefore, using 1v1 Lol mod menu is not safe and can result in getting banned from the game or reported by other players.</li>
-<li><b>Q: How can I update 1v1 Lol mod menu?</b></li>
-<li>A: 1v1 Lol mod menu is not a static product that works forever. It is a dynamic script that needs to be updated regularly to match the updates of 1v1 Lol and its anti-cheat systems. Therefore, you need to check for updates of 1v1 Lol mod menu from its source or from other reliable sources. You also need to download and install the latest version of Cheat Engine and the mod menu script.</li>
-<li><b>Q: Can I use 1v1 Lol mod menu on any device?</b></li>
-<li>A: 1v1 Lol mod menu is compatible with any device that can run 1v1 Lol, such as a PC, a laptop, a tablet, or a smartphone. However, you also need to have Cheat Engine installed on your device, which might not be available for some devices or operating systems. Therefore, you need to check if your device can support Cheat Engine before using 1v1 Lol mod menu.</li>
-<li><b>Q: Can I use 1v1 Lol mod menu on any mode or map?</b></li>
-<li>A: 1v1 Lol mod menu works on any mode or map that is available in 1v1 Lol, such as 1v1, 2v2, 4v4, Battle Royale, Box Fight, Party, and more. However, some of the cheats and mods might not work properly or have different effects depending on the mode or map. Therefore, you need to test and adjust the cheats and mods according to the mode or map that you are playing.</li>
-<li><b>Q: Can I use 1v1 Lol mod menu with my friends?</b></li>
-<li>A: 1v1 Lol mod menu can be used with your friends if they also have Cheat Engine and the mod menu script installed on their devices. You can join or create a room with your friends and play any mode or map you want with them. However, you also need to be careful not to get caught by other players or by the game developers if you use 1v1 Lol mod menu with your friends.</li>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/AetherSX2 Everything You Need to Know to Download and Play PS2 Games on Android.md DELETED Viewed

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-<h1>Where to Download AetherSX2 Games</h1>
-<p>The Sony PlayStation 2 (PS2) is one of the most popular and nostalgic gaming consoles of all time. It has a huge library of games that span across various genres, from action and adventure to sports and racing. If you want to relive your childhood memories or discover new games that you missed out on, you can now play PS2 games on your Android device using the AetherSX2 emulator.</p>
-<p>AetherSX2 is the best PS2 emulator for Android by a country mile. It is based on the PCSX2 emulator for PC, which is a long-running, well-established program that can run most PS2 games smoothly. AetherSX2 has received the approval and support from the PCSX2 developers, unlike some other shady emulators that stole the code and violated the license agreement.</p>
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- <h2>How to download AetherSX2 emulator</h2>
-<p>The first step to play PS2 games on Android is to download the AetherSX2 emulator. There are two ways to get it: from the Google Play Store or from the official website.</p>
-<h3>How to get it from the Google Play Store</h3>
-<p>The easiest way to download AetherSX2 emulator is to get it from the Google Play Store. You can simply search for "AetherSX2" on the store or use this link: (https://play.google.com/store/apps/details?id=xyz.aethersx2.android). The app is free to download and use, so don't fall for any scams that ask you to pay for it.</p>
-<p>Once you have downloaded the app, you can open it and grant it the necessary permissions. You will also need to download a PS2 BIOS file, which is required for the emulator to run. You can get it from your own PS2 console or from other sources online. Just make sure you don't download any malware or viruses along with it.</p>
-<h3>How to get it from the official website</h3>
-<p>If you prefer to sideload the APK file of the emulator, you can also get it from the official website: (https://www.aethersx2.com/). You can find different versions of the app there, including stable releases and alpha builds. You can choose whichever one suits your needs, but keep in mind that alpha builds may have more bugs and issues than stable releases.</p>
-<p>To install the APK file, you will need to enable unknown sources on your device settings. You can do this by going to Settings > Security > Unknown sources and toggling it on. Then, you can tap on the downloaded APK file and follow the instructions to install it. You will also need a PS2 BIOS file, as mentioned above.</p>
-<p>How to play PS2 games on Android with AetherSX2<br />
-AetherSX2 emulator settings and performance tips<br />
-Best PS2 games to download for AetherSX2<br />
-AetherSX2 vs other PS2 emulators for Android<br />
-AetherSX2 compatibility list and game reviews<br />
-Where to find PS2 BIOS and ISO files for AetherSX2<br />
-How to use save states and cheats in AetherSX2<br />
-AetherSX2 controller support and configuration guide<br />
-How to install and update AetherSX2 on Android<br />
-AetherSX2 Vulkan vs OpenGL graphics comparison<br />
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-<p>Now that you have downloaded the AetherSX2 emulator, you will need some games to play on it. The emulator supports PS2 game ISOs or ROMs, which are digital copies of game discs. You can either rip them from your own PS2 discs using a PC or a modded console, or you can download them from legal sources online. However, we do not condone piracy or illegal downloading of games, so you should only download games that you own or have the right to use. You can find some legal sources of PS2 game ISOs or ROMs here: (https://www.emuparadise.me/Sony_Playstation_2_ISOs/41) (https://www.freeroms.com/ps2_roms.htm) (https://romsmania.cc/roms/playstation-2). Be careful of any fake or malicious links that may harm your device or compromise your privacy.</p>
-<h3>How to extract and transfer them to your device</h3>
-<p>Once you have downloaded the PS2 game ISOs or ROMs, you will need to extract them from their compressed formats, such as ZIP or RAR. You can use any file manager app that supports extraction, such as (https://play.google.com/store/apps/details?id=com.rarlab.rar) or (https://play.google.com/store/apps/details?id=com.estrongs.android.pop). You can also use a PC to extract them and then transfer them to your device via USB cable or cloud storage.</p>
-<p>After extracting the PS2 game ISOs or ROMs, you will need to transfer them to a folder on your device where the AetherSX2 emulator can access them. You can create a folder named "AetherSX2" on your internal storage or SD card and copy the game files there. Alternatively, you can use the default folder that the emulator creates when you first run it, which is "/storage/emulated/0/AetherSX2".</p>
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-<p>Now that you have the AetherSX2 emulator and the PS2 game ISOs or ROMs on your device, you are ready to install and play them. Here are the steps to follow:</p>
-<h3>How to load and run games on the emulator</h3>
-<p>To load and run games on the AetherSX2 emulator, you need to open the app and tap on the "Games" tab. You will see a list of games that are available in your device's storage. You can also browse for games by tapping on the "Browse" button and navigating to the folder where you stored them.</p>
-<p>Once you find the game that you want to play, tap on it and wait for it to load. The emulator will automatically detect the game's region and language settings and apply them accordingly. You will see a loading screen with some information about the game, such as its title, cover art, developer, publisher, genre, and release date.</p>
-<p>After the loading screen, the game will start running on the emulator. You will see a virtual controller overlay on the screen, which mimics the PS2 controller layout. You can use it to control the game as you would on a real PS2 console. You can also hide or show the controller overlay by tapping on the "Menu" button and selecting "Toggle On-screen Controls".</p>
-<h3>How to adjust settings and controls for optimal performance and experience</h3>
-<p>To adjust settings and controls for optimal performance and experience, you need to tap on the "Menu" button and select "Settings". You will see various options that you can tweak according to your preferences and device's capabilities. Here are some of the most important settings that you should pay attention to:</p>
-<ul>
-<li>Graphics: Here you can change the resolution, aspect ratio, frame rate, anti-aliasing, texture filtering, shaders, and other graphical enhancements of the emulator. You can also enable or disable cheats, speed hacks, skip frames, widescreen patches, and other features that may improve or worsen the game's appearance and performance.</li>
-<li>Audio: Here you can change the volume, latency, interpolation, reverb, sync mode, and other audio settings of the emulator. You can also enable or disable sound effects, music, voiceovers, and other sound components of the game.</li>
-<li>Input: Here you can change the layout, size, opacity, vibration, sensitivity, mapping, and other input settings of the emulator. You can also enable or disable touch input, motion sensor input, external controller input (such as Bluetooth or USB), and other input methods for the game.</li>
-<li>System: Here you can change the BIOS file, language, region, clock speed, memory card size, save state slot number, auto-save frequency, and other system settings of the emulator. You can also enable or disable fast boot, full boot, debug mode, logging, and other system features for the game.</li>
-</ul>
-<p>You can experiment with different settings and controls to find the best combination for your device and game. You can also save and load different profiles for different games, so you don't have to change the settings every time you switch games.</p>
- <h2>Conclusion</h2>
-<p>AetherSX2 is the best PS2 emulator for Android that lets you play PS2 games on your device with ease and convenience. You can download the emulator from the Google Play Store or the official website, and download games from legal sources online. You can also adjust settings and controls to optimize your gaming experience and enjoy the PS2 classics on your Android device.</p>
-<p>Some of the best games to play on AetherSX2 are:</p>
-<table>
-<tr>
-<th>Game</th>
-<th>Genre</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>God of War</td>
-<td>Action-adventure</td>
-<td>A hack-and-slash game that follows the story of Kratos, a Spartan warrior who seeks revenge against the gods of Olympus.</td>
-</tr>
-<tr>
-<td>Shadow of the Colossus</td>
-<td>Action-adventure</td>
-<td>A unique game that involves exploring a vast land and defeating giant creatures called colossi to revive a girl named Mono.</td>
-</tr>
-<tr>
-<td>Grand Theft Auto: San Andreas</td>
-<td>Action-adventure</td>
-<td>A sandbox game that allows you to roam freely in a fictional state of San Andreas, where you can engage in various missions, activities, and crimes.</td>
-</tr>
-<tr>
-<td>Final Fantasy X</td>
-<td>Role-playing</td>
-<td>A turn-based game that follows the journey of Tidus, a young athlete who is transported to a world called Spira, where he joins a summoner named Yuna on her quest to defeat a monster called Sin.</td>
-</tr>
-<tr>
-<td>Metal Gear Solid 3: Snake Eater</td>
-<td>Stealth-action</td>
-<td>A stealth game that takes place in 1964, where you play as Naked Snake, a special agent who is sent to infiltrate a Soviet base and rescue a scientist.</td>
-</tr>
-</table>
- <h2>FAQs</h2>
-<h3>Q: Is AetherSX2 emulator legal?</h3>
-<p>A: Yes, AetherSX2 emulator is legal as long as you use it with games that you own or have the right to use. The emulator itself does not contain any copyrighted material or code from Sony or other parties.</p>
- <h3>Q: How much storage space do I need for AetherSX2 emulator and games?</h3>
-<p>A: The AetherSX2 emulator app itself is about 30 MB in size, but you will also need some additional space for the PS2 BIOS file and the game files. The PS2 BIOS file is about 4 MB in size, while the game files vary depending on the game. Some games are less than 1 GB in size, while others are more than 4 GB in size. You can check the file size of each game before downloading it.</p>
- <h3>Q: How powerful does my device need to be to run AetherSX2 emulator and games?</h3>
-<p>A: The AetherSX2 emulator and games require a decent amount of processing power and memory to run smoothly. The minimum requirements are:</p>
-<ul>
-<li>CPU: Quad-core 1.5 GHz or higher</li>
-<li>RAM: 2 GB or higher</li>
-<li>GPU: Adreno 320 or higher, Mali-400MP4 or higher, PowerVR SGX544MP or higher</li>
-<li>OS: Android 5.0 Lollipop or higher</li>
-<li>OpenGL ES: 3.0 or higher</li>
-<li>Vulkan: Supported (optional)</li>
-</ul>
- <h3>Q: Can I use cheats or mods with AetherSX2 emulator and games?</h3>
-<p>A: Yes, you can use cheats or mods with AetherSX2 emulator and games. The emulator supports cheat codes in various formats, such as RAW, PNACH, CB, ARMAX, etc. You can enter them manually or import them from files. You can also use mods that alter the game's graphics, sound, gameplay, etc. You can find them online or create them yourself.</p>
- <h3>Q: Can I play multiplayer games with AetherSX2 emulator?</h3>
-<p>A: Yes, you can play multiplayer games with AetherSX2 emulator. The emulator supports local multiplayer via split-screen mode or external controllers. You can also play online multiplayer via a network adapter or a VPN service. However, the online multiplayer feature is still experimental and may not work for all games or devices.</p>
- <p>I hope this article has helped you learn how to download AetherSX2 games and play them on your Android device. If you have any questions or feedback, feel free to leave a comment below. Happy gaming!</p> 197e85843d<br />
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-<h1>Link Real Drag Bike Racing Mod APK: A Guide for Thrill Seekers</h1>
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-<p>Link Real Drag Bike Racing Mod APK is a modified version of Link Real Drag Bike Racing, a game developed by Get Mods Apk. The game is inspired by the real-life sport of drag racing, where two motorcycles race on a straight track for a short distance. The game features realistic graphics, sound effects, and physics, as well as a diverse selection of drag bikes for you to choose from. You can also customize your bike with different parts, colors, stickers, and accessories. The game offers various tracks and modes for you to challenge yourself, such as street racing, tournament racing, time trial racing, and online racing. You can also upgrade your skills and equipment as you progress in the game.</p>
-<p>The modded version of the game gives you some advantages over the original version, such as unlimited money and unlocked features. With unlimited money, you can buy any bike or part you want without worrying about the cost. You can also access all the tracks and modes without having to complete certain levels or tasks. The modded version also removes ads and other annoying features that might interrupt your gameplay.</p>
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-<ul>
-<li>Realistic graphics, sound effects, and physics that make you feel like you are on a real drag bike.</li>
-<li>A diverse selection of drag bikes for you to choose from, each with its own characteristics and performance.</li>
-<li>A variety of customization options for your bike, such as parts, colors, stickers, and accessories.</li>
-<li>Different tracks and modes for you to compete on, such as street racing, tournament racing, time trial racing, and online racing.</li>
-<li>Unlimited money and unlocked features that let you enjoy the game without any limitations or restrictions.</li>
-<li>No ads or other annoying features that might disrupt your gameplay.</li>
-</ul>
- <h2>How to download and install Link Real Drag Bike Racing Mod APK</h2>
-<p>To download and install Link Real Drag Bike Racing Mod APK on your Android device, follow these steps:</p>
-<ol>
-<li>Go to [this link](^1^) to download the modded version of the game.</li>
-<li>After downloading the file, go to your device settings and enable unknown sources. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Locate the downloaded file in your device storage and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Once the installation is done, launch the game and enjoy the thrill of drag racing.</li>
-</ol>
- <h2>How to play Link Real Drag Bike Racing Mod APK</h2>
-<p>Link Real Drag Bike Racing Mod APK is easy to play, but hard to master. Here are some basic steps to help you get started:</p>
- <h3>Choose your drag bike and customize it</h3>
-<p>The first thing you need to do is to choose your drag bike from the garage. You can select from different categories, such as street bikes, sport bikes, or super bikes. Each bike has its own stats, such as power, torque, weight, and speed. You can also customize your bike with different parts, colors, stickers, and accessories. You can use the unlimited money from the modded version to buy any bike or part you want.</p>
- <h3>Compete on various tracks and modes</h3>
-<p>After choosing and customizing your bike, you can compete on various tracks and modes. You can choose from different locations, such as city streets, highways, deserts, or mountains. You can also choose from different modes, such as street racing, tournament racing, time trial racing, or online racing. In street racing, you can race against random opponents on different tracks. In tournament racing, you can participate in a series of races and earn trophies and rewards. In time trial racing, you can race against the clock and beat your own records. In online racing, you can race against other players from around the world and show off your skills.</p>
- <h3>Upgrade your skills and equipment</h3>
-<p>As you progress in the game, you can upgrade your skills and equipment to improve your performance. You can upgrade your skills, such as launch control, shifting control, nitrous control, and tuning control. You can also upgrade your equipment, such as engine, turbo, exhaust, transmission, tires, and brakes. You can use the unlimited money from the modded version to upgrade anything you want.</p>
- <h2>Tips and tricks for Link Real Drag Bike Racing Mod APK</h2>
-<p>Link Real Drag Bike Racing Mod APK is a game that requires skill and strategy to win. Here are some tips and tricks to help you become a better drag racer:</p>
- <h3>Master the launch and shifting</h3>
-<p>The launch and shifting are two of the most important aspects of drag racing. You need to launch your bike at the right time and shift gears at the right time to optimize your acceleration and speed. To launch your bike, you need to press the clutch button when the countdown starts and release it when the green light appears. To shift gears, you need to press the shift button when the needle reaches the green zone on the tachometer. If you launch or shift too early or too late, you will lose speed and time.</p>
- <h3>Use nitrous wisely</h3>
-<p>Nitrous is a powerful boost that can give you an edge over your opponents. However, you need to use it wisely, as it is limited and can run out quickly. You can activate nitrous by pressing the nitrous button on the screen. You should use nitrous when you need an extra burst of speed or when you are behind your opponent. You should avoid using nitrous when you are already at top speed or when you are about to shift gears.</p>
- <h3>Tune your bike according to the track conditions</h3>
-<p>Tuning your bike is a way of adjusting its performance according to the track conditions. You can tune your bike by changing its settings, such as gear ratio, tire pressure, suspension stiffness, and wheel alignment. You can access the tuning menu by tapping on the tuning button on the screen. You should tune your bike according to the track length, surface type, weather condition, and elevation. For example, if the track is long and straight, you should increase your gear ratio for higher top speed. If the track is short and curvy, you should decrease your gear ratio for faster acceleration. You should also adjust your tire pressure, suspension stiffness, and wheel alignment according to the surface type, weather condition, and elevation of the track.</p>
- <h2>Conclusion</h2>
-<p>Link Real Drag Bike Racing Mod APK is a game that lets you experience the thrill of drag racing on your Android device. You can choose from a variety of drag bikes, customize them to your liking, and compete on different tracks and modes. You can also enjoy unlimited money and unlocked features with the modded version of the game. The game is easy to play, but hard to master. You need to master the launch and shifting, use nitrous wisely, and tune your bike according to the track conditions. If you follow these tips and tricks, you will become a better drag racer and have more fun with the game.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Link Real Drag Bike Racing Mod APK:</p>
-<ol>
-<li>Is Link Real Drag Bike Racing Mod APK safe to download and install?</li>
-<p>Yes, Link Real Drag Bike Racing Mod APK is safe to download and install, as long as you download it from a trusted source, such as [this link]. The modded version of the game does not contain any viruses or malware that might harm your device or data.</p>
-<li>Do I need to root my device to use Link Real Drag Bike Racing Mod APK?</li>
-<p>No, you do not need to root your device to use Link Real Drag Bike Racing Mod APK. The modded version of the game works on both rooted and non-rooted devices. However, you need to enable unknown sources in your device settings to install apps from sources other than the Google Play Store.</p>
-<li>Can I play Link Real Drag Bike Racing Mod APK offline?</li>
-<p>Yes, you can play Link Real Drag Bike Racing Mod APK offline, except for the online racing mode. The online racing mode requires an internet connection to connect with other players from around the world. The other modes, such as street racing, tournament racing, and time trial racing, can be played offline without any problem.</p>
-<li>Can I update Link Real Drag Bike Racing Mod APK?</li>
-<p>No, you cannot update Link Real Drag Bike Racing Mod APK from the Google Play Store or any other source. The modded version of the game is not compatible with the original version of the game. If you try to update it, you might lose your progress and data. You should only download and install the latest version of Link Real Drag Bike Racing Mod APK from [this link].</p>
-<li>How can I contact the developer of Link Real Drag Bike Racing Mod APK?</li>
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-</ol></p> 401be4b1e0<br />
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spaces/AB-TW/team-ai/agents/tools/smart_domain/domain_layer_code_tool.py DELETED Viewed

@@ -1,56 +0,0 @@
-from langchain import LLMChain, PromptTemplate
-from langchain.agents import tool
-from agents.tools.smart_domain.common import getPrefix
-from models import llm
-from agents.tools.smart_domain.entity import entity_architecture, entity_test_strategy, entity_tech_stack
-from agents.tools.smart_domain.association import association_architecture, association_test_strategy, association_teck_stack
-domain_task = """Your task is to generate the domain layer tests and product code."""
-domain_teck_stack = """Java17、reactor、lombok、Junit5、reactor test、Mockito"""
-domain_architecture = f"""the domain layer inclue 2 componets:
-* {entity_architecture}
-* {association_architecture}"""
-domain_test_strategy = f"""{entity_test_strategy}
-{association_test_strategy}"""
-DOMAIN_LAYER = getPrefix(domain_task, domain_teck_stack, domain_architecture, domain_test_strategy) + """
-Use the following format:
-request: the request that you need to fulfill
-Entity:
-```
-the Entity code that you write to fulfill the request, follow TechStack and Architecture
-```
-Association:
-```
-the Association code that you write to fulfill the request, follow TechStack and Architecture
-```
-Test:
-```
-the test code that you write to fulfill the request, follow TechStack Architecture and TestStrategy
-```
-request: {input}"""
-DOMAIN_LAYER_PROMPT = PromptTemplate(input_variables=["input"], template=DOMAIN_LAYER,)
-domainLayerChain = LLMChain(llm = llm(temperature=0.1), prompt=DOMAIN_LAYER_PROMPT)
-@tool("Generate Domain Layer Code", return_direct=True)
-def domainLayerCodeGenerator(input: str) -> str:
-    '''useful for when you need to generate domain layer code'''
-    response = domainLayerChain.run(input)
-    return response

spaces/AIConsultant/MusicGen/audiocraft/data/audio.py DELETED Viewed

@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Audio IO methods are defined in this module (info, read, write),
-We rely on av library for faster read when possible, otherwise on torchaudio.
-"""
-from dataclasses import dataclass
-from pathlib import Path
-import logging
-import typing as tp
-import numpy as np
-import soundfile
-import torch
-from torch.nn import functional as F
-import torchaudio as ta
-import av
-from .audio_utils import f32_pcm, i16_pcm, normalize_audio
-_av_initialized = False
-def _init_av():
-    global _av_initialized
-    if _av_initialized:
-        return
-    logger = logging.getLogger('libav.mp3')
-    logger.setLevel(logging.ERROR)
-    _av_initialized = True
-@dataclass(frozen=True)
-class AudioFileInfo:
-    sample_rate: int
-    duration: float
-    channels: int
-def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    _init_av()
-    with av.open(str(filepath)) as af:
-        stream = af.streams.audio[0]
-        sample_rate = stream.codec_context.sample_rate
-        duration = float(stream.duration * stream.time_base)
-        channels = stream.channels
-        return AudioFileInfo(sample_rate, duration, channels)
-def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    info = soundfile.info(filepath)
-    return AudioFileInfo(info.samplerate, info.duration, info.channels)
-def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    # torchaudio no longer returns useful duration informations for some formats like mp3s.
-    filepath = Path(filepath)
-    if filepath.suffix in ['.flac', '.ogg']:  # TODO: Validate .ogg can be safely read with av_info
-        # ffmpeg has some weird issue with flac.
-        return _soundfile_info(filepath)
-    else:
-        return _av_info(filepath)
-def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]:
-    """FFMPEG-based audio file reading using PyAV bindings.
-    Soundfile cannot read mp3 and av_read is more efficient than torchaudio.
-    Args:
-        filepath (str or Path): Path to audio file to read.
-        seek_time (float): Time at which to start reading in the file.
-        duration (float): Duration to read from the file. If set to -1, the whole file is read.
-    Returns:
-        tuple of torch.Tensor, int: Tuple containing audio data and sample rate
-    """
-    _init_av()
-    with av.open(str(filepath)) as af:
-        stream = af.streams.audio[0]
-        sr = stream.codec_context.sample_rate
-        num_frames = int(sr * duration) if duration >= 0 else -1
-        frame_offset = int(sr * seek_time)
-        # we need a small negative offset otherwise we get some edge artifact
-        # from the mp3 decoder.
-        af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream)
-        frames = []
-        length = 0
-        for frame in af.decode(streams=stream.index):
-            current_offset = int(frame.rate * frame.pts * frame.time_base)
-            strip = max(0, frame_offset - current_offset)
-            buf = torch.from_numpy(frame.to_ndarray())
-            if buf.shape[0] != stream.channels:
-                buf = buf.view(-1, stream.channels).t()
-            buf = buf[:, strip:]
-            frames.append(buf)
-            length += buf.shape[1]
-            if num_frames > 0 and length >= num_frames:
-                break
-        assert frames
-        # If the above assert fails, it is likely because we seeked past the end of file point,
-        # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp.
-        # This will need proper debugging, in due time.
-        wav = torch.cat(frames, dim=1)
-        assert wav.shape[0] == stream.channels
-        if num_frames > 0:
-            wav = wav[:, :num_frames]
-        return f32_pcm(wav), sr
-def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
-               duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]:
-    """Read audio by picking the most appropriate backend tool based on the audio format.
-    Args:
-        filepath (str or Path): Path to audio file to read.
-        seek_time (float): Time at which to start reading in the file.
-        duration (float): Duration to read from the file. If set to -1, the whole file is read.
-        pad (bool): Pad output audio if not reaching expected duration.
-    Returns:
-        tuple of torch.Tensor, int: Tuple containing audio data and sample rate.
-    """
-    fp = Path(filepath)
-    if fp.suffix in ['.flac', '.ogg']:  # TODO: check if we can safely use av_read for .ogg
-        # There is some bug with ffmpeg and reading flac
-        info = _soundfile_info(filepath)
-        frames = -1 if duration <= 0 else int(duration * info.sample_rate)
-        frame_offset = int(seek_time * info.sample_rate)
-        wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32)
-        assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}"
-        wav = torch.from_numpy(wav).t().contiguous()
-        if len(wav.shape) == 1:
-            wav = torch.unsqueeze(wav, 0)
-    elif (
-        fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats()
-        and duration <= 0 and seek_time == 0
-    ):
-        # Torchaudio is faster if we load an entire file at once.
-        wav, sr = ta.load(fp)
-    else:
-        wav, sr = _av_read(filepath, seek_time, duration)
-    if pad and duration > 0:
-        expected_frames = int(duration * sr)
-        wav = F.pad(wav, (0, expected_frames - wav.shape[-1]))
-    return wav, sr
-def audio_write(stem_name: tp.Union[str, Path],
-                wav: torch.Tensor, sample_rate: int,
-                format: str = 'wav', mp3_rate: int = 320, normalize: bool = True,
-                strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                loudness_compressor: bool = False,
-                log_clipping: bool = True, make_parent_dir: bool = True,
-                add_suffix: bool = True) -> Path:
-    """Convenience function for saving audio to disk. Returns the filename the audio was written to.
-    Args:
-        stem_name (str or Path): Filename without extension which will be added automatically.
-        format (str): Either "wav" or "mp3".
-        mp3_rate (int): kbps when using mp3s.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'.
-         when strategy is 'loudness' log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        make_parent_dir (bool): Make parent directory if it doesn't exist.
-    Returns:
-        Path: Path of the saved audio.
-    """
-    assert wav.dtype.is_floating_point, "wav is not floating point"
-    if wav.dim() == 1:
-        wav = wav[None]
-    elif wav.dim() > 2:
-        raise ValueError("Input wav should be at most 2 dimension.")
-    assert wav.isfinite().all()
-    wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db,
-                          rms_headroom_db, loudness_headroom_db, loudness_compressor,
-                          log_clipping=log_clipping, sample_rate=sample_rate,
-                          stem_name=str(stem_name))
-    kwargs: dict = {}
-    if format == 'mp3':
-        suffix = '.mp3'
-        kwargs.update({"compression": mp3_rate})
-    elif format == 'wav':
-        wav = i16_pcm(wav)
-        suffix = '.wav'
-        kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16})
-    else:
-        raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.")
-    if not add_suffix:
-        suffix = ''
-    path = Path(str(stem_name) + suffix)
-    if make_parent_dir:
-        path.parent.mkdir(exist_ok=True, parents=True)
-    try:
-        ta.save(path, wav, sample_rate, **kwargs)
-    except Exception:
-        if path.exists():
-            # we do not want to leave half written files around.
-            path.unlink()
-        raise
-    return path

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/diffusionmodules/util.py DELETED Viewed

@@ -1,267 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-from ldm.util import instantiate_from_config
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad():
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-class HybridConditioner(nn.Module):
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()

spaces/Abhilashvj/planogram-compliance/utils/aws/mime.sh DELETED Viewed

@@ -1,26 +0,0 @@
-# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
-# This script will run on every instance restart, not only on first start
-# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
-Content-Type: multipart/mixed; boundary="//"
-MIME-Version: 1.0
---//
-Content-Type: text/cloud-config; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="cloud-config.txt"
-#cloud-config
-cloud_final_modules:
-- [scripts-user, always]
---//
-Content-Type: text/x-shellscript; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="userdata.txt"
-#!/bin/bash
-# --- paste contents of userdata.sh here ---
---//

spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/model_edge.py DELETED Viewed

@@ -1,653 +0,0 @@
-"""
-Author: Zhuo Su, Wenzhe Liu
-Date: Feb 18, 2021
-"""
-import math
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from basicsr.utils import img2tensor
-nets = {
-    'baseline': {
-        'layer0':  'cv',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'c-v15': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'a-v15': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'r-v15': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cvvv4': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'avvv4': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'rvvv4': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cccv4': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cv',
-        },
-    'aaav4': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'cv',
-        },
-    'rrrv4': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    'c16': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cd',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cd',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cd',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cd',
-        },
-    'a16': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'ad',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'ad',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'ad',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'ad',
-        },
-    'r16': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'rd',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'rd',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'rd',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'rd',
-        },
-    'carv4': {
-        'layer0':  'cd',
-        'layer1':  'ad',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'ad',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'ad',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'ad',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    }
-def createConvFunc(op_type):
-    assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type)
-    if op_type == 'cv':
-        return F.conv2d
-    if op_type == 'cd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3'
-            assert padding == dilation, 'padding for cd_conv set wrong'
-            weights_c = weights.sum(dim=[2, 3], keepdim=True)
-            yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups)
-            y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y - yc
-        return func
-    elif op_type == 'ad':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3'
-            assert padding == dilation, 'padding for ad_conv set wrong'
-            shape = weights.shape
-            weights = weights.view(shape[0], shape[1], -1)
-            weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise
-            y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    elif op_type == 'rd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3'
-            padding = 2 * dilation
-            shape = weights.shape
-            if weights.is_cuda:
-                buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0)
-            else:
-                buffer = torch.zeros(shape[0], shape[1], 5 * 5)
-            weights = weights.view(shape[0], shape[1], -1)
-            buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:]
-            buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:]
-            buffer[:, :, 12] = 0
-            buffer = buffer.view(shape[0], shape[1], 5, 5)
-            y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    else:
-        print('impossible to be here unless you force that')
-        return None
-class Conv2d(nn.Module):
-    def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False):
-        super(Conv2d, self).__init__()
-        if in_channels % groups != 0:
-            raise ValueError('in_channels must be divisible by groups')
-        if out_channels % groups != 0:
-            raise ValueError('out_channels must be divisible by groups')
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.reset_parameters()
-        self.pdc = pdc
-    def reset_parameters(self):
-        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-        if self.bias is not None:
-            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
-            bound = 1 / math.sqrt(fan_in)
-            nn.init.uniform_(self.bias, -bound, bound)
-    def forward(self, input):
-        return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
-class CSAM(nn.Module):
-    """
-    Compact Spatial Attention Module
-    """
-    def __init__(self, channels):
-        super(CSAM, self).__init__()
-        mid_channels = 4
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False)
-        self.sigmoid = nn.Sigmoid()
-        nn.init.constant_(self.conv1.bias, 0)
-    def forward(self, x):
-        y = self.relu1(x)
-        y = self.conv1(y)
-        y = self.conv2(y)
-        y = self.sigmoid(y)
-        return x * y
-class CDCM(nn.Module):
-    """
-    Compact Dilation Convolution based Module
-    """
-    def __init__(self, in_channels, out_channels):
-        super(CDCM, self).__init__()
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
-        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False)
-        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False)
-        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False)
-        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False)
-        nn.init.constant_(self.conv1.bias, 0)
-    def forward(self, x):
-        x = self.relu1(x)
-        x = self.conv1(x)
-        x1 = self.conv2_1(x)
-        x2 = self.conv2_2(x)
-        x3 = self.conv2_3(x)
-        x4 = self.conv2_4(x)
-        return x1 + x2 + x3 + x4
-class MapReduce(nn.Module):
-    """
-    Reduce feature maps into a single edge map
-    """
-    def __init__(self, channels):
-        super(MapReduce, self).__init__()
-        self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
-        nn.init.constant_(self.conv.bias, 0)
-    def forward(self, x):
-        return self.conv(x)
-class PDCBlock(nn.Module):
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock, self).__init__()
-        self.stride=stride
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-class PDCBlock_converted(nn.Module):
-    """
-    CPDC, APDC can be converted to vanilla 3x3 convolution
-    RPDC can be converted to vanilla 5x5 convolution
-    """
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock_converted, self).__init__()
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        if pdc == 'rd':
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False)
-        else:
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-class PiDiNet(nn.Module):
-    def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False):
-        super(PiDiNet, self).__init__()
-        self.sa = sa
-        if dil is not None:
-            assert isinstance(dil, int), 'dil should be an int'
-        self.dil = dil
-        self.fuseplanes = []
-        self.inplane = inplane
-        if convert:
-            if pdcs[0] == 'rd':
-                init_kernel_size = 5
-                init_padding = 2
-            else:
-                init_kernel_size = 3
-                init_padding = 1
-            self.init_block = nn.Conv2d(3, self.inplane,
-                    kernel_size=init_kernel_size, padding=init_padding, bias=False)
-            block_class = PDCBlock_converted
-        else:
-            self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1)
-            block_class = PDCBlock
-        self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane)
-        self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane)
-        self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # C
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2)
-        self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane)
-        self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane)
-        self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 2C
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2)
-        self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane)
-        self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane)
-        self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-        self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2)
-        self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane)
-        self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane)
-        self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-        self.conv_reduces = nn.ModuleList()
-        if self.sa and self.dil is not None:
-            self.attentions = nn.ModuleList()
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.attentions.append(CSAM(self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        elif self.sa:
-            self.attentions = nn.ModuleList()
-            for i in range(4):
-                self.attentions.append(CSAM(self.fuseplanes[i]))
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        elif self.dil is not None:
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        else:
-            for i in range(4):
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias
-        nn.init.constant_(self.classifier.weight, 0.25)
-        nn.init.constant_(self.classifier.bias, 0)
-        # print('initialization done')
-    def get_weights(self):
-        conv_weights = []
-        bn_weights = []
-        relu_weights = []
-        for pname, p in self.named_parameters():
-            if 'bn' in pname:
-                bn_weights.append(p)
-            elif 'relu' in pname:
-                relu_weights.append(p)
-            else:
-                conv_weights.append(p)
-        return conv_weights, bn_weights, relu_weights
-    def forward(self, x):
-        H, W = x.size()[2:]
-        x = self.init_block(x)
-        x1 = self.block1_1(x)
-        x1 = self.block1_2(x1)
-        x1 = self.block1_3(x1)
-        x2 = self.block2_1(x1)
-        x2 = self.block2_2(x2)
-        x2 = self.block2_3(x2)
-        x2 = self.block2_4(x2)
-        x3 = self.block3_1(x2)
-        x3 = self.block3_2(x3)
-        x3 = self.block3_3(x3)
-        x3 = self.block3_4(x3)
-        x4 = self.block4_1(x3)
-        x4 = self.block4_2(x4)
-        x4 = self.block4_3(x4)
-        x4 = self.block4_4(x4)
-        x_fuses = []
-        if self.sa and self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](self.dilations[i](xi)))
-        elif self.sa:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](xi))
-        elif self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.dilations[i](xi))
-        else:
-            x_fuses = [x1, x2, x3, x4]
-        e1 = self.conv_reduces[0](x_fuses[0])
-        e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False)
-        e2 = self.conv_reduces[1](x_fuses[1])
-        e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False)
-        e3 = self.conv_reduces[2](x_fuses[2])
-        e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False)
-        e4 = self.conv_reduces[3](x_fuses[3])
-        e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False)
-        outputs = [e1, e2, e3, e4]
-        output = self.classifier(torch.cat(outputs, dim=1))
-        #if not self.training:
-        #    return torch.sigmoid(output)
-        outputs.append(output)
-        outputs = [torch.sigmoid(r) for r in outputs]
-        return outputs
-def config_model(model):
-    model_options = list(nets.keys())
-    assert model in model_options, \
-        'unrecognized model, please choose from %s' % str(model_options)
-    # print(str(nets[model]))
-    pdcs = []
-    for i in range(16):
-        layer_name = 'layer%d' % i
-        op = nets[model][layer_name]
-        pdcs.append(createConvFunc(op))
-    return pdcs
-def pidinet():
-    pdcs = config_model('carv4')
-    dil = 24 #if args.dil else None
-    return PiDiNet(60, pdcs, dil=dil, sa=True)
-if __name__ == '__main__':
-    model = pidinet()
-    ckp = torch.load('table5_pidinet.pth')['state_dict']
-    model.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()})
-    im = cv2.imread('examples/test_my/cat_v4.png')
-    im = img2tensor(im).unsqueeze(0)/255.
-    res = model(im)[-1]
-    res = res>0.5
-    res = res.float()
-    res = (res[0,0].cpu().data.numpy()*255.).astype(np.uint8)
-    print(res.shape)
-    cv2.imwrite('edge.png', res)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/grid/Factory.js DELETED Viewed

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

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/cmd_inference.py DELETED Viewed

@@ -1,106 +0,0 @@
-"""该模块用于生成VITS文件
-使用方法
-python cmd_inference.py -m 模型路径 -c 配置文件路径 -o 输出文件路径 -l 输入的语言 -t 输入文本 -s 合成目标说话人名称
-可选参数
--ns 感情变化程度
--nsw 音素发音长度
--ls 整体语速
--on 输出文件的名称
-"""
-from pathlib import Path
-import utils
-from models import SynthesizerTrn
-import torch
-from torch import no_grad, LongTensor
-import librosa
-from text import text_to_sequence, _clean_text
-import commons
-import scipy.io.wavfile as wavf
-import os
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-language_marks = {
-    "Japanese": "",
-    "日本語": "[JA]",
-    "简体中文": "[ZH]",
-    "English": "[EN]",
-    "Mix": "",
-}
-def get_text(text, hps, is_symbol):
-    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-if __name__ == "__main__":
-    import argparse
-    parser = argparse.ArgumentParser(description='vits inference')
-    #必须参数
-    parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
-    parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
-    parser.add_argument('-o', '--output_path', type=str, default="output/vits", help='输出文件路径')
-    parser.add_argument('-l', '--language', type=str, default="日本語", help='输入的语言')
-    parser.add_argument('-t', '--text', type=str, help='输入文本')
-    parser.add_argument('-s', '--spk', type=str, help='合成目标说话人名称')
-    #可选参数
-    parser.add_argument('-on', '--output_name', type=str, default="output", help='输出文件的名称')
-    parser.add_argument('-ns', '--noise_scale', type=float,default= .667,help='感情变化程度')
-    parser.add_argument('-nsw', '--noise_scale_w', type=float,default=0.6, help='音素发音长度')
-    parser.add_argument('-ls', '--length_scale', type=float,default=1, help='整体语速')
-    args = parser.parse_args()
-    model_path = args.model_path
-    config_path = args.config_path
-    output_dir = Path(args.output_path)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    language = args.language
-    text = args.text
-    spk = args.spk
-    noise_scale = args.noise_scale
-    noise_scale_w = args.noise_scale_w
-    length = args.length_scale
-    output_name = args.output_name
-    hps = utils.get_hparams_from_file(config_path)
-    net_g = SynthesizerTrn(
-        len(hps.symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-    _ = utils.load_checkpoint(model_path, net_g, None)
-    speaker_ids = hps.speakers
-    if language is not None:
-        text = language_marks[language] + text + language_marks[language]
-        speaker_id = speaker_ids[spk]
-        stn_tst = get_text(text, hps, False)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(device)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-            sid = LongTensor([speaker_id]).to(device)
-            audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                                length_scale=1.0 / length)[0][0, 0].data.cpu().float().numpy()
-        del stn_tst, x_tst, x_tst_lengths, sid
-        wavf.write(str(output_dir)+"/"+output_name+".wav",hps.data.sampling_rate,audio)

spaces/AlhitawiMohammed22/HTD_HTR/README.md DELETED Viewed

@@ -1,20 +0,0 @@
----
-title: HTD HTR
-emoji: 📉
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-reference:
-https://github.com/kforcodeai/doctr-trocr
-https://github.com/mindee/doctr/issues/1307
-https://github.com/mindee/doctr/discussions/606

spaces/Andy1621/uniformer_image_detection/configs/gfl/gfl_r50_fpn_1x_coco.py DELETED Viewed

@@ -1,57 +0,0 @@
-_base_ = [
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    type='GFL',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs='on_output',
-        num_outs=5),
-    bbox_head=dict(
-        type='GFLHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            octave_base_scale=8,
-            scales_per_octave=1,
-            strides=[8, 16, 32, 64, 128]),
-        loss_cls=dict(
-            type='QualityFocalLoss',
-            use_sigmoid=True,
-            beta=2.0,
-            loss_weight=1.0),
-        loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
-        reg_max=16,
-        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(type='ATSSAssigner', topk=9),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False),
-    test_cfg=dict(
-        nms_pre=1000,
-        min_bbox_size=0,
-        score_thr=0.05,
-        nms=dict(type='nms', iou_threshold=0.6),
-        max_per_img=100))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

spaces/Andy1621/uniformer_image_detection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py DELETED Viewed

@@ -1,16 +0,0 @@
-_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://regnetx_1.6gf',
-    backbone=dict(
-        type='RegNet',
-        arch='regnetx_1.6gf',
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[72, 168, 408, 912],
-        out_channels=256,
-        num_outs=5))

spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py DELETED Viewed

@@ -1,34 +0,0 @@
-_base_ = [
-    '../_base_/models/faster_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-model = dict(
-    roi_head=dict(
-        bbox_head=dict(
-            _delete_=True,
-            type='SABLHead',
-            num_classes=80,
-            cls_in_channels=256,
-            reg_in_channels=256,
-            roi_feat_size=7,
-            reg_feat_up_ratio=2,
-            reg_pre_kernel=3,
-            reg_post_kernel=3,
-            reg_pre_num=2,
-            reg_post_num=1,
-            cls_out_channels=1024,
-            reg_offset_out_channels=256,
-            reg_cls_out_channels=256,
-            num_cls_fcs=1,
-            num_reg_fcs=0,
-            reg_class_agnostic=True,
-            norm_cfg=None,
-            bbox_coder=dict(
-                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
-                               loss_weight=1.0))))

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py DELETED Viewed

@@ -1,9 +0,0 @@
-_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))

spaces/AnishKumbhar/ChatBot/app.py DELETED Viewed

	@@ -1 +0,0 @@
1	- python /text-generation-webui/server.py --share --chat --wbits 4 --groupsize 128 --model_type llama

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/One-Click-Installers.md DELETED Viewed

@@ -1,72 +0,0 @@
-# Additional one-click installers info
-## Installing nvcc
-If you have an NVIDIA GPU and ever need to compile something, like ExLlamav2 (that currently doesn't have pre-built wheels), you can install `nvcc` by running the `cmd_` script for your OS and entering this command:
-```
-conda install cuda -c nvidia/label/cuda-11.7.1
-```
-## Using an AMD GPU in Linux
-Requires ROCm SDK 5.4.2 or 5.4.3 to be installed. Some systems may also
-need: sudo apt-get install libstdc++-12-dev
-Edit the "one_click.py" script using a text editor and un-comment and
-modify the lines near the top of the script according to your setup. In
-particular, modify the os.environ["ROCM_PATH"] = '/opt/rocm' line to
-point to your ROCm installation.
-## WSL instructions
-If you do not have WSL installed, see here:
-https://learn.microsoft.com/en-us/windows/wsl/install
-If you want to install Linux to a drive other than C
-Open powershell and enter these commands:
-cd D:\Path\To\Linux
-$ProgressPreference = 'SilentlyContinue'
-Invoke-WebRequest -Uri <LinuxDistroURL> -OutFile Linux.appx -UseBasicParsing
-mv Linux.appx Linux.zip
-Then open Linux.zip and you should see several .appx files inside.
-The one with _x64.appx contains the exe installer that you need.
-Extract the contents of that _x64.appx file and run <distro>.exe to install.
-Linux Distro URLs:
-https://learn.microsoft.com/en-us/windows/wsl/install-manual#downloading-distributions
-******************************************************************************
-*ENSURE THAT THE WSL LINUX DISTRO THAT YOU WISH TO USE IS SET AS THE DEFAULT!*
-******************************************************************************
-Do this by using these commands:
-wsl -l
-wsl -s <DistroName>
-### Web UI Installation
-Run the "start" script. By default it will install the web UI in WSL:
-/home/{username}/text-gen-install
-To launch the web UI in the future after it is already installed, run
-the same "start" script. Ensure that one_click.py and wsl.sh are next to it!
-### Updating the web UI
-As an alternative to running the "update" script, you can also run "wsl.sh update" in WSL.
-### Running an interactive shell
-As an alternative to running the "cmd" script, you can also run "wsl.sh cmd" in WSL.
-### Changing the default install location
-To change this, you will need to edit the scripts as follows:
-wsl.sh: line ~22   INSTALL_DIR="/path/to/install/dir"
-Keep in mind that there is a long-standing bug in WSL that significantly
-slows drive read/write speeds when using a physical drive as opposed to
-the virtual one that Linux is installed in.

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/util/util.py DELETED Viewed

@@ -1,76 +0,0 @@
-"""This module contains simple helper functions """
-from __future__ import print_function
-import torch
-import numpy as np
-import os
-import imageio
-def tensor2im(input_image, imtype=np.uint8):
-    """"Converts a Tensor array into a numpy image array.
-    Parameters:
-        input_image (tensor) --  the input image tensor array
-        imtype (type)        --  the desired type of the converted numpy array
-    """
-    if not isinstance(input_image, np.ndarray):
-        if isinstance(input_image, torch.Tensor):  # get the data from a variable
-            image_tensor = input_image.data
-        else:
-            return input_image
-        image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
-        if image_numpy.shape[0] == 1:  # grayscale to RGB
-            image_numpy = np.tile(image_numpy, (3, 1, 1))
-        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
-    else:  # if it is a numpy array, do nothing
-        image_numpy = input_image
-    return image_numpy.astype(imtype)
-def tensor2array(value_tensor):
-    """Converts a Tensor array into a numpy
-    :param value_tensor:
-    :return:
-    """
-    if value_tensor.dim() == 3:
-        numpy = value_tensor.view(-1).cpu().float().numpy()
-    else:
-        numpy = value_tensor[0].view(-1).cpu().float().numpy()
-    return numpy
-def save_image(image_numpy, image_path):
-    """Save a numpy image to the disk
-    Parameters:
-        image_numpy (numpy array) -- input numpy array
-        image_path (str)          -- the path of the image
-    """
-    if image_numpy.shape[2] == 1:
-        image_numpy = image_numpy.reshape(image_numpy.shape[0], image_numpy.shape[1])
-    imageio.imwrite(image_path, image_numpy)
-def mkdirs(paths):
-    """create empty directories if they don't exist
-    Parameters:
-        paths (str list) -- a list of directory paths
-    """
-    if isinstance(paths, list) and not isinstance(paths, str):
-        for path in paths:
-            mkdir(path)
-    else:
-        mkdir(paths)
-def mkdir(path):
-    """create a single empty directory if it didn't exist
-    Parameters:
-        path (str) -- a single directory path
-    """
-    if not os.path.exists(path):
-        os.makedirs(path)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/utils/__init__.py DELETED Viewed

@@ -1,19 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .flops_counter import get_model_complexity_info
-from .fuse_conv_bn import fuse_conv_bn
-from .sync_bn import revert_sync_batchnorm
-from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit,
-                          KaimingInit, NormalInit, PretrainedInit,
-                          TruncNormalInit, UniformInit, XavierInit,
-                          bias_init_with_prob, caffe2_xavier_init,
-                          constant_init, initialize, kaiming_init, normal_init,
-                          trunc_normal_init, uniform_init, xavier_init)
-__all__ = [
-    'get_model_complexity_info', 'bias_init_with_prob', 'caffe2_xavier_init',
-    'constant_init', 'kaiming_init', 'normal_init', 'trunc_normal_init',
-    'uniform_init', 'xavier_init', 'fuse_conv_bn', 'initialize',
-    'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
-    'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
-    'Caffe2XavierInit', 'revert_sync_batchnorm'
-]

spaces/Arnx/MusicGenXvAKN/audiocraft/utils/export.py DELETED Viewed

@@ -1,56 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Utility to export a training checkpoint to a lightweight release checkpoint.
-"""
-from pathlib import Path
-import typing as tp
-from omegaconf import OmegaConf, DictConfig
-import torch
-def _clean_lm_cfg(cfg: DictConfig):
-    OmegaConf.set_struct(cfg, False)
-    # This used to be set automatically in the LM solver, need a more robust solution
-    # for the future.
-    cfg['transformer_lm']['card'] = 2048
-    cfg['transformer_lm']['n_q'] = 4
-    # Experimental params no longer supported.
-    bad_params = ['spectral_norm_attn_iters', 'spectral_norm_ff_iters',
-                  'residual_balancer_attn', 'residual_balancer_ff', 'layer_drop']
-    for name in bad_params:
-        del cfg['transformer_lm'][name]
-    OmegaConf.set_struct(cfg, True)
-    return cfg
-def export_encodec(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['ema']['state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
-def export_lm(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['fsdp_best_state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(_clean_lm_cfg(pkg['xp.cfg']))
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/console.py DELETED Viewed

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spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/anchor_generator.py DELETED Viewed

@@ -1,382 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import collections
-import math
-from typing import List
-import torch
-from torch import nn
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec
-from detectron2.structures import Boxes, RotatedBoxes
-from detectron2.utils.registry import Registry
-ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR")
-ANCHOR_GENERATOR_REGISTRY.__doc__ = """
-Registry for modules that creates object detection anchors for feature maps.
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-class BufferList(nn.Module):
-    """
-    Similar to nn.ParameterList, but for buffers
-    """
-    def __init__(self, buffers):
-        super().__init__()
-        for i, buffer in enumerate(buffers):
-            # Use non-persistent buffer so the values are not saved in checkpoint
-            self.register_buffer(str(i), buffer, persistent=False)
-    def __len__(self):
-        return len(self._buffers)
-    def __iter__(self):
-        return iter(self._buffers.values())
-def _create_grid_offsets(size: List[int], stride: int, offset: float, device: torch.device):
-    grid_height, grid_width = size
-    shifts_x = torch.arange(
-        offset * stride, grid_width * stride, step=stride, dtype=torch.float32, device=device
-    )
-    shifts_y = torch.arange(
-        offset * stride, grid_height * stride, step=stride, dtype=torch.float32, device=device
-    )
-    shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
-    shift_x = shift_x.reshape(-1)
-    shift_y = shift_y.reshape(-1)
-    return shift_x, shift_y
-def _broadcast_params(params, num_features, name):
-    """
-    If one size (or aspect ratio) is specified and there are multiple feature
-    maps, we "broadcast" anchors of that single size (or aspect ratio)
-    over all feature maps.
-    If params is list[float], or list[list[float]] with len(params) == 1, repeat
-    it num_features time.
-    Returns:
-        list[list[float]]: param for each feature
-    """
-    assert isinstance(
-        params, collections.abc.Sequence
-    ), f"{name} in anchor generator has to be a list! Got {params}."
-    assert len(params), f"{name} in anchor generator cannot be empty!"
-    if not isinstance(params[0], collections.abc.Sequence):  # params is list[float]
-        return [params] * num_features
-    if len(params) == 1:
-        return list(params) * num_features
-    assert len(params) == num_features, (
-        f"Got {name} of length {len(params)} in anchor generator, "
-        f"but the number of input features is {num_features}!"
-    )
-    return params
-@ANCHOR_GENERATOR_REGISTRY.register()
-class DefaultAnchorGenerator(nn.Module):
-    """
-    Compute anchors in the standard ways described in
-    "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks".
-    """
-    box_dim: torch.jit.Final[int] = 4
-    """
-    the dimension of each anchor box.
-    """
-    @configurable
-    def __init__(self, *, sizes, aspect_ratios, strides, offset=0.5):
-        """
-        This interface is experimental.
-        Args:
-            sizes (list[list[float]] or list[float]):
-                If ``sizes`` is list[list[float]], ``sizes[i]`` is the list of anchor sizes
-                (i.e. sqrt of anchor area) to use for the i-th feature map.
-                If ``sizes`` is list[float], ``sizes`` is used for all feature maps.
-                Anchor sizes are given in absolute lengths in units of
-                the input image; they do not dynamically scale if the input image size changes.
-            aspect_ratios (list[list[float]] or list[float]): list of aspect ratios
-                (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies.
-            strides (list[int]): stride of each input feature.
-            offset (float): Relative offset between the center of the first anchor and the top-left
-                corner of the image. Value has to be in [0, 1).
-                Recommend to use 0.5, which means half stride.
-        """
-        super().__init__()
-        self.strides = strides
-        self.num_features = len(self.strides)
-        sizes = _broadcast_params(sizes, self.num_features, "sizes")
-        aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios")
-        self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios)
-        self.offset = offset
-        assert 0.0 <= self.offset < 1.0, self.offset
-    @classmethod
-    def from_config(cls, cfg, input_shape: List[ShapeSpec]):
-        return {
-            "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES,
-            "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS,
-            "strides": [x.stride for x in input_shape],
-            "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET,
-        }
-    def _calculate_anchors(self, sizes, aspect_ratios):
-        cell_anchors = [
-            self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios)
-        ]
-        return BufferList(cell_anchors)
-    @property
-    @torch.jit.unused
-    def num_cell_anchors(self):
-        """
-        Alias of `num_anchors`.
-        """
-        return self.num_anchors
-    @property
-    @torch.jit.unused
-    def num_anchors(self):
-        """
-        Returns:
-            list[int]: Each int is the number of anchors at every pixel
-                location, on that feature map.
-                For example, if at every pixel we use anchors of 3 aspect
-                ratios and 5 sizes, the number of anchors is 15.
-                (See also ANCHOR_GENERATOR.SIZES and ANCHOR_GENERATOR.ASPECT_RATIOS in config)
-                In standard RPN models, `num_anchors` on every feature map is the same.
-        """
-        return [len(cell_anchors) for cell_anchors in self.cell_anchors]
-    def _grid_anchors(self, grid_sizes: List[List[int]]):
-        """
-        Returns:
-            list[Tensor]: #featuremap tensors, each is (#locations x #cell_anchors) x 4
-        """
-        anchors = []
-        # buffers() not supported by torchscript. use named_buffers() instead
-        buffers: List[torch.Tensor] = [x[1] for x in self.cell_anchors.named_buffers()]
-        for size, stride, base_anchors in zip(grid_sizes, self.strides, buffers):
-            shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device)
-            shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
-            anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
-        return anchors
-    def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)):
-        """
-        Generate a tensor storing canonical anchor boxes, which are all anchor
-        boxes of different sizes and aspect_ratios centered at (0, 0).
-        We can later build the set of anchors for a full feature map by
-        shifting and tiling these tensors (see `meth:_grid_anchors`).
-        Args:
-            sizes (tuple[float]):
-            aspect_ratios (tuple[float]]):
-        Returns:
-            Tensor of shape (len(sizes) * len(aspect_ratios), 4) storing anchor boxes
-                in XYXY format.
-        """
-        # This is different from the anchor generator defined in the original Faster R-CNN
-        # code or Detectron. They yield the same AP, however the old version defines cell
-        # anchors in a less natural way with a shift relative to the feature grid and
-        # quantization that results in slightly different sizes for different aspect ratios.
-        # See also https://github.com/facebookresearch/Detectron/issues/227
-        anchors = []
-        for size in sizes:
-            area = size ** 2.0
-            for aspect_ratio in aspect_ratios:
-                # s * s = w * h
-                # a = h / w
-                # ... some algebra ...
-                # w = sqrt(s * s / a)
-                # h = a * w
-                w = math.sqrt(area / aspect_ratio)
-                h = aspect_ratio * w
-                x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0
-                anchors.append([x0, y0, x1, y1])
-        return torch.tensor(anchors)
-    def forward(self, features: List[torch.Tensor]):
-        """
-        Args:
-            features (list[Tensor]): list of backbone feature maps on which to generate anchors.
-        Returns:
-            list[Boxes]: a list of Boxes containing all the anchors for each feature map
-                (i.e. the cell anchors repeated over all locations in the feature map).
-                The number of anchors of each feature map is Hi x Wi x num_cell_anchors,
-                where Hi, Wi are resolution of the feature map divided by anchor stride.
-        """
-        grid_sizes = [feature_map.shape[-2:] for feature_map in features]
-        anchors_over_all_feature_maps = self._grid_anchors(grid_sizes)
-        return [Boxes(x) for x in anchors_over_all_feature_maps]
-@ANCHOR_GENERATOR_REGISTRY.register()
-class RotatedAnchorGenerator(nn.Module):
-    """
-    Compute rotated anchors used by Rotated RPN (RRPN), described in
-    "Arbitrary-Oriented Scene Text Detection via Rotation Proposals".
-    """
-    box_dim: int = 5
-    """
-    the dimension of each anchor box.
-    """
-    @configurable
-    def __init__(self, *, sizes, aspect_ratios, strides, angles, offset=0.5):
-        """
-        This interface is experimental.
-        Args:
-            sizes (list[list[float]] or list[float]):
-                If sizes is list[list[float]], sizes[i] is the list of anchor sizes
-                (i.e. sqrt of anchor area) to use for the i-th feature map.
-                If sizes is list[float], the sizes are used for all feature maps.
-                Anchor sizes are given in absolute lengths in units of
-                the input image; they do not dynamically scale if the input image size changes.
-            aspect_ratios (list[list[float]] or list[float]): list of aspect ratios
-                (i.e. height / width) to use for anchors. Same "broadcast" rule for `sizes` applies.
-            strides (list[int]): stride of each input feature.
-            angles (list[list[float]] or list[float]): list of angles (in degrees CCW)
-                to use for anchors. Same "broadcast" rule for `sizes` applies.
-            offset (float): Relative offset between the center of the first anchor and the top-left
-                corner of the image. Value has to be in [0, 1).
-                Recommend to use 0.5, which means half stride.
-        """
-        super().__init__()
-        self.strides = strides
-        self.num_features = len(self.strides)
-        sizes = _broadcast_params(sizes, self.num_features, "sizes")
-        aspect_ratios = _broadcast_params(aspect_ratios, self.num_features, "aspect_ratios")
-        angles = _broadcast_params(angles, self.num_features, "angles")
-        self.cell_anchors = self._calculate_anchors(sizes, aspect_ratios, angles)
-        self.offset = offset
-        assert 0.0 <= self.offset < 1.0, self.offset
-    @classmethod
-    def from_config(cls, cfg, input_shape: List[ShapeSpec]):
-        return {
-            "sizes": cfg.MODEL.ANCHOR_GENERATOR.SIZES,
-            "aspect_ratios": cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS,
-            "strides": [x.stride for x in input_shape],
-            "offset": cfg.MODEL.ANCHOR_GENERATOR.OFFSET,
-            "angles": cfg.MODEL.ANCHOR_GENERATOR.ANGLES,
-        }
-    def _calculate_anchors(self, sizes, aspect_ratios, angles):
-        cell_anchors = [
-            self.generate_cell_anchors(size, aspect_ratio, angle).float()
-            for size, aspect_ratio, angle in zip(sizes, aspect_ratios, angles)
-        ]
-        return BufferList(cell_anchors)
-    @property
-    def num_cell_anchors(self):
-        """
-        Alias of `num_anchors`.
-        """
-        return self.num_anchors
-    @property
-    def num_anchors(self):
-        """
-        Returns:
-            list[int]: Each int is the number of anchors at every pixel
-                location, on that feature map.
-                For example, if at every pixel we use anchors of 3 aspect
-                ratios, 2 sizes and 5 angles, the number of anchors is 30.
-                (See also ANCHOR_GENERATOR.SIZES, ANCHOR_GENERATOR.ASPECT_RATIOS
-                and ANCHOR_GENERATOR.ANGLES in config)
-                In standard RRPN models, `num_anchors` on every feature map is the same.
-        """
-        return [len(cell_anchors) for cell_anchors in self.cell_anchors]
-    def _grid_anchors(self, grid_sizes):
-        anchors = []
-        for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors):
-            shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device)
-            zeros = torch.zeros_like(shift_x)
-            shifts = torch.stack((shift_x, shift_y, zeros, zeros, zeros), dim=1)
-            anchors.append((shifts.view(-1, 1, 5) + base_anchors.view(1, -1, 5)).reshape(-1, 5))
-        return anchors
-    def generate_cell_anchors(
-        self,
-        sizes=(32, 64, 128, 256, 512),
-        aspect_ratios=(0.5, 1, 2),
-        angles=(-90, -60, -30, 0, 30, 60, 90),
-    ):
-        """
-        Generate a tensor storing canonical anchor boxes, which are all anchor
-        boxes of different sizes, aspect_ratios, angles centered at (0, 0).
-        We can later build the set of anchors for a full feature map by
-        shifting and tiling these tensors (see `meth:_grid_anchors`).
-        Args:
-            sizes (tuple[float]):
-            aspect_ratios (tuple[float]]):
-            angles (tuple[float]]):
-        Returns:
-            Tensor of shape (len(sizes) * len(aspect_ratios) * len(angles), 5)
-                storing anchor boxes in (x_ctr, y_ctr, w, h, angle) format.
-        """
-        anchors = []
-        for size in sizes:
-            area = size ** 2.0
-            for aspect_ratio in aspect_ratios:
-                # s * s = w * h
-                # a = h / w
-                # ... some algebra ...
-                # w = sqrt(s * s / a)
-                # h = a * w
-                w = math.sqrt(area / aspect_ratio)
-                h = aspect_ratio * w
-                anchors.extend([0, 0, w, h, a] for a in angles)
-        return torch.tensor(anchors)
-    def forward(self, features):
-        """
-        Args:
-            features (list[Tensor]): list of backbone feature maps on which to generate anchors.
-        Returns:
-            list[RotatedBoxes]: a list of Boxes containing all the anchors for each feature map
-                (i.e. the cell anchors repeated over all locations in the feature map).
-                The number of anchors of each feature map is Hi x Wi x num_cell_anchors,
-                where Hi, Wi are resolution of the feature map divided by anchor stride.
-        """
-        grid_sizes = [feature_map.shape[-2:] for feature_map in features]
-        anchors_over_all_feature_maps = self._grid_anchors(grid_sizes)
-        return [RotatedBoxes(x) for x in anchors_over_all_feature_maps]
-def build_anchor_generator(cfg, input_shape):
-    """
-    Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`.
-    """
-    anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME
-    return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/structures/rotated_boxes.py DELETED Viewed

@@ -1,503 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import math
-from typing import List, Tuple
-import torch
-from detectron2.layers.rotated_boxes import pairwise_iou_rotated
-from .boxes import Boxes
-class RotatedBoxes(Boxes):
-    """
-    This structure stores a list of rotated boxes as a Nx5 torch.Tensor.
-    It supports some common methods about boxes
-    (`area`, `clip`, `nonempty`, etc),
-    and also behaves like a Tensor
-    (support indexing, `to(device)`, `.device`, and iteration over all boxes)
-    """
-    def __init__(self, tensor: torch.Tensor):
-        """
-        Args:
-            tensor (Tensor[float]): a Nx5 matrix.  Each row is
-                (x_center, y_center, width, height, angle),
-                in which angle is represented in degrees.
-                While there's no strict range restriction for it,
-                the recommended principal range is between [-180, 180) degrees.
-        Assume we have a horizontal box B = (x_center, y_center, width, height),
-        where width is along the x-axis and height is along the y-axis.
-        The rotated box B_rot (x_center, y_center, width, height, angle)
-        can be seen as:
-        1. When angle == 0:
-           B_rot == B
-        2. When angle > 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;
-        3. When angle < 0:
-           B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.
-        Mathematically, since the right-handed coordinate system for image space
-        is (y, x), where y is top->down and x is left->right, the 4 vertices of the
-        rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from
-        the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)
-        in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians,
-        :math:`(y_c, x_c)` is the center of the rectangle):
-        .. math::
-            yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c,
-            xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c,
-        which is the standard rigid-body rotation transformation.
-        Intuitively, the angle is
-        (1) the rotation angle from y-axis in image space
-        to the height vector (top->down in the box's local coordinate system)
-        of the box in CCW, and
-        (2) the rotation angle from x-axis in image space
-        to the width vector (left->right in the box's local coordinate system)
-        of the box in CCW.
-        More intuitively, consider the following horizontal box ABCD represented
-        in (x1, y1, x2, y2): (3, 2, 7, 4),
-        covering the [3, 7] x [2, 4] region of the continuous coordinate system
-        which looks like this:
-        .. code:: none
-            O--------> x
-            |
-            |  A---B
-            |  |   |
-            |  D---C
-            |
-            v y
-        Note that each capital letter represents one 0-dimensional geometric point
-        instead of a 'square pixel' here.
-        In the example above, using (x, y) to represent a point we have:
-        .. math::
-            O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)
-        We name vector AB = vector DC as the width vector in box's local coordinate system, and
-        vector AD = vector BC as the height vector in box's local coordinate system. Initially,
-        when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis
-        in the image space, respectively.
-        For better illustration, we denote the center of the box as E,
-        .. code:: none
-            O--------> x
-            |
-            |  A---B
-            |  | E |
-            |  D---C
-            |
-            v y
-        where the center E = ((3+7)/2, (2+4)/2) = (5, 3).
-        Also,
-        .. math::
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-        Therefore, the corresponding representation for the same shape in rotated box in
-        (x_center, y_center, width, height, angle) format is:
-        (5, 3, 4, 2, 0),
-        Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees
-        CCW (counter-clockwise) by definition. It looks like this:
-        .. code:: none
-            O--------> x
-            |   B-C
-            |   | |
-            |   |E|
-            |   | |
-            |   A-D
-            v y
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CCW with regard to E:
-        A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)
-        Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to
-        vector AD or vector BC (the top->down height vector in box's local coordinate system),
-        or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right
-        width vector in box's local coordinate system).
-        .. math::
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-        Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)
-        by definition? It looks like this:
-        .. code:: none
-            O--------> x
-            |   D-A
-            |   | |
-            |   |E|
-            |   | |
-            |   C-B
-            v y
-        The center E is still located at the same point (5, 3), while the vertices
-        ABCD are rotated by 90 degrees CW with regard to E:
-        A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)
-        .. math::
-            width = |AB| = |CD| = 5 - 1 = 4,
-            height = |AD| = |BC| = 6 - 4 = 2.
-        This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU
-        will be 1. However, these two will generate different RoI Pooling results and
-        should not be treated as an identical box.
-        On the other hand, it's easy to see that (X, Y, W, H, A) is identical to
-        (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be
-        identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is
-        equivalent to rotating the same shape 90 degrees CW.
-        We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):
-        .. code:: none
-            O--------> x
-            |
-            |  C---D
-            |  | E |
-            |  B---A
-            |
-            v y
-        .. math::
-            A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),
-            width = |AB| = |CD| = 7 - 3 = 4,
-            height = |AD| = |BC| = 4 - 2 = 2.
-        Finally, this is a very inaccurate (heavily quantized) illustration of
-        how (5, 3, 4, 2, 60) looks like in case anyone wonders:
-        .. code:: none
-            O--------> x
-            |     B\
-            |    /  C
-            |   /E /
-            |  A  /
-            |   `D
-            v y
-        It's still a rectangle with center of (5, 3), width of 4 and height of 2,
-        but its angle (and thus orientation) is somewhere between
-        (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).
-        """
-        device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
-        tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
-        if tensor.numel() == 0:
-            # Use reshape, so we don't end up creating a new tensor that does not depend on
-            # the inputs (and consequently confuses jit)
-            tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)
-        assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()
-        self.tensor = tensor
-    def clone(self) -> "RotatedBoxes":
-        """
-        Clone the RotatedBoxes.
-        Returns:
-            RotatedBoxes
-        """
-        return RotatedBoxes(self.tensor.clone())
-    def to(self, device: torch.device):
-        # Boxes are assumed float32 and does not support to(dtype)
-        return RotatedBoxes(self.tensor.to(device=device))
-    def area(self) -> torch.Tensor:
-        """
-        Computes the area of all the boxes.
-        Returns:
-            torch.Tensor: a vector with areas of each box.
-        """
-        box = self.tensor
-        area = box[:, 2] * box[:, 3]
-        return area
-    def normalize_angles(self) -> None:
-        """
-        Restrict angles to the range of [-180, 180) degrees
-        """
-        self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0
-    def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None:
-        """
-        Clip (in place) the boxes by limiting x coordinates to the range [0, width]
-        and y coordinates to the range [0, height].
-        For RRPN:
-        Only clip boxes that are almost horizontal with a tolerance of
-        clip_angle_threshold to maintain backward compatibility.
-        Rotated boxes beyond this threshold are not clipped for two reasons:
-        1. There are potentially multiple ways to clip a rotated box to make it
-           fit within the image.
-        2. It's tricky to make the entire rectangular box fit within the image
-           and still be able to not leave out pixels of interest.
-        Therefore we rely on ops like RoIAlignRotated to safely handle this.
-        Args:
-            box_size (height, width): The clipping box's size.
-            clip_angle_threshold:
-                Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),
-                we do the clipping as horizontal boxes.
-        """
-        h, w = box_size
-        # normalize angles to be within (-180, 180] degrees
-        self.normalize_angles()
-        idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]
-        # convert to (x1, y1, x2, y2)
-        x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0
-        y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0
-        x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0
-        y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0
-        # clip
-        x1.clamp_(min=0, max=w)
-        y1.clamp_(min=0, max=h)
-        x2.clamp_(min=0, max=w)
-        y2.clamp_(min=0, max=h)
-        # convert back to (xc, yc, w, h)
-        self.tensor[idx, 0] = (x1 + x2) / 2.0
-        self.tensor[idx, 1] = (y1 + y2) / 2.0
-        # make sure widths and heights do not increase due to numerical errors
-        self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)
-        self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)
-    def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
-        """
-        Find boxes that are non-empty.
-        A box is considered empty, if either of its side is no larger than threshold.
-        Returns:
-            Tensor: a binary vector which represents
-            whether each box is empty (False) or non-empty (True).
-        """
-        box = self.tensor
-        widths = box[:, 2]
-        heights = box[:, 3]
-        keep = (widths > threshold) & (heights > threshold)
-        return keep
-    def __getitem__(self, item) -> "RotatedBoxes":
-        """
-        Returns:
-            RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.
-        The following usage are allowed:
-        1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.
-        2. `new_boxes = boxes[2:10]`: return a slice of boxes.
-        3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor
-           with `length = len(boxes)`. Nonzero elements in the vector will be selected.
-        Note that the returned RotatedBoxes might share storage with this RotatedBoxes,
-        subject to Pytorch's indexing semantics.
-        """
-        if isinstance(item, int):
-            return RotatedBoxes(self.tensor[item].view(1, -1))
-        b = self.tensor[item]
-        assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format(
-            item
-        )
-        return RotatedBoxes(b)
-    def __len__(self) -> int:
-        return self.tensor.shape[0]
-    def __repr__(self) -> str:
-        return "RotatedBoxes(" + str(self.tensor) + ")"
-    def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
-        """
-        Args:
-            box_size (height, width): Size of the reference box covering
-                [0, width] x [0, height]
-            boundary_threshold (int): Boxes that extend beyond the reference box
-                boundary by more than boundary_threshold are considered "outside".
-        For RRPN, it might not be necessary to call this function since it's common
-        for rotated box to extend to outside of the image boundaries
-        (the clip function only clips the near-horizontal boxes)
-        Returns:
-            a binary vector, indicating whether each box is inside the reference box.
-        """
-        height, width = box_size
-        cnt_x = self.tensor[..., 0]
-        cnt_y = self.tensor[..., 1]
-        half_w = self.tensor[..., 2] / 2.0
-        half_h = self.tensor[..., 3] / 2.0
-        a = self.tensor[..., 4]
-        c = torch.abs(torch.cos(a * math.pi / 180.0))
-        s = torch.abs(torch.sin(a * math.pi / 180.0))
-        # This basically computes the horizontal bounding rectangle of the rotated box
-        max_rect_dx = c * half_w + s * half_h
-        max_rect_dy = c * half_h + s * half_w
-        inds_inside = (
-            (cnt_x - max_rect_dx >= -boundary_threshold)
-            & (cnt_y - max_rect_dy >= -boundary_threshold)
-            & (cnt_x + max_rect_dx < width + boundary_threshold)
-            & (cnt_y + max_rect_dy < height + boundary_threshold)
-        )
-        return inds_inside
-    def get_centers(self) -> torch.Tensor:
-        """
-        Returns:
-            The box centers in a Nx2 array of (x, y).
-        """
-        return self.tensor[:, :2]
-    def scale(self, scale_x: float, scale_y: float) -> None:
-        """
-        Scale the rotated box with horizontal and vertical scaling factors
-        Note: when scale_factor_x != scale_factor_y,
-        the rotated box does not preserve the rectangular shape when the angle
-        is not a multiple of 90 degrees under resize transformation.
-        Instead, the shape is a parallelogram (that has skew)
-        Here we make an approximation by fitting a rotated rectangle to the parallelogram.
-        """
-        self.tensor[:, 0] *= scale_x
-        self.tensor[:, 1] *= scale_y
-        theta = self.tensor[:, 4] * math.pi / 180.0
-        c = torch.cos(theta)
-        s = torch.sin(theta)
-        # In image space, y is top->down and x is left->right
-        # Consider the local coordintate system for the rotated box,
-        # where the box center is located at (0, 0), and the four vertices ABCD are
-        # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)
-        # the midpoint of the left edge AD of the rotated box E is:
-        # E = (A+D)/2 = (-w / 2, 0)
-        # the midpoint of the top edge AB of the rotated box F is:
-        # F(0, -h / 2)
-        # To get the old coordinates in the global system, apply the rotation transformation
-        # (Note: the right-handed coordinate system for image space is yOx):
-        # (old_x, old_y) = (s * y + c * x, c * y - s * x)
-        # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)
-        # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)
-        # After applying the scaling factor (sfx, sfy):
-        # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)
-        # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)
-        # The new width after scaling tranformation becomes:
-        # w(new) = |E(new) - O| * 2
-        #        = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2
-        #        = sqrt[(sfx * c)^2 + (sfy * s)^2] * w
-        # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y
-        self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)
-        # h(new) = |F(new) - O| * 2
-        #        = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2
-        #        = sqrt[(sfx * s)^2 + (sfy * c)^2] * h
-        # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]
-        #
-        # For example,
-        # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;
-        # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x
-        self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)
-        # The angle is the rotation angle from y-axis in image space to the height
-        # vector (top->down in the box's local coordinate system) of the box in CCW.
-        #
-        # angle(new) = angle_yOx(O - F(new))
-        #            = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )
-        #            = atan2(sfx * s * h / 2, sfy * c * h / 2)
-        #            = atan2(sfx * s, sfy * c)
-        #
-        # For example,
-        # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)
-        self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi
-    @classmethod
-    def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes":
-        """
-        Concatenates a list of RotatedBoxes into a single RotatedBoxes
-        Arguments:
-            boxes_list (list[RotatedBoxes])
-        Returns:
-            RotatedBoxes: the concatenated RotatedBoxes
-        """
-        assert isinstance(boxes_list, (list, tuple))
-        if len(boxes_list) == 0:
-            return cls(torch.empty(0))
-        assert all([isinstance(box, RotatedBoxes) for box in boxes_list])
-        # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
-        cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
-        return cat_boxes
-    @property
-    def device(self) -> torch.device:
-        return self.tensor.device
-    @torch.jit.unused
-    def __iter__(self):
-        """
-        Yield a box as a Tensor of shape (5,) at a time.
-        """
-        yield from self.tensor
-def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None:
-    """
-    Given two lists of rotated boxes of size N and M,
-    compute the IoU (intersection over union)
-    between **all** N x M pairs of boxes.
-    The box order must be (x_center, y_center, width, height, angle).
-    Args:
-        boxes1, boxes2 (RotatedBoxes):
-            two `RotatedBoxes`. Contains N & M rotated boxes, respectively.
-    Returns:
-        Tensor: IoU, sized [N,M].
-    """
-    return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor)

spaces/BartPoint/VoiceChange/infer_pack/models.py DELETED Viewed

@@ -1,1124 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-class 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 SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = 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)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-class 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/Benson/text-generation/Examples/Dani Among Us 3d Download.md DELETED Viewed

@@ -1,53 +0,0 @@
-<br />
-<h1>Dani entre nosotros Descargar 3D: Cómo jugar el juego viral en 3D</h1>
-<p>Si eres un fan de <strong>Among Us</strong>, el exitoso juego multijugador online de engaño y trabajo en equipo, es posible que hayas oído hablar de <strong>Dani</strong>, un desarrollador de juegos indie noruego y YouTuber que hizo una versión en 3D del juego. En este artículo, te diremos todo lo que necesitas saber sobre <strong>Dani Among Us 3D</strong>, incluyendo lo que es, cómo se hizo, y cómo descargarlo y jugarlo. </p>
- <h2>¿Qué hay entre nosotros? </h2>
-<p>Entre nosotros es un juego donde juegas como uno de los compañeros de equipo o impostores en una nave espacial. Los tripulantes tienen que trabajar juntos para completar las tareas y encontrar a los impostores, mientras que los impostores tienen que matar a los tripulantes o sabotear la nave. El juego se puede jugar en línea o a través de WiFi local con 4-15 jugadores. </p>
-<h2>dani among us 3d download</h2><br /><p><b><b>Download File</b> &#128504; <a href="https://bltlly.com/2v6JpF">https://bltlly.com/2v6JpF</a></b></p><br /><br />
- <h3>Un popular juego multijugador en línea de engaño y trabajo en equipo</h3>
-<p>Among Us fue lanzado en 2018 por Innersloth, un estudio de juegos estadounidense, pero se hizo viral en 2020 gracias a los streamers y YouTubers que lo jugaron con sus amigos y fans. El juego ha sido elogiado por su juego simple pero adictivo, su interacción social y comunicación, y su valor de repetición. A partir de junio de 2021, Among Us tiene más de 500 millones de descargas en Google Play Store y más de 18 millones de propietarios en Steam.</p>
- <h3>Diferentes mapas, modos y opciones de personalización</h3>
-<p>Among Us ofrece cuatro mapas diferentes para jugar: The Skeld, MIRA HQ, Polus y The Airship. Cada mapa tiene su propio diseño, tareas, rejillas de ventilación, cámaras y sabotajes. El juego también tiene diferentes modos para elegir, como Classic o Hide n Seek. Además, los jugadores pueden personalizar sus personajes con varios colores, sombreros, pieles, mascotas y trajes. </p>
- <h2>¿Quién es Dani? </h2>
-<p>Dani es el alias en línea de Daniel William Sooman, un desarrollador de juegos indie noruego y YouTuber. Es conocido por crear juegos en Unity usando lenguaje de programación en C# y publicar devlogs (registros de desarrollo) en su canal de YouTube. </p>
-<p>Dani comenzó a programar cuando tenía 15 años, usando Java como su primer lenguaje. Luego cambió a Unity y C# ya que quería hacer mejores juegos. Creó su canal de YouTube en octubre de 2018 y subió su primer video en noviembre de 2018. Desde entonces, ha ganado más de 6 millones de suscriptores y más de 600 millones de visitas en su canal. </p>
- <h3>Conocido por crear juegos en Unity y publicar devlogs</h3>
-<p>Los vídeos de YouTube de Dani consisten principalmente en él desarrollando juegos en Unity y mostrando el proceso, los desafíos y los resultados de sus proyectos. Algunos de sus juegos más populares incluyen: - <strong>Karlson</strong>: un juego de disparos parkour basado en la física donde juegas como Karlson, un agente adicto a la leche que tiene que escapar de una instalación llena de enemigos y trampas. - <strong>Muck</strong>: un juego roguelike de supervivencia donde tienes que reunir recursos, crear objetos, luchar contra monstruos, y sobrevivir tanto como puedas en una isla generada por procedimientos. - <strong>Mobile Suit</strong>: un juego de combate mech donde piloteas un robot gigante y luchas contra otros mechs en varios entornos. Dani también hace juegos basados en títulos o géneros populares, como Fall Guys, Minecraft, Doom, GTA y más. A menudo añade su propio toque o humor a estos juegos, haciéndolos únicos y entretenidos. </p>
- <h2>¿Cómo hizo Dani Among Us 3D? </h2>
-<p>Dani’s Among Us 3D es uno de sus juegos más virales, ya que se basa en el juego original de Among Us pero con una perspectiva y gráficos en 3D. Dani hizo este juego en Unity usando C# y agregó nuevas características y mecánicas al juego. </p>
- <h3>Inspirado por los comentarios sobre su video clonado de Fall Guys</h3>
-<p>Dani tuvo la idea de hacer Among Us 3D de los comentarios en su video donde hizo un clon de Fall Guys en Unity. Mucha gente sugirió que debería hacer una versión en 3D de Among Us, ya que ambos juegos son similares en su estilo colorido y de dibujos animados. Dani decidió asumir el reto y comenzó a trabajar en el proyecto. </p>
-<p></p>
-<p>Dani utilizó Unity como motor de juego y C# como lenguaje de programación para crear una versión en 3D de Among Us. Siguió el mismo juego y reglas que el juego original, pero con una perspectiva en 3D y gráficos. También usó Blender para modelar los personajes, elementos y entornos. Documentó su progreso y desafíos en sus videos de YouTube, donde mostró cómo implementó características como: - El sistema de lobby - La visión del impostor - Las animaciones de eliminación - El sistema de ventilación - El sistema de votación - El sistema de tareas - El sistema de chat - Los efectos de sonido - La música </p>
- <h3>Añadidas nuevas características y mecánicas al juego</h3>
-<p>Además de recrear el juego original en 3D, Dani también agregó algunas nuevas características y mecánicas para hacer su versión más divertida e interesante. Algunas de estas características incluyen: - Una opción de cámara en primera persona - Un sistema de física ragdoll - Un artículo de jetpack - Un artículo de gancho de agarre - Un elemento de cáscara de plátano - Una opción de chat de voz de proximidad - Un editor de mapas personalizado </p>
- <h2>¿Cómo descargar y jugar a Dani Among Us 3D? </h2>
-<p>Si estás interesado en jugar a Dani Among Us 3D, quizás te estés preguntando cómo descargarlo y jugarlo. Desafortunadamente, no hay enlace de descarga oficial disponible todavía, ya que Dani todavía está trabajando en el juego y no lo ha lanzado públicamente. Sin embargo, hay algunas formas posibles de acceder al juego. </p>
- <h3>Todavía no hay enlace oficial de descarga</h3>
-<p>Dani no ha lanzado su juego 3D Among Us al público todavía, ya que todavía está trabajando en mejorarlo y agregar más características. Solo ha compartido algunas claves beta con algunos de sus amigos y fans que han probado el juego y le han dado retroalimentación. También ha declarado que no quiere lanzar el juego sin el permiso de Innersloth, ya que respeta su trabajo y no quiere causar ningún problema. </p>
- <h3>Formas posibles de acceder al juego</h3>
-<p>La mejor manera de mantenerse actualizado en Dani Among Us 3D es suscribirse al canal de YouTube de Dani y activar las notificaciones. Dani publica regularmente vídeos sobre sus proyectos de desarrollo de juegos, incluyendo Among Us 3D. También a veces da claves beta o pistas sobre cómo conseguirlas en sus vídeos o descripciones. También puedes comentar sus vídeos y pedirle educadamente una clave beta o más información sobre el juego. </p>
- <h4>Únete al servidor Discord de Dani y pide una clave beta</h4>
-<p>Otra forma de acceder a Dani Among Us 3D es unirse al servidor de Discord de Dani y pedir una clave beta o más información sobre el juego. Dani’s Discord server es una comunidad de más de 200.000 miembros que son fans de sus juegos y videos. Puede unirse al servidor haciendo clic en el enlace de la descripción de su canal de YouTube o utilizando este código de invitación: https://discord.gg/DaniDev. Una vez que se une al servidor, puede chatear con otros miembros, compartir sus comentarios y sugerencias, y participar en eventos y regalos. También puedes pedirle a Dani o a sus moderadores una clave beta o más información sobre Among Us 3D en el canal #among-us-3d o enviándoles un mensaje directo. </p>
- <h4>Apoyo Dani en Patreon y obtener recompensas exclusivas</h4>
- <h2>Conclusión</h2>
-<p>Dani Among Us 3D es un giro divertido y creativo en el juego original de Among Us, donde puedes jugar el juego en 3D con nuevas características y mecánicas. Dani es un desarrollador de juegos talentoso y entretenido y YouTuber que hace juegos en Unity y publica devlogs en su canal. Si quieres descargar y jugar Dani Among Us 3D, puedes suscribirte a su canal de YouTube, unirte a su servidor Discord, o apoyarlo en Patreon y esperar actualizaciones o claves beta. Alternativamente, también puedes ver sus videos o transmisiones donde muestra el juego y lo juega con otras personas. </p>
- <p>Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo sobre Dani Among Us 3D. Si tiene alguna pregunta o comentario, no dude en dejarlos abajo. ¡Gracias por leer! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Dani Among Us 3D:</p>
- <h3>Q: ¿Es Dani Among Us 3D gratis? </h3>
-<p>A: Dani aún no ha anunciado el precio de su juego Among Us 3D, pero ha dicho que podría hacerlo gratis o muy barato, ya que no quiere beneficiarse del trabajo de Innersloth o causar problemas legales. </p>
- <h3>Q: ¿Está Dani Among Us 3D disponible para dispositivos móviles? </h3>
-<p>A: Dani no ha lanzado su juego 3D Among Us para dispositivos móviles todavía, pero ha dicho que podría hacer una versión móvil en el futuro, ya que sabe que muchas personas juegan entre nosotros en sus teléfonos o tabletas. </p>
- <h3>Q: ¿Está Dani entre nosotros multijugador 3D? </h3>
-<p>A: Sí, Dani Among Us 3D es multijugador, al igual que el juego original. Puedes jugar online o a través de WiFi local con hasta 15 jugadores. También puede utilizar el chat de voz o de texto para comunicarse con otros jugadores. </p>
- <h3>Q: ¿Puedo jugar Dani entre nosotros 3D con mods? </h3>
-<p>A: Dani aún no ha lanzado ningún mod oficial para su juego Among Us 3D, pero ha dicho que podría hacer algunos mods o permitir que otras personas hagan mods en el futuro, ya que le gustan los juegos de modding y cree que agrega más diversión y variedad al juego. </p>
-<p>A: Puedes contactar a Dani o darle retroalimentación dejando un comentario en sus videos de YouTube, enviándole un mensaje en su servidor Discord, tuiteándolo en Twitter (@DaniDevYT), o enviándole un correo electrónico a [email protected]. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/irc.py DELETED Viewed

@@ -1,154 +0,0 @@
-"""
-    pygments.formatters.irc
-    ~~~~~~~~~~~~~~~~~~~~~~~
-    Formatter for IRC output
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.token import Keyword, Name, Comment, String, Error, \
-    Number, Operator, Generic, Token, Whitespace
-from pip._vendor.pygments.util import get_choice_opt
-__all__ = ['IRCFormatter']
-#: Map token types to a tuple of color values for light and dark
-#: backgrounds.
-IRC_COLORS = {
-    Token:              ('',            ''),
-    Whitespace:         ('gray',   'brightblack'),
-    Comment:            ('gray',   'brightblack'),
-    Comment.Preproc:    ('cyan',        'brightcyan'),
-    Keyword:            ('blue',    'brightblue'),
-    Keyword.Type:       ('cyan',        'brightcyan'),
-    Operator.Word:      ('magenta',      'brightcyan'),
-    Name.Builtin:       ('cyan',        'brightcyan'),
-    Name.Function:      ('green',   'brightgreen'),
-    Name.Namespace:     ('_cyan_',      '_brightcyan_'),
-    Name.Class:         ('_green_', '_brightgreen_'),
-    Name.Exception:     ('cyan',        'brightcyan'),
-    Name.Decorator:     ('brightblack',    'gray'),
-    Name.Variable:      ('red',     'brightred'),
-    Name.Constant:      ('red',     'brightred'),
-    Name.Attribute:     ('cyan',        'brightcyan'),
-    Name.Tag:           ('brightblue',        'brightblue'),
-    String:             ('yellow',       'yellow'),
-    Number:             ('blue',    'brightblue'),
-    Generic.Deleted:    ('brightred',        'brightred'),
-    Generic.Inserted:   ('green',  'brightgreen'),
-    Generic.Heading:    ('**',         '**'),
-    Generic.Subheading: ('*magenta*',   '*brightmagenta*'),
-    Generic.Error:      ('brightred',        'brightred'),
-    Error:              ('_brightred_',      '_brightred_'),
-}
-IRC_COLOR_MAP = {
-    'white': 0,
-    'black': 1,
-    'blue': 2,
-    'brightgreen': 3,
-    'brightred': 4,
-    'yellow': 5,
-    'magenta': 6,
-    'orange': 7,
-    'green': 7, #compat w/ ansi
-    'brightyellow': 8,
-    'lightgreen': 9,
-    'brightcyan': 9, # compat w/ ansi
-    'cyan': 10,
-    'lightblue': 11,
-    'red': 11, # compat w/ ansi
-    'brightblue': 12,
-    'brightmagenta': 13,
-    'brightblack': 14,
-    'gray': 15,
-}
-def ircformat(color, text):
-    if len(color) < 1:
-        return text
-    add = sub = ''
-    if '_' in color: # italic
-        add += '\x1D'
-        sub = '\x1D' + sub
-        color = color.strip('_')
-    if '*' in color: # bold
-        add += '\x02'
-        sub = '\x02' + sub
-        color = color.strip('*')
-    # underline (\x1F) not supported
-    # backgrounds (\x03FF,BB) not supported
-    if len(color) > 0: # actual color - may have issues with ircformat("red", "blah")+"10" type stuff
-        add += '\x03' + str(IRC_COLOR_MAP[color]).zfill(2)
-        sub = '\x03' + sub
-    return add + text + sub
-    return '<'+add+'>'+text+'</'+sub+'>'
-class IRCFormatter(Formatter):
-    r"""
-    Format tokens with IRC color sequences
-    The `get_style_defs()` method doesn't do anything special since there is
-    no support for common styles.
-    Options accepted:
-    `bg`
-        Set to ``"light"`` or ``"dark"`` depending on the terminal's background
-        (default: ``"light"``).
-    `colorscheme`
-        A dictionary mapping token types to (lightbg, darkbg) color names or
-        ``None`` (default: ``None`` = use builtin colorscheme).
-    `linenos`
-        Set to ``True`` to have line numbers in the output as well
-        (default: ``False`` = no line numbers).
-    """
-    name = 'IRC'
-    aliases = ['irc', 'IRC']
-    filenames = []
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.darkbg = get_choice_opt(options, 'bg',
-                                     ['light', 'dark'], 'light') == 'dark'
-        self.colorscheme = options.get('colorscheme', None) or IRC_COLORS
-        self.linenos = options.get('linenos', False)
-        self._lineno = 0
-    def _write_lineno(self, outfile):
-        if self.linenos:
-            self._lineno += 1
-            outfile.write("%04d: " % self._lineno)
-    def format_unencoded(self, tokensource, outfile):
-        self._write_lineno(outfile)
-        for ttype, value in tokensource:
-            color = self.colorscheme.get(ttype)
-            while color is None:
-                ttype = ttype[:-1]
-                color = self.colorscheme.get(ttype)
-            if color:
-                color = color[self.darkbg]
-                spl = value.split('\n')
-                for line in spl[:-1]:
-                    if line:
-                        outfile.write(ircformat(color, line))
-                    outfile.write('\n')
-                    self._write_lineno(outfile)
-                if spl[-1]:
-                    outfile.write(ircformat(color, spl[-1]))
-            else:
-                outfile.write(value)

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/archive_util.py DELETED Viewed

@@ -1,280 +0,0 @@
-"""distutils.archive_util
-Utility functions for creating archive files (tarballs, zip files,
-that sort of thing)."""
-import os
-from warnings import warn
-import sys
-try:
-    import zipfile
-except ImportError:
-    zipfile = None
-from distutils.errors import DistutilsExecError
-from distutils.spawn import spawn
-from distutils.dir_util import mkpath
-from distutils import log
-try:
-    from pwd import getpwnam
-except ImportError:
-    getpwnam = None
-try:
-    from grp import getgrnam
-except ImportError:
-    getgrnam = None
-def _get_gid(name):
-    """Returns a gid, given a group name."""
-    if getgrnam is None or name is None:
-        return None
-    try:
-        result = getgrnam(name)
-    except KeyError:
-        result = None
-    if result is not None:
-        return result[2]
-    return None
-def _get_uid(name):
-    """Returns an uid, given a user name."""
-    if getpwnam is None or name is None:
-        return None
-    try:
-        result = getpwnam(name)
-    except KeyError:
-        result = None
-    if result is not None:
-        return result[2]
-    return None
-def make_tarball(
-    base_name, base_dir, compress="gzip", verbose=0, dry_run=0, owner=None, group=None
-):
-    """Create a (possibly compressed) tar file from all the files under
-    'base_dir'.
-    'compress' must be "gzip" (the default), "bzip2", "xz", "compress", or
-    None.  ("compress" will be deprecated in Python 3.2)
-    'owner' and 'group' can be used to define an owner and a group for the
-    archive that is being built. If not provided, the current owner and group
-    will be used.
-    The output tar file will be named 'base_dir' +  ".tar", possibly plus
-    the appropriate compression extension (".gz", ".bz2", ".xz" or ".Z").
-    Returns the output filename.
-    """
-    tar_compression = {
-        'gzip': 'gz',
-        'bzip2': 'bz2',
-        'xz': 'xz',
-        None: '',
-        'compress': '',
-    }
-    compress_ext = {'gzip': '.gz', 'bzip2': '.bz2', 'xz': '.xz', 'compress': '.Z'}
-    # flags for compression program, each element of list will be an argument
-    if compress is not None and compress not in compress_ext.keys():
-        raise ValueError(
-            "bad value for 'compress': must be None, 'gzip', 'bzip2', "
-            "'xz' or 'compress'"
-        )
-    archive_name = base_name + '.tar'
-    if compress != 'compress':
-        archive_name += compress_ext.get(compress, '')
-    mkpath(os.path.dirname(archive_name), dry_run=dry_run)
-    # creating the tarball
-    import tarfile  # late import so Python build itself doesn't break
-    log.info('Creating tar archive')
-    uid = _get_uid(owner)
-    gid = _get_gid(group)
-    def _set_uid_gid(tarinfo):
-        if gid is not None:
-            tarinfo.gid = gid
-            tarinfo.gname = group
-        if uid is not None:
-            tarinfo.uid = uid
-            tarinfo.uname = owner
-        return tarinfo
-    if not dry_run:
-        tar = tarfile.open(archive_name, 'w|%s' % tar_compression[compress])
-        try:
-            tar.add(base_dir, filter=_set_uid_gid)
-        finally:
-            tar.close()
-    # compression using `compress`
-    if compress == 'compress':
-        warn("'compress' is deprecated.", DeprecationWarning)
-        # the option varies depending on the platform
-        compressed_name = archive_name + compress_ext[compress]
-        if sys.platform == 'win32':
-            cmd = [compress, archive_name, compressed_name]
-        else:
-            cmd = [compress, '-f', archive_name]
-        spawn(cmd, dry_run=dry_run)
-        return compressed_name
-    return archive_name
-def make_zipfile(base_name, base_dir, verbose=0, dry_run=0):  # noqa: C901
-    """Create a zip file from all the files under 'base_dir'.
-    The output zip file will be named 'base_name' + ".zip".  Uses either the
-    "zipfile" Python module (if available) or the InfoZIP "zip" utility
-    (if installed and found on the default search path).  If neither tool is
-    available, raises DistutilsExecError.  Returns the name of the output zip
-    file.
-    """
-    zip_filename = base_name + ".zip"
-    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
-    # If zipfile module is not available, try spawning an external
-    # 'zip' command.
-    if zipfile is None:
-        if verbose:
-            zipoptions = "-r"
-        else:
-            zipoptions = "-rq"
-        try:
-            spawn(["zip", zipoptions, zip_filename, base_dir], dry_run=dry_run)
-        except DistutilsExecError:
-            # XXX really should distinguish between "couldn't find
-            # external 'zip' command" and "zip failed".
-            raise DistutilsExecError(
-                (
-                    "unable to create zip file '%s': "
-                    "could neither import the 'zipfile' module nor "
-                    "find a standalone zip utility"
-                )
-                % zip_filename
-            )
-    else:
-        log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
-        if not dry_run:
-            try:
-                zip = zipfile.ZipFile(
-                    zip_filename, "w", compression=zipfile.ZIP_DEFLATED
-                )
-            except RuntimeError:
-                zip = zipfile.ZipFile(zip_filename, "w", compression=zipfile.ZIP_STORED)
-            with zip:
-                if base_dir != os.curdir:
-                    path = os.path.normpath(os.path.join(base_dir, ''))
-                    zip.write(path, path)
-                    log.info("adding '%s'", path)
-                for dirpath, dirnames, filenames in os.walk(base_dir):
-                    for name in dirnames:
-                        path = os.path.normpath(os.path.join(dirpath, name, ''))
-                        zip.write(path, path)
-                        log.info("adding '%s'", path)
-                    for name in filenames:
-                        path = os.path.normpath(os.path.join(dirpath, name))
-                        if os.path.isfile(path):
-                            zip.write(path, path)
-                            log.info("adding '%s'", path)
-    return zip_filename
-ARCHIVE_FORMATS = {
-    'gztar': (make_tarball, [('compress', 'gzip')], "gzip'ed tar-file"),
-    'bztar': (make_tarball, [('compress', 'bzip2')], "bzip2'ed tar-file"),
-    'xztar': (make_tarball, [('compress', 'xz')], "xz'ed tar-file"),
-    'ztar': (make_tarball, [('compress', 'compress')], "compressed tar file"),
-    'tar': (make_tarball, [('compress', None)], "uncompressed tar file"),
-    'zip': (make_zipfile, [], "ZIP file"),
-}
-def check_archive_formats(formats):
-    """Returns the first format from the 'format' list that is unknown.
-    If all formats are known, returns None
-    """
-    for format in formats:
-        if format not in ARCHIVE_FORMATS:
-            return format
-    return None
-def make_archive(
-    base_name,
-    format,
-    root_dir=None,
-    base_dir=None,
-    verbose=0,
-    dry_run=0,
-    owner=None,
-    group=None,
-):
-    """Create an archive file (eg. zip or tar).
-    'base_name' is the name of the file to create, minus any format-specific
-    extension; 'format' is the archive format: one of "zip", "tar", "gztar",
-    "bztar", "xztar", or "ztar".
-    'root_dir' is a directory that will be the root directory of the
-    archive; ie. we typically chdir into 'root_dir' before creating the
-    archive.  'base_dir' is the directory where we start archiving from;
-    ie. 'base_dir' will be the common prefix of all files and
-    directories in the archive.  'root_dir' and 'base_dir' both default
-    to the current directory.  Returns the name of the archive file.
-    'owner' and 'group' are used when creating a tar archive. By default,
-    uses the current owner and group.
-    """
-    save_cwd = os.getcwd()
-    if root_dir is not None:
-        log.debug("changing into '%s'", root_dir)
-        base_name = os.path.abspath(base_name)
-        if not dry_run:
-            os.chdir(root_dir)
-    if base_dir is None:
-        base_dir = os.curdir
-    kwargs = {'dry_run': dry_run}
-    try:
-        format_info = ARCHIVE_FORMATS[format]
-    except KeyError:
-        raise ValueError("unknown archive format '%s'" % format)
-    func = format_info[0]
-    for arg, val in format_info[1]:
-        kwargs[arg] = val
-    if format != 'zip':
-        kwargs['owner'] = owner
-        kwargs['group'] = group
-    try:
-        filename = func(base_name, base_dir, **kwargs)
-    finally:
-        if root_dir is not None:
-            log.debug("changing back to '%s'", save_cwd)
-            os.chdir(save_cwd)
-    return filename

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/dist.py DELETED Viewed

@@ -1,1286 +0,0 @@
-"""distutils.dist
-Provides the Distribution class, which represents the module distribution
-being built/installed/distributed.
-"""
-import sys
-import os
-import re
-import pathlib
-import contextlib
-from email import message_from_file
-try:
-    import warnings
-except ImportError:
-    warnings = None
-from distutils.errors import (
-    DistutilsOptionError,
-    DistutilsModuleError,
-    DistutilsArgError,
-    DistutilsClassError,
-)
-from distutils.fancy_getopt import FancyGetopt, translate_longopt
-from distutils.util import check_environ, strtobool, rfc822_escape
-from distutils import log
-from distutils.debug import DEBUG
-# Regex to define acceptable Distutils command names.  This is not *quite*
-# the same as a Python NAME -- I don't allow leading underscores.  The fact
-# that they're very similar is no coincidence; the default naming scheme is
-# to look for a Python module named after the command.
-command_re = re.compile(r'^[a-zA-Z]([a-zA-Z0-9_]*)$')
-def _ensure_list(value, fieldname):
-    if isinstance(value, str):
-        # a string containing comma separated values is okay.  It will
-        # be converted to a list by Distribution.finalize_options().
-        pass
-    elif not isinstance(value, list):
-        # passing a tuple or an iterator perhaps, warn and convert
-        typename = type(value).__name__
-        msg = "Warning: '{fieldname}' should be a list, got type '{typename}'"
-        msg = msg.format(**locals())
-        log.log(log.WARN, msg)
-        value = list(value)
-    return value
-class Distribution:
-    """The core of the Distutils.  Most of the work hiding behind 'setup'
-    is really done within a Distribution instance, which farms the work out
-    to the Distutils commands specified on the command line.
-    Setup scripts will almost never instantiate Distribution directly,
-    unless the 'setup()' function is totally inadequate to their needs.
-    However, it is conceivable that a setup script might wish to subclass
-    Distribution for some specialized purpose, and then pass the subclass
-    to 'setup()' as the 'distclass' keyword argument.  If so, it is
-    necessary to respect the expectations that 'setup' has of Distribution.
-    See the code for 'setup()', in core.py, for details.
-    """
-    # 'global_options' describes the command-line options that may be
-    # supplied to the setup script prior to any actual commands.
-    # Eg. "./setup.py -n" or "./setup.py --quiet" both take advantage of
-    # these global options.  This list should be kept to a bare minimum,
-    # since every global option is also valid as a command option -- and we
-    # don't want to pollute the commands with too many options that they
-    # have minimal control over.
-    # The fourth entry for verbose means that it can be repeated.
-    global_options = [
-        ('verbose', 'v', "run verbosely (default)", 1),
-        ('quiet', 'q', "run quietly (turns verbosity off)"),
-        ('dry-run', 'n', "don't actually do anything"),
-        ('help', 'h', "show detailed help message"),
-        ('no-user-cfg', None, 'ignore pydistutils.cfg in your home directory'),
-    ]
-    # 'common_usage' is a short (2-3 line) string describing the common
-    # usage of the setup script.
-    common_usage = """\
-Common commands: (see '--help-commands' for more)
-  setup.py build      will build the package underneath 'build/'
-  setup.py install    will install the package
-"""
-    # options that are not propagated to the commands
-    display_options = [
-        ('help-commands', None, "list all available commands"),
-        ('name', None, "print package name"),
-        ('version', 'V', "print package version"),
-        ('fullname', None, "print <package name>-<version>"),
-        ('author', None, "print the author's name"),
-        ('author-email', None, "print the author's email address"),
-        ('maintainer', None, "print the maintainer's name"),
-        ('maintainer-email', None, "print the maintainer's email address"),
-        ('contact', None, "print the maintainer's name if known, else the author's"),
-        (
-            'contact-email',
-            None,
-            "print the maintainer's email address if known, else the author's",
-        ),
-        ('url', None, "print the URL for this package"),
-        ('license', None, "print the license of the package"),
-        ('licence', None, "alias for --license"),
-        ('description', None, "print the package description"),
-        ('long-description', None, "print the long package description"),
-        ('platforms', None, "print the list of platforms"),
-        ('classifiers', None, "print the list of classifiers"),
-        ('keywords', None, "print the list of keywords"),
-        ('provides', None, "print the list of packages/modules provided"),
-        ('requires', None, "print the list of packages/modules required"),
-        ('obsoletes', None, "print the list of packages/modules made obsolete"),
-    ]
-    display_option_names = [translate_longopt(x[0]) for x in display_options]
-    # negative options are options that exclude other options
-    negative_opt = {'quiet': 'verbose'}
-    # -- Creation/initialization methods -------------------------------
-    def __init__(self, attrs=None):  # noqa: C901
-        """Construct a new Distribution instance: initialize all the
-        attributes of a Distribution, and then use 'attrs' (a dictionary
-        mapping attribute names to values) to assign some of those
-        attributes their "real" values.  (Any attributes not mentioned in
-        'attrs' will be assigned to some null value: 0, None, an empty list
-        or dictionary, etc.)  Most importantly, initialize the
-        'command_obj' attribute to the empty dictionary; this will be
-        filled in with real command objects by 'parse_command_line()'.
-        """
-        # Default values for our command-line options
-        self.verbose = 1
-        self.dry_run = 0
-        self.help = 0
-        for attr in self.display_option_names:
-            setattr(self, attr, 0)
-        # Store the distribution meta-data (name, version, author, and so
-        # forth) in a separate object -- we're getting to have enough
-        # information here (and enough command-line options) that it's
-        # worth it.  Also delegate 'get_XXX()' methods to the 'metadata'
-        # object in a sneaky and underhanded (but efficient!) way.
-        self.metadata = DistributionMetadata()
-        for basename in self.metadata._METHOD_BASENAMES:
-            method_name = "get_" + basename
-            setattr(self, method_name, getattr(self.metadata, method_name))
-        # 'cmdclass' maps command names to class objects, so we
-        # can 1) quickly figure out which class to instantiate when
-        # we need to create a new command object, and 2) have a way
-        # for the setup script to override command classes
-        self.cmdclass = {}
-        # 'command_packages' is a list of packages in which commands
-        # are searched for.  The factory for command 'foo' is expected
-        # to be named 'foo' in the module 'foo' in one of the packages
-        # named here.  This list is searched from the left; an error
-        # is raised if no named package provides the command being
-        # searched for.  (Always access using get_command_packages().)
-        self.command_packages = None
-        # 'script_name' and 'script_args' are usually set to sys.argv[0]
-        # and sys.argv[1:], but they can be overridden when the caller is
-        # not necessarily a setup script run from the command-line.
-        self.script_name = None
-        self.script_args = None
-        # 'command_options' is where we store command options between
-        # parsing them (from config files, the command-line, etc.) and when
-        # they are actually needed -- ie. when the command in question is
-        # instantiated.  It is a dictionary of dictionaries of 2-tuples:
-        #   command_options = { command_name : { option : (source, value) } }
-        self.command_options = {}
-        # 'dist_files' is the list of (command, pyversion, file) that
-        # have been created by any dist commands run so far. This is
-        # filled regardless of whether the run is dry or not. pyversion
-        # gives sysconfig.get_python_version() if the dist file is
-        # specific to a Python version, 'any' if it is good for all
-        # Python versions on the target platform, and '' for a source
-        # file. pyversion should not be used to specify minimum or
-        # maximum required Python versions; use the metainfo for that
-        # instead.
-        self.dist_files = []
-        # These options are really the business of various commands, rather
-        # than of the Distribution itself.  We provide aliases for them in
-        # Distribution as a convenience to the developer.
-        self.packages = None
-        self.package_data = {}
-        self.package_dir = None
-        self.py_modules = None
-        self.libraries = None
-        self.headers = None
-        self.ext_modules = None
-        self.ext_package = None
-        self.include_dirs = None
-        self.extra_path = None
-        self.scripts = None
-        self.data_files = None
-        self.password = ''
-        # And now initialize bookkeeping stuff that can't be supplied by
-        # the caller at all.  'command_obj' maps command names to
-        # Command instances -- that's how we enforce that every command
-        # class is a singleton.
-        self.command_obj = {}
-        # 'have_run' maps command names to boolean values; it keeps track
-        # of whether we have actually run a particular command, to make it
-        # cheap to "run" a command whenever we think we might need to -- if
-        # it's already been done, no need for expensive filesystem
-        # operations, we just check the 'have_run' dictionary and carry on.
-        # It's only safe to query 'have_run' for a command class that has
-        # been instantiated -- a false value will be inserted when the
-        # command object is created, and replaced with a true value when
-        # the command is successfully run.  Thus it's probably best to use
-        # '.get()' rather than a straight lookup.
-        self.have_run = {}
-        # Now we'll use the attrs dictionary (ultimately, keyword args from
-        # the setup script) to possibly override any or all of these
-        # distribution options.
-        if attrs:
-            # Pull out the set of command options and work on them
-            # specifically.  Note that this order guarantees that aliased
-            # command options will override any supplied redundantly
-            # through the general options dictionary.
-            options = attrs.get('options')
-            if options is not None:
-                del attrs['options']
-                for (command, cmd_options) in options.items():
-                    opt_dict = self.get_option_dict(command)
-                    for (opt, val) in cmd_options.items():
-                        opt_dict[opt] = ("setup script", val)
-            if 'licence' in attrs:
-                attrs['license'] = attrs['licence']
-                del attrs['licence']
-                msg = "'licence' distribution option is deprecated; use 'license'"
-                if warnings is not None:
-                    warnings.warn(msg)
-                else:
-                    sys.stderr.write(msg + "\n")
-            # Now work on the rest of the attributes.  Any attribute that's
-            # not already defined is invalid!
-            for (key, val) in attrs.items():
-                if hasattr(self.metadata, "set_" + key):
-                    getattr(self.metadata, "set_" + key)(val)
-                elif hasattr(self.metadata, key):
-                    setattr(self.metadata, key, val)
-                elif hasattr(self, key):
-                    setattr(self, key, val)
-                else:
-                    msg = "Unknown distribution option: %s" % repr(key)
-                    warnings.warn(msg)
-        # no-user-cfg is handled before other command line args
-        # because other args override the config files, and this
-        # one is needed before we can load the config files.
-        # If attrs['script_args'] wasn't passed, assume false.
-        #
-        # This also make sure we just look at the global options
-        self.want_user_cfg = True
-        if self.script_args is not None:
-            for arg in self.script_args:
-                if not arg.startswith('-'):
-                    break
-                if arg == '--no-user-cfg':
-                    self.want_user_cfg = False
-                    break
-        self.finalize_options()
-    def get_option_dict(self, command):
-        """Get the option dictionary for a given command.  If that
-        command's option dictionary hasn't been created yet, then create it
-        and return the new dictionary; otherwise, return the existing
-        option dictionary.
-        """
-        dict = self.command_options.get(command)
-        if dict is None:
-            dict = self.command_options[command] = {}
-        return dict
-    def dump_option_dicts(self, header=None, commands=None, indent=""):
-        from pprint import pformat
-        if commands is None:  # dump all command option dicts
-            commands = sorted(self.command_options.keys())
-        if header is not None:
-            self.announce(indent + header)
-            indent = indent + "  "
-        if not commands:
-            self.announce(indent + "no commands known yet")
-            return
-        for cmd_name in commands:
-            opt_dict = self.command_options.get(cmd_name)
-            if opt_dict is None:
-                self.announce(indent + "no option dict for '%s' command" % cmd_name)
-            else:
-                self.announce(indent + "option dict for '%s' command:" % cmd_name)
-                out = pformat(opt_dict)
-                for line in out.split('\n'):
-                    self.announce(indent + "  " + line)
-    # -- Config file finding/parsing methods ---------------------------
-    def find_config_files(self):
-        """Find as many configuration files as should be processed for this
-        platform, and return a list of filenames in the order in which they
-        should be parsed.  The filenames returned are guaranteed to exist
-        (modulo nasty race conditions).
-        There are multiple possible config files:
-        - distutils.cfg in the Distutils installation directory (i.e.
-          where the top-level Distutils __inst__.py file lives)
-        - a file in the user's home directory named .pydistutils.cfg
-          on Unix and pydistutils.cfg on Windows/Mac; may be disabled
-          with the ``--no-user-cfg`` option
-        - setup.cfg in the current directory
-        - a file named by an environment variable
-        """
-        check_environ()
-        files = [str(path) for path in self._gen_paths() if os.path.isfile(path)]
-        if DEBUG:
-            self.announce("using config files: %s" % ', '.join(files))
-        return files
-    def _gen_paths(self):
-        # The system-wide Distutils config file
-        sys_dir = pathlib.Path(sys.modules['distutils'].__file__).parent
-        yield sys_dir / "distutils.cfg"
-        # The per-user config file
-        prefix = '.' * (os.name == 'posix')
-        filename = prefix + 'pydistutils.cfg'
-        if self.want_user_cfg:
-            yield pathlib.Path('~').expanduser() / filename
-        # All platforms support local setup.cfg
-        yield pathlib.Path('setup.cfg')
-        # Additional config indicated in the environment
-        with contextlib.suppress(TypeError):
-            yield pathlib.Path(os.getenv("DIST_EXTRA_CONFIG"))
-    def parse_config_files(self, filenames=None):  # noqa: C901
-        from configparser import ConfigParser
-        # Ignore install directory options if we have a venv
-        if sys.prefix != sys.base_prefix:
-            ignore_options = [
-                'install-base',
-                'install-platbase',
-                'install-lib',
-                'install-platlib',
-                'install-purelib',
-                'install-headers',
-                'install-scripts',
-                'install-data',
-                'prefix',
-                'exec-prefix',
-                'home',
-                'user',
-                'root',
-            ]
-        else:
-            ignore_options = []
-        ignore_options = frozenset(ignore_options)
-        if filenames is None:
-            filenames = self.find_config_files()
-        if DEBUG:
-            self.announce("Distribution.parse_config_files():")
-        parser = ConfigParser()
-        for filename in filenames:
-            if DEBUG:
-                self.announce("  reading %s" % filename)
-            parser.read(filename)
-            for section in parser.sections():
-                options = parser.options(section)
-                opt_dict = self.get_option_dict(section)
-                for opt in options:
-                    if opt != '__name__' and opt not in ignore_options:
-                        val = parser.get(section, opt)
-                        opt = opt.replace('-', '_')
-                        opt_dict[opt] = (filename, val)
-            # Make the ConfigParser forget everything (so we retain
-            # the original filenames that options come from)
-            parser.__init__()
-        # If there was a "global" section in the config file, use it
-        # to set Distribution options.
-        if 'global' in self.command_options:
-            for (opt, (src, val)) in self.command_options['global'].items():
-                alias = self.negative_opt.get(opt)
-                try:
-                    if alias:
-                        setattr(self, alias, not strtobool(val))
-                    elif opt in ('verbose', 'dry_run'):  # ugh!
-                        setattr(self, opt, strtobool(val))
-                    else:
-                        setattr(self, opt, val)
-                except ValueError as msg:
-                    raise DistutilsOptionError(msg)
-    # -- Command-line parsing methods ----------------------------------
-    def parse_command_line(self):
-        """Parse the setup script's command line, taken from the
-        'script_args' instance attribute (which defaults to 'sys.argv[1:]'
-        -- see 'setup()' in core.py).  This list is first processed for
-        "global options" -- options that set attributes of the Distribution
-        instance.  Then, it is alternately scanned for Distutils commands
-        and options for that command.  Each new command terminates the
-        options for the previous command.  The allowed options for a
-        command are determined by the 'user_options' attribute of the
-        command class -- thus, we have to be able to load command classes
-        in order to parse the command line.  Any error in that 'options'
-        attribute raises DistutilsGetoptError; any error on the
-        command-line raises DistutilsArgError.  If no Distutils commands
-        were found on the command line, raises DistutilsArgError.  Return
-        true if command-line was successfully parsed and we should carry
-        on with executing commands; false if no errors but we shouldn't
-        execute commands (currently, this only happens if user asks for
-        help).
-        """
-        #
-        # We now have enough information to show the Macintosh dialog
-        # that allows the user to interactively specify the "command line".
-        #
-        toplevel_options = self._get_toplevel_options()
-        # We have to parse the command line a bit at a time -- global
-        # options, then the first command, then its options, and so on --
-        # because each command will be handled by a different class, and
-        # the options that are valid for a particular class aren't known
-        # until we have loaded the command class, which doesn't happen
-        # until we know what the command is.
-        self.commands = []
-        parser = FancyGetopt(toplevel_options + self.display_options)
-        parser.set_negative_aliases(self.negative_opt)
-        parser.set_aliases({'licence': 'license'})
-        args = parser.getopt(args=self.script_args, object=self)
-        option_order = parser.get_option_order()
-        log.set_verbosity(self.verbose)
-        # for display options we return immediately
-        if self.handle_display_options(option_order):
-            return
-        while args:
-            args = self._parse_command_opts(parser, args)
-            if args is None:  # user asked for help (and got it)
-                return
-        # Handle the cases of --help as a "global" option, ie.
-        # "setup.py --help" and "setup.py --help command ...".  For the
-        # former, we show global options (--verbose, --dry-run, etc.)
-        # and display-only options (--name, --version, etc.); for the
-        # latter, we omit the display-only options and show help for
-        # each command listed on the command line.
-        if self.help:
-            self._show_help(
-                parser, display_options=len(self.commands) == 0, commands=self.commands
-            )
-            return
-        # Oops, no commands found -- an end-user error
-        if not self.commands:
-            raise DistutilsArgError("no commands supplied")
-        # All is well: return true
-        return True
-    def _get_toplevel_options(self):
-        """Return the non-display options recognized at the top level.
-        This includes options that are recognized *only* at the top
-        level as well as options recognized for commands.
-        """
-        return self.global_options + [
-            (
-                "command-packages=",
-                None,
-                "list of packages that provide distutils commands",
-            ),
-        ]
-    def _parse_command_opts(self, parser, args):  # noqa: C901
-        """Parse the command-line options for a single command.
-        'parser' must be a FancyGetopt instance; 'args' must be the list
-        of arguments, starting with the current command (whose options
-        we are about to parse).  Returns a new version of 'args' with
-        the next command at the front of the list; will be the empty
-        list if there are no more commands on the command line.  Returns
-        None if the user asked for help on this command.
-        """
-        # late import because of mutual dependence between these modules
-        from distutils.cmd import Command
-        # Pull the current command from the head of the command line
-        command = args[0]
-        if not command_re.match(command):
-            raise SystemExit("invalid command name '%s'" % command)
-        self.commands.append(command)
-        # Dig up the command class that implements this command, so we
-        # 1) know that it's a valid command, and 2) know which options
-        # it takes.
-        try:
-            cmd_class = self.get_command_class(command)
-        except DistutilsModuleError as msg:
-            raise DistutilsArgError(msg)
-        # Require that the command class be derived from Command -- want
-        # to be sure that the basic "command" interface is implemented.
-        if not issubclass(cmd_class, Command):
-            raise DistutilsClassError(
-                "command class %s must subclass Command" % cmd_class
-            )
-        # Also make sure that the command object provides a list of its
-        # known options.
-        if not (
-            hasattr(cmd_class, 'user_options')
-            and isinstance(cmd_class.user_options, list)
-        ):
-            msg = (
-                "command class %s must provide "
-                "'user_options' attribute (a list of tuples)"
-            )
-            raise DistutilsClassError(msg % cmd_class)
-        # If the command class has a list of negative alias options,
-        # merge it in with the global negative aliases.
-        negative_opt = self.negative_opt
-        if hasattr(cmd_class, 'negative_opt'):
-            negative_opt = negative_opt.copy()
-            negative_opt.update(cmd_class.negative_opt)
-        # Check for help_options in command class.  They have a different
-        # format (tuple of four) so we need to preprocess them here.
-        if hasattr(cmd_class, 'help_options') and isinstance(
-            cmd_class.help_options, list
-        ):
-            help_options = fix_help_options(cmd_class.help_options)
-        else:
-            help_options = []
-        # All commands support the global options too, just by adding
-        # in 'global_options'.
-        parser.set_option_table(
-            self.global_options + cmd_class.user_options + help_options
-        )
-        parser.set_negative_aliases(negative_opt)
-        (args, opts) = parser.getopt(args[1:])
-        if hasattr(opts, 'help') and opts.help:
-            self._show_help(parser, display_options=0, commands=[cmd_class])
-            return
-        if hasattr(cmd_class, 'help_options') and isinstance(
-            cmd_class.help_options, list
-        ):
-            help_option_found = 0
-            for (help_option, short, desc, func) in cmd_class.help_options:
-                if hasattr(opts, parser.get_attr_name(help_option)):
-                    help_option_found = 1
-                    if callable(func):
-                        func()
-                    else:
-                        raise DistutilsClassError(
-                            "invalid help function %r for help option '%s': "
-                            "must be a callable object (function, etc.)"
-                            % (func, help_option)
-                        )
-            if help_option_found:
-                return
-        # Put the options from the command-line into their official
-        # holding pen, the 'command_options' dictionary.
-        opt_dict = self.get_option_dict(command)
-        for (name, value) in vars(opts).items():
-            opt_dict[name] = ("command line", value)
-        return args
-    def finalize_options(self):
-        """Set final values for all the options on the Distribution
-        instance, analogous to the .finalize_options() method of Command
-        objects.
-        """
-        for attr in ('keywords', 'platforms'):
-            value = getattr(self.metadata, attr)
-            if value is None:
-                continue
-            if isinstance(value, str):
-                value = [elm.strip() for elm in value.split(',')]
-                setattr(self.metadata, attr, value)
-    def _show_help(self, parser, global_options=1, display_options=1, commands=[]):
-        """Show help for the setup script command-line in the form of
-        several lists of command-line options.  'parser' should be a
-        FancyGetopt instance; do not expect it to be returned in the
-        same state, as its option table will be reset to make it
-        generate the correct help text.
-        If 'global_options' is true, lists the global options:
-        --verbose, --dry-run, etc.  If 'display_options' is true, lists
-        the "display-only" options: --name, --version, etc.  Finally,
-        lists per-command help for every command name or command class
-        in 'commands'.
-        """
-        # late import because of mutual dependence between these modules
-        from distutils.core import gen_usage
-        from distutils.cmd import Command
-        if global_options:
-            if display_options:
-                options = self._get_toplevel_options()
-            else:
-                options = self.global_options
-            parser.set_option_table(options)
-            parser.print_help(self.common_usage + "\nGlobal options:")
-            print('')
-        if display_options:
-            parser.set_option_table(self.display_options)
-            parser.print_help(
-                "Information display options (just display "
-                + "information, ignore any commands)"
-            )
-            print('')
-        for command in self.commands:
-            if isinstance(command, type) and issubclass(command, Command):
-                klass = command
-            else:
-                klass = self.get_command_class(command)
-            if hasattr(klass, 'help_options') and isinstance(klass.help_options, list):
-                parser.set_option_table(
-                    klass.user_options + fix_help_options(klass.help_options)
-                )
-            else:
-                parser.set_option_table(klass.user_options)
-            parser.print_help("Options for '%s' command:" % klass.__name__)
-            print('')
-        print(gen_usage(self.script_name))
-    def handle_display_options(self, option_order):
-        """If there were any non-global "display-only" options
-        (--help-commands or the metadata display options) on the command
-        line, display the requested info and return true; else return
-        false.
-        """
-        from distutils.core import gen_usage
-        # User just wants a list of commands -- we'll print it out and stop
-        # processing now (ie. if they ran "setup --help-commands foo bar",
-        # we ignore "foo bar").
-        if self.help_commands:
-            self.print_commands()
-            print('')
-            print(gen_usage(self.script_name))
-            return 1
-        # If user supplied any of the "display metadata" options, then
-        # display that metadata in the order in which the user supplied the
-        # metadata options.
-        any_display_options = 0
-        is_display_option = {}
-        for option in self.display_options:
-            is_display_option[option[0]] = 1
-        for (opt, val) in option_order:
-            if val and is_display_option.get(opt):
-                opt = translate_longopt(opt)
-                value = getattr(self.metadata, "get_" + opt)()
-                if opt in ['keywords', 'platforms']:
-                    print(','.join(value))
-                elif opt in ('classifiers', 'provides', 'requires', 'obsoletes'):
-                    print('\n'.join(value))
-                else:
-                    print(value)
-                any_display_options = 1
-        return any_display_options
-    def print_command_list(self, commands, header, max_length):
-        """Print a subset of the list of all commands -- used by
-        'print_commands()'.
-        """
-        print(header + ":")
-        for cmd in commands:
-            klass = self.cmdclass.get(cmd)
-            if not klass:
-                klass = self.get_command_class(cmd)
-            try:
-                description = klass.description
-            except AttributeError:
-                description = "(no description available)"
-            print("  %-*s  %s" % (max_length, cmd, description))
-    def print_commands(self):
-        """Print out a help message listing all available commands with a
-        description of each.  The list is divided into "standard commands"
-        (listed in distutils.command.__all__) and "extra commands"
-        (mentioned in self.cmdclass, but not a standard command).  The
-        descriptions come from the command class attribute
-        'description'.
-        """
-        import distutils.command
-        std_commands = distutils.command.__all__
-        is_std = {}
-        for cmd in std_commands:
-            is_std[cmd] = 1
-        extra_commands = []
-        for cmd in self.cmdclass.keys():
-            if not is_std.get(cmd):
-                extra_commands.append(cmd)
-        max_length = 0
-        for cmd in std_commands + extra_commands:
-            if len(cmd) > max_length:
-                max_length = len(cmd)
-        self.print_command_list(std_commands, "Standard commands", max_length)
-        if extra_commands:
-            print()
-            self.print_command_list(extra_commands, "Extra commands", max_length)
-    def get_command_list(self):
-        """Get a list of (command, description) tuples.
-        The list is divided into "standard commands" (listed in
-        distutils.command.__all__) and "extra commands" (mentioned in
-        self.cmdclass, but not a standard command).  The descriptions come
-        from the command class attribute 'description'.
-        """
-        # Currently this is only used on Mac OS, for the Mac-only GUI
-        # Distutils interface (by Jack Jansen)
-        import distutils.command
-        std_commands = distutils.command.__all__
-        is_std = {}
-        for cmd in std_commands:
-            is_std[cmd] = 1
-        extra_commands = []
-        for cmd in self.cmdclass.keys():
-            if not is_std.get(cmd):
-                extra_commands.append(cmd)
-        rv = []
-        for cmd in std_commands + extra_commands:
-            klass = self.cmdclass.get(cmd)
-            if not klass:
-                klass = self.get_command_class(cmd)
-            try:
-                description = klass.description
-            except AttributeError:
-                description = "(no description available)"
-            rv.append((cmd, description))
-        return rv
-    # -- Command class/object methods ----------------------------------
-    def get_command_packages(self):
-        """Return a list of packages from which commands are loaded."""
-        pkgs = self.command_packages
-        if not isinstance(pkgs, list):
-            if pkgs is None:
-                pkgs = ''
-            pkgs = [pkg.strip() for pkg in pkgs.split(',') if pkg != '']
-            if "distutils.command" not in pkgs:
-                pkgs.insert(0, "distutils.command")
-            self.command_packages = pkgs
-        return pkgs
-    def get_command_class(self, command):
-        """Return the class that implements the Distutils command named by
-        'command'.  First we check the 'cmdclass' dictionary; if the
-        command is mentioned there, we fetch the class object from the
-        dictionary and return it.  Otherwise we load the command module
-        ("distutils.command." + command) and fetch the command class from
-        the module.  The loaded class is also stored in 'cmdclass'
-        to speed future calls to 'get_command_class()'.
-        Raises DistutilsModuleError if the expected module could not be
-        found, or if that module does not define the expected class.
-        """
-        klass = self.cmdclass.get(command)
-        if klass:
-            return klass
-        for pkgname in self.get_command_packages():
-            module_name = "{}.{}".format(pkgname, command)
-            klass_name = command
-            try:
-                __import__(module_name)
-                module = sys.modules[module_name]
-            except ImportError:
-                continue
-            try:
-                klass = getattr(module, klass_name)
-            except AttributeError:
-                raise DistutilsModuleError(
-                    "invalid command '%s' (no class '%s' in module '%s')"
-                    % (command, klass_name, module_name)
-                )
-            self.cmdclass[command] = klass
-            return klass
-        raise DistutilsModuleError("invalid command '%s'" % command)
-    def get_command_obj(self, command, create=1):
-        """Return the command object for 'command'.  Normally this object
-        is cached on a previous call to 'get_command_obj()'; if no command
-        object for 'command' is in the cache, then we either create and
-        return it (if 'create' is true) or return None.
-        """
-        cmd_obj = self.command_obj.get(command)
-        if not cmd_obj and create:
-            if DEBUG:
-                self.announce(
-                    "Distribution.get_command_obj(): "
-                    "creating '%s' command object" % command
-                )
-            klass = self.get_command_class(command)
-            cmd_obj = self.command_obj[command] = klass(self)
-            self.have_run[command] = 0
-            # Set any options that were supplied in config files
-            # or on the command line.  (NB. support for error
-            # reporting is lame here: any errors aren't reported
-            # until 'finalize_options()' is called, which means
-            # we won't report the source of the error.)
-            options = self.command_options.get(command)
-            if options:
-                self._set_command_options(cmd_obj, options)
-        return cmd_obj
-    def _set_command_options(self, command_obj, option_dict=None):  # noqa: C901
-        """Set the options for 'command_obj' from 'option_dict'.  Basically
-        this means copying elements of a dictionary ('option_dict') to
-        attributes of an instance ('command').
-        'command_obj' must be a Command instance.  If 'option_dict' is not
-        supplied, uses the standard option dictionary for this command
-        (from 'self.command_options').
-        """
-        command_name = command_obj.get_command_name()
-        if option_dict is None:
-            option_dict = self.get_option_dict(command_name)
-        if DEBUG:
-            self.announce("  setting options for '%s' command:" % command_name)
-        for (option, (source, value)) in option_dict.items():
-            if DEBUG:
-                self.announce("    {} = {} (from {})".format(option, value, source))
-            try:
-                bool_opts = [translate_longopt(o) for o in command_obj.boolean_options]
-            except AttributeError:
-                bool_opts = []
-            try:
-                neg_opt = command_obj.negative_opt
-            except AttributeError:
-                neg_opt = {}
-            try:
-                is_string = isinstance(value, str)
-                if option in neg_opt and is_string:
-                    setattr(command_obj, neg_opt[option], not strtobool(value))
-                elif option in bool_opts and is_string:
-                    setattr(command_obj, option, strtobool(value))
-                elif hasattr(command_obj, option):
-                    setattr(command_obj, option, value)
-                else:
-                    raise DistutilsOptionError(
-                        "error in %s: command '%s' has no such option '%s'"
-                        % (source, command_name, option)
-                    )
-            except ValueError as msg:
-                raise DistutilsOptionError(msg)
-    def reinitialize_command(self, command, reinit_subcommands=0):
-        """Reinitializes a command to the state it was in when first
-        returned by 'get_command_obj()': ie., initialized but not yet
-        finalized.  This provides the opportunity to sneak option
-        values in programmatically, overriding or supplementing
-        user-supplied values from the config files and command line.
-        You'll have to re-finalize the command object (by calling
-        'finalize_options()' or 'ensure_finalized()') before using it for
-        real.
-        'command' should be a command name (string) or command object.  If
-        'reinit_subcommands' is true, also reinitializes the command's
-        sub-commands, as declared by the 'sub_commands' class attribute (if
-        it has one).  See the "install" command for an example.  Only
-        reinitializes the sub-commands that actually matter, ie. those
-        whose test predicates return true.
-        Returns the reinitialized command object.
-        """
-        from distutils.cmd import Command
-        if not isinstance(command, Command):
-            command_name = command
-            command = self.get_command_obj(command_name)
-        else:
-            command_name = command.get_command_name()
-        if not command.finalized:
-            return command
-        command.initialize_options()
-        command.finalized = 0
-        self.have_run[command_name] = 0
-        self._set_command_options(command)
-        if reinit_subcommands:
-            for sub in command.get_sub_commands():
-                self.reinitialize_command(sub, reinit_subcommands)
-        return command
-    # -- Methods that operate on the Distribution ----------------------
-    def announce(self, msg, level=log.INFO):
-        log.log(level, msg)
-    def run_commands(self):
-        """Run each command that was seen on the setup script command line.
-        Uses the list of commands found and cache of command objects
-        created by 'get_command_obj()'.
-        """
-        for cmd in self.commands:
-            self.run_command(cmd)
-    # -- Methods that operate on its Commands --------------------------
-    def run_command(self, command):
-        """Do whatever it takes to run a command (including nothing at all,
-        if the command has already been run).  Specifically: if we have
-        already created and run the command named by 'command', return
-        silently without doing anything.  If the command named by 'command'
-        doesn't even have a command object yet, create one.  Then invoke
-        'run()' on that command object (or an existing one).
-        """
-        # Already been here, done that? then return silently.
-        if self.have_run.get(command):
-            return
-        log.info("running %s", command)
-        cmd_obj = self.get_command_obj(command)
-        cmd_obj.ensure_finalized()
-        cmd_obj.run()
-        self.have_run[command] = 1
-    # -- Distribution query methods ------------------------------------
-    def has_pure_modules(self):
-        return len(self.packages or self.py_modules or []) > 0
-    def has_ext_modules(self):
-        return self.ext_modules and len(self.ext_modules) > 0
-    def has_c_libraries(self):
-        return self.libraries and len(self.libraries) > 0
-    def has_modules(self):
-        return self.has_pure_modules() or self.has_ext_modules()
-    def has_headers(self):
-        return self.headers and len(self.headers) > 0
-    def has_scripts(self):
-        return self.scripts and len(self.scripts) > 0
-    def has_data_files(self):
-        return self.data_files and len(self.data_files) > 0
-    def is_pure(self):
-        return (
-            self.has_pure_modules()
-            and not self.has_ext_modules()
-            and not self.has_c_libraries()
-        )
-    # -- Metadata query methods ----------------------------------------
-    # If you're looking for 'get_name()', 'get_version()', and so forth,
-    # they are defined in a sneaky way: the constructor binds self.get_XXX
-    # to self.metadata.get_XXX.  The actual code is in the
-    # DistributionMetadata class, below.
-class DistributionMetadata:
-    """Dummy class to hold the distribution meta-data: name, version,
-    author, and so forth.
-    """
-    _METHOD_BASENAMES = (
-        "name",
-        "version",
-        "author",
-        "author_email",
-        "maintainer",
-        "maintainer_email",
-        "url",
-        "license",
-        "description",
-        "long_description",
-        "keywords",
-        "platforms",
-        "fullname",
-        "contact",
-        "contact_email",
-        "classifiers",
-        "download_url",
-        # PEP 314
-        "provides",
-        "requires",
-        "obsoletes",
-    )
-    def __init__(self, path=None):
-        if path is not None:
-            self.read_pkg_file(open(path))
-        else:
-            self.name = None
-            self.version = None
-            self.author = None
-            self.author_email = None
-            self.maintainer = None
-            self.maintainer_email = None
-            self.url = None
-            self.license = None
-            self.description = None
-            self.long_description = None
-            self.keywords = None
-            self.platforms = None
-            self.classifiers = None
-            self.download_url = None
-            # PEP 314
-            self.provides = None
-            self.requires = None
-            self.obsoletes = None
-    def read_pkg_file(self, file):
-        """Reads the metadata values from a file object."""
-        msg = message_from_file(file)
-        def _read_field(name):
-            value = msg[name]
-            if value and value != "UNKNOWN":
-                return value
-        def _read_list(name):
-            values = msg.get_all(name, None)
-            if values == []:
-                return None
-            return values
-        metadata_version = msg['metadata-version']
-        self.name = _read_field('name')
-        self.version = _read_field('version')
-        self.description = _read_field('summary')
-        # we are filling author only.
-        self.author = _read_field('author')
-        self.maintainer = None
-        self.author_email = _read_field('author-email')
-        self.maintainer_email = None
-        self.url = _read_field('home-page')
-        self.license = _read_field('license')
-        if 'download-url' in msg:
-            self.download_url = _read_field('download-url')
-        else:
-            self.download_url = None
-        self.long_description = _read_field('description')
-        self.description = _read_field('summary')
-        if 'keywords' in msg:
-            self.keywords = _read_field('keywords').split(',')
-        self.platforms = _read_list('platform')
-        self.classifiers = _read_list('classifier')
-        # PEP 314 - these fields only exist in 1.1
-        if metadata_version == '1.1':
-            self.requires = _read_list('requires')
-            self.provides = _read_list('provides')
-            self.obsoletes = _read_list('obsoletes')
-        else:
-            self.requires = None
-            self.provides = None
-            self.obsoletes = None
-    def write_pkg_info(self, base_dir):
-        """Write the PKG-INFO file into the release tree."""
-        with open(
-            os.path.join(base_dir, 'PKG-INFO'), 'w', encoding='UTF-8'
-        ) as pkg_info:
-            self.write_pkg_file(pkg_info)
-    def write_pkg_file(self, file):
-        """Write the PKG-INFO format data to a file object."""
-        version = '1.0'
-        if (
-            self.provides
-            or self.requires
-            or self.obsoletes
-            or self.classifiers
-            or self.download_url
-        ):
-            version = '1.1'
-        # required fields
-        file.write('Metadata-Version: %s\n' % version)
-        file.write('Name: %s\n' % self.get_name())
-        file.write('Version: %s\n' % self.get_version())
-        def maybe_write(header, val):
-            if val:
-                file.write(f"{header}: {val}\n")
-        # optional fields
-        maybe_write("Summary", self.get_description())
-        maybe_write("Home-page", self.get_url())
-        maybe_write("Author", self.get_contact())
-        maybe_write("Author-email", self.get_contact_email())
-        maybe_write("License", self.get_license())
-        maybe_write("Download-URL", self.download_url)
-        maybe_write("Description", rfc822_escape(self.get_long_description() or ""))
-        maybe_write("Keywords", ",".join(self.get_keywords()))
-        self._write_list(file, 'Platform', self.get_platforms())
-        self._write_list(file, 'Classifier', self.get_classifiers())
-        # PEP 314
-        self._write_list(file, 'Requires', self.get_requires())
-        self._write_list(file, 'Provides', self.get_provides())
-        self._write_list(file, 'Obsoletes', self.get_obsoletes())
-    def _write_list(self, file, name, values):
-        values = values or []
-        for value in values:
-            file.write('{}: {}\n'.format(name, value))
-    # -- Metadata query methods ----------------------------------------
-    def get_name(self):
-        return self.name or "UNKNOWN"
-    def get_version(self):
-        return self.version or "0.0.0"
-    def get_fullname(self):
-        return "{}-{}".format(self.get_name(), self.get_version())
-    def get_author(self):
-        return self.author
-    def get_author_email(self):
-        return self.author_email
-    def get_maintainer(self):
-        return self.maintainer
-    def get_maintainer_email(self):
-        return self.maintainer_email
-    def get_contact(self):
-        return self.maintainer or self.author
-    def get_contact_email(self):
-        return self.maintainer_email or self.author_email
-    def get_url(self):
-        return self.url
-    def get_license(self):
-        return self.license
-    get_licence = get_license
-    def get_description(self):
-        return self.description
-    def get_long_description(self):
-        return self.long_description
-    def get_keywords(self):
-        return self.keywords or []
-    def set_keywords(self, value):
-        self.keywords = _ensure_list(value, 'keywords')
-    def get_platforms(self):
-        return self.platforms
-    def set_platforms(self, value):
-        self.platforms = _ensure_list(value, 'platforms')
-    def get_classifiers(self):
-        return self.classifiers or []
-    def set_classifiers(self, value):
-        self.classifiers = _ensure_list(value, 'classifiers')
-    def get_download_url(self):
-        return self.download_url
-    # PEP 314
-    def get_requires(self):
-        return self.requires or []
-    def set_requires(self, value):
-        import distutils.versionpredicate
-        for v in value:
-            distutils.versionpredicate.VersionPredicate(v)
-        self.requires = list(value)
-    def get_provides(self):
-        return self.provides or []
-    def set_provides(self, value):
-        value = [v.strip() for v in value]
-        for v in value:
-            import distutils.versionpredicate
-            distutils.versionpredicate.split_provision(v)
-        self.provides = value
-    def get_obsoletes(self):
-        return self.obsoletes or []
-    def set_obsoletes(self, value):
-        import distutils.versionpredicate
-        for v in value:
-            distutils.versionpredicate.VersionPredicate(v)
-        self.obsoletes = list(value)
-def fix_help_options(options):
-    """Convert a 4-tuple 'help_options' list as found in various command
-    classes to the 3-tuple form required by FancyGetopt.
-    """
-    new_options = []
-    for help_tuple in options:
-        new_options.append(help_tuple[0:3])
-    return new_options

spaces/Boranbruh/ehartford-WizardLM-7B-Uncensored/app.py DELETED Viewed

@@ -1,3 +0,0 @@
-import gradio as gr
-gr.Interface.load("models/ehartford/WizardLM-7B-Uncensored").launch()

spaces/CVPR/LIVE/thrust/examples/cpp_integration/device.h DELETED Viewed

@@ -1,7 +0,0 @@
-#pragma once
-#include <thrust/host_vector.h>
-// function prototype
-void sort_on_device(thrust::host_vector<int>& V);

spaces/CVPR/LIVE/thrust/thrust/iterator/detail/is_iterator_category.h DELETED Viewed

@@ -1,60 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-#pragma once
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_categories.h>
-#include <thrust/detail/type_traits.h>
-namespace thrust
-{
-namespace detail
-{
-template <typename T>
-  struct is_host_iterator_category
-    : thrust::detail::or_<
-        thrust::detail::is_convertible<T, thrust::input_host_iterator_tag>,
-        thrust::detail::is_convertible<T, thrust::output_host_iterator_tag>
-      >
-{
-}; // end is_host_iterator_category
-template <typename T>
-  struct is_device_iterator_category
-    : thrust::detail::or_<
-        thrust::detail::is_convertible<T, thrust::input_device_iterator_tag>,
-        thrust::detail::is_convertible<T, thrust::output_device_iterator_tag>
-      >
-{
-}; // end is_device_iterator_category
-template <typename T>
-  struct is_iterator_category
-    : thrust::detail::or_<
-        is_host_iterator_category<T>,
-        is_device_iterator_category<T>
-      >
-{
-}; // end is_iterator_category
-} // end detail
-} // end thrust

spaces/CVPR/lama-example/bin/report_from_tb.py DELETED Viewed

@@ -1,83 +0,0 @@
-#!/usr/bin/env python3
-import glob
-import os
-import re
-import tensorflow as tf
-from torch.utils.tensorboard import SummaryWriter
-GROUPING_RULES = [
-    re.compile(r'^(?P<group>train|test|val|extra_val_.*?(256|512))_(?P<title>.*)', re.I)
-]
-DROP_RULES = [
-    re.compile(r'_std$', re.I)
-]
-def need_drop(tag):
-    for rule in DROP_RULES:
-        if rule.search(tag):
-            return True
-    return False
-def get_group_and_title(tag):
-    for rule in GROUPING_RULES:
-        match = rule.search(tag)
-        if match is None:
-            continue
-        return match.group('group'), match.group('title')
-    return None, None
-def main(args):
-    os.makedirs(args.outdir, exist_ok=True)
-    ignored_events = set()
-    for orig_fname in glob.glob(args.inglob):
-        cur_dirpath = os.path.dirname(orig_fname)  # remove filename, this should point to "version_0" directory
-        subdirname = os.path.basename(cur_dirpath)  # == "version_0" most of time
-        exp_root_path = os.path.dirname(cur_dirpath)  # remove "version_0"
-        exp_name = os.path.basename(exp_root_path)
-        writers_by_group = {}
-        for e in tf.compat.v1.train.summary_iterator(orig_fname):
-            for v in e.summary.value:
-                if need_drop(v.tag):
-                    continue
-                cur_group, cur_title = get_group_and_title(v.tag)
-                if cur_group is None:
-                    if v.tag not in ignored_events:
-                        print(f'WARNING: Could not detect group for {v.tag}, ignoring it')
-                        ignored_events.add(v.tag)
-                    continue
-                cur_writer = writers_by_group.get(cur_group, None)
-                if cur_writer is None:
-                    if args.include_version:
-                        cur_outdir = os.path.join(args.outdir, exp_name, f'{subdirname}_{cur_group}')
-                    else:
-                        cur_outdir = os.path.join(args.outdir, exp_name, cur_group)
-                    cur_writer = SummaryWriter(cur_outdir)
-                    writers_by_group[cur_group] = cur_writer
-                cur_writer.add_scalar(cur_title, v.simple_value, global_step=e.step, walltime=e.wall_time)
-if __name__ == '__main__':
-    import argparse
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('inglob', type=str)
-    aparser.add_argument('outdir', type=str)
-    aparser.add_argument('--include-version', action='store_true',
-                         help='Include subdirectory name e.g. "version_0" into output path')
-    main(aparser.parse_args())

spaces/ChallengeHub/Chinese-LangChain/assets/custom.js DELETED Viewed

	@@ -1 +0,0 @@
1	- // custom javascript here

spaces/ChrisCaviar/ControlNet-v1-1/app_scribble_interactive.py DELETED Viewed

@@ -1,112 +0,0 @@
-#!/usr/bin/env python
-import gradio as gr
-import numpy as np
-from utils import randomize_seed_fn
-def create_canvas(w, h):
-    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                canvas_width = gr.Slider(label='Canvas width',
-                                         minimum=256,
-                                         maximum=512,
-                                         value=512,
-                                         step=1)
-                canvas_height = gr.Slider(label='Canvas height',
-                                          minimum=256,
-                                          maximum=512,
-                                          value=512,
-                                          step=1)
-                create_button = gr.Button('Open drawing canvas!')
-                image = gr.Image(tool='sketch', brush_radius=10)
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        create_button.click(fn=create_canvas,
-                            inputs=[canvas_width, canvas_height],
-                            outputs=image,
-                            queue=False)
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-    return demo
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='scribble')
-    demo = create_demo(model.process_scribble_interactive)
-    demo.queue().launch()

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/apps/request/request.js DELETED Viewed

@@ -1,65 +0,0 @@
-import { sendSocketList, Config, Version } from '../../components/index.js'
-Bot.on('request', async e => {
-    if (sendSocketList.length == 0) return false
-    let other = {}
-    switch (e.request_type) {
-        case 'friend':
-            other.request_type = 'friend'
-            switch (e.sub_type) {
-                case 'add':
-                    if (!Config.friendAdd) return false
-                    break;
-                default:
-                    return false
-            }
-            break;
-        case 'group':
-            other.request_type = 'group'
-            other.group_id = e.group_id
-            switch (e.sub_type) {
-                case 'invite':
-                    if (!Config.groupInvite) return false
-                    other.sub_type = 'invite'
-                    break;
-                case 'add':
-                    if (!Config.groupAdd) return false
-                    other.sub_type = 'add'
-                    break;
-                default:
-                    return false;
-            }
-            break;
-        default:
-            return false;
-    }
-    let msg = {
-        time: e.time,
-        self_id: e.self_id,
-        post_type: 'request',
-        flag: e.flag,
-        user_id: e.user_id,
-        comment: e.comment,
-        ...other
-    }
-    msg = JSON.stringify(msg)
-    sendSocketList.forEach(i => {
-        if (i.status == 1) {
-            switch (Number(i.type)) {
-                case 1:
-                case 2:
-                    if (Version.isTrss) {
-                        if (i.uin != e.self_id) return
-                        if (!Version.protocol.some(i => i == e.bot?.version?.name)) return
-                    }
-                    i.ws.send(msg)
-                    break;
-                default:
-                    break;
-            }
-        }
-    })
-})

spaces/CofAI/tv/README.md DELETED Viewed

@@ -1,44 +0,0 @@
----
-title: CofTV
-emoji: 📺☕📺
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
-app_port: 7860
-duplicated_from: TNR-5/AI-WebTV
----
-A generative AI WebTV, powered by Zeroscope and Hugging Face.
-This is just the frontend part, you will need the media-server (also open source) to make it work.
-Warning: this is an experimental, proof-of-concept project made in a few days.
-It is not ready for production use by other people! Also, this use models that should only be used for research purposes (no commercial usage).
-Note: because the stream uses FLV, it doesn't work on iPhone. There is however a [Twitch mirror here](https://www.twitch.tv/ai_webtv).
-The main code of the webtv is located inside the [media-server](https://huggingface.co/spaces/jbilcke-hf/media-server/tree/main) :
-manual steps:
-- human input to write a short paragraph describing a multi-shot video sequence
-- manual submit it to GPT-4 to generate a list of video captions for each shot (the system instructions are extracts from a stable diffusion guide)
-- commit the captions to the [playlist database](https://huggingface.co/spaces/jbilcke-hf/media-server/raw/main/database.json)
-Inside the `media-server` space (generation process running in the background):
-- for each prompt in the database
-- generate a silent 3 seconds video clip with   Zeroscope V2 576w (hosted on Hugging Face Spaces)
-- upscale the clip with  Zeroscope V2 XL (also a HF Space)
-- perform frame interpolation with FILM (also a HF Space)
-- storage in the Persistent Storage of the media-server Space
-Inside the `media-server` space (streaming process running in the foreground):
-- for each video file in the persistent storage folder
-- add it to a new FFmpeg playlist (it's just a .txt file)
-- broadcast it over the RTMP protocol using FFmpeg (in FLV format)
-- diffusion of the stream using node-media-server
-Inside the `AI-WebTV` space:
-- display the stream using `mpegts.js`
-- this doesn't work on iPhone, but now there is also a Twitch mirror

spaces/Dagfinn1962/stablediffusion-models/appworks.py DELETED Viewed

@@ -1,80 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-models = [
-    {"name": "Stable Diffusion 1.4","url": "CompVis/stable-diffusion-v1-4"},
-    {"name": "Stable Diffusion 1.5","url": "runwayml/stable-diffusion-v1-5"},
-    ]
-current_model = models[0]
-text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
-models2 = []
-for model in models:
-    model_url = f"models/{model['url']}"
-    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
-    models2.append(loaded_model)
-def text_it(inputs, text_gen=text_gen):
-    return text_gen(inputs)
-def set_model(current_model_index):
-    global current_model
-    current_model = models[current_model_index]
-    return gr.update(value=f"{current_model['name']}")
-def send_it(inputs, model_choice):
-    proc = models2[model_choice]
-    return proc(inputs)
-with gr.Blocks() as myface:
-    gr.HTML("""
-    <head> <style> with global {width: 500px; position; absolute; background-color: #000000; height: 100%; margin-left:2px; margin-right: 2px; font-weight:800; font-size: 24px; margin-right: 10px; padding: 10px;} </style> </head>"""
-    )
-    with gr.Row():
-        input_text = gr.Textbox(label=" ",placeholder="PROMPT HERE ",lines=4)
-        # Model selection dropdown
-        model_name1 = gr.Dropdown(
-        label=" ",
-        choices=[m["name"] for m in models],
-        type="index",
-        value=current_model["name"],
-        interactive=True,
-        )
-    with gr.Row():
-        see_prompts = gr.Button("Generate Prompts")
-        run = gr.Button("Generate Images", varant="primery")
-    with gr.Row():
-        output1 = gr.Image(label="")
-        output2 = gr.Image(label="")
-        output3 = gr.Image(label="")
-    with gr.Row():
-        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3,])
-    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
-    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
-    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)

spaces/Detomo/Depth_estimation/layers.py DELETED Viewed

@@ -1,55 +0,0 @@
-from tensorflow.keras.layers import Layer, InputSpec
-import keras.utils.conv_utils as conv_utils
-import tensorflow as tf
-import tensorflow.keras.backend as K
-def normalize_data_format(value):
-    if value is None:
-        value = K.image_data_format()
-    data_format = value.lower()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('The `data_format` argument must be one of '
-                         '"channels_first", "channels_last". Received: ' +
-                         str(value))
-    return data_format
-class BilinearUpSampling2D(Layer):
-    def __init__(self, size=(2, 2), data_format=None, **kwargs):
-        super(BilinearUpSampling2D, self).__init__(**kwargs)
-        self.data_format = normalize_data_format(data_format)
-        self.size = conv_utils.normalize_tuple(size, 2, 'size')
-        self.input_spec = InputSpec(ndim=4)
-    def compute_output_shape(self, input_shape):
-        if self.data_format == 'channels_first':
-            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
-            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
-            return (input_shape[0],
-                    input_shape[1],
-                    height,
-                    width)
-        elif self.data_format == 'channels_last':
-            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
-            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
-            return (input_shape[0],
-                    height,
-                    width,
-                    input_shape[3])
-    def call(self, inputs):
-        input_shape = K.shape(inputs)
-        if self.data_format == 'channels_first':
-            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
-            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
-        elif self.data_format == 'channels_last':
-            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
-            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
-        return tf.image.resize(inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR)
-    def get_config(self):
-        config = {'size': self.size, 'data_format': self.data_format}
-        base_config = super(BilinearUpSampling2D, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))

spaces/Dinoking/Garbage-Classifier-V4/app.py DELETED Viewed

@@ -1,31 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import tensorflow.keras as keras
-import keras.applications.vgg16 as vgg16
-from tensorflow.keras.models import load_model
-# load model
-model = load_model('model6904.h5')
-classnames = ['battery','cardboard','clothes','food','glass','medical','metal','paper','plastic','shoes']
-def predict_image(img):
-  img_4d=img.reshape(-1,224, 224,3)
-  prediction=model.predict(img_4d)[0]
-  return {classnames[i]: float(prediction[i]) for i in range(10)}
-image = gr.inputs.Image(shape=(224, 224))
-label = gr.outputs.Label(num_top_classes=3)
-article="<p style='text-align: center'>Made by Aditya Narendra with 🖤</p>"
-gr.Interface(fn=predict_image, inputs=image,  title="Garbage Classifier V4-VGG16+SVM",
-    description="This is a Garbage Classification Model Trained using VGG16+SVM(20 Epochs).Deployed to Hugging Faces using Gradio.",outputs=label,article=article,enable_queue=True,interpretation='default').launch(share="True")

spaces/DragGan/DragGan/stylegan_human/dnnlib/tflib/custom_ops.py DELETED Viewed

@@ -1,171 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-"""TensorFlow custom ops builder.
-"""
-import os
-import re
-import uuid
-import hashlib
-import tempfile
-import shutil
-import tensorflow as tf
-from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
-#----------------------------------------------------------------------------
-# Global options.
-cuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache')
-cuda_cache_version_tag = 'v1'
-do_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe!
-verbose = True # Print status messages to stdout.
-compiler_bindir_search_path = [
-    'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64',
-    'C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.23.28105/bin/Hostx64/x64',
-    'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin',
-]
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-def _find_compiler_bindir():
-    for compiler_path in compiler_bindir_search_path:
-        if os.path.isdir(compiler_path):
-            return compiler_path
-    return None
-def _get_compute_cap(device):
-    caps_str = device.physical_device_desc
-    m = re.search('compute capability: (\\d+).(\\d+)', caps_str)
-    major = m.group(1)
-    minor = m.group(2)
-    return (major, minor)
-def _get_cuda_gpu_arch_string():
-    gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']
-    if len(gpus) == 0:
-        raise RuntimeError('No GPU devices found')
-    (major, minor) = _get_compute_cap(gpus[0])
-    return 'sm_%s%s' % (major, minor)
-def _run_cmd(cmd):
-    with os.popen(cmd) as pipe:
-        output = pipe.read()
-        status = pipe.close()
-    if status is not None:
-        raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output))
-def _prepare_nvcc_cli(opts):
-    cmd = 'nvcc ' + opts.strip()
-    cmd += ' --disable-warnings'
-    cmd += ' --include-path "%s"' % tf.sysconfig.get_include()
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')
-    cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')
-    compiler_bindir = _find_compiler_bindir()
-    if compiler_bindir is None:
-        # Require that _find_compiler_bindir succeeds on Windows.  Allow
-        # nvcc to use whatever is the default on Linux.
-        if os.name == 'nt':
-            raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__)
-    else:
-        cmd += ' --compiler-bindir "%s"' % compiler_bindir
-    cmd += ' 2>&1'
-    return cmd
-#----------------------------------------------------------------------------
-# Main entry point.
-_plugin_cache = dict()
-def get_plugin(cuda_file):
-    cuda_file_base = os.path.basename(cuda_file)
-    cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
-    # Already in cache?
-    if cuda_file in _plugin_cache:
-        return _plugin_cache[cuda_file]
-    # Setup plugin.
-    if verbose:
-        print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
-    try:
-        # Hash CUDA source.
-        md5 = hashlib.md5()
-        with open(cuda_file, 'rb') as f:
-            md5.update(f.read())
-        md5.update(b'\n')
-        # Hash headers included by the CUDA code by running it through the preprocessor.
-        if not do_not_hash_included_headers:
-            if verbose:
-                print('Preprocessing... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
-                _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
-                with open(tmp_file, 'rb') as f:
-                    bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
-                    good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
-                    for ln in f:
-                        if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
-                            ln = ln.replace(bad_file_str, good_file_str)
-                            md5.update(ln)
-                    md5.update(b'\n')
-        # Select compiler options.
-        compile_opts = ''
-        if os.name == 'nt':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
-        elif os.name == 'posix':
-            compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')
-            compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\''
-        else:
-            assert False # not Windows or Linux, w00t?
-        compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()
-        compile_opts += ' --use_fast_math'
-        nvcc_cmd = _prepare_nvcc_cli(compile_opts)
-        # Hash build configuration.
-        md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
-        md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
-        md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
-        # Compile if not already compiled.
-        bin_file_ext = '.dll' if os.name == 'nt' else '.so'
-        bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
-        if not os.path.isfile(bin_file):
-            if verbose:
-                print('Compiling... ', end='', flush=True)
-            with tempfile.TemporaryDirectory() as tmp_dir:
-                tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
-                _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
-                os.makedirs(cuda_cache_path, exist_ok=True)
-                intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
-                shutil.copyfile(tmp_file, intermediate_file)
-                os.rename(intermediate_file, bin_file) # atomic
-        # Load.
-        if verbose:
-            print('Loading... ', end='', flush=True)
-        plugin = tf.load_op_library(bin_file)
-        # Add to cache.
-        _plugin_cache[cuda_file] = plugin
-        if verbose:
-            print('Done.', flush=True)
-        return plugin
-    except:
-        if verbose:
-            print('Failed!', flush=True)
-        raise
-#----------------------------------------------------------------------------

spaces/ECCV2022/storydalle/dalle/models/stage1/layers.py DELETED Viewed

@@ -1,373 +0,0 @@
-# ------------------------------------------------------------------------------------
-# Modified from VQGAN (https://github.com/CompVis/taming-transformers)
-# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer. All Rights Reserved.
-# ------------------------------------------------------------------------------------
-import torch
-import torch.nn as nn
-from typing import Tuple, Optional
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32,
-                              num_channels=in_channels,
-                              eps=1e-6,
-                              affine=True)
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0, 1, 0, 1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        assert temb_channels == 0
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-    def forward(self, x, temb=None):
-        assert temb is None
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-        return x+h
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)  # b,hw,c
-        k = k.reshape(b, c, h*w)  # b,c,hw
-        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-        # attend to values
-        v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b, c, h, w)
-        h_ = self.proj_out(h_)
-        return x+h_
-class Encoder(nn.Module):
-    def __init__(self,
-                 *,  # forced to use named arguments
-                 ch: int,
-                 out_ch: int,
-                 ch_mult: Tuple[int] = (1, 2, 4, 8),
-                 num_res_blocks: int,
-                 attn_resolutions: Tuple[int],
-                 pdrop: float = 0.0,
-                 resamp_with_conv: bool = True,
-                 in_channels: int,
-                 resolution: int,
-                 z_channels: int,
-                 double_z: Optional[bool] = None) -> None:
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=pdrop))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=pdrop)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=pdrop)
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-    def forward(self, x):
-        assert x.shape[2] == x.shape[3] == self.resolution, \
-               "{}, {}".format(x.shape, self.resolution)
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-            if i_level != self.num_resolutions-1:
-                h = self.down[i_level].downsample(h)
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-class Decoder(nn.Module):
-    def __init__(self,
-                 *,  # forced to use named arguments
-                 ch: int,
-                 out_ch: int,
-                 ch_mult: Tuple[int] = (1, 2, 4, 8),
-                 num_res_blocks: int,
-                 attn_resolutions: Tuple[int],
-                 pdrop: float = 0.0,
-                 resamp_with_conv: bool = True,
-                 in_channels: int,
-                 resolution: int,
-                 z_channels: int,
-                 double_z: bool) -> None:
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=pdrop)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=pdrop)
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=pdrop))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(AttnBlock(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-    def forward(self, z):
-        assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-        # z to block_in
-        h = self.conv_in(z)
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h

spaces/Eduger/webui/app.py DELETED Viewed

@@ -1,72 +0,0 @@
-import os
-from subprocess import getoutput
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/               queue=False,  /                /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-    os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
-    os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
-    os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.ckpt")
-    os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.yaml")
-    os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")

spaces/ElainaFanBoy/IRONY-Real-ESRGAN/FAQ.md DELETED Viewed

@@ -1,9 +0,0 @@
-# FAQ
-1. **What is the difference of `--netscale` and `outscale`?**
-A: TODO.
-1. **How to select models?**
-A: TODO.

spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_pipelines/satrn_pipeline.py DELETED Viewed

@@ -1,44 +0,0 @@
-img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='ResizeOCR',
-        height=32,
-        min_width=100,
-        max_width=100,
-        keep_aspect_ratio=False,
-        width_downsample_ratio=0.25),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio',
-            'resize_shape'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiRotateAugOCR',
-        rotate_degrees=[0, 90, 270],
-        transforms=[
-            dict(
-                type='ResizeOCR',
-                height=32,
-                min_width=100,
-                max_width=100,
-                keep_aspect_ratio=False,
-                width_downsample_ratio=0.25),
-            dict(type='ToTensorOCR'),
-            dict(type='NormalizeOCR', **img_norm_cfg),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=[
-                    'filename', 'ori_shape', 'img_shape', 'valid_ratio',
-                    'resize_shape', 'img_norm_cfg', 'ori_filename'
-                ]),
-        ])
-]