Update parquet files (step 54 of 476)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download NFS Underground 2 Vinyls Bin File and Customize Your Ride with Awesome Graphics.md

@@ -1,122 +0,0 @@
-<br />
-<h1>How to Download and Install Vinyls for NFS Underground 2</h1>
-<p>If you are a fan of <strong>Need for Speed Underground 2</strong>, you probably know how much fun it is to customize your cars with different <strong>vinyls</strong>. Vinyls are graphics that you can apply to your car's body, windows, hood, trunk, and spoiler. They can make your car look more stylish, unique, and eye-catching.</p>
-<h2>nfs underground 2 vinyls bin file download</h2><br /><p><b><b>Download Zip</b> ===> <a href="https://byltly.com/2uKxYY">https://byltly.com/2uKxYY</a></b></p><br /><br />
-<p>But did you know that you can download and install vinyls from other sources besides the ones that come with the game? There are many websites that offer vinyls for NFS Underground 2, created by talented and creative fans. You can find vinyls for almost any car model, theme, color, and design. You can even create your own vinyls using some tools and programs.</p>
-<p>In this article, we will show you how to download and install vinyls for NFS Underground 2 using a <strong>VINYLS.BIN</strong> file. This is a file that contains all the vinyls data for the game. By replacing this file with a new one, you can access new vinyls in the game. It's very easy and simple, and we will guide you through every step.</p>
- <h2>Downloading Vinyls for NFS Underground 2</h2>
-<p>The first thing you need to do is to find a website that offers vinyls for NFS Underground 2. There are many websites that you can choose from, but you need to be careful about some things. Make sure that the website is reliable and safe, and that it does not contain any viruses or malware. Also, make sure that the website has good reviews and ratings from other users.</p>
-<p>Some of the websites that we recommend are:</p>
-<ul>
-<li><a href="https://www.nfscars.net/need-for-speed-underground-2/6/files/">NFSCars</a>: This is one of the most popular and trusted websites for NFS games. It has a huge collection of vinyls for NFS Underground 2, as well as other mods, tools, cars, and tracks. You can browse by category, brand, date, rating, or popularity.</li>
-<li><a href="https://www.nfsaddons.com/nfsug2/">NFSAddons</a>: This is another great website for NFS games. It also has a large selection of vinyls for NFS Underground 2, as well as other content. You can filter by type, game, car, author, or name.</li>
-<li><a href="https://www.nfsg.net/">NFSG</a>: This is a Russian website that has a lot of vinyls for NFS Underground 2, as well as other games. You can use Google Translate to navigate the website if you don't speak Russian. You can sort by date or name.</li>
-</ul>
-<p>Once you have found a website that you like, you can start choosing the vinyls that you want to download. You can preview the vinyls on the website before downloading them. Look for vinyls that suit your preferences and style. You can download as many vinyls as you want, but remember that each VINYLS.BIN file can only contain a limited number of vinyls.</p>
-<p>nfsu2 vinyls bin file free download<br />
-how to install vinyls bin file in nfs underground 2<br />
-nfs underground 2 vinyls bin editor<br />
-download vinyls bin file for nfsu2 pc<br />
-nfs underground 2 custom vinyls bin file<br />
-nfsu2 vinyls bin file location<br />
-where to find vinyls bin file in nfs underground 2<br />
-nfs underground 2 vinyls bin file missing<br />
-how to fix vinyls bin file error in nfsu2<br />
-nfs underground 2 vinyls bin file corrupted<br />
-how to create vinyls bin file for nfs underground 2<br />
-nfsu2 vinyls bin file converter<br />
-download vinyls bin file for nfsu2 ps2<br />
-nfs underground 2 vinyls bin file mod<br />
-how to change vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file backup<br />
-how to restore vinyls bin file in nfsu2<br />
-nfsu2 vinyls bin file size<br />
-download vinyls bin file for nfsu2 xbox<br />
-nfs underground 2 vinyls bin file unlocker<br />
-how to extract vinyls bin file from nfsu2<br />
-nfsu2 vinyls bin file generator<br />
-download vinyls bin file for nfsu2 android<br />
-nfs underground 2 vinyls bin file patch<br />
-how to replace vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file original<br />
-how to open vinyls bin file in nfsu2<br />
-nfsu2 vinyls bin file viewer<br />
-download vinyls bin file for nfsu2 gamecube<br />
-nfs underground 2 vinyls bin file remover<br />
-how to delete vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file format<br />
-how to edit vinyls bin file in nfsu2<br />
-nfsu2 vinyls bin file maker<br />
-download vinyls bin file for nfsu2 psp<br />
-nfs underground 2 vinyls bin file changer<br />
-how to swap vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file compressor<br />
-download vinyls bin file for nfsu2 mac<br />
-nfsu2 vinyls bin file decrypter<br />
-how to copy vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file splitter<br />
-how to merge vinyls bin file in nfsu2<br />
-nfsu2 vinyls bin file encrypter<br />
-download vinyls bin file for nfsu2 linux<br />
-nfs underground 2 vinyls bin file renamer<br />
-how to move vinyls bin file in nfsu2<br />
-nfs underground 2 vinyls bin file extractor<br />
-download all vinyls.bin files for NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground 1 and NFS Underground</p>
-<p>After you have selected the vinyls that you want to download, you need to download them as ZIP or RAR files. These are compressed files that contain the VINYLS.BIN file and sometimes other files such as screenshots or readme files. To download them, simply click on the download button or link on the website.</p>
- <h2>Installing Vinyls for NFS Underground 2</h2>
-<p>Now that you have downloaded the ZIP or RAR files containing the VINYLS.BIN file, you need to extract them to a folder on your computer. You can use any program that can open ZIP or RAR files, such as WinRAR or 7-Zip. To extract them, right-click on the file and choose "Extract here" or "Extract to folder".</p>
-<p>After you have extracted the files, you need to locate the VINYLS.BIN file in your game directory. The game directory is where you installed NFS Underground 2 on your computer. It usually looks something like this: C:\\Program Files\\EA Games\\Need For Speed Underground 2\\</p>
-<p>Inside the game directory, you need to find the folder named CARS. This is where all the car models and data are stored. Inside this folder, you need to find the folder named SKYLINE. This is where all the data for Nissan Skyline car model are stored.</p>
-<p>Inside this folder, you will see a file named VINYLS.BIN. This is the original file that contains all the default vinyls for Nissan Skyline car model in NFS Underground 2. Before you replace this file with a new one, you need to back it up first. To do this, simply copy and paste this file to another folder on your computer.</p>
-<p>Now that you have backed up your original VINYLS.BIN file, you can copy and replace it with a new one. To do this, simply copy and paste the new VINYLS.BIN file that you downloaded from one of the websites into this folder: C:\\Program Files\\EA Games\\Need For Speed Underground 2\\CARS\\SKYLINE\\. If prompted, choose "Replace" or "Overwrite".</p>
-<p>Congratulations! You have successfully installed new vinyls for NFS Underground 2 using a VINYLS.BIN file. To see them in action, launch the game and go to Customize mode. Select Nissan Skyline car model and go to Paint menu. Then go to Vinyl menu and select ART FACTORY catalog. Here you will see all the new vinyls that you installed.</p>
- <h2>Tips and Tricks for Using Vinyls in NFS Underground 2</h2>
-<p>Now that you have new vinyls for NFS Underground 2, here are some tips and tricks on how to use them effectively:</p>
-<ul>
-<li>You can customize your vinyls using the in-game editor. You can change their color, size, position, rotation, opacity, layering order etc.</li>
-<li>You can mix and match different vinyls for different cars. For example: You can use a Nissan Skyline vinyl on a Honda Civic car model.</li>
-<li>You can share your vinyls with other players online. You can upload your VINYLS.BIN file to one of the websites mentioned above or send it directly to your friends via email or social media.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Vinyls are one of the best ways to customize your cars in NFS Underground 2. They can make your car look more stylish, unique, and eye-catching.</p>
- <p>In this article, we showed you how to download and install vinyls for NFS Underground 2 using a VINYLS.BIN file. This is a file that contains all the vinyl data for the game. By replacing this file with a new one, you can access new vinyls in the game. It's very easy and simple, and we guided you through every step.</p>
- <p>We also gave you some tips and tricks on how to use your new vinyls effectively. You can customize them using the in-game editor, mix and match them for different cars, and share them with other players online.</p>
- <p>We hope that this article was helpful and informative. If you want more resources on how to mod NFS Underground 2, you can check out these links:</p>
- <ul>
-<ul>
-<li><a href="https://www.nfstexed.com/">NFS TexEd</a>: This is a tool that allows you to edit the textures of NFS games, including vinyls. You can use it to create your own vinyls or modify existing ones.</li>
-<li><a href="https://www.nfsplanet.com/en/nfsug2/files/">NFSPlanet</a>: This is a website that has a lot of information and news about NFS games. It also has some downloads for NFS Underground 2, including vinyls, cars, tracks, and patches.</li>
-<li><a href="https://www.youtube.com/watch?v=Q0yJ7w9Zx6c">How to make your own vinyls for NFS Underground 2</a>: This is a video tutorial that shows you how to make your own vinyls for NFS Underground 2 using Photoshop and NFS TexEd.</li>
-</ul>
- <p>Thank you for reading this article. We hope that you enjoyed it and learned something new. Now go ahead and try out some vinyls for NFS Underground 2 and have fun!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about vinyls for NFS Underground 2:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What are the best vinyls for NFS Underground 2?</td>
-<td>There is no definitive answer to this question, as different vinyls may appeal to different players. However, some of the most popular and well-made vinyls for NFS Underground 2 are: -The Fast and the Furious movie car vinyls -The Need for Speed movie car vinyls -The NFS Most Wanted car vinyls -The NFS Carbon car vinyls -The NFS ProStreet car vinyls -The NFS Undercover car vinyls -The NFS Shift car vinyls -The NFS Hot Pursuit car vinyls -The NFS The Run car vinyls -The NFS Rivals car vinyls -The NFS Payback car vinyls -The NFS Heat car vinyls</td>
-</tr>
-<tr>
-<td>How can I uninstall vinyls from NFS Underground 2?</td>
-<td>If you want to uninstall vinyls from NFS Underground 2, you need to restore the original VINYLS.BIN file that you backed up before installing new ones. To do this, simply copy and paste the original VINYLS.BIN file back to the game directory (…\\Need For Speed Underground 2\\CARS\\SKYLINE\\). If prompted, choose "Replace" or "Overwrite".</td>
-</tr>
-<tr>
-<td>How can I fix the black screen issue when using vinyls in NFS Underground 2?</td>
-<td>Some players may experience a black screen issue when using some vinyls in NFS Underground 2. This is usually caused by incompatible or corrupted VINYLS.BIN files. To fix this issue, you need to download and install a patch that fixes the VINYLS.BIN file. You can find this patch here: <a href="https://www.nfscars.net/need-for-speed-underground-2/6/files/view/16488/">https://www.nfscars.net/need-for-speed-underground-2/6/files/view/16488/</a>. After installing this patch, you should be able to use any vinyl without any problems.</td>
-</tr>
-<tr>
-<td>How can I get more money in NFS Underground 2?</td>
-<td>If you want to get more money in NFS Underground 2, you can use some cheats or trainers that give you unlimited cash. However, this may ruin the fun and challenge of the game. Alternatively, you can earn more money by completing races, challenges, outruns, sponsorships, and hidden shops. You can also sell your unwanted cars or parts for extra cash.</td>
-</tr>
-<tr>
-<td>How can I unlock all cars in NFS Underground 2?</td>
-<td>If you want to unlock all cars in NFS Underground 2, you can use some cheats or trainers that give you access to all cars. However, this may spoil the excitement and progression of the game. Alternatively, you can unlock all cars by playing through the career mode and completing all stages, events, and magazines. You can also unlock some cars by finding them in hidden shops or winning them in outruns.</td>
-</tr>
-</table>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/EasyBCD 2.2 Portable Control and Configure Your Bootloader with a Simple Point-and-Click Interface.md

@@ -1,237 +0,0 @@
-
-<h1>How to Use EasyBCD 2.2 Portable to Dual-Boot Windows with Anything You Want</h1>
-<p>If you have ever wanted to dual-boot Windows with another operating system, such as Linux, Mac OS X, BSD or even older versions of Windows, you may have encountered some challenges with the bootloader. The bootloader is the program that runs before your operating system starts and lets you choose which one to boot. Windows has its own bootloader called BCD (Boot Configuration Data), which can be difficult to modify or repair if something goes wrong.</p>
-<p>Fortunately, there is a tool that can help you take control of your bootloader and make dual-booting easy and hassle-free. It's called <strong>EasyBCD</strong>, and it's a bootloader modification tool for Windows that lets you boot anything you want. In this article, we will show you how to use <strong>EasyBCD 2.2 Portable</strong>, which is a version of EasyBCD that you can run without installing it on your computer.</p>
-<h2>easybcd 2.2 portable</h2><br /><p><b><b>Download</b> &#128279; <a href="https://byltly.com/2uKzDS">https://byltly.com/2uKzDS</a></b></p><br /><br />
-<h2>What is EasyBCD and Why You Need It</h2>
-<p><strong>EasyBCD</strong> is a software developed by NeoSmart Technologies that extends and revamps the Windows BCD bootloader. It allows you to add, edit, delete, rename, reorder and configure boot entries for any operating system you have installed on your computer or on external devices. You can also change boot settings and options, such as the default boot target, the timeout, the boot menu appearance and more.</p>
-<p><strong>EasyBCD</strong> is useful for anyone who wants to dual-boot or multi-boot Windows with other operating systems, or who wants to have more control over their boot process. With <strong>EasyBCD</strong>, you can:</p>
-<ul>
-<li>Boot into Windows XP/Vista/7/8/10, Linux, Mac OS X, BSD and more.</li>
-<li>Boot from USB drives, network drives, ISO images, virtual disks, WinPE and more.</li>
-<li>Repair the Windows bootloader if it gets corrupted or damaged.</li>
-<li>Create bootable USB sticks with repair utilities that you can take with you anywhere.</li>
-<li>Use advanced features such as NeoGrub for custom scripting and boot scenarios.</li>
-</ul>
-<h3>EasyBCD Features and Benefits</h3>
-<p><strong>EasyBCD</strong> has many features that make it a powerful and versatile tool for managing your bootloader. Some of the main features are:</p>
-<ul>
-<li><strong>Painless editing:</strong> You can add, edit, delete, rename, reorder and configure boot entries with a few clicks. You don't need to know any complex commands or syntax.</li>
-<li><strong>Solve difficult problems:</strong> You can troubleshoot Windows, back up and restore the bootloader, fix boot errors, recover deleted partitions and more.</li>
-<li><strong>Boot anything:</strong> You can boot any operating system you want from any device you want. You can also create custom boot sequences, hide partitions, change active flags and more.</li>
-<li><strong>Powerful scripting with NeoGrub:</strong> You can use NeoGrub to write your own custom scripts and commands for advanced boot scenarios. You can also use it to hide partitions, chainload other bootloaders, load drivers and modules and more.</li>
-</ul>
-<p><strong>EasyBCD</strong> has many benefits that make it a must-have tool for anyone who wants to dual-boot or multi-boot Windows with other operating systems. Some of the main benefits are:</p>
-<p>easybcd 2.2 portable download<br />
-easybcd 2.2 portable free<br />
-easybcd 2.2 portable zip<br />
-easybcd 2.2 portable edition<br />
-easybcd 2.2 portable windows 10<br />
-easybcd 2.2 portable windows 7<br />
-easybcd 2.2 portable windows 8<br />
-easybcd 2.2 portable linux<br />
-easybcd 2.2 portable usb<br />
-easybcd 2.2 portable iso<br />
-easybcd 2.2 portable rar<br />
-easybcd 2.2 portable mega<br />
-easybcd 2.2 portable mediafire<br />
-easybcd 2.2 portable google drive<br />
-easybcd 2.2 portable dropbox<br />
-easybcd 2.2 portable torrent<br />
-easybcd 2.2 portable crack<br />
-easybcd 2.2 portable keygen<br />
-easybcd 2.2 portable serial<br />
-easybcd 2.2 portable license<br />
-easybcd 2.2 portable full version<br />
-easybcd 2.2 portable latest version<br />
-easybcd 2.2 portable updated version<br />
-easybcd 2.2 portable offline installer<br />
-easybcd 2.2 portable standalone installer<br />
-easybcd 2.2 portable multilingual<br />
-easybcd 2.2 portable for mac<br />
-easybcd 2.2 portable for pc<br />
-easybcd 2.2 portable for laptop<br />
-easybcd 2.2 portable for desktop<br />
-easybcd 2.2 portable software<br />
-easybcd 2.2 portable tool<br />
-easybcd 2.2 portable utility<br />
-easybcd 2.2 portable application<br />
-easybcd 2.2 portable program<br />
-easybcd 2.3 beta build xxx (replace xxx with a number)<br />
-how to use easybcd 2.3 beta build xxx (replace xxx with a number)<br />
-how to install easybcd 3 beta build xxx (replace xxx with a number)<br />
-how to uninstall easybcd beta build xxx (replace xxx with a number)<br />
-how to update easybcd beta build xxx (replace xxx with a number)<br />
-how to fix errors in easybcd beta build xxx (replace xxx with a number)<br />
-how to backup and restore boot configuration with easybcd beta build xxx (replace xxx with a number)<br />
-how to dual boot windows and linux with easybcd beta build xxx (replace xxx with a number)<br />
-how to create bootable usb with easybcd beta build xxx (replace xxx with a number)<br />
-how to repair windows boot loader with easybcd beta build xxx (replace xxx with a number)<br />
-how to add new entries to boot menu with easybcd beta build xxx (replace xxx with a number)<br />
-how to change boot order with easybcd beta build xxx (replace xxx with a number)<br />
-how to customize boot menu with easybcd beta build xxx (replace xxx with a number)<br />
-how to remove unwanted entries from boot menu with easybcd beta build xxx (replace xxx with a number)</p>
-<ul>
-<li><strong>It's free:</strong> <strong>EasyBCD</strong> is free for all private, non-commercial use. You can download it from the official website or from trusted sources such as TechSpot or Neowin.</li>
-<li><strong>It's easy:</strong> <strong>EasyBCD</strong> has a user-friendly interface that makes it easy to use even for beginners. You don't need any technical skills or knowledge to use it.</li>
-<li><strong>It's compatible:</strong> <strong>EasyBCD</strong> works with all versions of Windows from XP to 10. It also supports all types of disks (MBR, GPT) and file systems (FAT32, NTFS).</li>
-<li><strong>It's portable:</strong> <strong>EasyBCD 2.2 Portable</strong> is a version of <strong>EasyBCD</strong> that you can run without installing it on your computer. You can run it from a USB drive or any other removable device.</li>
-</ul>
-<h3>EasyBCD System Requirements and Compatibility</h3>
-<p>To use <strong>EasyBCD 2.2 Portable</strong>, you need a computer that meets the following system requirements:</p>
-<ul>
-<li>A processor that supports PAE/NX/SSE2 (most modern CPUs do).</li>
-<li>A minimum of 256 MB of RAM (512 MB recommended).</li>
-<li>A minimum of 50 MB of free disk space (100 MB recommended).</li>
-<li>A USB drive or any other removable device with at least 10 MB of free space.</li>
-<li>A working internet connection (optional but recommended).</li>
-</ul>
-<p><strong>EasyBCD 2.2 Portable</strong> is compatible with all versions of Windows from XP to 10 (32-bit and 64-bit). It also supports all types of disks (MBR, GPT) and file systems (FAT32, NTFS). However, some features may not work on older versions of Windows or on certain configurations.</p>
- <h2 id="how-to-download-and-run-easybcd-22-portable">How to Download and Run EasyBCD 2.2 Portable</h2>
- <p>To use <strong id="easybcd-22-portable">EasyBCD 2.2 Portable</strong>, you need to download it from a trusted source and run it without installing it on your computer. Here are the steps to follow:</p>
- <h3 id="downloading-easybcd-22-portable-from-a-trusted-source">Downloading EasyBCD 2.2 Portable from a Trusted Source</h3>
- <p>The first step is to download <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>. You can download it from the official website of NeoSmart Technologies or from other trusted sources such as TechSpot or Neowin.</p>
- <p>To download it from the official website of NeoSmart Technologies:</p>
- <ol type="1">
-	<li>Navigate to <a href="https://neosmart.net/EasyBCD/" rel="nofollow">https://neosmart.net/EasyBCD/</a>.</li>
- 	<li id="click-on-the-download-button-and-choose-the-free-for-personal-use-option">Click on the Download button and choose the Free for Personal Use option.</li>
- 	<li id="enter-your-name-and-email-address-and-click-on-the-register-button">Enter your name and email address and click on the Register button.</li>
- 	<li id="check-your-email-for-a-download-link-and-click-on-it-to-download-the-easybcdzip-file">Check your email for a download link and click on it to download the easybcd.zip file.</li>
- 	<li id="extract-the-easybcdzip-file-to-a-folder-of-your-choice-on-your-usb-drive-or-any-other-removable-device">Extract the easybcd.zip file to a folder of your choice on your USB drive or any other removable device.</li>
- <h3 id="running-easybcd-22-portable-without-installation">Running EasyBCD 2.2 Portable without Installation</h3>
- <p>The second step is to run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> without installing it on your computer. You can run it from your USB drive or any other removable device where you extracted the easybcd.zip file.</p>
- <p>To run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> without installation:</p>
- <ol type="1">
-	<li>Plug in your USB drive or any other removable device where you extracted the easybcd.zip file to your computer.</li>
- 	<li>Navigate to the folder where you extracted the easybcd.zip file and double-click on the EasyBCD.exe file.</li>
- 	<li>If you see a User Account Control (UAC) prompt, click on Yes to allow <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to run.</li>
- 	<li>You will see the main interface of <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, which looks like this:</p>
-<code><pre>
-+----------------------------------------+ |                                        | |   EasyBCD 2.2 Portable                 | |                                        | |   View Settings   Add New Entry        | |   Edit Boot Menu  BCD Backup/Repair    | |   BCD Deployment  Useful Utilities     | |                                        | +----------------------------------------+ </pre></code>
-<p>You can now use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to manage your bootloader as you wish.</li>
-</ol>
-<h2 id="how-to-use-easybcd-22-portable-to-manage-your-bootloader">How to Use EasyBCD 2.2 Portable to Manage Your Bootloader</h2>
- <p>The third step is to use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to manage your bootloader and dual-boot Windows with anything you want. You can use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to add, edit, delete, rename, reorder and configure boot entries for any operating system you have installed on your computer or on external devices. You can also change boot settings and options, such as the default boot target, the timeout, the boot menu appearance and more.</p>
- <p>To use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to manage your bootloader, you need to follow these steps:</p>
- <h3 id="adding-editing-and-deleting-boot-entries">Adding, Editing and Deleting Boot Entries</h3>
- <p>To add, edit or delete boot entries with <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, you need to go to the "Add New Entry" or "Edit Boot Menu" page.</p>
- <p>To go to the "Add New Entry" page:</p>
- <ol type="1">
-	<li>Run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> from your USB drive or any other removable device.</li>
- 	<li>Click on the "Add New Entry" button on the main interface.</li>
- 	<li>You will see the "Add New Entry" page, which looks like this:</p>
-<code><pre>
-+----------------------------------------+ |                                        | |   Add New Entry                        | |                                        | |   Windows      Linux/BSD               | |   Mac OS X     NeoGrub                 | |   WinPE        Floppy                  | |   ISO          VHD                     | |                                        | +----------------------------------------+ </pre></code>
-<p>You can choose the type of entry you want to add from the tabs on the left side.</li>
-</ol>
-<p>To go to the "Edit Boot Menu" page:</p>
- <ol type="1">
-	<li>Run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> from your USB drive or any other removable device.</li>
- 	<li>Click on the "Edit Boot Menu" button on the main interface.</li>
- 	<li>You will see the "Edit Boot Menu" page, which looks like this:</p>
-<code><pre>
-+----------------------------------------+ |                                        | |   Edit Boot Menu                       | |                                        | |   Windows 10                           | |   Ubuntu                               | |   Mac OS X                             | |                                        | |   Save Settings                        | |                                        | +----------------------------------------+ </pre></code>
-<p>You can see the list of existing boot entries and modify them as you wish.</li>
-</ol>
-<p>To add a new boot entry:</p>
- <ol type="1">
-	<li>Go to the "Add New Entry" page and choose the type of entry you want to add.</li>
- 	<li>Fill in the details of the entry, such as the name, the drive, the path and any other options.</li>
- 	<li>Click on the "Add Entry" button at the bottom of the page.</li>
- 	<li>You will see a confirmation message that says "Entry successfully added".</li>
-</ol>
-<p>To edit an existing boot entry:</p>
- <ol type="1">
-	<li>Go to the "Edit Boot Menu" page and select the entry you want to edit.</li>
- 	<li>Click on the "Rename" button at the bottom of the page and change the name of the entry as you wish.</li>
- 	<li>Click on the "Save Settings" button at the bottom of the page.</li>
- 	<li>You will see a confirmation message that says "Settings successfully saved".</li>
-</ol>
-<p>To delete an existing boot entry:</p>
- <ol type="1">
-	<li>Go to the "Edit Boot Menu" page and select the entry you want to delete.</li>
- <h3 id="changing-boot-settings-and-options">Changing Boot Settings and Options</h3>
- <p>To change boot settings and options with <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, you need to go to the "Edit Boot Menu" page and modify the menu options.</p>
- <p>To go to the "Edit Boot Menu" page:</p>
- <ol type="1">
-	<li>Run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> from your USB drive or any other removable device.</li>
- 	<li>Click on the "Edit Boot Menu" button on the main interface.</li>
- 	<li>You will see the "Edit Boot Menu" page, which looks like this:</p>
-<code><pre>
-+----------------------------------------+ |                                        | |   Edit Boot Menu                       | |                                        | |   Windows 10                           | |   Ubuntu                               | |   Mac OS X                             | |                                        | |   Save Settings                        | |                                        | +----------------------------------------+ </pre></code>
-<p>You can see the list of existing boot entries and modify them as you wish.</li>
-</ol>
-<p>To change boot settings and options:</p>
- <ol type="1">
-	<li>Go to the "Edit Boot Menu" page and scroll down to the "Menu Options" section.</li>
- 	<li>You will see the following options:</p>
-<code><pre>
-+----------------------------------------+ |                                        | |   Menu Options                         | |                                        | |   Skip the boot menu                   | |   Count down from: 30 seconds          | |   Display a timeout: 30 seconds        | |   Language: English                    | |                                        | +----------------------------------------+ </pre></code>
-<p>You can change these options as you wish.</li>
-</ol>
-<p>The "Skip the boot menu" option allows you to bypass the boot menu and boot into the default entry automatically. This option is useful if you are sure of the OS you want to boot into and don't want to see the menu every time.</p>
- <p>The "Count down from" option allows you to set a timer for the boot menu. The menu will display for the specified number of seconds before booting into the default entry. You can change the number of seconds by clicking on the arrows or typing a value. This option is useful if you want to have some time to choose an entry before the default one is selected.</p>
- <p>The "Display a timeout" option allows you to set a timeout for the boot menu. The menu will display until you press a key or select an entry. If you don't do anything for the specified number of seconds, the default entry will be booted. You can change the number of seconds by clicking on the arrows or typing a value. This option is useful if you want to have more control over when to boot into an entry.</p>
- <p>The "Language" option allows you to change the language of the boot menu. You can choose from a list of supported languages by clicking on the dropdown menu. This option is useful if you want to have the boot menu in your preferred language.</p>
- <p>After changing any of these options, click on the "Save Settings" button at the bottom of the page. You will see a confirmation message that says "Settings successfully saved".</p>
-<h3 id="troubleshooting-boot-problems-with-easybcd">Troubleshooting Boot Problems with EasyBCD</h3>
- <p>Sometimes, you may encounter some boot problems with your computer, such as missing or corrupted bootloader files, invalid boot entries, inaccessible partitions and more. These problems can prevent your computer from booting properly or at all. Fortunately, <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> can help you troubleshoot and fix these problems with its various tools and utilities.</p>
- <h4 id="creating-bootable-usb-sticks-with-repair-utilities">Creating Bootable USB Sticks with Repair Utilities</h4>
- <p>Another useful feature of <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> is the ability to create bootable USB sticks with repair utilities that you can take with you anywhere. These utilities can help you diagnose and fix various problems with your computer, such as corrupted files, missing drivers, malware infections and more. You can use these utilities to boot into different repair environments, such as Windows Recovery Environment (WinRE), Windows Preinstallation Environment (WinPE), Linux-based rescue disks and more.</p>
- <p>To create bootable USB sticks with repair utilities with <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, you need to follow these steps:</p>
- <ol type="1">
-	<li>Prepare a USB drive with enough space to hold the repair utilities you want to use. You can use any USB drive that is formatted with FAT32 or NTFS file system.</li>
- 	<li>Download the ISO images of the repair utilities you want to use from their official websites or trusted sources. Some examples of repair utilities are:</li>
-	<ul>
-	<li><strong>Windows Recovery Environment (WinRE):</strong> This is a built-in repair environment that comes with Windows 10 and 11. It can help you troubleshoot and fix common problems with Windows, such as startup issues, system restore, reset and more. You can download the WinRE ISO image from <a href="https://www.microsoft.com/en-us/software-download/windows10ISO" rel="nofollow">https://www.microsoft.com/en-us/software-download/windows10ISO</a> for Windows 10 or <a href="https://www.microsoft.com/en-us/software-download/windows11" rel="nofollow">https://www.microsoft.com/en-us/software-download/windows11</a> for Windows 11.</li>
-	<li><strong>Windows Preinstallation Environment (WinPE):</strong> This is a lightweight version of Windows that can be used to install, deploy or repair Windows on a PC. It can also run various tools and commands that are not available in WinRE. You can download the WinPE ISO image from <a href="https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/download-winpe--windows-pe" rel="nofollow">https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/download-winpe--windows-pe</a>.</li>
-	<li><strong>Linux-based rescue disks:</strong> These are bootable disks that run Linux operating systems and include various tools and utilities for repairing Windows and other systems. Some examples of Linux-based rescue disks are:</li>
-	<ul>
-	<li><strong>Hiren's BootCD PE:</strong> This is a popular rescue disk that includes various tools for backup, recovery, partitioning, antivirus, password reset and more. You can download the Hiren's BootCD PE ISO image from <a href="https://www.hirensbootcd.org/download/" rel="nofollow">https://www.hirensbootcd.org/download/</a>.</li>
-	<li><strong>Kali Linux:</strong> This is a powerful penetration testing and ethical hacking tool that includes various tools for security testing, forensics, malware analysis and more. You can download the Kali Linux ISO image from <a href="https://www.kali.org/get-kali/" rel="nofollow">https://www.kali.org/get-kali/</a>.</li>
-	<li><strong>SystemRescue:</strong> This is a versatile rescue disk that includes various tools for disk management, data recovery, system repair and more. You can download the SystemRescue ISO image from <a href="https://www.system-rescue.org/Download/" rel="nofollow">https://www.system-rescue.org/Download/</a>.</li>
-	</ul>
-	</ul>
-	<li>Copy the ISO images of the repair utilities to your USB drive or any other removable device where you extracted the easybcd.zip file.</li>
- 	<li>Run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> from your USB drive or any other removable device.</li>
- 	<li>Go to the "Add New Entry" page and click on the "ISO" tab on the left side.</li>
- 	<li>Add each ISO image as a new entry by following these steps:</li>
-	<ul>
-	<li>Enter a name for the entry in the "Name" box.</li>
- 	<li>Browse for the ISO image file in the "Path" box or type its location manually.</li>
- 	<li>Select a mode for loading the image: either "Run from Disk" or "Load from Memory". The former option loads the image from its location on-disk. This can perform slightly slower, but is more reliable. The latter option copies the image to the memory before loading it. This can perform faster, but may fail if the image is too large or if there is not enough memory available.</li>
- 	<li>Click on the "Add Entry" button at the bottom of the page.</li>
- 	<li>You will see a confirmation message that says "Entry successfully added".</li>
-	</ul>
-	<li>You have now created a bootable USB stick with repair utilities that you can use to boot into different repair environments.</li>
-</ol>
-<h4 id="using-neogrub-for-custom-scripting-and-boot-scenarios">Using NeoGrub for Custom Scripting and Boot Scenarios</h4>
- <p>The last advanced feature of <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> that we will cover in this article is NeoGrub. NeoGrub is a custom bootloader based on GRUB (Grand Unified Bootloader), which is a popular bootloader for Linux and other operating systems. NeoGrub allows you to write your own custom scripts and commands for advanced boot scenarios. You can also use it to hide partitions, chainload other bootloaders, load drivers and modules and more.</p>
- <p>To use NeoGrub with <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, you need to follow these steps:</p>
- <ol type="1">
-	<li>Go to the "Add New Entry" page and click on the "NeoGrub" tab on the left side.</li>
- 	<li>You will see a text editor where you can write your custom script or command for NeoGrub.</li>
- 	<li>You can use any valid GRUB syntax or command in your script or command. You can also use some special variables provided by EasyBCD, such as $WIN_DRIVE (the drive letter of your Windows installation), $LINUX_DRIVE (the drive letter of your Linux installation) and $NST_FOLDER (the folder where EasyBCD stores its files).</li>
- 	<li>You can also use some special commands provided by EasyBCD, such as hide/unhide (to hide or unhide partitions), makeactive/inactive (to change active flags), find/setroot (to find or set root partitions) and map/unmap (to map or unmap devices).</li>
- 	<li>You can find more information about GRUB syntax, commands, variables and options in the GRUB manual at <a href="https://www.gnu.org/software/grub/manual/grub/" rel="nofollow">https://www.gnu.org/software/grub/manual/grub/</a>.</li>
- 	<li>You can find some examples of NeoGrub scripts and commands in the EasyBCD documentation at <a href="https://neosmart.net/wiki/easybcd/neogrub/" rel="nofollow">https://neosmart.net/wiki/easybcd/neogrub/</a>.</li>
- 	<li>After writing your custom script or command for NeoGrub, click on the "Add Entry" button at the bottom of the page.</li>
- 	<li>You will see a confirmation message that says "Entry successfully added".</li>
- 	<li>You have now added a NeoGrub entry that you can use to boot into your custom script or command.</li>
-</ol>
-<h2 id="conclusion-and-faqs">Conclusion and FAQs</h2>
- <p>In this article, we have shown you how to use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, which is a bootloader modification tool for Windows that lets you boot anything you want. We have covered how to download and run <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, how to use it to manage your bootloader, how to use its advanced features and how to troubleshoot boot problems with it.</p>
- <p>has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Here are some frequently asked questions and answers about <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>:</p>
- <h3 id="faq-1-is-easybcd-22-portable-safe-to-use">FAQ 1: Is EasyBCD 2.2 Portable safe to use?</h3>
- <p>Answer: Yes, <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> is safe to use as long as you download it from a trusted source and use it with caution. However, you should always back up your data and your bootloader settings before using <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, as modifying the bootloader can be risky and may cause boot problems or data loss if done incorrectly.</p>
- <h3 id="faq-2-does-easybcd-22-portable-work-with-windows-11">FAQ 2: Does EasyBCD 2.2 Portable work with Windows 11?</h3>
- <p>Answer: Yes, <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> works with Windows 11 and supports its new features and requirements, such as Secure Boot and TPM 2.0. However, some features of <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> may not work on Windows 11 or on certain configurations, such as UEFI or GPT disks. You should always check the compatibility and requirements of the entries you want to add or edit with <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> before using it.</p>
- <h3 id="faq-3-can-i-use-easybcd-22-portable-on-a-mac-or-a-linux-pc">FAQ 3: Can I use EasyBCD 2.2 Portable on a Mac or a Linux PC?</h3>
- <p>Answer: No, <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> is designed for Windows PCs only and cannot run on a Mac or a Linux PC. However, you can use <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to add or edit entries for Mac OS X or Linux operating systems that are installed on your Windows PC or on external devices.</p>
- <h3 id="faq-4-how-can-i-update-easybcd-22-portable-to-the-latest-version">FAQ 4: How can I update EasyBCD 2.2 Portable to the latest version?</h3>
- <p>Answer: To update <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a> to the latest version, you need to download the latest version of the easybcd.zip file from the official website of NeoSmart Technologies or from other trusted sources and extract it to your USB drive or any other removable device where you extracted the previous version of the easybcd.zip file. You should also delete the old version of the easybcd.zip file to avoid confusion.</p>
- <h3 id="faq-5-how-can-i-get-more-help-or-support-for-easybcd-22-portable">FAQ 5: How can I get more help or support for EasyBCD 2.2 Portable?</h3>
- <p>Answer: To get more help or support for <a href="#easybcd-22-portable"><em id="easybcd-22-portable">EasyBCD 2.2 Portable</em></a>, you can visit the official website of NeoSmart Technologies at <a href="https://neosmart.net/EasyBCD/" rel="nofollow">https://neosmart.net/EasyBCD/</a>, where you can find more documentation, tutorials, forums and contact information.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Beach Head 2002 Free Download Full Version For 26 [VERIFIED].md

@@ -1,6 +0,0 @@
-<h2>beach head 2002 free download full version for 26</h2><br /><p><b><b>Download</b> &#10031; <a href="https://imgfil.com/2uy0M1">https://imgfil.com/2uy0M1</a></b></p><br /><br />
-
-2002..pc..installer...Bh2002..exe..download.blog....tempat....download....gratis.. 1fdad05405<br />
-<br />
-<br />
-<p></p>

spaces/1gistliPinn/ChatGPT4/Examples/Download Spyeye 1.3.48 Cracked T BETTER.md

@@ -1,12 +0,0 @@
-<h2>Download Spyeye 1.3.48 Cracked T</h2><br /><p><b><b>DOWNLOAD</b> &#10038; <a href="https://imgfil.com/2uxYif">https://imgfil.com/2uxYif</a></b></p><br /><br />
-
-spyeye 1.3.48 ed t rial
-spyeye 1.3.48 trial.
-Spyeye is a tool that allows you to receive email and SMS notifications while sitting at the computer.
-It's an intelligent, proprietary, and private tool for private information gathering.
-It's the perfect tool for people who want the privacy of their email, phone, Facebook, etc.
-Spyeye is an easy to use tool that's very convenient for people that need to collect email, phone codes, and more.
-It comes with a simple and easy-to-use interface, 8a78ff9644<br />
-<br />
-<br />
-<p></p>

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/DJ Studio 5 - Free music mixer The Most Comprehensive and User-Friendly App for Mobile DJs.md

@@ -1,131 +0,0 @@
-
-<h1>Studio DJ 5 APK: A Free Music Mixer for Android</h1>
-<p>If you are looking for a simple yet powerful audio recording and MIDI sequencing app, then definitely Studio DJ 5 APK is the right app for you. This amazing application gives you the opportunity to manipulate sound files from your Android device and share them with other mobile devices or online services, such as Dropbox and others.</p>
-<h2>studio dj 5 apk</h2><br /><p><b><b>Download File</b> &harr; <a href="https://urlin.us/2uT2dD">https://urlin.us/2uT2dD</a></b></p><br /><br />
-<h2>What is Studio DJ 5 APK?</h2>
-<p>Studio DJ 5 APK is a mobile DJ station that allows you to mix, remix, scratch, loop or pitch your music in the palm of your hands. Designed to be user-friendly and customizable, Studio DJ 5 APK lets you create your own music with a variety of tools and options. You can choose between a single deck or twin decks, adjust the volume, tempo, pitch and equalizer, add effects and samples, record your mixes live and share them on Soundcloud or other platforms.</p>
-<h3>Features of Studio DJ 5 APK</h3>
-<p>Some of the features that make Studio DJ 5 APK stand out from other similar apps are:</p>
-<ul>
-<li>It is completely free and does not require any in-app purchase or registration.</li>
-<li>It supports all popular audio formats, such as MP3, WAV, OGG and more.</li>
-<li>It has a wide range of effects and samples to enhance your mixes, such as flanger, phaser, reverb, delay, filter and more.</li>
-<li>It has a built-in BPM analyzer that automatically detects the tempo of your tracks and syncs them.</li>
-<li>It has a loop function that allows you to create seamless loops of any length.</li>
-<li>It has a scratch function that simulates the sound of vinyl records.</li>
-<li>It has a pitch function that allows you to change the pitch of your tracks without affecting the tempo.</li>
-<li>It has an equalizer function that allows you to adjust the sound levels of your tracks.</li>
-<li>It has a crossfader function that allows you to blend two tracks smoothly.</li>
-<li>It has a cue function that allows you to preview your tracks before playing them.</li>
-<li>It has a playlist function that allows you to organize your tracks by genre, artist, album or folder.</li>
-<li>It has a social function that allows you to share your mixes on Soundcloud or other platforms.</li>
-</ul>
-<h3>How to download and install Studio DJ 5 APK</h3>
-<p>To download and install Studio DJ 5 APK on your Android device, you need to follow these simple steps:</p>
-<p>DJ Studio 5 - Free music mixer download<br />
-DJ Studio 5 app for Android<br />
-How to use DJ Studio 5 on mobile<br />
-DJ Studio 5 - Free music mixer review<br />
-DJ Studio 5 - Music mixer Google Play<br />
-DJ Studio 5 - Free music mixer APK for Windows<br />
-DJ Studio 5 - Free music mixer features<br />
-DJ Studio 5 - Music mixer tips and tricks<br />
-DJ Studio 5 - Free music mixer alternatives<br />
-DJ Studio 5 - Music mixer latest version<br />
-DJ Studio 5 - Free music mixer tutorial<br />
-DJ Studio 5 app for iOS<br />
-How to install DJ Studio 5 on PC<br />
-DJ Studio 5 - Free music mixer ratings<br />
-DJ Studio 5 - Music mixer online<br />
-DJ Studio 5 - Free music mixer mod APK<br />
-DJ Studio 5 - Music mixer best settings<br />
-DJ Studio 5 - Free music mixer support<br />
-DJ Studio 5 - Music mixer free download<br />
-DJ Studio 5 app for Mac<br />
-How to update DJ Studio 5 on Android<br />
-DJ Studio 5 - Free music mixer feedback<br />
-DJ Studio 5 - Music mixer premium APK<br />
-DJ Studio 5 - Free music mixer FAQs<br />
-DJ Studio 5 - Music mixer offline mode<br />
-DJ Studio 5 - Free music mixer pro APK<br />
-DJ Studio 5 - Music mixer user guide<br />
-DJ Studio 5 - Free music mixer forum<br />
-DJ Studio 5 - Music mixer screenshots<br />
-DJ Studio 5 app for Windows Phone<br />
-How to uninstall DJ Studio 5 on Android<br />
-DJ Studio 5 - Free music mixer coupons<br />
-DJ Studio 5 - Music mixer license key<br />
-DJ Studio 5 - Free music mixer videos<br />
-DJ Studio 5 - Music mixer system requirements<br />
-DJ Studio 5 - Free music mixer old versions<br />
-DJ Studio 5 - Music mixer sound effects<br />
-DJ Studio 5 - Free music mixer blog<br />
-DJ Studio 5 - Music mixer social media<br />
-DJ Studio 5 app for Chromebook<br />
-How to backup DJ Studio 5 on Android<br />
-DJ Studio 5 - Free music mixer testimonials<br />
-DJ Studio 5 - Music mixer crack APK<br />
-DJ Studio 5 - Free music mixer newsletter<br />
-DJ Studio 5 - Music mixer comparison chart</p>
-<ol>
-<li>Go to the official website of Studio DJ 5 APK or click on this link: [Download Studio DJ 5 APK](^1^).</li>
-<li>Select the version that suits your device and click on the download button.</li>
-<li>Wait for the download to complete and then open the file manager on your device.</li>
-<li>Locate the downloaded file and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to the app.</li>
-<li>Once the installation is done, launch the app and enjoy mixing your music.</li>
-</ol>
- <h2>How to use Studio DJ 5 APK</h2>
- <p>To use Studio DJ 5 APK effectively , you need to master the basic functions of loading, mixing, remixing, scratching, looping and pitching your music tracks. Here are some tips on how to do that:</p>
-<h3>Loading music tracks</h3>
-<p>To load music tracks on Studio DJ 5 APK, you can either use the playlist function or the browse function. The playlist function allows you to access your tracks by genre, artist, album or folder. The browse function allows you to access your tracks by location, such as internal storage, external storage or cloud storage. To load a track, simply tap on it and drag it to the deck you want to play it on. You can also load a track by tapping on the load button on the deck and selecting the track from the list.</p>
-<h3>Mixing, remixing, scratching, looping and pitching</h3>
-<p>To mix, remix, scratch, loop or pitch your tracks on Studio DJ 5 APK, you need to use the various tools and options available on the app. Here are some of them:</p>
-<ul>
-<li>The crossfader allows you to blend two tracks smoothly by sliding it left or right.</li>
-<li>The volume slider allows you to adjust the volume of each track by sliding it up or down.</li>
-<li>The tempo slider allows you to adjust the tempo of each track by sliding it left or right.</li>
-<li>The pitch slider allows you to adjust the pitch of each track by sliding it up or down.</li>
-<li>The equalizer allows you to adjust the sound levels of each track by tapping on the EQ button and sliding the knobs.</li>
-<li>The effects allow you to add various effects to your tracks by tapping on the FX button and selecting the effect you want.</li>
-<li>The samples allow you to add various sounds to your tracks by tapping on the sampler button and selecting the sample you want.</li>
-<li>The scratch function allows you to simulate the sound of vinyl records by tapping on the scratch button and moving your finger on the deck.</li>
-<li>The loop function allows you to create seamless loops of any length by tapping on the loop button and selecting the loop size.</li>
-<li>The cue function allows you to preview your tracks before playing them by tapping on the cue button and listening through your headphones.</li>
-</ul>
-<h3>Recording and sharing mixes</h3>
-<p>To record and share your mixes on Studio DJ 5 APK, you need to use the record function and the social function. The record function allows you to record your mixes live and save them on your device. To record a mix, simply tap on the record button and start mixing. To stop recording, tap on the record button again. You can access your recorded mixes by tapping on the record button and selecting the mix from the list. The social function allows you to share your mixes on Soundcloud or other platforms. To share a mix, simply tap on the share button and choose the platform you want.</p>
- <h2>Pros and cons of Studio DJ 5 APK</h2>
- <p>Studio DJ 5 APK is a great app for aspiring DJs and music lovers who want to create their own music with their Android devices. However, like any other app, it has its pros and cons. Here are some of them:</p>
- <h3>Pros</h3>
- <h4>Free and comprehensive</h4>
- <p>One of the best things about Studio DJ 5 APK is that it is completely free and does not require any in-app purchase or registration. It also offers a comprehensive set of features and options that allow you to create professional-quality mixes with ease.</p>
- <h4>User-friendly and customizable</h4>
- <p>Another great thing about Studio DJ 5 APK is that it is user-friendly and customizable. It has a simple and intuitive interface that makes it easy to navigate and use. It also allows you to customize your decks, skins, layouts, colors and more according to your preferences.</p>
- <h4>Compatible and integrable</h4>
- <p>A third great thing about Studio DJ 5 APK is that it is compatible and integrable with various devices and platforms. It supports all popular audio formats, such as MP3, WAV, OGG and more. It also integrates with various online services, such as Dropbox, Soundcloud and others.</p>
- <h3>Cons</h3>
- <h4>Steep learning curve</h4>
- <p>One of the drawbacks of Studio DJ 5 APK is that it has a steep learning curve for beginners. It may take some time and practice to master all the functions and options of the app. It may also require some technical knowledge and skills to troubleshoot some issues that may arise.</p>
- <h4>Limited effects and samples</h4>
- <p>Another drawback of Studio DJ 5 APK is that it has limited effects and samples compared to other similar apps. It only offers a few basic effects and samples that may not be enough for some users who want to create more advanced and diverse mixes. It may also require some additional downloads and installations to access more effects and samples.</p>
- <h4>Some bugs and glitches</h4>
- <p>A third drawback of Studio DJ 5 APK is that it has some bugs and glitches that may affect its performance and functionality. Some users have reported issues such as crashing, freezing, lagging, skipping, distortion and more. It may also require some updates and fixes to improve its stability and compatibility.</p>
- <h2>Conclusion</h2>
- <p>Studio DJ 5 APK is a free music mixer for Android that allows you to create your own music with a variety of tools and options. It is a great app for aspiring DJs and music lovers who want to have fun and express their creativity. However, it also has some limitations and challenges that may require some patience and practice to overcome. Overall, Studio DJ 5 APK is a good app to try if you are looking for a simple yet powerful audio recording and MIDI sequencing app.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Studio DJ 5 APK:</p>
- <ol>
-<li>Is Studio DJ 5 APK safe to download and use?</li>
-<p>Yes, Studio DJ 5 APK is safe to download and use. It does not contain any virus, malware or spyware that may harm your device or data. However, you should always download it from the official website or a trusted source to avoid any fake or malicious versions.</p>
-<li>Does Studio DJ 5 APK require an internet connection?</li>
-<p>No, Studio DJ 5 APK does not require an internet connection to work. You can use it offline without any problem. However, you may need an internet connection to access some online services, such as Dropbox, Soundcloud and others.</p>
-<li>Can I use Studio DJ 5 APK on other devices besides Android?</li>
-<p>No, Studio DJ 5 APK is only compatible with Android devices. It does not support other devices or platforms, such as iOS, Windows or Mac. However, you may be able to use some alternative apps that offer similar features and functions on other devices.</p>
-<li>Can I use Studio DJ 5 APK with external devices or controllers?</li>
-<p>Yes, Studio DJ 5 APK can be used with external devices or controllers that support MIDI or USB connections. You can connect your device to a mixer, keyboard, turntable or other controller and use them with the app. However, you may need some additional adapters or cables to make the connection possible.</p>
-<li>How can I contact the developer of Studio DJ 5 APK?</li>
-<p>If you have any questions, feedback or suggestions about Studio DJ 5 APK, you can contact the developer by sending an email to [email protected]. You can also visit their website or follow them on social media for more information and updates.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Fake Low Battery APK and Enjoy the Ultimate Joke App.md

@@ -1,117 +0,0 @@
-
-<h1>Fake Low Battery APK: What Is It and How to Use It</h1>
-<p>Have you ever wanted to trick your kids, friends, or family into thinking that your phone has a low battery state? Maybe you want to avoid unwanted calls, messages, or games. Or maybe you just want to have some fun and prank them. Whatever your reason, there is an app that can help you do that. It's called fake low battery apk.</p>
-<h2>fake low battery apk</h2><br /><p><b><b>Download</b> &#9913;&#9913;&#9913; <a href="https://urlin.us/2uSW4k">https://urlin.us/2uSW4k</a></b></p><br /><br />
-<h2>Introduction</h2>
-<p>Fake low battery apk is a small app that can simulate a low battery state on your Android device. It can pop up a low battery dialog on your screen, making it look like your phone is about to die. You can use this app to fool anyone who wants to use your phone or check your battery level.</p>
-<p>But how does it work? And what are the benefits of using it? Let's find out in this article.</p>
-<h2>How to Download and Install Fake Low Battery APK</h2>
-<p>The first step to use fake low battery apk is to download and install it on your device. Here's how:</p>
-<ol>
-<li>Go to <a href="(^1^)">APKCombo</a> or <a href="(^2^)">APKFlash</a> and search for fake low battery apk.</li>
-<li>Select the app from the results and click on the download button.</li>
-<li>Once the download is complete, open the file manager on your device and locate the apk file.</li>
-<li>Tap on the file and follow the instructions to install it.</li>
-<li>If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources" under security options.</li>
-<li>After the installation is done, you will see an icon for fake low battery apk on your home screen or app drawer.</li>
-</ol>
-<h2>How to Use Fake Low Battery APK</h2>
-<p>Now that you have installed fake low battery apk, you can start using it to trick others. Here's how:</p>
-<ol>
-<li>Open the app and tap on the start button. This will activate the service that will pop up the low battery dialog on your screen.</li>
-<li>You can adjust the settings and options according to your preferences. For example, you can set the frequency, duration, and style of the dialog. You can also choose when to show or hide the dialog, such as when charging, when locked, or when in certain apps.</li>
-<li>You can also customize the appearance of the dialog by changing the color, font, size, and text. You can even add your own message or image to make it more convincing.</li>
-<li>To stop the service, simply tap on the stop button or reopen the app. You can also use a secret code or gesture to stop it if you want.</li>
-</ol>
-<h2>Pros and Cons of Fake Low Battery APK</h2>
-<p>Fake low battery apk is a fun and useful app that can help you avoid unwanted situations or prank others. But like any other app, it has its pros and cons. Here are some of them:</p>
- <table border="1">
-<tr><th> Pros</th><th>Cons</th></tr>
-<tr><td>
-- It can help you get out of awkward or annoying situations by pretending that your phone is low on battery. - It can make your phone look more realistic and authentic by showing a low battery dialog that matches your device model and theme. - It can be fun and entertaining to prank your friends, family, or coworkers with fake low battery apk. </td><td>
-- It can drain your actual battery if you use it for too long or too often. - It can cause confusion or frustration if you forget to turn it off or if someone else uses your phone without knowing about it. - It can be unethical or dishonest to use fake low battery apk to lie or deceive others for personal gain or malicious intent. </td></tr>
-</table>
- <h2>Alternatives to Fake Low Battery APK</h2>
-<p>If you are looking for some other apps that can simulate a low battery state on your Android device, here are some alternatives to fake low battery apk:</p>
-<ul>
-<li><a href="">Fake Battery</a>: This app can show a fake battery level on your status bar and lock screen. You can set the percentage and the charging status of the fake battery. You can also choose from different themes and icons.</li>
-<li><a href="">Low Battery Prank</a>: This app can display a low battery warning on your screen with a countdown timer. You can customize the warning message, the timer, and the sound. You can also set a password to stop the prank.</li>
-<li><a href="">Fake Low Battery - Prank Your Friends</a>: This app can show a low battery dialog on your screen with a funny message. You can choose from different messages and languages. You can also set a delay and a repeat interval for the dialog.</li>
-</ul>
-<p>These apps have similar features and functions as fake low battery apk, but they may have different designs and options. You can try them out and see which one suits your needs and preferences better.</p>
-<p>fake low battery app android<br />
-fake low battery prank apk<br />
-fake low battery download apk<br />
-fake low battery apk free<br />
-fake low battery apk latest version<br />
-fake low battery apk for samsung<br />
-fake low battery apk mod<br />
-fake low battery apk no ads<br />
-fake low battery apk pro<br />
-fake low battery apk premium<br />
-fake low battery apk cracked<br />
-fake low battery apk old version<br />
-fake low battery apk 2023<br />
-fake low battery apk 2022<br />
-fake low battery apk 2021<br />
-fake low battery apk 2020<br />
-fake low battery apk 2019<br />
-fake low battery apk 2018<br />
-fake low battery apk 2017<br />
-fake low battery apk 2016<br />
-fake low battery app for iphone<br />
-fake low battery app for ios<br />
-fake low battery app for ipad<br />
-fake low battery app for windows phone<br />
-fake low battery app for pc<br />
-fake low battery app for mac<br />
-fake low battery app for laptop<br />
-fake low battery app for chromebook<br />
-fake low battery app for fire tablet<br />
-fake low battery app for kindle fire<br />
-how to use fake low battery apk<br />
-how to install fake low battery apk<br />
-how to download fake low battery apk<br />
-how to uninstall fake low battery apk<br />
-how to update fake low battery apk<br />
-how to stop fake low battery apk<br />
-how to remove ads from fake low battery apk<br />
-how to get premium features on fake low battery apk<br />
-how to prank someone with fake low battery apk<br />
-how to trick your kids with fake low battery apk<br />
-is fake low battery apk safe<br />
-is fake low battery apk legit<br />
-is fake low battery apk virus free<br />
-is fake low battery apk malware free<br />
-is fake low battery apk ad free<br />
-is fake low battery apk worth it<br />
-is fake low battery apk fun<br />
-is fake low battery apk effective<br />
-is fake low battery apk reliable</p>
-<h2>Conclusion</h2>
-<p>Fake low battery apk is an app that can simulate a low battery state on your Android device. It can pop up a low battery dialog on your screen, making it look like your phone is about to die. You can use this app to trick anyone who wants to use your phone or check your battery level.</p>
-<p>To use fake low battery apk, you need to download and install it on your device. Then, you need to start the service and adjust the settings and options according to your liking. You can also customize the appearance of the dialog by changing the color, font, size, and text.</p>
-<p>Fake low battery apk has its pros and cons. It can help you avoid unwanted situations or prank others, but it can also drain your actual battery or cause confusion or frustration. You should use it responsibly and ethically.</p>
-<p>If you are looking for some alternatives to fake low battery apk, you can try some other apps that can simulate a low battery state on your device. They have similar features and functions as fake low battery apk, but they may have different designs and options.</p>
-<p>We hope this article has helped you learn more about fake low battery apk and how to use it. If you have any questions or feedback, please feel free to leave a comment below. And if you liked this article, please share it with your friends and family. Thank you for reading!</p>
- <h3>FAQs</h3>
-<ol>
-<li>Is fake low battery apk safe to use?</li>
-<p>Fake low battery apk is safe to use as long as you download it from a trusted source and scan it for viruses or malware. However, you should be careful not to use it for illegal or harmful purposes.</p>
-<li>Does fake low battery apk work on all Android devices?</li>
-<p>Fake low battery apk works on most Android devices that run Android 4.0 or higher. However, some devices may not support some features or options of the app due to different models or themes.</p>
-<li>Can I use fake low battery apk while charging my phone?</li>
-<p>Yes, you can use fake low battery apk while charging your phone. You can set the app to show or hide the dialog when charging in the settings menu.</p>
-<li>How can I stop fake low battery apk?</li>
-<p>You can stop fake low battery apk by tapping on the stop button in the app or by reopening the app. You can also use a secret code or gesture to stop it if you want.</p>
-<li>What are some other ways to prank someone with fake low battery apk?</li>
-<p>Some other ways to prank someone with fake low battery apk are:</p>
-<ul>
-<li>Show them a funny message or image on the dialog.</li>
-<li> - Set the timer to a very short or long duration.</li>
-<li>Change the battery level to a very high or low percentage.</li>
-<li>Make the dialog appear randomly or frequently.</li>
-</ul>
-<p>These are some of the ways you can prank someone with fake low battery apk. But remember, don't go too far or hurt anyone's feelings.</p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/DLS 17 Mega Mod APK The Ultimate Guide to Dream League Soccer 2017.md

@@ -1,237 +0,0 @@
-<br />
-<h1>DLS 17 Mega Mod APK Download: Everything You Need to Know</h1>
-<p>If you are a fan of soccer games on mobile devices, you have probably heard of Dream League Soccer, or DLS for short. This is one of the most popular and realistic soccer games on the market, with thousands of real-life players, stunning graphics, and addictive gameplay. But what if you want to take your gaming experience to the next level? What if you want to unlock all the features, players, and coins without spending any money or time? That's where DLS 17 Mega Mod APK comes in.</p>
-<h2>dls 17 mega mod apk download</h2><br /><p><b><b>Download Zip</b> ->>->>->> <a href="https://jinyurl.com/2uNLIF">https://jinyurl.com/2uNLIF</a></b></p><br /><br />
-<p>DLS 17 Mega Mod APK is a modified version of the original DLS 17 game that gives you access to unlimited resources, features, and modes. With this mod, you can build your dream team, customize your stadium, play in different leagues and cups, and challenge other players online. Sounds amazing, right? But before you download and install this mod, there are some things you need to know. In this article, we will tell you everything you need to know about DLS 17 Mega Mod APK, including what it is, how to download and install it, why you should use it, and some tips and tricks for playing it. Let's get started!</p>
- <h2>What is DLS 17?</h2>
-<p>DLS 17 is the latest version of Dream League Soccer, a soccer game developed by First Touch Games. It was released in 2017 and has since been updated with new features and improvements. DLS 17 allows you to create and manage your own soccer team, from choosing your players and kits to training them and playing matches. You can also compete in various leagues and cups, as well as play online against other players in Dream League Live mode.</p>
-<p>DLS 17 has a FIFPro license, which means that it features more than 4,000 real-life players from different teams and countries. You can also customize your team name, logo, colors, and stadium. The game has high-quality graphics and animations, realistic physics and gameplay, and immersive sound effects and commentary. DLS 17 is free to download and play, but it also offers in-app purchases for coins, which are the main currency of the game. You can use coins to buy new players, upgrade your stadium, or speed up your progress.</p>
- <h3>Features of DLS 17</h3>
-<p>Some of the main features of DLS 17 are:</p>
-<p>dls 17 apk mod unlimited coins<br />
-dls 17 mod apk android and ios<br />
-dls 17 mod apk create logo feature<br />
-dls 17 mod apk free download link<br />
-dls 17 mod apk hack cheat<br />
-dls 17 mod apk highly compressed<br />
-dls 17 mod apk latest version<br />
-dls 17 mod apk new ui and visuals<br />
-dls 17 mod apk obb data file<br />
-dls 17 mod apk smooth gameplay<br />
-download dream league soccer 2017 apk mod<br />
-download dream league soccer 2017 hack mod apk<br />
-download dream league soccer 2017 mega mod apk<br />
-download dream league soccer 2017 mod apk for android<br />
-download dream league soccer 2017 mod apk for ios<br />
-download dream league soccer 2017 mod apk with free coins<br />
-dream league soccer 2017 cheat for unlimited coins<br />
-dream league soccer 2017 hack mod apk android<br />
-dream league soccer 2017 hack mod apk ios<br />
-dream league soccer 2017 mega mod apk youtube<br />
-dream league soccer 2017 mod apk and obb download<br />
-dream league soccer 2017 mod apk bukasblog entertainment<br />
-dream league soccer 2017 mod apk first touch games<br />
-dream league soccer 2017 mod apk new soundtrack<br />
-dream league soccer 2017 mod apk technologysage<br />
-how to download and play dream league soccer 2017 on android and ios<br />
-how to download and install dream league soccer 2017 obb file<br />
-how to download dls 17 mega mod apk for android<br />
-how to get access to additional content in dls 17 mod version<br />
-how to get unlimited coins in dream league soccer 2017<br />
-how to upgrade your stadium fully in dream league soccer 2017<br />
-how to use the create logo feature in dls 17 mod apk<br />
-korea superconducting tokamak advanced research experiment korea institute of fusion energy<br />
-latest dream league soccer 2017 hack mod apk cheat for unlimited coins<br />
-latest nuclear fusion experiment south korea<br />
-nuclear fusion breakthrough as reactor runs seven times hotter than the sun<br />
-south korea's kstar sustained a 100m degree fusion reactor for 30 seconds<br />
-the ‘holy grail’ fusion experiment to create a mini sun after breakthrough in race for unlimited energy<br />
-what is new in the latest edition of dream league soccer 2017</p>
-<ul>
-<li>FIFPro licensed players with authentic stats and appearances</li>
-<li>Freedom to create, customize, and control your own dream team</li>
-<li>6 divisions to work your way through, and over 7 cup competitions to participate in</li>
-<li>Regular live events to win prizes and glory</li>
-<li>Build your own stadium and show off your superstars</li>
-<li>Develop your players with more accuracy and intent</li>
-<li>Season objectives to keep you engaged and coming back</li>
-<li>Google Play achievements and leaderboards to see who ranks on top</li>
-<li>Customize and import your own kits and logos</li>
-<li>Sync progress between devices with Google Play Cloud</li>
-<li>Exclusive soundtrack provided by The Luka State, Sunset Sons, Beth Thornton, Jack Wins, Vistas & Only The Poets!</li>
-</ul>
- <h3>How to download and install DLS 17 Mega Mod APK</h3>
-<p>If you want to enjoy all the features of DLS 17 without any limitations or restrictions, you can download and install DLS 17 Mega Mod APK. This is a modified version of the original game that gives you unlimited coins , as well as other features such as unlocked players, unlimited stamina, free shopping, and more. To download and install DLS 17 Mega Mod APK, follow these steps:</p>
-<ol>
-<li>Download the DLS 17 Mega Mod APK file from a trusted source. You can find many websites that offer this mod, but be careful of fake or malicious links. One of the sites that we recommend is [APKPure], which has a verified and safe download link.</li>
-<li>Before you install the mod, you need to uninstall the original DLS 17 game from your device. This is to avoid any conflicts or errors between the two versions. You can uninstall the game by going to your device settings, then apps, then DLS 17, and then tapping on uninstall.</li>
-<li>After you uninstall the original game, you need to enable the installation of apps from unknown sources on your device. This is to allow the mod to be installed without any issues. You can enable this option by going to your device settings, then security, then unknown sources, and then toggling it on.</li>
-<li>Now you can install the mod by locating the downloaded APK file on your device and tapping on it. Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Once the installation is done, you can launch the mod by tapping on its icon on your home screen or app drawer. You will see a new interface and menu with all the mod features and options. You can start playing the game with unlimited coins and resources.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed DLS 17 Mega Mod APK on your device. Now you can enjoy the game with all its benefits and advantages.</p>
- <h2>Why use DLS 17 Mega Mod APK?</h2>
-<p>You might be wondering why you should use DLS 17 Mega Mod APK instead of the original game. What are the benefits and risks of using this mod? Here are some of the reasons why you should use DLS 17 Mega Mod APK:</p>
- <h3>Benefits of using DLS 17 Mega Mod APK</h3>
-<p>Some of the benefits of using DLS 17 Mega Mod APK are:</p>
-<ul>
-<li>You can get unlimited coins for free, which you can use to buy new players, upgrade your stadium, or speed up your progress. You don't have to spend any real money or time to earn coins in the game.</li>
-<li>You can unlock all the players in the game, including the legendary ones. You can also customize their stats and appearances to suit your preferences. You can create your dream team with any players you want.</li>
-<li>You can get unlimited stamina for your players, which means they will never get tired or injured during matches. You can also use free shopping to buy any items or boosts you need in the game.</li>
-<li>You can access all the modes and features in the game, including Dream League Live, where you can play online against other players. You don't have to worry about any restrictions or limitations in the game.</li>
-<li>You can have more fun and excitement in playing DLS 17 with this mod. You can experiment with different strategies and tactics, score more goals and win more matches, and enjoy the realistic and immersive gameplay.</li>
-</ul>
- <h3>Risks of using DLS 17 Mega Mod APK</h3>
-<p>However, there are also some risks of using DLS 17 Mega Mod APK that you should be aware of. Some of these risks are:</p>
-<ul>
-<li>You might face some technical issues or errors while using this mod. This is because this mod is not an official version of the game and might not be compatible with your device or system. You might experience crashes, glitches, bugs, or lag while playing the game.</li>
-<li>You might lose your progress or data while using this mod. This is because this mod does not sync with Google Play Cloud or any other backup service. If you uninstall the mod or switch devices, you might lose all your coins, players, and achievements in the game.</li>
-<li>You might get banned or suspended from playing online while using this mod. This is because this mod violates the terms and conditions of the game and might be detected by the anti-cheat system. If you play online with this mod, you might face penalties such as account suspension or deletion.</li>
-<li>You might expose your device or data to malware or viruses while using this mod. This is because this mod is not verified or secured by any authority or platform. If you download this mod from an untrusted source, you might infect your device or data with harmful software.</li>
-</ul>
- <p>Therefore, you should use DLS 17 Mega Mod APK at your own risk and discretion. We do not endorse or recommend using this mod for any illegal or unethical purposes. We are only providing this information for educational and entertainment purposes. You should always respect the original developers and creators of the game and support them by playing the official version of DLS 17.</p>
- <h2>Tips and tricks for playing DLS 17</h2>
-<p>Whether you use DLS 17 Mega Mod APK or not, you might want to know some tips and tricks for playing DLS 17 better and smarter. Here are some of the best tips and tricks for playing DLS 17:</p>
- <h3>How to improve your team and players</h3>
-<p>One of the most important aspects of DLS 17 is building and improving your team and players. Here are some ways to do that:</p>
-<ul>
-<li>Use the transfer market to buy new players or sell unwanted ones. You can find players of different ratings, positions, and prices in the market. You can also use the scout feature to search for specific players that you need.</li>
-<li>Use the training mode to develop your players' skills and attributes. You can choose from different drills and exercises to improve your players' speed, strength, stamina, passing, shooting, dribbling, defending, and goalkeeping. You can also assign individual training plans to each player.</li>
-<li>Use the player development feature to upgrade your players' ratings and potentials. You can spend coins to increase your players' overall ratings or use gems to unlock their hidden potentials. You can also use special items such as boots, balls, or kits to boost your players' performance.</li>
-<li>Use the formation and tactics feature to arrange your team and players according to your strategy and style. You can choose from different formations such as 4-4-2, 4-3-3, or 3-5-2, and adjust your players' positions and roles. You can also change your tactics such as attacking, defending, or balanced, and tweak your settings such as passing style, pressing intensity, or offside trap.</li>
-</ul>
- <h3>How to score more goals and win more matches</h3>
-<p>Another important aspect of DLS 17 is scoring more goals and winning more matches. Here are some ways to do that:</p>
-<ul>
-<li>Use the swipe controls to control your players and the ball. You can swipe on the screen to move your players, pass the ball, shoot the ball, or tackle the opponents. You can also use buttons for more precise actions such as sprinting, sliding, or chipping.</li>
-<li>Use the skill moves to dribble past your opponents and create chances. You can perform different skill moves such as stepovers, roulettes, rainbows, or nutmegs by swiping on the screen in different directions. You can also use gestures such as double taps or long presses to activate special moves such as volleys, overhead kicks, or bicycle kicks.</li>
-<li>Use the power bar to adjust your shots and passes. You can see a power bar on the top of the screen when you swipe to shoot or pass. You can increase or decrease the power by swiping longer or shorter. You can also curve your shots or passes by swiping in an arc shape.</li>
-<li>Use the camera angles to see the field better and make better decisions. You can change the camera angle by tapping on the camera icon on the top right corner of the screen. You can choose from different angles such as broadcast, dynamic, end-to-end, or sideline.</li>
-</ul>
- <h3>How to play against human opponents in Dream League Live</h3>
-<p>One of the most exciting features of DLS 17 is Dream League Live, where you can play online against other human opponents from around the world. Here are some ways to play better in Dream League Live:</p>
-<ul>
-<li>Use the matchmaking feature to find suitable opponents for you. You can tap on the play button on the bottom right corner of the screen to enter Dream League Live mode. You will see a list of available opponents with their ratings, divisions, and countries. You can choose an opponent that matches your level and preference.</li>
-<li>Use the chat feature to communicate with your opponent before, during, or after the match. You can tap on the chat icon on the top left corner of the screen to open the chat window. You will see a list of predefined messages that you can send to your opponent such as "Good luck", "Well played", or "Rematch?". You can also use emojis to express your emotions.</li>
-<li>Use the replay feature to watch your highlights or mistakes after the match. You can tap on the replay icon on the top right corner of the screen to access the replay mode. You will see a timeline of the match with different icons representing goals, fouls, cards, or substitutions. You can tap on any icon to watch the corresponding moment of the match. You can also use the controls to pause, play, rewind, or fast forward the replay.</li>
-<li>Use the stats feature to analyze your performance and improvement after the match. You can tap on the stats icon on the bottom left corner of the screen to see the detailed statistics of the match. You will see various metrics such as possession, shots, passes, tackles, fouls, or ratings. You can also compare your stats with your opponent's stats and see who performed better.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>DLS 17 is a fantastic soccer game that lets you create and manage your own dream team, compete in various leagues and cups, and play online against other players. However, if you want to enjoy the game without any limitations or restrictions, you can download and install DLS 17 Mega Mod APK, which gives you unlimited coins and resources, as well as other features such as unlocked players, unlimited stamina, free shopping, and more.</p>
-<p>However, you should also be aware of the risks of using DLS 17 Mega Mod APK, such as technical issues, data loss, ban risk, or malware risk. You should use this mod at your own risk and discretion, and respect the original developers and creators of the game. We hope this article has helped you learn everything you need to know about DLS 17 Mega Mod APK, including what it is, how to download and install it, why you should use it, and some tips and tricks for playing it. Now you can enjoy DLS 17 with all its benefits and advantages.</p>
- <h3>Call to action</h3>
-<p>If you are ready to download and install DLS 17 Mega Mod APK on your device, you can click on the link below to get started. This link will take you to a trusted and verified source where you can download the mod safely and easily. You can also find more information and reviews about the mod on this site. Don't wait any longer and start playing DLS 17 with this amazing mod today!</p>
-<p><a href="">Download DLS 17 Mega Mod APK here</a></p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about DLS 17 Mega Mod APK:</p>
-<ul>
-<li><b>Q: Is DLS 17 Mega Mod APK safe to use?</b></li>
-<li>A: DLS 17 Mega Mod APK is safe to use if you download it from a trusted and verified source. However, you should always be careful of fake or malicious links that might harm your device or data. You should also scan the mod file with an antivirus software before installing it.</li>
-<li><b>Q: Is DLS 17 Mega Mod APK compatible with my device?</b></li>
-<li>A: DLS 17 Mega Mod APK is compatible with most Android devices that have Android 4.4 or higher versions. However, some devices might not support some features or modes of the mod due to hardware or software limitations. You should check the compatibility of your device before downloading and installing the mod.</li>
-<li><b>Q: Can I play online with DLS 17 Mega Mod APK?</b></li>
-<li>A: Yes, you can play online with DLS 17 Mega Mod APK in Dream League Live mode. However, you might face some issues or risks while playing online with this mod. For example, you might encounter lag or disconnect problems due to server overload or instability. You might also get banned or suspended from playing online if the anti-cheat system detects that you are using a modded version of the game.</li>
-<li><b>Q: Can I update DLS 17 Mega Mod APK?</b></li>
-<li>A: Yes, you can update DLS 17 Mega Mod APK whenever there is a new version available. However, you should always backup your progress and data before updating the mod. You should also uninstall the previous version of the mod before installing the new one. You can find the latest version of DLS 17 Mega Mod APK on [APKPure] or other reliable sources.</li>
-<li><b>Q: Can I use DLS 17 Mega Mod APK with other mods or hacks?</b></li>
-<li>A: No, you cannot use DLS 17 Mega Mod APK with other mods or hacks for DLS 17. This is because this mod already includes all the features and resources that you need in the game. Using other mods or hacks might cause conflicts or errors in the game. You should only use one mod at a time for DLS 17.</li>
-</ul <p>I have finished writing the article. Here is the final version of the article with HTML formatting:</p>
- <h1>DLS 17 Mega Mod APK Download: Everything You Need to Know</h1>
-<p>If you are a fan of soccer games on mobile devices, you have probably heard of Dream League Soccer, or DLS for short. This is one of the most popular and realistic soccer games on the market, with thousands of real-life players, stunning graphics, and addictive gameplay. But what if you want to take your gaming experience to the next level? What if you want to unlock all the features, players, and coins without spending any money or time? That's where DLS 17 Mega Mod APK comes in.</p>
-<p>DLS 17 Mega Mod APK is a modified version of the original DLS 17 game that gives you access to unlimited resources, features, and modes. With this mod, you can build your dream team, customize your stadium, play in different leagues and cups, and challenge other players online. Sounds amazing, right? But before you download and install this mod, there are some things you need to know. In this article, we will tell you everything you need to know about DLS 17 Mega Mod APK, including what it is, how to download and install it, why you should use it, and some tips and tricks for playing it. Let's get started!</p>
- <h2>What is DLS 17?</h2>
-<p>DLS 17 is the latest version of Dream League Soccer, a soccer game developed by First Touch Games. It was released in 2017 and has since been updated with new features and improvements. DLS 17 allows you to create and manage your own soccer team, from choosing your players and kits to training them and playing matches. You can also compete in various leagues and cups, as well as play online against other players in Dream League Live mode.</p>
-<p>DLS 17 has a FIFPro license, which means that it features more than 4,000 real-life players from different teams and countries. You can also customize your team name, logo, colors, and stadium. The game has high-quality graphics and animations, realistic physics and gameplay, and immersive sound effects and commentary. DLS 17 is free to download and play, but it also offers in-app purchases for coins, which are the main currency of the game. You can use coins to buy new players, upgrade your stadium, or speed up your progress.</p>
- <h3>Features of DLS 17</h3>
-<p>Some of the main features of DLS 17 are:</p>
-<ul>
-<li>FIFPro licensed players with authentic stats and appearances</li>
-<li>Freedom to create, customize, and control your own dream team</li>
-<li>6 divisions to work your way through, and over 7 cup competitions to participate in</li>
-<li>Regular live events to win prizes and glory</li>
-<li>Build your own stadium and show off your superstars</li>
-<li>Develop your players with more accuracy and intent</li>
-<li>Season objectives to keep you engaged and coming back</li>
-<li>Google Play achievements and leaderboards to see who ranks on top</li>
-<li>Customize and import your own kits and logos</li>
-<li>Sync progress between devices with Google Play Cloud</li>
-<li>Exclusive soundtrack provided by The Luka State, Sunset Sons, Beth Thornton, Jack Wins, Vistas & Only The Poets!</li>
-</ul>
- <h3>How to download and install DLS 17 Mega Mod APK</h3>
-<p>If you want to enjoy all the features of DLS 17 without any limitations or restrictions, you can download and install DLS 17 Mega Mod APK. This is a modified version of the original game that gives you unlimited coins and resources, as well as other features such as unlocked players, unlimited stamina, free shopping, and more. To download and install DLS 17 Mega Mod APK, follow these steps:</p>
-<ol>
-<li>Download the DLS 17 Mega Mod APK file from a trusted source. You can find many websites that offer this mod, but be careful of fake or malicious links. One of the sites that we recommend is [APKPure], which has a verified and safe download link.</li>
-<li>Before you install the mod, you need to uninstall the original DLS 17 game from your device. This is to avoid any conflicts or errors between the two versions. You can uninstall the game by going to your device settings, then apps, then DLS 17, and then tapping on uninstall.</li>
-<li>After you uninstall the original game, you need to enable the installation of apps from unknown sources on your device. This is to allow the mod to be installed without any issues. You can enable this option by going to your device settings, then security, then unknown sources, and then toggling it on.</li>
-<li>Now you can install the mod by locating the downloaded APK file on your device and tapping on it. Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Once the installation is done, you can launch the mod by tapping on its icon on your home screen or app drawer. You will see a new interface and menu with all the mod features and options. You can start playing the game with unlimited coins and resources.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed DLS 17 Mega Mod APK on your device. Now you can enjoy the game with all its benefits and advantages.</p>
- <h2>Why use DLS 17 Mega Mod APK?</h2>
-<p>You might be wondering why you should use DLS 17 Mega Mod APK instead of the original game. What are the benefits and risks of using this mod? Here are some of the reasons why you should use DLS 17 Mega Mod APK:</p>
- <h3>Benefits of using DLS 17 Mega Mod APK</h3>
-<p>Some of the benefits of using DLS 17 Mega Mod APK are:</p>
-<ul>
-<li>You can get unlimited coins for free, which you can use to buy new players, upgrade your stadium, or speed up your progress. You don't have to spend any real money or time to earn coins in the game.</li>
-<li>You can unlock all the players in the game, including the legendary ones. You can also customize their stats and appearances to suit your preferences. You can create your dream team with any players you want.</li>
-<li>You can get unlimited stamina for your players, which means they will never get tired or injured during matches. You can also use free shopping to buy any items or boosts you need in the game.</li>
-<li>You can access all the modes and features in the game, including Dream League Live, where you can play online against other players. You don't have to worry about any restrictions or limitations in the game.</li>
-<li>You can have more fun and excitement in playing DLS 17 with this mod. You can experiment with different strategies and tactics, score more goals and win more matches, and enjoy the realistic and immersive gameplay.</li>
-</ul>
- <h3>Risks of using DLS 17 Mega Mod APK</h3>
-<p>However, there are also some risks of using DLS 17 Mega Mod APK that you should be aware of. Some of these risks are:</p>
-<ul>
-<li>You might face some technical issues or errors while using this mod. This is because this mod is not an official version of the game and might not be compatible with your device or system. You might experience crashes, glitches, bugs, or lag while playing the game.</li>
-<li>You might lose your progress or data while using this mod. This is because this mod does not sync with Google Play Cloud or any other backup service. If you uninstall the mod or switch devices, you might lose all your coins, players, and achievements in the game.</li>
-<li>You might get banned or suspended from playing online while using this mod. This is because this mod violates the terms and conditions of the game and might be detected by the anti-cheat system. If you play online with this mod, you might face penalties such as account suspension or deletion.</li>
-<li>You might expose your device or data to malware or viruses while using this mod. This is because this mod is not verified or secured by any authority or platform. If you download this mod from an untrusted source, you might infect your device or data with harmful software.</li>
-</ul>
- <p>Therefore, you should use DLS 17 Mega Mod APK at your own risk and discretion. We do not endorse or recommend using this mod for any illegal or unethical purposes. We are only providing this information for educational and entertainment purposes. You should always respect the original developers and creators of the game and support them by playing the official version of DLS 17.</p>
- <h2>Tips and tricks for playing DLS 17</h2>
-<p>Whether you use DLS 17 Mega Mod APK or not, you might want to know some tips and tricks for playing DLS 17 better and smarter. Here are some of the best tips and tricks for playing DLS 17:</p>
- <h3>How to improve your team and players</h3>
-<p>One of the most important aspects of DLS 17 is building and improving your team and players. Here are some ways to do that:</p>
-<ul>
-<li>Use the transfer market to buy new players or sell unwanted ones. You can find players of different ratings, positions, and prices in the market. You can also use the scout feature to search for specific players that you need.</li>
-<li>Use the training mode to develop your players' skills and attributes. You can choose from different drills and exercises to improve your players' speed, strength, stamina, passing, shooting, dribbling, defending, and goalkeeping. You can also assign individual training plans to each player.</li>
-<li>Use the player development feature to upgrade your players' ratings and potentials. You can spend coins to increase your players' overall ratings or use gems to unlock their hidden potentials. You can also use special items such as boots, balls, or kits to boost your players' performance.</li>
-<li>Use the formation and tactics feature to arrange your team and players according to your strategy and style. You can choose from different formations such as 4-4-2, 4-3-3, or 3-5-2, and adjust your players' positions and roles. You can also change your tactics such as attacking, defending, or balanced, and tweak your settings such as passing style, pressing intensity, or offside trap.</li>
-</ul>
- <h3>How to score more goals and win more matches</h3>
-<p>Another important aspect of DLS 17 is scoring more goals and winning more matches. Here are some ways to do that:</p>
-<ul>
-<li>Use the swipe controls to control your players and the ball. You can swipe on the screen to move your players, pass the ball, shoot the ball, or tackle the opponents. You can also use buttons for more precise actions such as sprinting, sliding, or chipping.</li>
-<li>Use the skill moves to dribble past your opponents and create chances. You can perform different skill moves such as stepovers, roulettes, rainbows, or nutmegs by swiping on the screen in different directions. You can also use gestures such as double taps or long presses to activate special moves such as volleys, overhead kicks, or bicycle kicks.</li>
-<li>Use the power bar to adjust your shots and passes. You can see a power bar on the top of the screen when you swipe to shoot or pass. You can increase or decrease the power by swiping longer or shorter. You can also curve your shots or passes by swiping in an arc shape.</li>
-<li>Use the camera angles to see the field better and make better decisions. You can change the camera angle by tapping on the camera icon on the top right corner of the screen. You can choose from different angles such as broadcast, dynamic, end-to-end, or sideline.</li>
-</ul>
- <h3>How to play against human opponents in Dream League Live</h3>
-<p>One of the most exciting features of DLS 17 is Dream League Live, where you can play online against other human opponents from around the world. Here are some ways to play better in Dream League Live:</p>
-<ul>
-<li>Use the matchmaking feature to find suitable opponents for you. You can tap on the play button on the bottom right corner of the screen to enter Dream League Live mode. You will see a list of available opponents with their ratings, divisions, and countries. You can choose an opponent that matches your level and preference.</li>
-<li>Use the chat feature to communicate with your opponent before, during, or after the match. You can tap on the chat icon on the top left corner of the screen to open the chat window. You will see a list of predefined messages that you can send to your opponent such as "Good luck", "Well played", or "Rematch?". You can also use emojis to express your emotions.</li>
-<li>Use the replay feature to watch your highlights or mistakes after the match. You can tap on the replay icon on the top right corner of the screen to access the replay mode. You will see a timeline of the match with different icons representing goals, fouls, cards, or substitutions. You can tap on any icon to watch the corresponding moment of the match. You can also use the controls to pause, play, rewind, or fast forward the replay.</li>
-<li>Use the stats feature to analyze your performance and improvement after the match. You can tap on the stats icon on the bottom left corner of the screen to see the detailed statistics of the match. You will see various metrics such as possession, shots, passes, tackles, fouls, or ratings. You can also compare your stats with your opponent's stats and see who performed better.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>DLS 17 is a fantastic soccer game that lets you create and manage your own dream team, compete in various leagues and cups, and play online against other players. However, if you want to enjoy the game without any limitations or restrictions, you can download and install DLS 17 Mega Mod APK, which gives you unlimited coins and resources, as well as other features such as unlocked players, unlimited stamina, free shopping, and more.</p>
-<p>However, you should also be aware of the risks of using DLS 17 Mega Mod APK, such as technical issues, data loss, ban risk, or malware risk. You should use this mod at your own risk and discretion, and respect the original developers and creators of the game. We hope this article has helped you learn everything you need to know about DLS 17 Mega Mod APK, including what it is, how to download and install it, why you should use it, and some tips and tricks for playing it. Now you can enjoy DLS 17 with all its benefits and advantages.</p>
- <h3>Call to action</h3>
-<p>If you are ready to download and install DLS 17 Mega Mod APK on your device, you can click on the link below to get started. This link will take you to a trusted and verified source where you can download the mod safely and easily. You can also find more information and reviews about the mod on this site. Don't wait any longer and start playing DLS 17 with this amazing mod today!</p>
-<p><a href="">Download DLS 17 Mega Mod APK here</a></p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about DLS 17 Mega Mod APK:</p>
-<ul>
-<li><b>Q: Is DLS 17 Mega Mod APK safe to use?</b></li>
-<li>A: DLS 17 Mega Mod APK is safe to use if you download it from a trusted and verified source. However, you should always be careful of fake or malicious links that might harm your device or data. You should also scan the mod file with an antivirus software before installing it.</li>
-<li><b>Q: Is DLS 17 Mega Mod APK compatible with my device?</b></li>
-<li>A: DLS 17 Mega Mod APK is compatible with most Android devices that have Android 4.4 or higher versions. However, some devices might not support some features or modes of the mod due to hardware or software limitations. You should check the compatibility of your device before downloading and installing the mod.</li>
-<li><b>Q: Can I play online with DLS 17 Mega Mod APK?</b></li>
-<li>A: Yes, you can play online with DLS 17 Mega Mod APK in Dream League Live mode. However, you might face some issues or risks while playing online with this mod. For example, you might encounter lag or disconnect problems due to server overload or instability. You might also get banned or suspended from playing online if the anti-cheat system detects that you are using a modded version of the game.</li>
-<li><b>Q: Can I update DLS 17 Mega Mod APK?</b></li>
-<li>A: Yes, you can update DLS 17 Mega Mod APK whenever there is a new version available. However, you should always backup your progress and data before updating the mod. You should also uninstall the previous version of the mod before installing the new one. You can find the latest version of DLS 17 Mega Mod APK on [APKPure] or other reliable sources.</li>
-<li><b>Q: Can I use DLS 17 Mega Mod APK with other mods or hacks?</b></li>
-<li>A: No, you cannot use DLS 17 Mega Mod APK with other mods or hacks for DLS 17. This is because this mod already includes all the features and resources that you need in the game. Using other mods or hacks might cause conflicts or errors in the game. You should only use one mod at a time for DLS 17.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Hunter Assassin and Unlock New Characters Themes and Rewards.md

@@ -1,118 +0,0 @@
-
-<h1>How to Download Hunter Assassin Game</h1>
-<p>If you are looking for a thrilling and addictive game that tests your stealth and strategy skills, then you should try <strong>Hunter Assassin</strong> – a popular mobile game that has millions of downloads and positive reviews. In this game, you play as a hunter with a deadly knife who has to take down targets without being detected by enemies. You will have to use your surroundings and shadows to hide from flashlights, avoid enemies with automatic machine guns, activate laser traps, unlock new characters with different abilities, and complete challenging missions in various themes and maps. In this article, we will show you how to download Hunter Assassin game for Android, iOS, and PC devices, as well as some tips and tricks for playing the game.</p>
- <h2>Features of Hunter Assassin Game</h2>
-<p>Before we get into the details of how to download Hunter Assassin game, let's take a look at some of the features that make this game so fun and exciting:</p>
-<h2>download hunter assassin game</h2><br /><p><b><b>Download Zip</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://jinyurl.com/2uNMFV">https://jinyurl.com/2uNMFV</a></b></p><br /><br />
-<ul>
-<li><strong>Stealth and strategy gameplay:</strong> You will have to plan your moves carefully and avoid being seen by enemies as you hunt them down one by one. You will also have to be quick and agile, as the enemies will chase you if they spot you.</li>
-<li><strong>Multiple enemies and missions:</strong> You will face different types of enemies, such as guards, snipers, dogs, and bosses, each with their own behavior and difficulty level. You will also have to complete various missions, such as eliminating all targets, rescuing hostages, or escaping from the enemy base.</li>
-<li><strong>Unique characters and abilities:</strong> You can unlock and play as different characters, each with their own unique abilities and play styles. For example, you can play as the Ninja Assassin, who can throw shurikens and dash through enemies, or the Cyber Assassin, who can hack into security systems and use drones.</li>
-<li><strong>Different themes and maps:</strong> You can explore different themes and maps, such as the desert, the jungle, the city, or the snow. Each theme and map has its own design, layout, and obstacles that you have to overcome.</li>
-</ul>
-<p>With these features, Hunter Assassin game offers you hours of fun and challenge. Now, let's see how you can download Hunter Assassin game for your device.</p>
- <h2>How to Download Hunter Assassin Game for Android</h2>
-<p>If you have an Android device, such as a smartphone or a tablet, you can download Hunter Assassin game easily from the Google Play Store. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to the Google Play Store on your device. You can either tap on the app icon on your home screen or app drawer, or open your web browser and go to <a href="">https://play.google.com/store</a>.</li>
-<li>Search for Hunter Assassin in the search bar. You can either type in the name of the game or use the voice search feature.</li>
-<li>Tap on the Install button next to the game icon. This will start downloading and installing the game on your device.</li>
-<li>Enjoy the game. Once the installation is complete, you can tap on the Open button to launch the game. You can also find the game icon on your home screen or app drawer.</li>
-</ol>
-<p>That's it. You have successfully downloaded Hunter Assassin game for your Android device. Now you can start playing and hunting down your targets.</p>
- <h2>How to Download Hunter Assassin Game for iOS</h2>
-<p>If you have an iOS device, such as an iPhone or an iPad, you can download Hunter Assassin game easily from the App Store. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to the App Store on your device. You can either tap on the app icon on your home screen or app drawer, or open your web browser and go to <a href="">https://apps.apple.com/us/app/hunter-assassin/id1479584550</a>.</li>
-<li>Search for Hunter Assassin in the search bar. You can either type in the name of the game or use the voice search feature.</li>
-<li>Tap on the Get button next to the game icon. This will start downloading and installing the game on your device.</li>
-<li>Enjoy the game. Once the installation is complete, you can tap on the Open button to launch the game. You can also find the game icon on your home screen or app drawer.</li>
-</ol>
-<p>That's it. You have successfully downloaded Hunter Assassin game for your iOS device. Now you can start playing and hunting down your targets.</p>
- <h2>How to Download Hunter Assassin Game for PC</h2>
-<p>If you want to play Hunter Assassin game on your PC, such as a laptop or a desktop computer, you will need to use an emulator. An emulator is a software that allows you to run mobile apps on your PC. There are many emulators available online, but we recommend using <a href="">BlueStacks</a>, which is one of the most popular and reliable emulators. Here are the steps you need to follow:</p>
-<ol>
-<li>Download BlueStacks from its official website: <a href="">https://www.bluestacks.com/</a>. You can choose between Windows or Mac versions depending on your operating system.</li>
-<li>Install BlueStacks on your PC by following the instructions on the screen. This may take some time depending on your internet speed and PC performance.</li>
-<li>Launch BlueStacks and sign in with your Google account. This will allow you to access the Google Play Store and other Google services on BlueStacks.</li>
-<li>Search for Hunter Assassin in the Google Play Store app on BlueStacks. You can either type in the name of the game or use the voice search feature.</li>
-<li>Install Hunter Assassin by clicking on the Install button next to the game icon. This will start downloading and installing the game on your PC.</li>
-<li>Play Hunter Assassin by clicking on the Open button or the game icon on the BlueStacks home screen. You can also find the game icon on your PC desktop.</li>
-</ol>
-<p>That's it. You have successfully downloaded Hunter Assassin game for your PC using BlueStacks. Now you can enjoy the game on a bigger screen and with better controls.</p>
-<p>download hunter assassin game for pc<br />
-download hunter assassin game for android<br />
-download hunter assassin game for windows 10<br />
-download hunter assassin game apk<br />
-download hunter assassin game mod apk<br />
-download hunter assassin game online<br />
-download hunter assassin game free<br />
-download hunter assassin game latest version<br />
-download hunter assassin game from play store<br />
-download hunter assassin game from microsoft store<br />
-how to download hunter assassin game<br />
-where to download hunter assassin game<br />
-best site to download hunter assassin game<br />
-download hunter assassin game hack<br />
-download hunter assassin game cheats<br />
-download hunter assassin game tips and tricks<br />
-download hunter assassin game guide<br />
-download hunter assassin game walkthrough<br />
-download hunter assassin game review<br />
-download hunter assassin game gameplay<br />
-download hunter assassin game trailer<br />
-download hunter assassin game video<br />
-download hunter assassin game wallpaper<br />
-download hunter assassin game theme song<br />
-download hunter assassin game soundtrack<br />
-is it safe to download hunter assassin game<br />
-is it legal to download hunter assassin game<br />
-is it worth to download hunter assassin game<br />
-what is the size of hunter assassin game to download<br />
-what are the requirements to download hunter assassin game<br />
-what are the features of hunter assassin game to download<br />
-what are the benefits of downloading hunter assassin game<br />
-what are the drawbacks of downloading hunter assassin game<br />
-what are the alternatives to downloading hunter assassin game<br />
-how to install hunter assassin game after downloading it<br />
-how to uninstall hunter assassin game after downloading it<br />
-how to update hunter assassin game after downloading it<br />
-how to play hunter assassin game after downloading it<br />
-how to unlock characters in hunter assassin game after downloading it<br />
-how to earn gems in hunter assassin game after downloading it<br />
-how to complete missions in hunter assassin game after downloading it<br />
-how to avoid traps in hunter assassin game after downloading it<br />
-how to kill enemies in hunter assassin game after downloading it<br />
-how to use laser traps in hunter assassin game after downloading it<br />
-how to use freeze mines in hunter assassin game after downloading it<br />
-how to use rockets in hunter assassin game after downloading it<br />
-how to spin the wheel in hunter assassin game after downloading it <br />
-how to get VIP membership in hunter assassin game after downloading it <br />
-how to cancel VIP membership in hunter assassin game after downloading it</p>
- <h2>Tips and Tricks for Playing Hunter Assassin Game</h2>
-<p>Now that you know how to download Hunter Assassin game for your device, you may want to learn some tips and tricks for playing the game better and having more fun. Here are some of the best tips and tricks we have gathered for you:</p>
-<ul>
-<li><strong>Tip 1: Use your surroundings and shadows to hide from enemies:</strong> The key to success in Hunter Assassin game is to be stealthy and avoid being seen by enemies. You can use your surroundings, such as walls, crates, barrels, or cars, to hide behind and sneak up on your targets. You can also use the shadows to blend in with the dark and avoid the flashlights of the enemies.</li>
-<li><strong>Tip 2: Avoid enemies with automatic machine guns:</strong> Some enemies in Hunter Assassin game have automatic machine guns that can shoot you down in seconds if they spot you. You should avoid these enemies as much as possible, or take them out quickly before they alert other enemies. You can also use laser traps to distract them or make them shoot each other.</li>
-<li><strong>Tip 3: Activate laser traps to lure enemies into your trap:</strong> Some levels in Hunter Assassin game have laser traps that you can activate by tapping on them. These traps will emit a red laser beam that will attract the attention of the enemies. You can use this to lure them into your trap and take them out easily. You can also use this to make them walk into other traps, such as mines or spikes.</li>
-<li><strong>Tip 4: Unlock new characters with different abilities and play styles:</strong> As you play Hunter Assassin game, you will earn gems that you can use to unlock new characters. Each character has a different ability and play style that can help you in different situations. For example, the Ninja Assassin can throw shurikens and dash through enemies, the Cyber Assassin can hack into security systems and use drones, and the Zombie Assassin can infect enemies and turn them into zombies.</li>
-<li><strong>Tip 5: Spin the wheel for more gems and rewards:</strong> Every day, you can spin the wheel for free and get more gems and rewards, such as extra lives, keys, or boosters. You can also watch ads or use gems to spin the wheel more times. This is a great way to get more resources and make your gameplay easier and more enjoyable.</li>
-</ul>
-<p>With these tips and tricks, you will be able to master Hunter Assassin game and become the ultimate hunter assassin.</p>
- <h2>Conclusion and FAQs</h2>
-<p>In conclusion, Hunter Assassin game is a fun and addictive game that tests your stealth and strategy skills. You can download Hunter Assassin game for Android, iOS, or PC devices easily from the app stores or using an emulator. You can also use some tips and tricks to improve your gameplay and have more fun. If you are looking for a thrilling and challenging game that keeps you on the edge of your seat, then you should download Hunter Assassin game today and start hunting down your targets.</p>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Hunter Assassin game:</p>
-<h4>Q1: Is Hunter Assassin free to play?</h4>
-<p>A1: Yes, Hunter Assassin is free to play, but it contains ads and in-app purchases. You can also subscribe to a VIP membership for more benefits.</p>
-<h4>Q2: How much is the VIP membership?</h4>
-<p>A2: The VIP membership costs $7.99 per week, and it gives you access to the Ninja Assassin character, all ads removing, 5000 gems and permanent +100% level gem earning. You can also get a free trial for three days before you decide to buy it.</p>
-<h4>Q3: How many levels are there in Hunter Assassin?</h4>
-<p>A3: There are currently over 500 levels in Hunter Assassin, each with different enemies, traps, and challenges. The developers are also adding new levels regularly, so you will never run out of fun.</p>
-<h4>Q4: Can I play Hunter Assassin offline?</h4>
-<p>A4: Yes, you can play Hunter Assassin offline, as long as you have downloaded the game on your device. However, some features may not work properly without an internet connection, such as spinning the wheel or watching ads for extra gems.</p>
-<h4>Q5: Can I play Hunter Assassin with my friends?</h4>
-<p>A5: Unfortunately, Hunter Assassin does not have a multiplayer mode at the moment, so you cannot play with your friends directly. However, you can still compare your scores and achievements with your friends through the leaderboards and social media.</p>
-<p>I hope this article has answered all your questions about Hunter Assassin game and helped you download and play the game. If you have any other questions or feedback, please feel free to leave a comment below. Thank you for reading and happy hunting!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Free Bingo Daily Quests and More with Bingo Life App.md

@@ -1,128 +0,0 @@
-<br />
-<h1>Download Bingo Life App: The Ultimate Guide to Enjoying Bingo Anytime, Anywhere</h1>
-<p>Do you love playing bingo but don't have time or money to go to a bingo hall? Do you want to experience the thrill of bingo from the comfort of your own home or on the go? If you answered yes, then you need to download Bingo Life app right now!</p>
-<p>Bingo Life is a popular global free live stream app and platform that lets you enjoy unlimited free bingo, complete daily quests, build your collection of cute creatures, play mini games at the funfair and much, much more! It's the ultimate game for bingo lovers!</p>
-<h2>download bingo life app</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://jinyurl.com/2uNS5T">https://jinyurl.com/2uNS5T</a></b></p><br /><br />
-<p>In this article, we will show you how to download Bingo Life app for free, how to play it, how to join the community, how to become a social media influencer with it, and some tips and tricks for having more fun. We will also answer some frequently asked questions about the app. So let's get started!</p>
-<h2>How to Download Bingo Life App for Free</h2>
-<p>Downloading Bingo Life app is easy and fast. You can download it on any device that supports Android, iOS or Mac. Here are the step-by-step instructions for each device:</p>
-<ul>
-<li><strong>Android:</strong> Go to [Google Play Store](^1^) and search for "Bingo Life". Tap on "Install" and wait for the app to download. Once it's done, tap on "Open" and enjoy!</li>
-<li><strong>iOS:</strong> Go to [App Store](^2^) and search for "My Bingo Life". Tap on "Get" and wait for the app to download. Once it's done, tap on "Open" and enjoy!</li>
-<li><strong>Mac:</strong> Go to [Mac App Store](^2^) and search for "My Bingo Life". Click on "Get" and wait for the app to download. Once it's done, click on "Open" and enjoy!</li>
-</ul>
-<p>Congratulations! You have successfully downloaded Bingo Life app. Now let's see how to play it.</p>
-<h2>How to Play Bingo Life App</h2>
-<p>Bingo Life app is very easy to play. You can choose from different game modes, such as live streaming, live chat, live game streaming, live voice chat and more. You can also play free 1, 2 or 4 card bingo, travel to the funfair to play mini games, unlock awesome new avatars and collect cute creatures, complete daily quests and special events for rewards, and use powerful bingo boosters to increase your chances of winning. Let's take a closer look at each of these features.</p>
-<h3>Play Free 1, 2 or 4 Card Bingo</h3>
-<p>One of the main attractions of Bingo Life app is the free bingo games. You can choose to play with 1, 2 or 4 bingo cards, depending on your preference and skill level. Each bingo card has 25 numbers, and you need to daub them as they are called out. You can daub them manually or use the auto daub feature to save time and effort. You can also use the free space feature to mark the center square of each card.</p>
-<p>To win a bingo game, you need to complete a pattern on your card, such as a line, a column, a diagonal, a four corners, or a full house. The first player to complete the pattern wins the game and gets a prize. The prize can be coins, power ups, funfair tokens, high roller tokens, or even real cash! You can also win extra prizes by getting multiple bingos on the same card or by getting a jackpot bingo.</p>
-<h3>Travel to the Funfair to Play Mini Games</h3>
-<p>If you want to take a break from bingo, you can travel to the funfair and play some exciting mini games. The funfair is a place where you can earn more coins, power ups, high roller tokens, and other rewards by playing different games. Some of the games you can play are:</p>
-<p>How to download bingo life app for free<br />
-Download bingo life app and play with friends<br />
-Bingo life app download for android and ios devices<br />
-Best bingo games to download - bingo life app review<br />
-Download bingo life app and win real money prizes<br />
-Bingo life app download - tips and tricks to win big<br />
-Download bingo life app and enjoy daily bonuses and rewards<br />
-Bingo life app download - how to join the bingo community<br />
-Download bingo life app and get access to exclusive bingo rooms<br />
-Bingo life app download - how to customize your bingo cards<br />
-Download bingo life app and play offline anytime<br />
-Bingo life app download - how to earn loyalty points and redeem them for gifts<br />
-Download bingo life app and participate in weekly tournaments and challenges<br />
-Bingo life app download - how to invite your friends and get free coins<br />
-Download bingo life app and chat with other bingo players<br />
-Bingo life app download - how to play bingo with different themes and modes<br />
-Download bingo life app and enjoy the best graphics and sound effects<br />
-Bingo life app download - how to contact the customer support team<br />
-Download bingo life app and rate it on the app store<br />
-Bingo life app download - how to share your feedback and suggestions<br />
-Download bingo life app and learn the rules of bingo<br />
-Bingo life app download - how to play multiple cards at once<br />
-Download bingo life app and use the auto-daub feature<br />
-Bingo life app download - how to activate the power-ups and boosters<br />
-Bingo life app download - how to claim your daily free spins<br />
-Download bingo life app and watch videos to earn extra coins<br />
-Bingo life app download - how to sync your progress across different devices<br />
-Download bingo life app and unlock new levels and features<br />
-Bingo life app download - how to update the app regularly<br />
-Bingo life app download - how to troubleshoot common issues<br />
-Download bingo life app and have fun with the mini-games<br />
-Bingo life app download - how to connect with Facebook and Twitter<br />
-Download bingo life app and enjoy the seasonal events and promotions<br />
-Bingo life app download - how to become a VIP member and get more benefits<br />
-Download bingo life app and discover new bingo sites and apps<br />
-Download bingo life app and compare it with other bingo apps<br />
-Bingo life app download - pros and cons of the app<br />
-Download bingo life app and check out the testimonials from other users<br />
-Bingo life app download - how to uninstall the app if needed<br />
-Download bingo life app and find out the latest news and updates from the developers</p>
-<ul>
-<li><strong>Donkey Derby:</strong> Bet on your favorite donkey and watch it race against other donkeys. If your donkey wins, you get a prize.</li>
-<li><strong>Duck Diver:</strong> Pick a duck from the pond and see what prize it has under its wing.</li>
-<li><strong>Wheel of Fortune:</strong> Spin the wheel and see what prize it lands on.</li>
-<li><strong>Balloon Popper:</strong> Pop as many balloons as you can before the time runs out and get a prize based on how many you popped.</li>
-<li><strong>Coin Pusher:</strong> Drop coins into the machine and try to push them off the edge. You get to keep all the coins and prizes that fall off.</li>
-</ul>
-<p>To play these mini games, you need funfair tokens. You can earn funfair tokens by playing bingo games or by completing daily quests and special events. You can also buy them with real money if you want.</p>
-<h3>Unlock Awesome New Avatars and Collect Cute Creatures</h3>
-<p>Bingo Life app is not just about playing bingo. It's also about expressing yourself and collecting cute creatures. You can customize your profile with different avatars that represent your personality and style. You can unlock new avatars by playing bingo games, completing daily quests and special events, or by buying them with real money.</p>
-<p>You can also collect cute creatures from different magical worlds, such as fairyland, wonderland, candyland, and more. These creatures are adorable and have unique abilities that can help you in your bingo games. For example, some creatures can give you extra time, extra coins, extra power ups, or extra bingos. You can unlock new creatures by playing bingo games, completing daily quests and special events, or by buying them with real money.</p>
-<h3>Complete Daily Quests and Special Events for Rewards</h3>
-<p>Bingo Life app is always full of surprises and challenges. Every day, you can complete different quests that will test your bingo skills and reward you with coins, power ups, funfair tokens, high roller tokens, and more. Some of the quests you can complete are: - Play a certain number of bingo games - Win a certain number of bingos - Use a certain number of power ups - Spend a certain amount of coins or funfair tokens - Collect a certain number of creatures - And more! You can also participate in special events that happen regularly and offer even more rewards and challenges. Some of the events you can join are: - Bingo Bash: Play bingo with thousands of players from around the world and compete for huge prizes - Bingo Blitz: Play bingo with a faster pace and more excitement - Bingo Bonanza: Play bingo with special rules and surprises - Bingo Party: Play bingo with your friends and club members and have fun together - And more! To join these events, you need to pay an entry fee with coins or high roller tokens. You can earn high roller tokens by playing bingo games or by buying them with real money.</p>
-<h3>Use Powerful Bingo Boosters to Increase Your Chances of Winning</h3>
-<p>Bingo Life app is not only about luck. It's also about strategy and skill. You can use different boosters to enhance your bingo experience and increase your chances of winning. Some of the boosters you can use are: - Extra Time: Gives you more time to daub your numbers - Auto Daub: Automatically daubs your numbers for you - Free Space: Marks the center square of each card for free - Double Daub: Daubs two numbers at once - Instant Bingo: Gives you an instant bingo on one card - And more! You can get boosters by playing bingo games, completing daily quests and special events, or by buying them with real money.</p>
-<h2>How to Join the Bingo Life Community</h2>
-<p>Bingo Life app is not just a game. It's also a community. You can chat with other players, make friends, join clubs, and participate in live events. You can also send and receive gifts, messages, and emojis to show your appreciation and support.</p>
-<p>To chat with other players, you can use the chat feature that is available in every bingo room. You can also use the voice chat feature to talk to other players in real time. You can choose to chat with everyone, your friends, or your club members.</p>
-<p>To make friends, you can send friend requests to other players that you like or admire. You can also accept friend requests from other players who want to be your friend. You can see your friends list and their online status on your profile.</p>
-<p>To join a club, you can browse the list of available clubs and apply to join one that suits your interests and goals. You can also create your own club and invite other players to join it. You can see your club information and members on your profile.</p>
-<p>To participate in live events, you can join the live stream feature that is available in some bingo rooms. You can watch other players play bingo live, chat with them, send them gifts, and cheer them on. You can also go live yourself and share your bingo moments with the world.</p>
-<h2>How to Become a Social Media Influencer with Bingo Life App</h2>
-<p>Bingo Life app is not just a community. It's also a platform. You can become a social media influencer with Bingo Life app and gain fans, followers, and fame. You can also earn money by monetizing your live streams and content.</p>
-<p>To become a social media influencer with Bingo Life app, you need to do the following:</p>
-<ul>
-<li><strong>Go live:</strong> Use the live stream feature to broadcast your bingo games live to the world. You can also use the live voice chat feature to talk to your viewers and interact with them.</li>
-<li><strong>Share your bingo moments:</strong> Use the share feature to post your bingo screenshots, videos, stories, tips, tricks, and more on social media platforms like Facebook, Instagram, Twitter, YouTube, TikTok, etc.</li>
-<li><strong>Invite friends:</strong> Use the invite feature to invite your friends from other social media platforms to join Bingo Life app and play with you.</li>
-<li><strong>Gain fans:</strong> Use the fan feature to attract more viewers and followers to your live streams and content. You can also use the fan club feature to create a loyal fan base that will support you and reward you.</li>
-</ul>
-<p>By doing these things, you can increase your popularity and influence on social media platforms. You can also earn money by monetizing your live streams and content through ads, donations, subscriptions, sponsorships, etc.</p>
-<h2>Tips and Tricks for Playing Bingo Life App</h2>
-<p>Bingo Life app is a fun and easy game to play. But if you want to have more fun and success, you can follow these tips and tricks: - Play with more cards: The more cards you play with, the more chances you have to win. You can play with up to 4 cards in each bingo game. However, playing with more cards also requires more attention and concentration, so make sure you can handle it. - Use power ups wisely: Power ups can give you an edge in your bingo games, but they are not unlimited. You need to use them strategically and sparingly. For example, you can use the extra time power up when you are running out of time, or the instant bingo power up when you are close to completing a pattern. - Play in different rooms: Bingo Life app has many different rooms to choose from, each with its own theme, difficulty level, and prize pool. You can play in different rooms to explore new worlds, meet new players, and win different rewards. You can also play in special rooms that offer higher stakes and bigger prizes. - Join a club: Joining a club can give you many benefits, such as access to exclusive bingo rooms, chat features, gifts, and events. You can also cooperate with your club members to complete club quests and earn club points. Club points can be used to rank up your club and unlock more perks and rewards. - Have fun: The most important tip for playing Bingo Life app is to have fun. Bingo is a game of luck and chance, so don't take it too seriously or get frustrated if you lose. Enjoy the game, chat with other players, make friends, and have a good time.</p>
-<h2>Frequently Asked Questions about Bingo Life App</h2>
-<p>Here are some of the most common questions and answers about Bingo Life app:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Bingo Life app free to play?</td>
-<td>Yes, Bingo Life app is free to play. You can download it for free and play unlimited free bingo games. However, some features and items may require real money purchases.</td>
-</tr>
-<tr>
-<td>How do I contact customer support?</td>
-<td>You can contact customer support by tapping on the settings icon on the top right corner of the screen and then tapping on "Contact Us". You can also email them at [email protected].</td>
-</tr>
-<tr>
-<td>How do I update the app?</td>
-<td>You can update the app by going to the Google Play Store or App Store and tapping on "Update". You can also enable automatic updates on your device settings.</td>
-</tr>
-<tr>
-<td>How do I delete the app?</td>
-<td>You can delete the app by going to your device settings and tapping on "Apps". Then find Bingo Life app and tap on "Uninstall". Please note that deleting the app will erase all your data and progress.</td>
-</tr>
-<tr>
-<td>How do I restore my purchases?</td>
-<td>You can restore your purchases by tapping on the settings icon on the top right corner of the screen and then tapping on "Restore Purchases". You will need to log in with the same account that you used to make the purchases.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Bingo Life app is a fun and exciting game that lets you enjoy bingo anytime, anywhere. You can play free bingo games, travel to the funfair, unlock new avatars and creatures, complete quests and events, use boosters, join the community, become a social media influencer, and more. It's the ultimate game for bingo lovers!</p>
-<h4>Download Bingo Life App Now and Start Your Epic Bingo Adventure!</h4>
-<p>What are you waiting for? Download Bingo Life app now and join millions of players from around the world who are having a blast with bingo. You will never get bored with this game. There is always something new and exciting to do. Download Bingo Life app now and start your epic bingo adventure!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/2ndelement/voicevox/Makefile

@@ -1,125 +0,0 @@
-CMD=
-NOCACHE=
-
-ARGS:=
-ifeq ($(NOCACHE),1)
-	ARGS:=$(ARGS) --no-cache
-endif
-
-# Ubuntu 20.04
-.PHONY: build-linux-docker-ubuntu20.04
-build-linux-docker-ubuntu20.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:cpu-ubuntu20.04-latest \
-		--target runtime-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:20.04 \
-		--build-arg BASE_RUNTIME_IMAGE=ubuntu:20.04 \
-		--build-arg ONNXRUNTIME_URL=https://github.com/microsoft/onnxruntime/releases/download/v1.13.1/onnxruntime-linux-x64-1.13.1.tgz \
-		--build-arg VOICEVOX_CORE_LIBRARY_NAME=libcore_cpu_x64.so $(ARGS)
-
-.PHONY: run-linux-docker-ubuntu20.04
-run-linux-docker-ubuntu20.04:
-	docker run --rm -it \
-		-p '127.0.0.1:50021:50021' $(ARGS) \
-		voicevox/voicevox_engine:cpu-ubuntu20.04-latest $(CMD)
-
-.PHONY: build-linux-docker-nvidia-ubuntu20.04
-build-linux-docker-nvidia-ubuntu20.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:nvidia-ubuntu20.04-latest \
-		--target runtime-nvidia-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:20.04 \
-		--build-arg BASE_RUNTIME_IMAGE=nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04 \
-		--build-arg ONNXRUNTIME_URL=https://github.com/microsoft/onnxruntime/releases/download/v1.13.1/onnxruntime-linux-x64-gpu-1.13.1.tgz \
-		--build-arg VOICEVOX_CORE_LIBRARY_NAME=libcore_gpu_x64_nvidia.so $(ARGS)
-
-.PHONY: run-linux-docker-nvidia-ubuntu20.04
-run-linux-docker-nvidia-ubuntu20.04:
-	docker run --rm -it \
-		--gpus all \
-		-p '127.0.0.1:50021:50021' $(ARGS) \
-		voicevox/voicevox_engine:nvidia-ubuntu20.04-latest $(CMD)
-
-
-# Ubuntu 18.04
-.PHONY: build-linux-docker-ubuntu18.04
-build-linux-docker-ubuntu18.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:cpu-ubuntu18.04-latest \
-		--target runtime-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:18.04 \
-		--build-arg BASE_RUNTIME_IMAGE=ubuntu:18.04 \
-		--build-arg ONNXRUNTIME_URL=https://github.com/microsoft/onnxruntime/releases/download/v1.13.1/onnxruntime-linux-x64-1.13.1.tgz \
-		--build-arg VOICEVOX_CORE_LIBRARY_NAME=libcore_cpu_x64.so $(ARGS)
-
-.PHONY: run-linux-docker-ubuntu18.04
-run-linux-docker-ubuntu18.04:
-	docker run --rm -it \
-		-p '127.0.0.1:50021:50021' $(ARGS) \
-		voicevox/voicevox_engine:cpu-ubuntu18.04-latest $(CMD)
-
-.PHONY: build-linux-docker-nvidia-ubuntu18.04
-build-linux-docker-nvidia-ubuntu18.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:nvidia-ubuntu18.04-latest \
-		--target runtime-nvidia-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:18.04 \
-		--build-arg BASE_RUNTIME_IMAGE=nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu18.04 \
-		--build-arg ONNXRUNTIME_URL=https://github.com/microsoft/onnxruntime/releases/download/v1.13.1/onnxruntime-linux-x64-gpu-1.13.1.tgz \
-		--build-arg VOICEVOX_CORE_LIBRARY_NAME=libcore_gpu_x64_nvidia.so $(ARGS)
-
-.PHONY: run-linux-docker-nvidia-ubuntu18.04
-run-linux-docker-nvidia-ubuntu18.04:
-	docker run --rm -it \
-		--gpus all \
-		-p '127.0.0.1:50021:50021' $(ARGS) \
-		voicevox/voicevox_engine:nvidia-ubuntu18.04-latest $(CMD)
-
-
-# VOICEVOX Core env for test
-.PHONY: build-linux-docker-download-core-env-ubuntu18.04
-build-linux-docker-download-core-env-ubuntu18.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:download-core-env-ubuntu18.04 \
-		--target download-core-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:18.04 $(ARGS)
-
-.PHONY: run-linux-docker-download-core-env-ubuntu18.04
-run-linux-docker-download-core-env-ubuntu18.04:
-	docker run --rm -it $(ARGS) \
-		voicevox/voicevox_engine:download-core-env-ubuntu18.04 $(CMD)
-
-
-# ONNX Runtime env for test
-.PHONY: build-linux-docker-download-onnxruntime-env-ubuntu18.04
-build-linux-docker-download-onnxruntime-env-ubuntu18.04:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:download-onnxruntime-env-ubuntu18.04 \
-		--target download-onnxruntime-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:18.04 $(ARGS)
-
-.PHONY: run-linux-docker-download-onnxruntime-env-ubuntu18.04
-run-linux-docker-download-onnxruntime-env-ubuntu18.04:
-	docker run --rm -it $(ARGS) \
-		voicevox/voicevox_engine:download-onnxruntime-env-ubuntu18.04 $(CMD)
-
-
-# Python env for test
-.PHONY: build-linux-docker-compile-python-env
-build-linux-docker-compile-python-env:
-	docker buildx build . \
-		-t voicevox/voicevox_engine:compile-python-env \
-		--target compile-python-env \
-		--progress plain \
-		--build-arg BASE_IMAGE=ubuntu:20.04 $(ARGS)
-
-.PHONY: run-linux-docker-compile-python-env
-run-linux-docker-compile-python-env:
-	docker run --rm -it $(ARGS) \
-		voicevox/voicevox_engine:compile-python-env $(CMD)

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/onnx_helper.py

@@ -1,250 +0,0 @@
-from __future__ import division
-import datetime
-import os
-import os.path as osp
-import glob
-import numpy as np
-import cv2
-import sys
-import onnxruntime
-import onnx
-import argparse
-from onnx import numpy_helper
-from insightface.data import get_image
-
-class ArcFaceORT:
-    def __init__(self, model_path, cpu=False):
-        self.model_path = model_path
-        # providers = None will use available provider, for onnxruntime-gpu it will be "CUDAExecutionProvider"
-        self.providers = ['CPUExecutionProvider'] if cpu else None
-
-    #input_size is (w,h), return error message, return None if success
-    def check(self, track='cfat', test_img = None):
-        #default is cfat
-        max_model_size_mb=1024
-        max_feat_dim=512
-        max_time_cost=15
-        if track.startswith('ms1m'):
-            max_model_size_mb=1024
-            max_feat_dim=512
-            max_time_cost=10
-        elif track.startswith('glint'):
-            max_model_size_mb=1024
-            max_feat_dim=1024
-            max_time_cost=20
-        elif track.startswith('cfat'):
-            max_model_size_mb = 1024
-            max_feat_dim = 512
-            max_time_cost = 15
-        elif track.startswith('unconstrained'):
-            max_model_size_mb=1024
-            max_feat_dim=1024
-            max_time_cost=30
-        else:
-            return "track not found"
-
-        if not os.path.exists(self.model_path):
-            return "model_path not exists"
-        if not os.path.isdir(self.model_path):
-            return "model_path should be directory"
-        onnx_files = []
-        for _file in os.listdir(self.model_path):
-            if _file.endswith('.onnx'):
-                onnx_files.append(osp.join(self.model_path, _file))
-        if len(onnx_files)==0:
-            return "do not have onnx files"
-        self.model_file = sorted(onnx_files)[-1]
-        print('use onnx-model:', self.model_file)
-        try:
-            session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
-        except:
-            return "load onnx failed"
-        input_cfg = session.get_inputs()[0]
-        input_shape = input_cfg.shape
-        print('input-shape:', input_shape)
-        if len(input_shape)!=4:
-            return "length of input_shape should be 4"
-        if not isinstance(input_shape[0], str):
-            #return "input_shape[0] should be str to support batch-inference"
-            print('reset input-shape[0] to None')
-            model = onnx.load(self.model_file)
-            model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None'
-            new_model_file = osp.join(self.model_path, 'zzzzrefined.onnx')
-            onnx.save(model, new_model_file)
-            self.model_file = new_model_file
-            print('use new onnx-model:', self.model_file)
-            try:
-                session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
-            except:
-                return "load onnx failed"
-            input_cfg = session.get_inputs()[0]
-            input_shape = input_cfg.shape
-            print('new-input-shape:', input_shape)
-
-        self.image_size = tuple(input_shape[2:4][::-1])
-        #print('image_size:', self.image_size)
-        input_name = input_cfg.name
-        outputs = session.get_outputs()
-        output_names = []
-        for o in outputs:
-            output_names.append(o.name)
-            #print(o.name, o.shape)
-        if len(output_names)!=1:
-            return "number of output nodes should be 1"
-        self.session = session
-        self.input_name = input_name
-        self.output_names = output_names
-        #print(self.output_names)
-        model = onnx.load(self.model_file)
-        graph = model.graph
-        if len(graph.node)<8:
-            return "too small onnx graph"
-
-        input_size = (112,112)
-        self.crop = None
-        if track=='cfat':
-            crop_file = osp.join(self.model_path, 'crop.txt')
-            if osp.exists(crop_file):
-                lines = open(crop_file,'r').readlines()
-                if len(lines)!=6:
-                    return "crop.txt should contain 6 lines"
-                lines = [int(x) for x in lines]
-                self.crop = lines[:4]
-                input_size = tuple(lines[4:6])
-        if input_size!=self.image_size:
-            return "input-size is inconsistant with onnx model input, %s vs %s"%(input_size, self.image_size)
-
-        self.model_size_mb = os.path.getsize(self.model_file) / float(1024*1024)
-        if self.model_size_mb > max_model_size_mb:
-            return "max model size exceed, given %.3f-MB"%self.model_size_mb
-
-        input_mean = None
-        input_std = None
-        if track=='cfat':
-            pn_file = osp.join(self.model_path, 'pixel_norm.txt')
-            if osp.exists(pn_file):
-                lines = open(pn_file,'r').readlines()
-                if len(lines)!=2:
-                    return "pixel_norm.txt should contain 2 lines"
-                input_mean = float(lines[0])
-                input_std = float(lines[1])
-        if input_mean is not None or input_std is not None:
-            if input_mean is None or input_std is None:
-                return "please set input_mean and input_std simultaneously"
-        else:
-            find_sub = False
-            find_mul = False
-            for nid, node in enumerate(graph.node[:8]):
-                print(nid, node.name)
-                if node.name.startswith('Sub') or node.name.startswith('_minus'):
-                    find_sub = True
-                if node.name.startswith('Mul') or node.name.startswith('_mul') or node.name.startswith('Div'):
-                    find_mul = True
-            if find_sub and find_mul:
-                print("find sub and mul")
-                #mxnet arcface model
-                input_mean = 0.0
-                input_std = 1.0
-            else:
-                input_mean = 127.5
-                input_std = 127.5
-        self.input_mean = input_mean
-        self.input_std = input_std
-        for initn in graph.initializer:
-            weight_array = numpy_helper.to_array(initn)
-            dt = weight_array.dtype
-            if dt.itemsize<4:
-                return 'invalid weight type - (%s:%s)' % (initn.name, dt.name)
-        if test_img is None:
-            test_img = get_image('Tom_Hanks_54745')
-            test_img = cv2.resize(test_img, self.image_size)
-        else:
-            test_img = cv2.resize(test_img, self.image_size)
-        feat, cost = self.benchmark(test_img)
-        batch_result = self.check_batch(test_img)
-        batch_result_sum = float(np.sum(batch_result))
-        if batch_result_sum in [float('inf'), -float('inf')] or batch_result_sum != batch_result_sum:
-            print(batch_result)
-            print(batch_result_sum)
-            return "batch result output contains NaN!"
-
-        if len(feat.shape) < 2:
-           return "the shape of the feature must be two, but get {}".format(str(feat.shape))
-
-        if feat.shape[1] > max_feat_dim:
-            return "max feat dim exceed, given %d"%feat.shape[1]
-        self.feat_dim = feat.shape[1]
-        cost_ms = cost*1000
-        if cost_ms>max_time_cost:
-            return "max time cost exceed, given %.4f"%cost_ms
-        self.cost_ms = cost_ms
-        print('check stat:, model-size-mb: %.4f, feat-dim: %d, time-cost-ms: %.4f, input-mean: %.3f, input-std: %.3f'%(self.model_size_mb, self.feat_dim, self.cost_ms, self.input_mean, self.input_std))
-        return None
-
-    def check_batch(self, img):
-        if not isinstance(img, list):
-            imgs = [img, ] * 32
-        if self.crop is not None:
-            nimgs = []
-            for img in imgs:
-                nimg = img[self.crop[1]:self.crop[3], self.crop[0]:self.crop[2], :]
-                if nimg.shape[0] != self.image_size[1] or nimg.shape[1] != self.image_size[0]:
-                    nimg = cv2.resize(nimg, self.image_size)
-                nimgs.append(nimg)
-            imgs = nimgs
-        blob = cv2.dnn.blobFromImages(
-            images=imgs, scalefactor=1.0 / self.input_std, size=self.image_size,
-            mean=(self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        net_out = self.session.run(self.output_names, {self.input_name: blob})[0]
-        return net_out
-
-
-    def meta_info(self):
-        return {'model-size-mb':self.model_size_mb, 'feature-dim':self.feat_dim, 'infer': self.cost_ms}
-
-
-    def forward(self, imgs):
-        if not isinstance(imgs, list):
-            imgs = [imgs]
-        input_size = self.image_size
-        if self.crop is not None:
-            nimgs = []
-            for img in imgs:
-                nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
-                if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
-                    nimg = cv2.resize(nimg, input_size)
-                nimgs.append(nimg)
-            imgs = nimgs
-        blob = cv2.dnn.blobFromImages(imgs, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
-        return net_out
-
-    def benchmark(self, img):
-        input_size = self.image_size
-        if self.crop is not None:
-            nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
-            if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
-                nimg = cv2.resize(nimg, input_size)
-            img = nimg
-        blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        costs = []
-        for _ in range(50):
-            ta = datetime.datetime.now()
-            net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
-            tb = datetime.datetime.now()
-            cost = (tb-ta).total_seconds()
-            costs.append(cost)
-        costs = sorted(costs)
-        cost = costs[5]
-        return net_out, cost
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='')
-    # general
-    parser.add_argument('workdir', help='submitted work dir', type=str)
-    parser.add_argument('--track', help='track name, for different challenge', type=str, default='cfat')
-    args = parser.parse_args()
-    handler = ArcFaceORT(args.workdir)
-    err = handler.check(args.track)
-    print('err:', err)

spaces/AI4PD/hexviz/hexviz/config.py

@@ -1 +0,0 @@
-URL = "https://aksell-hexviz.hf.space/"

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

@@ -1,182 +0,0 @@
-import copy
-import json
-
-import numpy as np
-import fire
-
-
-def evaluate_annotation(key2refs, scorer):
-    if scorer.method() == "Bleu":
-        scores = np.array([ 0.0 for n in range(4) ])
-    else:
-        scores = 0
-    num_cap_per_audio = len(next(iter(key2refs.values())))
-
-    for i in range(num_cap_per_audio):
-        if i > 0:
-            for key in key2refs:
-                key2refs[key].insert(0, res[key][0])
-        res = { key: [refs.pop(),] for key, refs in key2refs.items() }
-        score, _ = scorer.compute_score(key2refs, res)
-        
-        if scorer.method() == "Bleu":
-            scores += np.array(score)
-        else:
-            scores += score
-    
-    score = scores / num_cap_per_audio
-    return score
-   
-def evaluate_prediction(key2pred, key2refs, scorer):
-    if scorer.method() == "Bleu":
-        scores = np.array([ 0.0 for n in range(4) ])
-    else:
-        scores = 0
-    num_cap_per_audio = len(next(iter(key2refs.values())))
-
-    for i in range(num_cap_per_audio):
-        key2refs_i = {}
-        for key, refs in key2refs.items():
-            key2refs_i[key] = refs[:i] + refs[i+1:]
-        score, _ = scorer.compute_score(key2refs_i, key2pred)
-        
-        if scorer.method() == "Bleu":
-            scores += np.array(score)
-        else:
-            scores += score
-    
-    score = scores / num_cap_per_audio
-    return score
-
-
-class Evaluator(object):
-
-    def eval_annotation(self, annotation, output):
-        captions = json.load(open(annotation, "r"))["audios"]
-
-        key2refs = {}
-        for audio_idx in range(len(captions)):
-            audio_id = captions[audio_idx]["audio_id"]
-            key2refs[audio_id] = []
-            for caption in captions[audio_idx]["captions"]:
-                key2refs[audio_id].append(caption["caption"])
-
-        from fense.fense import Fense
-        scores = {}
-        scorer = Fense()
-        scores[scorer.method()] = evaluate_annotation(copy.deepcopy(key2refs), scorer)
-
-        refs4eval = {}
-        for key, refs in key2refs.items():
-            refs4eval[key] = []
-            for idx, ref in enumerate(refs):
-                refs4eval[key].append({
-                    "audio_id": key,
-                    "id": idx,
-                    "caption": ref
-                })
-
-        from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
-
-        tokenizer = PTBTokenizer()
-        key2refs = tokenizer.tokenize(refs4eval)
-
-
-        from pycocoevalcap.bleu.bleu import Bleu
-        from pycocoevalcap.cider.cider import Cider
-        from pycocoevalcap.rouge.rouge import Rouge
-        from pycocoevalcap.meteor.meteor import Meteor
-        from pycocoevalcap.spice.spice import Spice
-        
-
-        scorers = [Bleu(), Rouge(), Cider(), Meteor(), Spice()]
-        for scorer in scorers:
-            scores[scorer.method()] = evaluate_annotation(copy.deepcopy(key2refs), scorer)
-
-        spider = 0
-        with open(output, "w") as f:
-            for name, score in scores.items():
-                if name == "Bleu":
-                    for n in range(4):
-                        f.write("Bleu-{}: {:6.3f}\n".format(n + 1, score[n]))
-                else:
-                    f.write("{}: {:6.3f}\n".format(name, score))
-                    if name in ["CIDEr", "SPICE"]:
-                        spider += score
-            f.write("SPIDEr: {:6.3f}\n".format(spider / 2))
-
-    def eval_prediction(self, prediction, annotation, output):
-        ref_captions = json.load(open(annotation, "r"))["audios"]
-
-        key2refs = {}
-        for audio_idx in range(len(ref_captions)):
-            audio_id = ref_captions[audio_idx]["audio_id"]
-            key2refs[audio_id] = []
-            for caption in ref_captions[audio_idx]["captions"]:
-                key2refs[audio_id].append(caption["caption"])
-
-        pred_captions = json.load(open(prediction, "r"))["predictions"]
-
-        key2pred = {}
-        for audio_idx in range(len(pred_captions)):
-            item = pred_captions[audio_idx]
-            audio_id = item["filename"]
-            key2pred[audio_id] = [item["tokens"]]
-
-        from fense.fense import Fense
-        scores = {}
-        scorer = Fense()
-        scores[scorer.method()] = evaluate_prediction(key2pred, key2refs, scorer)
-
-        refs4eval = {}
-        for key, refs in key2refs.items():
-            refs4eval[key] = []
-            for idx, ref in enumerate(refs):
-                refs4eval[key].append({
-                    "audio_id": key,
-                    "id": idx,
-                    "caption": ref
-                })
-
-        preds4eval = {}
-        for key, preds in key2pred.items():
-            preds4eval[key] = []
-            for idx, pred in enumerate(preds):
-                preds4eval[key].append({
-                    "audio_id": key,
-                    "id": idx,
-                    "caption": pred
-                })
-
-        from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
-
-        tokenizer = PTBTokenizer()
-        key2refs = tokenizer.tokenize(refs4eval)
-        key2pred = tokenizer.tokenize(preds4eval)
-
-
-        from pycocoevalcap.bleu.bleu import Bleu
-        from pycocoevalcap.cider.cider import Cider
-        from pycocoevalcap.rouge.rouge import Rouge
-        from pycocoevalcap.meteor.meteor import Meteor
-        from pycocoevalcap.spice.spice import Spice
-
-        scorers = [Bleu(), Rouge(), Cider(), Meteor(), Spice()]
-        for scorer in scorers:
-            scores[scorer.method()] = evaluate_prediction(key2pred, key2refs, scorer)
-
-        spider = 0
-        with open(output, "w") as f:
-            for name, score in scores.items():
-                if name == "Bleu":
-                    for n in range(4):
-                        f.write("Bleu-{}: {:6.3f}\n".format(n + 1, score[n]))
-                else:
-                    f.write("{}: {:6.3f}\n".format(name, score))
-                    if name in ["CIDEr", "SPICE"]:
-                        spider += score
-            f.write("SPIDEr: {:6.3f}\n".format(spider / 2))
-
-
-if __name__ == "__main__":
-    fire.Fire(Evaluator)

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/losses_audio/__init__.py

@@ -1,7 +0,0 @@
-from ldm.modules.losses_audio.vqperceptual import DummyLoss
-
-# relative imports pain
-import os
-import sys
-path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'vggishish')
-sys.path.append(path)

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

@@ -1,6 +0,0 @@
-// Override
-var GetChildrenWidth = function () {
-    return 0;
-}
-
-export default GetChildrenWidth;

spaces/AlexWang/lama/bin/sample_from_dataset.py

@@ -1,87 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import numpy as np
-import tqdm
-from skimage import io
-from skimage.segmentation import mark_boundaries
-
-from saicinpainting.evaluation.data import InpaintingDataset
-from saicinpainting.evaluation.vis import save_item_for_vis
-
-def save_mask_for_sidebyside(item, out_file):
-    mask = item['mask']# > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    mask = np.clip(mask * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, mask)
-
-def save_img_for_sidebyside(item, out_file):
-    img = np.transpose(item['image'], (1, 2, 0))
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-def save_masked_img_for_sidebyside(item, out_file):
-    mask = item['mask']
-    img  = item['image']
-
-    img = (1-mask) * img + mask
-    img = np.transpose(img, (1, 2, 0))
-
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-def main(args):
-    dataset = InpaintingDataset(args.datadir, img_suffix='.png')
-
-    area_bins = np.linspace(0, 1, args.area_bins + 1)
-
-    heights = []
-    widths = []
-    image_areas = []
-    hole_areas = []
-    hole_area_percents = []
-    area_bins_count = np.zeros(args.area_bins)
-    area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]
-
-    bin2i = [[] for _ in range(args.area_bins)]
-
-    for i, item in enumerate(tqdm.tqdm(dataset)):
-        h, w = item['image'].shape[1:]
-        heights.append(h)
-        widths.append(w)
-        full_area = h * w
-        image_areas.append(full_area)
-        hole_area = (item['mask'] == 1).sum()
-        hole_areas.append(hole_area)
-        hole_percent = hole_area / full_area
-        hole_area_percents.append(hole_percent)
-        bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)
-        area_bins_count[bin_i] += 1
-        bin2i[bin_i].append(i)
-
-    os.makedirs(args.outdir, exist_ok=True)
-   
-    for bin_i in range(args.area_bins):
-        bindir = os.path.join(args.outdir, area_bin_titles[bin_i])
-        os.makedirs(bindir, exist_ok=True)
-        bin_idx = bin2i[bin_i]
-        for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):
-            item = dataset[sample_i]
-            path = os.path.join(bindir, dataset.img_filenames[sample_i].split('/')[-1])
-            save_masked_img_for_sidebyside(item, path)
-           
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('--datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('--outdir', type=str, help='Where to put results')
-    aparser.add_argument('--samples-n', type=int, default=10,
-                         help='Number of sample images with masks to copy for visualization for each area bin')
-    aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')
-
-    main(aparser.parse_args())

spaces/Alexxggs/ggvpnewen/utils.py

@@ -1,52 +0,0 @@
-import json
-import numpy as np
-import httpx
-
-from constants import MUBERT_TAGS, MUBERT_LICENSE, MUBERT_MODE, MUBERT_TOKEN
-
-
-def get_mubert_tags_embeddings(w2v_model):
-    return w2v_model.encode(MUBERT_TAGS)
-
-
-
-def get_pat(email: str):
-    r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
-                   json={
-                       "method": "GetServiceAccess",
-                       "params": {
-                           "email": email,
-                           "license": MUBERT_LICENSE,
-                           "token": MUBERT_TOKEN,
-                           "mode": MUBERT_MODE,
-               
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, "probably incorrect e-mail"
-    pat = rdata['data']['pat']
-    return pat
-
-
-def find_similar(em, embeddings, method='cosine'):
-    scores = []
-    for ref in embeddings:
-        if method == 'cosine':
-            scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em)))
-        if method == 'norm':
-            scores.append(np.linalg.norm(ref - em))
-    return np.array(scores), np.argsort(scores)
-
-
-def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False):
-    prompts_embeddings = w2v_model.encode(prompts)
-    ret = []
-    for i, pe in enumerate(prompts_embeddings):
-        scores, idxs = find_similar(pe, mubert_tags_embeddings)
-        top_tags = MUBERT_TAGS[idxs[:top_n]]
-        top_prob = 1 - scores[idxs[:top_n]]
-        if debug:
-            print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n")
-        ret.append((prompts[i], list(top_tags)))
-    return ret

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/读文章写摘要.py

@@ -1,67 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

spaces/Anandbheesetti/MNIST_digit_predictor/README.md

@@ -1,13 +0,0 @@
----
-title: MNIST Digit Predictor
-emoji: 🏃
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_480x480_80k_pascal_context.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

spaces/AnimalEquality/chatbot/scripts/pin_requirements.sh

@@ -1 +0,0 @@
-pip freeze | grep -F -f requirements/core.txt | sed 's/+cpu//g' > requirements.txt

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/upsampling.py

@@ -1,104 +0,0 @@
-from pathlib import Path
-
-import cv2
-from diffusers.utils import logging
-from huggingface_hub import hf_hub_download
-from PIL import Image
-from torch import nn
-
-try:
-    from basicsr.archs.rrdbnet_arch import RRDBNet
-    from realesrgan import RealESRGANer
-except ImportError as e:
-    raise ImportError(
-        "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
-        "pip install realesrgan"
-    )
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class RealESRGANModel(nn.Module):
-    def __init__(self, model_path, tile=0, tile_pad=10, pre_pad=0, fp32=False):
-        super().__init__()
-        try:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
-            from realesrgan import RealESRGANer
-        except ImportError as e:
-            raise ImportError(
-                "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
-                "pip install realesrgan"
-            )
-
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        self.upsampler = RealESRGANer(
-            scale=4, model_path=model_path, model=model, tile=tile, tile_pad=tile_pad, pre_pad=pre_pad, half=not fp32
-        )
-
-    def forward(self, image, outscale=4, convert_to_pil=True):
-        """Upsample an image array or path.
-        Args:
-            image (Union[np.ndarray, str]): Either a np array or an image path. np array is assumed to be in RGB format,
-                and we convert it to BGR.
-            outscale (int, optional): Amount to upscale the image. Defaults to 4.
-            convert_to_pil (bool, optional): If True, return PIL image. Otherwise, return numpy array (BGR). Defaults to True.
-        Returns:
-            Union[np.ndarray, PIL.Image.Image]: An upsampled version of the input image.
-        """
-        if isinstance(image, (str, Path)):
-            img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
-        else:
-            img = image
-            img = (img * 255).round().astype("uint8")
-            img = img[:, :, ::-1]
-
-        image, _ = self.upsampler.enhance(img, outscale=outscale)
-
-        if convert_to_pil:
-            image = Image.fromarray(image[:, :, ::-1])
-
-        return image
-
-    @classmethod
-    def from_pretrained(cls, model_name_or_path="nateraw/real-esrgan"):
-        """Initialize a pretrained Real-ESRGAN upsampler.
-        Example:
-            ```python
-            >>> from stable_diffusion_videos import PipelineRealESRGAN
-            >>> pipe = PipelineRealESRGAN.from_pretrained('nateraw/real-esrgan')
-            >>> im_out = pipe('input_img.jpg')
-            ```
-        Args:
-            model_name_or_path (str, optional): The Hugging Face repo ID or path to local model. Defaults to 'nateraw/real-esrgan'.
-        Returns:
-            stable_diffusion_videos.PipelineRealESRGAN: An instance of `PipelineRealESRGAN` instantiated from pretrained model.
-        """
-        # reuploaded form official ones mentioned here:
-        # https://github.com/xinntao/Real-ESRGAN
-        if Path(model_name_or_path).exists():
-            file = model_name_or_path
-        else:
-            file = hf_hub_download(model_name_or_path, "RealESRGAN_x4plus.pth")
-        return cls(file)
-
-    def upsample_imagefolder(self, in_dir, out_dir, suffix="out", outfile_ext=".png", recursive=False, force=False):
-        in_dir, out_dir = Path(in_dir), Path(out_dir)
-        if not in_dir.exists():
-            raise FileNotFoundError(f"Provided input directory {in_dir} does not exist")
-
-        out_dir.mkdir(exist_ok=True, parents=True)
-
-        generator = in_dir.rglob("*") if recursive else in_dir.glob("*")
-        image_paths = [x for x in generator if x.suffix.lower() in [".png", ".jpg", ".jpeg"]]
-        n_img = len(image_paths)
-        for i, image in enumerate(image_paths):
-            out_filepath = out_dir / (str(image.relative_to(in_dir).with_suffix("")) + suffix + outfile_ext)
-            if not force and out_filepath.exists():
-                logger.info(
-                    f"[{i}/{n_img}] {out_filepath} already exists, skipping. To avoid skipping, pass force=True."
-                )
-                continue
-            logger.info(f"[{i}/{n_img}] upscaling {image}")
-            im = self(str(image))
-            out_filepath.parent.mkdir(parents=True, exist_ok=True)
-            im.save(out_filepath)

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/transformer.py

@@ -1,959 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR Transformer class.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from typing import Optional
-
-import torch
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-
-from groundingdino.util.misc import inverse_sigmoid
-
-from .fuse_modules import BiAttentionBlock
-from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
-from .transformer_vanilla import TransformerEncoderLayer
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    get_sine_pos_embed,
-)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        nhead=8,
-        num_queries=300,
-        num_encoder_layers=6,
-        num_unicoder_layers=0,
-        num_decoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.0,
-        activation="relu",
-        normalize_before=False,
-        return_intermediate_dec=False,
-        query_dim=4,
-        num_patterns=0,
-        # for deformable encoder
-        num_feature_levels=1,
-        enc_n_points=4,
-        dec_n_points=4,
-        # init query
-        learnable_tgt_init=False,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
-        embed_init_tgt=False,
-        # for text
-        use_text_enhancer=False,
-        use_fusion_layer=False,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-        use_text_cross_attention=False,
-        text_dropout=0.1,
-        fusion_dropout=0.1,
-        fusion_droppath=0.0,
-    ):
-        super().__init__()
-        self.num_feature_levels = num_feature_levels
-        self.num_encoder_layers = num_encoder_layers
-        self.num_unicoder_layers = num_unicoder_layers
-        self.num_decoder_layers = num_decoder_layers
-        self.num_queries = num_queries
-        assert query_dim == 4
-
-        # choose encoder layer type
-        encoder_layer = DeformableTransformerEncoderLayer(
-            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
-        )
-
-        if use_text_enhancer:
-            text_enhance_layer = TransformerEncoderLayer(
-                d_model=d_model,
-                nhead=nhead // 2,
-                dim_feedforward=dim_feedforward // 2,
-                dropout=text_dropout,
-            )
-        else:
-            text_enhance_layer = None
-
-        if use_fusion_layer:
-            feature_fusion_layer = BiAttentionBlock(
-                v_dim=d_model,
-                l_dim=d_model,
-                embed_dim=dim_feedforward // 2,
-                num_heads=nhead // 2,
-                dropout=fusion_dropout,
-                drop_path=fusion_droppath,
-            )
-        else:
-            feature_fusion_layer = None
-
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        assert encoder_norm is None
-        self.encoder = TransformerEncoder(
-            encoder_layer,
-            num_encoder_layers,
-            d_model=d_model,
-            num_queries=num_queries,
-            text_enhance_layer=text_enhance_layer,
-            feature_fusion_layer=feature_fusion_layer,
-            use_checkpoint=use_checkpoint,
-            use_transformer_ckpt=use_transformer_ckpt,
-        )
-
-        # choose decoder layer type
-        decoder_layer = DeformableTransformerDecoderLayer(
-            d_model,
-            dim_feedforward,
-            dropout,
-            activation,
-            num_feature_levels,
-            nhead,
-            dec_n_points,
-            use_text_cross_attention=use_text_cross_attention,
-        )
-
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(
-            decoder_layer,
-            num_decoder_layers,
-            decoder_norm,
-            return_intermediate=return_intermediate_dec,
-            d_model=d_model,
-            query_dim=query_dim,
-            num_feature_levels=num_feature_levels,
-        )
-
-        self.d_model = d_model
-        self.nhead = nhead
-        self.dec_layers = num_decoder_layers
-        self.num_queries = num_queries  # useful for single stage model only
-        self.num_patterns = num_patterns
-        if not isinstance(num_patterns, int):
-            Warning("num_patterns should be int but {}".format(type(num_patterns)))
-            self.num_patterns = 0
-
-        if num_feature_levels > 1:
-            if self.num_encoder_layers > 0:
-                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-            else:
-                self.level_embed = None
-
-        self.learnable_tgt_init = learnable_tgt_init
-        assert learnable_tgt_init, "why not learnable_tgt_init"
-        self.embed_init_tgt = embed_init_tgt
-        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
-            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
-            nn.init.normal_(self.tgt_embed.weight.data)
-        else:
-            self.tgt_embed = None
-
-        # for two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type == "standard":
-            # anchor selection at the output of encoder
-            self.enc_output = nn.Linear(d_model, d_model)
-            self.enc_output_norm = nn.LayerNorm(d_model)
-            self.two_stage_wh_embedding = None
-
-        if two_stage_type == "no":
-            self.init_ref_points(num_queries)  # init self.refpoint_embed
-
-        self.enc_out_class_embed = None
-        self.enc_out_bbox_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-        for m in self.modules():
-            if isinstance(m, MSDeformAttn):
-                m._reset_parameters()
-        if self.num_feature_levels > 1 and self.level_embed is not None:
-            nn.init.normal_(self.level_embed)
-
-    def get_valid_ratio(self, mask):
-        _, H, W = mask.shape
-        valid_H = torch.sum(~mask[:, :, 0], 1)
-        valid_W = torch.sum(~mask[:, 0, :], 1)
-        valid_ratio_h = valid_H.float() / H
-        valid_ratio_w = valid_W.float() / W
-        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
-        return valid_ratio
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
-
-    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
-        """
-        Input:
-            - srcs: List of multi features [bs, ci, hi, wi]
-            - masks: List of multi masks [bs, hi, wi]
-            - refpoint_embed: [bs, num_dn, 4]. None in infer
-            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
-            - tgt: [bs, num_dn, d_model]. None in infer
-
-        """
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        spatial_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            bs, c, h, w = src.shape
-            spatial_shape = (h, w)
-            spatial_shapes.append(spatial_shape)
-
-            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
-            mask = mask.flatten(1)  # bs, hw
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
-            if self.num_feature_levels > 1 and self.level_embed is not None:
-                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
-            else:
-                lvl_pos_embed = pos_embed
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-            src_flatten.append(src)
-            mask_flatten.append(mask)
-        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
-        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
-        spatial_shapes = torch.as_tensor(
-            spatial_shapes, dtype=torch.long, device=src_flatten.device
-        )
-        level_start_index = torch.cat(
-            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
-        )
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
-        # two stage
-        enc_topk_proposals = enc_refpoint_embed = None
-
-        #########################################################
-        # Begin Encoder
-        #########################################################
-        memory, memory_text = self.encoder(
-            src_flatten,
-            pos=lvl_pos_embed_flatten,
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            key_padding_mask=mask_flatten,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-            position_ids=text_dict["position_ids"],
-            text_self_attention_masks=text_dict["text_self_attention_masks"],
-        )
-        #########################################################
-        # End Encoder
-        # - memory: bs, \sum{hw}, c
-        # - mask_flatten: bs, \sum{hw}
-        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
-        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        #########################################################
-        text_dict["encoded_text"] = memory_text
-        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-        #     if memory.isnan().any() | memory.isinf().any():
-        #         import ipdb; ipdb.set_trace()
-
-        if self.two_stage_type == "standard":
-            output_memory, output_proposals = gen_encoder_output_proposals(
-                memory, mask_flatten, spatial_shapes
-            )
-            output_memory = self.enc_output_norm(self.enc_output(output_memory))
-
-            if text_dict is not None:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
-            else:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
-
-            topk_logits = enc_outputs_class_unselected.max(-1)[0]
-            enc_outputs_coord_unselected = (
-                self.enc_out_bbox_embed(output_memory) + output_proposals
-            )  # (bs, \sum{hw}, 4) unsigmoid
-            topk = self.num_queries
-
-            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
-
-            # gather boxes
-            refpoint_embed_undetach = torch.gather(
-                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            )  # unsigmoid
-            refpoint_embed_ = refpoint_embed_undetach.detach()
-            init_box_proposal = torch.gather(
-                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            ).sigmoid()  # sigmoid
-
-            # gather tgt
-            tgt_undetach = torch.gather(
-                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
-            )
-            if self.embed_init_tgt:
-                tgt_ = (
-                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-                )  # nq, bs, d_model
-            else:
-                tgt_ = tgt_undetach.detach()
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-        elif self.two_stage_type == "no":
-            tgt_ = (
-                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, d_model
-            refpoint_embed_ = (
-                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, 4
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-            if self.num_patterns > 0:
-                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
-                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
-                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
-                    self.num_queries, 1
-                )  # 1, n_q*n_pat, d_model
-                tgt = tgt_embed + tgt_pat
-
-            init_box_proposal = refpoint_embed_.sigmoid()
-
-        else:
-            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
-        #########################################################
-        # End preparing tgt
-        # - tgt: bs, NQ, d_model
-        # - refpoint_embed(unsigmoid): bs, NQ, d_model
-        #########################################################
-
-        #########################################################
-        # Begin Decoder
-        #########################################################
-        hs, references = self.decoder(
-            tgt=tgt.transpose(0, 1),
-            memory=memory.transpose(0, 1),
-            memory_key_padding_mask=mask_flatten,
-            pos=lvl_pos_embed_flatten.transpose(0, 1),
-            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            tgt_mask=attn_mask,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-        )
-        #########################################################
-        # End Decoder
-        # hs: n_dec, bs, nq, d_model
-        # references: n_dec+1, bs, nq, query_dim
-        #########################################################
-
-        #########################################################
-        # Begin postprocess
-        #########################################################
-        if self.two_stage_type == "standard":
-            hs_enc = tgt_undetach.unsqueeze(0)
-            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
-        else:
-            hs_enc = ref_enc = None
-        #########################################################
-        # End postprocess
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
-        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
-        #########################################################
-
-        return hs, references, hs_enc, ref_enc, init_box_proposal
-        # hs: (n_dec, bs, nq, d_model)
-        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
-        # ref_enc: sigmoid coordinates. \
-        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        encoder_layer,
-        num_layers,
-        d_model=256,
-        num_queries=300,
-        enc_layer_share=False,
-        text_enhance_layer=None,
-        feature_fusion_layer=None,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-    ):
-        """_summary_
-
-        Args:
-            encoder_layer (_type_): _description_
-            num_layers (_type_): _description_
-            norm (_type_, optional): _description_. Defaults to None.
-            d_model (int, optional): _description_. Defaults to 256.
-            num_queries (int, optional): _description_. Defaults to 300.
-            enc_layer_share (bool, optional): _description_. Defaults to False.
-
-        """
-        super().__init__()
-        # prepare layers
-        self.layers = []
-        self.text_layers = []
-        self.fusion_layers = []
-        if num_layers > 0:
-            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
-
-            if text_enhance_layer is not None:
-                self.text_layers = _get_clones(
-                    text_enhance_layer, num_layers, layer_share=enc_layer_share
-                )
-            if feature_fusion_layer is not None:
-                self.fusion_layers = _get_clones(
-                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
-                )
-        else:
-            self.layers = []
-            del encoder_layer
-
-            if text_enhance_layer is not None:
-                self.text_layers = []
-                del text_enhance_layer
-            if feature_fusion_layer is not None:
-                self.fusion_layers = []
-                del feature_fusion_layer
-
-        self.query_scale = None
-        self.num_queries = num_queries
-        self.num_layers = num_layers
-        self.d_model = d_model
-
-        self.use_checkpoint = use_checkpoint
-        self.use_transformer_ckpt = use_transformer_ckpt
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (H_, W_) in enumerate(spatial_shapes):
-
-            ref_y, ref_x = torch.meshgrid(
-                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
-                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
-            )
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(
-        self,
-        # for images
-        src: Tensor,
-        pos: Tensor,
-        spatial_shapes: Tensor,
-        level_start_index: Tensor,
-        valid_ratios: Tensor,
-        key_padding_mask: Tensor,
-        # for texts
-        memory_text: Tensor = None,
-        text_attention_mask: Tensor = None,
-        pos_text: Tensor = None,
-        text_self_attention_masks: Tensor = None,
-        position_ids: Tensor = None,
-    ):
-        """
-        Input:
-            - src: [bs, sum(hi*wi), 256]
-            - pos: pos embed for src. [bs, sum(hi*wi), 256]
-            - spatial_shapes: h,w of each level [num_level, 2]
-            - level_start_index: [num_level] start point of level in sum(hi*wi).
-            - valid_ratios: [bs, num_level, 2]
-            - key_padding_mask: [bs, sum(hi*wi)]
-
-            - memory_text: bs, n_text, 256
-            - text_attention_mask: bs, n_text
-                False for no padding; True for padding
-            - pos_text: bs, n_text, 256
-
-            - position_ids: bs, n_text
-        Intermedia:
-            - reference_points: [bs, sum(hi*wi), num_level, 2]
-        Outpus:
-            - output: [bs, sum(hi*wi), 256]
-        """
-
-        output = src
-
-        # preparation and reshape
-        if self.num_layers > 0:
-            reference_points = self.get_reference_points(
-                spatial_shapes, valid_ratios, device=src.device
-            )
-
-        if self.text_layers:
-            # generate pos_text
-            bs, n_text, text_dim = memory_text.shape
-            if pos_text is None and position_ids is None:
-                pos_text = (
-                    torch.arange(n_text, device=memory_text.device)
-                    .float()
-                    .unsqueeze(0)
-                    .unsqueeze(-1)
-                    .repeat(bs, 1, 1)
-                )
-                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
-            if position_ids is not None:
-                pos_text = get_sine_pos_embed(
-                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
-                )
-
-        # main process
-        for layer_id, layer in enumerate(self.layers):
-            # if output.isnan().any() or memory_text.isnan().any():
-            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #         import ipdb; ipdb.set_trace()
-            if self.fusion_layers:
-                if self.use_checkpoint:
-                    output, memory_text = checkpoint.checkpoint(
-                        self.fusion_layers[layer_id],
-                        output,
-                        memory_text,
-                        key_padding_mask,
-                        text_attention_mask,
-                    )
-                else:
-                    output, memory_text = self.fusion_layers[layer_id](
-                        v=output,
-                        l=memory_text,
-                        attention_mask_v=key_padding_mask,
-                        attention_mask_l=text_attention_mask,
-                    )
-
-            if self.text_layers:
-                memory_text = self.text_layers[layer_id](
-                    src=memory_text.transpose(0, 1),
-                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
-                    src_key_padding_mask=text_attention_mask,
-                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
-                ).transpose(0, 1)
-
-            # main process
-            if self.use_transformer_ckpt:
-                output = checkpoint.checkpoint(
-                    layer,
-                    output,
-                    pos,
-                    reference_points,
-                    spatial_shapes,
-                    level_start_index,
-                    key_padding_mask,
-                )
-            else:
-                output = layer(
-                    src=output,
-                    pos=pos,
-                    reference_points=reference_points,
-                    spatial_shapes=spatial_shapes,
-                    level_start_index=level_start_index,
-                    key_padding_mask=key_padding_mask,
-                )
-
-        return output, memory_text
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(
-        self,
-        decoder_layer,
-        num_layers,
-        norm=None,
-        return_intermediate=False,
-        d_model=256,
-        query_dim=4,
-        num_feature_levels=1,
-    ):
-        super().__init__()
-        if num_layers > 0:
-            self.layers = _get_clones(decoder_layer, num_layers)
-        else:
-            self.layers = []
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-        assert return_intermediate, "support return_intermediate only"
-        self.query_dim = query_dim
-        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
-        self.num_feature_levels = num_feature_levels
-
-        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
-        self.query_pos_sine_scale = None
-
-        self.query_scale = None
-        self.bbox_embed = None
-        self.class_embed = None
-
-        self.d_model = d_model
-
-        self.ref_anchor_head = None
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
-        # for memory
-        level_start_index: Optional[Tensor] = None,  # num_levels
-        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        valid_ratios: Optional[Tensor] = None,
-        # for text
-        memory_text: Optional[Tensor] = None,
-        text_attention_mask: Optional[Tensor] = None,
-    ):
-        """
-        Input:
-            - tgt: nq, bs, d_model
-            - memory: hw, bs, d_model
-            - pos: hw, bs, d_model
-            - refpoints_unsigmoid: nq, bs, 2/4
-            - valid_ratios/spatial_shapes: bs, nlevel, 2
-        """
-        output = tgt
-
-        intermediate = []
-        reference_points = refpoints_unsigmoid.sigmoid()
-        ref_points = [reference_points]
-
-        for layer_id, layer in enumerate(self.layers):
-
-            if reference_points.shape[-1] == 4:
-                reference_points_input = (
-                    reference_points[:, :, None]
-                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
-                )  # nq, bs, nlevel, 4
-            else:
-                assert reference_points.shape[-1] == 2
-                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
-            query_sine_embed = gen_sineembed_for_position(
-                reference_points_input[:, :, 0, :]
-            )  # nq, bs, 256*2
-
-            # conditional query
-            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
-            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
-            query_pos = pos_scale * raw_query_pos
-            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-            #     if query_pos.isnan().any() | query_pos.isinf().any():
-            #         import ipdb; ipdb.set_trace()
-
-            # main process
-            output = layer(
-                tgt=output,
-                tgt_query_pos=query_pos,
-                tgt_query_sine_embed=query_sine_embed,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                tgt_reference_points=reference_points_input,
-                memory_text=memory_text,
-                text_attention_mask=text_attention_mask,
-                memory=memory,
-                memory_key_padding_mask=memory_key_padding_mask,
-                memory_level_start_index=level_start_index,
-                memory_spatial_shapes=spatial_shapes,
-                memory_pos=pos,
-                self_attn_mask=tgt_mask,
-                cross_attn_mask=memory_mask,
-            )
-            if output.isnan().any() | output.isinf().any():
-                print(f"output layer_id {layer_id} is nan")
-                try:
-                    num_nan = output.isnan().sum().item()
-                    num_inf = output.isinf().sum().item()
-                    print(f"num_nan {num_nan}, num_inf {num_inf}")
-                except Exception as e:
-                    print(e)
-                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-                    #     import ipdb; ipdb.set_trace()
-
-            # iter update
-            if self.bbox_embed is not None:
-                # box_holder = self.bbox_embed(output)
-                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
-                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
-
-                reference_before_sigmoid = inverse_sigmoid(reference_points)
-                delta_unsig = self.bbox_embed[layer_id](output)
-                outputs_unsig = delta_unsig + reference_before_sigmoid
-                new_reference_points = outputs_unsig.sigmoid()
-
-                reference_points = new_reference_points.detach()
-                # if layer_id != self.num_layers - 1:
-                ref_points.append(new_reference_points)
-
-            intermediate.append(self.norm(output))
-
-        return [
-            [itm_out.transpose(0, 1) for itm_out in intermediate],
-            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
-        ]
-
-
-class DeformableTransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-    ):
-        super().__init__()
-
-        # self attention
-        self.self_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn)
-        self.dropout2 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, src):
-        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
-        src = src + self.dropout3(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(
-        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
-    ):
-        # self attention
-        # import ipdb; ipdb.set_trace()
-        src2 = self.self_attn(
-            query=self.with_pos_embed(src, pos),
-            reference_points=reference_points,
-            value=src,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            key_padding_mask=key_padding_mask,
-        )
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-
-        # ffn
-        src = self.forward_ffn(src)
-
-        return src
-
-
-class DeformableTransformerDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-        use_text_feat_guide=False,
-        use_text_cross_attention=False,
-    ):
-        super().__init__()
-
-        # cross attention
-        self.cross_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # cross attention text
-        if use_text_cross_attention:
-            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-            self.catext_norm = nn.LayerNorm(d_model)
-
-        # self attention
-        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm2 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
-        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm3 = nn.LayerNorm(d_model)
-
-        self.key_aware_proj = None
-        self.use_text_feat_guide = use_text_feat_guide
-        assert not use_text_feat_guide
-        self.use_text_cross_attention = use_text_cross_attention
-
-    def rm_self_attn_modules(self):
-        self.self_attn = None
-        self.dropout2 = None
-        self.norm2 = None
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, tgt):
-        with torch.cuda.amp.autocast(enabled=False):
-            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout4(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward(
-        self,
-        # for tgt
-        tgt: Optional[Tensor],  # nq, bs, d_model
-        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
-        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
-        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
-        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
-        # for memory
-        memory: Optional[Tensor] = None,  # hw, bs, d_model
-        memory_key_padding_mask: Optional[Tensor] = None,
-        memory_level_start_index: Optional[Tensor] = None,  # num_levels
-        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        memory_pos: Optional[Tensor] = None,  # pos for memory
-        # sa
-        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
-        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
-    ):
-        """
-        Input:
-            - tgt/tgt_query_pos: nq, bs, d_model
-            -
-        """
-        assert cross_attn_mask is None
-
-        # self attention
-        if self.self_attn is not None:
-            # import ipdb; ipdb.set_trace()
-            q = k = self.with_pos_embed(tgt, tgt_query_pos)
-            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
-            tgt = tgt + self.dropout2(tgt2)
-            tgt = self.norm2(tgt)
-
-        if self.use_text_cross_attention:
-            tgt2 = self.ca_text(
-                self.with_pos_embed(tgt, tgt_query_pos),
-                memory_text.transpose(0, 1),
-                memory_text.transpose(0, 1),
-                key_padding_mask=text_attention_mask,
-            )[0]
-            tgt = tgt + self.catext_dropout(tgt2)
-            tgt = self.catext_norm(tgt)
-
-        tgt2 = self.cross_attn(
-            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
-            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
-            value=memory.transpose(0, 1),
-            spatial_shapes=memory_spatial_shapes,
-            level_start_index=memory_level_start_index,
-            key_padding_mask=memory_key_padding_mask,
-        ).transpose(0, 1)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # ffn
-        tgt = self.forward_ffn(tgt)
-
-        return tgt
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        num_queries=args.num_queries,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-        query_dim=args.query_dim,
-        activation=args.transformer_activation,
-        num_patterns=args.num_patterns,
-        num_feature_levels=args.num_feature_levels,
-        enc_n_points=args.enc_n_points,
-        dec_n_points=args.dec_n_points,
-        learnable_tgt_init=True,
-        # two stage
-        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
-        embed_init_tgt=args.embed_init_tgt,
-        use_text_enhancer=args.use_text_enhancer,
-        use_fusion_layer=args.use_fusion_layer,
-        use_checkpoint=args.use_checkpoint,
-        use_transformer_ckpt=args.use_transformer_ckpt,
-        use_text_cross_attention=args.use_text_cross_attention,
-        text_dropout=args.text_dropout,
-        fusion_dropout=args.fusion_dropout,
-        fusion_droppath=args.fusion_droppath,
-    )

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

@@ -1,94 +0,0 @@
-from datetime import datetime
-from typing import Iterable, List, Optional, TYPE_CHECKING, Union, Callable
-
-
-from .text import Text, TextType
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleRenderable, RenderableType
-    from .table import Table
-
-FormatTimeCallable = Callable[[datetime], Text]
-
-
-class LogRender:
-    def __init__(
-        self,
-        show_time: bool = True,
-        show_level: bool = False,
-        show_path: bool = True,
-        time_format: Union[str, FormatTimeCallable] = "[%x %X]",
-        omit_repeated_times: bool = True,
-        level_width: Optional[int] = 8,
-    ) -> None:
-        self.show_time = show_time
-        self.show_level = show_level
-        self.show_path = show_path
-        self.time_format = time_format
-        self.omit_repeated_times = omit_repeated_times
-        self.level_width = level_width
-        self._last_time: Optional[Text] = None
-
-    def __call__(
-        self,
-        console: "Console",
-        renderables: Iterable["ConsoleRenderable"],
-        log_time: Optional[datetime] = None,
-        time_format: Optional[Union[str, FormatTimeCallable]] = None,
-        level: TextType = "",
-        path: Optional[str] = None,
-        line_no: Optional[int] = None,
-        link_path: Optional[str] = None,
-    ) -> "Table":
-        from .containers import Renderables
-        from .table import Table
-
-        output = Table.grid(padding=(0, 1))
-        output.expand = True
-        if self.show_time:
-            output.add_column(style="log.time")
-        if self.show_level:
-            output.add_column(style="log.level", width=self.level_width)
-        output.add_column(ratio=1, style="log.message", overflow="fold")
-        if self.show_path and path:
-            output.add_column(style="log.path")
-        row: List["RenderableType"] = []
-        if self.show_time:
-            log_time = log_time or console.get_datetime()
-            time_format = time_format or self.time_format
-            if callable(time_format):
-                log_time_display = time_format(log_time)
-            else:
-                log_time_display = Text(log_time.strftime(time_format))
-            if log_time_display == self._last_time and self.omit_repeated_times:
-                row.append(Text(" " * len(log_time_display)))
-            else:
-                row.append(log_time_display)
-                self._last_time = log_time_display
-        if self.show_level:
-            row.append(level)
-
-        row.append(Renderables(renderables))
-        if self.show_path and path:
-            path_text = Text()
-            path_text.append(
-                path, style=f"link file://{link_path}" if link_path else ""
-            )
-            if line_no:
-                path_text.append(":")
-                path_text.append(
-                    f"{line_no}",
-                    style=f"link file://{link_path}#{line_no}" if link_path else "",
-                )
-            row.append(path_text)
-
-        output.add_row(*row)
-        return output
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from pip._vendor.rich.console import Console
-
-    c = Console()
-    c.print("[on blue]Hello", justify="right")
-    c.log("[on blue]hello", justify="right")

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/samplers/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .distributed_sampler import (
-    InferenceSampler,
-    RandomSubsetTrainingSampler,
-    RepeatFactorTrainingSampler,
-    TrainingSampler,
-)
-
-from .grouped_batch_sampler import GroupedBatchSampler
-
-__all__ = [
-    "GroupedBatchSampler",
-    "TrainingSampler",
-    "RandomSubsetTrainingSampler",
-    "InferenceSampler",
-    "RepeatFactorTrainingSampler",
-]

spaces/Benson/text-generation/Examples/Ajedrez Sin Conexin Apk Para Pc.md

@@ -1,69 +0,0 @@
-<br />
-<h1>Cómo jugar al ajedrez sin conexión en el PC con APK</h1>
-<p>El ajedrez es uno de los juegos de estrategia más antiguos y populares del mundo. Es un juego que desafía tu mente, mejora tu concentración y mejora tu pensamiento lógico. Sin embargo, no todo el mundo tiene acceso a un tablero de ajedrez o un socio de ajedrez en todo momento. Es por eso que jugar ajedrez sin conexión en tu PC con un archivo APK puede ser una gran opción para los amantes del ajedrez. </p>
-<h2>ajedrez sin conexión apk para pc</h2><br /><p><b><b>DOWNLOAD</b> &bull;&bull;&bull; <a href="https://bltlly.com/2v6Mdy">https://bltlly.com/2v6Mdy</a></b></p><br /><br />
-<p>En este artículo, le mostraremos lo que es el ajedrez sin conexión APK, cómo descargar e instalar en su PC, y cómo jugar al ajedrez sin conexión en su PC con facilidad y diversión. ¡Vamos a empezar! </p>
- <h2>¿Qué es el ajedrez fuera de línea APK? </h2>
-<p>Ajedrez sin conexión APK es una aplicación para Android que le permite jugar al ajedrez contra el ordenador sin conexión a Internet. Usted puede elegir entre diferentes niveles de dificultad, desde principiante a experto, y disfrutar de una experiencia de ajedrez realista y suave. También puede personalizar la apariencia del tablero y las piezas, y cambiar entre vistas 2D y 3D. </p>
- <h3>Características del ajedrez fuera de línea APK</h3>
-<p>Algunas de las características del ajedrez fuera de línea APK son:</p>
-<p></p>
-<ul>
-<li>Gratis y fácil de jugar</li>
-<li>8 niveles de dificultad</li>
-<li>Funciones de sugerencia y deshacer</li>
-<li>Estadísticas personales y progreso</li>
-<li>Increíbles gráficos y efectos de sonido</li>
-<li>Bajo tamaño de aplicación y consumo de batería</li>
-</ul>
- <h3>Beneficios de Jugar Ajedrez Offline</h3>
-<p>Jugar ajedrez sin conexión tiene muchos beneficios, como:</p>
-<ul>
-<li> Puede jugar en cualquier momento y en cualquier lugar, sin preocuparse por la conexión a Internet o el uso de datos</li>
-<li>Puedes practicar tus habilidades de ajedrez y aprender nuevas estrategias a tu propio ritmo</li>
-<li>Puedes desafiarte a ti mismo con diferentes niveles de dificultad y mejorar tu juego</li>
-<li>Puedes divertirte y relajarte mientras estimulas tu cerebro</li>
-</ul>
- <h2> Cómo descargar e instalar ajedrez sin conexión APK en PC</h2>
-
- <h3>Usando el emulador de BlueStacks</h3>
-<p>Para usar el emulador de BlueStacks, siga estos pasos:</p>
-<ol>
-<li>Descargar BlueStacks desde su sitio web oficial <a href="( 1 )">aquí</a></li>
-<li>Instalar BlueStacks en su PC siguiendo las instrucciones en la pantalla</li>
-<li>Abra BlueStacks e inicie sesión con su cuenta de Google (o cree una si no tiene una)</li>
-<li>Búsqueda de ajedrez - Juego de mesa sin conexión por GamoVation en la barra de búsqueda en la esquina superior derecha</li>
-<li>Haga clic para instalar Chess - Offline Board Game desde los resultados de búsqueda</li>
-<li>Haga clic en el Ajedrez - Offline Board Game icono en la pantalla de inicio para comenzar a jugar</li>
-</ol>
- <h3>Usando otros emuladores</h3>
-<p>Si prefiere usar otros emuladores, también puede descargar e instalar ajedrez sin conexión APK en su PC siguiendo estos pasos:</p>
-<ol>
-<li>Descargar ajedrez sin conexión APK de una fuente de confianza <a href="( 2 )">aquí</a></li>
-<li>Descargue un emulador de Android de su elección desde su sitio web oficial (como NoxPlayer, LDPlayer, MEmu, etc.)</li>
-<li>Instale el emulador en su PC siguiendo las instrucciones en la pantalla</li>
-<li>Abra el emulador y arrastre y suelte el archivo APK sin conexión de ajedrez en él (o use el navegador incorporado para localizarlo)</li>
-<li>Espere a que la instalación se complete y haga clic en el icono de ajedrez sin conexión para comenzar a jugar</li>
-</ol>
- <h2. <h2>Cómo jugar ajedrez sin conexión en PC</h2>
-<p>Ahora que ha descargado e instalado el ajedrez sin conexión APK en su PC, usted está listo para jugar ajedrez sin conexión en su PC. Aquí hay algunos consejos y trucos para ayudarle a disfrutar del juego. </p>
- <h3>Elegir el nivel de dificultad</h3>
-<p>Antes de comenzar un juego, puede elegir el nivel de dificultad que se adapte a su habilidad y preferencia. Hay 8 niveles de dificultad, desde principiante hasta experto. Puede cambiar el nivel en cualquier momento durante el juego haciendo clic en el icono de configuración en la esquina superior derecha. Cuanto más alto sea el nivel, más desafiante e inteligente será el oponente de la computadora. </p>
-
-<p>Si está atascado o necesita alguna orientación, puede usar la función de sugerencia para obtener una sugerencia para su próximo movimiento. Simplemente haga clic en el icono de la bombilla en la esquina inferior izquierda y el mejor movimiento se resaltará en el tablero. También puede usar la función de deshacer para recuperar su último movimiento si cometió un error o cambió de opinión. Simplemente haga clic en el icono de flecha en la esquina inferior derecha y su movimiento se invertirá. </p>
- <h3>Seguimiento de su progreso y estadísticas</h3>
-<p>Puede realizar un seguimiento de su progreso y estadísticas haciendo clic en el icono del trofeo en la esquina superior izquierda. Puedes ver cuántos juegos has jugado, ganado, perdido y sorteado, así como tu tasa de ganancias y calificación. También puedes ver tus mejores movimientos, las rachas más largas y las puntuaciones más altas. Puede restablecer sus estadísticas en cualquier momento haciendo clic en el botón de reinicio en la parte inferior de la pantalla. </p>
- <h2>Conclusión</h2>
-<p>Jugar ajedrez sin conexión en su PC con un archivo APK es una gran manera de disfrutar del ajedrez sin conexión a Internet. Puede descargar e instalar ajedrez sin conexión APK en su PC fácilmente con un emulador de Android, como BlueStacks u otros. Puede elegir entre diferentes niveles de dificultad, usar las funciones de sugerencia y deshacer, y realizar un seguimiento de su progreso y estadísticas. Jugar ajedrez offline puede ayudarte a mejorar tus habilidades de ajedrez, estimular tu cerebro y divertirte. </p>
-<p>Esperamos que este artículo te haya ayudado a aprender a jugar ajedrez sin conexión en PC con APK. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Feliz jugando! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre jugar al ajedrez sin conexión en el PC con APK:</p>
-<tabla>
-<tr><td><b>Q: ¿Es el ajedrez sin conexión APK seguro para descargar e instalar? </b></td><td><b>A: Sí, el ajedrez sin conexión APK es seguro para descargar e instalar siempre y cuando lo obtenga de una fuente de confianza, como <a href="">here</a>. Sin embargo, siempre debe escanear cualquier archivo APK con un software antivirus antes de instalarlo en su PC.</b></td></tr>
-
-<tr><td><b>Q: ¿Puedo personalizar el tablero y las piezas de ajedrez fuera de línea APK? </b></td><td><b>A: Sí, puede personalizar el tablero y las piezas de ajedrez sin conexión APK haciendo clic en el icono de configuración en la esquina superior derecha. Puede elegir entre diferentes colores, estilos y temas para el tablero y las piezas. También puede cambiar entre vistas 2D y 3D haciendo clic en el icono 3D en la parte inferior de la pantalla. </b></td></tr>
-<tr><td><b>Q: ¿Cómo puedo mejorar mis habilidades de ajedrez jugando ajedrez fuera de línea? </b></td><td><b>A: Jugar ajedrez sin conexión puede ayudarte a mejorar tus habilidades de ajedrez al darte práctica, retroalimentación y desafío. Puedes practicar tus movimientos y estrategias jugando contra diferentes niveles de dificultad. Puede obtener retroalimentación mediante el uso de la función de sugerencia o comprobar sus estadísticas. Puede desafiarse a sí mismo aumentando el nivel de dificultad o estableciendo un límite de tiempo para cada movimiento. </b></td></tr>
-<tr><td><b>Q: ¿Cuáles son algunas otras buenas aplicaciones de ajedrez para PC? </b></td><td><b>A: Algunas otras buenas aplicaciones de ajedrez para PC son Chess.com, Lichess.org, Chess24.com, ChessBase.com, etc. Estas aplicaciones le permiten jugar al ajedrez en línea con millones de jugadores de todo el mundo, así como acceder a varias características como puzzles, lecciones, torneos, etc.</b></td></tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar El Depredador 2018.md

@@ -1,151 +0,0 @@
-
-<h1>Cómo descargar la película Predator 2018 legalmente y gratis</h1>
-<p>The Predator es una película de acción de ciencia ficción de 2018 dirigida por Shane Black y protagonizada por Boyd Holbrook, Trevante Rhodes, Jacob Tremblay, Keegan-Michael Key, Olivia Munn, Thomas Jane, Alfie Allen y Sterling K. Brown. Es la cuarta entrega de la franquicia Predator y sigue a un grupo de soldados y un científico que deben luchar contra un par invasor de depredadores y descubrir sus planes para la humanidad. </p>
-<p>Si eres un fan de la serie Predator o disfrutas de películas emocionantes y sangrientas, es posible que quieras ver la película The Predator 2018. Sin embargo, encontrar y descargar películas de forma legal y gratuita puede ser un reto, ya que muchos sitios web ofrecen contenido pirata o ilegal que puede meterte en problemas con la ley o exponerte a virus o malware. Además, descargar películas puede ocupar mucho espacio en tu dispositivo y requerir una conexión a Internet rápida y estable. </p>
-<h2>descargar el depredador 2018</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6MV3">https://bltlly.com/2v6MV3</a></b></p><br /><br />
-<p>Afortunadamente, hay algunas maneras de descargar la película de The Predator 2018 legalmente y gratis, sin comprometer su seguridad o calidad. En este artículo, le mostraremos dos métodos para hacerlo: el uso de servicios de transmisión que permiten la visualización sin conexión y el uso de sitios de películas gratuitas que ofrecen contenido de dominio público o con licencia. También explicaremos cómo usar cada método, así como sus ventajas y desventajas. </p>
- <h2>Método 1: Uso de servicios de streaming que permiten ver sin conexión</h2>
-<p>Una de las formas más fáciles de descargar la película de The Predator 2018 de forma legal y gratuita es utilizar servicios de streaming que permiten la visualización sin conexión. Estas son plataformas que te permiten ver películas en línea con una suscripción, pero también te permiten descargarlas en tu dispositivo para verlas más tarde sin conexión a Internet. Algunos de los servicios de streaming más populares que ofrecen esta función son Netflix y Amazon Prime Video.</p>
- <h3>Netflix</h3>
-
-<p>Las ventajas de usar Netflix para descargar The Predator 2018 de forma legal y gratuita son:</p>
-<ul>
-<li> Puedes disfrutar de vídeo y audio de alta calidad. </li>
-<li>Puede elegir entre diferentes resoluciones y formatos dependiendo del espacio de almacenamiento de su dispositivo. </li>
-<li> Puede acceder a otras características como subtítulos, calificaciones, recomendaciones, etc.</li>
-<li>Puede beneficiarse de velocidades de transferencia rápidas y una interfaz de usuario fácil. </li>
-</ul>
-<p>Las desventajas de usar Netflix para descargar la película de The Predator 2018 legalmente y gratis son:</p>
-<ul>
-<li>Necesitas tener una suscripción de pago para usar Netflix.</li>
-<li>Necesitas tener un dispositivo compatible que soporte descargas de Netflix, como iOS, Android, Windows 10 o tabletas Fire. </li>
-<li>Necesitas tener suficiente espacio de almacenamiento en tu dispositivo para descargar la película. </li>
-<li>Necesitas tener una buena conexión a Internet para descargar la película. </li>
-<li> Solo puede ver la película dentro de la aplicación de Netflix y no en otros jugadores o dispositivos. </li>
-<li> Solo puede mantener la película en su dispositivo por un tiempo limitado, dependiendo del título y su región. </li>
-</ul>
-<p>Para descargar la película The Predator 2018 de forma legal y gratuita en Netflix, sigue estos pasos:</p>
-<ol>
-<li>Abra la aplicación de Netflix en su dispositivo e inicie sesión con su cuenta. </li>
-<li>Buscar la película Predator 2018 en la barra de búsqueda de la aplicación o navegar por las categorías. </li>
-<li>Toque en el título de la película para abrir su página de descripción. </li>
-<li> Si la película está disponible para su descarga, verá un botón de descarga con un icono de flecha hacia abajo. Toque en él para comenzar a descargar la película. </li>
-<li>Puede comprobar el progreso de su descarga en la sección Descargas de la aplicación. También puede pausar, reanudar o cancelar su descarga allí. </li>
-<li>Una vez completada la descarga, puede ver la película sin conexión en la sección Descargas de la aplicación. También puede eliminar la película de su dispositivo cuando haya terminado de verla. </li>
-</ol>
- <h3>Video de Amazon Prime</h3>
-
-<p>Las ventajas de usar Amazon Prime Video para descargar la película The Predator 2018 legalmente y gratis son:</p>
-<ul>
-<li> Puedes disfrutar de vídeo y audio de alta calidad. </li>
-<li>Puede elegir entre diferentes resoluciones y formatos dependiendo del espacio de almacenamiento de su dispositivo. </li>
-<li> Puede acceder a otras características como subtítulos, calificaciones, recomendaciones, etc.</li>
-<li>Puede beneficiarse de velocidades de transferencia rápidas y una interfaz de usuario fácil. </li>
-</ul>
-<p>Las desventajas de usar Amazon Prime Video para descargar The Predator 2018 película legalmente y gratis son:</p>
-<ul>
-<li>Necesitas tener una membresía Prime pagada para usar Amazon Prime Video.</li>
-<li>Es necesario tener un dispositivo compatible que soporta descargas de Amazon Prime Video, tales como iOS, Android, tabletas de fuego, o Fire TV stick. </li>
-<li>Necesitas tener suficiente espacio de almacenamiento en tu dispositivo para descargar la película. </li>
-<li>Necesitas tener una buena conexión a Internet para descargar la película. </li>
-<li>Solo puede ver la película dentro de la aplicación Amazon Prime Video y no en otros reproductores o dispositivos. </li>
-<li> Solo puede mantener la película en su dispositivo por un tiempo limitado, dependiendo del título y su región. </li>
-</ul>
-<p>Para descargar la película Predator 2018 legalmente y gratis en Amazon Prime Video, siga estos pasos:</p>
-<p></p>
-<ol>
-<li>Abra la aplicación Amazon Prime Video en su dispositivo e inicie sesión con su cuenta. </li>
-<li>Buscar la película Predator 2018 en la barra de búsqueda de la aplicación o navegar por las categorías. </li>
-<li>Toque en el título de la película para abrir su página de detalles. </li>
-<li> Si la película es elegible para descargar, verá un botón de descarga con un icono de flecha hacia abajo. Toque en él para comenzar a descargar la película. </li>
-<li>Puede comprobar el progreso de su descarga en la sección Mis cosas de la aplicación. También puede pausar, reanudar o cancelar su descarga allí. </li>
-
-</ol>
- <h2>Método 2: Usando sitios de películas gratis que ofrecen dominio público o contenido con licencia</h2>
-<p>Otra forma de descargar la película The Predator 2018 de forma legal y gratuita es utilizar sitios de películas gratuitas que ofrecen contenido de dominio público o con licencia. Estos son sitios web que alojan películas que están en el dominio público (lo que significa que ya no están protegidos por derechos de autor) o con licencia de sus propietarios para su distribución gratuita. Algunos de estos sitios web también pueden utilizar la tecnología peer-to-peer (P2P) como BitTorrent para compartir archivos entre los usuarios. Sin embargo, no todos los sitios de películas gratuitas son legales o seguros, por lo que debe tener cuidado al elegir uno y verificar su legitimidad y reputación antes de descargar cualquier película. Aquí hay dos ejemplos de sitios de películas gratuitas que ofrecen contenido de dominio público o con licencia que puede usar para descargar la película The Predator 2018 legalmente y de forma gratuita. </p>
- <h3>Torrentes de dominio público</h3>
-<p>Public Domain Torrents es un sitio web que ofrece una gran colección de películas que están en el dominio público y se pueden descargar legalmente y de forma gratuita utilizando BitTorrent. BitTorrent es un protocolo P2P que permite a los usuarios compartir archivos entre sí sin un servidor central. Necesitará un cliente BitTorrent, como uTorrent o BitTorrent, para descargar películas de Torrents de dominio público. </p>
-<p>Las ventajas de usar torrents de dominio público para descargar la película The Predator 2018 legalmente y gratis son:</p>
-<ul>
-<li>Puedes encontrar una variedad de géneros y categorías de películas, desde clásicos hasta terror y ciencia ficción. </li>
-<li> Puede elegir entre diferentes resoluciones y formatos dependiendo de la compatibilidad y las preferencias de su dispositivo. </li>
-<li>Puede beneficiarse de velocidades de transferencia rápidas y bajo consumo de ancho de banda, siempre y cuando haya suficientes sembradoras (usuarios que tienen el archivo completo) y pares (usuarios que tienen partes del archivo). </li>
-</ul>
-<p>Las desventajas de usar torrents de dominio público para descargar la película The Predator 2018 legalmente y gratis son:</p>
-<ul>
-
-<li>Necesitas tener suficiente espacio de almacenamiento en tu dispositivo para descargar la película. </li>
-<li>Necesitas tener una buena conexión a Internet para descargar la película. </li>
-<li> Puede encontrar algunos anuncios o ventanas emergentes en el sitio web que pueden ser molestos o engañosos. </li>
-<li>Es posible que no encuentre las últimas o más populares películas en Torrents de dominio público, ya que son en su mayoría títulos más antiguos o menos conocidos. </li>
-</ul>
-<p>Para descargar la película de Predator 2018 de forma legal y gratuita en Public Domain Torrents, siga estos pasos:</p>
-<ol>
-<li>Abra su navegador web y vaya a <a href="">Torrents de dominio público</a>. </li>
-<li>Buscar la película Predator 2018 en la barra de búsqueda del sitio web o navegar por las categorías. </li>
-<li>Haga clic en el título de la película para abrir su página de detalles. </li>
-<li>Elija la resolución y el formato que desea descargar, como DivX, iPod, PSP, etc.</li>
-<li>Haga clic en el botón Descargar con un icono magnético para abrir el archivo torrent en su cliente BitTorrent. </li>
-<li>Su cliente BitTorrent comenzará a descargar la película de otros usuarios que la tienen. Puede comprobar el progreso de su descarga en la interfaz de su cliente. También puede pausar, reanudar o cancelar su descarga allí. </li>
-<li>Una vez completada la descarga, puede ver la película sin conexión en su dispositivo utilizando cualquier reproductor multimedia que soporte el formato de archivo. </li>
-</ol>
- <h3>Crepitar</h3>
-<p>Crackle es un sitio web que ofrece una selección de películas, programas de televisión, originales y más que están licenciados por Sony Pictures Entertainment para su transmisión y descarga gratuitas. Puedes ver películas en línea en Crackle con anuncios, o puedes descargarlas para verlas sin conexión en Crackle con anuncios usando su aplicación o sitio web. </p>
-<p>Las ventajas de usar Crackle para descargar la película The Predator 2018 legalmente y gratis son:</p>
-<ul>
-<li>Puedes encontrar una variedad de géneros y categorías de películas, desde acción hasta comedia y drama. </li>
-<li> Puedes disfrutar de vídeo y audio de alta calidad. </li>
-
-<li> Usted puede beneficiarse de la interfaz de usuario fácil y la navegación. </li>
-</ul>
-<p>Las desventajas de usar Crackle para descargar la película The Predator 2018 legalmente y gratis son:</p>
-<ul>
-<li>Necesitas crear una cuenta gratuita o iniciar sesión con tu cuenta de Facebook para usar Crackle.</li>
-<li>Necesitas tener un dispositivo compatible que soporte descargas de Crackle, como iOS, Android, Roku, Amazon Fire TV, Apple TV, Chromecast, PlayStation 4, Xbox One o Smart TV.</li>
-<li>Necesitas tener suficiente espacio de almacenamiento en tu dispositivo para descargar la película. </li>
-<li>Necesitas tener una buena conexión a Internet para descargar la película. </li>
-<li>Tienes que ver anuncios antes y durante la película, incluso cuando lo ves sin conexión. </li>
-<li> Solo puede mantener la película en su dispositivo por un tiempo limitado, dependiendo del título y su región. </li>
-</ul>
-<p>Para descargar The Predator 2018 película legalmente y gratis en Crackle, siga estos pasos:</p>
-<ol>
-<li>Abra la aplicación o sitio web Crackle en su dispositivo e inicie sesión con su cuenta o cuenta de Facebook. </li>
-<li>Buscar la película Predator 2018 en la barra de búsqueda de la aplicación o sitio web o navegar por las categorías. </li>
-<li>Toque en el título de la película para abrir su página de detalles. </li>
-<li> Si la película está disponible para su descarga, verá un botón de descarga con un icono de flecha hacia abajo. Toque en él para comenzar a descargar la película. </li>
-<li>Puede comprobar el progreso de su descarga en la sección Mi Crackle de la aplicación o sitio web. También puede pausar, reanudar o cancelar su descarga allí. </li>
-<li>Una vez completada la descarga, puedes ver la película sin conexión en la sección My Crackle de la aplicación o sitio web. También puede eliminar la película de su dispositivo cuando haya terminado de verla. </li>
-</ol>
- <h2>Conclusión</h2>
-
-<p>El mejor método para descargar la película The Predator 2018 de forma legal y gratuita depende de tus preferencias y necesidades. Si desea disfrutar de vídeo y audio de alta calidad, acceder a otras funciones y beneficiarse de una interfaz de usuario y navegación fáciles, es posible que desee utilizar servicios de transmisión como Netflix o Amazon Prime Video. Sin embargo, necesitará tener una suscripción de pago, un dispositivo compatible, suficiente espacio de almacenamiento, una buena conexión a Internet y ver anuncios. También tendrá un tiempo limitado para mantener la película en su dispositivo. </p>
-<p>Si desea encontrar una variedad de géneros y categorías de películas, elegir entre diferentes resoluciones y formatos, y beneficiarse de velocidades de transferencia rápidas y bajo consumo de ancho de banda, es posible que desee utilizar sitios de películas gratuitas como Public Domain Torrents o Crackle. Sin embargo, necesitará tener un cliente BitTorrent, una cuenta gratuita, suficiente espacio de almacenamiento, una buena conexión a Internet y ver anuncios. También puede encontrar algunos anuncios o ventanas emergentes en el sitio web que pueden ser molestos o engañosos. Es posible que no encuentres las películas más recientes o más populares en estos sitios, ya que en su mayoría son títulos más antiguos o menos conocidos. </p>
-<p>Cualquiera que sea el método que elija, asegúrese de descargar la película The Predator 2018 legalmente y de forma gratuita de fuentes confiables y seguras. No descargue películas de sitios web piratas o ilegales que puedan meterlo en problemas con la ley o exponerlo a virus o malware. Asimismo, respetar los derechos de los creadores y propietarios de las películas y no distribuirlas o venderlas sin su permiso. </p>
-<p>Esperamos que este artículo te haya ayudado a aprender a descargar The Predator 2018 de forma legal y gratuita. Si tiene alguna pregunta o comentario, por favor háganoslo saber en los comentarios a continuaci��n. ¡Feliz viendo! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre la descarga de la película de The Predator 2018 de forma legal y gratuita:</p>
- <h3>¿Cuáles son algunos otros servicios de streaming que permiten la visualización sin conexión? </h3>
-
- <h3>¿Cuáles son algunos otros sitios de películas gratuitas que ofrecen contenido legal? </h3>
-<p>Algunos otros sitios de películas gratuitas que ofrecen contenido legal son Tubi TV, Pluto TV, Popcornflix, Vudu Movies on Us, Kanopy, SnagFilms, Yidio, etc. Sin embargo, no todo el contenido de estos sitios web se puede descargar, y algunos de ellos pueden tener una disponibilidad o calidad limitadas. También tendrá que ver anuncios y comprobar su legitimidad y seguridad antes de descargar cualquier película. </p>
- <h3>¿Cómo puedo comprobar si una película está en el dominio público o con licencia? </h3>
-<p>Una forma de comprobar si una película está en el dominio público o con licencia es buscar su información de derechos de autor en su sitio web o fuente. También puede utilizar herramientas en línea como <a href=">Public Domain Sherpa</a> o <a href=">Creative Commons Search</a> para encontrar películas que están en el dominio público o con licencia bajo diferentes términos. Sin embargo, estas herramientas no siempre son precisas o actualizadas, por lo que siempre debes hacer tu propia investigación y verificación antes de descargar cualquier película. </p>
- <h3>¿Cómo puedo evitar virus o malware al descargar películas? </h3>
-<p>Algunas maneras de evitar virus o malware al descargar películas son:</p>
-<ul>
-<li>Utilice fuentes confiables y seguras que ofrecen contenido legal y gratuito. </li>
-<li>Utilice un software antivirus confiable y un firewall en su dispositivo y escanee sus descargas regularmente. </li>
-<li>Utilice un servicio VPN para proteger su privacidad y seguridad en línea. </li>
-<li>Evite hacer clic en enlaces, anuncios o ventanas emergentes sospechosos que puedan redirigirle a sitios web o descargas maliciosos. </li>
-<li>Evite abrir o ejecutar archivos o programas desconocidos que puedan contener virus o malware. </li>
-</ul>
- <h3>¿Cómo puedo ver películas descargadas en diferentes dispositivos? </h3>
-<p>Algunas formas de ver películas descargadas en diferentes dispositivos son:</p>
-<ul>
-<li>Utilice un reproductor multimedia compatible que soporte el formato de archivo de la película. </li>
-<li> Utilice un software de conversión de vídeo o una herramienta en línea para cambiar el formato de archivo de la película para adaptarse a su dispositivo. </li>
-
-<li>Utilice un dispositivo de transmisión, como Chromecast, Roku, Apple TV, etc., para emitir la película desde su dispositivo a su TV.</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/resolvelib/providers.py

@@ -1,133 +0,0 @@
-class AbstractProvider(object):
-    """Delegate class to provide the required interface for the resolver."""
-
-    def identify(self, requirement_or_candidate):
-        """Given a requirement, return an identifier for it.
-
-        This is used to identify a requirement, e.g. whether two requirements
-        should have their specifier parts merged.
-        """
-        raise NotImplementedError
-
-    def get_preference(
-        self,
-        identifier,
-        resolutions,
-        candidates,
-        information,
-        backtrack_causes,
-    ):
-        """Produce a sort key for given requirement based on preference.
-
-        The preference is defined as "I think this requirement should be
-        resolved first". The lower the return value is, the more preferred
-        this group of arguments is.
-
-        :param identifier: An identifier as returned by ``identify()``. This
-            identifies the dependency matches which should be returned.
-        :param resolutions: Mapping of candidates currently pinned by the
-            resolver. Each key is an identifier, and the value is a candidate.
-            The candidate may conflict with requirements from ``information``.
-        :param candidates: Mapping of each dependency's possible candidates.
-            Each value is an iterator of candidates.
-        :param information: Mapping of requirement information of each package.
-            Each value is an iterator of *requirement information*.
-        :param backtrack_causes: Sequence of requirement information that were
-            the requirements that caused the resolver to most recently backtrack.
-
-        A *requirement information* instance is a named tuple with two members:
-
-        * ``requirement`` specifies a requirement contributing to the current
-          list of candidates.
-        * ``parent`` specifies the candidate that provides (depended on) the
-          requirement, or ``None`` to indicate a root requirement.
-
-        The preference could depend on various issues, including (not
-        necessarily in this order):
-
-        * Is this package pinned in the current resolution result?
-        * How relaxed is the requirement? Stricter ones should probably be
-          worked on first? (I don't know, actually.)
-        * How many possibilities are there to satisfy this requirement? Those
-          with few left should likely be worked on first, I guess?
-        * Are there any known conflicts for this requirement? We should
-          probably work on those with the most known conflicts.
-
-        A sortable value should be returned (this will be used as the ``key``
-        parameter of the built-in sorting function). The smaller the value is,
-        the more preferred this requirement is (i.e. the sorting function
-        is called with ``reverse=False``).
-        """
-        raise NotImplementedError
-
-    def find_matches(self, identifier, requirements, incompatibilities):
-        """Find all possible candidates that satisfy the given constraints.
-
-        :param identifier: An identifier as returned by ``identify()``. This
-            identifies the dependency matches of which should be returned.
-        :param requirements: A mapping of requirements that all returned
-            candidates must satisfy. Each key is an identifier, and the value
-            an iterator of requirements for that dependency.
-        :param incompatibilities: A mapping of known incompatibilities of
-            each dependency. Each key is an identifier, and the value an
-            iterator of incompatibilities known to the resolver. All
-            incompatibilities *must* be excluded from the return value.
-
-        This should try to get candidates based on the requirements' types.
-        For VCS, local, and archive requirements, the one-and-only match is
-        returned, and for a "named" requirement, the index(es) should be
-        consulted to find concrete candidates for this requirement.
-
-        The return value should produce candidates ordered by preference; the
-        most preferred candidate should come first. The return type may be one
-        of the following:
-
-        * A callable that returns an iterator that yields candidates.
-        * An collection of candidates.
-        * An iterable of candidates. This will be consumed immediately into a
-          list of candidates.
-        """
-        raise NotImplementedError
-
-    def is_satisfied_by(self, requirement, candidate):
-        """Whether the given requirement can be satisfied by a candidate.
-
-        The candidate is guaranteed to have been generated from the
-        requirement.
-
-        A boolean should be returned to indicate whether ``candidate`` is a
-        viable solution to the requirement.
-        """
-        raise NotImplementedError
-
-    def get_dependencies(self, candidate):
-        """Get dependencies of a candidate.
-
-        This should return a collection of requirements that `candidate`
-        specifies as its dependencies.
-        """
-        raise NotImplementedError
-
-
-class AbstractResolver(object):
-    """The thing that performs the actual resolution work."""
-
-    base_exception = Exception
-
-    def __init__(self, provider, reporter):
-        self.provider = provider
-        self.reporter = reporter
-
-    def resolve(self, requirements, **kwargs):
-        """Take a collection of constraints, spit out the resolution result.
-
-        This returns a representation of the final resolution state, with one
-        guarenteed attribute ``mapping`` that contains resolved candidates as
-        values. The keys are their respective identifiers.
-
-        :param requirements: A collection of constraints.
-        :param kwargs: Additional keyword arguments that subclasses may accept.
-
-        :raises: ``self.base_exception`` or its subclass.
-        """
-        raise NotImplementedError

spaces/CForGETaass/vits-uma-genshin-honkai/text/cleaners.py

@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text

spaces/CVPR/WALT/mmdet/core/bbox/assigners/grid_assigner.py

@@ -1,155 +0,0 @@
-import torch
-
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .assign_result import AssignResult
-from .base_assigner import BaseAssigner
-
-
-@BBOX_ASSIGNERS.register_module()
-class GridAssigner(BaseAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-
-    Each proposals will be assigned with `-1`, `0`, or a positive integer
-    indicating the ground truth index.
-
-    - -1: don't care
-    - 0: negative sample, no assigned gt
-    - positive integer: positive sample, index (1-based) of assigned gt
-
-    Args:
-        pos_iou_thr (float): IoU threshold for positive bboxes.
-        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
-        min_pos_iou (float): Minimum iou for a bbox to be considered as a
-            positive bbox. Positive samples can have smaller IoU than
-            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
-        gt_max_assign_all (bool): Whether to assign all bboxes with the same
-            highest overlap with some gt to that gt.
-    """
-
-    def __init__(self,
-                 pos_iou_thr,
-                 neg_iou_thr,
-                 min_pos_iou=.0,
-                 gt_max_assign_all=True,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.pos_iou_thr = pos_iou_thr
-        self.neg_iou_thr = neg_iou_thr
-        self.min_pos_iou = min_pos_iou
-        self.gt_max_assign_all = gt_max_assign_all
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-
-    def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None):
-        """Assign gt to bboxes. The process is very much like the max iou
-        assigner, except that positive samples are constrained within the cell
-        that the gt boxes fell in.
-
-        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
-        will be assigned with -1, 0, or a positive number. -1 means don't care,
-        0 means negative sample, positive number is the index (1-based) of
-        assigned gt.
-        The assignment is done in following steps, the order matters.
-
-        1. assign every bbox to -1
-        2. assign proposals whose iou with all gts <= neg_iou_thr to 0
-        3. for each bbox within a cell, if the iou with its nearest gt >
-            pos_iou_thr and the center of that gt falls inside the cell,
-            assign it to that bbox
-        4. for each gt bbox, assign its nearest proposals within the cell the
-            gt bbox falls in to itself.
-
-        Args:
-            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
-            box_responsible_flags (Tensor): flag to indicate whether box is
-                responsible for prediction, shape(n, )
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
-
-        # compute iou between all gt and bboxes
-        overlaps = self.iou_calculator(gt_bboxes, bboxes)
-
-        # 1. assign -1 by default
-        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
-                                             -1,
-                                             dtype=torch.long)
-
-        if num_gts == 0 or num_bboxes == 0:
-            # No ground truth or boxes, return empty assignment
-            max_overlaps = overlaps.new_zeros((num_bboxes, ))
-            if num_gts == 0:
-                # No truth, assign everything to background
-                assigned_gt_inds[:] = 0
-            if gt_labels is None:
-                assigned_labels = None
-            else:
-                assigned_labels = overlaps.new_full((num_bboxes, ),
-                                                    -1,
-                                                    dtype=torch.long)
-            return AssignResult(
-                num_gts,
-                assigned_gt_inds,
-                max_overlaps,
-                labels=assigned_labels)
-
-        # 2. assign negative: below
-        # for each anchor, which gt best overlaps with it
-        # for each anchor, the max iou of all gts
-        # shape of max_overlaps == argmax_overlaps == num_bboxes
-        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-        if isinstance(self.neg_iou_thr, float):
-            assigned_gt_inds[(max_overlaps >= 0)
-                             & (max_overlaps <= self.neg_iou_thr)] = 0
-        elif isinstance(self.neg_iou_thr, (tuple, list)):
-            assert len(self.neg_iou_thr) == 2
-            assigned_gt_inds[(max_overlaps > self.neg_iou_thr[0])
-                             & (max_overlaps <= self.neg_iou_thr[1])] = 0
-
-        # 3. assign positive: falls into responsible cell and above
-        # positive IOU threshold, the order matters.
-        # the prior condition of comparision is to filter out all
-        # unrelated anchors, i.e. not box_responsible_flags
-        overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1.
-
-        # calculate max_overlaps again, but this time we only consider IOUs
-        # for anchors responsible for prediction
-        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-        # for each gt, which anchor best overlaps with it
-        # for each gt, the max iou of all proposals
-        # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts
-        gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
-
-        pos_inds = (max_overlaps >
-                    self.pos_iou_thr) & box_responsible_flags.type(torch.bool)
-        assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
-
-        # 4. assign positive to max overlapped anchors within responsible cell
-        for i in range(num_gts):
-            if gt_max_overlaps[i] > self.min_pos_iou:
-                if self.gt_max_assign_all:
-                    max_iou_inds = (overlaps[i, :] == gt_max_overlaps[i]) & \
-                         box_responsible_flags.type(torch.bool)
-                    assigned_gt_inds[max_iou_inds] = i + 1
-                elif box_responsible_flags[gt_argmax_overlaps[i]]:
-                    assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
-
-        # assign labels of positive anchors
-        if gt_labels is not None:
-            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
-            pos_inds = torch.nonzero(
-                assigned_gt_inds > 0, as_tuple=False).squeeze()
-            if pos_inds.numel() > 0:
-                assigned_labels[pos_inds] = gt_labels[
-                    assigned_gt_inds[pos_inds] - 1]
-
-        else:
-            assigned_labels = None
-
-        return AssignResult(
-            num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)

spaces/CVPR/lama-example/bin/predict.py

@@ -1,89 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
-#       indir=<path to input data> \
-#       outdir=<where to store predicts>
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint
-from saicinpainting.utils import register_debug_signal_handlers
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
-def main(predict_config: OmegaConf):
-    try:
-        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
-
-        device = torch.device(predict_config.device)
-
-        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
-        with open(train_config_path, 'r') as f:
-            train_config = OmegaConf.create(yaml.safe_load(f))
-        
-        train_config.training_model.predict_only = True
-
-        out_ext = predict_config.get('out_ext', '.png')
-
-        checkpoint_path = os.path.join(predict_config.model.path, 
-                                       'models', 
-                                       predict_config.model.checkpoint)
-        model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
-        model.freeze()
-        model.to(device)
-
-        if not predict_config.indir.endswith('/'):
-            predict_config.indir += '/'
-
-        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
-        with torch.no_grad():
-            for img_i in tqdm.trange(len(dataset)):
-                mask_fname = dataset.mask_filenames[img_i]
-                cur_out_fname = os.path.join(
-                    predict_config.outdir, 
-                    os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
-                )
-                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-                batch = move_to_device(default_collate([dataset[img_i]]), device)
-                batch['mask'] = (batch['mask'] > 0) * 1
-                batch = model(batch)
-                cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
-
-                cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-                cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-                cv2.imwrite(cur_out_fname, cur_res)
-    except KeyboardInterrupt:
-        LOGGER.warning('Interrupted by user')
-    except Exception as ex:
-        LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    main()

spaces/Cam-Brazy/BearTest/README.md

@@ -1,13 +0,0 @@
----
-title: BearTest
-emoji: 🐢
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ChallengeHub/Chinese-LangChain/assets/Kelpy-Codos.js

@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. 
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-
-(function () {
-    'use strict';
-
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();

spaces/ChandraMohanNayal/AutoGPT/autogpt/utils.py

@@ -1,77 +0,0 @@
-import os
-
-import requests
-import yaml
-from colorama import Fore
-from git import Repo
-
-
-def clean_input(prompt: str = ""):
-    try:
-        return input(prompt)
-    except KeyboardInterrupt:
-        print("You interrupted Auto-GPT")
-        print("Quitting...")
-        exit(0)
-
-
-def validate_yaml_file(file: str):
-    try:
-        with open(file, encoding="utf-8") as fp:
-            yaml.load(fp.read(), Loader=yaml.FullLoader)
-    except FileNotFoundError:
-        return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found")
-    except yaml.YAMLError as e:
-        return (
-            False,
-            f"There was an issue while trying to read with your AI Settings file: {e}",
-        )
-
-    return (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")
-
-
-def readable_file_size(size, decimal_places=2):
-    """Converts the given size in bytes to a readable format.
-    Args:
-        size: Size in bytes
-        decimal_places (int): Number of decimal places to display
-    """
-    for unit in ["B", "KB", "MB", "GB", "TB"]:
-        if size < 1024.0:
-            break
-        size /= 1024.0
-    return f"{size:.{decimal_places}f} {unit}"
-
-
-def get_bulletin_from_web() -> str:
-    try:
-        response = requests.get(
-            "https://raw.githubusercontent.com/Significant-Gravitas/Auto-GPT/master/BULLETIN.md"
-        )
-        if response.status_code == 200:
-            return response.text
-    except:
-        return ""
-
-
-def get_current_git_branch() -> str:
-    try:
-        repo = Repo(search_parent_directories=True)
-        branch = repo.active_branch
-        return branch.name
-    except:
-        return ""
-
-
-def get_latest_bulletin() -> str:
-    exists = os.path.exists("CURRENT_BULLETIN.md")
-    current_bulletin = ""
-    if exists:
-        current_bulletin = open("CURRENT_BULLETIN.md", "r", encoding="utf-8").read()
-    new_bulletin = get_bulletin_from_web()
-    is_new_news = new_bulletin != current_bulletin
-
-    if new_bulletin and is_new_news:
-        open("CURRENT_BULLETIN.md", "w", encoding="utf-8").write(new_bulletin)
-        return f" {Fore.RED}::UPDATED:: {Fore.CYAN}{new_bulletin}{Fore.RESET}"
-    return current_bulletin

spaces/CofAI/chat.b4/client/css/typing.css

@@ -1,15 +0,0 @@
-.typing {
-	position: absolute;
-	top: -25px;
-	left: 0;
-	font-size: 14px;
-	animation: show_popup 0.4s;
-}
-
-.typing-hiding {
-	animation: hide_popup 0.4s;
-}
-
-.typing-hidden {
-	display: none;
-}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/_common.py

@@ -1,43 +0,0 @@
-"""
-Common code used in multiple modules.
-"""
-
-
-class weekday(object):
-    __slots__ = ["weekday", "n"]
-
-    def __init__(self, weekday, n=None):
-        self.weekday = weekday
-        self.n = n
-
-    def __call__(self, n):
-        if n == self.n:
-            return self
-        else:
-            return self.__class__(self.weekday, n)
-
-    def __eq__(self, other):
-        try:
-            if self.weekday != other.weekday or self.n != other.n:
-                return False
-        except AttributeError:
-            return False
-        return True
-
-    def __hash__(self):
-        return hash((
-          self.weekday,
-          self.n,
-        ))
-
-    def __ne__(self, other):
-        return not (self == other)
-
-    def __repr__(self):
-        s = ("MO", "TU", "WE", "TH", "FR", "SA", "SU")[self.weekday]
-        if not self.n:
-            return s
-        else:
-            return "%s(%+d)" % (s, self.n)
-
-# vim:ts=4:sw=4:et

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/visualization/__init__.py

@@ -1,4 +0,0 @@
-"""
-@date: 2021/06/19
-@description:
-"""

spaces/Datasculptor/DescriptionGPT/detic/data/transforms/custom_transform.py

@@ -1,114 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py 
-# Modified by Xingyi Zhou
-# The original code is under Apache-2.0 License
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.transforms.transform import (
-    CropTransform,
-    HFlipTransform,
-    NoOpTransform,
-    Transform,
-    TransformList,
-)
-from PIL import Image
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-__all__ = [
-    "EfficientDetResizeCropTransform",
-]
-
-class EfficientDetResizeCropTransform(Transform):
-    """
-    """
-
-    def __init__(self, scaled_h, scaled_w, offset_y, offset_x, img_scale, \
-        target_size, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            new_h, new_w (int): new image size
-            interp: PIL interpolation methods, defaults to bilinear.
-        """
-        # TODO decide on PIL vs opencv
-        super().__init__()
-        if interp is None:
-            interp = Image.BILINEAR
-        self._set_attributes(locals())
-
-    def apply_image(self, img, interp=None):
-        assert len(img.shape) <= 4
-
-        if img.dtype == np.uint8:
-            pil_image = Image.fromarray(img)
-            interp_method = interp if interp is not None else self.interp
-            pil_image = pil_image.resize((self.scaled_w, self.scaled_h), interp_method)
-            ret = np.asarray(pil_image)
-            right = min(self.scaled_w, self.offset_x + self.target_size[1])
-            lower = min(self.scaled_h, self.offset_y + self.target_size[0])
-            if len(ret.shape) <= 3:
-                ret = ret[self.offset_y: lower, self.offset_x: right]
-            else:
-                ret = ret[..., self.offset_y: lower, self.offset_x: right, :]
-        else:
-            # PIL only supports uint8
-            img = torch.from_numpy(img)
-            shape = list(img.shape)
-            shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
-            img = img.view(shape_4d).permute(2, 3, 0, 1)  # hw(c) -> nchw
-            _PIL_RESIZE_TO_INTERPOLATE_MODE = {Image.BILINEAR: "bilinear", Image.BICUBIC: "bicubic"}
-            mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[self.interp]
-            img = F.interpolate(img, (self.scaled_h, self.scaled_w), mode=mode, align_corners=False)
-            shape[:2] = (self.scaled_h, self.scaled_w)
-            ret = img.permute(2, 3, 0, 1).view(shape).numpy()  # nchw -> hw(c)
-            right = min(self.scaled_w, self.offset_x + self.target_size[1])
-            lower = min(self.scaled_h, self.offset_y + self.target_size[0])
-            if len(ret.shape) <= 3:
-                ret = ret[self.offset_y: lower, self.offset_x: right]
-            else:
-                ret = ret[..., self.offset_y: lower, self.offset_x: right, :]
-        return ret
-
-
-    def apply_coords(self, coords):
-        coords[:, 0] = coords[:, 0] * self.img_scale
-        coords[:, 1] = coords[:, 1] * self.img_scale
-        coords[:, 0] -= self.offset_x
-        coords[:, 1] -= self.offset_y
-        return coords
-
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-
-
-    def inverse(self):
-        raise NotImplementedError
-
-
-    def inverse_apply_coords(self, coords):
-        coords[:, 0] += self.offset_x
-        coords[:, 1] += self.offset_y
-        coords[:, 0] = coords[:, 0] / self.img_scale
-        coords[:, 1] = coords[:, 1] / self.img_scale
-        return coords
-
-
-    def inverse_apply_box(self, box: np.ndarray) -> np.ndarray:
-        """
-        """
-        idxs = np.array([(0, 1), (2, 1), (0, 3), (2, 3)]).flatten()
-        coords = np.asarray(box).reshape(-1, 4)[:, idxs].reshape(-1, 2)
-        coords = self.inverse_apply_coords(coords).reshape((-1, 4, 2))
-        minxy = coords.min(axis=1)
-        maxxy = coords.max(axis=1)
-        trans_boxes = np.concatenate((minxy, maxxy), axis=1)
-        return trans_boxes

spaces/DeclK/pose/tools/apis.py

@@ -1,90 +0,0 @@
-import torch
-from mmengine.registry import MODELS
-from mmengine.dataset import Compose, pseudo_collate
-from mmengine.model.utils import revert_sync_batchnorm
-from mmengine.registry import init_default_scope
-from mmengine.runner import load_checkpoint
-from mmengine.config import Config
-
-from mmdeploy.utils import get_input_shape, load_config
-from mmdeploy.apis.utils import build_task_processor
-
-def build_model(cfg, checkpoint=None, device='cpu'):
-    """ Build model from config and load checkpoint
-    checkpoint_meta usually contains dataset classes information
-    """
-    if isinstance(cfg, str):
-        cfg = Config.fromfile(cfg)
-    # scope of model, e.g. mmdet, mmseg, mmpose...
-    init_default_scope(cfg.default_scope)
-    model = MODELS.build(cfg.model)
-    model = revert_sync_batchnorm(model)
-    if checkpoint is not None:
-        ckpt = load_checkpoint(model, checkpoint,
-                            map_location='cpu')
-        checkpoint_meta = ckpt.get('meta', {})
-        # usually classes and pallate are in checkpoint_meta
-        model.checkpoint_meta = checkpoint_meta
-    model.to(device)
-    model.eval()
-    return model
-
-def inference(model, cfg, img):
-    """ Given model, config and image, return inference results.
-    Models in mmlab does not share the same inference api. So this
-    function is just a memo for me...
-    """
-    if isinstance(cfg, str):
-        cfg = Config.fromfile(cfg)
-    # process pipline
-    test_pipeline = cfg.test_dataloader.dataset.pipeline
-    # Use 'LoadImage' to handle both cases of img and img_path
-    # This is specially designed for mmdet config, which uses 'LoadImageFromFile'
-    for pipeline in test_pipeline:
-        if 'LoadImage' in pipeline['type']:
-            pipeline['type'] = 'mmpose.LoadImage'
-
-    init_default_scope(cfg.default_scope)
-    pipeline = Compose(test_pipeline)
-
-    if isinstance(img, str):
-        # img_id is useless...but to be compatible with mmdet
-        data_info = dict(img_path=img, img_id=0)
-    else:
-        data_info = dict(img=img, img_id=0)
-
-    data = pipeline(data_info)
-    batch = pseudo_collate([data])
-    
-    with torch.no_grad():
-        results = model.test_step(batch)
-
-    return results
-
-def build_onnx_model_and_task_processor(model_cfg, deploy_cfg, backend_files, device):
-
-    deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
-
-    task_processor = build_task_processor(model_cfg, deploy_cfg, device)
-
-    model = task_processor.build_backend_model(
-        backend_files, task_processor.update_data_preprocessor)
-
-    return model, task_processor
-
-def inference_onnx_model(model, task_processor, deploy_cfg, img):
-    input_shape = get_input_shape(deploy_cfg)
-    model_inputs, _ = task_processor.create_input(img, input_shape)
-    
-    with torch.no_grad():
-        result = model.test_step(model_inputs)
-
-    return result
-    
-if __name__ == '__main__':
-    config = '/github/Tennis.ai/model_zoo/rtmpose/rtmpose-t_8xb256-420e_aic-coco-256x192/rtmpose-t_8xb256-420e_aic-coco-256x192.py'
-    ckpt = '/github/Tennis.ai/model_zoo/rtmpose/rtmpose-t_8xb256-420e_aic-coco-256x192/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth'
-    img = '/github/Tennis.ai/assets/000000197388.jpg'
-
-    detector = build_model(config, checkpoint=ckpt)
-    result = inference(detector, config, img)

spaces/DragGan/DragGan/stylegan_human/torch_utils/ops/bias_act.cpp

@@ -1,101 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
-    if (x.dim() != y.dim())
-        return false;
-    for (int64_t i = 0; i < x.dim(); i++)
-    {
-        if (x.size(i) != y.size(i))
-            return false;
-        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
-            return false;
-    }
-    return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
-    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
-    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
-    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
-    TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
-    // Validate layout.
-    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
-    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
-    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
-    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
-    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    torch::Tensor y = torch::empty_like(x);
-    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
-    // Initialize CUDA kernel parameters.
-    bias_act_kernel_params p;
-    p.x     = x.data_ptr();
-    p.b     = (b.numel()) ? b.data_ptr() : NULL;
-    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
-    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
-    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
-    p.y     = y.data_ptr();
-    p.grad  = grad;
-    p.act   = act;
-    p.alpha = alpha;
-    p.gain  = gain;
-    p.clamp = clamp;
-    p.sizeX = (int)x.numel();
-    p.sizeB = (int)b.numel();
-    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
-    // Choose CUDA kernel.
-    void* kernel;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        kernel = choose_bias_act_kernel<scalar_t>(p);
-    });
-    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
-    // Launch CUDA kernel.
-    p.loopX = 4;
-    int blockSize = 4 * 32;
-    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------

spaces/Eddycrack864/Applio-Inference/infer/lib/infer_pack/attentions.py

@@ -1,417 +0,0 @@
-import copy
-import math
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer.lib.infer_pack import commons, modules
-from infer.lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        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.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        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.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x

spaces/FKBaffour/Gradio_App_for_Sentiment_Analysis/README.md

@@ -1,12 +0,0 @@
----
-title: Gradio App For Sentiment Analysis
-emoji: 🚀
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ferion/image-matting-app/ppmatting/datasets/matting_dataset.py

@@ -1,251 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import math
-
-import cv2
-import numpy as np
-import random
-import paddle
-from paddleseg.cvlibs import manager
-
-import ppmatting.transforms as T
-
-
-@manager.DATASETS.add_component
-class MattingDataset(paddle.io.Dataset):
-    """
-    Pass in a dataset that conforms to the format.
-        matting_dataset/
-        |--bg/
-        |
-        |--train/
-        |  |--fg/
-        |  |--alpha/
-        |
-        |--val/
-        |  |--fg/
-        |  |--alpha/
-        |  |--trimap/ (if existing)
-        |
-        |--train.txt
-        |
-        |--val.txt
-    See README.md for more information of dataset.
-
-    Args:
-        dataset_root(str): The root path of dataset.
-        transforms(list):  Transforms for image.
-        mode (str, optional): which part of dataset to use. it is one of ('train', 'val', 'trainval'). Default: 'train'.
-        train_file (str|list, optional): File list is used to train. It should be `foreground_image.png background_image.png`
-            or `foreground_image.png`. It shold be provided if mode equal to 'train'. Default: None.
-        val_file (str|list, optional): File list is used to evaluation. It should be `foreground_image.png background_image.png`
-            or `foreground_image.png` or ``foreground_image.png background_image.png trimap_image.png`.
-            It shold be provided if mode equal to 'val'. Default: None.
-        get_trimap (bool, optional): Whether to get triamp. Default: True.
-        separator (str, optional): The separator of train_file or val_file. If file name contains ' ', '|' may be perfect. Default: ' '.
-        key_del (tuple|list, optional): The key which is not need will be delete to accellect data reader. Default: None.
-        if_rssn (bool, optional): Whether to use RSSN while Compositing image. Including denoise and blur. Default: False.
-    """
-
-    def __init__(self,
-                 dataset_root,
-                 transforms,
-                 mode='train',
-                 train_file=None,
-                 val_file=None,
-                 get_trimap=True,
-                 separator=' ',
-                 key_del=None,
-                 if_rssn=False):
-        super().__init__()
-        self.dataset_root = dataset_root
-        self.transforms = T.Compose(transforms)
-        self.mode = mode
-        self.get_trimap = get_trimap
-        self.separator = separator
-        self.key_del = key_del
-        self.if_rssn = if_rssn
-
-        # check file
-        if mode == 'train' or mode == 'trainval':
-            if train_file is None:
-                raise ValueError(
-                    "When `mode` is 'train' or 'trainval', `train_file must be provided!"
-                )
-            if isinstance(train_file, str):
-                train_file = [train_file]
-            file_list = train_file
-
-        if mode == 'val' or mode == 'trainval':
-            if val_file is None:
-                raise ValueError(
-                    "When `mode` is 'val' or 'trainval', `val_file must be provided!"
-                )
-            if isinstance(val_file, str):
-                val_file = [val_file]
-            file_list = val_file
-
-        if mode == 'trainval':
-            file_list = train_file + val_file
-
-        # read file
-        self.fg_bg_list = []
-        for file in file_list:
-            file = os.path.join(dataset_root, file)
-            with open(file, 'r') as f:
-                lines = f.readlines()
-                for line in lines:
-                    line = line.strip()
-                    self.fg_bg_list.append(line)
-        if mode != 'val':
-            random.shuffle(self.fg_bg_list)
-
-    def __getitem__(self, idx):
-        data = {}
-        fg_bg_file = self.fg_bg_list[idx]
-        fg_bg_file = fg_bg_file.split(self.separator)
-        data['img_name'] = fg_bg_file[0]  # using in save prediction results
-        fg_file = os.path.join(self.dataset_root, fg_bg_file[0])
-        alpha_file = fg_file.replace('/fg', '/alpha')
-        fg = cv2.imread(fg_file)
-        alpha = cv2.imread(alpha_file, 0)
-        data['alpha'] = alpha
-        data['gt_fields'] = []
-
-        # line is: fg [bg] [trimap]
-        if len(fg_bg_file) >= 2:
-            bg_file = os.path.join(self.dataset_root, fg_bg_file[1])
-            bg = cv2.imread(bg_file)
-            data['img'], data['fg'], data['bg'] = self.composite(fg, alpha, bg)
-            if self.mode in ['train', 'trainval']:
-                data['gt_fields'].append('fg')
-                data['gt_fields'].append('bg')
-                data['gt_fields'].append('alpha')
-            if len(fg_bg_file) == 3 and self.get_trimap:
-                if self.mode == 'val':
-                    trimap_path = os.path.join(self.dataset_root, fg_bg_file[2])
-                    if os.path.exists(trimap_path):
-                        data['trimap'] = trimap_path
-                        data['gt_fields'].append('trimap')
-                        data['ori_trimap'] = cv2.imread(trimap_path, 0)
-                    else:
-                        raise FileNotFoundError(
-                            'trimap is not Found: {}'.format(fg_bg_file[2]))
-        else:
-            data['img'] = fg
-            if self.mode in ['train', 'trainval']:
-                data['fg'] = fg.copy()
-                data['bg'] = fg.copy()
-                data['gt_fields'].append('fg')
-                data['gt_fields'].append('bg')
-                data['gt_fields'].append('alpha')
-
-        data['trans_info'] = []  # Record shape change information
-
-        # Generate trimap from alpha if no trimap file provided
-        if self.get_trimap:
-            if 'trimap' not in data:
-                data['trimap'] = self.gen_trimap(
-                    data['alpha'], mode=self.mode).astype('float32')
-                data['gt_fields'].append('trimap')
-                if self.mode == 'val':
-                    data['ori_trimap'] = data['trimap'].copy()
-
-        # Delete key which is not need
-        if self.key_del is not None:
-            for key in self.key_del:
-                if key in data.keys():
-                    data.pop(key)
-                if key in data['gt_fields']:
-                    data['gt_fields'].remove(key)
-        data = self.transforms(data)
-
-        # When evaluation, gt should not be transforms.
-        if self.mode == 'val':
-            data['gt_fields'].append('alpha')
-
-        data['img'] = data['img'].astype('float32')
-        for key in data.get('gt_fields', []):
-            data[key] = data[key].astype('float32')
-
-        if 'trimap' in data:
-            data['trimap'] = data['trimap'][np.newaxis, :, :]
-        if 'ori_trimap' in data:
-            data['ori_trimap'] = data['ori_trimap'][np.newaxis, :, :]
-
-        data['alpha'] = data['alpha'][np.newaxis, :, :] / 255.
-
-        return data
-
-    def __len__(self):
-        return len(self.fg_bg_list)
-
-    def composite(self, fg, alpha, ori_bg):
-        if self.if_rssn:
-            if np.random.rand() < 0.5:
-                fg = cv2.fastNlMeansDenoisingColored(fg, None, 3, 3, 7, 21)
-                ori_bg = cv2.fastNlMeansDenoisingColored(ori_bg, None, 3, 3, 7,
-                                                         21)
-            if np.random.rand() < 0.5:
-                radius = np.random.choice([19, 29, 39, 49, 59])
-                ori_bg = cv2.GaussianBlur(ori_bg, (radius, radius), 0, 0)
-        fg_h, fg_w = fg.shape[:2]
-        ori_bg_h, ori_bg_w = ori_bg.shape[:2]
-
-        wratio = fg_w / ori_bg_w
-        hratio = fg_h / ori_bg_h
-        ratio = wratio if wratio > hratio else hratio
-
-        # Resize ori_bg if it is smaller than fg.
-        if ratio > 1:
-            resize_h = math.ceil(ori_bg_h * ratio)
-            resize_w = math.ceil(ori_bg_w * ratio)
-            bg = cv2.resize(
-                ori_bg, (resize_w, resize_h), interpolation=cv2.INTER_LINEAR)
-        else:
-            bg = ori_bg
-
-        bg = bg[0:fg_h, 0:fg_w, :]
-        alpha = alpha / 255
-        alpha = np.expand_dims(alpha, axis=2)
-        image = alpha * fg + (1 - alpha) * bg
-        image = image.astype(np.uint8)
-        return image, fg, bg
-
-    @staticmethod
-    def gen_trimap(alpha, mode='train', eval_kernel=7):
-        if mode == 'train':
-            k_size = random.choice(range(2, 5))
-            iterations = np.random.randint(5, 15)
-            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
-                                               (k_size, k_size))
-            dilated = cv2.dilate(alpha, kernel, iterations=iterations)
-            eroded = cv2.erode(alpha, kernel, iterations=iterations)
-            trimap = np.zeros(alpha.shape)
-            trimap.fill(128)
-            trimap[eroded > 254.5] = 255
-            trimap[dilated < 0.5] = 0
-        else:
-            k_size = eval_kernel
-            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
-                                               (k_size, k_size))
-            dilated = cv2.dilate(alpha, kernel)
-            trimap = np.zeros(alpha.shape)
-            trimap.fill(128)
-            trimap[alpha >= 250] = 255
-            trimap[dilated <= 5] = 0
-
-        return trimap

spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/nsf_hifigan/utils.py

@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-

spaces/FrankZxShen/so-vits-svc-models-pcr/vdecoder/nsf_hifigan/models.py

@@ -1,439 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
-    h = load_config(model_path)
-
-    generator = Generator(h).to(device)
-
-    cp_dict = torch.load(model_path, map_location=device)
-    generator.load_state_dict(cp_dict['generator'])
-    generator.eval()
-    generator.remove_weight_norm()
-    del cp_dict
-    return generator, h
-
-def load_config(model_path):
-    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
-    with open(config_file) as f:
-        data = f.read()
-
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-    return h
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class 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):
-        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
-
-    @torch.no_grad()
-    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)
-        """
-        f0 = f0.unsqueeze(-1)
-        fn = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1)))
-        rad_values = (fn / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-        rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-        is_half = rad_values.dtype is not torch.float32
-        tmp_over_one = torch.cumsum(rad_values.double(), 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-        if is_half:
-            tmp_over_one = tmp_over_one.half()
-        else:
-            tmp_over_one = tmp_over_one.float()
-        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
-        rad_values = rad_values.double()
-        cumsum_shift = cumsum_shift.double()
-        sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
-        if is_half:
-            sine_waves = sine_waves.half()
-        else:
-            sine_waves = sine_waves.float()
-        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):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # 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):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=h.sampling_rate,
-            harmonic_num=8
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-            if i + 1 < len(h.upsample_rates):  #
-                stride_f0 = int(np.prod(h.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()
-        ch = h.upsample_initial_channel
-        for i in range(len(self.ups)):
-            ch //= 2
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.upp = int(np.prod(h.upsample_rates))
-
-    def forward(self, x, f0):
-        har_source = self.m_source(f0, self.upp).transpose(1, 2)
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, 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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, periods=None):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
-        self.discriminators = nn.ModuleList()
-        for period in self.periods:
-            self.discriminators.append(DiscriminatorP(period))
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses

spaces/Freiburg-AI-Research/dermoscopic_image_generation/glide_text2im/clip/model_creation.py

@@ -1,117 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Any, Callable, Dict, List, Optional, Tuple
-
-import attr
-import numpy as np
-import torch
-import torch.nn as nn
-import yaml
-from glide_text2im.tokenizer.simple_tokenizer import SimpleTokenizer
-
-from .encoders import ImageEncoder, TextEncoder
-
-
-@lru_cache()
-def default_config_path() -> str:
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.yaml")
-
-
-@attr.s
-class CLIPModel:
-    config: Dict[str, Any] = attr.ib()
-    text_encoder: nn.Module = attr.ib()
-    image_encoder: nn.Module = attr.ib()
-    logit_scale: torch.Tensor = attr.ib()
-    device: torch.device = attr.ib()
-    tokenizer: SimpleTokenizer = attr.ib()
-
-    def encode_prompts(self, prompts: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
-        tokens = []
-        lens = []
-        for prompt in prompts:
-            sub_tokens, sub_len = self.tokenizer.padded_tokens_and_len(
-                self.tokenizer.encode(prompt), self.text_encoder.max_text_len
-            )
-            tokens.append(sub_tokens)
-            lens.append(sub_len)
-        return (
-            torch.tensor(tokens).to(dtype=torch.long, device=self.device),
-            torch.tensor(lens).to(dtype=torch.long, device=self.device),
-        )
-
-    def text_embeddings(self, prompts: List[str]) -> torch.Tensor:
-        tokens, lens = self.encode_prompts(prompts)
-        z_t = self.text_encoder(tokens, lens)
-        return z_t / (torch.linalg.norm(z_t, dim=-1, keepdim=True) + 1e-12)
-
-    def image_embeddings(self, images: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
-        z_i = self.image_encoder((images + 1) * 127.5, t)
-        return z_i / (torch.linalg.norm(z_i, dim=-1, keepdim=True) + 1e-12)
-
-    def cond_fn(self, prompts: List[str], grad_scale: float) -> Callable[..., torch.Tensor]:
-        with torch.no_grad():
-            z_t = self.text_embeddings(prompts)
-
-        def cond_fn(x, t, grad_scale=grad_scale, **kwargs):
-            with torch.enable_grad():
-                x_var = x.detach().requires_grad_(True)
-                z_i = self.image_embeddings(x_var, t)
-                loss = torch.exp(self.logit_scale) * (z_t * z_i).sum()
-                grad = torch.autograd.grad(loss, x_var)[0].detach()
-            return grad * grad_scale
-
-        return cond_fn
-
-
-def create_clip_model(
-    config_path: Optional[str] = None,
-    device: Optional[torch.device] = None,
-    tokenizer: Optional[SimpleTokenizer] = None,
-) -> CLIPModel:
-    if config_path is None:
-        config_path = default_config_path()
-    if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if tokenizer is None:
-        tokenizer = SimpleTokenizer()
-
-    with open(config_path, "r") as f:
-        config = yaml.load(f, Loader=yaml.SafeLoader)
-
-    text_encoder = TextEncoder(
-        n_bpe_vocab=config["n_vocab"],
-        max_text_len=config["max_text_len"],
-        n_embd=config["n_embd"],
-        n_head=config["n_head_text"],
-        n_xf_blocks=config["n_xf_blocks_text"],
-        n_head_state=config["n_head_state_text"],
-        device=device,
-    )
-
-    image_encoder = ImageEncoder(
-        image_size=config["image_size"],
-        patch_size=config["patch_size"],
-        n_embd=config["n_embd"],
-        n_head=config["n_head_image"],
-        n_xf_blocks=config["n_xf_blocks_image"],
-        n_head_state=config["n_head_state_image"],
-        n_timestep=config["n_timesteps"],
-        device=device,
-    )
-
-    logit_scale = torch.tensor(
-        np.log(config["logit_scale"]),
-        dtype=torch.float32,
-        device=device,
-        requires_grad=False,
-    )
-
-    return CLIPModel(
-        config=config,
-        text_encoder=text_encoder,
-        image_encoder=image_encoder,
-        logit_scale=logit_scale,
-        device=device,
-        tokenizer=tokenizer,
-    )

spaces/GAIR/Factool/factool/scientific/pipeline.py

@@ -1,204 +0,0 @@
-import time
-import json
-import math
-import os
-import yaml
-from typing import Dict, List
-
-from factool.scientific.tool import google_scholar
-from factool.utils.base.pipeline import pipeline
-
-class scientific_pipeline(pipeline):
-    def __init__(self, foundation_model):
-        super().__init__('scientific', foundation_model)
-
-        self.tool = google_scholar()
-
-        with open(os.path.join(self.prompts_path, "claim_extraction.yaml"), 'r') as file:
-            data = yaml.load(file, Loader=yaml.FullLoader)
-        self.claim_prompt = data['scientific']
-
-        with open(os.path.join(self.prompts_path, 'agreement_verification.yaml'), 'r') as file:
-            data = yaml.load(file, Loader=yaml.FullLoader)
-        self.verification_prompt = data['scientific']
-
-    async def _claim_extraction(self, responses):
-        messages_list = [
-            [
-                {"role": "system", "content": self.claim_prompt['system']},
-                {"role": "user", "content": self.claim_prompt['user'].format(input=response)},
-            ]
-            for response in responses
-        ]
-        return await self.chat.async_run(messages_list, List)
-    
-    async def _check_authors(self, authors):
-        messages_list = [
-            [
-                {"role": "system", "content": self.verification_prompt['system']},
-                {"role": "user", "content": self.verification_prompt['user'].format(string1=claim_author, list2=real_author)},
-            ]
-            for claim_author, real_author in authors
-        ]
-
-        return await self.chat.async_run(messages_list, Dict)
-
-    async def _verification(self, claims, responses):
-        authors = [(claim['paper_author(s)'], response['author']) for claim, response in zip(claims, responses)]
-        check_authors_results = await self._check_authors(authors)
-        final_responses = []
-        for i, (claim, response) in enumerate(zip(claims, responses)):
-            final_response = {
-                'generated_paper_title': claim['paper_title'],
-                'generated_paper_author(s)': claim['paper_author(s)'],
-                'generated_paper_pub_year': claim['paper_pub_year'],
-                'actual_paper_title': response['title'],
-                'actual_paper_author(s)': response['author'],
-                'actual_paper_pub_year': response['pub_year'],
-            }
-
-            errors = []
-            if final_response['generated_paper_title'].lower() != final_response['actual_paper_title'].lower() and final_response['generated_paper_title'].lower() not in final_response['actual_paper_title'].lower() and final_response['actual_paper_title'].lower() not in final_response['generated_paper_title'].lower() :
-                errors.append('wrong_paper_title')
-            if check_authors_results[i]['factuality'] == False:
-                errors.append('wrong_paper_author(s)')
-            if final_response['generated_paper_pub_year'] != final_response['actual_paper_pub_year']:
-                errors.append('wrong_paper_pub_year')
-
-            final_response['error'] = errors
-            final_response['factuality'] = len(errors) == 0
-
-            final_responses.append(final_response)
-
-        return final_responses
-
-    async def run_with_tool_live(self, samples):
-        claims_in_responses = await self._claim_extraction(samples)
-        queries_in_responses = []
-        evidences_in_responses = []
-        verifications_in_responses = []
-        for claims_in_response in claims_in_responses:
-            queries = [claim['paper_title'] for claim in claims_in_response]
-            queries_in_responses.append(queries)
-            evidences = [self.tool.run(paper_title) for paper_title in queries]
-            evidences_in_responses.append(evidences)
-            verifications = await self._verification(claims_in_response, evidences)
-            verifications_in_responses.append(verifications)
-
-        return claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses
-        
-    async def run_with_tool_live_without_claim_extraction(self, claims):
-        # claims = [{"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013"}, {"paper_title": "BERT", "paper_author(s)": "John Smith", "paper_pub_year": "2020"}]
-        papers_titles = [claim['paper_title'] for claim in claims]
-        responses = [self.tool.run(paper_title) for paper_title in papers_titles]
-        final_response = await self._verification(claims, responses)
-        return final_response
-
-    async def run_with_tool_api_call(self, prompts, responses):
-        batch_size = 5
-        num_batches = math.ceil(len(prompts) / batch_size)
-
-        self.sample_list = [{"prompt": prompt, "response": response, "category": 'scientific'} for prompt, response in zip(prompts, responses)]
-
-        for i in range(num_batches):
-            print(i)
-            batch_start = i * batch_size
-            batch_end = min((i + 1) * batch_size, len(responses))
-
-            claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses = await self.run_with_tool_live(responses[batch_start:batch_end])
-
-            for j, (claims_in_response, queries_in_response, evidences_in_response, verifications_in_response) in enumerate(zip(claims_in_responses, queries_in_responses, evidences_in_responses, verifications_in_responses)):
-                index = batch_start + j
-
-                self.sample_list[index].update({
-                    'claims': claims_in_response,
-                    'queries': queries_in_response,
-                    'evidences': evidences_in_response,
-                    'claim_level_factuality': verifications_in_response,
-                    'response_level_factuality': all([verification['factuality'] if verification != None else True for verification in verifications_in_response])
-                })
-        return self.sample_list
-
-    async def run_with_tool_dataset(self, annotated_dataset_path: str, with_tool_classified_dataset_path: str, rerun: bool = False, rerun_indices: list = []):
-        # Example of a line: 
-        # {"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013", "label": True / False}
-        if rerun == False:
-            with open(annotated_dataset_path, 'r') as f:
-                data = [json.loads(line) for line in f]
-            self.sample_list = [claim for sample in data for claim in sample['claims']]
-            rerun_elements = self.sample_list
-        else:
-            with open(with_tool_classified_dataset_path, 'r') as f:
-                data = [json.loads(line) for line in f]
-            self.sample_list = data
-            rerun_elements = [self.sample_list[i] for i in rerun_indices]
-
-        batch_size = 5
-        num_batches = math.ceil(len(rerun_elements) / batch_size) # 5
-
-        for i in range(num_batches):
-            print(i)
-            batch_start = i * batch_size
-            batch_end = (i + 1) * batch_size if (i + 1) * batch_size < len(rerun_elements) else len(rerun_elements)
-
-            responses = await self.run_with_tool_live_without_claim_extraction(rerun_elements[batch_start:batch_end])
-            for j, response in enumerate(responses):
-                index = batch_start + j if rerun == False else rerun_indices[batch_start + j]
-                if response == None:
-                    self.sample_list[index]['with_tool_classification'] = 'None'
-                    self.sample_list[index]['error'] = 'None'
-                else:
-                    self.sample_list[index]['with_tool_classification'] = response.get('factuality', 'None')
-                    self.sample_list[index]['error'] = response.get('error', 'None')
-        
-            # save everything after each batch to prevent data loss
-            with open(with_tool_classified_dataset_path, 'w') as f:
-                for item in self.sample_list:
-                    json_str = json.dumps(item)
-                    f.write(json_str + '\n')
-
-    async def run_self_check_live(self, fewshot, batch):
-        user_prompt_key = 'user_3_shot_CoT' if fewshot else 'user_zero_shot_CoT'
-        messages_list = [
-            [
-                {"role": "system", "content": self.self_check_prompt['system']},
-                {"role": "user", "content": self.self_check_prompt[user_prompt_key].format(scientific_literature=response)}
-            ]
-            for response in batch
-        ]
-        return await self.chat.async_run(messages_list, Dict)
-
-    async def run_self_check_dataset(self, annotated_dataset_path: str, self_check_classified_dataset_path: str, fewshot: bool = False, rerun: bool = False, rerun_indices: list = []):
-        # Example of a line: 
-        # {"paper_title": "A Survey of Modern Authorship Attribution Methods", "paper_author(s)": "Stamatatos, Efstathios", "paper_pub_year": "2013", "annotation": True / False}
-        data_path = annotated_dataset_path if not rerun else self_check_classified_dataset_path
-        with open(data_path, 'r') as f:
-            data = [json.loads(line) for line in f]
-        self.sample_list = data if rerun else [claim for sample in data for claim in sample['claims']]
-        rerun_elements = self.sample_list if not rerun else [self.sample_list[i] for i in rerun_indices]
-
-        batch_size = 5
-        num_batches = math.ceil(len(rerun_elements) / batch_size)
-
-        for i in range(num_batches):
-            print(i)
-            batch_start = i * batch_size
-            batch_end = (i + 1) * batch_size
-            batch = rerun_elements[batch_start:batch_end]
-            batch = [{k:v for k,v in d.items() if k != "label"} for d in batch]
-
-            responses = await self.run_self_check_live(fewshot, batch)
-            for j, response in enumerate(responses):
-                index = batch_start + j if rerun == False else rerun_indices[batch_start + j]
-                if response == None:
-                    self.sample_list[index]['self_check_classification'] = 'None'
-                    self.sample_list[index]['self_check_reasoning'] = 'None'
-                else:
-                    self.sample_list[index]['self_check_classification'] = response.get('factuality', 'None')
-                    self.sample_list[index]['self_check_reasoning'] = response.get('reasoning', 'None')
-        
-            # save everything after each batch to prevent data loss
-            with open(self_check_classified_dataset_path, 'w') as f:
-                for item in self.sample_list:
-                    json_str = json.dumps(item)
-                    f.write(json_str + '\n')

spaces/GeekTony/Examples-Of-AI/README.md

@@ -1,13 +0,0 @@
----
-title: Examples Of AI
-emoji: 🐢
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/GorroRojo/nitrosocke-Ghibli-Diffusion/README.md

@@ -1,12 +0,0 @@
----
-title: Nitrosocke Ghibli Diffusion
-emoji: ⚡
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference