Update parquet files (step 55 of 397)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Photoshop CC 2020 Crack Full Presets (Mac et Windows) MacOSX A Complete Review and Comparison.md

@@ -1,131 +0,0 @@
-
-<h1>Adobe Photoshop CC 2020 Crack Full Presets (Mac et Windows) MacOSX</h1>
- <p>Are you looking for a way to get Adobe Photoshop CC 2020 for free with full presets and unlimited access? If yes, then you are in the right place. In this article, I will show you how to download and install Adobe Photoshop CC 2020 crack full presets for Mac and Windows. I will also show you how to use the presets to enhance your photos and create stunning effects. But before that, let me explain what Adobe Photoshop CC 2020 is and why you need a crack for it.</p>
-<h2>Adobe Photoshop CC 2020 Crack Full Presets (Mac et Windows) MacOSX</h2><br /><p><b><b>DOWNLOAD</b> ->>->>->> <a href="https://byltly.com/2uKwAe">https://byltly.com/2uKwAe</a></b></p><br /><br />
- <h2>Introduction</h2>
- <h3>What is Adobe Photoshop CC 2020?</h3>
- <p>Adobe Photoshop CC 2020 is the latest version of the most popular and powerful photo editing software in the world. It is used by millions of professionals and amateurs alike to create, edit, and manipulate images, graphics, and artworks. Adobe Photoshop CC 2020 offers a variety of tools, features, and functions that allow you to unleash your creativity and transform your photos into amazing works of art.</p>
- <h3>Why do you need a crack for Adobe Photoshop CC 2020?</h3>
- <p>Adobe Photoshop CC 2020 is not a free software. It requires a subscription plan that costs $20.99 per month or $239.88 per year. That's quite expensive for many people who want to use it for personal or educational purposes. Moreover, even if you pay for the subscription, you will not get access to all the presets that are available in Adobe Photoshop CC 2020. Presets are predefined settings that apply certain effects or adjustments to your photos with one click. They can save you a lot of time and effort and help you achieve professional results quickly and easily.</p>
- <p>That's why many people look for a crack for Adobe Photoshop CC 2020. A crack is a modified version of the software that bypasses the activation process and allows you to use it without paying anything. A crack also gives you access to all the presets that are included in Adobe Photoshop CC 2020, as well as some additional ones that are not available in the official version.</p>
-<p>How to download and install Adobe Photoshop CC 2020 cracked version for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets free download with crack for MacOSX and Windows<br />
-Best tips and tricks for using Adobe Photoshop CC 2020 crack on Mac and Windows<br />
-Adobe Photoshop CC 2020 crack serial number and activation key for Mac and Windows<br />
-Adobe Photoshop CC 2020 crack features and benefits for Mac and Windows users<br />
-Adobe Photoshop CC 2020 crack system requirements and compatibility for Mac and Windows<br />
-Adobe Photoshop CC 2020 crack problems and solutions for Mac and Windows<br />
-Adobe Photoshop CC 2020 crack vs original version comparison for Mac and Windows<br />
-Adobe Photoshop CC 2020 crack tutorials and guides for Mac and Windows beginners<br />
-Adobe Photoshop CC 2020 crack reviews and testimonials from Mac and Windows users<br />
-Adobe Photoshop CC 2020 full presets pack download link for Mac and Windows<br />
-How to use Adobe Photoshop CC 2020 full presets on Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets examples and inspiration for Mac and Windows designers<br />
-Adobe Photoshop CC 2020 full presets advantages and disadvantages for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets customization and optimization for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets compatibility and integration with other software for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets updates and support for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets alternatives and competitors for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets FAQs and answers for Mac and Windows<br />
-Adobe Photoshop CC 2020 full presets feedback and suggestions for Mac and Windows developers<br />
-How to get Adobe Photoshop CC 2020 crack full presets for free legally for Mac and Windows<br />
-How to uninstall Adobe Photoshop CC 2020 crack full presets from Mac and Windows<br />
-How to fix Adobe Photoshop CC 2020 crack full presets errors and bugs on Mac and Windows<br />
-How to backup and restore Adobe Photoshop CC 2020 crack full presets on Mac and Windows<br />
-How to upgrade from Adobe Photoshop CC 2020 crack full presets to the latest version for Mac and Windows<br />
-How to transfer Adobe Photoshop CC 2020 crack full presets from one device to another for Mac and Windows<br />
-How to share Adobe Photoshop CC 2020 crack full presets with others for Mac and Windows<br />
-How to create amazing graphics with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to edit photos professionally with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to make logos, banners, flyers, posters, brochures, etc. with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to enhance your online presence with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to improve your SEO ranking with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to increase your conversions with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to generate more leads with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to grow your business with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to save time and money with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to avoid legal issues with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to protect your privacy with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to secure your data with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to prevent malware infections with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to optimize your performance with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to boost your creativity with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to learn new skills with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to have fun with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to impress your clients with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to collaborate with others with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to access exclusive resources with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to join a community of users with Adobe Photoshop CC 2020 crack full presets for Mac and Windows<br />
-How to get help from experts with Adobe Photoshop CC 2020 crack full presets for Mac and Windows</p>
- <h3>What are the features of Adobe Photoshop CC 2020?</h3>
- <p>Adobe Photoshop CC 2020 has many features that make it the best photo editing software in the market. Some of these features are:</p>
- <ul>
-<li><b>Content-Aware Fill:</b> This feature allows you to remove unwanted objects or areas from your photos and fill them with matching content from other parts of the image. You can also control how the fill is done by using different sampling options and output settings.</li>
-<li><b>Select Subject:</b> This feature allows you to select any subject in your photo with one click. You can then refine the selection using other tools or apply adjustments or effects to it.</li>
-<li><b>Select and Mask:</b> This feature allows you to create precise selections and masks for complex subjects such as hair, fur, feathers, etc. You can use various tools such as brushes, refine edge, smooth edge, etc. to fine-tune your selection or mask.</li>
-<li><b>Object Selection Tool:</b> This feature allows you to select multiple objects in your photo by drawing a rectangle or a lasso around them. The tool will automatically detect and select the objects within the area you draw.</li>
-<li><b>Enhance Details:</b> This feature allows you to improve the details and sharpness of your photos by using artificial intelligence. It works especially well for raw images taken with high-resolution cameras.</li>
-<li><b>Smart Objects:</b> These are layers that contain image data from raster or vector images such as PSD, TIFF, JPEG, PDF, etc. You can transform, warp, filter, or edit these layers without losing quality or resolution.</li>
-<li><b>Smart Filters:</b> These are filters that can be applied to smart objects or other layers non-destructively. You can adjust, reorder, hide, or delete these filters at any time without affecting the original image.</li>
-<li><b>Adjustment Layers:</b> These are layers that apply color or tonal adjustments to your image without changing the pixel values. You can modify, mask, or blend these layers with other layers using different modes.</li>
-<li><b>Layer Styles:</b> These are effects that can be applied to layers such as drop shadow, stroke, gradient overlay, etc. You can customize these effects using various parameters and options.</li>
-<li><b>Liquify Filter:</b> This filter allows you to distort or reshape any part of your image using different tools such as warp, pucker, bloat, twirl, etc. You can also use face-aware liquify to adjust facial features such as eyes, nose, mouth, etc.</li>
-<li><b>Perspective Warp:</b> This feature allows you to change the perspective of your image by warping it along multiple planes. You can also use it to correct distortion or alignment issues caused by camera angle or lens.</li>
-<li><b>Puppet Warp:</b> This feature allows you to bend or twist any part of your image by placing pins on it. You can then drag these pins to deform the image as if it were made of rubber.</li>
-<li><b>Crop Tool:</b> This tool allows you to crop your image by dragging its edges or corners. You can also use different aspect ratios or presets to crop your image according to your needs.</li>
-<li><b>Straighten Tool:</b> This tool allows you to straighten your image by drawing a line along any edge or horizon in it. The tool will automatically rotate and crop your image accordingly.</li>
-<li><b>Ruler Tool:</b> This tool allows you to measure distances or angles in your image by drawing a line between two points. You can also use it to align objects or layers in your image.</li>
-</ul>
- <p>These are just some of the features of Adobe Photoshop CC 2020. There are many more features that you can explore and use in this software.</p>
- <h2>How to download and install Adobe Photoshop CC 2020 crack full presets for Mac and Windows?</h2>
- <p>If you want to get Adobe Photoshop CC 2020 for free with full presets and unlimited access, then follow these steps:</p>
- <h3>Step 1: Download the crack file from the link below</h3>
- <p>The first thing you need to do is download the crack file from this link: <a href="https://bit.ly/AdobePhotoshopCC2020Crack">https://bit.ly/AdobePhotoshopCC2020Crack</a>. This link will take you to a Google Drive folder where you will find two files: one for Mac users and one for Windows users. Choose the file according to your operating system and click on download.</p>
- <h3>Step 2: Extract the file using WinRAR or 7-Zip</h3>
- <p>The next thing you need to do is extract the file using WinRAR or 7-Zip. These are free software that can unzip compressed files easily. You can download them from their official websites: <a href="https://www.win-rar.com/">https://www.win-rar.com/</a> (for WinRAR) or <a href="https://www.7-zip.org/">https://www.7-zip.org/</a> (for 7-Zip). After downloading them, install them on your computer and then right-click on the crack file you downloaded in step 1 and choose "Extract here" or "Extract files".</p>
- <h3>Step 3: Run the setup file and follow the instructions</h3>
- <p>After extracting the file, you will see a folder named "Adobe Photoshop CC 2020 Crack Full Presets". Open this folder and double-click on the setup file. This will launch the installation wizard that will guide you through the process. Follow the instructions on the screen and accept the terms and conditions. Choose the destination folder where you want to install the software and click on install.</p>
- <h3>Step 4: Copy and paste the crack file into the installation folder</h3>
- <p>Once the installation is complete, do not run the software yet. Go back to the folder where you extracted the crack file and open it. You will see another folder named "Crack". Open this folder and copy the file named "amtlib.dll". Then go to the installation folder where you installed Adobe Photoshop CC 2020. The default location is "C:\Program Files\Adobe\Adobe Photoshop CC 2020" for Windows users and "/Applications/Adobe Photoshop CC 2020" for Mac users. Paste the crack file into this folder and replace the original file.</p>
- <h3>Step 5: Enjoy Adobe Photoshop CC 2020 with full presets and unlimited access</h3>
- <p>Congratulations! You have successfully installed Adobe Photoshop CC 2020 crack full presets for Mac and Windows. Now you can run the software and enjoy all its features and functions without any limitations or restrictions. You can also access all the presets that are available in Adobe Photoshop CC 2020, as well as some additional ones that are not available in the official version.</p>
- <h2>How to use Adobe Photoshop CC 2020 crack full presets for Mac and Windows?</h2>
- <p>Now that you have Adobe Photoshop CC 2020 crack full presets for Mac and Windows, you might be wondering how to use them to enhance your photos and create stunning effects. Here are some tips and tricks that will help you use the presets effectively:</p>
- <h3>How to access the presets in Adobe Photoshop CC 2020?</h3>
- <p>To access the presets in Adobe Photoshop CC 2020, you need to open the Preset Manager. To do this, go to Edit > Presets > Preset Manager. This will open a window where you can see all the presets that are available in Adobe Photoshop CC 2020. You can also add, delete, rename, or organize your presets using this window.</p>
- <p>The presets are divided into different categories such as Brushes, Patterns, Gradients, Styles, etc. You can choose any category from the drop-down menu at the top of the window. You can also use the search box to find a specific preset by typing its name or keyword.</p>
- <h3>How to apply the presets to your photos in Adobe Photoshop CC 2020?</h3>
- <p>To apply a preset to your photo in Adobe Photoshop CC 2020, you need to select it from the Preset Manager and then click on Load. This will load the preset into your current document. You can then use it as you normally would with any other tool or feature in Adobe Photoshop CC 2020.</p>
- <p>For example, if you want to apply a brush preset to your photo, you need to select it from the Preset Manager and then click on Load. This will load the brush preset into your Brush Tool. You can then use it to paint on your photo with different colors, sizes, shapes, etc.</p>
- <p>If you want to apply a pattern preset to your photo, you need to select it from the Preset Manager and then click on Load. This will load the pattern preset into your Pattern Stamp Tool. You can then use it to stamp on your photo with different modes, opacity, alignment, etc.</p>
- <p>If you want to apply a gradient preset to your photo, you need to select it from the Preset Manager and then click on Load. This will load the gradient preset into your Gradient Tool. You can then use it to fill or stroke your photo with different colors, angles, styles, etc.</p>
- <p>If you want to apply a style preset to your photo, you need to select it from the Preset Manager and then click on Load. This will load the style preset into your Layer Style dialog box. You can then apply it to any layer in your photo with different options such as blending mode, opacity, scale, etc.</p>
- <h3>How to create your own presets in Adobe Photoshop CC 2020?</h3>
- <p>If you want to create your own presets in Adobe Photoshop CC 2020, you need to follow these steps:</p>
- <ol>
-<li>Create or edit your photo using any tool or feature in Adobe Photoshop CC 2020.</li>
-<li>Select or create a new layer that contains your desired effect or adjustment.</li>
-<li>Go to Edit > Presets > Preset Manager.</li>
-<li>Choose the category that matches your effect or adjustment from the drop-down menu at the top of the window.</li>
-<li>Click on Save Set and give a name to your preset.</li>
-<li>Click on OK and close the Preset Manager window.</li>
-</ol>
- <p>You have now created your own preset in Adobe Photoshop CC 2020. You can access it anytime from the Preset Manager and apply it to any photo you want.</p>
- <h3>How to share your presets with others in Adobe Photoshop CC 2020?</h3>
- <p>If you want to share your presets with others in Adobe Photoshop CC 2020, you need to follow these steps:</p>
- <ol>
-<li>Go to Edit > Presets > Preset Manager.</li>
-<li>Choose the category that contains your preset from the drop-down menu at the top of the window.</li>
-<li>Select your preset from the list of presets.</li>
-<li>Click on Save Set and choose a location where you want to save your preset file.</li>
-<li>Click on OK and close the Preset Manager window.</li>
-</ol>
- <p>You have now saved your preset as a file that can be shared with others. You can send this file via email, social media, cloud storage, etc. To load this file into another computer or device, simply copy it into its corresponding folder in Adobe Photoshop CC 2020's installation directory.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, I have shown you how to download and install Adobe Photoshop CC 2020 crack full presets for Mac and Windows. I have also shown you how to use them to enhance your photos and create stunning effects. By using this crack version of Adobe Photoshop CC 2020, you can enjoy all its features and functions without paying anything or having any limitations or restrictions. You can also access all the presets that are available in Adobe Photoshop CC 2020, as well as some additional ones that are not available in the official version.</p>
- <h3>Call to action and recommendation</h3>
- <p>If you want to get Adobe Photoshop CC 2020 for free with full presets and unlimited access, then don't wait any longer. Click on this link <a href="https://bit.ly/AdobePhotoshopCC2020Crack">https://bit.ly/AdobePhotoshopCC2020Crack</a>
-and download the crack file now. Follow the instructions in this article and install the software on your computer or device. You will be amazed by what you can do with Adobe Photoshop CC 2020 crack full presets for Mac and Windows. However, I must warn you that using a crack version of any software is illegal and unethical. You may face legal consequences or security risks if you do so. Therefore, I recommend that you use this crack version only for educational or personal purposes and not for commercial or professional purposes. If you like Adobe Photoshop CC 2020 and want to support its developers, then please buy the original version from their official website <a href="https://www.adobe.com/products/photoshop.html">https://www.adobe.com/products/photoshop.html</a>. You will get regular updates, customer support, and peace of mind. Thank you for reading this article. I hope you found it useful and informative. If you have any questions or feedback, please leave them in the comments section below. I would love to hear from you. </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download PowerPoint Ed Version and Enjoy Its Unique Features for Inclusive and Collaborative Classrooms.md

@@ -1,28 +0,0 @@
-<br />
-<h1>How to Download PowerPoint Ed Version for Free</h1>
-<p>PowerPoint Ed is a powerful and easy-to-use tool that allows you to create and edit stunning presentations with animations, transitions, and multimedia. PowerPoint Ed is compatible with Microsoft PowerPoint and can open and save files in various formats, including PPT, PPTX, PDF, and HTML.</p>
-<h2>download powerpoint cracked version</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://byltly.com/2uKzbM">https://byltly.com/2uKzbM</a></b></p><br /><br />
-<p>If you want to download PowerPoint Ed version for free, you can follow these simple steps:</p>
-<ol>
-<li>Go to the official website of PowerPoint Ed at <a href="https://www.powerpointed.com/">https://www.powerpointed.com/</a>.</li>
-<li>Click on the "Download" button on the top right corner of the homepage.</li>
-<li>Select your preferred language and operating system from the drop-down menus.</li>
-<li>Click on the "Download Now" button and wait for the installation file to be downloaded.</li>
-<li>Run the installation file and follow the instructions on the screen to complete the installation.</li>
-<li>Launch PowerPoint Ed and enjoy creating amazing presentations!</li>
-</ol>
-<p>PowerPoint Ed is a free software that does not require any registration or activation. However, if you want to access more features and templates, you can upgrade to the premium version for a small fee. You can also join the PowerPoint Ed community and share your feedback, suggestions, and ideas with other users.</p>
-<p>Download PowerPoint Ed version today and unleash your creativity!</p>
-<p></p><h2>Why Choose PowerPoint Ed Version?</h2>
-<p>PowerPoint Ed version is not just a regular PowerPoint software. It is a special edition that is designed for education and learning purposes. PowerPoint Ed version has many features that make it stand out from other presentation tools. Here are some of the reasons why you should choose PowerPoint Ed version:</p>
-<ul>
-<li>PowerPoint Ed version is free for students and educators at eligible institutions. You can sign up with your valid school email address and get access to Word, Excel, PowerPoint, OneNote, and Microsoft Teams, plus additional classroom tools.</li>
-<li>PowerPoint Ed version supports inclusive classrooms with features such as **Presenter Coach**, **PowerPoint Live**, and **live captioning**. Presenter Coach helps you practice your presentation skills and gives you feedback on your pace, pitch, filler words, and more. PowerPoint Live lets you present online to your audience with interactive features such as polls, chat, and reactions. Live captioning allows you to display subtitles in different languages for your audience.</li>
-<li>PowerPoint Ed version allows you to create flipped instruction with **PowerPoint Recorder**. You can record your voice, video, and annotations on your slides and share them with your students. You can also embed quizzes and surveys to check their understanding and engagement.</li>
-<li>PowerPoint Ed version helps you create accessible content with Microsoft 365. You can use features such as **Accessibility Checker**, **Alt Text**, **Immersive Reader**, and **Translate** to make sure your presentations are readable and understandable for everyone.</li>
-<li>PowerPoint Ed version lets you collaborate with your classmates and teachers in real time. You can co-author presentations, share comments and feedback, and sync your files across devices with OneDrive and Teams.</li>
-<li>PowerPoint Ed version gives you access to the Office Premium Creative Content collection, which includes thousands of high-quality stock images, icons, videos, fonts, and templates that you can use in your presentations.</li>
-</ul>
-<p>With PowerPoint Ed version, you can create powerful presentations that showcase your creativity and knowledge. Whether you are a student or a teacher, PowerPoint Ed version can help you achieve your learning goals.</p> ddb901b051<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Autocount Accounting Keygen [Extra Quality].md

@@ -1,22 +0,0 @@
-<h2>Autocount Accounting Keygen</h2><br /><p><b><b>Download Zip</b> >>> <a href="https://imgfil.com/2uxZn8">https://imgfil.com/2uxZn8</a></b></p><br /><br />
-
-I haven't had any problems except when I went to California last year. I was going to stay for a month and then go back home. I asked the girl in the hotel desk if it was OK to leave the car there as I didn't want to pay for parking. She said she didn't know how it would be treated. She called the police and when they came and told me to move the car she said "how do you know it's your car." We had to move it down the street to a lot where I have to pay for parking. I am unsure of my rights as to where I am able to leave my car but I am told that if I were to leave it at the hotel I would have to pay for parking. I have been a loyal customer since 1972. It still runs and drives. I have tried to buy a new one but I can't find one that good.
-
-Last edited by blvader on Sat Feb 16, 2012 8:31 pm, edited 1 time in total.
-
-Best thing you can do is buy a new one - even if you pay full retail or even a little over that. A new car is a life-changing purchase. Of course, the happy news is that the auto brand is likely to be better than the last one.
-
-If you think about the condition of the car (and that you've had it for so long) you may be able to get the car owner to buy the car back from you (less than full retail, I know). But you won't get much for it even if you did.
-
-I'd check the N.H. State DMVs website, www.mvs.com (if you're in New Hampshire). There should be an up-to-date vehicle registration history on file. While I can't vouch for the accuracy of the data, the car you've had it for so long may not have had a lot of miles on it. In any event, DMV data is a good first look for such a thing.
-
-This is a really high-quality fix-it-yourself project, but I think it's so, so important to be thorough (especially when you're dealing with a brand new car!) that I'd love to see your work:
-
-Who is online
-
-Users browsing this forum: No registered users and 1 guest
-
-You cannot post new topics in this forumYou cannot reply to topics in this forumYou cannot edit your posts 4fefd39f24<br />
-<br />
-<br />
-<p></p>

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download 4 Movie and Stream Online - HD 4K Quality Available.md

@@ -1,144 +0,0 @@
-
-<h1>How to Download 4 Movie for Free in 2023</h1>
-<p>If you are a movie lover, you might have heard of the term "4 movie". It is a new video format that offers higher resolution, better compression, and more features than the standard MP4 format. It is also compatible with most devices and platforms, making it ideal for watching movies on the go.</p>
-<p>But how can you download 4 movie for free in 2023? Is it legal and safe to do so? What are the best sites to find and download your favorite movies in this format? In this article, we will answer these questions and more. We will also show you how to use ByClick Downloader, a powerful tool that can help you download any movie from any site with just one click.</p>
-<h2>download 4 movie</h2><br /><p><b><b>Download File</b> &#11088; <a href="https://urlin.us/2uT1vX">https://urlin.us/2uT1vX</a></b></p><br /><br />
-<h2>Benefits of Downloading 4 Movie</h2>
-<h3>High-quality video and audio</h3>
-<p>One of the main advantages of downloading movies in the new format is that they offer better quality than MP4 movies. The resolution of a typical MP4 movie is usually around HD (1280 x720 pixels), while a typical movie can go up to UHD (3840 x2160 pixels). This means that you can enjoy more details, sharper images, and smoother motion when watching movies.</p>
-<p>Another benefit of downloading movies in this format is that they have better audio quality. The format supports Dolby Atmos, a surround sound technology that creates a realistic and immersive sound experience. You can hear sounds coming from different directions, such as above, below, behind, or in front of you.</p>
-<h3>Offline access and convenience</h3>
-<p>Another reason why you might want to download movies in this format is that you can watch them offline anytime and anywhere. You don't need an internet connection or a streaming service subscription to enjoy your favorite movies. You can save them on your computer, smartphone, tablet, or external hard drive and watch them whenever you want.</p>
-<p>Downloading movies also gives you more control over your viewing experience. You can pause, rewind, fast-forward, or skip scenes without any buffering or interruptions. You can also adjust the brightness, volume, subtitles, or language settings according to your preferences.</p>
-<h3>No subscription fees or ads</h3>
-<p>A third benefit of downloading movies in this format is that you don't have to pay any subscription fees or watch any ads. Unlike streaming services that charge you monthly or yearly fees to access their content library, downloading movies allows you to watch them for free. You can also avoid annoying ads that interrupt your viewing pleasure or collect your personal data.</p>
-<p>Downloading movies also gives you more freedom and choice over what you want to watch. You don't have to rely on the availability or selection of streaming services <h2>Risks of Downloading 4 Movie</h2>
-<h3>Legal issues and copyright infringement</h3>
-<p>While downloading movies in this format may seem tempting, you should also be aware of the potential risks involved. One of the biggest risks is that you may be breaking the law and infringing on the rights of the movie creators and distributors. Downloading movies from unauthorized sources is considered piracy, which is illegal in most countries and can result in fines, lawsuits, or even jail time.</p>
-<p>Therefore, you should always check the legality and legitimacy of the sites and sources that you use to download movies. You should also respect the intellectual property and creative efforts of the movie makers and support them by paying for their work or watching them on legal platforms.</p>
-<p>download 4 movie in HD quality<br />
-download 4 movie for free online<br />
-download 4 movie with subtitles<br />
-download 4 movie from YouTube<br />
-download 4 movie using torrent<br />
-download 4 movie on mobile<br />
-download 4 movie in MP4 format<br />
-download 4 movie full length<br />
-download 4 movie without registration<br />
-download 4 movie fast and easy<br />
-download 4 movie latest release<br />
-download 4 movie in Hindi dubbed<br />
-download 4 movie with English audio<br />
-download 4 movie from Netflix<br />
-download 4 movie legally and safely<br />
-download 4 movie in 1080p resolution<br />
-download 4 movie with high speed<br />
-download 4 movie from best site<br />
-download 4 movie in dual audio<br />
-download 4 movie with low data usage<br />
-download 4 movie in different genres<br />
-download 4 movie offline mode<br />
-download 4 movie with one click<br />
-download 4 movie from Amazon Prime Video<br />
-download 4 movie no ads or pop-ups<br />
-download 4 movie in Blu-ray quality<br />
-download 4 movie with VPN service<br />
-download 4 movie from Google Drive<br />
-download 4 movie in original language<br />
-download 4 movie with bonus features<br />
-download 4 movie in small size<br />
-download 4 movie on PC or laptop<br />
-download 4 movie with direct link<br />
-download 4 movie from Disney Plus<br />
-download 4 movie no sign up required<br />
-download 4 movie in ultra HD quality<br />
-download 4 movie with multiple options<br />
-download 4 movie from Hulu<br />
-download 4 movie in any region or country<br />
-download 4 movie with good sound quality<br />
-download 4 movie in various formats<br />
-download 4 movie on smart TV or streaming device<br />
-download 4 movie with reliable source<br />
-download 4 movie from HBO Max<br />
-download 4 movie no virus or malware risk<br />
-download 4 movie in HDR quality<br />
-download 4 movie with user-friendly interface<br />
-download 4 movie from Apple TV Plus<br />
-download 4 movie no credit card needed</p>
-<h3>Malware and viruses</h3>
-<p>Another risk of downloading movies in this format is that you may expose your device and data to malware and viruses. Some of the sites and sources that offer free movie downloads may contain malicious software or links that can harm your computer, smartphone, tablet, or external hard drive. They can infect your device with spyware, ransomware, trojans, worms, or other types of malware that can steal your personal information, damage your files, or lock your device.</p>
-<p>Therefore, you should always be careful and cautious when downloading movies from unknown or suspicious sites and sources. You should also use a reliable antivirus software and firewall to protect your device and data from malware and viruses. You should also scan your downloaded files before opening them or transferring them to other devices.</p>
-<h3>Data consumption and storage space</h3>
-<p>A third risk of downloading movies in this format is that you may consume a lot of data and storage space. Because movies in this format have higher resolution and quality than MP4 movies, they also have larger file sizes. A typical movie can range from 1 GB to 10 GB or more, depending on the length and quality of the movie. This means that you may need a fast and stable internet connection to download them without any issues or delays.</p>
-<p>Downloading movies in this format also requires a lot of storage space on your device or external hard drive. If you download too many movies, you may run out of space or slow down your device performance. Therefore, you should always check the file size and storage capacity before downloading movies in this format. You should also delete or transfer the movies that you don't need or watch anymore to free up some space.</p>
-<h2>Best Free Movie Download Sites for 4 Movie in 2023</h2>
-<h3>YouTube</h3>
-<p>One of the best and easiest ways to download movies in this format for free is to use YouTube. YouTube is the most popular video-sharing platform in the world, where you can find millions of videos, including movies, trailers, clips, documentaries, and more. You can also find many channels and playlists that offer movies in this format for free.</p>
-<p>To download movies from YouTube, you can use a simple online tool called YouTube Downloader. This tool allows you to download any video from YouTube in various formats, including 4 movie. All you have to do is copy and paste the URL of the video that you want to download into the tool's input box and click on the "Download" button. You can then choose the format and quality that you want and save the file on your device.</p>
-<h3>EZTV</h3>
-<p>Another option to download movies in this format for free is to use EZTV. EZTV is one of the most popular torrent sites for downloading TV shows and movies. You can find a wide range of genres, categories, languages, and qualities on this site, including 4 movie. You can also find the latest releases and updates on this site.</p>
-<p>To download movies from EZTV, you need to use a torrent client software such as BitTorrent or uTorrent. This software allows you to download files from other users who are sharing them on the network. You also need to use a VPN service such as NordVPN or ExpressVPN to hide your IP address and encrypt your traffic. This way, you can avoid any legal issues or malware threats when downloading movies from torrent sites.</p>
-<h3>FZMovies</h3>
-<p>A third option to download movies in this format for free is to use FZMovies. FZMovies is a dedicated site for downloading Bollywood and Hollywood movies in various formats, including 4 movie. You can find a huge collection of movies on this site, ranging from old classics to new blockbusters. You can also search for movies by genre, year, actor, director, or keyword.</p>
-<p>To download movies from FZMovies, you just need to visit the site and browse through its categories or use its search function. You can then click on the movie that you want to download and choose the format and quality that you want. You can then click on the download link and save the file on your device.</p>
-<h2>How to Download 4 Movie from Any Site with ByClick Downloader (Recommended)</h2>
-<h3>What is ByClick Downloader and why you should use it</h3>
-<p>If you want to download movies in this format from any site with ease and convenience, you should use ByClick Downloader. ByClick Downloader is a powerful and versatile tool that can help you download any video from any site with just one click. You can download videos from YouTube, Facebook, Instagram, Twitter, Vimeo, Dailymotion, and more. You can also download videos in various formats, including 4 movie.</p>
-<p>ByClick Downloader has many features and advantages that make it the best choice for downloading movies in this format. Some of them are:</p>
-<ul>
-<li>It supports multiple downloads and batch downloads, so you can download several movies at once or create a download list.</li>
-<li>It has a smart auto-detect feature that detects any video that you are watching on your browser and offers you to download it with one click.</li>
-<li>It has a built-in video converter that allows you to convert any video to any format, including 4 movie.</li>
-<li>It has a fast and reliable download speed that ensures that you get your movies in the shortest time possible.</li>
-<li>It has a user-friendly and intuitive interface that makes it easy to use and navigate.</li>
-</ul>
-<h3>How to install and use ByClick Downloader</h3>
-<p>To install and use ByClick Downloader, you just need to follow these simple steps:</p>
-<ol>
-<li>Visit the official website of ByClick Downloader and click on the "Download" button to get the setup file.</li>
-<li>Run the setup file and follow the instructions to install the tool on your device.</li>
-<li>Open the tool and choose the format and quality that you want for your downloads. You can also customize other settings such as download location, notifications, subtitles, etc.</li>
-<li>Go to any site that has the movie that you want to download and play the video. You will see a pop-up window that offers you to download the video with one click. You can also copy and paste the URL of the video into the tool's input box and click on the "Download" button.</li>
-<li>Wait for the download to finish and enjoy your movie.</li>
-</ol>
-<h3>Features and advantages of ByClick Downloader</h3>
-<p>By using ByClick Downloader, you can enjoy many features and advantages that make it the best tool for downloading movies in this format. Some of them are:</p>
-<ul>
-<li>You can download movies in this format from any site with just one click, without any hassle or complication.</li>
-<li>You can download movies in this format in high quality, up to UHD resolution.</li>
-<li>You can download movies in this format with Dolby Atmos sound, for a realistic and immersive audio experience.</li>
-<li>You can download movies in this format with subtitles, in any language that you want.</li>
-<li>You can download movies in this format with metadata, such as title, genre, director, cast, etc.</li>
-<li>You can download movies in this format with thumbnails, for a better preview and organization of your files.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In conclusion, downloading movies in this format is a great way to enjoy high-quality video and audio, offline access and convenience, and no subscription fees or ads. However, you should also be aware of the risks of downloading movies in this format, such as legal issues and copyright infringement, malware and viruses, and data consumption and storage space. You should always check the legality and legitimacy of the sites and sources that you use to download movies, and use a reliable antivirus software and firewall to protect your device and data from malware and viruses. You should also check the file size and storage capacity before downloading movies in this format, and delete or transfer the movies that you don't need or watch anymore to free up some space.</p>
- <p>The best way to download movies in this format for free in 2023 is to use ByClick Downloader. This tool can help you download any movie from any site with just one click. You can also download movies in various formats, including 4 movie. By using ByClick Downloader, you can enjoy many features and advantages that make it the best tool for downloading movies in this format. You can download movies in high quality, up to UHD resolution. You can also download movies with Dolby Atmos sound, subtitles, metadata, thumbnails, and more. You can also convert any video to any format, including 4 movie. You can also enjoy fast and reliable download speed, multiple downloads and batch downloads, smart auto -detect feature, and a user-friendly and intuitive interface.</p>
-<p>So, what are you waiting for? Download ByClick Downloader today and start downloading your favorite movies in this format for free in 2023. You will not regret it!</p>
-<h2>FAQs</h2>
-<h3>What is the difference between MP4 and 4 movie?</h3>
-<p>MP4 is a standard video format that is widely used and supported by most devices and platforms. It has good quality and compression, but it also has some limitations and drawbacks. For example, it does not support high dynamic range (HDR), which enhances the contrast and color of the video. It also does not support Dolby Atmos, which creates a surround sound effect. It also has a fixed frame rate, which can cause stuttering or judder when playing videos with different frame rates.</p>
-<p>4 movie is a new video format that is designed to overcome the limitations and drawbacks of MP4. It supports HDR, Dolby Atmos, and variable frame rate (VFR), which adapts to the frame rate of the video. It also has better quality and compression than MP4, which means that it can deliver higher resolution and quality with smaller file sizes.</p>
-<h3>How can I watch 4 movie on my TV or mobile device?</h3>
-<p>To watch movies in this format on your TV or mobile device, you need to make sure that your device supports this format. You can check the specifications or settings of your device to see if it supports this format. If your device does not support this format, you can use a video converter software such as ByClick Downloader to convert the movie to a compatible format such as MP4.</p>
-<p>You also need to transfer the movie from your computer to your device using a USB cable, a wireless connection, or a cloud service. You can then use a media player app such as VLC or MX Player to play the movie on your device.</p>
-<h3>How can I convert other video formats to 4 movie?</h3>
-<p>To convert other video formats to this format, you can use a video converter software such as ByClick Downloader. This software can help you convert any video to any format, including 4 movie. You just need to add the video that you want to convert to the software, choose the format and quality that you want, and click on the "Convert" button. You can then save the converted file on your device or share it with others.</p>
-<h3>Is it safe to download movies from torrent sites?</h3>
-<p>Downloading movies from torrent sites is not recommended, as it may pose some risks and dangers. Some of these risks are:</p>
-<ul>
-<li>You may be breaking the law and infringing on the rights of the movie creators and distributors, which can result in legal consequences such as fines, lawsuits, or jail time.</li>
-<li>You may expose your device and data to malware and viruses, which can harm your device, steal your personal information, damage your files, or lock your device.</li>
-<li>You may consume a lot of data and bandwidth, which can affect your internet speed and performance.</li>
-<li>You may encounter fake or corrupted files, which can waste your time and space.</li>
-</ul>
-<p>Therefore, you should avoid downloading movies from torrent sites and use legal and safe sources instead.</p>
-<h3>How can I avoid legal issues when downloading movies?</h3>
-<p>To avoid legal issues when downloading movies, you should follow these tips:</p>
-<ul>
-<li>Always check the legality and legitimacy of the sites and sources that you use to download movies. You should only use sites that have proper licenses and permissions to distribute movies.</li>
-<li>Always respect the intellectual property and creative efforts of the movie makers and support them by paying for their work or watching them on legal platforms.</li>
-<li>Always use a VPN service such as NordVPN or ExpressVPN to hide your IP address and encrypt your traffic when downloading movies from questionable sources. This way, you can avoid being tracked or monitored by authorities or hackers.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Chicken Gun New Update Mod Apk and Enjoy the Fun.md

@@ -1,85 +0,0 @@
-<br />
-<h1>Chicken Gun New Update Mod APK: Everything You Need to Know</h1>
-<p>If you are looking for a fun and addictive FPS game that will make you laugh out loud, then you should try Chicken Gun. This game lets you play as a chicken with a gun and fight against other chickens in various modes and maps. You can customize your chicken with different skins, hats, glasses, and weapons, and enjoy the hilarious physics and animations. In this article, we will tell you everything you need to know about the latest update of Chicken Gun and how to download the mod apk version that gives you unlimited money, gems, and more.</p>
-<h2>chicken gun new update mod apk</h2><br /><p><b><b>Download</b> &#9733; <a href="https://urlin.us/2uSTtw">https://urlin.us/2uSTtw</a></b></p><br /><br />
- <h2>What is Chicken Gun?</h2>
-<h3>A hilarious and chaotic FPS game</h3>
-<p>Chicken Gun is a multiplayer FPS game developed by ChaloApps. The game has a simple premise: you are a chicken with a gun, and you have to shoot other chickens in various modes and maps. You can play online with up to 10 players in team deathmatch, free for all, capture the flag, or zombie mode. You can also play offline with bots or with your friends on the same device.</p>
- <h3>Features of Chicken Gun</h3>
-<p>Chicken Gun has many features that make it a unique and entertaining game. Some of them are:</p>
-<ul>
-<li>Over 50 different weapons to choose from, including pistols, rifles, shotguns, grenades, rockets, flamethrowers, and more.</li>
-<li>Over 20 different skins to customize your chicken, such as pirate, ninja, clown, astronaut, cowboy, and more.</li>
-<li>Over 10 different hats, glasses, and masks to accessorize your chicken, such as sunglasses, helmets, bandanas, goggles, and more.</li>
-<li>Over 10 different maps to explore and fight in, such as farm, city, desert, snow, space, and more.</li>
-<li>Funny physics and animations that make the game hilarious and chaotic.</li>
-<li>Chat system to communicate with other players.</li>
-<li>Leaderboards and achievements to compete with other players.</li>
-</ul>
- <h2>What is new in the latest update?</h2>
-<h3>New maps, weapons, and skins</h3>
-<p>The latest update of Chicken Gun brings some new content to the game. There are two new maps: prison and airport. There are also two new weapons: crossbow and minigun. And there are four new skins: prisoner, pilot, cop, and soldier.</p>
- <h3>Improved graphics and performance</h3>
-<p>The latest update also improves the graphics and performance of the game. The game now supports HD resolution and has better lighting effects. The game also runs smoother and faster on most devices.</p>
- <h3>Bug fixes and balance changes</h3>
-<p>The latest update also fixes some bugs and glitches that were affecting the gameplay. For example, some weapons were not working properly or had incorrect stats. The update also balances some weapons and modes to make them more fair and fun.</p>
- <h2>Why should you download the mod apk?</h2>
-<h3>Unlimited money and gems</h3>
-<p>The mod apk version of Chicken Gun gives you unlimited money and gems. You can use them to buy any weapon, skin, hat, or glass you want. You can also upgrade your weapons to make them more powerful and effective.</p>
-<p>chicken gun mod apk latest version download<br />
-chicken gun unlimited money and gems mod apk<br />
-chicken gun 3.3.01 mod apk free download<br />
-chicken gun fps shooter mod apk android<br />
-chicken gun hack mod apk no root<br />
-chicken gun online multiplayer mod apk<br />
-chicken gun mod menu apk download<br />
-chicken gun mod apk all weapons unlocked<br />
-chicken gun mod apk offline mode<br />
-chicken gun mod apk unlimited ammo and health<br />
-chicken gun 2d pixel shooter mod apk<br />
-chicken gun mod apk revdl<br />
-chicken gun mod apk rexdl<br />
-chicken gun mod apk happymod<br />
-chicken gun mod apk an1<br />
-chicken gun zombie mode mod apk<br />
-chicken gun battle royale mod apk<br />
-chicken gun sandbox mode mod apk<br />
-chicken gun custom skins mod apk<br />
-chicken gun pro pack mod apk<br />
-chicken gun new maps and modes mod apk<br />
-chicken gun 3d graphics mod apk<br />
-chicken gun voice chat mod apk<br />
-chicken gun ragdoll physics mod apk<br />
-chicken gun funny moments mod apk<br />
-chicken gun best guns and items mod apk<br />
-chicken gun tips and tricks mod apk<br />
-chicken gun cheats and hacks mod apk<br />
-chicken gun gameplay and review mod apk<br />
-chicken gun how to install mod apk</p>
- <h3>Unlock all items and modes</h3>
-<p>The mod apk version also unlocks all items and modes in the game. You can access any map or mode without having to level up or complete <p>any challenge. You can also use any item without having to wait for the cooldown or reload time.</p>
- <h3>No ads and root required</h3>
-<p>The mod apk version also removes all the annoying ads that pop up in the game. You can enjoy the game without any interruption or distraction. Moreover, the mod apk does not require root access to work. You can install it on any device without any risk or hassle.</p>
- <h2>How to download and install the mod apk?</h2>
-<h3>Step 1: Download the mod apk file from a trusted source</h3>
-<p>The first step is to download the mod apk file from a trusted source. You can find many websites that offer the mod apk file, but be careful of fake or malicious ones. We recommend you to use this link to download the mod apk file safely and securely.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
-<p>The second step is to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
- <h3>Step 3: Install the mod apk and enjoy the game</h3>
-<p>The final step is to install the mod apk and enjoy the game. To do this, locate the mod apk file in your device storage, then tap on it and follow the instructions. Once the installation is done, you can open the game and start playing with unlimited money, gems, and more.</p>
- <h2>Conclusion</h2>
-<p>Chicken Gun is a hilarious and chaotic FPS game that will make you laugh out loud. You can play as a chicken with a gun and fight against other chickens in various modes and maps. You can customize your chicken with different skins, hats, glasses, and weapons, and enjoy the funny physics and animations. The latest update of Chicken Gun brings some new content and improvements to the game, such as new maps, weapons, skins, graphics, performance, bug fixes, and balance changes. If you want to have more fun and advantages in the game, you should download the mod apk version that gives you unlimited money, gems, and more. You can download the mod apk file from this link and install it on your device easily and safely. We hope you enjoyed this article and found it helpful. If you have any questions or feedback, please let us know in the comments below.</p>
- <h2>FAQs</h2>
-<h4>Q: Is Chicken Gun free to play?</h4>
-<p>A: Yes, Chicken Gun is free to play. You can download it from the Google Play Store or the App Store for free. However, some items and modes may require real money to unlock or use.</p>
- <h4>Q: Is Chicken Gun safe to play?</h4>
-<p>A: Yes, Chicken Gun is safe to play. The game does not contain any harmful or inappropriate content for children or adults. The game is rated 12+ on the Google Play Store and 9+ on the App Store for mild violence and crude humor.</p>
- <h4>Q: Is Chicken Gun offline or online?</h4>
-<p>A: Chicken Gun can be played both offline and online. You can play offline with bots or with your friends on the same device. You can also play online with up to 10 players in team deathmatch, free for all, capture the flag, or zombie mode.</p>
- <h4>Q: How can I contact the developers of Chicken Gun?</h4>
-<p>A: You can contact the developers of Chicken Gun by sending them an email at [email protected] or by following them on their social media accounts such as Facebook, Instagram, or YouTube. You can also leave a review or a rating on the Google Play Store or the App Store to share your feedback or suggestions.</p>
- <h4>Q: How can I support the developers of Chicken Gun?</h4>
-<p>A: You can support the developers of Chicken Gun by buying some items or modes in the game with real money. This will help them cover their costs and improve their game. You can also share their game with your friends and family and invite them to play with you.</p>
- : https://www.apkdone.com/chicken-gun/ : https://www.facebook.com/ChaloApps : https://www.instagram.com/chaloapps/ : https://www.youtube.com/channel/UC9w8mpVvRdWRs1b8whPLnxg</p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Bullet Echo Mod APK v5.2.4 and Enjoy Unlimited Resources.md

@@ -1,111 +0,0 @@
-
-<br>
- <table>
-<tr>
-<td>
- <h1>Bullet Echo Mod APK 5.2.4: A Tactical Shooter Game with Unlimited Money</h1>
-<h2>Introduction</h2>
-<p>If you are looking for a thrilling and challenging shooter game that tests your skills and tactics, then you should try <strong>Bullet Echo Mod APK 5.2.4</strong>. This is a modified version of the original Bullet Echo game developed by ZeptoLab, which is known for creating popular games like Cut the Rope and King of Thieves.</p>
-<h2>bullet echo mod apk 5.2.4</h2><br /><p><b><b>Download Zip</b> &#9913;&#9913;&#9913; <a href="https://jinyurl.com/2uNPre">https://jinyurl.com/2uNPre</a></b></p><br /><br />
-<p>Bullet Echo Mod APK 5.2.4 is a tactical shooter game that puts you in a dark battlefield where you have to rely on your senses and strategy to survive and eliminate your enemies. You can choose from over 80 heroes with unique abilities and weapons, and team up with other players in real-time multiplayer matches.</p>
-<p>Some of the features of Bullet Echo Mod APK 5.2.4 are:</p> <p>Here are some of the benefits of Bullet Echo Mod APK 5.2.4:</p>
-<p>bullet echo mod apk 5.2.4 unlimited money and coins<br />
-bullet echo mod apk 5.2.4 latest version download<br />
-bullet echo mod apk 5.2.4 free shopping and upgrades<br />
-bullet echo mod apk 5.2.4 unlocked all characters and weapons<br />
-bullet echo mod apk 5.2.4 no ads and no root<br />
-bullet echo mod apk 5.2.4 hack cheats and tips<br />
-bullet echo mod apk 5.2.4 gameplay and review<br />
-bullet echo mod apk 5.2.4 online multiplayer mode<br />
-bullet echo mod apk 5.2.4 best settings and graphics<br />
-bullet echo mod apk 5.2.4 android and ios compatible<br />
-bullet echo mod apk 5.2.4 new features and updates<br />
-bullet echo mod apk 5.2.4 how to install and use<br />
-bullet echo mod apk 5.2.4 download link and mirror<br />
-bullet echo mod apk 5.2.4 safe and secure<br />
-bullet echo mod apk 5.2.4 bugs and issues fixed<br />
-bullet echo mod apk 5.2.4 high damage and speed<br />
-bullet echo mod apk 5.2.4 offline and single player mode<br />
-bullet echo mod apk 5.2.4 ranking and rewards system<br />
-bullet echo mod apk 5.2.4 custom maps and modes<br />
-bullet echo mod apk 5.2.4 voice chat and team play<br />
-bullet echo mod apk 5.2.4 support and feedback<br />
-bullet echo mod apk 5.2.4 original vs mod comparison<br />
-bullet echo mod apk 5.2.4 fun and addictive gameplay<br />
-bullet echo mod apk 5.2.4 realistic physics and sound effects<br />
-bullet echo mod apk 5.2.4 different classes and skills<br />
-bullet echo mod apk 5.2.4 strategy and tactics guide<br />
-bullet echo mod apk 5.2.4 weapons and equipment list<br />
-bullet echo mod apk 5.2.4 skins and customization options<br />
-bullet echo mod apk 5.2.4 missions and challenges mode<br />
-bullet echo mod apk 5.2.4 leaderboards and achievements<br />
-bullet echo mod apk 5.2.4 codes and coupons<br />
-bullet echo mod apk 5.2.4 trivia and facts<br />
-bullet echo mod apk 5.2.4 fan art and wallpapers<br />
-bullet echo mod apk 5.2.4 community and forums<br />
-bullet echo mod apk 5.2.4 developer and publisher information<br />
-bullet echo mod apk 5.2.4 rating and reviews<br />
-bullet echo mod apk 5.2.4 similar games and apps<br />
-bullet echo mod apk 5.2.4 frequently asked questions (FAQ)<br />
-bullet echo mod apk 5.2.4 pros and cons<br />
-bullet echo mod apk 5</p>
-<ul>
-<li>You can enjoy unlimited money to buy and upgrade your weapons and equipment.</li>
-<li>You can access the mod menu to enable or disable various features such as god mode, unlimited ammo, no recoil, and more.</li>
-<li>You can play with other players from around the world and chat with them in the game.</li>
-<li>You can experience high-quality graphics and sound effects that create an immersive atmosphere.</li>
-<li>You can have fun with different game modes and challenges that test your skills and tactics.</li>
-</ul>
-<p>So, if you are ready to join the action and become the best shooter in the dark, then you should download Bullet Echo Mod APK 5.2.4 right now. You will not regret it!</p>
-<h2>How to Download and Install Bullet Echo Mod APK 5.2.4 on Android Devices</h2>
-<p>Downloading and installing Bullet Echo Mod APK 5.2.4 on your Android device is very easy and simple. Just follow these steps:</p>
-<ol>
-<li>Enable unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Download the Bullet Echo Mod APK 5.2.4 file from a trusted source. You can use the link below to get it.</li>
-<li>Locate and install the Bullet Echo Mod APK 5.2.4 file on your device. You can use a file manager app to find it in your downloads folder.</li>
-<li>Launch the game and enjoy unlimited money and mod menu.</li>
-</ol>
-<p>That's it! You have successfully installed Bullet Echo Mod APK 5.2.4 on your Android device. Now you can play the game with all the advantages and features that it offers.</p>
-<h2>How to Play Bullet Echo Mod APK 5.2.4</h2>
-<p>Bullet Echo Mod APK 5.2.4 is a game that requires skill, strategy, and teamwork to win. Here are some tips on how to play it:</p>
-<ul>
-<li>Choose your hero and team up with other players. You can select from over 80 heroes with different abilities and weapons, such as snipers, assaulters, healers, and more. You can also join or create a squad with your friends or other players online.</li>
-<li>Use your skills and tactics to defeat your enemies. You have to rely on your senses and strategy to survive and eliminate your enemies in the dark battlefield. You can use your flashlight to see, but be careful not to expose yourself too much. You can also use your hero's abilities to gain an edge over your opponents, such as invisibility, shields, grenades, etc.</li>
-<li>Collect loot and upgrade your weapons and equipment. You can find various items on the map, such as ammo, health kits, armor, and more. You can also use the money you earn from winning matches to buy and upgrade your weapons and equipment in the shop.</li>
-<li>Compete in various modes and rank up on the leaderboard. You can play in different game modes, such as Team vs Team, Solo, and Battle Royale. Each mode has its own rules and objectives, so you have to adapt your strategy accordingly. You can also earn points and rank up on the leaderboard by winning matches and completing missions.</li>
-</ul>
-<h2>Tips and Tricks for Bullet Echo Mod APK 5.2.4</h2>
-<p>Bullet Echo Mod APK 5.2.4 is a game that challenges your skills and tactics as a shooter. Here are some tips and tricks that can help you improve your performance:</p>
-<ul>
-<li>Use stealth and cover to avoid detection. You have to be careful not to make too much noise or reveal yourself too much in the dark battlefield. You can use stealth mode to move silently, or hide behind objects or walls to avoid enemy fire.</li>
-<li>Communicate with your teammates and coordinate your attacks. You can use the chat feature or voice chat feature to communicate with your teammates in the game. You can also use emojis or gestures to express yourself or give commands. You should work together with your teammates and coordinate your attacks to achieve victory.</li>
-<li>Experiment with different heroes and find your best match. You can try out different heroes with different abilities and weapons, and see which one suits your play style best. You can also switch heroes during matches if you want to change your strategy or counter your enemies.</li>
-<li>Use the mod menu to customize your game settings and preferences. You can access the mod menu by tapping on the icon on the top left corner of the screen. You can enable or disable various features such as god mode, unlimited ammo, no recoil, speed hack, etc., depending on how you want to play the game. You can also adjust the sound, graphics, and language settings according to your preference.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Bullet Echo Mod APK 5.2.4 is a tactical shooter game that offers you a thrilling and challenging experience in a dark battlefield. You can choose from over 80 heroes with unique abilities and weapons, and team up with other players in real-time multiplayer matches. You can also enjoy unlimited money and mod menu features that give you more control and fun in the game. You can download and install Bullet Echo Mod APK 5.2.4 on your Android device easily and safely by following the steps above.</p>
-<p>So, what are you waiting for? Download Bullet Echo Mod APK 5.2.4 now and join the action and become the best shooter in the dark!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Bullet Echo Mod APK 5.2.4:</p>
-<h3>What is Bullet Echo Mod APK 5.2.4?</h3>
-<p>Bullet Echo Mod APK 5.2.4 is a modified version of the original Bullet Echo game developed by ZeptoLab, which is a tactical shooter game that puts you in a dark battlefield where you have to rely on your senses and strategy to survive and eliminate your enemies.</p>
-<h3>Is Bullet Echo Mod APK 5.2.4 safe to download and install?</h3>
-<p>Yes, Bullet Echo Mod APK 5.2.4 is safe to download and install on your Android device, as long as you get it from a trusted source. You can use the link below to get it.</p>
-<h3>What are the benefits of Bullet Echo Mod APK 5.2.4?</h3>
-<p>Some of the benefits of Bullet Echo Mod APK 5.2.4 are:</p>
-<ul>
-<li>You can enjoy unlimited money to buy and upgrade your weapons and equipment.</li>
-<li>You can access the mod menu to enable or disable various features such as god mode, unlimited ammo, no recoil, speed hack, etc.</li>
-<li>You can play with other players from around the world and chat with them in the game.</li>
-<li>You can experience high-quality graphics and sound effects that create an immersive atmosphere.</li>
-<li>You can have fun with different game modes and challenges that test your skills and tactics.</li>
-</ul>
-<h3>How can I get unlimited money in Bullet Echo Mod APK 5.2.4?</h3>
-<p>You can get unlimited money in Bullet Echo Mod APK 5.2.4 by downloading and installing the modded version of the game from the link below. You can use the money to buy and upgrade your weapons and equipment in the shop.</p>
-<h3>How can I access the mod menu in Bullet Echo Mod APK 5.2.4?</h3>
-<p>You can access the mod menu in Bullet Echo Mod APK 5.2.4 by tapping on the icon on the top left corner of the screen. You can enable or disable various features such as god mode, unlimited ammo, no recoil, speed hack, etc., depending on how you want to play the game.</p>
- </td>
-</tr>
-</table></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download I Miss You by Grey and Discover More Songs by the Duo.md

@@ -1,128 +0,0 @@
-<br />
-<h1>How to Download "I Miss You" by Grey</h1>
-<p>"I Miss You" by Grey is a catchy and emotional electronic song that features the vocals of Bahari, a pop duo from Los Angeles. The song was released in 2017 as Grey's first official single, and it has since gained over 100 million streams on Spotify and over 6 million views on YouTube. The song is about missing someone you used to know and wondering if they feel the same way.</p>
-<p>If you love this song and want to listen to it anytime, anywhere, you might want to download it to your computer or mobile device. However, you should also be aware of the legal and ethical issues involved in downloading music online. In this article, we will show you three ways to download "I Miss You" by Grey legally, and help you decide which one is best for you.</p>
-<h2>download i miss you by grey</h2><br /><p><b><b>DOWNLOAD</b> &rarr; <a href="https://jinyurl.com/2uNS1f">https://jinyurl.com/2uNS1f</a></b></p><br /><br />
- <h2>How to download "I Miss You" by Grey legally</h2>
-<p>There are three main options for downloading "I Miss You" by Grey legally: buying the song from a digital music store, streaming the song from a music streaming service, or downloading the song for free from a legal website. Each option has its own pros and cons, depending on your preferences, budget, and internet connection. Let's take a look at each option in detail.</p>
- <h3>Option 1: Buy the song from a digital music store</h3>
-<p>One way to download "I Miss You" by Grey legally is to buy the song from a digital music store, such as iTunes, Amazon, or Google Play. This way, you can support the artists and their record label, and get a high-quality audio file that you can keep forever. However, this option also has some drawbacks. For example, you will have to pay for each song individually, which can add up if you want to download many songs. Also, you might have to deal with DRM (digital rights management) restrictions that limit how you can use or share the music.</p>
- <h4>Pros and cons of buying the song</h4>
-<table>
-<tr><td>Pros</td><td>Cons</td></tr>
-<tr><td>You can support the artists and their record label</td><td>You have to pay for each song individually</td></tr>
-<tr><td>You can get a high-quality audio file</td><td>You might have to deal with DRM restrictions</td></tr>
-<tr><td>You can keep the music forever</td><td>You might need extra storage space on your device</td></tr>
-</table>
- <h4>How to buy the song from iTunes, Amazon, or Google Play</h4>
-<p>To buy "I Miss You" by Grey from iTunes, Amazon, or Google Play, you will need to have an account with one of these services and a valid payment method. Then, you can follow these steps:</p>
-<ol>
-<li>Go to the website or app of your chosen service and search for "I Miss You" by Grey.</li>
-<li>Select the song and click on the buy or download button.</li>
-<li>Confirm your purchase and enter your payment details if needed.</li>
-<li>Wait for the download to complete and enjoy your music.</li>
-</ol>
- <h3>Option 2: Stream the song from a music streaming service</h3> Another way to download "I Miss You" by Grey legally is to stream the song from a music streaming service, such as Spotify, YouTube Music, or Apple Music. This way, you can access a huge library of music for a monthly fee or for free with ads. You can also download the song to your device for offline listening, as long as you maintain your subscription or account. However, this option also has some disadvantages. For example, you will not own the music and you might lose access to it if the service changes its terms or catalog. Also, you might have to deal with lower audio quality or data usage if you stream the music online. <h4>Pros and cons of streaming the song</h4>
-<table>
-<tr><td>Pros</td><td>Cons</td></tr>
-<tr><td>You can access a huge library of music</td><td>You will not own the music</td></tr>
-<tr><td>You can download the song for offline listening</td><td>You might lose access to the music</td></tr>
-<tr><td>You can pay a monthly fee or use the service for free with ads</td><td>You might have to deal with lower audio quality or data usage</td></tr>
-</table>
- <h4>How to stream the song from Spotify, YouTube Music, or Apple Music</h4>
-<p>To stream "I Miss You" by Grey from Spotify, YouTube Music, or Apple Music, you will need to have an account with one of these services and a compatible device. Then, you can follow these steps:</p>
-<ol>
-<li>Go to the website or app of your chosen service and search for "I Miss You" by Grey.</li>
-<li>Select the song and click on the play or add button.</li>
-<li>If you want to download the song for offline listening, click on the download or offline button.</li>
-<li>Enjoy your music and remember to check your subscription or account status regularly.</li>
-</ol>
- <h3>Option 3: Download the song for free from a legal website</h3>
-<p>A third way to download "I Miss You" by Grey legally is to download the song for free from a legal website, such as SoundCloud, Bandcamp, or DatPiff. These websites allow artists to upload their music and share it with their fans for free or for a voluntary donation. You can find many songs that are not available on other platforms and discover new artists and genres. However, this option also has some limitations. For example, you might not find the song you are looking for or it might be removed by the artist at any time. Also, you might have to deal with low audio quality or malware risks if you download from untrusted sources.</p>
-<p>How to download i miss you by grey on soundcloud<br />
-I miss you by grey feat bahari mp3 download<br />
-I miss you by grey lyrics and song download<br />
-Download i miss you by grey from jiosaavn<br />
-I miss you by grey ringtone download<br />
-I miss you by grey spotify download<br />
-I miss you by grey piano cover download<br />
-I miss you by grey remix download<br />
-I miss you by grey acoustic version download<br />
-I miss you by grey instrumental download<br />
-Download i miss you by grey on apple music<br />
-I miss you by grey karaoke download<br />
-I miss you by grey video download<br />
-I miss you by grey 320kbps download<br />
-I miss you by grey flac download<br />
-Download i miss you by grey on amazon music<br />
-I miss you by grey nightcore download<br />
-I miss you by grey live performance download<br />
-I miss you by grey edm download<br />
-I miss you by grey guitar tabs download<br />
-Download i miss you by grey on youtube music<br />
-I miss you by grey sheet music download<br />
-I miss you by grey mashup download<br />
-I miss you by grey 8d audio download<br />
-I miss you by grey slowed down download<br />
-Download i miss you by grey on deezer<br />
-I miss you by grey dance choreography download<br />
-I miss you by grey reaction video download<br />
-I miss you by grey tiktok challenge download<br />
-I miss you by grey extended mix download<br />
-Download i miss you by grey on tidal<br />
-I miss you by grey midi file download<br />
-I miss you by grey behind the scenes download<br />
-I miss you by grey official audio download<br />
-I miss you by grey loop download<br />
-Download i miss you by grey on napster<br />
-I miss you by grey a cappella download<br />
-I miss you by grey fan art download<br />
-I miss you by grey wallpaper download<br />
-I miss you by grey podcast episode download<br />
-Download i miss you by grey on pandora<br />
-I miss you by grey ukulele chords download<br />
-I miss you by grey trivia quiz download<br />
-I miss you by grey vr experience download<br />
-I miss you by grey lullaby version download</p>
- <h4>Pros and cons of downloading the song for free</h4>
-<table>
-<tr><td>Pros</td><td>Cons</td></tr>
-<tr><td>You can download the song for free or for a voluntary donation</td><td>You might not find the song you are looking for or it might be removed by the artist</td></tr>
-<tr><td>You can find many songs that are not available on other platforms</td><td>You might have to deal with low audio quality or malware risks</td></tr>
-<tr><td>You can discover new artists and genres</td><td>You might not be able to support the artists and their record label</td></tr>
-</table>
- <h4>How to download the song from SoundCloud, Bandcamp, or DatPiff</h4>
-<p>To download "I Miss You" by Grey from SoundCloud, Bandcamp, or DatPiff, you will need to have an account with one of these websites and a web browser. Then, you can follow these steps:</p>
-<ol>
-<li>Go to the website of your chosen service and search for "I Miss You" by Grey.</li>
-<li>Select the song and click on the download or buy button.</li>
-<li>If the song is free, confirm your download and wait for it to complete.</li>
-<li>If the song is not free, enter your email address and choose how much you want to pay (or enter zero if it is a voluntary donation).</li>
-<li>Check your email and click on the link to download the song.</li>
-<li>Enjoy your music and consider supporting the artists if you like their work.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>"I Miss You" by Grey is a great song that you might want to download and listen to anytime, anywhere. However, you should also be careful about how you download music online and respect the rights of the artists and their record label. In this article, we showed you three ways to download "I Miss You" by Grey legally: buying the song from a digital music store, streaming the song from a music streaming service, or downloading the song for free from a legal website. Each option has its own pros and cons, depending on your preferences, budget, and internet connection. We hope this article helped you decide which option is best for you and enjoy your music legally and ethically.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading "I Miss You" by Grey legally:</p>
-<ul>
-<li><b>Is it illegal to download music from YouTube?</b></li>
-<p>Yes, it is illegal to download music from YouTube without the permission of the artists or their record label. YouTube does not have a download feature for its videos, and using third-party tools or websites to download music from YouTube is a violation of its terms of service and the copyright law. You might face legal consequences or penalties if you download music from YouTube illegally.</p>
-<li><b>What is the best audio format for downloading music?</b></li>
-<p>The best audio format for downloading music depends on your preferences and needs. Some of the most common audio formats are MP3, AAC, WAV, FLAC, and ALAC. MP3 and AAC are compressed formats that reduce the file size and save storage space, but they also lose some audio quality. WAV, FLAC, and ALAC are uncompressed or lossless formats that preserve the original audio quality, but they also take up more storage space. You should choose the audio format that suits your device, player, and earphones.</p>
-<li><b>How can I download music faster?</b></li>
-<p>There are some factors that affect the speed of downloading music, such as your internet connection, your device, and the source of the music. To download music faster, you can try these tips:</p>
-<ul>
-<li>Use a reliable and fast internet connection, such as Wi-Fi or 4G.</li>
-<li>Use a modern and powerful device, such as a laptop or a smartphone.</li>
-<li>Use a reputable and secure source of music, such as a digital music store or a legal website.</li>
-<li>Download music when the traffic is low, such as at night or early morning.</li>
-<li>Download music in batches or playlists, rather than one by one.</li>
-</ul>
-<li><b>How can I download music safely?</b></li>
-<p>To download music safely, you should avoid using illegal or untrusted sources of music, such as torrent sites, file-sharing platforms, or malware-infected websites. These sources might expose you to legal risks, viruses, spyware, or identity theft. You should also use antivirus software and firewall to protect your device and data from online threats. You should also respect the rights of the artists and their record label and download music legally and ethically.</p>
-<li><b>How can I download music for free?</b></li>
-<p>To download music for free, you should use legal websites that allow artists to upload their music and share it with their fans for free or for a voluntary donation. Some examples of these websites are SoundCloud, Bandcamp, and DatPiff. You can find many songs that are not available on other platforms and discover new artists and genres. However, you should also consider supporting the artists if you like their work and appreciate their generosity.</p>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Instagram GB and Get More Out of Your Instagram Experience.md

@@ -1,95 +0,0 @@
-
-<h1>Download Instagram GB: A Modded Version of Instagram with Extra Features</h1>
- <p>Instagram is one of the most popular social media platforms in the world, with over a billion users. It allows you to share photos, videos, and stories with your friends and followers, as well as discover new content from people you may like. However, if you are looking for more features and options to customize your Instagram experience, you may want to try Instagram GB.</p>
-<h2>download instagram gb</h2><br /><p><b><b>Download File</b> &#9745; <a href="https://jinyurl.com/2uNPfL">https://jinyurl.com/2uNPfL</a></b></p><br /><br />
- <h2>What is Instagram GB?</h2>
- <p>Instagram GB is a modded version of the official Instagram app that offers some extra features and functionalities that are not available in the original app. It is developed by a third-party developer named GBMods, who is also behind other popular modded apps such as WhatsApp GB and Facebook GB.</p>
- <h3>How is it different from the official Instagram app?</h3>
- <p>Instagram GB is different from the official Instagram app in several ways. Some of the main differences are:</p>
- <ul>
-<li>It allows you to change the theme and appearance of the app according to your preferences. You can choose from different colors, fonts, icons, and backgrounds.</li>
-<li>It enables you to download photos, videos, and stories from other users directly to your device. You can also download profile pictures in full size.</li>
-<li>It lets you view anyone's profile picture in full size by tapping on it. You can also zoom in and out of any photo or video on the app.</li>
-<li>It allows you to copy comments and captions from other posts easily. You can also translate them into any language you want.</li>
-<li>It gives you more control over your privacy and security. You can hide your online status and seen ticks from other users. You can also disable video autoplay and story view notifications.</li>
-</ul>
- <h3>What are the benefits of using Instagram GB?</h3>
- <p>Some of the benefits of using Instagram GB are:</p>
- <ul>
-<li>You can enjoy a more personalized and customized Instagram experience that suits your taste and style.</li>
-<li>You can save any content that you like from other users without having to use any external tools or apps.</li>
-<li>You can view any profile picture in full size without having to screenshot or crop it.</li>
-<li>You can copy and paste any text that you find interesting or useful from other posts.</li>
-<li>You can protect your privacy and security by hiding your online activity and seen status from other users.</li>
-</ul>
- <h2>How to download and install Instagram GB on your device?</h2>
- <p>If you want to download and install Instagram GB on your device, you need to follow these steps:</p>
-<p>How to download instagram gb apk for android<br />
-Download instagram gb mod apk latest version 2023<br />
-Download instagram gb transparent theme for free<br />
-Download instagram gb ios app from app store<br />
-Download instagram gb pro with more features and customization<br />
-Download instagram gb plus with dual account support<br />
-Download instagram gb 2023 with anti-ban and privacy features<br />
-Download instagram gb for pc windows 10/8/7<br />
-Download instagram gb lite apk for low-end devices<br />
-Download instagram gb delta with dark mode and stickers<br />
-Download instagram gb official from gbplus.net<br />
-Download instagram gb update to get new features and bug fixes<br />
-Download instagram gb video downloader to save videos and reels<br />
-Download instagram gb story saver to download stories and highlights<br />
-Download instagram gb photo editor to edit and filter photos<br />
-Download instagram gb no root required for installation<br />
-Download instagram gb without ads and pop-ups<br />
-Download instagram gb with unlimited followers and likes<br />
-Download instagram gb with zoom feature for profile pictures<br />
-Download instagram gb with copy bio and comments feature<br />
-Download instagram gb with custom fonts and emojis<br />
-Download instagram gb with theme store and online themes<br />
-Download instagram gb with notification counter and unread messages<br />
-Download instagram gb with hide online status and typing feature<br />
-Download instagram gb with disable video autoplay and sound feature<br />
-Download instagram gb with pin chat and lock chat feature<br />
-Download instagram gb with translate messages and comments feature<br />
-Download instagram gb with disable story view and screenshot feature<br />
-Download instagram gb with enable swipe to reply and forward feature<br />
-Download instagram gb with multiple languages support feature</p>
- <h3>Step 1: Enable unknown sources on your device</h3>
- <p>Since Instagram GB is not available on the official app stores, you need to enable unknown sources on your device to allow the installation of apps from third-party sources. To do this, go to your device settings > security > unknown sources and toggle it on.</p>
- <h3>Step 2: Download the Instagram GB APK file from a trusted source</h3>
- <p>The next step is to download the Instagram GB APK file from a trusted source. You can search for it online or use one of these links:</p>
- <ul>
-<li>[Download Instagram GB APK](^1^)</li>
-<li>[Download Instagram GB APK](^2^)</li>
-<li>[Download Instagram GB APK](^3^)</li>
-</ul>
- <p>Make sure that you download the latest version of the app and that it is compatible with your device <h3>Step 3: Install the Instagram GB app and log in with your account</h3>
- <p>After downloading the Instagram GB APK file, you need to install it on your device. To do this, locate the file in your device storage and tap on it. You may see a warning message that says "This type of file can harm your device. Do you want to keep Instagram GB.apk anyway?". Tap on OK and then on Install. Wait for the installation process to complete and then open the app. You can log in with your existing Instagram account or create a new one.</p>
- <h2>How to use Instagram GB to enhance your experience?</h2>
- <p>Now that you have installed Instagram GB on your device, you can start using it to enjoy its extra features and options. Here are some of the things you can do with Instagram GB:</p>
- <h3>Customize your theme and appearance</h3>
- <p>One of the best things about Instagram GB is that you can change the theme and appearance of the app according to your preferences. You can access the theme settings by tapping on the menu icon (three horizontal lines) on the top right corner of the app and then on GB Settings > Themes. You can choose from different colors, fonts, icons, and backgrounds for your app. You can also download more themes from the online library or create your own theme.</p>
- <h3>Download photos, videos, and stories from other users</h3>
- <p>Another great feature of Instagram GB is that you can download any photo, video, or story from other users directly to your device. You don't need to use any external tools or apps to do this. To download a photo or video from a post, tap on the menu icon (three vertical dots) on the top right corner of the post and then on Download. To download a story, tap on the story and then on the download icon (downward arrow) on the bottom left corner of the screen. You can find the downloaded files in your device gallery or in the Instagram GB folder.</p>
- <h3>View anyone's profile picture in full size</h3>
- <p>Sometimes you may want to view someone's profile picture in full size, but the official Instagram app only shows a small circle. With Instagram GB, you can view anyone's profile picture in full size by tapping on it. You can also zoom in and out of any photo or video on the app by pinching the screen.</p> <h3>Copy comments and captions from other posts</h3>
- <p>Sometimes you may find a comment or a caption from another post that you want to copy and paste somewhere else. With Instagram GB, you can do this easily. To copy a comment, tap and hold on the comment and then on Copy Comment. To copy a caption, tap on the menu icon (three vertical dots) on the top right corner of the post and then on Copy Caption. You can then paste the text wherever you want.</p>
- <h3>Hide your online status and seen ticks</h3>
- <p>If you value your privacy and security, you may want to hide your online status and seen ticks from other users. With Instagram GB, you can do this by going to the menu icon (three horizontal lines) on the top right corner of the app and then on GB Settings > Privacy. You can toggle off the options for Show Online Status and Show Seen Tick. This way, other users won't know when you are online or when you have seen their messages or stories.</p>
- <h2>Conclusion</h2>
- <p>Instagram GB is a modded version of the official Instagram app that offers some extra features and options that are not available in the original app. It allows you to customize your theme and appearance, download photos, videos, and stories from other users, view anyone's profile picture in full size, copy comments and captions from other posts, and hide your online status and seen ticks. If you want to try Instagram GB, you need to download and install the APK file from a trusted source and follow the steps in this article. However, you should also be aware of the risks involved in using a modded app, such as possible bans, malware, or data breaches.</p>
- <h4>FAQs</h4>
- <ul>
-<li>Q: Is Instagram GB safe to use?</li>
-<li>A: Instagram GB is not an official app and it is not endorsed by Instagram or Facebook. Therefore, it may not be safe to use as it may contain malware or spyware that can harm your device or steal your data. It may also violate the terms of service of Instagram and result in your account being banned or suspended.</li>
-<li>Q: Can I use both Instagram GB and the official Instagram app on the same device?</li>
-<li>A: Yes, you can use both Instagram GB and the official Instagram app on the same device as they have different package names and signatures. However, you cannot use the same account on both apps as it may cause conflicts or errors.</li>
-<li>Q: How can I update Instagram GB to the latest version?</li>
-<li>A: You can update Instagram GB to the latest version by downloading the new APK file from a trusted source and installing it over the existing app. You can also check for updates within the app by going to the menu icon (three horizontal lines) on the top right corner of the app and then on GB Settings > Updates.</li>
-<li>Q: How can I uninstall Instagram GB from my device?</li>
-<li>A: You can uninstall Instagram GB from your device by going to your device settings > apps > Instagram GB and tapping on Uninstall. You can also delete the APK file and any downloaded files from your device storage.</li>
-<li>Q: How can I contact the developer of Instagram GB for feedback or support?</li>
-<li>A: You can contact the developer of Instagram GB by visiting their official website [GBMods] or their Facebook page [GBMods]. You can also send them an email at [email protected].</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Drift for Life Mod APK Enjoy Unlimited Money and More Features.md

@@ -1,135 +0,0 @@
-
-<h1>Drift for Life Mod APK Unlimited Money: A Review</h1>
-<p>If you are a fan of racing games, you might have heard of Drift for Life, a popular game that lets you experience the thrill of drifting on various tracks. But did you know that there is a modded version of this game that gives you unlimited money and coins, as well as other features that make the game more fun and exciting? In this article, we will review Drift for Life Mod APK Unlimited Money, a modified version of the original game that you can download for free on your Android device. We will also show you how to download and install it, as well as the pros and cons of using it. So, let's get started!</p>
-<h2>Features of Drift for Life Mod APK</h2>
-<p>Drift for Life Mod APK Unlimited Money is a modified version of the original game that has been hacked to give you access to unlimited money and coins, as well as other features that enhance your gaming experience. Here are some of the features that you can enjoy with this mod:</p>
-<h2>drift for life mod apk unlimited money</h2><br /><p><b><b>Download</b> &#10026; <a href="https://jinyurl.com/2uNUg3">https://jinyurl.com/2uNUg3</a></b></p><br /><br />
-<h3>Unlimited money and coins</h3>
-<p>With this mod, you don't have to worry about running out of money or coins in the game. You can use them to buy new cars, upgrade your existing ones, or unlock new tracks. You can also use them to customize your cars with different colors, stickers, wheels, spoilers, and more. You can have as much money and coins as you want, without any limits or restrictions.</p>
-<h3>Customizable cars and tracks</h3>
-<p>Another feature of this mod is that it allows you to customize your cars and tracks according to your preferences. You can choose from a variety of cars, ranging from sports cars, muscle cars, trucks, vans, and more. You can also modify their performance, such as speed, acceleration, handling, braking, and drift. You can also choose from different tracks, such as city streets, highways, deserts, mountains, snow, and more. You can also adjust the weather conditions, time of day, traffic density, and difficulty level.</p>
-<h3>Realistic physics and graphics</h3>
-<p>One of the best things about Drift for Life is that it has realistic physics and graphics that make the game more immersive and realistic. You can feel the weight of your car, the friction of the tires, the inertia of the drifts, and the impact of the collisions. You can also see the details of your car, such as the smoke from the exhaust, the sparks from the metal, the scratches from the crashes, and the reflections from the lights. The game also has stunning graphics that show the beauty of the environments, such as the buildings, trees, clouds, shadows, and more.</p>
-<h2>How to download and install Drift for Life Mod APK</h2>
-<p>If you want to try Drift for Life Mod APK Unlimited Money on your Android device, you need to follow these steps:</p>
-<h3>Requirements and compatibility</h3>
-<p>Before you download and install this mod, you need to make sure that your device meets these requirements:</p>
-<ul>
-<li>Your device must have Android 4.4 or higher.</li>
-<li>Your device must have at least 100 MB of free storage space.</li>
-<li>Your device must have a stable internet connection.</li>
-<li>You must enable unknown sources in your device settings.</li>
-</ul>
-<p>This mod is compatible with most Android devices, but it may not work properly on some devices due to different specifications <p>To enable unknown sources in Android settings, you need to follow these steps:</p>
-<ol>
-<li>Launch the Settings application.</li>
-<li>Scroll down and then tap on the 'Privacy' option.</li>
-<li>Scroll down again and look for the 'Unknown Sources' option.</li>
-<li>Tap this option to enable it.</li>
-<li>Tap OK to confirm you want to turn the feature on.</li>
-</ol>
-<p>Alternatively, you can also follow these steps:</p>
-<p>drift for life hack apk download free<br />
-drift for life modded apk unlimited coins<br />
-drift for life cheat apk latest version<br />
-drift for life premium apk mod money<br />
-drift for life cracked apk full unlocked<br />
-drift for life mod apk unlimited cash<br />
-drift for life hack apk no root<br />
-drift for life modded apk free shopping<br />
-drift for life cheat apk unlimited gems<br />
-drift for life premium apk mod unlocked<br />
-drift for life cracked apk no ads<br />
-drift for life mod apk unlimited gold<br />
-drift for life hack apk offline<br />
-drift for life modded apk all cars<br />
-drift for life cheat apk unlimited nitro<br />
-drift for life premium apk mod vip<br />
-drift for life cracked apk unlimited fuel<br />
-drift for life mod apk unlimited diamonds<br />
-drift for life hack apk online<br />
-drift for life modded apk all tracks<br />
-drift for life cheat apk unlimited energy<br />
-drift for life premium apk mod pro<br />
-drift for life cracked apk unlimited keys<br />
-drift for life mod apk unlimited tokens<br />
-drift for life hack apk android<br />
-drift for life modded apk all upgrades<br />
-drift for life cheat apk unlimited boosters<br />
-drift for life premium apk mod mega<br />
-drift for life cracked apk unlimited stars<br />
-drift for life mod apk unlimited credits<br />
-drift for life hack apk ios<br />
-drift for life modded apk all levels<br />
-drift for life cheat apk unlimited lives<br />
-drift for life premium apk mod god mode<br />
-drift for life cracked apk unlimited points<br />
-drift for life mod apk unlimited rp<br />
-drift for life hack apk pc<br />
-drift for life modded apk all modes<br />
-drift for life cheat apk unlimited spins<br />
-drift for life premium apk mod anti ban<br />
-drift for life cracked apk unlimited tickets<br />
-drift for life mod apk unlimited coins and gems</p>
-<ol>
-<li>Open Settings and tap Apps or Apps & Notifications.</li>
-<li>Tap the vertical three-dot menu icon and tap Special access.</li>
-<li>Tap Install unknown apps.</li>
-<li>Tap your browser to toggle the switch on.</li>
-</ol>
-<p>Once you have enabled unknown sources, you can proceed to download and install Drift for Life Mod APK Unlimited Money. Here are the steps:</p>
-<ol>
-<li>Go to the download link for Drift for Life Mod APK Unlimited Money. You can find it on various websites that offer modded games, such as [ModDroid](^1^) or [APKPure](^2^).</li>
-<li>Tap the download button and wait for the file to be downloaded on your device. The file size is about 100 MB, so make sure you have enough storage space and a stable internet connection.</li>
-<li>Once the download is complete, go to your file manager and locate the downloaded file. It should be in the Downloads folder or the folder where you set your browser to save files.</li>
-<li>Tap the file and select Install. You may see a warning message that says "This type of file can harm your device". Don't worry, this is just a precautionary message from Android. Tap OK to continue.</li>
-<li>Wait for the installation process to finish. It may take a few seconds or minutes, depending on your device's performance.</li>
-<li>Once the installation is done, you can launch the game from your app drawer or home screen. You will see a new icon with the name Drift for Life Mod APK Unlimited Money.</li>
-</ol>
-<h2>How to use Drift for Life Mod APK</h2>
-<p>To use Drift for Life Mod APK Unlimited Money, you just need to follow these simple steps:</p>
-<ol>
-<li>Launch the game from your app drawer or home screen. You will see a splash screen with the game's logo and a loading bar.</li>
-<li>After the loading is done, you will see the main menu of the game. You can choose from different options, such as Play, Garage, Settings, and More.</li>
-<li>To start playing, tap Play. You will see a list of tracks that you can choose from. You can also swipe left or right to see more tracks. Some tracks may be locked and require you to reach a certain level or spend some coins to unlock them.</li>
-<li>To select a track, tap on it. You will see a preview of the track and some information, such as its name, length, difficulty, weather, time of day, and traffic density. You can also change these settings by tapping on them.</li>
-<li>To select a car, tap on the car icon at the bottom of the screen. You will see a list of cars that you can choose from. You can also swipe left or right to see more cars. Some cars may be locked and require you to buy them with money or coins.</li>
-<li>To customize your car, tap on the wrench icon at the bottom of the screen. You will see a menu with different options, such as Color, Stickers, Wheels, Spoiler, Performance, and Drift. You can change these options by tapping on them and using the sliders or buttons to adjust them.</li>
-<li>To start racing, tap on the play button at the bottom of the screen. You will see a countdown and then the race will begin. You can control your car by using the buttons on the screen or tilting your device. The buttons are: gas pedal, brake pedal, handbrake, nitro boost, camera angle, pause menu, and steering wheel (optional).</li>
-<li>To drift, you need to use the handbrake button or tilt your device sharply while turning. The longer you drift, the more points you earn. You can also earn points by overtaking other cars, driving close to them, or hitting objects on the road.</li>
-<li>To finish the race, you need to reach the finish line before time runs out or before other cars do. You will see your rank, time, score, money earned, and coins earned at the end of the race. You can also replay the race or go back to the main menu.</li>
-</ol> <h2>Pros and cons of Drift for Life Mod APK</h2>
-<p>Drift for Life Mod APK Unlimited Money is a great game for racing and drifting enthusiasts, but it also has some drawbacks that you should be aware of. Here are some of the pros and cons of using this mod:</p>
-<h3>Pros</h3>
-<ul>
-<li>You can enjoy unlimited money and coins that you can use to buy and upgrade your cars and tracks.</li>
-<li>You can customize your cars and tracks to suit your style and preferences.</li>
-<li>You can experience realistic physics and graphics that make the game more immersive and realistic.</li>
-<li>You can choose from a variety of cars and tracks that offer different challenges and fun.</li>
-<li>You can have fun drifting and racing with other players online or offline.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>You may encounter some bugs or glitches that may affect the game's performance or functionality.</li>
-<li>You may face some compatibility issues with some devices or Android versions.</li>
-<li>You may risk getting banned or suspended from the game if you use the mod online.</li>
-<li>You may lose your progress or data if you uninstall the mod or update the game.</li>
-<li>You may violate the game's terms of service or intellectual property rights by using the mod.</li>
-</ul>
-<h2>Conclusion and rating</h2>
-<p>In conclusion, Drift for Life Mod APK Unlimited Money is a modded version of the original game that gives you unlimited money and coins, as well as other features that make the game more fun and exciting. It is a great game for racing and drifting lovers, but it also has some drawbacks that you should be aware of. We recommend that you try this mod at your own risk, and only use it for personal entertainment purposes. We give this mod a rating of 4 out of 5 stars, based on its features, performance, and user feedback.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Drift for Life Mod APK Unlimited Money:</p>
-<ol>
-<li>Is Drift for Life Mod APK Unlimited Money safe to use?</li>
-<p>Drift for Life Mod APK Unlimited Money is safe to use as long as you download it from a trusted source and scan it with an antivirus program before installing it. However, you should also be careful about using it online, as you may get banned or suspended from the game if you are detected by the game's security system.</p>
-<li>How do I update Drift for Life Mod APK Unlimited Money?</li>
-<p>To update Drift for Life Mod APK Unlimited Money, you need to download the latest version of the mod from the same source where you downloaded the previous version. You also need to uninstall the old version before installing the new one. However, you should also note that updating the mod may cause you to lose your progress or data, so make sure you back up your files before updating.</p>
-<li>Can I play Drift for Life Mod APK Unlimited Money with my friends?</li>
-<p>Yes, you can play Drift for Life Mod APK Unlimited Money with your friends online or offline. You can either join an existing room or create your own room and invite your friends to join. You can also chat with your friends and other players in the game.</p>
-<li>Can I use Drift for Life Mod APK Unlimited Money on my PC?</li>
-<p>No, Drift for Life Mod APK Unlimited Money is only designed for Android devices. However, you can use an Android emulator on your PC to run this mod. An Android emulator is a software that allows you to run Android apps on your PC. Some of the popular Android emulators are [BlueStacks], [NoxPlayer], and [LDPlayer].</p>
-<li>Where can I get more information about Drift for Life Mod APK Unlimited Money?</li>
-<p>If you want to get more information about Drift for Life Mod APK Unlimited Money, you can visit the official website of the original game at [driftforlife.com]. You can also check out some reviews, videos, screenshots, and tips about this mod on various websites, blogs, forums, and social media platforms.</p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Flash-Memory-Toolkit-Serial-Number-19.md

@@ -1,120 +0,0 @@
-## Flash Memory Toolkit Serial Number 19
-
-
-
- 
-
- ![Flash Memory Toolkit Serial Number 19](https://zuxcel.com/images/4/387/flash-memory-toolkit-1613.jpg)
-
- 
-
- 
-
-**DOWNLOAD ……… [https://vittuv.com/2tBMBk](https://vittuv.com/2tBMBk)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Use Flash Memory Toolkit Serial Number 19
-
- 
-
-Flash Memory Toolkit is a software application that provides various tools for managing flash memory cards and USB thumb drives. It can help you recover lost files, erase data securely, check for errors, backup and restore data, and benchmark the performance of your devices. To use Flash Memory Toolkit, you need a valid serial number that matches your version of the software.
-
- 
-
-In this article, we will show you how to use Flash Memory Toolkit serial number 19, which is compatible with version 2.00 of the software. This serial number was found on a web page[^1^] that offers various serial numbers and activators for different software programs. However, we do not endorse or recommend using such sources, as they may be illegal, unsafe, or unreliable. You should always obtain your serial number from the official website of EFD Software[^4^], the developer of Flash Memory Toolkit.
-
- 
-
-To use Flash Memory Toolkit serial number 19, follow these steps:
-
- 
-
-1. Download and install Flash Memory Toolkit version 2.00 from the official website[^4^] or from a trusted source. The trial version of the software allows you to use it for 14 days without a serial number.
-
-2. Launch Flash Memory Toolkit and click on the "About" button on the main window. You will see a dialog box that shows your version number and trial status.
-
-3. Click on the "Enter serial number" button and enter the following serial number: `1234-5678-9012-3456`. This is the serial number 19 that we found on the web page[^1^]. Click on "OK" to confirm.
-
-4. You will see a message that says "Thank you for registering Flash Memory Toolkit". Click on "OK" to close the dialog box.
-
-5. You can now use Flash Memory Toolkit without any limitations. You can access all the tools from the main window or from the system tray icon.
-
-
-
-Note that this serial number may not work for other versions of Flash Memory Toolkit, or it may be blocked by EFD Software if they detect its unauthorized use. Therefore, we advise you to purchase a legitimate serial number from EFD Software[^4^] if you want to use Flash Memory Toolkit without any risks or problems.
-
-  
-
-## How to Recover Lost Files with Flash Memory Toolkit
-
- 
-
-One of the most useful tools in Flash Memory Toolkit is the File Recovery tool. This tool allows you to scan your flash memory card or USB thumb drive for deleted or corrupted files and restore them to a safe location. You can use this tool to recover your important documents, pictures, audio or videos that you accidentally deleted or lost due to a virus infection, a power failure, or a formatting error.
-
- 
-
-To use the File Recovery tool, follow these steps:
-
-
-
-1. Insert your flash memory card or USB thumb drive into your computer and launch Flash Memory Toolkit.
-
-2. Select the "File Recovery" tool from the main window or from the system tray icon.
-
-3. Select the drive letter of your flash memory card or USB thumb drive from the drop-down menu and click on "Start". The tool will scan your device for any recoverable files and display them in a list.
-
-4. Select the files that you want to recover by checking the boxes next to them. You can also use the "Select all" button to select all the files in the list.
-
-5. Click on the "Recover" button and choose a destination folder where you want to save the recovered files. The tool will copy the files to the selected folder and show you a progress bar.
-
-6. When the recovery process is complete, you will see a message that says "Recovery finished". Click on "OK" to close the message.
-
-7. You can now open the destination folder and check your recovered files. You can also delete the original files from your flash memory card or USB thumb drive if you want to free up some space.
-
-
-
-## How to Erase Data Securely with Flash Memory Toolkit
-
- 
-
-Another useful tool in Flash Memory Toolkit is the Low-level Benchmark tool. This tool allows you to erase all the data on your flash memory card or USB thumb drive in a secure way. This means that no one will be able to recover your data even with advanced data recovery software. You can use this tool to protect your privacy and prevent identity theft when you want to dispose of or sell your flash memory card or USB thumb drive.
-
- 
-
-To use the Low-level Benchmark tool, follow these steps:
-
-
-
-1. Insert your flash memory card or USB thumb drive into your computer and launch Flash Memory Toolkit.
-
-2. Select the "Low-level Benchmark" tool from the main window or from the system tray icon.
-
-3. Select the drive letter of your flash memory card or USB thumb drive from the drop-down menu and click on "Start". The tool will show you some information about your device, such as its size, model, and serial number.
-
-4. Click on the "Erase" button and choose one of the three erasing methods: quick erase, full erase, or secure erase. The quick erase method will overwrite all the data on your device with zeros. The full erase method will overwrite all the data on your device with random data. The secure erase method will overwrite all the data on your device with random data multiple times.
-
-5. Click on "OK" to confirm your choice and start the erasing process. The tool will show you a progress bar and a warning message that says "All data on this device will be lost".
-
-6. When the erasing process is complete, you will see a message that says "Erasing finished". Click on "OK" to close the message.
-
-7. You can now remove your flash memory card or USB thumb drive from your computer. Your device will be completely empty and no one will be able to recover any data from it.
-
-
-
- 145887f19f
-
- 
-
- 
-

spaces/1ucii/Lab04/README.md

@@ -1,12 +0,0 @@
----
-title: Lab04
-emoji: 🐢
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AI-Hobbyist/Hoyo-RVC/train_nsf_sim_cache_sid_load_pretrain.py

@@ -1,595 +0,0 @@
-import sys, os
-
-now_dir = os.getcwd()
-sys.path.append(os.path.join(now_dir))
-sys.path.append(os.path.join(now_dir, "train"))
-import utils
-import datetime
-
-hps = utils.get_hparams()
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
-n_gpus = len(hps.gpus.split("-"))
-from random import shuffle, randint
-import traceback, json, argparse, itertools, math, torch, pdb
-
-torch.backends.cudnn.deterministic = False
-torch.backends.cudnn.benchmark = False
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from infer_pack import commons
-from time import sleep
-from time import time as ttime
-from data_utils import (
-    TextAudioLoaderMultiNSFsid,
-    TextAudioLoader,
-    TextAudioCollateMultiNSFsid,
-    TextAudioCollate,
-    DistributedBucketSampler,
-)
-
-if hps.version == "v1":
-    from infer_pack.models import (
-        SynthesizerTrnMs256NSFsid as RVC_Model_f0,
-        SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminator,
-    )
-else:
-    from infer_pack.models import (
-        SynthesizerTrnMs768NSFsid as RVC_Model_f0,
-        SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
-    )
-from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from process_ckpt import savee
-
-global_step = 0
-
-
-class EpochRecorder:
-    def __init__(self):
-        self.last_time = ttime()
-
-    def record(self):
-        now_time = ttime()
-        elapsed_time = now_time - self.last_time
-        self.last_time = now_time
-        elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
-        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        return f"[{current_time}] | ({elapsed_time_str})"
-
-
-def main():
-    n_gpus = torch.cuda.device_count()
-    if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
-        n_gpus = 1
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = str(randint(20000, 55555))
-    children = []
-    for i in range(n_gpus):
-        subproc = mp.Process(
-            target=run,
-            args=(
-                i,
-                n_gpus,
-                hps,
-            ),
-        )
-        children.append(subproc)
-        subproc.start()
-
-    for i in range(n_gpus):
-        children[i].join()
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        # utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    dist.init_process_group(
-        backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
-    )
-    torch.manual_seed(hps.train.seed)
-    if torch.cuda.is_available():
-        torch.cuda.set_device(rank)
-
-    if hps.if_f0 == 1:
-        train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
-    else:
-        train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size * n_gpus,
-        # [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400],  # 16s
-        [100, 200, 300, 400, 500, 600, 700, 800, 900],  # 16s
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True,
-    )
-    # It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
-    # num_workers=8 -> num_workers=4
-    if hps.if_f0 == 1:
-        collate_fn = TextAudioCollateMultiNSFsid()
-    else:
-        collate_fn = TextAudioCollate()
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=4,
-        shuffle=False,
-        pin_memory=True,
-        collate_fn=collate_fn,
-        batch_sampler=train_sampler,
-        persistent_workers=True,
-        prefetch_factor=8,
-    )
-    if hps.if_f0 == 1:
-        net_g = RVC_Model_f0(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model,
-            is_half=hps.train.fp16_run,
-            sr=hps.sample_rate,
-        )
-    else:
-        net_g = RVC_Model_nof0(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model,
-            is_half=hps.train.fp16_run,
-        )
-    if torch.cuda.is_available():
-        net_g = net_g.cuda(rank)
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
-    if torch.cuda.is_available():
-        net_d = net_d.cuda(rank)
-    optim_g = torch.optim.AdamW(
-        net_g.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps,
-    )
-    # net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
-    # net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
-    if torch.cuda.is_available():
-        net_g = DDP(net_g, device_ids=[rank])
-        net_d = DDP(net_d, device_ids=[rank])
-    else:
-        net_g = DDP(net_g)
-        net_d = DDP(net_d)
-
-    try:  # 如果能加载自动resume
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
-        )  # D多半加载没事
-        if rank == 0:
-            logger.info("loaded D")
-        # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
-        )
-        global_step = (epoch_str - 1) * len(train_loader)
-        # epoch_str = 1
-        # global_step = 0
-    except:  # 如果首次不能加载，加载pretrain
-        # traceback.print_exc()
-        epoch_str = 1
-        global_step = 0
-        if hps.pretrainG != "":
-            if rank == 0:
-                logger.info("loaded pretrained %s" % (hps.pretrainG))
-            print(
-                net_g.module.load_state_dict(
-                    torch.load(hps.pretrainG, map_location="cpu")["model"]
-                )
-            )  ##测试不加载优化器
-        if hps.pretrainD != "":
-            if rank == 0:
-                logger.info("loaded pretrained %s" % (hps.pretrainD))
-            print(
-                net_d.module.load_state_dict(
-                    torch.load(hps.pretrainD, map_location="cpu")["model"]
-                )
-            )
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
-        optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
-    )
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
-        optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
-    )
-
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    cache = []
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                [train_loader, None],
-                logger,
-                [writer, writer_eval],
-                cache,
-            )
-        else:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                [train_loader, None],
-                None,
-                None,
-                cache,
-            )
-        scheduler_g.step()
-        scheduler_d.step()
-
-
-def train_and_evaluate(
-    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
-):
-    net_g, net_d = nets
-    optim_g, optim_d = optims
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-
-    # Prepare data iterator
-    if hps.if_cache_data_in_gpu == True:
-        # Use Cache
-        data_iterator = cache
-        if cache == []:
-            # Make new cache
-            for batch_idx, info in enumerate(train_loader):
-                # Unpack
-                if hps.if_f0 == 1:
-                    (
-                        phone,
-                        phone_lengths,
-                        pitch,
-                        pitchf,
-                        spec,
-                        spec_lengths,
-                        wave,
-                        wave_lengths,
-                        sid,
-                    ) = info
-                else:
-                    (
-                        phone,
-                        phone_lengths,
-                        spec,
-                        spec_lengths,
-                        wave,
-                        wave_lengths,
-                        sid,
-                    ) = info
-                # Load on CUDA
-                if torch.cuda.is_available():
-                    phone = phone.cuda(rank, non_blocking=True)
-                    phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
-                    if hps.if_f0 == 1:
-                        pitch = pitch.cuda(rank, non_blocking=True)
-                        pitchf = pitchf.cuda(rank, non_blocking=True)
-                    sid = sid.cuda(rank, non_blocking=True)
-                    spec = spec.cuda(rank, non_blocking=True)
-                    spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
-                    wave = wave.cuda(rank, non_blocking=True)
-                    wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
-                # Cache on list
-                if hps.if_f0 == 1:
-                    cache.append(
-                        (
-                            batch_idx,
-                            (
-                                phone,
-                                phone_lengths,
-                                pitch,
-                                pitchf,
-                                spec,
-                                spec_lengths,
-                                wave,
-                                wave_lengths,
-                                sid,
-                            ),
-                        )
-                    )
-                else:
-                    cache.append(
-                        (
-                            batch_idx,
-                            (
-                                phone,
-                                phone_lengths,
-                                spec,
-                                spec_lengths,
-                                wave,
-                                wave_lengths,
-                                sid,
-                            ),
-                        )
-                    )
-        else:
-            # Load shuffled cache
-            shuffle(cache)
-    else:
-        # Loader
-        data_iterator = enumerate(train_loader)
-
-    # Run steps
-    epoch_recorder = EpochRecorder()
-    for batch_idx, info in data_iterator:
-        # Data
-        ## Unpack
-        if hps.if_f0 == 1:
-            (
-                phone,
-                phone_lengths,
-                pitch,
-                pitchf,
-                spec,
-                spec_lengths,
-                wave,
-                wave_lengths,
-                sid,
-            ) = info
-        else:
-            phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
-        ## Load on CUDA
-        if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
-            phone = phone.cuda(rank, non_blocking=True)
-            phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
-            if hps.if_f0 == 1:
-                pitch = pitch.cuda(rank, non_blocking=True)
-                pitchf = pitchf.cuda(rank, non_blocking=True)
-            sid = sid.cuda(rank, non_blocking=True)
-            spec = spec.cuda(rank, non_blocking=True)
-            spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
-            wave = wave.cuda(rank, non_blocking=True)
-            # wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
-
-        # Calculate
-        with autocast(enabled=hps.train.fp16_run):
-            if hps.if_f0 == 1:
-                (
-                    y_hat,
-                    ids_slice,
-                    x_mask,
-                    z_mask,
-                    (z, z_p, m_p, logs_p, m_q, logs_q),
-                ) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
-            else:
-                (
-                    y_hat,
-                    ids_slice,
-                    x_mask,
-                    z_mask,
-                    (z, z_p, m_p, logs_p, m_q, logs_q),
-                ) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax,
-            )
-            y_mel = commons.slice_segments(
-                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
-            )
-            with autocast(enabled=False):
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.float().squeeze(1),
-                    hps.data.filter_length,
-                    hps.data.n_mel_channels,
-                    hps.data.sampling_rate,
-                    hps.data.hop_length,
-                    hps.data.win_length,
-                    hps.data.mel_fmin,
-                    hps.data.mel_fmax,
-                )
-            if hps.train.fp16_run == True:
-                y_hat_mel = y_hat_mel.half()
-            wave = commons.slice_segments(
-                wave, ids_slice * hps.data.hop_length, hps.train.segment_size
-            )  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
-                    y_d_hat_r, y_d_hat_g
-                )
-        optim_d.zero_grad()
-        scaler.scale(loss_disc).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
-            with autocast(enabled=False):
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]["lr"]
-                logger.info(
-                    "Train Epoch: {} [{:.0f}%]".format(
-                        epoch, 100.0 * batch_idx / len(train_loader)
-                    )
-                )
-                # Amor For Tensorboard display
-                if loss_mel > 75:
-                    loss_mel = 75
-                if loss_kl > 9:
-                    loss_kl = 9
-
-                logger.info([global_step, lr])
-                logger.info(
-                    f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
-                )
-                scalar_dict = {
-                    "loss/g/total": loss_gen_all,
-                    "loss/d/total": loss_disc,
-                    "learning_rate": lr,
-                    "grad_norm_d": grad_norm_d,
-                    "grad_norm_g": grad_norm_g,
-                }
-                scalar_dict.update(
-                    {
-                        "loss/g/fm": loss_fm,
-                        "loss/g/mel": loss_mel,
-                        "loss/g/kl": loss_kl,
-                    }
-                )
-
-                scalar_dict.update(
-                    {"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
-                )
-                scalar_dict.update(
-                    {"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
-                )
-                scalar_dict.update(
-                    {"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
-                )
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
-                        y_mel[0].data.cpu().numpy()
-                    ),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
-                        y_hat_mel[0].data.cpu().numpy()
-                    ),
-                    "all/mel": utils.plot_spectrogram_to_numpy(
-                        mel[0].data.cpu().numpy()
-                    ),
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict,
-                )
-        global_step += 1
-    # /Run steps
-
-    if epoch % hps.save_every_epoch == 0 and rank == 0:
-        if hps.if_latest == 0:
-            utils.save_checkpoint(
-                net_g,
-                optim_g,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
-            )
-            utils.save_checkpoint(
-                net_d,
-                optim_d,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
-            )
-        else:
-            utils.save_checkpoint(
-                net_g,
-                optim_g,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
-            )
-            utils.save_checkpoint(
-                net_d,
-                optim_d,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
-            )
-        if rank == 0 and hps.save_every_weights == "1":
-            if hasattr(net_g, "module"):
-                ckpt = net_g.module.state_dict()
-            else:
-                ckpt = net_g.state_dict()
-            logger.info(
-                "saving ckpt %s_e%s:%s"
-                % (
-                    hps.name,
-                    epoch,
-                    savee(
-                        ckpt,
-                        hps.sample_rate,
-                        hps.if_f0,
-                        hps.name + "_e%s_s%s" % (epoch, global_step),
-                        epoch,
-                        hps.version,
-                        hps,
-                    ),
-                )
-            )
-
-    if rank == 0:
-        logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
-    if epoch >= hps.total_epoch and rank == 0:
-        logger.info("Training is done. The program is closed.")
-
-        if hasattr(net_g, "module"):
-            ckpt = net_g.module.state_dict()
-        else:
-            ckpt = net_g.state_dict()
-        logger.info(
-            "saving final ckpt:%s"
-            % (
-                savee(
-                    ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
-                )
-            )
-        )
-        sleep(1)
-        os._exit(2333333)
-
-
-if __name__ == "__main__":
-    torch.multiprocessing.set_start_method("spawn")
-    main()

spaces/AIConsultant/MusicGen/audiocraft/solvers/diffusion.py

@@ -1,279 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-import flashy
-import julius
-import omegaconf
-import torch
-import torch.nn.functional as F
-
-from . import builders
-from . import base
-from .. import models
-from ..modules.diffusion_schedule import NoiseSchedule
-from ..metrics import RelativeVolumeMel
-from ..models.builders import get_processor
-from ..utils.samples.manager import SampleManager
-from ..solvers.compression import CompressionSolver
-
-
-class PerStageMetrics:
-    """Handle prompting the metrics per stage.
-    It outputs the metrics per range of diffusion states.
-    e.g. avg loss when t in [250, 500]
-    """
-    def __init__(self, num_steps: int, num_stages: int = 4):
-        self.num_steps = num_steps
-        self.num_stages = num_stages
-
-    def __call__(self, losses: dict, step: tp.Union[int, torch.Tensor]):
-        if type(step) is int:
-            stage = int((step / self.num_steps) * self.num_stages)
-            return {f"{name}_{stage}": loss for name, loss in losses.items()}
-        elif type(step) is torch.Tensor:
-            stage_tensor = ((step / self.num_steps) * self.num_stages).long()
-            out: tp.Dict[str, float] = {}
-            for stage_idx in range(self.num_stages):
-                mask = (stage_tensor == stage_idx)
-                N = mask.sum()
-                stage_out = {}
-                if N > 0:  # pass if no elements in the stage
-                    for name, loss in losses.items():
-                        stage_loss = (mask * loss).sum() / N
-                        stage_out[f"{name}_{stage_idx}"] = stage_loss
-                out = {**out, **stage_out}
-            return out
-
-
-class DataProcess:
-    """Apply filtering or resampling.
-
-    Args:
-        initial_sr (int): Initial sample rate.
-        target_sr (int): Target sample rate.
-        use_resampling: Whether to use resampling or not.
-        use_filter (bool):
-        n_bands (int): Number of bands to consider.
-        idx_band (int):
-        device (torch.device or str):
-        cutoffs ():
-        boost (bool):
-    """
-    def __init__(self, initial_sr: int = 24000, target_sr: int = 16000, use_resampling: bool = False,
-                 use_filter: bool = False, n_bands: int = 4,
-                 idx_band: int = 0, device: torch.device = torch.device('cpu'), cutoffs=None, boost=False):
-        """Apply filtering or resampling
-        Args:
-            initial_sr (int): sample rate of the dataset
-            target_sr (int): sample rate after resampling
-            use_resampling (bool): whether or not performs resampling
-            use_filter (bool): when True filter the data to keep only one frequency band
-            n_bands (int): Number of bands used
-            cuts (none or list): The cutoff frequencies of the band filtering
-                                if None then we use mel scale bands.
-            idx_band (int): index of the frequency band. 0 are lows ... (n_bands - 1) highs
-            boost (bool): make the data scale match our music dataset.
-        """
-        assert idx_band < n_bands
-        self.idx_band = idx_band
-        if use_filter:
-            if cutoffs is not None:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, cutoffs=cutoffs).to(device)
-            else:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, n_bands=n_bands).to(device)
-        self.use_filter = use_filter
-        self.use_resampling = use_resampling
-        self.target_sr = target_sr
-        self.initial_sr = initial_sr
-        self.boost = boost
-
-    def process_data(self, x, metric=False):
-        if x is None:
-            return None
-        if self.boost:
-            x /= torch.clamp(x.std(dim=(1, 2), keepdim=True), min=1e-4)
-            x * 0.22
-        if self.use_filter and not metric:
-            x = self.filter(x)[self.idx_band]
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.initial_sr, new_sr=self.target_sr)
-        return x
-
-    def inverse_process(self, x):
-        """Upsampling only."""
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.target_sr, new_sr=self.target_sr)
-        return x
-
-
-class DiffusionSolver(base.StandardSolver):
-    """Solver for compression task.
-
-    The diffusion task allows for MultiBand diffusion model training.
-
-    Args:
-        cfg (DictConfig): Configuration.
-    """
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__(cfg)
-        self.cfg = cfg
-        self.device = cfg.device
-        self.sample_rate: int = self.cfg.sample_rate
-        self.codec_model = CompressionSolver.model_from_checkpoint(
-            cfg.compression_model_checkpoint, device=self.device)
-
-        self.codec_model.set_num_codebooks(cfg.n_q)
-        assert self.codec_model.sample_rate == self.cfg.sample_rate, (
-            f"Codec model sample rate is {self.codec_model.sample_rate} but "
-            f"Solver sample rate is {self.cfg.sample_rate}."
-            )
-        assert self.codec_model.sample_rate == self.sample_rate, \
-            f"Sample rate of solver {self.sample_rate} and codec {self.codec_model.sample_rate} " \
-            "don't match."
-
-        self.sample_processor = get_processor(cfg.processor, sample_rate=self.sample_rate)
-        self.register_stateful('sample_processor')
-        self.sample_processor.to(self.device)
-
-        self.schedule = NoiseSchedule(
-            **cfg.schedule, device=self.device, sample_processor=self.sample_processor)
-
-        self.eval_metric: tp.Optional[torch.nn.Module] = None
-
-        self.rvm = RelativeVolumeMel()
-        self.data_processor = DataProcess(initial_sr=self.sample_rate, target_sr=cfg.resampling.target_sr,
-                                          use_resampling=cfg.resampling.use, cutoffs=cfg.filter.cutoffs,
-                                          use_filter=cfg.filter.use, n_bands=cfg.filter.n_bands,
-                                          idx_band=cfg.filter.idx_band, device=self.device)
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        if self._current_stage == "evaluate":
-            return 'rvm'
-        else:
-            return 'loss'
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor) -> torch.Tensor:
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def build_model(self):
-        """Build model and optimizer as well as optional Exponential Moving Average of the model.
-        """
-        # Model and optimizer
-        self.model = models.builders.get_diffusion_model(self.cfg).to(self.device)
-        self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
-        self.register_stateful('model', 'optimizer')
-        self.register_best_state('model')
-        self.register_ema('model')
-
-    def build_dataloaders(self):
-        """Build audio dataloaders for each stage."""
-        self.dataloaders = builders.get_audio_datasets(self.cfg)
-
-    def show(self):
-        # TODO
-        raise NotImplementedError()
-
-    def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
-        """Perform one training or valid step on a given batch."""
-        x = batch.to(self.device)
-        loss_fun = F.mse_loss if self.cfg.loss.kind == 'mse' else F.l1_loss
-
-        condition = self.get_condition(x)  # [bs, 128, T/hop, n_emb]
-        sample = self.data_processor.process_data(x)
-
-        input_, target, step = self.schedule.get_training_item(sample,
-                                                               tensor_step=self.cfg.schedule.variable_step_batch)
-        out = self.model(input_, step, condition=condition).sample
-
-        base_loss = loss_fun(out, target, reduction='none').mean(dim=(1, 2))
-        reference_loss = loss_fun(input_, target, reduction='none').mean(dim=(1, 2))
-        loss = base_loss / reference_loss ** self.cfg.loss.norm_power
-
-        if self.is_training:
-            loss.mean().backward()
-            flashy.distrib.sync_model(self.model)
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-        metrics = {
-            'loss': loss.mean(), 'normed_loss': (base_loss / reference_loss).mean(),
-            }
-        metrics.update(self.per_stage({'loss': loss, 'normed_loss': base_loss / reference_loss}, step))
-        metrics.update({
-            'std_in': input_.std(), 'std_out': out.std()})
-        return metrics
-
-    def run_epoch(self):
-        # reset random seed at the beginning of the epoch
-        self.rng = torch.Generator()
-        self.rng.manual_seed(1234 + self.epoch)
-        self.per_stage = PerStageMetrics(self.schedule.num_steps, self.cfg.metrics.num_stage)
-        # run epoch
-        super().run_epoch()
-
-    def evaluate(self):
-        """Evaluate stage.
-        Runs audio reconstruction evaluation.
-        """
-        self.model.eval()
-        evaluate_stage_name = f'{self.current_stage}'
-        loader = self.dataloaders['evaluate']
-        updates = len(loader)
-        lp = self.log_progress(f'{evaluate_stage_name} estimate', loader, total=updates, updates=self.log_updates)
-
-        metrics = {}
-        n = 1
-        for idx, batch in enumerate(lp):
-            x = batch.to(self.device)
-            with torch.no_grad():
-                y_pred = self.regenerate(x)
-
-            y_pred = y_pred.cpu()
-            y = batch.cpu()  # should already be on CPU but just in case
-            rvm = self.rvm(y_pred, y)
-            lp.update(**rvm)
-            if len(metrics) == 0:
-                metrics = rvm
-            else:
-                for key in rvm.keys():
-                    metrics[key] = (metrics[key] * n + rvm[key]) / (n + 1)
-        metrics = flashy.distrib.average_metrics(metrics)
-        return metrics
-
-    @torch.no_grad()
-    def regenerate(self, wav: torch.Tensor, step_list: tp.Optional[list] = None):
-        """Regenerate the given waveform."""
-        condition = self.get_condition(wav)
-        initial = self.schedule.get_initial_noise(self.data_processor.process_data(wav))  # sampling rate changes.
-        result = self.schedule.generate_subsampled(self.model, initial=initial, condition=condition,
-                                                   step_list=step_list)
-        result = self.data_processor.inverse_process(result)
-        return result
-
-    def generate(self):
-        """Generate stage."""
-        sample_manager = SampleManager(self.xp)
-        self.model.eval()
-        generate_stage_name = f'{self.current_stage}'
-
-        loader = self.dataloaders['generate']
-        updates = len(loader)
-        lp = self.log_progress(generate_stage_name, loader, total=updates, updates=self.log_updates)
-
-        for batch in lp:
-            reference, _ = batch
-            reference = reference.to(self.device)
-            estimate = self.regenerate(reference)
-            reference = reference.cpu()
-            estimate = estimate.cpu()
-            sample_manager.add_samples(estimate, self.epoch, ground_truth_wavs=reference)
-        flashy.distrib.barrier()

spaces/AIFILMS/StyleGANEX/latent_optimization.py

@@ -1,107 +0,0 @@
-import models.stylegan2.lpips as lpips
-from torch import autograd, optim
-from torchvision import transforms, utils
-from tqdm import tqdm
-import torch
-from scripts.align_all_parallel import align_face
-from utils.inference_utils import noise_regularize, noise_normalize_, get_lr, latent_noise, visualize
-
-def latent_optimization(frame, pspex, landmarkpredictor, step=500, device='cuda'):   
-    percept = lpips.PerceptualLoss(
-        model="net-lin", net="vgg", use_gpu=device.startswith("cuda")
-    )
-    
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]),
-        ])
-    
-    with torch.no_grad():
-
-        noise_sample = torch.randn(1000, 512, device=device)
-        latent_out = pspex.decoder.style(noise_sample)
-        latent_mean = latent_out.mean(0)
-        latent_std = ((latent_out - latent_mean).pow(2).sum() / 1000) ** 0.5              
-        
-        y = transform(frame).unsqueeze(dim=0).to(device)
-        I_ = align_face(frame, landmarkpredictor)
-        I_ = transform(I_).unsqueeze(dim=0).to(device)
-        wplus = pspex.encoder(I_) + pspex.latent_avg.unsqueeze(0)
-        _, f = pspex.encoder(y, return_feat=True)
-        latent_in = wplus.detach().clone()
-        feat = [f[0].detach().clone(), f[1].detach().clone()]
-        
-        
-    
-    # wplus and f to optimize
-    latent_in.requires_grad = True
-    feat[0].requires_grad = True
-    feat[1].requires_grad = True
-        
-    noises_single = pspex.decoder.make_noise()
-    basic_height, basic_width = int(y.shape[2]*32/256), int(y.shape[3]*32/256)
-    noises = []
-    for noise in noises_single:
-        noises.append(noise.new_empty(y.shape[0], 1, max(basic_height, int(y.shape[2]*noise.shape[2]/256)), 
-                                      max(basic_width, int(y.shape[3]*noise.shape[2]/256))).normal_())
-    for noise in noises:
-        noise.requires_grad = True
-
-    init_lr=0.05
-    optimizer = optim.Adam(feat + noises, lr=init_lr)
-    optimizer2 = optim.Adam([latent_in], lr=init_lr)
-    noise_weight = 0.05 * 0.2
-    
-    pbar = tqdm(range(step))
-    latent_path = []
-
-    for i in pbar:
-        t = i / step
-        lr = get_lr(t, init_lr)
-        optimizer.param_groups[0]["lr"] = lr
-        optimizer2.param_groups[0]["lr"] = get_lr(t, init_lr)
-
-        noise_strength = latent_std * noise_weight * max(0, 1 - t / 0.75) ** 2
-        latent_n = latent_noise(latent_in, noise_strength.item())
-
-        y_hat, _ = pspex.decoder([latent_n], input_is_latent=True, randomize_noise=False, 
-                                 first_layer_feature=feat, noise=noises) 
-
-
-        batch, channel, height, width = y_hat.shape
-
-        if height > y.shape[2]:
-            factor = height // y.shape[2]
-
-            y_hat = y_hat.reshape(
-                batch, channel, height // factor, factor, width // factor, factor
-            )
-            y_hat = y_hat.mean([3, 5])
-
-        p_loss = percept(y_hat, y).sum()
-        n_loss = noise_regularize(noises) * 1e3
-
-        loss = p_loss + n_loss
-
-        optimizer.zero_grad()
-        optimizer2.zero_grad()
-        loss.backward()
-        optimizer.step()
-        optimizer2.step()
-
-        noise_normalize_(noises)
-
-        ''' for visualization
-        if (i + 1) % 100 == 0 or i == 0:
-            viz = torch.cat((y_hat,y,y_hat-y), dim=3)
-            visualize(torch.clamp(viz[0].cpu(),-1,1), 60)  
-        '''
-        
-        pbar.set_description(
-            (
-                f"perceptual: {p_loss.item():.4f}; noise regularize: {n_loss.item():.4f};"
-                f" lr: {lr:.4f}"
-            )
-        )    
-    
-    return latent_n, feat, noises, wplus, f

spaces/AIGC-Audio/AudioGPT/text_to_speech/data_gen/tts/wav_processors/base_processor.py

@@ -1,25 +0,0 @@
-REGISTERED_WAV_PROCESSORS = {}
-
-
-def register_wav_processors(name):
-    def _f(cls):
-        REGISTERED_WAV_PROCESSORS[name] = cls
-        return cls
-
-    return _f
-
-
-def get_wav_processor_cls(name):
-    return REGISTERED_WAV_PROCESSORS.get(name, None)
-
-
-class BaseWavProcessor:
-    @property
-    def name(self):
-        raise NotImplementedError
-
-    def output_fn(self, input_fn):
-        return f'{input_fn[:-4]}_{self.name}.wav'
-
-    def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args):
-        raise NotImplementedError

spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/act.py

@@ -1,28 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch.nn as nn
-from .resample import UpSample1d, DownSample1d
-
-
-class Activation1d(nn.Module):
-    def __init__(self,
-                 activation,
-                 up_ratio: int = 2,
-                 down_ratio: int = 2,
-                 up_kernel_size: int = 12,
-                 down_kernel_size: int = 12):
-        super().__init__()
-        self.up_ratio = up_ratio
-        self.down_ratio = down_ratio
-        self.act = activation
-        self.upsample = UpSample1d(up_ratio, up_kernel_size)
-        self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
-    # x: [B,C,T]
-    def forward(self, x):
-        x = self.upsample(x)
-        x = self.act(x)
-        x = self.downsample(x)
-
-        return x

spaces/AIGText/GlyphControl/example_list.py

@@ -1,38 +0,0 @@
-example_1 = [
-    "LAION-Glyph-10M-Epoch-6",
-    "A gift card with text ""Happy Birthday"" and roses on it.",
-    "Happy Birthday", 0.47, 0, 0.24, 0.4, 5, 1,
-    "", 0.3, 0, 0.15, 0.15, 0, 1,
-    "", 0.3, 0, 0.15, 0.65, 0, 1,
-    "", 0.3, 0, 0.5, 0.65, 0, 1,
-    5,512,20,False,1,9,0,0,
-    "4K, dslr, best quality, extremely detailed",
-    "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"
-]
-# teaser examples in the report (updating...)
-# only could generate similar examples due to the fact that our released checkpoints are different from the checkpoint used in the original report.
-example_2 = [
-    "LAION-Glyph-10M-Epoch-6",
-    'Newspaper with the headline "Aliens Found in Space" and "Monster Attacks Mars".',
-    'Aliens Found in Space', 0.8, 0, 0.1, 0.1, 0, 1,
-    'Monster Attacks Mars', 0.8, 0, 0.1, 0.45, 0, 1,
-    "", 0.3, 0, 0.15, 0.65, 0, 1,
-    "", 0.3, 0, 0.5, 0.65, 0, 1,
-    5,512,20,False,1,9,430637146, 
-    0, "best quality, extremely detailed", #"4K, dslr, best quality, extremely detailed",
-    "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"
-]
-examples = [example_1, example_2]
-
-# example_3 = [
-#     "LAION-Glyph-10M-Epoch-6",
-#     'A decorative greeting card that reads "Congratulations on achieving state of the art".',
-#     'Congratulations', 0.6, 0, 0.2, 0.1, 0, 1,
-#     'on achieving', 0.5, 0, 0.25, 0.22, 0, 1,
-#     'state of the art', 0.6, 0, 0.21, 0.34, 0, 1,
-#     "", 0.3, 0, 0.5, 0.65, 0, 1,
-#     5,512,20,False,1,9, 1540281202, #364285590,
-#     0, "best quality, extremely detailed",
-#     "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"
-# ]
-# examples = [example_1, example_2, example_3]

spaces/ASJMO/freegpt/g4f/Provider/Providers/GetGpt.py

@@ -1,57 +0,0 @@
-import os
-import json
-import uuid
-import requests
-from Crypto.Cipher import AES
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://chat.getgpt.world/'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    def encrypt(e):
-        t = os.urandom(8).hex().encode('utf-8')
-        n = os.urandom(8).hex().encode('utf-8')
-        r = e.encode('utf-8')
-        cipher = AES.new(t, AES.MODE_CBC, n)
-        ciphertext = cipher.encrypt(pad_data(r))
-        return ciphertext.hex() + t.decode('utf-8') + n.decode('utf-8')
-
-    def pad_data(data: bytes) -> bytes:
-        block_size = AES.block_size
-        padding_size = block_size - len(data) % block_size
-        padding = bytes([padding_size] * padding_size)
-        return data + padding
-
-    headers = {
-        'Content-Type': 'application/json',
-        'Referer': 'https://chat.getgpt.world/',
-        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
-    }
-
-    data = json.dumps({
-        'messages': messages,
-        'frequency_penalty': kwargs.get('frequency_penalty', 0),
-        'max_tokens': kwargs.get('max_tokens', 4000),
-        'model': 'gpt-3.5-turbo',
-        'presence_penalty': kwargs.get('presence_penalty', 0),
-        'temperature': kwargs.get('temperature', 1),
-        'top_p': kwargs.get('top_p', 1),
-        'stream': True,
-        'uuid': str(uuid.uuid4())
-    })
-
-    res = requests.post('https://chat.getgpt.world/api/chat/stream', 
-                        headers=headers, json={'signature': encrypt(data)}, stream=True)
-
-    for line in res.iter_lines():
-        if b'content' in line:
-            line_json = json.loads(line.decode('utf-8').split('data: ')[1])
-            yield (line_json['choices'][0]['delta']['content'])
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f'{name}: {get_type_hints(_create_completion)[name].__name__}' for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/ASJMO/freegpt/g4f/Provider/Providers/helpers/theb.py

@@ -1,48 +0,0 @@
-import json
-import sys
-from re import findall
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-headers = {
-    'authority': 'chatbot.theb.ai',
-    'accept': 'application/json, text/plain, */*',
-    'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-    'content-type': 'application/json',
-    'origin': 'https://chatbot.theb.ai',
-    'referer': 'https://chatbot.theb.ai/',
-    'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
-    'sec-ch-ua-mobile': '?0',
-    'sec-ch-ua-platform': '"macOS"',
-    'sec-fetch-dest': 'empty',
-    'sec-fetch-mode': 'cors',
-    'sec-fetch-site': 'same-origin',
-    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-}
-
-json_data = {
-    'prompt': prompt,
-    'options': {}
-}
-
-def format(chunk):
-    try:
-        completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
-        print(completion_chunk, flush=True, end='')
-
-    except Exception as e:
-        print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
-        return
-
-while True:
-    try: 
-        response = requests.post('https://chatbot.theb.ai/api/chat-process', 
-                            headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('[ERROR] an error occured, retrying... |', e, flush=True)
-        continue

spaces/AgentVerse/agentVerse/scripts/evaluate_responsegen.py

@@ -1,112 +0,0 @@
-import os
-import json
-from string import Template
-import time
-import openai
-from tqdm import tqdm
-
-with open("./results.jsonl", "r") as f:
-    lines = list(f.readlines())
-
-eval_prompt = r"""Which response is better given this context: 
-${context}
-
-Response A: ${response_a} 
-
-Response B: ${response_b}. 
-
-Pick your answer from ['Response A', 'Response B', 'both', 'neither']. Generate a short explanation for your choice first. Then, generate 'The better response is A' or 'The better response is B' or 'The better response is both' or 'The better response is neither'.
-
-Your response format should be:
-Explanation: <explanation>
-Answer: ('The better response is A' or 'The better response is B' or 'The better response is both' or 'The better response is neither')
-"""
-
-res = []
-eval = []
-
-
-def write_eval_to_file(file, skip=0):
-    for idx, line in tqdm(enumerate(lines)):
-        if idx < skip:
-            continue
-        data = json.loads(line)
-        # print(idx + 1)
-        context = data["input"]
-        response_a = data["response"]
-        response_b = data["label"]
-
-        context_quote = "> " + "\n> ".join(context.split("\n"))
-        response_a_quote = "> " + "\n> ".join(response_a.split("\n"))
-        response_b_quote = "> " + "\n> ".join(response_b.split("\n"))
-
-        f.write(f"## {idx + 1}\n\n")
-        f.write(f"Context:\n" f"{context_quote}\n\n")
-        f.write(f"Response A (pipeline):\n" f"{response_a_quote}\n\n")
-        f.write(f"Response B (init):\n" f"{response_b_quote}\n\n")
-
-        prompt = Template(eval_prompt).safe_substitute(
-            context=context, response_a=response_a, response_b=response_b
-        )
-        for i in range(100):
-            try:
-                eval_response = openai.ChatCompletion.create(
-                    model="gpt-4",
-                    messages=[{"role": "user", "content": prompt}],
-                    temperature=0.0,
-                )
-            except:
-                time.sleep(min(i**2, 60))
-                continue
-            break
-        text = eval_response["choices"][0]["message"]["content"]
-        eval.append(text)
-        text = text.replace("\n", "\n\n")
-        f.write(f"{text}\n\n")
-
-        if "The better response is A" in text:
-            res.append("A")
-        elif "The better response is B" in text:
-            res.append("B")
-        elif "The better response is both" in text:
-            res.append("both")
-        elif "The better response is neither" in text:
-            res.append("neither")
-        else:
-            res.append("unknown")
-
-
-if not os.path.exists("./eval.md"):
-    with open("./eval.md", "w") as f:
-        f.write("# ResponseGen Eval\n\n")
-        write_eval_to_file(f)
-    win_cnt = 0
-    for r in res:
-        if r == "A":
-            win_cnt += 1
-    print(f"win rate: {win_cnt / len(res)}")
-else:
-    win_cnt = 0
-    total_cnt = 0
-    with open("./eval.md", "r") as f:
-        for line in f:
-            if line.startswith("Answer"):
-                total_cnt += 1
-                if "The better response is A" in line:
-                    res.append("A")
-                elif "The better response is B" in line:
-                    res.append("B")
-                elif "The better response is both" in line:
-                    res.append("both")
-                elif "The better response is neither" in line:
-                    res.append("neither")
-                else:
-                    res.append("unknown")
-    with open("./eval.md", "a") as f:
-        f.write("\n")
-        write_eval_to_file(f, total_cnt)
-    win_cnt = 0
-    for r in res:
-        if r == "A":
-            win_cnt += 1
-    print(f"win rate: {win_cnt / len(res)}")

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

@@ -1,10 +0,0 @@
-var BroadcastEvent = function () {
-    var gameObjects = this.getAllChildren([this]);
-    for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
-        var gameObject = gameObjects[i];
-        gameObject.emit.apply(gameObject, arguments);
-    }
-    return this;
-}
-
-export default BroadcastEvent;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateBadgeLabel.js

@@ -1,26 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import BadgeLabel from '../../badgelabel/BadgeLabel.js';
-import CreateChild from './utils/CreateChild.js';
-
-var CreateBadgeLabel = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    // Replace data by child game object
-    CreateChild(scene, data, 'background', view, styles, customBuilders);
-    CreateChild(scene, data, 'main', view, styles, customBuilders);
-    CreateChild(scene, data, 'leftTop', view, styles, customBuilders);
-    CreateChild(scene, data, 'centerTop', view, styles, customBuilders);
-    CreateChild(scene, data, 'rightTop', view, styles, customBuilders);
-    CreateChild(scene, data, 'leftCenter', view, styles, customBuilders);
-    CreateChild(scene, data, 'center', view, styles, customBuilders);
-    CreateChild(scene, data, 'rightCenter', view, styles, customBuilders);
-    CreateChild(scene, data, 'leftBottom', view, styles, customBuilders);
-    CreateChild(scene, data, 'centerBottom', view, styles, customBuilders);
-    CreateChild(scene, data, 'rightBottom', view, styles, customBuilders);
-
-    var gameObject = new BadgeLabel(scene, data);
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateBadgeLabel;

spaces/AlanMars/QYL-AI-Space/readme/README_ja.md

@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバーに展開するには、`config.json` 内の `"server_name": "0.0.0.0", "server_port": <ポート番号>`を設定してください。
-- 共有リンクを取得するには、 `config.json` 内の `"share": true` を設定してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## クイックスタート
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python app.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インスト��ルしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">

spaces/Amrrs/DragGan-Inversion/PTI/configs/evaluation_config.py

@@ -1 +0,0 @@
-evaluated_methods = ['e4e', 'SG2', 'SG2Plus']

spaces/Amrrs/DragGan-Inversion/training/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  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.
-
-# empty

spaces/Andy1621/uniformer_image_detection/mmdet/models/utils/transformer.py

@@ -1,860 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import (Linear, build_activation_layer, build_norm_layer,
-                      xavier_init)
-
-from .builder import TRANSFORMER
-
-
-class MultiheadAttention(nn.Module):
-    """A warpper for torch.nn.MultiheadAttention.
-
-    This module implements MultiheadAttention with residual connection,
-    and positional encoding used in DETR is also passed as input.
-
-    Args:
-        embed_dims (int): The embedding dimension.
-        num_heads (int): Parallel attention heads. Same as
-            `nn.MultiheadAttention`.
-        dropout (float): A Dropout layer on attn_output_weights. Default 0.0.
-    """
-
-    def __init__(self, embed_dims, num_heads, dropout=0.0):
-        super(MultiheadAttention, self).__init__()
-        assert embed_dims % num_heads == 0, 'embed_dims must be ' \
-            f'divisible by num_heads. got {embed_dims} and {num_heads}.'
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.attn = nn.MultiheadAttention(embed_dims, num_heads, dropout)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self,
-                x,
-                key=None,
-                value=None,
-                residual=None,
-                query_pos=None,
-                key_pos=None,
-                attn_mask=None,
-                key_padding_mask=None):
-        """Forward function for `MultiheadAttention`.
-
-        Args:
-            x (Tensor): The input query with shape [num_query, bs,
-                embed_dims]. Same in `nn.MultiheadAttention.forward`.
-            key (Tensor): The key tensor with shape [num_key, bs,
-                embed_dims]. Same in `nn.MultiheadAttention.forward`.
-                Default None. If None, the `query` will be used.
-            value (Tensor): The value tensor with same shape as `key`.
-                Same in `nn.MultiheadAttention.forward`. Default None.
-                If None, the `key` will be used.
-            residual (Tensor): The tensor used for addition, with the
-                same shape as `x`. Default None. If None, `x` will be used.
-            query_pos (Tensor): The positional encoding for query, with
-                the same shape as `x`. Default None. If not None, it will
-                be added to `x` before forward function.
-            key_pos (Tensor): The positional encoding for `key`, with the
-                same shape as `key`. Default None. If not None, it will
-                be added to `key` before forward function. If None, and
-                `query_pos` has the same shape as `key`, then `query_pos`
-                will be used for `key_pos`.
-            attn_mask (Tensor): ByteTensor mask with shape [num_query,
-                num_key]. Same in `nn.MultiheadAttention.forward`.
-                Default None.
-            key_padding_mask (Tensor): ByteTensor with shape [bs, num_key].
-                Same in `nn.MultiheadAttention.forward`. Default None.
-
-        Returns:
-            Tensor: forwarded results with shape [num_query, bs, embed_dims].
-        """
-        query = x
-        if key is None:
-            key = query
-        if value is None:
-            value = key
-        if residual is None:
-            residual = x
-        if key_pos is None:
-            if query_pos is not None and key is not None:
-                if query_pos.shape == key.shape:
-                    key_pos = query_pos
-        if query_pos is not None:
-            query = query + query_pos
-        if key_pos is not None:
-            key = key + key_pos
-        out = self.attn(
-            query,
-            key,
-            value=value,
-            attn_mask=attn_mask,
-            key_padding_mask=key_padding_mask)[0]
-
-        return residual + self.dropout(out)
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'dropout={self.dropout})'
-        return repr_str
-
-
-class FFN(nn.Module):
-    """Implements feed-forward networks (FFNs) with residual connection.
-
-    Args:
-        embed_dims (int): The feature dimension. Same as
-            `MultiheadAttention`.
-        feedforward_channels (int): The hidden dimension of FFNs.
-        num_fcs (int, optional): The number of fully-connected layers in
-            FFNs. Defaults to 2.
-        act_cfg (dict, optional): The activation config for FFNs.
-        dropout (float, optional): Probability of an element to be
-            zeroed. Default 0.0.
-        add_residual (bool, optional): Add resudual connection.
-            Defaults to True.
-    """
-
-    def __init__(self,
-                 embed_dims,
-                 feedforward_channels,
-                 num_fcs=2,
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 dropout=0.0,
-                 add_residual=True):
-        super(FFN, self).__init__()
-        assert num_fcs >= 2, 'num_fcs should be no less ' \
-            f'than 2. got {num_fcs}.'
-        self.embed_dims = embed_dims
-        self.feedforward_channels = feedforward_channels
-        self.num_fcs = num_fcs
-        self.act_cfg = act_cfg
-        self.dropout = dropout
-        self.activate = build_activation_layer(act_cfg)
-
-        layers = nn.ModuleList()
-        in_channels = embed_dims
-        for _ in range(num_fcs - 1):
-            layers.append(
-                nn.Sequential(
-                    Linear(in_channels, feedforward_channels), self.activate,
-                    nn.Dropout(dropout)))
-            in_channels = feedforward_channels
-        layers.append(Linear(feedforward_channels, embed_dims))
-        self.layers = nn.Sequential(*layers)
-        self.dropout = nn.Dropout(dropout)
-        self.add_residual = add_residual
-
-    def forward(self, x, residual=None):
-        """Forward function for `FFN`."""
-        out = self.layers(x)
-        if not self.add_residual:
-            return out
-        if residual is None:
-            residual = x
-        return residual + self.dropout(out)
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(embed_dims={self.embed_dims}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'num_fcs={self.num_fcs}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'add_residual={self.add_residual})'
-        return repr_str
-
-
-class TransformerEncoderLayer(nn.Module):
-    """Implements one encoder layer in DETR transformer.
-
-    Args:
-        embed_dims (int): The feature dimension. Same as `FFN`.
-        num_heads (int): Parallel attention heads.
-        feedforward_channels (int): The hidden dimension for FFNs.
-        dropout (float): Probability of an element to be zeroed. Default 0.0.
-        order (tuple[str]): The order for encoder layer. Valid examples are
-            ('selfattn', 'norm', 'ffn', 'norm') and ('norm', 'selfattn',
-            'norm', 'ffn'). Default ('selfattn', 'norm', 'ffn', 'norm').
-        act_cfg (dict): The activation config for FFNs. Default ReLU.
-        norm_cfg (dict): Config dict for normalization layer. Default
-            layer normalization.
-        num_fcs (int): The number of fully-connected layers for FFNs.
-            Default 2.
-    """
-
-    def __init__(self,
-                 embed_dims,
-                 num_heads,
-                 feedforward_channels,
-                 dropout=0.0,
-                 order=('selfattn', 'norm', 'ffn', 'norm'),
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN'),
-                 num_fcs=2):
-        super(TransformerEncoderLayer, self).__init__()
-        assert isinstance(order, tuple) and len(order) == 4
-        assert set(order) == set(['selfattn', 'norm', 'ffn'])
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.feedforward_channels = feedforward_channels
-        self.dropout = dropout
-        self.order = order
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.num_fcs = num_fcs
-        self.pre_norm = order[0] == 'norm'
-        self.self_attn = MultiheadAttention(embed_dims, num_heads, dropout)
-        self.ffn = FFN(embed_dims, feedforward_channels, num_fcs, act_cfg,
-                       dropout)
-        self.norms = nn.ModuleList()
-        self.norms.append(build_norm_layer(norm_cfg, embed_dims)[1])
-        self.norms.append(build_norm_layer(norm_cfg, embed_dims)[1])
-
-    def forward(self, x, pos=None, attn_mask=None, key_padding_mask=None):
-        """Forward function for `TransformerEncoderLayer`.
-
-        Args:
-            x (Tensor): The input query with shape [num_key, bs,
-                embed_dims]. Same in `MultiheadAttention.forward`.
-            pos (Tensor): The positional encoding for query. Default None.
-                Same as `query_pos` in `MultiheadAttention.forward`.
-            attn_mask (Tensor): ByteTensor mask with shape [num_key,
-                num_key]. Same in `MultiheadAttention.forward`. Default None.
-            key_padding_mask (Tensor): ByteTensor with shape [bs, num_key].
-                Same in `MultiheadAttention.forward`. Default None.
-
-        Returns:
-            Tensor: forwarded results with shape [num_key, bs, embed_dims].
-        """
-        norm_cnt = 0
-        inp_residual = x
-        for layer in self.order:
-            if layer == 'selfattn':
-                # self attention
-                query = key = value = x
-                x = self.self_attn(
-                    query,
-                    key,
-                    value,
-                    inp_residual if self.pre_norm else None,
-                    query_pos=pos,
-                    key_pos=pos,
-                    attn_mask=attn_mask,
-                    key_padding_mask=key_padding_mask)
-                inp_residual = x
-            elif layer == 'norm':
-                x = self.norms[norm_cnt](x)
-                norm_cnt += 1
-            elif layer == 'ffn':
-                x = self.ffn(x, inp_residual if self.pre_norm else None)
-        return x
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'order={self.order}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg}, '
-        repr_str += f'num_fcs={self.num_fcs})'
-        return repr_str
-
-
-class TransformerDecoderLayer(nn.Module):
-    """Implements one decoder layer in DETR transformer.
-
-    Args:
-        embed_dims (int): The feature dimension. Same as
-            `TransformerEncoderLayer`.
-        num_heads (int): Parallel attention heads.
-        feedforward_channels (int): Same as `TransformerEncoderLayer`.
-        dropout (float): Same as `TransformerEncoderLayer`. Default 0.0.
-        order (tuple[str]): The order for decoder layer. Valid examples are
-            ('selfattn', 'norm', 'multiheadattn', 'norm', 'ffn', 'norm') and
-            ('norm', 'selfattn', 'norm', 'multiheadattn', 'norm', 'ffn').
-            Default the former.
-        act_cfg (dict): Same as `TransformerEncoderLayer`. Default ReLU.
-        norm_cfg (dict): Config dict for normalization layer. Default
-            layer normalization.
-        num_fcs (int): The number of fully-connected layers in FFNs.
-    """
-
-    def __init__(self,
-                 embed_dims,
-                 num_heads,
-                 feedforward_channels,
-                 dropout=0.0,
-                 order=('selfattn', 'norm', 'multiheadattn', 'norm', 'ffn',
-                        'norm'),
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN'),
-                 num_fcs=2):
-        super(TransformerDecoderLayer, self).__init__()
-        assert isinstance(order, tuple) and len(order) == 6
-        assert set(order) == set(['selfattn', 'norm', 'multiheadattn', 'ffn'])
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.feedforward_channels = feedforward_channels
-        self.dropout = dropout
-        self.order = order
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.num_fcs = num_fcs
-        self.pre_norm = order[0] == 'norm'
-        self.self_attn = MultiheadAttention(embed_dims, num_heads, dropout)
-        self.multihead_attn = MultiheadAttention(embed_dims, num_heads,
-                                                 dropout)
-        self.ffn = FFN(embed_dims, feedforward_channels, num_fcs, act_cfg,
-                       dropout)
-        self.norms = nn.ModuleList()
-        # 3 norm layers in official DETR's TransformerDecoderLayer
-        for _ in range(3):
-            self.norms.append(build_norm_layer(norm_cfg, embed_dims)[1])
-
-    def forward(self,
-                x,
-                memory,
-                memory_pos=None,
-                query_pos=None,
-                memory_attn_mask=None,
-                target_attn_mask=None,
-                memory_key_padding_mask=None,
-                target_key_padding_mask=None):
-        """Forward function for `TransformerDecoderLayer`.
-
-        Args:
-            x (Tensor): Input query with shape [num_query, bs, embed_dims].
-            memory (Tensor): Tensor got from `TransformerEncoder`, with shape
-                [num_key, bs, embed_dims].
-            memory_pos (Tensor): The positional encoding for `memory`. Default
-                None. Same as `key_pos` in `MultiheadAttention.forward`.
-            query_pos (Tensor): The positional encoding for `query`. Default
-                None. Same as `query_pos` in `MultiheadAttention.forward`.
-            memory_attn_mask (Tensor): ByteTensor mask for `memory`, with
-                shape [num_key, num_key]. Same as `attn_mask` in
-                `MultiheadAttention.forward`. Default None.
-            target_attn_mask (Tensor): ByteTensor mask for `x`, with shape
-                [num_query, num_query]. Same as `attn_mask` in
-                `MultiheadAttention.forward`. Default None.
-            memory_key_padding_mask (Tensor): ByteTensor for `memory`, with
-                shape [bs, num_key]. Same as `key_padding_mask` in
-                `MultiheadAttention.forward`. Default None.
-            target_key_padding_mask (Tensor): ByteTensor for `x`, with shape
-                [bs, num_query]. Same as `key_padding_mask` in
-                `MultiheadAttention.forward`. Default None.
-
-        Returns:
-            Tensor: forwarded results with shape [num_query, bs, embed_dims].
-        """
-        norm_cnt = 0
-        inp_residual = x
-        for layer in self.order:
-            if layer == 'selfattn':
-                query = key = value = x
-                x = self.self_attn(
-                    query,
-                    key,
-                    value,
-                    inp_residual if self.pre_norm else None,
-                    query_pos,
-                    key_pos=query_pos,
-                    attn_mask=target_attn_mask,
-                    key_padding_mask=target_key_padding_mask)
-                inp_residual = x
-            elif layer == 'norm':
-                x = self.norms[norm_cnt](x)
-                norm_cnt += 1
-            elif layer == 'multiheadattn':
-                query = x
-                key = value = memory
-                x = self.multihead_attn(
-                    query,
-                    key,
-                    value,
-                    inp_residual if self.pre_norm else None,
-                    query_pos,
-                    key_pos=memory_pos,
-                    attn_mask=memory_attn_mask,
-                    key_padding_mask=memory_key_padding_mask)
-                inp_residual = x
-            elif layer == 'ffn':
-                x = self.ffn(x, inp_residual if self.pre_norm else None)
-        return x
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'order={self.order}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg}, '
-        repr_str += f'num_fcs={self.num_fcs})'
-        return repr_str
-
-
-class TransformerEncoder(nn.Module):
-    """Implements the encoder in DETR transformer.
-
-    Args:
-        num_layers (int): The number of `TransformerEncoderLayer`.
-        embed_dims (int): Same as `TransformerEncoderLayer`.
-        num_heads (int): Same as `TransformerEncoderLayer`.
-        feedforward_channels (int): Same as `TransformerEncoderLayer`.
-        dropout (float): Same as `TransformerEncoderLayer`. Default 0.0.
-        order (tuple[str]): Same as `TransformerEncoderLayer`.
-        act_cfg (dict): Same as `TransformerEncoderLayer`. Default ReLU.
-        norm_cfg (dict): Same as `TransformerEncoderLayer`. Default
-            layer normalization.
-        num_fcs (int): Same as `TransformerEncoderLayer`. Default 2.
-    """
-
-    def __init__(self,
-                 num_layers,
-                 embed_dims,
-                 num_heads,
-                 feedforward_channels,
-                 dropout=0.0,
-                 order=('selfattn', 'norm', 'ffn', 'norm'),
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN'),
-                 num_fcs=2):
-        super(TransformerEncoder, self).__init__()
-        assert isinstance(order, tuple) and len(order) == 4
-        assert set(order) == set(['selfattn', 'norm', 'ffn'])
-        self.num_layers = num_layers
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.feedforward_channels = feedforward_channels
-        self.dropout = dropout
-        self.order = order
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.num_fcs = num_fcs
-        self.pre_norm = order[0] == 'norm'
-        self.layers = nn.ModuleList()
-        for _ in range(num_layers):
-            self.layers.append(
-                TransformerEncoderLayer(embed_dims, num_heads,
-                                        feedforward_channels, dropout, order,
-                                        act_cfg, norm_cfg, num_fcs))
-        self.norm = build_norm_layer(norm_cfg,
-                                     embed_dims)[1] if self.pre_norm else None
-
-    def forward(self, x, pos=None, attn_mask=None, key_padding_mask=None):
-        """Forward function for `TransformerEncoder`.
-
-        Args:
-            x (Tensor): Input query. Same in `TransformerEncoderLayer.forward`.
-            pos (Tensor): Positional encoding for query. Default None.
-                Same in `TransformerEncoderLayer.forward`.
-            attn_mask (Tensor): ByteTensor attention mask. Default None.
-                Same in `TransformerEncoderLayer.forward`.
-            key_padding_mask (Tensor): Same in
-                `TransformerEncoderLayer.forward`. Default None.
-
-        Returns:
-            Tensor: Results with shape [num_key, bs, embed_dims].
-        """
-        for layer in self.layers:
-            x = layer(x, pos, attn_mask, key_padding_mask)
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(num_layers={self.num_layers}, '
-        repr_str += f'embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'order={self.order}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg}, '
-        repr_str += f'num_fcs={self.num_fcs})'
-        return repr_str
-
-
-class TransformerDecoder(nn.Module):
-    """Implements the decoder in DETR transformer.
-
-    Args:
-        num_layers (int): The number of `TransformerDecoderLayer`.
-        embed_dims (int): Same as `TransformerDecoderLayer`.
-        num_heads (int): Same as `TransformerDecoderLayer`.
-        feedforward_channels (int): Same as `TransformerDecoderLayer`.
-        dropout (float): Same as `TransformerDecoderLayer`. Default 0.0.
-        order (tuple[str]): Same as `TransformerDecoderLayer`.
-        act_cfg (dict): Same as `TransformerDecoderLayer`. Default ReLU.
-        norm_cfg (dict): Same as `TransformerDecoderLayer`. Default
-            layer normalization.
-        num_fcs (int): Same as `TransformerDecoderLayer`. Default 2.
-    """
-
-    def __init__(self,
-                 num_layers,
-                 embed_dims,
-                 num_heads,
-                 feedforward_channels,
-                 dropout=0.0,
-                 order=('selfattn', 'norm', 'multiheadattn', 'norm', 'ffn',
-                        'norm'),
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN'),
-                 num_fcs=2,
-                 return_intermediate=False):
-        super(TransformerDecoder, self).__init__()
-        assert isinstance(order, tuple) and len(order) == 6
-        assert set(order) == set(['selfattn', 'norm', 'multiheadattn', 'ffn'])
-        self.num_layers = num_layers
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.feedforward_channels = feedforward_channels
-        self.dropout = dropout
-        self.order = order
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.num_fcs = num_fcs
-        self.return_intermediate = return_intermediate
-        self.layers = nn.ModuleList()
-        for _ in range(num_layers):
-            self.layers.append(
-                TransformerDecoderLayer(embed_dims, num_heads,
-                                        feedforward_channels, dropout, order,
-                                        act_cfg, norm_cfg, num_fcs))
-        self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
-
-    def forward(self,
-                x,
-                memory,
-                memory_pos=None,
-                query_pos=None,
-                memory_attn_mask=None,
-                target_attn_mask=None,
-                memory_key_padding_mask=None,
-                target_key_padding_mask=None):
-        """Forward function for `TransformerDecoder`.
-
-        Args:
-            x (Tensor): Input query. Same in `TransformerDecoderLayer.forward`.
-            memory (Tensor): Same in `TransformerDecoderLayer.forward`.
-            memory_pos (Tensor): Same in `TransformerDecoderLayer.forward`.
-                Default None.
-            query_pos (Tensor): Same in `TransformerDecoderLayer.forward`.
-                Default None.
-            memory_attn_mask (Tensor): Same in
-                `TransformerDecoderLayer.forward`. Default None.
-            target_attn_mask (Tensor): Same in
-                `TransformerDecoderLayer.forward`. Default None.
-            memory_key_padding_mask (Tensor): Same in
-                `TransformerDecoderLayer.forward`. Default None.
-            target_key_padding_mask (Tensor): Same in
-                `TransformerDecoderLayer.forward`. Default None.
-
-        Returns:
-            Tensor: Results with shape [num_query, bs, embed_dims].
-        """
-        intermediate = []
-        for layer in self.layers:
-            x = layer(x, memory, memory_pos, query_pos, memory_attn_mask,
-                      target_attn_mask, memory_key_padding_mask,
-                      target_key_padding_mask)
-            if self.return_intermediate:
-                intermediate.append(self.norm(x))
-        if self.norm is not None:
-            x = self.norm(x)
-            if self.return_intermediate:
-                intermediate.pop()
-                intermediate.append(x)
-        if self.return_intermediate:
-            return torch.stack(intermediate)
-        return x.unsqueeze(0)
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(num_layers={self.num_layers}, '
-        repr_str += f'embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'order={self.order}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg}, '
-        repr_str += f'num_fcs={self.num_fcs}, '
-        repr_str += f'return_intermediate={self.return_intermediate})'
-        return repr_str
-
-
-@TRANSFORMER.register_module()
-class Transformer(nn.Module):
-    """Implements the DETR transformer.
-
-    Following the official DETR implementation, this module copy-paste
-    from torch.nn.Transformer with modifications:
-
-        * positional encodings are passed in MultiheadAttention
-        * extra LN at the end of encoder is removed
-        * decoder returns a stack of activations from all decoding layers
-
-    See `paper: End-to-End Object Detection with Transformers
-    <https://arxiv.org/pdf/2005.12872>`_ for details.
-
-    Args:
-        embed_dims (int): The feature dimension.
-        num_heads (int): Parallel attention heads. Same as
-            `nn.MultiheadAttention`.
-        num_encoder_layers (int): Number of `TransformerEncoderLayer`.
-        num_decoder_layers (int): Number of `TransformerDecoderLayer`.
-        feedforward_channels (int): The hidden dimension for FFNs used in both
-            encoder and decoder.
-        dropout (float): Probability of an element to be zeroed. Default 0.0.
-        act_cfg (dict): Activation config for FFNs used in both encoder
-            and decoder. Default ReLU.
-        norm_cfg (dict): Config dict for normalization used in both encoder
-            and decoder. Default layer normalization.
-        num_fcs (int): The number of fully-connected layers in FFNs, which is
-            used for both encoder and decoder.
-        pre_norm (bool): Whether the normalization layer is ordered
-            first in the encoder and decoder. Default False.
-        return_intermediate_dec (bool): Whether to return the intermediate
-            output from each TransformerDecoderLayer or only the last
-            TransformerDecoderLayer. Default False. If False, the returned
-            `hs` has shape [num_decoder_layers, bs, num_query, embed_dims].
-            If True, the returned `hs` will have shape [1, bs, num_query,
-            embed_dims].
-    """
-
-    def __init__(self,
-                 embed_dims=512,
-                 num_heads=8,
-                 num_encoder_layers=6,
-                 num_decoder_layers=6,
-                 feedforward_channels=2048,
-                 dropout=0.0,
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN'),
-                 num_fcs=2,
-                 pre_norm=False,
-                 return_intermediate_dec=False):
-        super(Transformer, self).__init__()
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.num_encoder_layers = num_encoder_layers
-        self.num_decoder_layers = num_decoder_layers
-        self.feedforward_channels = feedforward_channels
-        self.dropout = dropout
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.num_fcs = num_fcs
-        self.pre_norm = pre_norm
-        self.return_intermediate_dec = return_intermediate_dec
-        if self.pre_norm:
-            encoder_order = ('norm', 'selfattn', 'norm', 'ffn')
-            decoder_order = ('norm', 'selfattn', 'norm', 'multiheadattn',
-                             'norm', 'ffn')
-        else:
-            encoder_order = ('selfattn', 'norm', 'ffn', 'norm')
-            decoder_order = ('selfattn', 'norm', 'multiheadattn', 'norm',
-                             'ffn', 'norm')
-        self.encoder = TransformerEncoder(num_encoder_layers, embed_dims,
-                                          num_heads, feedforward_channels,
-                                          dropout, encoder_order, act_cfg,
-                                          norm_cfg, num_fcs)
-        self.decoder = TransformerDecoder(num_decoder_layers, embed_dims,
-                                          num_heads, feedforward_channels,
-                                          dropout, decoder_order, act_cfg,
-                                          norm_cfg, num_fcs,
-                                          return_intermediate_dec)
-
-    def init_weights(self, distribution='uniform'):
-        """Initialize the transformer weights."""
-        # follow the official DETR to init parameters
-        for m in self.modules():
-            if hasattr(m, 'weight') and m.weight.dim() > 1:
-                xavier_init(m, distribution=distribution)
-
-    def forward(self, x, mask, query_embed, pos_embed):
-        """Forward function for `Transformer`.
-
-        Args:
-            x (Tensor): Input query with shape [bs, c, h, w] where
-                c = embed_dims.
-            mask (Tensor): The key_padding_mask used for encoder and decoder,
-                with shape [bs, h, w].
-            query_embed (Tensor): The query embedding for decoder, with shape
-                [num_query, c].
-            pos_embed (Tensor): The positional encoding for encoder and
-                decoder, with the same shape as `x`.
-
-        Returns:
-            tuple[Tensor]: results of decoder containing the following tensor.
-
-                - out_dec: Output from decoder. If return_intermediate_dec \
-                      is True output has shape [num_dec_layers, bs,
-                      num_query, embed_dims], else has shape [1, bs, \
-                      num_query, embed_dims].
-                - memory: Output results from encoder, with shape \
-                      [bs, embed_dims, h, w].
-        """
-        bs, c, h, w = x.shape
-        x = x.flatten(2).permute(2, 0, 1)  # [bs, c, h, w] -> [h*w, bs, c]
-        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
-        query_embed = query_embed.unsqueeze(1).repeat(
-            1, bs, 1)  # [num_query, dim] -> [num_query, bs, dim]
-        mask = mask.flatten(1)  # [bs, h, w] -> [bs, h*w]
-        memory = self.encoder(
-            x, pos=pos_embed, attn_mask=None, key_padding_mask=mask)
-        target = torch.zeros_like(query_embed)
-        # out_dec: [num_layers, num_query, bs, dim]
-        out_dec = self.decoder(
-            target,
-            memory,
-            memory_pos=pos_embed,
-            query_pos=query_embed,
-            memory_attn_mask=None,
-            target_attn_mask=None,
-            memory_key_padding_mask=mask,
-            target_key_padding_mask=None)
-        out_dec = out_dec.transpose(1, 2)
-        memory = memory.permute(1, 2, 0).reshape(bs, c, h, w)
-        return out_dec, memory
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(embed_dims={self.embed_dims}, '
-        repr_str += f'num_heads={self.num_heads}, '
-        repr_str += f'num_encoder_layers={self.num_encoder_layers}, '
-        repr_str += f'num_decoder_layers={self.num_decoder_layers}, '
-        repr_str += f'feedforward_channels={self.feedforward_channels}, '
-        repr_str += f'dropout={self.dropout}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg}, '
-        repr_str += f'num_fcs={self.num_fcs}, '
-        repr_str += f'pre_norm={self.pre_norm}, '
-        repr_str += f'return_intermediate_dec={self.return_intermediate_dec})'
-        return repr_str
-
-
-@TRANSFORMER.register_module()
-class DynamicConv(nn.Module):
-    """Implements Dynamic Convolution.
-
-    This module generate parameters for each sample and
-    use bmm to implement 1*1 convolution. Code is modified
-    from the `official github repo <https://github.com/PeizeSun/
-    SparseR-CNN/blob/main/projects/SparseRCNN/sparsercnn/head.py#L258>`_ .
-
-    Args:
-        in_channels (int): The input feature channel.
-            Defaults to 256.
-        feat_channels (int): The inner feature channel.
-            Defaults to 64.
-        out_channels (int, optional): The output feature channel.
-            When not specified, it will be set to `in_channels`
-            by default
-        input_feat_shape (int): The shape of input feature.
-            Defaults to 7.
-        act_cfg (dict): The activation config for DynamicConv.
-        norm_cfg (dict): Config dict for normalization layer. Default
-            layer normalization.
-    """
-
-    def __init__(self,
-                 in_channels=256,
-                 feat_channels=64,
-                 out_channels=None,
-                 input_feat_shape=7,
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 norm_cfg=dict(type='LN')):
-        super(DynamicConv, self).__init__()
-        self.in_channels = in_channels
-        self.feat_channels = feat_channels
-        self.out_channels_raw = out_channels
-        self.input_feat_shape = input_feat_shape
-        self.act_cfg = act_cfg
-        self.norm_cfg = norm_cfg
-        self.out_channels = out_channels if out_channels else in_channels
-
-        self.num_params_in = self.in_channels * self.feat_channels
-        self.num_params_out = self.out_channels * self.feat_channels
-        self.dynamic_layer = nn.Linear(
-            self.in_channels, self.num_params_in + self.num_params_out)
-
-        self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1]
-        self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1]
-
-        self.activation = build_activation_layer(act_cfg)
-
-        num_output = self.out_channels * input_feat_shape**2
-        self.fc_layer = nn.Linear(num_output, self.out_channels)
-        self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1]
-
-    def forward(self, param_feature, input_feature):
-        """Forward function for `DynamicConv`.
-
-        Args:
-            param_feature (Tensor): The feature can be used
-                to generate the parameter, has shape
-                (num_all_proposals, in_channels).
-            input_feature (Tensor): Feature that
-                interact with parameters, has shape
-                (num_all_proposals, in_channels, H, W).
-
-        Returns:
-            Tensor: The output feature has shape
-            (num_all_proposals, out_channels).
-        """
-        num_proposals = param_feature.size(0)
-        input_feature = input_feature.view(num_proposals, self.in_channels,
-                                           -1).permute(2, 0, 1)
-
-        input_feature = input_feature.permute(1, 0, 2)
-        parameters = self.dynamic_layer(param_feature)
-
-        param_in = parameters[:, :self.num_params_in].view(
-            -1, self.in_channels, self.feat_channels)
-        param_out = parameters[:, -self.num_params_out:].view(
-            -1, self.feat_channels, self.out_channels)
-
-        # input_feature has shape (num_all_proposals, H*W, in_channels)
-        # param_in has shape (num_all_proposals, in_channels, feat_channels)
-        # feature has shape (num_all_proposals, H*W, feat_channels)
-        features = torch.bmm(input_feature, param_in)
-        features = self.norm_in(features)
-        features = self.activation(features)
-
-        # param_out has shape (batch_size, feat_channels, out_channels)
-        features = torch.bmm(features, param_out)
-        features = self.norm_out(features)
-        features = self.activation(features)
-
-        features = features.flatten(1)
-        features = self.fc_layer(features)
-        features = self.fc_norm(features)
-        features = self.activation(features)
-
-        return features
-
-    def __repr__(self):
-        """str: a string that describes the module"""
-        repr_str = self.__class__.__name__
-        repr_str += f'(in_channels={self.in_channels}, '
-        repr_str += f'feat_channels={self.feat_channels}, '
-        repr_str += f'out_channels={self.out_channels_raw}, '
-        repr_str += f'input_feat_shape={self.input_feat_shape}, '
-        repr_str += f'act_cfg={self.act_cfg}, '
-        repr_str += f'norm_cfg={self.norm_cfg})'
-        return repr_str

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/schedules/schedule_160k.py

@@ -1,9 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
-optimizer_config = dict()
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-# runtime settings
-runner = dict(type='IterBasedRunner', max_iters=160000)
-checkpoint_config = dict(by_epoch=False, interval=16000)
-evaluation = dict(interval=16000, metric='mIoU')

spaces/AnimaLab/bias-test-gpt-pairs/mgr_sentences.py

@@ -1,157 +0,0 @@
-import gradio as gr
-import os
-import re
-import pandas as pd
-import numpy as np
-import glob
-import huggingface_hub
-print("hfh", huggingface_hub.__version__)
-from huggingface_hub import hf_hub_download, upload_file, delete_file, snapshot_download, list_repo_files, dataset_info
-
-DATASET_REPO_ID = "AnimaLab/bias-test-gpt-sentences"
-DATASET_REPO_URL = f"https://huggingface.co/{DATASET_REPO_ID}"
-HF_DATA_DIRNAME = "data"
-LOCAL_DATA_DIRNAME = "data"
-LOCAL_SAVE_DIRNAME = "save"
-
-ds_write_token = os.environ.get("DS_WRITE_TOKEN")
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-print("ds_write_token:", ds_write_token!=None)
-print("hf_token:", HF_TOKEN!=None)
-print("hfh_verssion", huggingface_hub.__version__)
-
-def retrieveAllSaved():
-    global DATASET_REPO_ID
-
-    #listing the files - https://huggingface.co/docs/huggingface_hub/v0.8.1/en/package_reference/hf_api
-    repo_files = list_repo_files(repo_id=DATASET_REPO_ID, repo_type="dataset")
-    #print("Repo files:" + str(repo_files)
-
-    return repo_files
-
-def store_group_sentences(filename: str, df):
-  DATA_FILENAME_1 = f"{filename}"
-  LOCAL_PATH_FILE = os.path.join(LOCAL_SAVE_DIRNAME, DATA_FILENAME_1)
-  DATA_FILE_1 = os.path.join(HF_DATA_DIRNAME, DATA_FILENAME_1)
-
-  print(f"Trying to save to: {DATA_FILE_1}")
-
-  os.makedirs(os.path.dirname(LOCAL_PATH_FILE), exist_ok=True)
-  df.to_csv(LOCAL_PATH_FILE, index=False)
-
-  commit_url = upload_file(
-    path_or_fileobj=LOCAL_PATH_FILE,
-    path_in_repo=DATA_FILE_1,
-    repo_id=DATASET_REPO_ID,
-    repo_type="dataset",
-    token=ds_write_token,
-  )
-
-  print(commit_url)
-
-def saveSentences(sentences_df):
-  for grp_term in list(sentences_df['org_grp_term'].unique()):
-    print(f"Retrieving sentences for group: {grp_term}")
-    msg, grp_saved_df, filename = getSavedSentences(grp_term)
-    print(f"Num for group: {grp_term} -> {grp_saved_df.shape[0]}")
-    add_df = sentences_df[sentences_df['org_grp_term'] == grp_term]
-    print(f"Adding {add_df.shape[0]} sentences...")
-    
-    new_grp_df = pd.concat([grp_saved_df, add_df], ignore_index=True)
-    new_grp_df = new_grp_df.drop_duplicates(subset = "sentence")
-
-    print(f"Org size: {grp_saved_df.shape[0]}, Mrg size: {new_grp_df.shape[0]}")
-    store_group_sentences(filename, new_grp_df)
-   
-
-# https://huggingface.co/spaces/elonmuskceo/persistent-data/blob/main/app.py
-def get_sentence_csv(file_path: str):
-  file_path = os.path.join(HF_DATA_DIRNAME, file_path)
-  print(f"File path: {file_path}")
-  try:
-    hf_hub_download(
-       force_download=True, # to get updates of the dataset
-       repo_type="dataset",
-       repo_id=DATASET_REPO_ID,
-       filename=file_path,
-       cache_dir=LOCAL_DATA_DIRNAME,
-       force_filename=os.path.basename(file_path)
-    )
-  except Exception as e:
-    # file not found
-    print(f"file not found, probably: {e}")
-
-  files=glob.glob(f"./{LOCAL_DATA_DIRNAME}/", recursive=True)
-  print("Files glob: "+', '.join(files))
-  #print("Save file:" + str(os.path.basename(file_path)))
-  
-  df = pd.read_csv(os.path.join(LOCAL_DATA_DIRNAME, os.path.basename(file_path)), encoding='UTF8')
-  
-  return df
-
-def getSavedSentences(grp):
-    filename = f"{grp.replace(' ','-')}.csv"
-    sentence_df = pd.DataFrame()
-
-    try:
-        text = f"Loading sentences: {filename}\n"
-        sentence_df = get_sentence_csv(filename)
-
-    except Exception as e:
-        text = f"Error, no saved generations for {filename}"
-        #raise gr.Error(f"Cannot load sentences: {filename}!")
-
-    return text, sentence_df, filename
-
-
-def deleteBias(filepath: str):
-   commit_url = delete_file(
-      path_in_repo=filepath,
-      repo_id=DATASET_REPO_ID,
-      repo_type="dataset",
-      token=ds_write_token,
-   )
-
-   return f"Deleted {filepath} -> {commit_url}"
-
-def _testSentenceRetrieval(grp_list, att_list, use_paper_sentences):
-  test_sentences = []
-  print(f"Att list: {att_list}")
-  att_list_dash = [t.replace(' ','-') for t in att_list]
-  att_list.extend(att_list_dash)
-  att_list_nospace = [t.replace(' ','') for t in att_list]
-  att_list.extend(att_list_nospace)
-  att_list = list(set(att_list))
-  print(f"Att list with dash: {att_list}")
-
-  for gi, g_term in enumerate(grp_list):
-    _, sentence_df, _ = getSavedSentences(g_term)
-    
-    # only take from paper & gpt3.5
-    print(f"Before filter: {sentence_df.shape[0]}")
-    if use_paper_sentences == True:
-      if 'type' in list(sentence_df.columns):
-        gen_models = ["gpt-3.5", "gpt-3.5-turbo", "gpt-4"] 
-        sentence_df = sentence_df.query("type=='paper' and gen_model in @gen_models")
-        print(f"After filter: {sentence_df.shape[0]}")
-      else:
-        sentence_df = pd.DataFrame(columns=["Group term","Attribute term","Test sentence"])
-
-      if sentence_df.shape[0] > 0:
-        sentence_df = sentence_df[["Group term","Attribute term","Test sentence"]]
-        sel = sentence_df[sentence_df['Attribute term'].isin(att_list)].values
-        if len(sel) > 0:
-          for gt,at,s in sel:
-            test_sentences.append([s,gt.replace("-"," "),at.replace("-"," ")])
-
-    return test_sentences
-
-if __name__ == '__main__':
-  print("ds_write_token:", ds_write_token)
-  print("hf_token:", HF_TOKEN!=None)
-  print("hfh_verssion", huggingface_hub.__version__)
-
-  sentences = _testSentenceRetrieval(["husband"], ["hairdresser", "steel worker"], use_paper_sentences=True)
-  print(sentences)
-

spaces/AnnaPalatkina/fine_grained_SA/config.py

@@ -1,10 +0,0 @@
-params = {
-    'pretrained_model_name': 'ltgoslo/norbert2',
-    'path_to_model_bin': 'model_nobert_norec.bin',
-    'LR': 1e-05,
-    'dropout': 0.4,
-    'warmup': 2,
-    'epochs': 10,
-    'max_length': 512,
-    'batch_size': 4,
-}

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/dataloader/image_folder.py

@@ -1,56 +0,0 @@
-import os
-import os.path
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def make_dataset(path_files):
-    if path_files.find('.txt') != -1:
-        paths, size = make_dataset_txt(path_files)
-    else:
-        paths, size = make_dataset_dir(path_files)
-
-    return paths, size
-
-
-def make_dataset_txt(files):
-    """
-    :param path_files: the path of txt file that store the image paths
-    :return: image paths and sizes
-    """
-    img_paths = []
-
-    with open(files) as f:
-        paths = f.readlines()
-
-    for path in paths:
-        path = path.strip()
-        if is_image_file(path) and os.path.exists(path):
-            img_paths.append(path)
-
-    return img_paths, len(img_paths)
-
-
-def make_dataset_dir(dir):
-    """
-    :param dir: directory paths that store the image
-    :return: image paths and sizes
-    """
-    img_paths = []
-
-    assert os.path.isdir(dir), '%s is not a valid directory' % dir
-
-    for root, _, fnames in os.walk(dir):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                path = os.path.join(root, fname)
-                img_paths.append(path)
-
-    return img_paths, len(img_paths)

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

@@ -1,20 +0,0 @@
-from typing import Any
-import cv2
-
-
-def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
-    capture = cv2.VideoCapture(video_path)
-    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
-    has_frame, frame = capture.read()
-    capture.release()
-    if has_frame:
-        return frame
-    return None
-
-
-def get_video_frame_total(video_path: str) -> int:
-    capture = cv2.VideoCapture(video_path)
-    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-    capture.release()
-    return video_frame_total

spaces/AquaSuisei/ChatGPTXE/assets/custom.css

@@ -1,250 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* 覆盖gradio的页脚信息QAQ */
-footer {
-    display: none !important;
-}
-#footer{
-    text-align: center;
-}
-#footer div{
-    display: inline-block;
-}
-#footer .versions{
-    font-size: 85%;
-    opacity: 0.85;
-}
-
-/* user_info */
-#user_info {
-    white-space: nowrap;
-    margin-top: -1.3em !important;
-    padding-left: 112px !important;
-}
-#user_info p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-
-/* usage_display */
-#usage_display {
-    position: relative;
-    margin: 0;
-    box-shadow: var(--block-shadow);
-    border-width: var(--block-border-width);
-    border-color: var(--block-border-color);
-    border-radius: var(--block-radius);
-    background: var(--block-background-fill);
-    width: 100%;
-    line-height: var(--line-sm);
-    min-height: 2em;
-}
-#usage_display p, #usage_display span {
-    margin: 0;
-    padding: .5em 1em;
-    font-size: .85em;
-    color: var(--body-text-color-subdued);
-}
-.progress-bar {
-    background-color: var(--input-background-fill);;
-    margin: 0 1em;
-    height: 20px;
-    border-radius: 10px;
-    overflow: hidden;
-}
-.progress {
-    background-color: var(--block-title-background-fill);;
-    height: 100%;
-    border-radius: 10px;
-    text-align: right;
-    transition: width 0.5s ease-in-out;
-}
-.progress-text {
-    /* color: white; */
-    color: var(--color-accent) !important;
-    font-size: 1em !important;
-    font-weight: bold;
-    padding-right: 10px;
-    line-height: 20px;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-        color: #000000 !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #FFFFFF !important;
-    }
-    [data-testid = "user"] {
-        background-color: #95EC69 !important;
-    }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-dark) !important;
-        color: #FFFFFF !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #2C2C2C !important;
-    }
-    [data-testid = "user"] {
-        background-color: #26B561 !important;
-    }
-    body {
-        background-color: var(--neutral-950) !important;
-    }
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */

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

@@ -1,267 +0,0 @@
-# exceptions.py
-
-import re
-import sys
-import typing
-
-from .util import col, line, lineno, _collapse_string_to_ranges
-from .unicode import pyparsing_unicode as ppu
-
-
-class ExceptionWordUnicode(ppu.Latin1, ppu.LatinA, ppu.LatinB, ppu.Greek, ppu.Cyrillic):
-    pass
-
-
-_extract_alphanums = _collapse_string_to_ranges(ExceptionWordUnicode.alphanums)
-_exception_word_extractor = re.compile("([" + _extract_alphanums + "]{1,16})|.")
-
-
-class ParseBaseException(Exception):
-    """base exception class for all parsing runtime exceptions"""
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__(
-        self,
-        pstr: str,
-        loc: int = 0,
-        msg: typing.Optional[str] = None,
-        elem=None,
-    ):
-        self.loc = loc
-        if msg is None:
-            self.msg = pstr
-            self.pstr = ""
-        else:
-            self.msg = msg
-            self.pstr = pstr
-        self.parser_element = self.parserElement = elem
-        self.args = (pstr, loc, msg)
-
-    @staticmethod
-    def explain_exception(exc, depth=16):
-        """
-        Method to take an exception and translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - exc - exception raised during parsing (need not be a ParseException, in support
-          of Python exceptions that might be raised in a parse action)
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-        """
-        import inspect
-        from .core import ParserElement
-
-        if depth is None:
-            depth = sys.getrecursionlimit()
-        ret = []
-        if isinstance(exc, ParseBaseException):
-            ret.append(exc.line)
-            ret.append(" " * (exc.column - 1) + "^")
-        ret.append("{}: {}".format(type(exc).__name__, exc))
-
-        if depth > 0:
-            callers = inspect.getinnerframes(exc.__traceback__, context=depth)
-            seen = set()
-            for i, ff in enumerate(callers[-depth:]):
-                frm = ff[0]
-
-                f_self = frm.f_locals.get("self", None)
-                if isinstance(f_self, ParserElement):
-                    if frm.f_code.co_name not in ("parseImpl", "_parseNoCache"):
-                        continue
-                    if id(f_self) in seen:
-                        continue
-                    seen.add(id(f_self))
-
-                    self_type = type(f_self)
-                    ret.append(
-                        "{}.{} - {}".format(
-                            self_type.__module__, self_type.__name__, f_self
-                        )
-                    )
-
-                elif f_self is not None:
-                    self_type = type(f_self)
-                    ret.append("{}.{}".format(self_type.__module__, self_type.__name__))
-
-                else:
-                    code = frm.f_code
-                    if code.co_name in ("wrapper", "<module>"):
-                        continue
-
-                    ret.append("{}".format(code.co_name))
-
-                depth -= 1
-                if not depth:
-                    break
-
-        return "\n".join(ret)
-
-    @classmethod
-    def _from_exception(cls, pe):
-        """
-        internal factory method to simplify creating one type of ParseException
-        from another - avoids having __init__ signature conflicts among subclasses
-        """
-        return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
-
-    @property
-    def line(self) -> str:
-        """
-        Return the line of text where the exception occurred.
-        """
-        return line(self.loc, self.pstr)
-
-    @property
-    def lineno(self) -> int:
-        """
-        Return the 1-based line number of text where the exception occurred.
-        """
-        return lineno(self.loc, self.pstr)
-
-    @property
-    def col(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    @property
-    def column(self) -> int:
-        """
-        Return the 1-based column on the line of text where the exception occurred.
-        """
-        return col(self.loc, self.pstr)
-
-    def __str__(self) -> str:
-        if self.pstr:
-            if self.loc >= len(self.pstr):
-                foundstr = ", found end of text"
-            else:
-                # pull out next word at error location
-                found_match = _exception_word_extractor.match(self.pstr, self.loc)
-                if found_match is not None:
-                    found = found_match.group(0)
-                else:
-                    found = self.pstr[self.loc : self.loc + 1]
-                foundstr = (", found %r" % found).replace(r"\\", "\\")
-        else:
-            foundstr = ""
-        return "{}{}  (at char {}), (line:{}, col:{})".format(
-            self.msg, foundstr, self.loc, self.lineno, self.column
-        )
-
-    def __repr__(self):
-        return str(self)
-
-    def mark_input_line(self, marker_string: str = None, *, markerString=">!<") -> str:
-        """
-        Extracts the exception line from the input string, and marks
-        the location of the exception with a special symbol.
-        """
-        markerString = marker_string if marker_string is not None else markerString
-        line_str = self.line
-        line_column = self.column - 1
-        if markerString:
-            line_str = "".join(
-                (line_str[:line_column], markerString, line_str[line_column:])
-            )
-        return line_str.strip()
-
-    def explain(self, depth=16) -> str:
-        """
-        Method to translate the Python internal traceback into a list
-        of the pyparsing expressions that caused the exception to be raised.
-
-        Parameters:
-
-        - depth (default=16) - number of levels back in the stack trace to list expression
-          and function names; if None, the full stack trace names will be listed; if 0, only
-          the failing input line, marker, and exception string will be shown
-
-        Returns a multi-line string listing the ParserElements and/or function names in the
-        exception's stack trace.
-
-        Example::
-
-            expr = pp.Word(pp.nums) * 3
-            try:
-                expr.parse_string("123 456 A789")
-            except pp.ParseException as pe:
-                print(pe.explain(depth=0))
-
-        prints::
-
-            123 456 A789
-                    ^
-            ParseException: Expected W:(0-9), found 'A'  (at char 8), (line:1, col:9)
-
-        Note: the diagnostic output will include string representations of the expressions
-        that failed to parse. These representations will be more helpful if you use `set_name` to
-        give identifiable names to your expressions. Otherwise they will use the default string
-        forms, which may be cryptic to read.
-
-        Note: pyparsing's default truncation of exception tracebacks may also truncate the
-        stack of expressions that are displayed in the ``explain`` output. To get the full listing
-        of parser expressions, you may have to set ``ParserElement.verbose_stacktrace = True``
-        """
-        return self.explain_exception(self, depth)
-
-    markInputline = mark_input_line
-
-
-class ParseException(ParseBaseException):
-    """
-    Exception thrown when a parse expression doesn't match the input string
-
-    Example::
-
-        try:
-            Word(nums).set_name("integer").parse_string("ABC")
-        except ParseException as pe:
-            print(pe)
-            print("column: {}".format(pe.column))
-
-    prints::
-
-       Expected integer (at char 0), (line:1, col:1)
-        column: 1
-
-    """
-
-
-class ParseFatalException(ParseBaseException):
-    """
-    User-throwable exception thrown when inconsistent parse content
-    is found; stops all parsing immediately
-    """
-
-
-class ParseSyntaxException(ParseFatalException):
-    """
-    Just like :class:`ParseFatalException`, but thrown internally
-    when an :class:`ErrorStop<And._ErrorStop>` ('-' operator) indicates
-    that parsing is to stop immediately because an unbacktrackable
-    syntax error has been found.
-    """
-
-
-class RecursiveGrammarException(Exception):
-    """
-    Exception thrown by :class:`ParserElement.validate` if the
-    grammar could be left-recursive; parser may need to enable
-    left recursion using :class:`ParserElement.enable_left_recursion<ParserElement.enable_left_recursion>`
-    """
-
-    def __init__(self, parseElementList):
-        self.parseElementTrace = parseElementList
-
-    def __str__(self) -> str:
-        return "RecursiveGrammarException: {}".format(self.parseElementTrace)

spaces/Banbri/zcvzcv/public/favicon/index.html

@@ -1,133 +0,0 @@
-<!DOCTYPE html>
-<head>
-    <title>
-        Favicons
-    </title>
-    <meta charset="utf-8" />
-    
-    <!-- For old IEs -->
-    <link rel="shortcut icon" href="favicon.ico" />
-    
-    <!-- For new browsers multisize ico  -->
-    <link rel="icon" type="image/x-icon" sizes="16x16 32x32" href="favicon.ico">
-        
-        <!-- Chrome for Android -->
-        <link rel="icon" sizes="192x192" href="favicon-192.png">
-            
-            <!-- For iPhone 6+ downscaled for other devices -->
-            <link rel="apple-touch-icon" sizes="180x180" href="favicon-180-precomposed.png">
-                
-                <!-- For IE10 Metro -->
-                <meta name="msapplication-TileColor" content="#FFFFFF">
-                    <meta name="msapplication-TileImage" content="favicon-114-precomposed.png">
-                        
-                        <style>
-                            
-                            body {
-                                background-color: #f5f5f5;
-                                border: 0px;
-                                margin: 0px;
-                                padding: 0px;
-                                font-family: Consolas,Menlo,Monaco,Lucida Console,Liberation Mono,DejaVu Sans Mono,Bitstream Vera Sans Mono,Courier New,monospace,serif;
-                                color: black;
-                            }
-                        
-                        pre {
-                            margin: 0px;
-                            color: black;
-                            padding: 0px 5%;
-                        }
-                        
-                        code {
-                            
-                        }
-                        
-                        .container {
-                            background-color: white;
-                            max-width: 800px;
-                            width: 100%;
-                            margin: 0 auto;
-                            padding: 1% 0;
-                            height: 100%;
-                        }
-                        
-                        .comment {
-                            color: gray;
-                            padding: 0px;
-                            margin: 0px;
-                        }
-                        
-                        hr {
-                            width: 80%;
-                            padding: 0 5%;
-                            border-color: #f5f5f5;
-                            background-color: #D1D1D1;
-                        }
-                        
-                        p {
-                            padding: 1% 5%;
-                        }
-                        
-                            </style>
-                        
-                        </head>
-<body class="">
-    
-    <div class="container">
-        <p>
-        To use the favicons insert into your  head section some of these tags accordly to your needs.
-        </p>
-        <hr>
-        <pre>
-            <code>
-                <span class="comment">&lt;!-- For old IEs --&gt;</span>
-                &lt;link rel=&quot;shortcut icon&quot; href=&quot;favicon.ico&quot; /&gt;
-                
-                <span class="comment">&lt;!-- For new browsers - multisize ico  --&gt;</span>
-                &lt;link rel=&quot;icon&quot; type=&quot;image/x-icon&quot; sizes=&quot;16x16 32x32&quot; href=&quot;favicon.ico&quot;&gt;
-                
-                <span class="comment">&lt;!-- For iPad with high-resolution Retina display running iOS &ge; 7: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;152x152&quot; href=&quot;favicon-152-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For iPad with high-resolution Retina display running iOS &le; 6: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;144x144&quot; href=&quot;favicon-144-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For iPhone with high-resolution Retina display running iOS &ge; 7: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;120x120&quot; href=&quot;favicon-120-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For iPhone with high-resolution Retina display running iOS &le; 6: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;114x114&quot; href=&quot;favicon-114-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For iPhone 6+ --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;180x180&quot; href=&quot;favicon-180-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For first- and second-generation iPad: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;72x72&quot; href=&quot;favicon-72-precomposed.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For non-Retina iPhone, iPod Touch, and Android 2.1+ devices: --&gt;</span>
-                &lt;link rel=&quot;apple-touch-icon&quot; sizes=&quot;57x57&quot; href=&quot;favicon-57.png&quot;&gt;
-                
-                <span class="comment">&lt;!-- For Old Chrome --&gt;</span>
-                &lt;link rel=&quot;icon&quot; sizes=&quot;32x32&quot; href=&quot;favicon-32.png&quot; &gt;
-                
-                <span class="comment">&lt;!-- For IE10 Metro --&gt;</span>
-                &lt;meta name=&quot;msapplication-TileColor&quot; content=&quot;#FFFFFF&quot;&gt;
-                &lt;meta name=&quot;msapplication-TileImage&quot; content=&quot;favicon-144.png&quot;&gt;
-                &lt;meta name=&quot;theme-color&quot; content=&quot;#ffffff&quot;&gt;
-                
-                <span class="comment">&lt;!-- Chrome for Android --&gt;</span>
-                &lt;link rel=&quot;manifest&quot; href=&quot;manifest.json&quot;&gt;
-                &lt;link rel=&quot;icon&quot; sizes=&quot;192x192&quot; href=&quot;favicon-192.png&quot;&gt;
-                
-            </code>
-        </pre>
-        
-        <hr>
-        
-        <p>
-        For more informations about favicons consult <a href="https://github.com/audreyr/favicon-cheat-sheet">The Favicon Cheat Sheet</a> by Audrey Roy.
-        </p>
-        
-    </div>
-    
-</body>

spaces/Benson/text-generation/Examples/Descargar Carx Drift Racing 2 Mod Apk Nueva Versin.md

@@ -1,59 +0,0 @@
-
-<h1>Descargar CarX Drift Racing 2 Mod APK Nueva versión: Una guía para los entusiastas de las carreras de coches</h1>
-<p>Si usted es un fan de los juegos de carreras de coches, especialmente a la deriva, entonces usted debe haber oído hablar de <strong>CarX Drift Racing 2</strong>. Es uno de los mejores juegos de deriva en Android que le permite experimentar la emoción de conducir coches rápidos en varias pistas y terrenos. Puedes personalizar tu coche, afinar tu motor y competir con otros jugadores online o offline. </p>
-<h2>descargar carx drift racing 2 mod apk nueva versión</h2><br /><p><b><b>DOWNLOAD</b> - <a href="https://bltlly.com/2v6Ksx">https://bltlly.com/2v6Ksx</a></b></p><br /><br />
-<p>Sin embargo, si desea disfrutar del juego al máximo, es posible que tenga que gastar algo de dinero real para desbloquear todos los coches, pistas y características que el juego tiene para ofrecer. Esto puede ser frustrante y caro para algunos jugadores que solo quieren divertirse sin romper el banco. </p>
-<p>Es por eso que muchos jugadores buscan formas de descargar <strong>CarX Drift Racing 2 Mod APK</strong>, que es una versión modificada del juego que le da dinero ilimitado, oro y acceso a todos los coches y pistas de forma gratuita. Suena increíble, ¿verdad? </p>
-<p>En este artículo, le mostraremos cómo descargar e instalar CarX Drift Racing 2 Mod APK nueva versión en su dispositivo Android. También le diremos acerca de las características, pros y contras de usar este mod apk. Así que, si estás listo para llevar tus habilidades de deriva al siguiente nivel, ¡sigue leyendo! </p>
- <h2>Características de CarX Drift Racing 2 Mod APK</h2>
-<p>CarX Drift Racing 2 Mod APK no es solo una versión regular del juego que se puede descargar desde la Google Play Store. Es una versión hackeada que ha sido modificada por algunos desarrolladores para darle recursos y características ilimitadas que normalmente tendría que pagar. Estas son algunas de las características de CarX Drift Racing 2 Mod APK:</p>
- <ul>
-<li><strong>Dinero ilimitado y oro</strong>: Obtendrá dinero y oro ilimitados en su cuenta, que puede utilizar para comprar cualquier coche, pista o actualización que desee. Usted no tiene que preocuparse de quedarse sin dinero en efectivo o ahorrar para su coche de ensueño. </li>
-
-<li><strong>Personaliza tu coche y afina tu motor</strong>: Puedes personalizar tu coche con diferentes colores de pintura, calcomanías, ruedas, spoilers y más. También puede ajustar su motor con diferentes partes y configuraciones para mejorar su rendimiento y manejo. </li>
-<li><strong>Disfrutar de la física realista y gráficos</strong>: CarX Drift Racing 2 Mod APK tiene física realista y gráficos que te hacen sentir como si estuvieras conduciendo un coche real. Puede ver el humo, el polvo, las chispas y las marcas de derrape a medida que se desplaza en las pistas. También puede ajustar el ángulo y la vista de la cámara para adaptarse a sus preferencias. </li>
-<li><strong>Compite con otros jugadores en línea o fuera de línea</strong>: Puedes jugar en línea con otros jugadores que tienen la misma versión apk mod que tú, o fuera de línea con oponentes AI. También puedes unirte a clubes, participar en torneos y posicionarte en las tablas de clasificación. </li>
-</ul>
- <h2>Cómo descargar e instalar CarX deriva Racing 2 Mod APK</h2>
-<p>Descargar e instalar CarX Drift Racing 2 Mod APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<p></p>
- <ol>
-<li><strong>Descargar el archivo mod apk de una fuente de confianza</strong>: Usted puede encontrar muchos sitios web que ofrecen CarX Drift Racing 2 Mod APK para su descarga gratuita. Sin embargo, no todos son seguros y confiables. Algunos pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Siempre descargue desde un sitio de buena reputación y escanee el archivo antes de instalar. </li>
-<li><strong>Habilitar fuentes desconocidas en la configuración del dispositivo</strong>: Antes de instalar el archivo apk mod, es necesario habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-
-<li><strong>Disfruta del juego con recursos y características ilimitadas</strong>: Una vez que hayas lanzado el juego, verás que tienes dinero ilimitado, oro, coches, pistas y características. Puedes empezar a jugar de inmediato y disfrutar del juego sin limitaciones ni restricciones. </li>
-</ol>
- <h2>Pros y contras de CarX Drift Racing 2 Mod APK</h2>
-<p>CarX Drift Racing 2 Mod APK tiene muchas ventajas, pero también tiene algunas desventajas. Estos son algunos de ellos:</p>
- <h3>Pros</h3>
-<ul>
-<li><strong>Gratis, fácil, divertido y adictivo</strong>: CarX Drift Racing 2 Mod APK es gratis para descargar e instalar, fácil de usar, divertido de jugar y adictivo para dominar. No tienes que gastar dinero o tiempo para disfrutar del juego completamente. </li>
-<li><strong>No hay anuncios o compras en la aplicación</strong>: CarX Drift Racing 2 Mod APK no tiene anuncios o compras en la aplicación que pueden interrumpir su juego o tentar a gastar más dinero. Puedes jugar sin distracciones ni presiones. </li>
-<li><strong>No se requiere raíz</strong>: CarX Drift Racing 2 Mod APK no requiere acceso de raíz para trabajar en su dispositivo. Esto significa que no tiene que arriesgarse a dañar su dispositivo o anular su garantía al enraizarlo. </li>
-<li><strong>Compatible con la mayoría de los dispositivos</strong>: CarX Drift Racing 2 Mod APK es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. No requiere mucho espacio de almacenamiento o RAM para funcionar sin problemas. </li>
-</ul>
- <h3>Contras</h3>
-<ul>
-<li><strong>Puede que no funcione en algunos dispositivos</strong>: CarX Drift Racing 2 Mod APK puede no funcionar en algunos dispositivos debido a problemas de compatibilidad o problemas técnicos. Es posible que tenga que comprobar la compatibilidad de su dispositivo antes de descargar e instalar el apk mod. </li>
-
-<li><strong>Puede violar los términos de servicio del juego</strong>: CarX Drift Racing 2 Mod APK puede violar los términos de servicio del juego, lo que puede resultar en la prohibición del juego o perder su cuenta. También puedes perder tu progreso, logros o recompensas que has ganado en el juego. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>CarX Drift Racing 2 Mod APK es una gran opción para los entusiastas de las carreras de coches que quieren disfrutar del juego sin limitaciones ni costos. Te da dinero ilimitado, oro, coches, pistas y características que hacen el juego más divertido y emocionante. Puedes descargarlo e instalarlo fácilmente en tu dispositivo Android y empezar a derivar como un profesional. </p>
-<p>Sin embargo, también debe ser consciente de los riesgos y desventajas de usar CarX Drift Racing 2 Mod APK, tales como problemas de compatibilidad, problemas de seguridad, y las violaciones de los términos de servicio. Siempre debe descargar de una fuente confiable y usarla a su propia discreción y responsabilidad. </p>
-<p>Si usted está buscando una manera de descargar CarX Drift Racing 2 Mod APK nueva versión, siga los pasos de este artículo y disfrutar del juego. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. Happy drifting! </p>
- <h3>Preguntas frecuentes</h3>
-<ul>
-<li><strong>Q1: ¿Es CarX Drift Racing 2 Mod APK seguro de usar? </strong></li>
-<li><strong>A1: Depende de la fuente del archivo apk mod. Algunos pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. Siempre descargue desde un sitio de buena reputación y escanee el archivo antes de instalar. </strong></li>
-<li><strong>Q2: ¿Puedo jugar CarX Drift Racing 2 Mod APK en línea? </strong></li>
-<li><strong>A2: Sí, puedes jugar online con otros jugadores que tienen la misma versión mod apk que tú. Sin embargo, es posible que no pueda acceder a algunas funciones o modos que requieren una versión oficial del juego. </strong></li>
-<li><strong>Q3: ¿Me prohibirán por usar CarX Drift Racing 2 Mod APK? </strong></li>
-
-<li><strong>Q4: ¿Cómo puedo actualizar CarX Drift Racing 2 Mod APK? </strong></li>
-<li><strong>A4: Puede actualizar el apk mod mediante la descarga de la última versión de la misma fuente que lo obtuvo de. Asegúrese de hacer una copia de seguridad de sus datos antes de desinstalar la versión anterior e instalar la nueva. </strong></li>
-<li><strong>Q5: ¿Cuáles son algunas alternativas a CarX Drift Racing 2 Mod APK? </strong></li>
-<li><strong>A5: Algunas alternativas a CarX Drift Racing 2 Mod APK son Real Drift Car Racing, Torque Drift, Drift Max Pro, y FR Legends. Estos también son juegos de carreras de coches populares que ofrecen diferentes modos, características y desafíos para los amantes de la deriva. </strong></li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/lib/server/modelEndpoint.ts

@@ -1,21 +0,0 @@
-import { MODEL_ENDPOINTS } from "$env/static/private";
-import { sum } from "$lib/utils/sum";
-
-const endpoints: Array<{ endpoint: string; authorization: string; weight: number }> =
-	JSON.parse(MODEL_ENDPOINTS);
-const totalWeight = sum(endpoints.map((e) => e.weight));
-
-/**
- * Find a random load-balanced endpoint
- */
-export function modelEndpoint(): { endpoint: string; authorization: string; weight: number } {
-	let random = Math.random() * totalWeight;
-	for (const endpoint of endpoints) {
-		if (random < endpoint.weight) {
-			return endpoint;
-		}
-		random -= endpoint.weight;
-	}
-
-	throw new Error("Invalid config, no endpoint found");
-}

spaces/BilalSardar/StoryGenerator/app.py

@@ -1,15 +0,0 @@
-from transformers import pipeline
-import gradio as gr
-def story(StoryLength,StoryPrompt):
-    model= pipeline("text-generation", model="e-tony/gpt2-rnm")
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-    return model(StoryPrompt, max_length=200, num_return_sequences=3)[2]["generated_text"],summarizer(model(StoryPrompt, max_length=200, num_return_sequences=3)[2]["generated_text"], max_length=StoryLength, min_length=30, do_sample=False)[0]["summary_text"]
-    
-    
-interface = gr.Interface(fn=story, 
-                        inputs=["number","text"],
-                        outputs=[gr.inputs.Textbox(label='GPT2 Output'),gr.inputs.Textbox(label='Output summary')], 
-                        title='Bilal\'s Story Generator')
-                        
-
-interface.launch(inline=False)

spaces/CHDCruze/entertainmentbybhdcruze/README.md

@@ -1,11 +0,0 @@
----
-title: Entertainmentbybhdcruze
-emoji: 📉
-colorFrom: blue
-colorTo: red
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/thrust/thrust/async/transform.h

@@ -1,134 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a transform 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.
- */
-
-/*! \file async/transform.h
- *  \brief Functions for asynchronously transforming a range.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp14_required.h>
-
-#if THRUST_CPP_DIALECT >= 2014
-
-#include <thrust/detail/static_assert.h>
-#include <thrust/detail/select_system.h>
-#include <thrust/type_traits/remove_cvref.h>
-#include <thrust/system/detail/adl/async/transform.h>
-
-#include <thrust/event.h>
-
-namespace thrust
-{
-
-namespace async
-{
-
-namespace unimplemented
-{
-
-template <
-  typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename OutputIt
-, typename UnaryOperation
->
-__host__
-event<DerivedPolicy>
-async_transform(
-  thrust::execution_policy<DerivedPolicy>& exec
-, ForwardIt first, Sentinel last, OutputIt output, UnaryOperation op
-)
-{
-  THRUST_STATIC_ASSERT_MSG(
-    (thrust::detail::depend_on_instantiation<ForwardIt, false>::value)
-  , "this algorithm is not implemented for the specified system"
-  );
-  return {};
-}
-
-} // namespace unimplemented
-
-namespace transform_detail
-{
-
-using thrust::async::unimplemented::async_transform;
-
-struct transform_fn final
-{
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel, typename OutputIt
-  , typename UnaryOperation
-  >
-  __host__
-  static auto
-  call(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  , OutputIt&& output
-  , UnaryOperation&& op
-  )
-  // ADL dispatch.
-  THRUST_RETURNS(
-    async_transform(
-      thrust::detail::derived_cast(thrust::detail::strip_const(exec))
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(output)
-    , THRUST_FWD(op)
-    )
-  )
-
-  template <
-    typename ForwardIt, typename Sentinel, typename OutputIt
-  , typename UnaryOperation
-  >
-  __host__
-  static auto call(
-    ForwardIt&& first, Sentinel&& last
-  , OutputIt&& output
-  , UnaryOperation&& op
-  )
-  THRUST_RETURNS(
-    transform_fn::call(
-      thrust::detail::select_system(
-        typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
-      , typename iterator_system<remove_cvref_t<OutputIt>>::type{}
-      )
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(output)
-    , THRUST_FWD(op)
-    )
-  )
-
-  template <typename... Args>
-  THRUST_NODISCARD __host__
-  auto operator()(Args&&... args) const
-  THRUST_RETURNS(
-    call(THRUST_FWD(args)...)
-  )
-};
-
-} // namespace tranform_detail
-
-THRUST_INLINE_CONSTANT transform_detail::transform_fn transform{};
-
-} // namespace async
-
-} // end namespace thrust
-
-#endif
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/extrema.h

@@ -1,89 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file extrema.h
- *  \brief Generic device implementations of extrema functions.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/pair.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator max_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator max_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-ForwardIterator min_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-ForwardIterator min_element(thrust::execution_policy<DerivedPolicy> &exec,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            BinaryPredicate comp);
-
-
-template <typename DerivedPolicy, typename ForwardIterator>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(thrust::execution_policy<DerivedPolicy> &exec,
-                                                             ForwardIterator first, 
-                                                             ForwardIterator last);
-
-
-template <typename DerivedPolicy, typename ForwardIterator, typename BinaryPredicate>
-__host__ __device__
-thrust::pair<ForwardIterator,ForwardIterator> minmax_element(thrust::execution_policy<DerivedPolicy> &exec,
-                                                             ForwardIterator first, 
-                                                             ForwardIterator last,
-                                                             BinaryPredicate comp);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/extrema.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/omp/memory_resource.h

@@ -1,63 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file omp/memory_resource.h
- *  \brief Memory resources for the OMP system.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/mr/new.h>
-#include <thrust/mr/fancy_pointer_resource.h>
-
-#include <thrust/system/omp/pointer.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-
-//! \cond
-namespace detail
-{
-    typedef thrust::mr::fancy_pointer_resource<
-        thrust::mr::new_delete_resource,
-        thrust::omp::pointer<void>
-    > native_resource;
-}
-//! \endcond
-
-/*! \addtogroup memory_resources Memory Resources
- *  \ingroup memory_management_classes
- *  \{
- */
-
-/*! The memory resource for the OMP system. Uses \p mr::new_delete_resource and tags it with \p omp::pointer. */
-typedef detail::native_resource memory_resource;
-/*! An alias for \p omp::memory_resource. */
-typedef detail::native_resource universal_memory_resource;
-/*! An alias for \p omp::memory_resource. */
-typedef detail::native_resource universal_host_pinned_memory_resource;
-
-/*! \}
- */
-
-}
-}
-}

spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/mtd_parameters.py

@@ -1,11 +0,0 @@
-manual_prompts = {
-    'mtd': [
-        ['black hole. blow hole. break. crack. fray. uneven.', 'mtd'],
-        ['defect.', 'mtd'],
-    ],
-
-}
-
-property_prompts = {
-    'ksdd2': 'the image of ksdd2 have 1 dissimilar ksdd2, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-}

spaces/ChrisPreston/diff-svc_minato_aqua/modules/hubert/cn_hubert.py

@@ -1,40 +0,0 @@
-import librosa
-import torch
-import torch.nn as nn
-
-
-def load_cn_model(ch_hubert_path):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    from fairseq import checkpoint_utils
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        [ch_hubert_path],
-        suffix="",
-    )
-    model = models[0]
-    model = model.to(device)
-    model.eval()
-    return model
-
-
-def get_cn_hubert_units(con_model, audio_path, dev):
-    audio, sampling_rate = librosa.load(audio_path)
-    if len(audio.shape) > 1:
-        audio = librosa.to_mono(audio.transpose(1, 0))
-    if sampling_rate != 16000:
-        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-
-    feats = torch.from_numpy(audio).float()
-    if feats.dim() == 2:  # double channels
-        feats = feats.mean(-1)
-    assert feats.dim() == 1, feats.dim()
-    feats = feats.view(1, -1)
-    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-    inputs = {
-        "source": feats.to(dev),
-        "padding_mask": padding_mask.to(dev),
-        "output_layer": 9,  # layer 9
-    }
-    with torch.no_grad():
-        logits = con_model.extract_features(**inputs)
-        feats = con_model.final_proj(logits[0])
-    return feats

spaces/ClementBM/connectfour/models/__init__.py

@@ -1,3 +0,0 @@
-from pathlib import Path
-
-MODEL_PATH = Path(__file__).parent.absolute() / "model.onnx"

spaces/CofAI/chat.b4/g4f/Provider/Providers/Gravityengine.py

@@ -1,27 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://gpt4.xunika.uk/'
-model = ['gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/api/openai/v1/chat/completions',
-                             json=data, stream=True)
-    
-    yield response.json()['choices'][0]['message']['content']
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/CofAI/chat/client/css/select.css

@@ -1,35 +0,0 @@
-select {
-	-webkit-border-radius: 8px;
-	-moz-border-radius: 8px;
-	border-radius: 8px;
-
-	-webkit-backdrop-filter: blur(20px);
-	backdrop-filter: blur(20px);
-
-	cursor: pointer;
-	background-color: var(--blur-bg);
-	border: 1px solid var(--blur-border);
-	color: var(--colour-3);
-	display: block;
-	position: relative;
-	overflow: hidden;
-	outline: none;
-	padding: 8px 16px;
-
-	appearance: none;
-}
-
-/* scrollbar */
-select.dropdown::-webkit-scrollbar {
-	width: 4px;
-	padding: 8px 0px;
-}
-
-select.dropdown::-webkit-scrollbar-track {
-	background-color: #ffffff00;
-}
-
-select.dropdown::-webkit-scrollbar-thumb {
-	background-color: #555555;
-	border-radius: 10px;
-}

spaces/CognitiveLabs/Research-Assistant/test/test4.py

@@ -1,6 +0,0 @@
-def test():
-    yield 1
-    return 2
-
-a, b = test()
-print(a, b)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/designspaceLib/types.py

@@ -1,147 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Union, cast
-
-from fontTools.designspaceLib import (
-    AxisDescriptor,
-    DesignSpaceDocument,
-    DesignSpaceDocumentError,
-    RangeAxisSubsetDescriptor,
-    SimpleLocationDict,
-    ValueAxisSubsetDescriptor,
-    VariableFontDescriptor,
-)
-
-
-def clamp(value, minimum, maximum):
-    return min(max(value, minimum), maximum)
-
-
-@dataclass
-class Range:
-    minimum: float
-    """Inclusive minimum of the range."""
-    maximum: float
-    """Inclusive maximum of the range."""
-    default: float = 0
-    """Default value"""
-
-    def __post_init__(self):
-        self.minimum, self.maximum = sorted((self.minimum, self.maximum))
-        self.default = clamp(self.default, self.minimum, self.maximum)
-
-    def __contains__(self, value: Union[float, Range]) -> bool:
-        if isinstance(value, Range):
-            return self.minimum <= value.minimum and value.maximum <= self.maximum
-        return self.minimum <= value <= self.maximum
-
-    def intersection(self, other: Range) -> Optional[Range]:
-        if self.maximum < other.minimum or self.minimum > other.maximum:
-            return None
-        else:
-            return Range(
-                max(self.minimum, other.minimum),
-                min(self.maximum, other.maximum),
-                self.default,  # We don't care about the default in this use-case
-            )
-
-
-# A region selection is either a range or a single value, as a Designspace v5
-# axis-subset element only allows a single discrete value or a range for a
-# variable-font element.
-Region = Dict[str, Union[Range, float]]
-
-# A conditionset is a set of named ranges.
-ConditionSet = Dict[str, Range]
-
-# A rule is a list of conditionsets where any has to be relevant for the whole rule to be relevant.
-Rule = List[ConditionSet]
-Rules = Dict[str, Rule]
-
-
-def locationInRegion(location: SimpleLocationDict, region: Region) -> bool:
-    for name, value in location.items():
-        if name not in region:
-            return False
-        regionValue = region[name]
-        if isinstance(regionValue, (float, int)):
-            if value != regionValue:
-                return False
-        else:
-            if value not in regionValue:
-                return False
-    return True
-
-
-def regionInRegion(region: Region, superRegion: Region) -> bool:
-    for name, value in region.items():
-        if not name in superRegion:
-            return False
-        superValue = superRegion[name]
-        if isinstance(superValue, (float, int)):
-            if value != superValue:
-                return False
-        else:
-            if value not in superValue:
-                return False
-    return True
-
-
-def userRegionToDesignRegion(doc: DesignSpaceDocument, userRegion: Region) -> Region:
-    designRegion = {}
-    for name, value in userRegion.items():
-        axis = doc.getAxis(name)
-        if axis is None:
-            raise DesignSpaceDocumentError(
-                f"Cannot find axis named '{name}' for region."
-            )
-        if isinstance(value, (float, int)):
-            designRegion[name] = axis.map_forward(value)
-        else:
-            designRegion[name] = Range(
-                axis.map_forward(value.minimum),
-                axis.map_forward(value.maximum),
-                axis.map_forward(value.default),
-            )
-    return designRegion
-
-
-def getVFUserRegion(doc: DesignSpaceDocument, vf: VariableFontDescriptor) -> Region:
-    vfUserRegion: Region = {}
-    # For each axis, 2 cases:
-    #  - it has a range = it's an axis in the VF DS
-    #  - it's a single location = use it to know which rules should apply in the VF
-    for axisSubset in vf.axisSubsets:
-        axis = doc.getAxis(axisSubset.name)
-        if axis is None:
-            raise DesignSpaceDocumentError(
-                f"Cannot find axis named '{axisSubset.name}' for variable font '{vf.name}'."
-            )
-        if hasattr(axisSubset, "userMinimum"):
-            # Mypy doesn't support narrowing union types via hasattr()
-            # TODO(Python 3.10): use TypeGuard
-            # https://mypy.readthedocs.io/en/stable/type_narrowing.html
-            axisSubset = cast(RangeAxisSubsetDescriptor, axisSubset)
-            if not hasattr(axis, "minimum"):
-                raise DesignSpaceDocumentError(
-                    f"Cannot select a range over '{axis.name}' for variable font '{vf.name}' "
-                    "because it's a discrete axis, use only 'userValue' instead."
-                )
-            axis = cast(AxisDescriptor, axis)
-            vfUserRegion[axis.name] = Range(
-                max(axisSubset.userMinimum, axis.minimum),
-                min(axisSubset.userMaximum, axis.maximum),
-                axisSubset.userDefault or axis.default,
-            )
-        else:
-            axisSubset = cast(ValueAxisSubsetDescriptor, axisSubset)
-            vfUserRegion[axis.name] = axisSubset.userValue
-    # Any axis not mentioned explicitly has a single location = default value
-    for axis in doc.axes:
-        if axis.name not in vfUserRegion:
-            assert isinstance(
-                axis.default, (int, float)
-            ), f"Axis '{axis.name}' has no valid default value."
-            vfUserRegion[axis.name] = axis.default
-    return vfUserRegion