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-<h1>Download PICBASIC PRO 3.0.7 Full Crack: A Guide for Beginners</h1>
- <p>If you are looking for a fast and easy way to develop microcontroller-based projects using Microchip's PIC microcontrollers, you might want to try PICBASIC PRO. PICBASIC PRO is a powerful BASIC compiler that generates optimized, machine-ready code for PIC MCUs. It has been used by professionals and hobbyists alike for over 15 years, thanks to its simplicity, stability, and speed.</p>
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-<li>Stealing your personal information, such as passwords, credit card numbers, or bank accounts.</li>
-<li>Encrypting your files and demanding a ransom to unlock them.</li>
-<li>Installing more malware or allowing remote access to hackers.</li>
-</ul>
-<p>Some examples of malware found in cracked software are:</p>
-<ul>
-<li>The Shlayer Trojan, which was found in cracked versions of Adobe Photoshop and Microsoft Office for Mac. It infected over 10% of all Macs in 2019.</li>
-<li>The Crackonosh Malware, which was found in cracked versions of popular games like GTA V and NBA 2K19. It infected over 220,000 computers and earned over $2 million for its creators by mining cryptocurrency.</li>
-<li>The STOP Ransomware, which was found in cracked versions of Adobe Illustrator and Photoshop. It encrypted over 460,000 files and demanded $980 to unlock them.</li>
-</ul> <h3>Deactivation of Antivirus Software During Installation</h3>
-<p>Another risk of using cracked software is that some cracks require you to deactivate your antivirus software during installation. This is because the antivirus software may detect the crack as a threat and block it or delete it. However, by disabling your antivirus software, you are also exposing your computer to other threats that may be lurking in the background or online.</p>
-<p>Some cracking sites or instructions may claim that the crack is safe and that the antivirus software is just giving a false positive. However, this is not always true and you should not trust them blindly. Even if the crack itself is not malicious, it may still compromise your system security by altering your settings, registry, or firewall.</p>
-<h3>Downloading from Untrusted Sources</h3>
-<p>A third risk of using cracked software is that you have to download it from untrusted sources, such as cracking sites, forums, or peer-to-peer networks. These sources are often unreliable, unsafe, and full of popups or redirects that can lead you to more dangerous sites or downloads.</p>
-<p>Some of the problems that you may encounter when downloading from untrusted sources are:</p>
-<ul>
-<li>The crack or the cracked software may not work as advertised or may not work at all.</li>
-<li>The crack or the cracked software may be outdated, incomplete, or corrupted.</li>
-<li>The crack or the cracked software may contain malware or unwanted programs.</li>
-<li>The download link may be broken, expired, or fake.</li>
-<li>The download speed may be slow, unstable, or limited.</li>
-<li>The download may be illegal, infringing on the intellectual property rights of the software developer.</li>
-</ul>
-<h3>Lack of Updates, Leaving the Software Exposed to Security Threats</h3>
-<p>A fourth risk of using cracked software is that it cannot receive updates from the official source, which can leave it vulnerable to bugs, errors, or exploits. Updates are important for software because they can fix issues, improve performance, add features, or enhance security. However, cracked software cannot connect to the official server or verify its license, so it cannot download or install updates.</p>
-<p>This means that cracked software may have:</p>
-<ul>
-<li>Unresolved bugs or errors that can affect its functionality or stability.</li>
-<li>Outdated features or performance that can make it less efficient or compatible with other software or hardware.</li>
-<li>Unpatched vulnerabilities that can expose it to hackers or malware attacks.</li>
-</ul> <h3>Poorly Functioning Functions</h3>
-<p>A fifth risk of using cracked software is that it may not work as intended, have missing or corrupted features, or cause compatibility issues with other software or hardware. Cracked software may have been modified or damaged by the cracking process, which can affect its quality and performance.</p>
-<p>Some of the problems that you may encounter when using cracked software are:</p>
-<ul>
-<li>The software may crash, freeze, or display error messages frequently.</li>
-<li>The software may have reduced functionality, such as disabled features, limited options, or lower quality output.</li>
-<li>The software may have corrupted files, such as missing icons, fonts, or images.</li>
-<li>The software may have compatibility issues with other software or hardware, such as conflicts, errors, or crashes.</li>
-</ul>
-<h3>Loss of Revenue for Software Developers</h3>
-<p>A sixth risk of using cracked software is that it harms the software industry, reduces the incentive for innovation, and deprives developers of their rightful income. Software development is a costly and time-consuming process that requires a lot of resources, skills, and creativity. Software developers deserve to be compensated for their work and to protect their intellectual property rights.</p>
-<p>When you use cracked software, you are:</p>
-<ul>
-<li>Violating the terms and conditions of the software license agreement.</li>
-<li>Infringing on the copyright and trademark of the software developer.</li>
-<li>Stealing the revenue that the software developer would have earned from your purchase or subscription.</li>
-<li>Discouraging the software developer from investing in further development, improvement, or support of the software.</li>
-</ul>
-<h2>What Are the Alternatives to Cracked Software?</h2>
-<p>Now that you know the risks of using cracked software, you may be wondering what are the alternatives to cracked software. Fortunately, there are many legal and ethical ways to get access to AI 2021 or similar software without breaking the law or compromising your security. Here are some of them:</p>
-<h3>Free Trial Version</h3>
-<p>One of the easiest and safest ways to try AI 2021 for free is to download a free trial version from Adobe's website. The free trial version allows you to use AI 2021 for 7 days without any limitations. You can access all the features and functions of AI 2021 and create as many projects as you want. You can also save and export your work in various formats.</p>
-<p>To download the free trial version of AI 2021, you need to create a free Adobe account and install the Creative Cloud desktop app on your computer. Then you can download and install AI 2021 from the app. You can also cancel the trial anytime before it expires without any charges.</p>
-<p>You can download the free trial version of AI 2021 from this link: <a href="https://www.adobe.com/products/illustrator/free-trial-download.html">https://www.adobe.com/products/illustrator/free-trial-download.html</a></p> <h3>Student or Teacher Discount</h3>
-<p>If you are a student or a teacher, you can get a 60% discount on AI 2021 and other Creative Cloud apps. This means that you can get access to AI 2021 and over 20 other apps for only $19.99 per month or $239.88 per year. You can also get access to 100 GB of cloud storage, Adobe Fonts, Adobe Portfolio, and Adobe Spark.</p>
-<p>To get the student or teacher discount, you need to verify your eligibility by providing proof of enrollment or employment at an accredited educational institution. You can do this online or by contacting Adobe customer service. Once you are verified, you can purchase the discounted plan from Adobe's website.</p>
-<p>You can get the student or teacher discount from this link: <a href="https://www.adobe.com/creativecloud/buy/students.html">https://www.adobe.com/creativecloud/buy/students.html</a></p>
-<h3>Open Source AI Software</h3>
-<p>Another alternative to cracked software is to use open source AI software. Open source software is software that is free, legal, and often comparable to AI 2021 in terms of features and performance. Open source software is developed and maintained by a community of volunteers who share their code and contributions with the public.</p>
-<p>Some of the benefits of using open source software are:</p>
-<ul>
-<li>You can use it for any purpose, personal or commercial, without any restrictions or fees.</li>
-<li>You can modify it, customize it, or improve it according to your needs or preferences.</li>
-<li>You can access the latest updates, bug fixes, or new features from the developers or the community.</li>
-<li>You can support the open source movement and contribute to its development or promotion.</li>
-</ul>
-<p>Some examples of open source AI software are:</p>
-<ul>
-<li>Inkscape: A vector graphics editor that supports SVG, PNG, PDF, EPS, and other formats. It has many tools and features similar to AI 2021, such as paths, shapes, gradients, filters, text, clones, markers, transformations, and more. You can download Inkscape from this link: <a href="https://inkscape.org/">https://inkscape.org/</a></li>
-<li>GIMP: A raster graphics editor that supports JPEG, PNG, GIF, TIFF, PSD, and other formats. It has many tools and features similar to AI 2021, such as layers, masks, brushes, gradients, filters, text, vectors, transformations, and more. You can download GIMP from this link: <a href="https://www.gimp.org/">https://www.gimp.org/</a></li>
-<li>Krita: A digital painting and illustration software that supports PSD, PNG, JPEG, BMP, TIFF, and other formats. It has many tools and features similar to AI 2021, such as brushes, vectors, shapes, gradients, filters, text, layers, masks, transformations, and more. You can download Krita from this link: <a href="https://krita.org/en/">https://krita.org/en/</a></li>
-</ul> <h2>Conclusion</h2>
-<p>In conclusion, downloading AI 2021 full crack is not worth it. It is illegal, unethical, and risky. You may end up with malware infections, security threats, poor performance, compatibility issues, or legal troubles. You may also harm the software industry and discourage innovation.</p>
-<p>Instead of using cracked software, you should consider the alternatives that are legal and ethical. You can try AI 2021 for free for 7 days, get a student or teacher discount, or use open source software. These options will give you access to quality software without breaking the law or compromising your security.</p>
-<p>We hope this article has helped you understand the risks of using cracked software and the alternatives to cracked software. If you have any questions or comments, please feel free to leave them below.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions related to the topic of this article:</p>
-<ul>
-<li><b>Q: How can I tell if a software is cracked or not?</b></li>
-<li>A: There are some signs that can indicate if a software is cracked or not, such as:</li>
-<ul>
-<li>The software is downloaded from an untrusted source, such as a cracking site, a forum, or a peer-to-peer network.</li>
-<li>The software requires you to disable your antivirus software during installation or run a crack file before using it.</li>
-<li>The software does not ask for a license key or activation code when you install or use it.</li>
-<li>The software does not receive updates from the official source or shows an error message when you try to update it.</li>
-<li>The software has missing or corrupted features, files, or icons.</li>
-</ul>
-<li><b>Q: Is it legal to use cracked software for personal use?</b></li>
-<li>A: No, it is not legal to use cracked software for personal use. Cracked software is considered a form of piracy, which is the unauthorized copying or distribution of software. Piracy is illegal in most countries and can result in fines, lawsuits, or criminal charges.</li>
-<li><b>Q: What are the penalties for using cracked software?</b></li>
-<li>A: The penalties for using cracked software vary depending on the country, the type of software, and the extent of the infringement. However, some of the possible penalties are:</li>
-<ul>
-<li>Fines ranging from hundreds to thousands of dollars per infringement.</li>
-<li>Lawsuits from the software developer or the owner of the intellectual property rights.</li>
-<li>Criminal charges that can lead to imprisonment or community service.</li>
-</ul>
-<li><b>Q: How can I protect myself from malware infections when downloading software?</b></li>
-<li>A: There are some steps that you can take to protect yourself from malware infections when downloading software, such as:</li>
-<ul>
-<li>Download software only from trusted sources, such as the official website of the developer or a reputable online store.</li>
-<li>Use a reliable antivirus software and keep it updated regularly.</li>
-<li>Scan any file that you download before opening or running it.</li>
-<li>Avoid clicking on suspicious links, popups, or redirects that may lead you to malicious sites or downloads.</li>
-</ul>
-<li><b>Q: How can I support the software industry and encourage innovation?</b></li>
-<li>A: There are some ways that you can support the software industry and encourage innovation, such as:</li>
-<ul>
-<li>Purchase or subscribe to software from the official source and pay the fair price for it.</li>
-<li>Respect the terms and conditions of the software license agreement and do not share or distribute it without permission.</li>
-<li>Provide feedback, reviews, or suggestions to the software developer or the community.</li>
-<li>Donate to open source projects or contribute to their development or promotion.</li>
-</ul></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Facebook Old Version APK for Android from Uptodown.md DELETED Viewed

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-<h1>How to Download Facebook Old Version APK from Uptodown</h1>
-<h2>Introduction</h2>
-<p>Facebook is one of the most popular social media platforms in the world, with over 2.8 billion monthly active users as of 2021. However, not everyone is happy with the latest version of the Facebook app, which can be slow, buggy, and full of ads. If you are one of those people who prefer the old version of Facebook, you might be wondering how to download it on your Android device. Fortunately, there is a way to do that, thanks to a website called Uptodown.</p>
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-<p>APK stands for Android Package Kit, which is a file format that contains the code and resources of an Android app. An APK file can be installed on an Android device manually, without using the Google Play Store. This is also known as sideloading. A Facebook Old Version APK is an APK file that contains an older version of the Facebook app, which may have different features, design, and performance than the current version.</p>
-<h3>Why would you want to download Facebook Old Version APK?</h3>
-<p>There are many reasons why you might want to download Facebook Old Version APK on your Android device. Some of them are:</p>
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-<li>You like the old interface and layout of Facebook better than the new one.</li>
-<li>You want to use some features that are no longer available in the latest version, such as chat heads, stickers, or games.</li>
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-<li>You want to save data and battery by using a lighter and simpler version of Facebook.</li>
-<li>You want to avoid ads and permissions that are annoying or intrusive.</li>
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-<h2>How to download Facebook Old Version APK from Uptodown</h2>
-<p>If you are interested in downloading Facebook Old Version APK on your Android device, you can follow these simple steps:</p>
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-<h3>Step 1: Visit the Uptodown website</h3>
-<p>Uptodown is a website that offers free downloads of apps and games for various platforms, including Android, Windows, Mac, iOS, and more. It also has a large collection of older versions of popular apps, such as Facebook Messenger. To visit the Uptodown website, you can use any browser on your device and go to <a href="(^1^)">https://en.uptodown.com/</a>.</p>
-<h3>Step 2: Search for Facebook Messenger</h3>
-<p>Once you are on the Uptodown website, you can use the search bar at the top to look for Facebook Messenger. You can also browse through the categories or use the filters to narrow down your search. You should see a list of results that match your query. Click on the one that says "Facebook Messenger" with the blue logo.</p>
-<h3>Step 3: Choose an older version of Facebook Messenger</h3>
-<p>After clicking on Facebook Messenger, you will be taken to a page that shows information about the app, such as its description, screenshots, ratings, and more. You will also see a button that says "Download Latest Version". However, you don't want to download the latest version, you want to download an older version. To do that, you need to scroll down to the bottom of the page, where you will see a section that says "Previous versions". Here, you will see a table that shows the different versions of Facebook Messenger that are available on Uptodown, along with their release date, size, and download link. You can choose any version that you like, but make sure that it is compatible with your device and Android version. For example, if you have Android 4.0 or higher, you can choose version 229.0.0.8.118, which was released on June 11, 2020.</p>
-<h3>Step 4: Download and install the APK file</h3>
-<p>Once you have chosen an older version of Facebook Messenger, you can click on the green button that says "Download" next to it. This will start the download process of the APK file on your device. Depending on your browser settings, you may need to confirm the download or choose a location to save the file. After the download is complete, you need to install the APK file on your device. To do that, you need to enable the option to install apps from unknown sources in your device settings. This may vary depending on your device model and Android version, but generally, you can find it under Security or Applications. Once you have enabled this option, you can locate the APK file in your device storage and tap on it to install it. You may need to grant some permissions or accept some warnings before the installation is complete. After the installation is complete, you can open Facebook Messenger and enjoy the old version.</p>
-<h2>Benefits of using Facebook Old Version APK from Uptodown</h2>
-<p>By downloading Facebook Old Version APK from Uptodown, you can enjoy some benefits that are not available in the latest version of Facebook Messenger. Some of them are:</p>
-<h3>Access to features that are no longer available in the latest version</h3>
-<p>Some features that were popular and useful in the old version of Facebook Messenger have been removed or changed in the latest version. For example, chat heads, which allowed you to chat with your friends without leaving other apps, are no longer supported by some devices or Android versions. Stickers, which added fun and expression to your messages, are now limited and require a separate app to use them. Games, which let you play with your friends within Messenger, are now gone and replaced by a new gaming platform called Facebook Gaming. By using an older version of Facebook Messenger, you can still access these features and enjoy them as before.</p>
-<h3>Faster and smoother performance on older devices</h3>
-<p>The latest version of Facebook Messenger may be too heavy and demanding for some older devices or Android versions. It may cause lagging, crashing, freezing, or draining of battery and data. By using an older version of Facebook Messenger, you can avoid these problems and have a faster and smoother performance on your device. The older version may also have a simpler and cleaner interface that is easier to use and navigate.</p>
-<h3>Less intrusive ads and permissions</h3>
-<p>The latest version of Facebook Messenger may have more ads and permissions than the older version. Ads may pop up or appear in your chats or stories, which can be annoying or distracting. Permissions may ask for access to your contacts, location, camera, microphone, or other sensitive information, which can be invasive or risky. By using an older version of Facebook Messenger, you can reduce the amount of ads and permissions that you have to deal with and have more control over your privacy and security.</p>
-<h2>Risks of using Facebook Old Version APK from Uptodown</h2>
-<p>While there are some benefits of using Facebook Old Version APK from Uptodown, there are also some risks that you should be aware of before downloading it. Some of them are:</p>
-<h3>Security and privacy issues</h3>
-<p>By downloading an APK file from an unknown source like Uptodown, you may expose your device and data to potential threats such as malware, viruses, spyware, or hackers. These threats may harm your device or steal your personal information such as passwords, credit card numbers, or photos. You may also lose access to some security features or updates that are available in the latest version of Facebook Messenger, such as encryption, verification codes, or bug fixes. These features or updates may protect your account and messages from unauthorized access or interception.</p>
-<h3>Compatibility and stability issues</h3>
-<p>By using an older version of Facebook Messenger that is not compatible with your device or Android version, you may encounter some compatibility and stability issues that may affect your user experience. For example, you may not be able to chat with some of your friends who are using the latest version of Facebook Messenger, or you may not be able to send or receive some types of messages, such as voice notes, videos, or GIFs. You may also experience some glitches, errors, or crashes that may interrupt your chats or cause data loss.</p>
-<h3>Missing out on new updates and features</h3>
-<p>By using an older version of Facebook Messenger, you may miss out on some new updates and features that are available in the latest version. These updates and features may improve the functionality, design, or performance of the app, or add some new options or capabilities that may enhance your communication or entertainment. For example, you may not be able to use some of the new features that are available in the latest version of Facebook Messenger, such as dark mode, vanish mode, watch together, or rooms.</p>
-<h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, downloading Facebook Old Version APK from Uptodown is a way to use an older version of Facebook Messenger on your Android device. This may have some benefits, such as accessing features that are no longer available in the latest version, having faster and smoother performance on older devices, or avoiding ads and permissions that are intrusive. However, this may also have some risks, such as security and privacy issues, compatibility and stability issues, or missing out on new updates and features. Therefore, you should weigh the pros and cons carefully before deciding to download Facebook Old Version APK from Uptodown.</p>
-<h4>Call to action</h4>
-<p>If you are still interested in downloading Facebook Old Version APK from Uptodown, you can follow the steps that we have outlined in this article. However, if you want to avoid the risks and enjoy the latest features and updates of Facebook Messenger, you can always download the latest version from the Google Play Store. Either way, we hope that this article has been helpful and informative for you. Thank you for reading!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Is it legal to download Facebook Old Version APK from Uptodown?</b></li>
-<p>It is not illegal to download Facebook Old Version APK from Uptodown, as long as you do not use it for any malicious or fraudulent purposes. However, it may violate the terms of service of Facebook or Google Play Store, which may result in some consequences such as account suspension or termination.</p>
-<li><b>Is it safe to download Facebook Old Version APK from Uptodown?</b></li>
-<p>It is not completely safe to download Facebook Old Version APK from Uptodown, as there may be some security and privacy risks involved. You may expose your device and data to potential threats such as malware, viruses, spyware, or hackers. You may also lose access to some security features or updates that are available in the latest version of Facebook Messenger. Therefore, you should be careful and cautious when downloading Facebook Old Version APK from Uptodown.</p>
-<li><b>How can I update Facebook Old Version APK from Uptodown?</b></li>
-<p>You cannot update Facebook Old Version APK from Uptodown directly, as Uptodown does not provide automatic updates for its apps. You will have to manually check for new versions on the Uptodown website and download them if you want to update your app. However, this may not be advisable, as you may encounter some compatibility and stability issues with newer versions. Alternatively, you can uninstall Facebook Old Version APK from Uptodown and install the latest version from the Google Play Store.</p>
-<li><b>How can I uninstall Facebook Old Version APK from Uptodown?</b></li>
-<p>You can uninstall Facebook Old Version APK from Uptodown by following these steps:</p>
-<ol>
-<li>Go to your device settings and tap on Apps or Applications.</li>
-<li>Find and tap on Facebook Messenger.</li>
-<li>Tap on Uninstall and confirm your action.</li>
-</ol>
-<p>This will remove Facebook Old Version APK from Uptodown from your device completely.</p>
-<li><b>What are some alternatives to downloading Facebook Old Version APK from Uptodown?</b></li>
-<p>If you are not satisfied with downloading Facebook Old Version APK from Uptodown, you can try some alternatives such as:</p>
-<ul>
-<li>Downloading the latest version of Facebook Messenger from the Google Play Store.</li>
-<li>Using a different messaging app that suits your needs and preferences better.</li>
-<li>Using the web version of Facebook Messenger on your browser.</li>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Free 3D House Models in Various Formats and Styles.md DELETED Viewed

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-<h1>Download 3D House Model Free: A Guide for Beginners</h1>
-<p>If you are interested in creating or exploring virtual houses in three dimensions, you might want to download a 3D house model for free. A 3D house model is a digital representation of a real or imaginary house that can be viewed and manipulated in three dimensions. You can use it for various purposes, such as architecture, design, gaming, education, entertainment, etc.</p>
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-<p>A 3D house model is a digital representation of a real or imaginary house that can be viewed and manipulated in three dimensions. It consists of vertices (points), edges (lines), faces (surfaces), and textures (images) that define the shape and appearance of the house.</p>
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-<ul>
-<li><b>Architecture:</b> 3D house models can help architects and engineers design and visualize new houses or renovate existing ones. They can also help clients and contractors understand and communicate the plans and specifications of the houses.</li>
-<li><b>Design:</b> 3D house models can help designers and artists create and showcase different styles and themes of houses. They can also help homeowners and decorators plan and arrange the interior and exterior of their houses.</li>
-<li><b>Gaming:</b> 3D house models can help game developers and players create and explore immersive and interactive environments in various genres of games, such as simulation, adventure, horror, etc.</li>
-<li><b>Education:</b> 3D house models can help teachers and students learn and teach various subjects related to houses, such as history, culture, geography, math, physics, etc.</li>
-<li><b>Entertainment:</b> 3D house models can help filmmakers and animators create and animate realistic or fantastical scenes involving houses. They can also help hobbyists and enthusiasts enjoy and share their passion for houses.</li>
-</ul>
- <h2>Why Download 3D House Model Free?</h2>
-<p>If you want to use a 3D house model for any of the purposes mentioned above, you might want to download it for free. There are many benefits of downloading free 3D house models, such as:</p>
-<ul>
-<li><b>Saving money:</b> You don't have to pay anything to download a free 3D house model. You can save your money for other expenses or investments.</li>
-<li><b>Saving time:</b> You don't have to spend hours or days creating your own 3D house model from scratch. You can save your time for other tasks or activities.</li>
-<li><b>Saving resources:</b> You don't have to use a lot of computer power or storage space to create or store your own 3D house model. You can save your resources for other purposes or applications.</li>
-<li><b>Accessing a wide variety of models:</b> You can choose from thousands of free 3D house models available online. You can find different types, styles, sizes, and qualities of models to suit your needs and preferences.</li>
-<li><b>Learning new skills:</b> You can learn from the free 3D house models created by other people. You can study how they modeled, textured, lit, and rendered their models. You can also improve your skills by editing or modifying their models.</li>
-</ul>
- <h3>Sources of Free 3D House Models</h3>
-<p>There are many websites that offer free 3D house models for download. Some of the best ones are:</p>
-<ul>
-<li><a href="">Free3D</a>: This is one of the largest online repositories of free 3D models. You can find over 10,000 free 3D house models in various formats, such as OBJ, FBX, STL, etc. You can also filter by category, rating, license, etc.</li>
-<li><a href="">TurboSquid</a>: This is one of the most popular online marketplaces of 3D models. You can find over 4,000 free 3D house models in various formats, such as OBJ, FBX, STL, etc. You can also filter by category, rating, license, etc.</li>
-<li><a href="">Sketchfab</a>: This is one of the most innovative online platforms of 3D models. You can find over 3,000 free 3D house models in various formats, such as OBJ, FBX, STL, etc. You can also view and interact with the models in 3D and VR.</li>
-</ul>
- <h3>Tips for Choosing the Right 3D House Model</h3>
-<p>Before you download a free 3D house model, you should consider some factors that can affect your choice, such as:</p>
-<ul>
-<li><b>Format:</b> You should check the format of the 3D house model and make sure it is compatible with the software you want to use. Some of the most common formats are OBJ, FBX, STL, etc. You can also use online converters to change the format of the model if needed.</li>
-<li><b>Quality:</b> You should check the quality of the 3D house model and make sure it meets your expectations. Some of the indicators of quality are poly count, texture resolution, lighting, shading, etc. You can also use online tools to optimize or enhance the quality of the model if needed.</li>
-<li><b>License:</b> You should check the license of the 3D house model and make sure it allows you to use it for your intended purpose. Some of the common licenses are CC0 (public domain), CC BY (attribution), CC BY-SA (attribution-share alike), etc. You should also respect the rights and credits of the original creators of the model.</li>
-<li><b>Compatibility:</b> You should check the compatibility of the 3D house model and make sure it works well with your hardware and software. Some of the factors that can affect compatibility are file size, memory usage, rendering speed, etc. You should also test the model before using it for your project.</li>
-</ul>
- <h2>How to Download 3D House Model Free?</h2>
-<p>Once you have chosen the right 3D house model for your needs, you can download it for free from the website that offers it. The steps involved in downloading a free 3D house model are:</p>
-<ol>
-<li><b>Search:</b> You can use the search bar or the categories to find the 3D house model you want.</li>
-<li><b>Browse:</b> You can browse through the results and see the thumbnails and details of each model.</li>
-<li><b>Filter:</b> You can use the filters to narrow down your results and sort them by relevance, popularity, date, etc.</li>
-<li><b>Preview:</b> You can preview the 3D house model in 3D and see how it looks from different angles and perspectives.</li>
-<li><b>Download:</b> You can click on the download button and choose the format and quality of the model. You can also agree to the terms and conditions of the license.</li>
-</ol>
- <h3>How to Open and Edit a 3D House Model?</h3>
-<p>After you have downloaded a free 3D house model, you can open and edit it with different software. Some of the best software that can open and edit a 3D house model are:</p>
-<ul>
-<li><a href="">Blender</a>: This is one of the most powerful and versatile open-source software for 3D modeling, animation, rendering, etc. You can import and export various formats of 3D models, such as OBJ, FBX, STL, etc. You can also edit and modify the 3D house model with various tools, such as sculpting, painting, rigging, etc.</li>
-<li><a href="">SketchUp</a>: This is one of the most user-friendly and intuitive software for 3D modeling, design, and visualization. You can import and export various formats of 3D models, such as OBJ, FBX, STL, etc. You can also edit and modify the 3D house model with various tools, such as drawing, extruding, scaling, etc.</li>
-<li><a href="">Maya</a>: This is one of the most professional and advanced software for 3D modeling, animation, rendering, etc. You can import and export various formats of 3D models, such as OBJ, FBX, STL, etc. You can also edit and modify the 3D house model with various tools, such as modeling, texturing, lighting, etc.</li>
-</ul>
- <h3>How to Export and Share a 3D House Model?</h3>
-<p>After you have opened and edited a free 3D house model, you can export and share it with different formats. Some of the common formats that can be used to export and share a 3D house model are:</p>
-<ul>
-<li><b>OBJ:</b> This is one of the most widely used and supported formats for 3D models. It can store the geometry and texture of the model in a simple and readable way. It can be opened by almost any software that can handle 3D models.</li>
-<li><b>FBX:</b> This is one of the most versatile and flexible formats for 3D models. It can store the geometry, texture, animation, and other attributes of the model in a compact and efficient way. It can be opened by many software that can handle 3D models.</li>
-<li><b>STL:</b> This is one of the most popular formats for 3D printing. It can store the geometry of the model in a binary or ASCII format. It can be opened by many software that can handle 3D printing.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In this article, we have explained what is a 3D house model, why it is useful, and how to download it for free. We have also provided some tips on how to choose the right 3D house model for your needs and how to open and edit it with different software. We hope you have learned something new and useful from this article.</p>
-<p>If you want to download free 3D house models for your projects, you can visit some of the websites we mentioned above, such as Free3D, TurboSquid, Sketchfab, etc. You can also use some of the software we recommended above, such as Blender, SketchUp, Maya, etc.</p>
-<p>Thank you for reading this article. We hope you enjoyed it and found it helpful. If you have any questions or feedback, please feel free to leave a comment below. Happy downloading!</p>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about downloading free 3D house models:</p>
-<ul>
-<li><b>Q: How do I know if a 3D house model is free or not?</b></li>
-<li>A: You can check the license of the 3D house model on the website that offers it. Usually, there will be a label or a link that indicates whether the model is free or not. You can also read the terms and conditions of the license to understand what you can and cannot do with the model.</li>
-<li><b>Q: How do I know if a 3D house model is good or not?</b></li>
-<li>A: You can check the quality of the 3D house model by previewing it in 3D on the website that offers it. You can also read the reviews and ratings of other users who have downloaded or used the model. You can also compare different models and see which one suits your needs and preferences better.</li>
-<li><b>Q: How do I know if a 3D house model is compatible with my software or not?</b></li>
-<li>A: You can check the format of the 3D house model on the website that offers it. Usually, there will be a list or a dropdown menu that shows which formats are available for download. You can also check the specifications or documentation of your software to see which formats it can import or export.</li>
-<li><b>Q: How do I download multiple 3D house models at once?</b></li>
-<li>A: Some websites may allow you to download multiple 3D house models at once by selecting them and adding them to your cart or download folder. However, some websites may limit the number of downloads per day or per user. You may have to register or sign in to access more downloads. You may also have to pay a fee or subscribe to a plan to download more models.</li>
-<li><b>Q: How do I share my 3D house model with others?</b></li>
-<li>A: You can share your 3D house model with others by exporting it to a common format, such as OBJ, FBX, STL, etc. and sending it via email, cloud storage, social media, etc. You can also upload your 3D house model to a website that allows you to showcase and share your 3D models, such as Sketchfab, ArtStation, etc.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Autocad Error Unable To Load The Modeler Dlls.md DELETED Viewed

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-## Autocad Error Unable To Load The Modeler Dlls
- ![Autocad Error Unable To Load The Modeler Dlls](https://www.ketcausoft.com/images/f19/p2015032002.png)
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-# How to Fix Autocad Error Unable To Load The Modeler Dlls
-If you are using Autocad and encounter a fatal error that says "unable to load the modeler dlls", you may be wondering what causes this problem and how to solve it. In this article, we will explain what are the modeler dlls, why they are needed, and what are some possible solutions to fix this error.
-## What are the modeler dlls?
-The modeler dlls are dynamic link libraries that are used by Autocad to create and manipulate 3D objects and solids. They are essential for working with 3D modeling and rendering in Autocad. The modeler dlls are located in the \uE000Acgex19\uE001 folder under the Autocad installation directory.
-## Why do they fail to load?
-There are several possible reasons why the modeler dlls fail to load when you start Autocad or open a drawing that contains 3D blocks. Some of the common causes are:
-- The modeler dlls are missing, corrupted, or outdated.
-- The \uE000Acgex19\uE001 folder is missing or has incorrect permissions.
-- The \uE000Acgex19\uE001 folder is not registered in the Windows registry.
-- The drawing file is corrupted or contains invalid 3D blocks.
-- The system resources are insufficient or conflicting.
-## How to fix the error?
-Depending on the cause of the error, there are different solutions that you can try to fix it. Here are some of the possible methods:
-1. Run Disk Cleanup to delete temporary files that may interfere with the loading of the modeler dlls[^1^].
-2. Repair or reinstall Autocad to restore the missing or corrupted modeler dlls.
-3. Copy the \uE000Acgex19\uE001 folder from another working computer or from the installation media to your Autocad installation directory.
-4. Register the \uE000Acgex19\uE001 folder in the Windows registry by running the command `regsvr32 "C:\Program Files\Autodesk\AutoCAD 2019\Acgex19\acgex19.dll"` as administrator.
-5. Purge and audit the drawing file to remove any invalid 3D blocks or data.
-6. Update your graphics card driver and adjust your graphics settings in Autocad.
-7. Increase your system memory and disk space to improve performance.
-We hope this article has helped you understand and fix the Autocad error unable to load the modeler dlls. If you need further assistance, please contact Autodesk support or visit their community forums.
-## How to improve your skills and productivity in Autocad?
-Autocad is a powerful and versatile software for design and drafting, but it can also be challenging and complex to master. If you want to improve your skills and productivity in Autocad, you need to learn some tips and tricks that can help you work faster, smarter, and more efficiently. Here are some of the best tips and tricks that every Autocad user should know:
-### Use keyboard shortcuts
-Keyboard shortcuts are one of the easiest ways to save time and reduce mouse clicks in Autocad. You can use the default shortcuts or create your own custom ones. To access the keyboard shortcuts menu, go to Manage tab > Customization panel > User Interface, or type CUI into the command line. You can drag and drop commands from the Command List pane to the Shortcut Keys node in the Customizations In pane. You can also modify or delete existing shortcuts by selecting them from under the Shortcut Keys node[^1^].
-### Enable autosave
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-### Customize the Quick Access Toolbar
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-- iDRAWPRO: A blog that provides useful tips and tricks for AutoCAD users, such as keyboard shortcuts, blocks palette, right-click menus, etc[^1^].
-- Freelancer: A platform that connects freelancers with clients who need various services, including AutoCAD design and drafting. Freelancer also has a community section that offers articles on various topics, such as AutoCAD tricks and shortcuts[^3^].
-We hope these tips and tricks will help you improve your skills and productivity in Autocad. If you have any questions or feedback, please feel free to leave a comment below.
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spaces/1phancelerku/anime-remove-background/Call of Duty Black Ops Cold War The Next Generation of COD on PC.md DELETED Viewed

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-<br />
-<br>
- <table>
-<tr>
-<td>
- <h1>How to Download Call of Duty on PC</h1>
-<h2>Introduction</h2>
-<p>Call of Duty is one of the most popular and successful first-person shooter games in the world. It has millions of fans who enjoy its thrilling gameplay, realistic graphics, immersive storylines, and competitive multiplayer modes. Whether you want to fight in historical wars, futuristic battles, or zombie apocalypses, there is a Call of Duty game for you.</p>
-<p>But did you know that you can also play Call of Duty on your PC? Playing Call of Duty on PC has many benefits, such as better performance, higher resolution, more customization options, and access to mods and community servers. Plus, you can use your keyboard and mouse for more precise aiming and control.</p>
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-<p>In this article, we will show you how to download Call of Duty on PC. We will cover three main topics:</p>
-<ul>
-<li>How to download Call of Duty: Modern Warfare on PC</li>
-<li> <li>How to download Call of Duty: Warzone on PC</li>
-<li>How to download other Call of Duty games on PC</li>
-</ul>
-<p>By the end of this article, you will be able to enjoy Call of Duty on your PC with ease. So, let's get started!</p>
-<h2>How to Download Call of Duty: Modern Warfare on PC</h2>
-<h3>Requirements</h3>
-<p>Before you download Call of Duty: Modern Warfare on PC, you need to make sure that your PC meets the minimum or recommended specifications for the game. Here are the requirements for Call of Duty: Modern Warfare on PC:</p>
-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>OS: Windows 7 64-Bit (SP1) or Windows 10 64-Bit (1709 or later)</td>
-<td>OS: Windows 10 64-Bit (latest update)</td>
-</tr>
-<tr>
-<td>CPU: Intel Core i3-4340 or AMD FX-6300</td>
-<td>CPU: Intel Core i5-2500K or AMD Ryzen R5 1600X</td>
-</tr>
-<tr>
-<td>RAM: 8 GB</td>
-<td>RAM: 12 GB</td>
-</tr>
-<tr>
-<td>GPU: NVIDIA GeForce GTX 670 / NVIDIA GeForce GTX 1650 or AMD Radeon HD 7950</td>
-<td>GPU: NVIDIA GeForce GTX 970 / NVIDIA GeForce GTX 1660 or AMD Radeon R9 390 / AMD Radeon RX 580</td>
-</tr>
-<tr>
-<td>HDD: 175 GB available space</td>
-<td>HDD: 175 GB available space</td>
-</tr>
-<tr>
-<td>DirectX: Version 12</td>
-<td>DirectX: Version 12</td>
-</tr>
-<tr>
-<td>Network: Broadband Internet connection</td>
-<td>Network: Broadband Internet connection</td>
-</tr>
-<tr>
-<td>Sound Card: DirectX Compatible</td>
-<td>Sound Card: DirectX Compatible</td>
-</tr> </table>
-<p>If your PC does not meet the minimum requirements, you may experience low frame rates, crashes, or errors while playing the game. If your PC meets the recommended requirements, you will be able to enjoy the game with higher settings and smoother performance.</p>
-<p>Another thing you need to consider is the hard drive space. Call of Duty: Modern Warfare is a very large game that requires 175 GB of available space on your PC. You may need to delete some files or uninstall some programs to free up some space before you download the game.</p>
-<h3>Steps</h3>
-<p>Now that you have checked the requirements and the hard drive space, you are ready to download Call of Duty: Modern Warfare on PC. Here are the steps you need to follow:</p>
-<ol>
-<li>Purchase Call of Duty: Modern Warfare on PC. You can buy the game from the official website or from other online retailers. The price may vary depending on the edition and the region. You will receive a digital code that you can redeem on Blizzard Entertainment.</li>
-<li>Download and install Blizzard Entertainment. Blizzard Entertainment is a gaming platform that allows you to download, install, and play Call of Duty: Modern Warfare and other games on PC. You can download Blizzard Entertainment from here. You will need to create a free account or log in with an existing one.</li>
-<li>Redeem your digital code on Blizzard Entertainment. After you have installed Blizzard Entertainment, launch it and log in with your account. Click on the "Games" tab and then click on "Redeem a Code". Enter your digital code and follow the instructions to add Call of Duty: Modern Warfare to your library.</li>
-<li>Download and install Call of Duty: Modern Warfare on PC. After you have redeemed your code, click on the "Call of Duty: MW" icon on the left side of Blizzard Entertainment. Click on the "Install" button and choose a location for the game files. The download and installation process may take some time depending on your internet speed and PC performance.</li>
-<li>Launch and play Call of Duty: Modern Warfare on PC. After the installation is complete, click on the "Play" button to launch the game. You may need to update the game or download some additional content before you can play. You can adjust the settings and preferences according to your preferences. Enjoy playing Call of Duty: Modern Warfare on PC!</li>
-</ol>
-<h2>How to Download Call of Duty: Warzone on PC</h2>
-<h3>Requirements</h3>
-<p>Call of Duty: Warzone is a free-to-play battle royale game that is part of Call of Duty: Modern Warfare. It features up to 150 players competing in solo, duo, trio, or quad modes in a large map called Verdansk. You can play Call of Duty: Warzone even if you do not own Call of Duty: Modern Warfare.</p>
-<p>However, you still need to make sure that your PC meets the minimum or recommended specifications for the game. Here are the requirements for Call of Duty: Warzone on PC:</p>
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-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>OS: Windows 7 64-Bit (SP1) or Windows 10 64-Bit (1709 or later)</td>
-<td>OS: Windows 10 64-Bit (latest update)</td>
-</tr>
-<tr>
-<td>CPU: Intel Core i3-4340 or AMD FX-6300</td>
-<td>CPU: Intel Core i5-2500K or AMD Ryzen R5 1600X</td>
-</tr>
-<tr>
-<td>RAM: 8 GB</td>
-<td>RAM: 12 GB</td>
-</tr>
-<tr>
-<td>GPU: NVIDIA GeForce GTX 670 / NVIDIA GeForce GTX 1650 or AMD Radeon HD 7950</td>
-<td>GPU: NVIDIA GeForce GTX 970 / NVIDIA GeForce GTX 1660 or AMD Radeon R9 390 / AMD Radeon RX 580</td>
-</tr>
-<tr>
-<td>HDD: 175 GB available space</td>
-<td>HDD: 175 GB available space</td>
-</tr>
-<tr>
-<td>DirectX: Version 12</td>
-<td>DirectX: Version 12</td>
-</tr>
-<tr>
-<td>Network: Broadband Internet connection</td>
-<td>Network: Broadband Internet connection</td>
-</tr>
-<tr>
-<td>Sound Card: DirectX Compatible</td>
-<td>Sound Card: DirectX Compatible</td>
-</tr> </table>
-<p>As you can see, the requirements for Call of Duty: Warzone are the same as Call of Duty: Modern Warfare. However, you may need more hard drive space if you want to download both games. You can also choose to download only Call of Duty: Warzone if you do not want to play Call of Duty: Modern Warfare.</p>
-<h3>Steps</h3>
-<p>Downloading Call of Duty: Warzone on PC is similar to downloading Call of Duty: Modern Warfare on PC. However, there are some differences that you need to know. Here are the steps you need to follow:</p>
-<ol>
-<li>Download and install Blizzard Entertainment. If you already have Blizzard Entertainment on your PC, you can skip this step. If not, you can download Blizzard Entertainment from here. You will need to create a free account or log in with an existing one.</li>
-<li>Download Call of Duty: Warzone for free on PC. After you have installed Blizzard Entertainment, launch it and log in with your account. Click on the "Games" tab and then click on "Call of Duty: MW". You will see a screen that shows the options for Call of Duty: Modern Warfare and Call of Duty: Warzone. Click on the "Play for Free" button under Call of Duty: Warzone to start the download.</li>
-<li>Install and update Call of Duty: Warzone on PC. After the download is complete, click on the "Install" button and choose a location for the game files. The installation process may take some time depending on your PC performance. You may also need to update the game or download some additional content before you can play.</li>
-<li>Launch and play Call of Duty: Warzone on PC. After the installation and update are complete, click on the "Play" button to launch the game. You can adjust the settings and preferences according to your preferences. Enjoy playing Call of Duty: Warzone on PC!</li>
-</ol>
-<h2>How to Download Other Call of Duty Games on PC</h2>
-<h3>Blizzard Entertainment</h3>
-<p>If you want to play other Call of Duty games on PC, you can use Blizzard Entertainment as well. Blizzard Entertainment is a gaming platform that allows you to download, install, and play various games on PC, including some Call of Duty games.</p>
-<p>Here are the other Call of Duty games available on Blizzard Entertainment:</p>
-<ul>
-<li>Call of Duty: Black Ops 4</li>
-<li>Call of Duty: Black Ops Cold War</li>
-<li>Call of Duty: Vanguard (coming soon)</li>
-</ul>
-<p>To download and install these games, you need to follow the same steps as downloading Call of Duty: Modern Warfare or Call of Duty: Warzone on PC. However, you need to purchase these games from the official website or from other online retailers before you can redeem them on Blizzard Entertainment.</p>
-<h3>Steam</h3>
-<p>If you prefer to use another gaming platform, you can use Steam. Steam is a popular gaming platform that allows you to download, install, and play thousands of games on PC, including some Call of Duty games.</p>
-<p>Here are the other Call of Duty games available on Steam:</p>
-<ul>
-<li>Call of Duty</li>
-<li>Call of Duty 2</li>
-<li>Call of Duty 4: Modern Warfare</li>
-<li>Call of Duty: World at War</li>
-<li>Call of Duty: Modern Warfare 2</li>
-<li>Call of Duty: Black Ops</li>
-<li>Call of Duty: Modern Warfare 3</li>
-<li>Call of Duty: Black Ops II</li>
-<li>Call of Duty: Ghosts</li>
-<li>Call of Duty: Advanced Warfare</li>
-<li>Call of Duty: Black Ops III</li>
-<li>Call of Duty: Infinite Warfare</li>
-<li>Call of Duty: WWII</li>
-</ul> <p>To download and install these games, you need to follow these steps:</p>
-<ol>
-<li>Download and install Steam. If you already have Steam on your PC, you can skip this step. If not, you can download Steam from here. You will need to create a free account or log in with an existing one.</li>
-<li>Purchase the Call of Duty games you want on Steam. You can buy the games from the Steam store or from other online retailers. The price may vary depending on the game and the region. You will receive a digital code that you can redeem on Steam.</li>
-<li>Redeem your digital code on Steam. After you have installed Steam, launch it and log in with your account. Click on the "Games" menu and then click on "Activate a Product on Steam". Enter your digital code and follow the instructions to add the game to your library.</li>
-<li>Download and install the Call of Duty games on PC. After you have redeemed your code, click on the "Library" tab and then click on the game you want to play. Click on the "Install" button and choose a location for the game files. The download and installation process may take some time depending on your internet speed and PC performance.</li>
-<li>Launch and play the Call of Duty games on PC. After the installation is complete, click on the "Play" button to launch the game. You may need to update the game or download some additional content before you can play. You can adjust the settings and preferences according to your preferences. Enjoy playing the Call of Duty games on PC!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Call of Duty on PC. We have covered three main topics:</p>
-<ul>
-<li>How to download Call of Duty: Modern Warfare on PC</li>
-<li>How to download Call of Duty: Warzone on PC</li>
-<li>How to download other Call of Duty games on PC</li>
-</ul>
-<p>We hope that this article has been helpful and informative for you. Playing Call of Duty on PC has many advantages, such as better graphics, smoother performance, more customization options, and access to mods and community servers. Plus, you can use your keyboard and mouse for more precise aiming and control.</p>
-<p>Here are some tips and tricks for playing Call of Duty on PC:</p>
-<ul>
-<li>Check the system requirements and hard drive space before downloading any game.</li>
-<li>Use Blizzard Entertainment or Steam as your gaming platform for downloading and installing Call of Duty games.</li>
-<li>Update your drivers and software regularly for optimal performance and security.</li>
-<li>Adjust the settings and preferences according to your PC specifications and personal preferences.</li>
-<li>Join online communities and forums for tips, guides, news, and support.</li>
-</ul>
-<p>We would love to hear from you. What are your thoughts and feedback on this article? What are your favorite Call of Duty games on PC? How do you like playing Call of Duty on PC? Let us know in the comments below!</p>
- <h2>Frequently Asked Questions</h2>
- <h3>Q: Is Call of Duty free on PC?</h3>
- <p>A: Some Call of Duty games are free on PC, such as Call of Duty: Warzone, which is a free-to-play battle royale game that is part of Call of Duty: Modern Warfare. However, most Call of Duty games are not free on PC, such as Call of Duty: Modern Warfare, Call of Duty: Black Ops 4, Call of Duty: Black Ops Cold War, etc. You need to purchase these games from the official website or from other online retailers before you can download them on PC.</p>
- <h3>Q: Can I play Call of Duty on PC with a controller?</h3>
- <p>A: Yes, you can play Call of Duty on PC with a controller if you prefer. Most Call of Duty games support controller input on PC, such as Xbox One controller, PlayStation 4 controller, etc. You can connect your controller to your PC via USB cable or wireless adapter. You can also adjust the controller settings and sensitivity in the game options.</p>
- <h3>Q: Can I play Call of Duty on PC with my friends who play on console?</h3>
- <p>A: Yes, you can play Call of Duty on PC with your friends who play on console if the game supports cross-play feature. Cross-play feature allows players from different platforms, such as PC, PlayStation 4, Xbox One, etc., to play together online in the same lobby or match. Some Call of Duty games that support cross-play feature are Call of Duty: Modern Warfare, Call of Duty: Warzone, Call of Duty: Black Ops Cold War, etc. You need to enable cross-play feature in the game settings and link your Blizzard Entertainment account with your Activision account. You can also add your friends from different platforms to your in-game friends list and invite them to join your party or match.</p>
- <h3>Q: How can I improve my performance and FPS in Call of Duty on PC?</h3>
- <p>A: There are several ways you can improve your performance and FPS (frames per second) in Call of Duty on PC, such as:</p>
-<ul>
-<li>Update your drivers and software regularly for optimal performance and security.</li>
-<li>Close any unnecessary programs or background processes that may consume your CPU, RAM, or GPU resources.</li>
-<li>Adjust the game settings and preferences according to your PC specifications and personal preferences. You can lower the graphics quality, resolution, or render scale to increase the FPS.</li>
-<li>Use a wired internet connection or a high-speed Wi-Fi connection to reduce the latency and lag.</li>
-<li>Clean your PC and monitor regularly to prevent dust, dirt, or overheating.</li>
-</ul>
- <h3>Q: How can I get better at Call of Duty on PC?</h3>
- <p>A: There are several ways you can get better at Call of Duty on PC, such as:</p>
-<ul>
-<li>Practice regularly and learn from your mistakes. You can play the campaign mode, the co-op mode, or the custom games to improve your skills and knowledge.</li>
-<li>Watch and learn from other players who are more experienced or skilled than you. You can watch live streams, videos, or tutorials online to get some tips and tricks.</li>
-<li>Experiment with different weapons, attachments, perks, killstreaks, and loadouts to find the ones that suit your playstyle and preferences.</li>
-<li>Communicate and cooperate with your teammates. You can use voice chat, text chat, or ping system to coordinate your strategies and tactics.</li>
-<li>Have fun and enjoy the game. Do not let frustration, anger, or stress affect your performance or attitude. Remember that it is just a game and you are here to have fun.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Clash of Clans Apk Para Hileli Nasl Yklenir? - Adm Adm Anlatm.md DELETED Viewed

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-<h1>Clash of Clans Apk Para Hileli: How to Download and Play with Unlimited Resources</h1>
- <p>Do you love playing Clash of Clans but wish you had more gold, elixir, gems, and dark elixir to build your village, train your troops, and crush your enemies? If so, you might be interested in trying out Clash of Clans Apk Para Hileli, a modified version of the game that gives you unlimited resources for free. In this article, we will tell you everything you need to know about Clash of Clans Apk Para Hileli, including what it is, how to download and install it, how to play it, and some tips and tricks to make your gaming experience more fun and exciting.</p>
- <h2>What is Clash of Clans?</h2>
- <p>Clash of Clans is a strategy game where you build your village, raise a clan, and compete in epic clan wars. You can customize your village with various buildings, defenses, traps, walls, decorations, and more. You can also train different types of troops with unique abilities and spells to attack other players' villages or defend your own. You can join or create a clan with other players from around the world and participate in clan wars, clan games, clan war leagues, and other events. You can also chat with your clan mates, donate and request troops, and share replays and strategies. Clash of Clans is one of the most popular and highest-grossing mobile games of all time, with over 500 million downloads and millions of active players. It has also received many awards and positive reviews from critics and fans alike.</p>
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- <h2>What is an Apk?</h2>
- <p>An apk is an Android application package file that contains all the files and code needed to run an app on your device. You can download apk files from various sources on the internet, such as websites, blogs, forums, or file-sharing platforms. Unlike the official app store downloads, apk files are not verified or regulated by Google or the app developers. This means that they can offer features or functions that are not available in the original version of the app, such as unlocked content, premium access, ad-free experience, or unlimited resources. However, this also means that they can pose risks or drawbacks, such as malware, viruses, bugs, compatibility issues, or legal consequences. Therefore, you should always be careful and cautious when downloading and installing apk files on your device.</p>
- <h2>What is a Money Cheat?</h2>
- <p>A money cheat is a hack or a mod that gives you unlimited resources in Clash of Clans. Resources are the currency of the game that you use to build your village, train your troops, upgrade your buildings and troops, research new technologies, and more. There are four types of resources in Clash of Clans: gold, elixir, gems, and dark elixir. Gold and elixir are obtained by raiding other players' villages, collecting from mines and collectors, completing achievements, winning clan wars, and participating in events. Gems are the premium currency of the game that can be purchased with real money or earned by completing achievements, removing obstacles, or opening gem boxes. Dark elixir is a rare resource that is used to train and upgrade dark troops and heroes. It is obtained by raiding other players' villages, collecting from drills, completing achievements, winning clan wars, and participating in events.</p>
- <p>A money cheat works by modifying the game code or data to give you unlimited amounts of resources without spending any time or effort. It can also bypass the game's security measures and detection systems to prevent you from getting banned or suspended. A money cheat can offer you many advantages and benefits in the game, such as faster progress, higher levels, stronger troops and defenses, more options and flexibility, and more fun and enjoyment. However, a money cheat can also have some negative impacts on the game, such as unfairness, imbalance, boredom, loss of challenge, loss of interest, or loss of respect. Moreover, a money cheat can be illegal or immoral depending on the laws and ethics of your country or region. Therefore, you should always be aware and responsible when using a money cheat in Clash of Clans.</p>
- <h2>How to Download and Install Clash of Clans Apk Para Hileli?</h2>
- <p>If you want to try out Clash of Clans Apk Para Hileli for yourself, you will need to follow these steps:</p>
- <ol>
-<li>Find and download the apk file from a reliable and trustworthy source on the internet. You can search for it on Google or use one of these links:   . Make sure that the apk file is compatible with your device model and Android version.</li>
-<li>Before installing the apk file on your device, you will need to enable the unknown sources option in your settings. This will allow you to install apps from sources other than the official app store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Locate the downloaded apk file on your device using a file manager app or your browser's downloads folder. Tap on it to start the installation process. You may need to grant some permissions or accept some terms and conditions before proceeding.</li>
-<li>Wait for the installation to finish and then launch the app from your home screen or app drawer. You may need to sign in with your Google account or create a new account if you don't have one already.</li>
-<li>Enjoy playing Clash of Clans Apk Para Hileli with unlimited resources!</li>
-</ol>
- <h2>How to Play Clash of Clans Apk Para Hileli?</h2>
- <p>Playing Clash of Clans Apk Para Hileli is similar to playing the original version of the game. You will still need to build your village, train your troops, join a clan, and fight in clan wars. However, you will have some differences and advantages that will make your gameplay more enjoyable and easier. Here are some of the features and differences of Clash of Clans Apk Para Hileli:</p>
- <table>
-<tr>
-<th>Feature</th>
-<th>Original Version</th>
-<th>Apk Para Hileli Version</th>
-</tr>
-<tr>
-<td>Gold</td>
-<td>Limited by storage capacity and production rate</td>
-<td>Unlimited and instant</td>
-</tr>
-<tr>
-<td>Elixir</td>
-<td>Limited by storage capacity and production rate</td>
-<td>Unlimited and instant</td>
-</tr>
-<tr>
-<td>Gems</td>
-<td>Limited by purchase or achievement</td>
-<td>Unlimited and free</td>
-</tr>
-<tr>
-<td>Dark Elixir</td>
-<td>Limited by storage capacity and production rate</td>
-<td>Unlimited and instant</td>
-</tr>
-<tr>
-<td>Building Time</td>
-<td>Depends on the level and type of the building</td>
-<td>Zero or reduced</td>
-</tr>
-<tr>
-<td>Troop Training Time</td>
-<td>Depends on the level and type of the troop</td>
-<td>Zero or reduced</td>
-</tr>
-<tr>
-<td>Research Time</td>
-<td>Depends on the level and type of the research</td>
-<td>Zero or reduced</td>
-</tr>
-<tr>
-<td>Town Hall Level</td>
-<td>Capped at 14 (as of June 2021)</td>
-<td>Capped at 16 (with additional buildings and troops)</td>
-</tr>
- <tr><td colspan="3">Source: </td></tr>
- : https://clashofclansmodapk.net/clash-of-clans-mod-apk-para-hileli/ : https://www.apkmody.com/174-clash-of-clans-mod-unlimited-money.html : https://www.thesun.co.uk/news/17143468/holy-grail-fusion-experiments-breakthrough-race-unlimited-energy/ : https://www.newscientist.com/article/2336385-korean-nuclear-fusion-reactor-achieves-100-millionc-for-30-seconds/ : https://news.yahoo.com/nuclear-fusion-breakthrough-reactor-runs-130157687.html : https://en.wikipedia.org/wiki/Solar_core : https://nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html : http://curious.astro.cornell.edu/about-us/54-our-solar-system/the-sun/interior/206-how-hot-is-each-one-of-the-layers-of-the-sun-beginner : https://solar.physics.montana.edu/YPOP/Spotlight/SunInfo/Core.html : https://en.wikipedia.org/wiki/Sun#:~:text=The%20core%20of%20the%20Sun%20extends%20from%20the,the%20Sun%27s%20surface%20temperature%20is%20approximately%205800%20K). <p>As you can see, Clash of Clans Apk Para Hileli gives you a lot of benefits and advantages over the original version of the game. You can build your village faster, train your troops easier, research new technologies quicker, and have more options and flexibility in your gameplay. You can also experiment with different strategies, layouts, combinations, and tactics without worrying about losing resources or time. You can also enjoy the game more without spending any money or watching any ads.</p>
- <p>However, you should also be aware that Clash of Clans Apk Para Hileli is not an official or authorized version of the game. It is a modified version that is created by third-party developers or hackers who have no affiliation or connection with Supercell, the original developer of the game. Therefore, you should not expect any support, updates, or bug fixes from Supercell. You should also not use your main account or connect to your social media accounts when playing Clash of Clans Apk Para Hileli. You may risk losing your account, data, or progress if you do so. You may also face legal or ethical issues if you use a money cheat in Clash of Clans.</p>
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- <h2>Conclusion</h2>
- <p>In conclusion, Clash of Clans Apk Para Hileli is a modified version of the game that gives you unlimited resources for free. It can make your gameplay more fun and easy by allowing you to build your village faster, train your troops easier, research new technologies quicker, and have more options and flexibility in your gameplay. However, you should also be careful and responsible when using Clash of Clans Apk Para Hileli, as it is not an official or authorized version of the game. It can pose risks or drawbacks, such as malware, viruses, bugs, compatibility issues, or legal consequences. You should also not use your main account or connect to your social media accounts when playing Clash of Clans Apk Para Hileli. You may risk losing your account, data, or progress if you do so. You should also respect the game rules and the other players when playing Clash of Clans Apk Para Hileli. You should not abuse or exploit the money cheat to gain an unfair advantage or ruin the game experience for others.</p>
- <p>If you are looking for a fun and easy way to play Clash of Clans with unlimited resources, you can give Clash of Clans Apk Para Hileli a try. However, if you want to play the original and authentic version of the game, you can download it from the official app store or visit the official website of Supercell. You can also check out other games similar to Clash of Clans, such as Clash Royale, Boom Beach, Hay Day, or Brawl Stars. They are all developed by Supercell and offer different genres and gameplay styles.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers related to Clash of Clans Apk Para Hileli:</p>
- <ol>
-<li>Q: Is Clash of Clans Apk Para Hileli safe to use?<br>
-A: Clash of Clans Apk Para Hileli is not guaranteed to be safe to use, as it is a modified version of the game that is created by third-party developers or hackers who have no affiliation or connection with Supercell, the original developer of the game. It can contain malware, viruses, bugs, or compatibility issues that can harm your device or data. Therefore, you should always scan the apk file before installing it and use a trusted antivirus app on your device.</li>
-<li>Q: Is Clash of Clans Apk Para Hileli legal to use?<br>
-A: Clash of Clans Apk Para Hileli is not legal to use, as it violates the terms and conditions of Supercell and the game. It also infringes on the intellectual property rights and trademarks of Supercell and the game. Therefore, you may face legal consequences if you use Clash of Clans Apk Para Hileli. You may also get banned or suspended from the game if you are detected by the game's security measures and detection systems.</li>
-<li>Q: Is Clash of Clans Apk Para Hileli updated regularly?<br>
-A: Clash of Clans Apk Para Hileli is not updated regularly, as it is not an official or authorized version of the game. It depends on the availability and activity of the third-party developers or hackers who create and maintain it. Therefore, you may not get the latest features, updates, or bug fixes from Supercell and the game when you use Clash of Clans Apk Para Hileli. You may also experience glitches or errors when playing Clash of Clans Apk Para Hileli.</li>
-<li>Q: Can I play Clash of Clans Apk Para Hileli online with other players?<br>
-A: Yes, you can play Clash of Clans Apk Para Hileli online with other players who are also using the same version of the game. However, you cannot play with players who are using the original version of the game or other versions of the game. You may also face difficulties or restrictions when joining or creating a clan in Clash of Clans Apk Para Hileli.</li>
-<li>Q: Can I switch between Clash of Clans Apk Para Hileli and the original version of the game?<br>
-A: Yes, you can switch between Clash of Clans Apk Para Hileli and the original version of the game by uninstalling one version and installing another version on your device. However, you should not use your main account or connect to your social media accounts when switching between versions. You may lose your account, data, or progress if you do so. You should also backup your data before switching between versions.</li>
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spaces/1phancelerku/anime-remove-background/Download Haryana Super 100 Level 1 Admit Card 2023 - Check Exam Date and Details.md DELETED Viewed

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-<h1>Haryana Super 100 Level 1 Admit Card Download: Everything You Need to Know</h1>
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- <p>To download your admit card for Haryana Super 100 level 1 exam online, you need to follow these simple steps:</p>
- <ol>
-<li>Go to the official website of Haryana Super 100 scheme: www.haryanasuper100.com.</li>
-<li>On the home page, click on the link that says "Download Admit Card".</li>
-<li>Enter your registration number or SRN number or WhatsApp number or Aadhar number and date of birth in the given fields.</li>
-<li>Click on "Submit" button.</li>
-<li>Your admit card will be displayed on the screen. Check all the details carefully and download it.</li>
-<li>Take a printout of your admit card and keep it safe for future reference.</li>
-</ol>
- <h2>What are the details mentioned on the admit card and how to verify them?</h2>
- <p>Your admit card for Haryana Super 100 level 1 exam contains some important information that you need to check and verify before appearing for the exam. These details are:</p>
-<p>How to download haryana super 100 level 1 admit card online<br />
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-Haryana super</p>
- <ul>
-<li>Exam name</li>
-<li>Applicant's name</li>
-<li>Father's name</li>
-<li>Mother's name</li>
-<li>Gender</li>
-<li>Registration number</li>
-<li>Date of birth</li>
-<li>Roll number</li>
-<li>Exam date</li>
-<li>Exam centre</li>
-<li>Exam timing</li>
-<li>Category of PwD (if applicable)</li>
-<li>Admit card ID</li>
-<li>Subjects in which appearing with date of examination</li>
-<li>Important instructions for the exam</li>
-</ul>
- <p>You should verify all these details with your identity proof <h2>What are the exam pattern, syllabus, and marking scheme for Haryana Super 100 level 1 exam?</h2>
- <p>The exam pattern, syllabus, and marking scheme for Haryana Super 100 level 1 exam are as follows:</p>
- <h3>Exam Pattern</h3>
- <p>The Haryana Super 100 level 1 exam is an online objective type test that consists of two papers: General Studies (GS) and Civil Services Aptitude Test (CSAT). Each paper has 100 questions of 1 mark each and the duration of each paper is 2 hours. The total marks of the exam are 200. The GS paper covers topics such as General Science, Current Affairs, History, Geography, Culture, Economy, Polity, Mental Ability, etc. The CSAT paper tests the candidates' logical reasoning, analytical ability, decision making, problem solving, basic numeracy, data interpretation, etc.</p>
- <h3>Syllabus</h3>
- <p>The syllabus for Haryana Super 100 level 1 exam is based on the NCERT books of class 6th to 10th and the state board books of class 11th and 12th. The candidates are expected to have a general awareness of the state of Haryana as well as the national and international issues. The detailed syllabus for each paper is given below:</p>
- <table>
-<tr>
-<th>GS Paper</th>
-<th>CSAT Paper</th>
-</tr>
-<tr>
-<td>
-<ul>
-<li>General Science: Physics, Chemistry, Biology, Environment, etc.</li>
-<li>Current Affairs: Important National and International Events, Awards and Honours, Sports, Books and Authors, etc.</li>
-<li>History of India and Indian National Movement: Ancient, Medieval and Modern History of India, Freedom Struggle, etc.</li>
-<li>Indian and World Geography: Physical, Social and Economic Geography of India and the World.</li>
-<li>Indian Culture, Economy and Polity: Art and Culture, Constitution, Governance, Planning, Budgeting, etc.</li>
-<li>General Mental Ability: Verbal and Non-Verbal Reasoning, Analogies, Classification, Series, Coding-Decoding, etc.</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Logical Reasoning: Syllogism, Statement and Assumptions, Statement and Arguments, Statement and Conclusions, etc.</li>
-<li>Analytical Ability: Blood Relations, Direction Sense Test, Seating Arrangement, Ranking Test, etc.</li>
-<li>Decision Making: Decision Making in Various Situations Based on Given Information.</li>
-<li>Problem Solving: Problems Based on Arithmetic Operations, Algebra, Geometry, Mensuration, etc.</li>
-<li>Basic Numeracy: Numbers and their Relations, Fractions and Decimals, Percentage and Average, Ratio and Proportion, etc.</li>
-<li>Data Interpretation: Data Presented in Tables, Graphs, Charts or Diagrams.</li>
-</ul>
-</td>
-</tr>
-</table>
- <h3>Marking Scheme</h3>
- <p>The marking scheme for Haryana Super 100 level 1 exam is as follows:</p>
- <ul>
-<li>Each question carries 1 mark.</li>
-<li>There is negative marking of 0.25 marks for each wrong answer.</li>
-<li>The GS paper is counted for merit while the CSAT paper is only qualifying in nature.</li>
-<li>The candidates have to score at least 33% marks in the CSAT paper to qualify for the next stage.</li>
-</ul>
- <p>I hope this information helps you to prepare well for the Haryana Super 100 level 1 exam. All the best! ?</p> <h2>What are the exam date, timing, and venue for Haryana Super 100 level 1 exam?</h2>
- <p>The exam date, timing, and venue for Haryana Super 100 level 1 exam are as follows:</p>
- <h3>Exam Date</h3>
- <p>The Haryana Super 100 level 1 exam will be conducted on June 4, 2023. The candidates who qualify the level 1 exam will be eligible to appear for the level 2 exam, which will be held on July 2, 2023. The final selection of the candidates will be based on their performance in both the exams and their academic records.</p>
- <h3>Exam Timing</h3>
- <p>The Haryana Super 100 level 1 exam will be held in two shifts: morning and afternoon. The morning shift will start from 10:00 am and end at 12:00 pm. The afternoon shift will start from 2:00 pm and end at 4:00 pm. The candidates have to report at the exam centre at least one hour before the commencement of the exam. They have to carry their admit card, identity proof, and other required documents with them.</p>
- <h3>Exam Venue</h3>
- <p>The Haryana Super 100 level 1 exam will be conducted at various online centres across the state of Haryana. The candidates have to choose their preferred exam centre while filling the online application form. The allotment of the exam centre will be done on the basis of the availability of seats and the preference of the candidates. The name and address of the exam centre will be mentioned on the admit card of the candidates. The candidates have to follow the instructions and guidelines given by the exam authorities at the exam centre.</p> <h2>How to prepare for Haryana Super 100 level 1 exam and what are the best books and resources?</h2>
- <p>Preparing for Haryana Super 100 level 1 exam is not an easy task. It requires a lot of hard work, dedication, and smart strategy. Here are some tips and suggestions that can help you to ace the exam:</p>
- <ul>
-<li>Know the exam pattern, syllabus, and marking scheme thoroughly and plan your study accordingly.</li>
-<li>Revise the NCERT books of class 6th to 10th and the state board books of class 11th and 12th for all the subjects.</li>
-<li>Practice previous year papers, mock tests, and sample papers regularly to improve your speed, accuracy, and time management.</li>
-<li>Read newspapers, magazines, and online sources to update your current affairs and general knowledge.</li>
-<li>Focus on your weak areas and work on them. Also, revise your strong areas and don't neglect them.</li>
-<li>Solve different types of questions and problems from various sources to enhance your logical reasoning, analytical ability, decision making, problem solving, basic numeracy, and data interpretation skills.</li>
-<li>Make short notes, flashcards, mnemonics, and diagrams to remember the important facts, formulas, concepts, and dates.</li>
-<li>Take care of your health and well-being. Eat a balanced diet, drink plenty of water, exercise regularly, sleep well, and avoid stress.</li>
-</ul>
- <p>Some of the best books and resources that you can refer to for Haryana Super 100 level 1 exam are:</p>
- <table>
-<tr>
-<th>Subject</th>
-<th>Book/Resource</th>
-</tr>
-<tr>
-<td>General Science</td>
-<td>NCERT Books of Class 6th to 10th<br>Lucent's General Science</td>
-</tr>
-<tr>
-<td>Current Affairs</td>
-<td>The Hindu Newspaper<br>Monthly Current Affairs Magazine<br>Haryana Current Affairs by Arihant Publications</td>
-</tr>
-<tr>
-<td>History of India and Indian National Movement</td>
-<td>NCERT Books of Class 6th to 12th<br>A Brief History of Modern India by Rajiv Ahir<br>India's Struggle for Independence by Bipan Chandra</td>
-</tr>
-<tr>
-<td>Indian and World Geography</td>
-<td>NCERT Books of Class 6th to 12th<br>Certificate Physical and Human Geography by G.C. Leong<br>Oxford School Atlas</td>
-</tr>
-<tr>
-<td>Indian Culture, Economy and Polity</td>
-<td>NCERT Books of Class 6th to 12th<br>Indian Art and Culture by Nitin Singhania<br>Indian Economy by Ramesh Singh<br>Indian Polity by M. Laxmikanth</td>
-</tr>
-<tr>
-<td>General Mental Ability</td>
-<td>A Modern Approach to Verbal and Non-Verbal Reasoning by R.S. Aggarwal<br>Analytical Reasoning by M.K. Pandey<br>A New Approach to Reasoning Verbal and Non-Verbal by B.S. Sijwali and Indu Sijwali</td>
-</tr>
-<tr>
-<td>Logical Reasoning</td>
-<td>A Modern Approach to Logical Reasoning by R.S. Aggarwal<br>Analytical Reasoning by M.K. Pandey<br>A New Approach to Reasoning Verbal and Non-Verbal by B.S. Sijwali and Indu Sijwali</td>
-</tr>
-<tr>
-<td>Analytical Ability</td>
-<td>A Modern Approach to Logical Reasoning by R.S. Aggarwal<br>Analytical Reasoning by M.K. Pandey<br>A New Approach to Reasoning Verbal and Non-Verbal by B.S. Sijwali and Indu Sijwali</td>
-</tr>
-<tr>
-<td>Decision Making</td>
-<td>Data Sufficiency & Decision Making by Arihant Publications<br>Critical Thinking: A Beginner's Guide by Sharon M. Kaye<br>The Power of Logical Thinking by Marilyn Vos Savant</td>
-</tr>
-<tr>
-<td>Problem Solving</td ><td>A Modern Approach to Logical Reasoning by R.S. Aggarwal<br>Analytical Reasoning by M.K. Pandey<br>A New Approach to Reasoning Verbal and Non-Verbal by B.S. Sijwali and Indu Sijwali</td ></tr ><tr ><td >Basic Numeracy ></td ><td >Quantitative Aptitude for Competitive Examinations by R.S. Aggarwal<br >Fundamentals of Mathematics for Competitive Exams by Sanjeev Kumar Jha<br >Numerical Ability & Mathematical Aptitude by Abhijit Banerjee</td>
-</tr>
-<tr>
-<td>Data Interpretation</td>
-<td>Data Interpretation and Data Sufficiency by Arihant Publications<br>Data Analysis and Interpretation by Disha Experts<br>How to Prepare for Data Interpretation for CAT by Arun Sharma</td>
-</tr>
-</table>
- <h2>What are the do's and don'ts for Haryana Super 100 level 1 exam day?</h2>
- <p>On the day of the Haryana Super 100 level 1 exam, you should follow some do's and don'ts to avoid any hassle and perform well in the exam. Here are some of them:</p>
- <h3>Do's</h3>
- <ul>
-<li>Carry your admit card, identity proof, and other required documents with you.</li>
-<li>Reach the exam centre at least one hour before the exam time.</li>
-<li>Read the instructions given on the admit card and the question paper carefully.</li>
-<li>Attempt the questions that you are confident about first and then move on to the difficult ones.</li>
-<li>Use the elimination method to guess the answers if you are not sure.</li>
-<li>Mark your answers on the computer screen correctly and carefully.</li>
-<li>Manage your time wisely and don't spend too much time on one question.</li>
-<li>Review your answers before submitting them.</li>
-</ul>
- <h3>Don'ts</h3>
- <ul>
-<li>Don't forget to carry your admit card, identity proof, and other required documents with you.</li>
-<li>Don't reach the exam centre late or after the exam time.</li>
-<li>Don't ignore the instructions given on the admit card and the question paper.</li>
-<li>Don't attempt the questions that you are not sure about first and waste your time.</li>
-<li>Don't mark your answers randomly or blindly without any logic.</li>
-<li>Don't make any changes or corrections on the admit card or the question paper.</li>
-<li>Don't use any unfair means or indulge in any malpractice during the exam.</li>
-<li>Don't leave the exam hall before the end of the exam time.</li>
-</ul>
- <p>I hope these do's and don'ts will help you to avoid any mistakes and score well in the Haryana Super 100 level 1 exam. Good luck! ?</p>
- <h2>How to check Haryana Super 100 level 1 result and what are the cut-off marks and merit list?</h2>
- <p>The result of Haryana Super 100 level 1 exam will be declared on June 15, 2023, on the official website of the scheme: www.haryanasuper100.com. To check your result, you need to follow these steps:</p>
- <ol>
-<li>Go to the official website of Haryana Super 100 scheme: www.haryanasuper100.com.</li>
-<li>On the home page, click on the link that says "Result".</li>
-<li>Enter your registration number or SRN number or WhatsApp number or Aadhar number and date of birth in the given fields.</li>
-<li>Click on "Submit" button.</li>
-<li>Your result will be displayed on the screen. Check your marks, rank, and qualifying status.</li>
-<li>Download your result and take a printout of it for future reference.</li>
-</ol>
- <p>The cut-off marks for Haryana Super 100 level 1 exam are the minimum marks that a candidate needs to score to qualify for the next stage. The cut-off marks are decided by the exam authorities based on various factors such as number of candidates appeared, difficulty level of the exam, number of seats available, etc. The cut-off marks for Haryana Super 100 level 1 exam will be released along with the result on June 15, 2023. The candidates who score equal to or more than the cut-off marks will be eligible to appear for Haryana Super 100 level 2 exam.</p>
- <p>The merit list for Haryana Super 100 level 1 exam is the list of candidates who have qualified for the next stage. The merit list is prepared by the exam authorities based on the marks obtained by the candidates in the GS paper. The merit list will also be released along with the result on June 15, 2023. The candidates who are included in the merit list will be called for document verification and counselling before being admitted to the coaching centres.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have covered everything you need to know about Haryana Super 100 level 1 admit card download. We have also provided you with some useful information about the exam date, timing, venue, pattern, syllabus, marking scheme, preparation tips, do's and don'ts, result, cut-off marks, and merit list. We hope that this article has helped you to clear your doubts and queries regarding the exam. We wish you all the best for your exam and future endeavours. Remember, you have the potential to achieve your dreams and goals. Just work hard, stay focused, and be confident. You can do it! ?</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions related to Haryana Super 100 level 1 admit card download:</p>
- <h3>Q1. What is the official website of Haryana Super 100 scheme?</h3>
- <p>A1. The official website of Haryana Super 100 scheme is www.haryanasuper100.com. You can visit this website to get all the latest updates and information about the scheme.</p>
- <h3>Q2. How can I contact the exam authorities if I have any issue or query regarding the exam?</h3>
- <p>A2. You can contact the exam authorities through the following modes:</p>
- <ul>
-<li>Email: [email protected]</li>
-<li>Phone: 0172-2560206</li>
-<li>WhatsApp: 9416010101</li>
-<li>Address: Directorate of School Education, Haryana, Shiksha Sadan, Sector-5, Panchkula-134105</li>
-</ul>
- <h3>Q3. What if I forget my registration number or SRN number or WhatsApp number or Aadhar number?</h3>
- <p>A3. If you forget any of these details, you can retrieve them by using the "Forgot Details" option on the admit card download page. You have to enter your name and date of birth and click on "Submit" button. You will get your details on your registered email id or mobile number.</p>
- <h3>Q4. What if I find any discrepancy or error in my admit card?</h3>
- <p>A4. If you find any discrepancy or error in your admit card, such as spelling mistake, wrong photograph, incorrect details, etc., you should immediately report it to the exam authorities through email or phone or WhatsApp or in person and get it rectified before the exam.</p>
- <h3>Q5. Can I change my exam centre after downloading my admit card?</h3>
- <p>A5. No, you cannot change your exam centre after downloading your admit card. The exam centre once allotted is final and binding. No request for change of exam centre will be entertained by the exam authorities under any circumstances.</p> 401be4b1e0<br />
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- <h3>Step 1: Go to the LiveZilla website and download the zip file</h3>
-<p>Go to <a href="">https://www.livezilla.net/downloads/en/</a> and click on the Download button for Linux. You will get a file named livezilla_8.x.x.zip that contains all the files that you need to run LiveZilla on Linux.</p>
- <h3>Step 2: Unzip the file and upload it to your web server</h3>
-<p>Unzip the livezilla_8.x.x.zip file on your computer using a tool like WinZip or 7-Zip. You will see a folder named livezilla that contains all the files that you need to upload to your web server. You can use an FTP client or a web hosting control panel to upload these files to your web server. Make sure that you upload them under your website root folder.</p>
- <h3>Step 3: Create a database for LiveZilla and grant <h3>Step 3: Create a database for LiveZilla and grant privileges</h3>
-<p>Before you can run the installation wizard, you need to create a database for LiveZilla and grant the necessary privileges to a user. You can use a tool like phpMyAdmin or the command line to do that. For example, you can use the following commands to create a database named livezilla and a user named livezilla_user with the password livezilla_pass:</p>
-<code>
-CREATE DATABASE livezilla; CREATE USER 'livezilla_user'@'localhost' IDENTIFIED BY 'livezilla_pass'; GRANT ALL PRIVILEGES ON livezilla.* TO 'livezilla_user'@'localhost'; FLUSH PRIVILEGES; </code>
-<p>Make sure that you replace the database name, user name, and password with your own values.</p>
- <h3>Step 4: Open your web browser and run the installation wizard</h3>
-<p>Now you can open your web browser and go to <a href="">https://yourdomain.com/livezilla/</a> to start the installation wizard. You will see a welcome screen that asks you to choose a language and accept the license agreement. Then you will see a screen that asks you to enter the database information that you created in the previous step. After that, you will see a screen that asks you to create an administrator account for LiveZilla. Finally, you will see a screen that confirms that the installation is complete and gives you some options to customize your chat widget, forms, buttons, etc.</p>
- <h3>Step 5: Start using LiveZilla to chat with your website visitors</h3>
-<p>Congratulations! You have successfully installed LiveZilla on Linux. You can now log in to your LiveZilla web interface from any browser by going to <a href="">https://yourdomain.com/livezilla/</a>. From there, you can manage your chats, tickets, operators, chatbots, etc.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and install LiveZilla on Windows and Linux, and how to use it to chat with your website visitors and provide customer support. LiveZilla is a powerful and versatile customer service platform that includes a live chat software, a visitor monitoring tool, and a help desk system. It has many features and benefits that can help you increase your sales and customer satisfaction. You can try LiveZilla for free for 30 days or choose from one of its affordable pricing plans. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to contact us or leave a comment below.</p>
- <h2>FAQs</h2>
-<h4>Q: How do I update LiveZilla?</h4>
-<p>A: You can update LiveZilla by downloading the latest version from the LiveZilla website and running the setup file on Windows or unzipping the zip file on Linux. The update process will preserve your settings and data.</p>
- <h4>Q: How do I uninstall LiveZilla?</h4>
-<p>A: You can uninstall LiveZilla by deleting the LiveZilla folder from your computer and web server. You can also delete the database that you created for LiveZilla if you don't need it anymore.</p>
- <h4>Q: How do I add more operators or chatbots to LiveZilla?</h4>
-<p>A: You can add more operators or chatbots to LiveZilla by logging in to your LiveZilla web interface and going to User Management. There you can create new operator or chatbot accounts and assign them roles and permissions.</p>
- <h4>Q: How do I customize my chat widget, forms, buttons, etc.?</h4>
-<p>A: You can customize your chat widget, forms, buttons, etc. by logging in to your LiveZilla web interface and going to Link Generator. There you can choose from different templates, colors, sizes, positions, etc. and generate HTML code that you can embed on your website.</p>
- <h4>Q: How do I integrate LiveZilla with other apps?</h4>
-<p>A: You can integrate LiveZilla with other apps by using its API or its Zapier integration. You can find more information about these options on the LiveZilla website or documentation.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Experience the Thrill of Into the Dead Mod APK with Unlimited Ammo and Money.md DELETED Viewed

@@ -1,100 +0,0 @@
-<h1>Into the Dead Mod APK: A Zombie Survival Game with Unlimited Ammo and Money</h1>
-<p>If you are looking for a thrilling and immersive zombie game for your Android device, you might want to check out Into the Dead mod APK. This is a modified version of the popular endless runner game by Pikpok, where you have to run for your life in a post-apocalyptic world infested by the undead. In this article, we will tell you how to download and install Into the Dead mod APK on your Android device, what features it offers, and some tips and tricks for playing it.</p>
- <h2>How to Download and Install Into the Dead Mod APK on Android</h2>
-<p>Before you can enjoy Into the Dead mod APK, you need to follow these steps to install it on your device:</p>
-<h2>into the dead mod apk unlimited ammo and money andropalace</h2><br /><p><b><b>Download Zip</b> &#11088; <a href="https://jinyurl.com/2uNJOU">https://jinyurl.com/2uNJOU</a></b></p><br /><br />
-<ol>
-<li>Allow unknown apps on your device. To do this, go to your device settings and tap on Security. Then, scroll down until you see Unknown sources and check the box. This will let you install apps from sources other than the Google Play Store.</li>
-<li>Download the mod APK file from a reputable source. You can find many websites that offer Into the Dead mod APK, but be careful as some of them may contain malware or viruses. One of the trusted sources we recommend is [Andropalace](^1^), where you can download Into the Dead mod APK v2.7.1 with unlimited ammo and money.</li>
-<li>Install the mod APK using a file manager app. After downloading the mod APK file, you need to locate it on your device using a file manager app. You can use any file manager app you like, such as Cx File Explorer or File Manager. Once you find the mod APK file, tap on it and follow the instructions to install it.</li>
-</ol>
-<p>Congratulations! You have successfully installed Into the Dead mod APK on your Android device. Now you can launch the game and enjoy its features.</p>
- <h2>Features of Into the Dead Mod APK</h2>
-<p>Into the Dead mod APK offers many features that make it more fun and exciting than the original game. Here are some of them:</p>
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-<ul>
-<li>Unlimited ammo and money. With this feature, you don't have to worry about running out of bullets or coins in the game. You can shoot as many zombies as you want and buy any weapon or perk you like.</li>
-<li>All weapons unlocked. With this feature, you can access all the weapons in the game without having to complete missions or challenges. You can choose from pistols, shotguns, chainsaws, grenades, miniguns, and more.</li>
-<li>No ads. With this feature, you can enjoy the game without any annoying interruptions or distractions. You can focus on running and killing zombies without having to watch ads or buy premium items.</li>
-</ul>
- <h2>Tips and Tricks for Playing Into the Dead Mod APK</h2>
-<p>Into the Dead mod APK is a simple but challenging game that requires quick reflexes and strategy. Here are some tips and tricks that can help you survive longer and score higher in the game:</p>
-<ul>
-<li>Use headphones for better immersion. The game has excellent sound effects that create a <p>scary and realistic atmosphere. You can hear the zombies growling, the bullets whizzing, and the wind howling. Using headphones can enhance your immersion and help you react faster to the sounds around you.</li>
-<li>Avoid obstacles and fences. The game is full of obstacles and fences that can slow you down or stop you completely. You need to avoid them as much as possible, as they can make you vulnerable to zombie attacks. You can use the tilt or touch controls to steer left or right, or jump over low obstacles by tapping the screen.</li>
-<li>Save your weapons for emergencies. The game gives you a weapon at the start of each run, but you have limited ammo and it can run out quickly. You should save your weapons for situations where you are surrounded by zombies or facing a large horde. You can also find ammo crates along the way, but they are rare and random.</li>
-<li>Complete missions and challenges. The game has various missions and challenges that you can complete to earn coins and unlock new weapons and perks. Some of the missions include killing a certain number of zombies, running a certain distance, or using a specific weapon. Some of the challenges include running in fog, running at night, or running with no weapons.</li>
-</ul>
- <h2>Pros and Cons of Into the Dead Mod APK</h2>
-<p>Into the Dead mod APK is a fun and addictive game that can keep you entertained for hours. However, it also has some drawbacks that you should be aware of. Here are some of the pros and cons of Into the Dead mod APK:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Simple controls. The game has easy and intuitive controls that anyone can master. You can choose between tilt or touch controls, depending on your preference.</td>
-<td>Repetitive levels. The game has only one mode and one map, which can get boring after a while. The levels are randomly generated, but they have little variation in terms of scenery and layout.</td>
-</tr>
-<tr>
-<td>Atmospheric graphics. The game has stunning graphics that create a dark and eerie mood. The game uses realistic lighting and shadows, fog effects, and blood splatters to enhance the horror theme.</td>
-<td>Lack of story. The game has no story or plot, which makes it feel shallow and meaningless. You don't know why you are running, where you are going, or what happened to the world.</td>
-</tr>
-<tr>
-<td>Addictive gameplay. The game has a simple but addictive gameplay that keeps you hooked. You always want to run farther, kill more zombies, and unlock more weapons and perks.</td>
-<td>Potential security risks. The game is a modded version of the original game, which means it may contain malware or viruses that can harm your device or steal your data. You should always download mod APKs from trusted sources and scan them before installing them.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Into the Dead mod APK is a zombie survival game that offers unlimited ammo and money, all weapons unlocked, and no ads. It is a modified version of the original game by Pikpok, where you have to run for your life in a post-apocalyptic world infested by the undead. It has simple controls, atmospheric graphics, and addictive gameplay that make it a great choice for zombie fans. However, it also has some drawbacks, such as repetitive levels, lack of story, and potential security risks. You should always be careful when downloading and installing mod APKs on your device.</p>
- <p>If you are interested in playing Into the Dead mod APK, you can download it from [Andropalace] and follow the steps we mentioned above to install it on your Android device. We hope you enjoy this game and have fun killing zombies!</p>
- <h3>Frequently Asked Questions</h3>
-<p>Here are some of the common questions that people ask about Into the Dead mod APK:</p>
- <h4>Q: Is Into the Dead mod APK safe to use?</h4>
-<p>A: Into the Dead mod APK is generally safe to use if you download it from a reputable source like Andropalace. However, there is always a risk of malware or viruses when downloading mod APKs from unknown sources. You should always scan the mod APK file before installing it on your device.</p>
- <h4>Q: How do I update Into the Dead mod APK?</h4>
-<p>A: To update Into the Dead mod APK, you need to download the latest version of the mod APK file from Andropalace or another trusted source. Then, you need to uninstall the previous version of the mod APK from your device and install the new version using a file manager app.</p>
- <h4>Q: Can Q: Can I play Into the Dead mod APK online with other players?</h4>
-<p>A: No, Into the Dead mod APK is an offline game that does not require an internet connection to play. You can only play it solo on your device.</p>
- <h4>Q: What are the perks in Into the Dead mod APK?</h4>
-<p>A: Perks are special abilities that you can use in the game to enhance your performance. You can buy perks with coins or unlock them by completing missions or challenges. Some of the perks include faster reload, longer sprint, more health, and more ammo.</p>
- <h4>Q: What are the best weapons in Into the Dead mod APK?</h4>
-<p>A: The best weapons in Into the Dead mod APK depend on your preference and playstyle. However, some of the most powerful and popular weapons are the chainsaw, the minigun, the grenade launcher, and the crossbow.</p> 401be4b1e0<br />
-<br />
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spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/lp_train.py DELETED Viewed

@@ -1,301 +0,0 @@
-import json
-import logging
-import math
-import os
-import time
-from contextlib import suppress
-import numpy as np
-import torch
-import torch.nn.functional as F
-try:
-    import wandb
-except ImportError:
-    wandb = None
-from open_clip import LPLoss, LPMetrics, lp_gather_features
-from open_clip.utils import do_mixup, get_mix_lambda
-from .distributed import is_master
-from .zero_shot import zero_shot_eval
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-    def __init__(self):
-        self.reset()
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-def unwrap_model(model):
-    if hasattr(model, "module"):
-        return model.module
-    else:
-        return model
-def train_one_epoch(
-    model,
-    data,
-    epoch,
-    optimizer,
-    scaler,
-    scheduler,
-    args,
-    tb_writer=None,
-    extra_suffix="",
-):
-    device = torch.device(args.device)
-    autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
-    model.train()
-    loss = LPLoss(args.lp_loss)
-    dataloader, sampler = data["train"].dataloader, data["train"].sampler
-    if args.distributed and sampler is not None:
-        sampler.set_epoch(epoch)
-    num_batches_per_epoch = dataloader.num_batches
-    sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
-    # for toy dataset
-    if args.dataset_type == "toy":
-        dataloader.dataset.generate_queue()
-    loss_m = AverageMeter()
-    batch_time_m = AverageMeter()
-    data_time_m = AverageMeter()
-    end = time.time()
-    for i, batch in enumerate(dataloader):
-        step = num_batches_per_epoch * epoch + i
-        if isinstance(scheduler, dict):
-            for s in scheduler.values():
-                s(step)
-        else:
-            scheduler(step)
-        audio = batch  # contains mel_spec, wavform, and longer list
-        class_label = batch["class_label"]
-        # audio = audio.to(device=device, non_blocking=True)
-        class_label = class_label.to(device=device, non_blocking=True)
-        if args.mixup:
-            # https://github.com/RetroCirce/HTS-Audio-Transformer/blob/main/utils.py#L146
-            mix_lambda = torch.from_numpy(
-                get_mix_lambda(0.5, len(audio["waveform"]))
-            ).to(device)
-            class_label = do_mixup(class_label, mix_lambda)
-        else:
-            mix_lambda = None
-        data_time_m.update(time.time() - end)
-        if isinstance(optimizer, dict):
-            for o_ in optimizer.values():
-                o_.zero_grad()
-        else:
-            optimizer.zero_grad()
-        with autocast():
-            pred = model(audio, mix_lambda=mix_lambda, device=device)
-            total_loss = loss(pred, class_label)
-        if isinstance(optimizer, dict):
-            if scaler is not None:
-                scaler.scale(total_loss).backward()
-                for o_ in optimizer.values():
-                    if args.horovod:
-                        o_.synchronize()
-                        scaler.unscale_(o_)
-                        with o_.skip_synchronize():
-                            scaler.step(o_)
-                    else:
-                        scaler.step(o_)
-                scaler.update()
-            else:
-                total_loss.backward()
-                for o_ in optimizer.values():
-                    o_.step()
-        else:
-            if scaler is not None:
-                scaler.scale(total_loss).backward()
-                if args.horovod:
-                    optimizer.synchronize()
-                    scaler.unscale_(optimizer)
-                    with optimizer.skip_synchronize():
-                        scaler.step(optimizer)
-                else:
-                    scaler.step(optimizer)
-                scaler.update()
-            else:
-                total_loss.backward()
-                optimizer.step()
-        # Note: we clamp to 4.6052 = ln(100), as in the original paper.
-        with torch.no_grad():
-            unwrap_model(model).clap_model.logit_scale_a.clamp_(0, math.log(100))
-            unwrap_model(model).clap_model.logit_scale_t.clamp_(0, math.log(100))
-        batch_time_m.update(time.time() - end)
-        end = time.time()
-        batch_count = i + 1
-        if is_master(args) and (i % 100 == 0 or batch_count == num_batches_per_epoch):
-            if isinstance(audio, dict):
-                batch_size = len(audio["waveform"])
-            else:
-                batch_size = len(audio)
-            num_samples = batch_count * batch_size * args.world_size
-            samples_per_epoch = dataloader.num_samples
-            percent_complete = 100.0 * batch_count / num_batches_per_epoch
-            # NOTE loss is coarsely sampled, just master node and per log update
-            loss_m.update(total_loss.item(), batch_size)
-            if isinstance(optimizer, dict):
-                logging.info(
-                    f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
-                    f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
-                    f"Data (t): {data_time_m.avg:.3f} "
-                    f"Batch (t): {batch_time_m.avg:.3f} "
-                    f"LR: {[o_.param_groups[0]['lr'] for o_ in optimizer.values()]}"
-                )
-                log_data = {
-                    "loss": loss_m.val,
-                    "data_time": data_time_m.val,
-                    "batch_time": batch_time_m.val,
-                    "lr": [o_.param_groups[0]["lr"] for o_ in optimizer.values()],
-                }
-            else:
-                logging.info(
-                    f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
-                    f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
-                    f"Data (t): {data_time_m.avg:.3f} "
-                    f"Batch (t): {batch_time_m.avg:.3f} "
-                    f"LR: {optimizer.param_groups[0]['lr']:5f} "
-                )
-                # Save train loss / etc. Using non avg meter values as loggers have their own smoothing
-                log_data = {
-                    "loss": loss_m.val,
-                    "data_time": data_time_m.val,
-                    "batch_time": batch_time_m.val,
-                    "lr": optimizer.param_groups[0]["lr"],
-                }
-            for name, val in log_data.items():
-                name = f"train{extra_suffix}/{name}"
-                if tb_writer is not None:
-                    tb_writer.add_scalar(name, val, step)
-                if args.wandb:
-                    assert wandb is not None, "Please install wandb."
-                    wandb.log({name: val, "step": step})
-            # resetting batch / data time meters per log window
-            batch_time_m.reset()
-            data_time_m.reset()
-    # end for
-def evaluate(model, data, epoch, args, tb_writer=None, extra_suffix=""):
-    metrics = {}
-    if not args.parallel_eval:
-        if not is_master(args):
-            return metrics
-    device = torch.device(args.device)
-    model.eval()
-    # CHANGE
-    # zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
-    # metrics.update(zero_shot_metrics)
-    if is_master(args):
-        print("Evaluating...")
-        metric_names = args.lp_metrics.split(",")
-        eval_tool = LPMetrics(metric_names=metric_names)
-    autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
-    if "val" in data and (
-        args.val_frequency
-        and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)
-    ):
-        if args.parallel_eval:
-            dataloader, sampler = data["val"].dataloader, data["val"].sampler
-            if args.distributed and sampler is not None:
-                sampler.set_epoch(epoch)
-            samples_per_val = dataloader.num_samples
-        else:
-            dataloader = data["val"].dataloader
-            num_samples = 0
-            samples_per_val = dataloader.num_samples
-        eval_info = {"pred": [], "target": []}
-        with torch.no_grad():
-            for i, batch in enumerate(dataloader):
-                audio = batch  # contains mel_spec, wavform, and longer list
-                class_label = batch["class_label"]
-                # audio = audio.to(device=device, non_blocking=True)
-                class_label = class_label.to(device=device, non_blocking=True)
-                with autocast():
-                    pred = model(audio, device=device)
-                    if args.parallel_eval:
-                        pred, class_label = lp_gather_features(
-                            pred, class_label, args.world_size, args.horovod
-                        )
-                    eval_info["pred"].append(pred)
-                    eval_info["target"].append(class_label)
-                num_samples += class_label.shape[0]
-                if (i % 100) == 0:  # and i != 0:
-                    logging.info(
-                        f"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]"
-                    )
-            if is_master(args):
-                eval_info["pred"] = torch.cat(eval_info["pred"], 0).cpu()
-                eval_info["target"] = torch.cat(eval_info["target"], 0).cpu()
-                metric_dict = eval_tool.evaluate_mertics(
-                    eval_info["pred"], eval_info["target"]
-                )
-                metrics.update(metric_dict)
-                if "epoch" not in metrics.keys():
-                    metrics.update({"epoch": epoch})
-    if is_master(args):
-        if not metrics:
-            return metrics
-        logging.info(
-            f"Eval Epoch: {epoch} "
-            + "\n".join(
-                ["\t".join([f"{m}: {round(metrics[m], 4):.4f}"]) for m in metrics]
-            )
-        )
-        if args.save_logs:
-            for name, val in metrics.items():
-                if tb_writer is not None:
-                    tb_writer.add_scalar(f"val{extra_suffix}/{name}", val, epoch)
-            with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
-                f.write(json.dumps(metrics))
-                f.write("\n")
-        if args.wandb:
-            assert wandb is not None, "Please install wandb."
-            for name, val in metrics.items():
-                wandb.log({f"val{extra_suffix}/{name}": val, "epoch": epoch})
-        return metrics
-    else:
-        return metrics

spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/pytorch/inference.py DELETED Viewed

@@ -1,206 +0,0 @@
-import os
-import sys
-sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
-import numpy as np
-import argparse
-import librosa
-import matplotlib.pyplot as plt
-import torch
-from utilities import create_folder, get_filename
-from models import *
-from pytorch_utils import move_data_to_device
-import config
-def audio_tagging(args):
-    """Inference audio tagging result of an audio clip.
-    """
-    # Arugments & parameters
-    sample_rate = args.sample_rate
-    window_size = args.window_size
-    hop_size = args.hop_size
-    mel_bins = args.mel_bins
-    fmin = args.fmin
-    fmax = args.fmax
-    model_type = args.model_type
-    checkpoint_path = args.checkpoint_path
-    audio_path = args.audio_path
-    device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
-    classes_num = config.classes_num
-    labels = config.labels
-    # Model
-    Model = eval(model_type)
-    model = Model(sample_rate=sample_rate, window_size=window_size,
-        hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax,
-        classes_num=classes_num)
-    checkpoint = torch.load(checkpoint_path, map_location=device)
-    model.load_state_dict(checkpoint['model'])
-    # Parallel
-    if 'cuda' in str(device):
-        model.to(device)
-        print('GPU number: {}'.format(torch.cuda.device_count()))
-        model = torch.nn.DataParallel(model)
-    else:
-        print('Using CPU.')
-    # Load audio
-    (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
-    waveform = waveform[None, :]    # (1, audio_length)
-    waveform = move_data_to_device(waveform, device)
-    # Forward
-    with torch.no_grad():
-        model.eval()
-        batch_output_dict = model(waveform, None)
-    clipwise_output = batch_output_dict['clipwise_output'].data.cpu().numpy()[0]
-    """(classes_num,)"""
-    sorted_indexes = np.argsort(clipwise_output)[::-1]
-    # Print audio tagging top probabilities
-    for k in range(10):
-        print('{}: {:.3f}'.format(np.array(labels)[sorted_indexes[k]],
-            clipwise_output[sorted_indexes[k]]))
-    # Print embedding
-    if 'embedding' in batch_output_dict.keys():
-        embedding = batch_output_dict['embedding'].data.cpu().numpy()[0]
-        print('embedding: {}'.format(embedding.shape))
-    return clipwise_output, labels
-def sound_event_detection(args):
-    """Inference sound event detection result of an audio clip.
-    """
-    # Arugments & parameters
-    sample_rate = args.sample_rate
-    window_size = args.window_size
-    hop_size = args.hop_size
-    mel_bins = args.mel_bins
-    fmin = args.fmin
-    fmax = args.fmax
-    model_type = args.model_type
-    checkpoint_path = args.checkpoint_path
-    audio_path = args.audio_path
-    device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
-    classes_num = config.classes_num
-    labels = config.labels
-    frames_per_second = sample_rate // hop_size
-    # Paths
-    fig_path = os.path.join('results', '{}.png'.format(get_filename(audio_path)))
-    create_folder(os.path.dirname(fig_path))
-    # Model
-    Model = eval(model_type)
-    model = Model(sample_rate=sample_rate, window_size=window_size,
-        hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax,
-        classes_num=classes_num)
-    checkpoint = torch.load(checkpoint_path, map_location=device)
-    model.load_state_dict(checkpoint['model'])
-    # Parallel
-    print('GPU number: {}'.format(torch.cuda.device_count()))
-    model = torch.nn.DataParallel(model)
-    if 'cuda' in str(device):
-        model.to(device)
-    # Load audio
-    (waveform, _) = librosa.core.load(audio_path, sr=sample_rate, mono=True)
-    waveform = waveform[None, :]    # (1, audio_length)
-    waveform = move_data_to_device(waveform, device)
-    # Forward
-    with torch.no_grad():
-        model.eval()
-        batch_output_dict = model(waveform, None)
-    framewise_output = batch_output_dict['framewise_output'].data.cpu().numpy()[0]
-    """(time_steps, classes_num)"""
-    print('Sound event detection result (time_steps x classes_num): {}'.format(
-        framewise_output.shape))
-    sorted_indexes = np.argsort(np.max(framewise_output, axis=0))[::-1]
-    top_k = 10  # Show top results
-    top_result_mat = framewise_output[:, sorted_indexes[0 : top_k]]
-    """(time_steps, top_k)"""
-    # Plot result
-    stft = librosa.core.stft(y=waveform[0].data.cpu().numpy(), n_fft=window_size,
-        hop_length=hop_size, window='hann', center=True)
-    frames_num = stft.shape[-1]
-    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(10, 4))
-    axs[0].matshow(np.log(np.abs(stft)), origin='lower', aspect='auto', cmap='jet')
-    axs[0].set_ylabel('Frequency bins')
-    axs[0].set_title('Log spectrogram')
-    axs[1].matshow(top_result_mat.T, origin='upper', aspect='auto', cmap='jet', vmin=0, vmax=1)
-    axs[1].xaxis.set_ticks(np.arange(0, frames_num, frames_per_second))
-    axs[1].xaxis.set_ticklabels(np.arange(0, frames_num / frames_per_second))
-    axs[1].yaxis.set_ticks(np.arange(0, top_k))
-    axs[1].yaxis.set_ticklabels(np.array(labels)[sorted_indexes[0 : top_k]])
-    axs[1].yaxis.grid(color='k', linestyle='solid', linewidth=0.3, alpha=0.3)
-    axs[1].set_xlabel('Seconds')
-    axs[1].xaxis.set_ticks_position('bottom')
-    plt.tight_layout()
-    plt.savefig(fig_path)
-    print('Save sound event detection visualization to {}'.format(fig_path))
-    return framewise_output, labels
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Example of parser. ')
-    subparsers = parser.add_subparsers(dest='mode')
-    parser_at = subparsers.add_parser('audio_tagging')
-    parser_at.add_argument('--sample_rate', type=int, default=32000)
-    parser_at.add_argument('--window_size', type=int, default=1024)
-    parser_at.add_argument('--hop_size', type=int, default=320)
-    parser_at.add_argument('--mel_bins', type=int, default=64)
-    parser_at.add_argument('--fmin', type=int, default=50)
-    parser_at.add_argument('--fmax', type=int, default=14000)
-    parser_at.add_argument('--model_type', type=str, required=True)
-    parser_at.add_argument('--checkpoint_path', type=str, required=True)
-    parser_at.add_argument('--audio_path', type=str, required=True)
-    parser_at.add_argument('--cuda', action='store_true', default=False)
-    parser_sed = subparsers.add_parser('sound_event_detection')
-    parser_sed.add_argument('--sample_rate', type=int, default=32000)
-    parser_sed.add_argument('--window_size', type=int, default=1024)
-    parser_sed.add_argument('--hop_size', type=int, default=320)
-    parser_sed.add_argument('--mel_bins', type=int, default=64)
-    parser_sed.add_argument('--fmin', type=int, default=50)
-    parser_sed.add_argument('--fmax', type=int, default=14000)
-    parser_sed.add_argument('--model_type', type=str, required=True)
-    parser_sed.add_argument('--checkpoint_path', type=str, required=True)
-    parser_sed.add_argument('--audio_path', type=str, required=True)
-    parser_sed.add_argument('--cuda', action='store_true', default=False)
-    args = parser.parse_args()
-    if args.mode == 'audio_tagging':
-        audio_tagging(args)
-    elif args.mode == 'sound_event_detection':
-        sound_event_detection(args)
-    else:
-        raise Exception('Error argument!')

spaces/Ababababababbababa/Ashaar/poetry_diacritizer/util/text_encoders.py DELETED Viewed

@@ -1,160 +0,0 @@
-from . import text_cleaners
-from typing import Dict, List, Optional
-from .constants import ALL_POSSIBLE_HARAQAT
-import sentencepiece as spm
-class TextEncoder:
-    pad = "P"
-    def __init__(
-        self,
-        input_chars: List[str],
-        target_charts: List[str],
-        cleaner_fn: Optional[str] = None,
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        if cleaner_fn:
-            self.cleaner_fn = getattr(text_cleaners, cleaner_fn)
-        else:
-            self.cleaner_fn = None
-        self.input_symbols: List[str] = [TextEncoder.pad] + input_chars
-        self.target_symbols: List[str] = [TextEncoder.pad] + target_charts
-        if sp_model_path is None:
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: i for i, s in enumerate(self.input_symbols)
-            }
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for i, s in enumerate(self.input_symbols)
-            }
-        else:
-            sp_model = spm.SentencePieceProcessor()
-            sp_model.load(sp_model_path + "/sp.model")
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: sp_model.PieceToId(s+'▁') for s in self.input_symbols
-            }
-            self.input_symbol_to_id[" "] = sp_model.PieceToId("|")  # encode space
-            self.input_symbol_to_id[TextEncoder.pad] = 0  # encode padding
-            self.input_space_id = sp_model.PieceToId("|")
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for s, i in self.input_symbol_to_id.items()
-            }
-        self.target_symbol_to_id: Dict[str, int] = {
-            s: i for i, s in enumerate(self.target_symbols)
-        }
-        self.target_id_to_symbol: Dict[int, str] = {
-            i: s for i, s in enumerate(self.target_symbols)
-        }
-        self.reverse_input = reverse_input
-        self.reverse_target = reverse_target
-        self.input_pad_id = self.input_symbol_to_id[self.pad]
-        self.target_pad_id = self.target_symbol_to_id[self.pad]
-        self.start_symbol_id = None
-    def input_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_input:
-            text = "".join(list(reversed(text)))
-        sequence = [self.input_symbol_to_id[s] for s in text if s not in [self.pad]]
-        return sequence
-    def target_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_target:
-            text = "".join(list(reversed(text)))
-        sequence = [self.target_symbol_to_id[s] for s in text if s not in [self.pad]]
-        return sequence
-    def sequence_to_input(self, sequence: List[int]):
-        return [
-            self.input_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.input_id_to_symbol and symbol not in [self.input_pad_id]
-        ]
-    def sequence_to_target(self, sequence: List[int]):
-        return [
-            self.target_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.target_id_to_symbol and symbol not in [self.target_pad_id]
-        ]
-    def clean(self, text):
-        if self.cleaner_fn:
-            return self.cleaner_fn(text)
-        return text
-    def combine_text_and_haraqat(self, input_ids: List[int], output_ids: List[int]):
-        """
-        Combines the  input text with its corresponding  haraqat
-        Args:
-            inputs: a list of ids representing the input text
-            outputs: a list of ids representing the output text
-        Returns:
-        text: the text after merging the inputs text representation with the output
-        representation
-        """
-        output = ""
-        for i, input_id in enumerate(input_ids):
-            if input_id == self.input_pad_id:
-                break
-            output += self.input_id_to_symbol[input_id]
-            # if input_id == self.input_space_id:
-            #   continue
-            output += self.target_id_to_symbol[output_ids[i]]
-        return output
-    def __str__(self):
-        return type(self).__name__
-class BasicArabicEncoder(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys())
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-class ArabicEncoderWithStartSymbol(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        # the only difference from the basic encoder is adding the start symbol
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys()) + ["s"]
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-        self.start_symbol_id = self.target_symbol_to_id["s"]

spaces/AchyuthGamer/ImMagician/README.md DELETED Viewed

@@ -1,10 +0,0 @@
----
-title: ImMagician
-emoji: 🪄
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: true
----

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/childbehaviors/Move.js DELETED Viewed

@@ -1,156 +0,0 @@
-import IndexOf from '../../../../plugins/utils/object/IndexOf.js';
-import { GetDisplayWidth, GetDisplayHeight } from '../../../../plugins/utils/size/GetDisplaySize.js';
-import { WaitComplete } from '../../utils/WaitEvent.js';
-export default {
-    moveChild(child, duration, ease, distance) {
-        var key;
-        if (typeof (child) === 'string') {
-            key = child;
-            child = this.sizerChildren[key];
-        } else {
-            key = IndexOf(this.sizerChildren, child);
-        }
-        if (duration === undefined) {
-            duration = 500;
-        }
-        var isShownChild = (this.currentChildKey === key);
-        if (distance === undefined) {
-            switch (key) {
-                case 'leftSide':
-                case 'rightSide':
-                    distance = GetDisplayWidth(child);
-                    break;
-                case 'topSide':
-                case 'bottomSide':
-                    distance = GetDisplayHeight(child);
-                    break;
-                default: // 'panel'
-                    if (isShownChild) { // Show panel
-                        switch (this.previousChildKey) {
-                            case 'leftSide':
-                            case 'rightSide':
-                                distance = GetDisplayWidth(this.sizerChildren[this.previousChildKey]);
-                                break;
-                            case 'topSide':
-                            case 'bottomSide':
-                                distance = GetDisplayHeight(this.sizerChildren[this.previousChildKey]);
-                                break;
-                            default:
-                                distance = 0;
-                                break;
-                        }
-                    } else { // Hide panel
-                        switch (this.currentChildKey) {
-                            case 'leftSide':
-                            case 'rightSide':
-                                distance = GetDisplayWidth(this.sizerChildren[this.currentChildKey]);
-                                break;
-                            case 'topSide':
-                            case 'bottomSide':
-                                distance = GetDisplayHeight(this.sizerChildren[this.currentChildKey]);
-                                break;
-                            default:
-                                distance = 0;
-                                break;
-                        }
-                    }
-                    break;
-            }
-        }
-        var moveLeft, moveRight, moveUp, moveDown;
-        if (isShownChild) {
-            switch (key) {
-                case 'panel':
-                    switch (this.previousChildKey) {
-                        case 'leftSide':
-                            moveLeft = true;
-                            break;
-                        case 'rightSide':
-                            moveRight = true;
-                            break;
-                        case 'topSide':
-                            moveUp = true;
-                            break;
-                        case 'bottomSide':
-                            moveDown = true;
-                            break;
-                    }
-                    break;
-                case 'leftSide':
-                    moveRight = true;
-                    break;
-                case 'rightSide':
-                    moveLeft = true;
-                    break;
-                case 'topSide':
-                    moveDown = true;
-                    break;
-                case 'bottomSide':
-                    moveUp = true;
-                    break;
-            }
-        } else { // Hide
-            switch (key) {
-                case 'panel':
-                    switch (this.currentChildKey) {
-                        case 'leftSide':
-                            moveRight = true;
-                            break;
-                        case 'rightSide':
-                            moveLeft = true;
-                            break;
-                        case 'topSide':
-                            moveDown = true;
-                            break;
-                        case 'bottomSide':
-                            moveUp = true;
-                            break;
-                    }
-                    break;
-                case 'leftSide':
-                    moveLeft = true;
-                    break;
-                case 'rightSide':
-                    moveRight = true;
-                    break;
-                case 'topSide':
-                    moveUp = true;
-                    break;
-                case 'bottomSide':
-                    moveDown = true;
-                    break;
-            }
-        }
-        if (moveLeft) {
-            child.moveTo(duration, `-=${distance}`, undefined, ease);
-        } else if (moveRight) {
-            child.moveTo(duration, `+=${distance}`, undefined, ease);
-        } else if (moveUp) {
-            child.moveTo(duration, undefined, `-=${distance}`, ease);
-        } else if (moveDown) {
-            child.moveTo(duration, undefined, `+=${distance}`, ease);
-        } else {
-            child.moveTo(0);
-        }
-        return this;
-    },
-    moveChildPromise(child, duration, ease, distance) {
-        if (typeof (child) === 'string') {
-            child = this.sizerChildren[key];
-        }
-        this.moveChild(child, duration, ease, distance);
-        if (child._easeMove) {
-            return WaitComplete(child._easeMove);
-        } else {
-            return Promise.resolve();
-        }
-    }
-}

spaces/Alcedo/yunmedia/resources/chatgpt-plugin/js/app-legacy.8305dfab.js DELETED Viewed

The diff for this file is too large to render. See raw diff

spaces/AlexWang/lama/predict.py DELETED Viewed

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

spaces/AlexZou/Deploy_Restoration/net/Ushape_Trans.py DELETED Viewed

@@ -1,378 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Author  : Lintao Peng
-# @File    : Ushape_Trans.py
-# coding=utf-8
-# Design based on the pix2pix
-import torch.nn as nn
-import torch.nn.functional as F
-import torch
-import datetime
-import os
-import time
-import timeit
-import copy
-import numpy as np
-from torch.nn import ModuleList
-from torch.nn import Conv2d
-from torch.nn import LeakyReLU
-from net.block import *
-from net.block import _equalized_conv2d
-from net.SGFMT import TransformerModel
-from net.PositionalEncoding import FixedPositionalEncoding,LearnedPositionalEncoding
-from net.CMSFFT import ChannelTransformer
-##权重初始化
-def weights_init_normal(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find("BatchNorm2d") != -1:
-        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
-        torch.nn.init.constant_(m.bias.data, 0.0)
-class Generator(nn.Module):
-	"""
-	MSG-Unet-GAN的生成器部分
-	"""
-	def __init__(self,
-		img_dim=256,
-		patch_dim=16,
-		embedding_dim=512,
-		num_channels=3,
-		num_heads=8,
-		num_layers=4,
-		hidden_dim=256,
-		dropout_rate=0.0,
-		attn_dropout_rate=0.0,
-		in_ch=3,
-		out_ch=3,
-		conv_patch_representation=True,
-		positional_encoding_type="learned",
-		use_eql=True):
-		super(Generator, self).__init__()
-		assert embedding_dim % num_heads == 0
-		assert img_dim % patch_dim == 0
-		self.out_ch=out_ch #输出通道数
-		self.in_ch=in_ch #输入通道数
-		self.img_dim = img_dim   #输入图片尺寸
-		self.embedding_dim = embedding_dim  #512
-		self.num_heads = num_heads  #多头注意力中头的数量
-		self.patch_dim = patch_dim  #每个patch的尺寸
-		self.num_channels = num_channels  #图片通道数?
-		self.dropout_rate = dropout_rate  #drop-out比率
-		self.attn_dropout_rate = attn_dropout_rate  #注意力模块的dropout比率
-		self.conv_patch_representation = conv_patch_representation  #True
-		self.num_patches = int((img_dim // patch_dim) ** 2)  #将三通道图片分成多少块
-		self.seq_length = self.num_patches  #每个sequence的长度为patches的大小
-		self.flatten_dim = 128 * num_channels  #128*3=384
-        #线性编码
-		self.linear_encoding = nn.Linear(self.flatten_dim, self.embedding_dim)
-		#位置编码
-		if positional_encoding_type == "learned":
-			self.position_encoding = LearnedPositionalEncoding(
-				self.seq_length, self.embedding_dim, self.seq_length
-			)
-		elif positional_encoding_type == "fixed":
-			self.position_encoding = FixedPositionalEncoding(
-				self.embedding_dim,
-			)
-		self.pe_dropout = nn.Dropout(p=self.dropout_rate)
-		self.transformer = TransformerModel(
-			embedding_dim, #512
-			num_layers, #4
-			num_heads,  #8
-			hidden_dim,  #4096
-			self.dropout_rate,
-			self.attn_dropout_rate,
-        )
-		#layer Norm
-		self.pre_head_ln = nn.LayerNorm(embedding_dim)
-		if self.conv_patch_representation:
-			self.Conv_x = nn.Conv2d(
-				256,
-				self.embedding_dim,  #512
-				kernel_size=3,
-				stride=1,
-				padding=1
-		    )
-		self.bn = nn.BatchNorm2d(256)
-		self.relu = nn.ReLU(inplace=True)
-		#modulelist
-		self.rgb_to_feature=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
-		self.feature_to_rgb=ModuleList([to_rgb(32),to_rgb(64),to_rgb(128),to_rgb(256)])
-		self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
-		self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
-		self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
-		self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
-		self.Maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
-		self.Conv1=conv_block(self.in_ch, 16)
-		self.Conv1_1 = conv_block(16, 32)
-		self.Conv2 = conv_block(32, 32)
-		self.Conv2_1 = conv_block(32, 64)
-		self.Conv3 = conv_block(64,64)
-		self.Conv3_1 = conv_block(64,128)
-		self.Conv4 = conv_block(128,128)
-		self.Conv4_1 = conv_block(128,256)
-		self.Conv5 = conv_block(512,256)
-		#self.Conv_x = conv_block(256,512)
-		self.mtc = ChannelTransformer(channel_num=[32,64,128,256],
-									patchSize=[32, 16, 8, 4])
-		self.Up5 = up_conv(256, 256)
-		self.coatt5 = CCA(F_g=256, F_x=256)
-		self.Up_conv5 = conv_block(512, 256)
-		self.Up_conv5_1 = conv_block(256, 256)
-		self.Up4 = up_conv(256, 128)
-		self.coatt4 = CCA(F_g=128, F_x=128)
-		self.Up_conv4 = conv_block(256, 128)
-		self.Up_conv4_1 = conv_block(128, 128)
-		self.Up3 = up_conv(128, 64)
-		self.coatt3 = CCA(F_g=64, F_x=64)
-		self.Up_conv3 = conv_block(128, 64)
-		self.Up_conv3_1 = conv_block(64, 64)
-		self.Up2 = up_conv(64, 32)
-		self.coatt2 = CCA(F_g=32, F_x=32)
-		self.Up_conv2 = conv_block(64, 32)
-		self.Up_conv2_1 = conv_block(32, 32)
-		self.Conv = nn.Conv2d(32, self.out_ch, kernel_size=1, stride=1, padding=0)
-		# self.active = torch.nn.Sigmoid()
-		#
-	def reshape_output(self,x): #将transformer的输出resize为原来的特征图尺寸
-		x = x.view(
-			x.size(0),
-			int(self.img_dim / self.patch_dim),
-			int(self.img_dim / self.patch_dim),
-			self.embedding_dim,
-			)#B,16,16,512
-		x = x.permute(0, 3, 1, 2).contiguous()
-		return x
-	def forward(self, x):
-		#print(x.shape)
-		output=[]
-		x_1=self.Maxpool(x)
-		x_2=self.Maxpool(x_1)
-		x_3=self.Maxpool(x_2)
-		e1 = self.Conv1(x)
-		#print(e1.shape)
-		e1 = self.Conv1_1(e1)
-		e2 = self.Maxpool1(e1)
-		#32*128*128
-		x_1=self.rgb_to_feature[0](x_1)
-		#e2=torch.cat((x_1,e2), dim=1)
-		e2=x_1+e2
-		e2 = self.Conv2(e2)
-		e2 = self.Conv2_1(e2)
-		e3 = self.Maxpool2(e2)
-		#64*64*64
-		x_2=self.rgb_to_feature[1](x_2)
-		#e3=torch.cat((x_2,e3), dim=1)
-		e3=x_2+e3
-		e3 = self.Conv3(e3)
-		e3 = self.Conv3_1(e3)
-		e4 = self.Maxpool3(e3)
-		#128*32*32
-		x_3=self.rgb_to_feature[2](x_3)
-		#e4=torch.cat((x_3,e4), dim=1)
-		e4=x_3+e4
-		e4 = self.Conv4(e4)
-		e4 = self.Conv4_1(e4)
-		e5 = self.Maxpool4(e4)
-		#256*16*16
-		#channel-wise transformer-based attention
-		e1,e2,e3,e4,att_weights = self.mtc(e1,e2,e3,e4)
-		#spatial-wise transformer-based attention
-		residual=e5
-		#中间的隐变量
-		#conv_x应该接受256通道，输出512通道的中间隐变量
-		e5= self.bn(e5)
-		e5=self.relu(e5)
-		e5= self.Conv_x(e5) #out->512*16*16 shape->B,512,16,16
-		e5= e5.permute(0, 2, 3, 1).contiguous()  # B,512,16,16->B,16,16,512
-		e5= e5.view(e5.size(0), -1, self.embedding_dim) #B,16,16,512->B,16*16,512 线性映射层
-		e5= self.position_encoding(e5) #位置编码
-		e5= self.pe_dropout(e5)	 #预dropout层
-		# apply transformer
-		e5= self.transformer(e5)
-		e5= self.pre_head_ln(e5)
-		e5= self.reshape_output(e5)#out->512*16*16 shape->B,512,16,16
-		e5=self.Conv5(e5) #out->256,16,16 shape->B,256,16,16
-		#residual是否要加bn和relu？
-		e5=e5+residual
-		d5 = self.Up5(e5)
-		e4_att = self.coatt5(g=d5, x=e4)
-		d5 = torch.cat((e4_att, d5), dim=1)
-		d5 = self.Up_conv5(d5)
-		d5 = self.Up_conv5_1(d5)
-		#256
-		out3=self.feature_to_rgb[3](d5)
-		output.append(out3)#32*32orH/8,W/8
-		d4 = self.Up4(d5)
-		e3_att = self.coatt4(g=d4, x=e3)
-		d4 = torch.cat((e3_att, d4), dim=1)
-		d4 = self.Up_conv4(d4)
-		d4 = self.Up_conv4_1(d4)
-		#128
-		out2=self.feature_to_rgb[2](d4)
-		output.append(out2)#64*64orH/4,W/4
-		d3 = self.Up3(d4)
-		e2_att = self.coatt3(g=d3, x=e2)
-		d3 = torch.cat((e2_att, d3), dim=1)
-		d3 = self.Up_conv3(d3)
-		d3 = self.Up_conv3_1(d3)
-		#64
-		out1=self.feature_to_rgb[1](d3)
-		output.append(out1)#128#128orH/2,W/2
-		d2 = self.Up2(d3)
-		e1_att = self.coatt2(g=d2, x=e1)
-		d2 = torch.cat((e1_att, d2), dim=1)
-		d2 = self.Up_conv2(d2)
-		d2 = self.Up_conv2_1(d2)
-		#32
-		out0=self.feature_to_rgb[0](d2)
-		output.append(out0)#256*256
-		#out = self.Conv(d2)
-		#d1 = self.active(out)
-		#output=np.array(output)
-		return output[3]
-class Discriminator(nn.Module):
-    def __init__(self, in_channels=3,use_eql=True):
-        super(Discriminator, self).__init__()
-        self.use_eql=use_eql
-        self.in_channels=in_channels
-        #modulelist
-        self.rgb_to_feature1=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
-        self.rgb_to_feature2=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
-        self.layer=_equalized_conv2d(self.in_channels*2, 64, (1, 1), bias=True)
-        # pixel_wise feature normalizer:
-        self.pixNorm = PixelwiseNorm()
-        # leaky_relu:
-        self.lrelu = LeakyReLU(0.2)
-        self.layer0=DisGeneralConvBlock(64,64,use_eql=self.use_eql)
-        #128*128*32
-        self.layer1=DisGeneralConvBlock(128,128,use_eql=self.use_eql)
-        #64*64*64
-        self.layer2=DisGeneralConvBlock(256,256,use_eql=self.use_eql)
-        #32*32*128
-        self.layer3=DisGeneralConvBlock(512,512,use_eql=self.use_eql)
-        #16*16*256
-        self.layer4=DisFinalBlock(512,use_eql=self.use_eql)
-        #8*8*512
-    def forward(self, img_A, inputs):
-    	#inputs图片尺寸从小到大
-        # Concatenate image and condition image by channels to produce input
-        #img_input = torch.cat((img_A, img_B), 1)
-        #img_A_128= F.interpolate(img_A, size=[128, 128])
-        #img_A_64= F.interpolate(img_A, size=[64, 64])
-        #img_A_32= F.interpolate(img_A, size=[32, 32])
-        x=torch.cat((img_A[3], inputs[3]), 1)
-        y = self.pixNorm(self.lrelu(self.layer(x)))
-        y=self.layer0(y)
-        #128*128*64
-        x1=self.rgb_to_feature1[0](img_A[2])
-        x2=self.rgb_to_feature2[0](inputs[2])
-        x=torch.cat((x1,x2),1)
-        y=torch.cat((x,y),1)
-        y=self.layer1(y)
-        #64*64*128
-        x1=self.rgb_to_feature1[1](img_A[1])
-        x2=self.rgb_to_feature2[1](inputs[1])
-        x=torch.cat((x1,x2),1)
-        y=torch.cat((x,y),1)
-        y=self.layer2(y)
-        #32*32*256
-        x1=self.rgb_to_feature1[2](img_A[0])
-        x2=self.rgb_to_feature2[2](inputs[0])
-        x=torch.cat((x1,x2),1)
-        y=torch.cat((x,y),1)
-        y=self.layer3(y)
-        #16*16*512
-        y=self.layer4(y)
-        #8*8*512
-        return y

spaces/Alycer/VITS-Umamusume-voice-synthesizer/ONNXVITS_modules.py DELETED Viewed

@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-import commons
-from commons import init_weights, get_padding
-from ONNXVITS_transforms import piecewise_rational_quadratic_transform
-LRELU_SLOPE = 0.1
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-    if g is not None:
-      g = self.cond_layer(g)
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp DELETED Viewed

@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-#include "libipc/memory/resource.h"
-namespace ipc {
-namespace mem {
-void* pool_alloc::alloc(std::size_t size) {
-    return async_pool_alloc::alloc(size);
-}
-void pool_alloc::free(void* p, std::size_t size) {
-    async_pool_alloc::free(p, size);
-}
-} // namespace mem
-} // namespace ipc

spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py DELETED Viewed

@@ -1,13 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py DELETED Viewed

@@ -1,13 +0,0 @@
-_base_ = './ms_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/approx_max_iou_assigner.py DELETED Viewed

@@ -1,145 +0,0 @@
-import torch
-from ..builder import BBOX_ASSIGNERS
-from ..iou_calculators import build_iou_calculator
-from .max_iou_assigner import MaxIoUAssigner
-@BBOX_ASSIGNERS.register_module()
-class ApproxMaxIoUAssigner(MaxIoUAssigner):
-    """Assign a corresponding gt bbox or background to each bbox.
-    Each proposals will be assigned with an integer indicating the ground truth
-     index. (semi-positive index: gt label (0-based), -1: background)
-    - -1: negative sample, no assigned gt
-    - semi-positive integer: positive sample, index (0-based) of assigned gt
-    Args:
-        pos_iou_thr (float): IoU threshold for positive bboxes.
-        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
-        min_pos_iou (float): Minimum iou for a bbox to be considered as a
-            positive bbox. Positive samples can have smaller IoU than
-            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
-        gt_max_assign_all (bool): Whether to assign all bboxes with the same
-            highest overlap with some gt to that gt.
-        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
-            `gt_bboxes_ignore` is specified). Negative values mean not
-            ignoring any bboxes.
-        ignore_wrt_candidates (bool): Whether to compute the iof between
-            `bboxes` and `gt_bboxes_ignore`, or the contrary.
-        match_low_quality (bool): Whether to allow quality matches. This is
-            usually allowed for RPN and single stage detectors, but not allowed
-            in the second stage.
-        gpu_assign_thr (int): The upper bound of the number of GT for GPU
-            assign. When the number of gt is above this threshold, will assign
-            on CPU device. Negative values mean not assign on CPU.
-    """
-    def __init__(self,
-                 pos_iou_thr,
-                 neg_iou_thr,
-                 min_pos_iou=.0,
-                 gt_max_assign_all=True,
-                 ignore_iof_thr=-1,
-                 ignore_wrt_candidates=True,
-                 match_low_quality=True,
-                 gpu_assign_thr=-1,
-                 iou_calculator=dict(type='BboxOverlaps2D')):
-        self.pos_iou_thr = pos_iou_thr
-        self.neg_iou_thr = neg_iou_thr
-        self.min_pos_iou = min_pos_iou
-        self.gt_max_assign_all = gt_max_assign_all
-        self.ignore_iof_thr = ignore_iof_thr
-        self.ignore_wrt_candidates = ignore_wrt_candidates
-        self.gpu_assign_thr = gpu_assign_thr
-        self.match_low_quality = match_low_quality
-        self.iou_calculator = build_iou_calculator(iou_calculator)
-    def assign(self,
-               approxs,
-               squares,
-               approxs_per_octave,
-               gt_bboxes,
-               gt_bboxes_ignore=None,
-               gt_labels=None):
-        """Assign gt to approxs.
-        This method assign a gt bbox to each group of approxs (bboxes),
-        each group of approxs is represent by a base approx (bbox) and
-        will be assigned with -1, or a semi-positive number.
-        background_label (-1) means negative sample,
-        semi-positive number is the index (0-based) of assigned gt.
-        The assignment is done in following steps, the order matters.
-        1. assign every bbox to background_label (-1)
-        2. use the max IoU of each group of approxs to assign
-        2. assign proposals whose iou with all gts < neg_iou_thr to background
-        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
-           assign it to that bbox
-        4. for each gt bbox, assign its nearest proposals (may be more than
-           one) to itself
-        Args:
-            approxs (Tensor): Bounding boxes to be assigned,
-                shape(approxs_per_octave*n, 4).
-            squares (Tensor): Base Bounding boxes to be assigned,
-                shape(n, 4).
-            approxs_per_octave (int): number of approxs per octave
-            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
-            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
-                labelled as `ignored`, e.g., crowd boxes in COCO.
-            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        num_squares = squares.size(0)
-        num_gts = gt_bboxes.size(0)
-        if num_squares == 0 or num_gts == 0:
-            # No predictions and/or truth, return empty assignment
-            overlaps = approxs.new(num_gts, num_squares)
-            assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
-            return assign_result
-        # re-organize anchors by approxs_per_octave x num_squares
-        approxs = torch.transpose(
-            approxs.view(num_squares, approxs_per_octave, 4), 0,
-            1).contiguous().view(-1, 4)
-        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
-            num_gts > self.gpu_assign_thr) else False
-        # compute overlap and assign gt on CPU when number of GT is large
-        if assign_on_cpu:
-            device = approxs.device
-            approxs = approxs.cpu()
-            gt_bboxes = gt_bboxes.cpu()
-            if gt_bboxes_ignore is not None:
-                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
-            if gt_labels is not None:
-                gt_labels = gt_labels.cpu()
-        all_overlaps = self.iou_calculator(approxs, gt_bboxes)
-        overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares,
-                                        num_gts).max(dim=0)
-        overlaps = torch.transpose(overlaps, 0, 1)
-        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
-                and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0):
-            if self.ignore_wrt_candidates:
-                ignore_overlaps = self.iou_calculator(
-                    squares, gt_bboxes_ignore, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
-            else:
-                ignore_overlaps = self.iou_calculator(
-                    gt_bboxes_ignore, squares, mode='iof')
-                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
-            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
-        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
-        if assign_on_cpu:
-            assign_result.gt_inds = assign_result.gt_inds.to(device)
-            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
-            if assign_result.labels is not None:
-                assign_result.labels = assign_result.labels.to(device)
-        return assign_result

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/pascal_context_59.py DELETED Viewed

@@ -1,60 +0,0 @@
-# dataset settings
-dataset_type = 'PascalContextDataset59'
-data_root = 'data/VOCdevkit/VOC2010/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (520, 520)
-crop_size = (480, 480)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', reduce_zero_label=True),
-    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=img_scale,
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/train.txt',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='JPEGImages',
-        ann_dir='SegmentationClassContext',
-        split='ImageSets/SegmentationContext/val.txt',
-        pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fpn_r50.py DELETED Viewed

@@ -1,36 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 1, 1),
-        strides=(1, 2, 2, 2),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=4),
-    decode_head=dict(
-        type='FPNHead',
-        in_channels=[256, 256, 256, 256],
-        in_index=[0, 1, 2, 3],
-        feature_strides=[4, 8, 16, 32],
-        channels=128,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- _base_ = './dnl_r50-d8_512x1024_80k_cityscapes.py'
2	- model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py DELETED Viewed

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_512x512_160k_ade20k.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

spaces/Anonymous-sub/Rerender/gmflow_module/scripts/train_gmflow.sh DELETED Viewed

@@ -1,108 +0,0 @@
-#!/usr/bin/env bash
-# GMFlow without refinement
-# number of gpus for training, please set according to your hardware
-# by default use all gpus on a machine
-# can be trained on 4x 16GB V100 or 2x 32GB V100 or 2x 40GB A100 gpus
-NUM_GPUS=4
-# chairs
-CHECKPOINT_DIR=checkpoints/chairs-gmflow && \
-mkdir -p ${CHECKPOINT_DIR} && \
-python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \
---launcher pytorch \
---checkpoint_dir ${CHECKPOINT_DIR} \
---batch_size 16 \
---val_dataset chairs sintel kitti \
---lr 4e-4 \
---image_size 384 512 \
---padding_factor 16 \
---upsample_factor 8 \
---with_speed_metric \
---val_freq 10000 \
---save_ckpt_freq 10000 \
---num_steps 100000 \
-2>&1 | tee -a ${CHECKPOINT_DIR}/train.log
-# things (our final model is trained for 800K iterations, for ablation study, you can train for 200K)
-CHECKPOINT_DIR=checkpoints/things-gmflow && \
-mkdir -p ${CHECKPOINT_DIR} && \
-python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \
---launcher pytorch \
---checkpoint_dir ${CHECKPOINT_DIR} \
---resume checkpoints/chairs-gmflow/step_100000.pth \
---stage things \
---batch_size 8 \
---val_dataset things sintel kitti \
---lr 2e-4 \
---image_size 384 768 \
---padding_factor 16 \
---upsample_factor 8 \
---with_speed_metric \
---val_freq 40000 \
---save_ckpt_freq 50000 \
---num_steps 800000 \
-2>&1 | tee -a ${CHECKPOINT_DIR}/train.log
-# sintel
-CHECKPOINT_DIR=checkpoints/sintel-gmflow && \
-mkdir -p ${CHECKPOINT_DIR} && \
-python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \
---launcher pytorch \
---checkpoint_dir ${CHECKPOINT_DIR} \
---resume checkpoints/things-gmflow/step_800000.pth \
---stage sintel \
---batch_size 8 \
---val_dataset sintel kitti \
---lr 2e-4 \
---image_size 320 896 \
---padding_factor 16 \
---upsample_factor 8 \
---with_speed_metric \
---val_freq 20000 \
---save_ckpt_freq 20000 \
---num_steps 200000 \
-2>&1 | tee -a ${CHECKPOINT_DIR}/train.log
-# kitti
-CHECKPOINT_DIR=checkpoints/kitti-gmflow && \
-mkdir -p ${CHECKPOINT_DIR} && \
-python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \
---launcher pytorch \
---checkpoint_dir ${CHECKPOINT_DIR} \
---resume checkpoints/sintel-gmflow/step_200000.pth \
---stage kitti \
---batch_size 8 \
---val_dataset kitti \
---lr 2e-4 \
---image_size 320 1152 \
---padding_factor 16 \
---upsample_factor 8 \
---with_speed_metric \
---val_freq 10000 \
---save_ckpt_freq 10000 \
---num_steps 100000 \
-2>&1 | tee -a ${CHECKPOINT_DIR}/train.log
-# a final note: if your training is terminated unexpectedly, you can resume from the latest checkpoint
-# an example: resume chairs training
-# CHECKPOINT_DIR=checkpoints/chairs-gmflow && \
-# mkdir -p ${CHECKPOINT_DIR} && \
-# python -m torch.distributed.launch --nproc_per_node=${NUM_GPUS} --master_port=9989 main.py \
-# --launcher pytorch \
-# --checkpoint_dir ${CHECKPOINT_DIR} \
-# --resume checkpoints/chairs-gmflow/checkpoint_latest.pth \
-# --batch_size 16 \
-# --val_dataset chairs sintel kitti \
-# --lr 4e-4 \
-# --image_size 384 512 \
-# --padding_factor 16 \
-# --upsample_factor 8 \
-# --with_speed_metric \
-# --val_freq 10000 \
-# --save_ckpt_freq 10000 \
-# --num_steps 100000 \
-# 2>&1 | tee -a ${CHECKPOINT_DIR}/train.log

spaces/Artrajz/vits-simple-api/bert_vits2/transforms.py DELETED Viewed

@@ -1,192 +0,0 @@
-import torch
-from torch.nn import functional as F
-import numpy as np
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-def piecewise_rational_quadratic_transform(inputs,
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None,
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-    return outputs, logabsdet
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-    num_bins = unnormalized_widths.shape[-1]
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, logabsdet

spaces/AutoBG/Auto-BoardGame/Stream_to_Output/GameCleaner.py DELETED Viewed

@@ -1,144 +0,0 @@
-import pandas as pd
-import numpy as np
-import re
-import nltk
-from nltk.corpus import stopwords
-from gensim.parsing import preprocess_string, strip_tags, strip_numeric, strip_multiple_whitespaces, stem_text, strip_punctuation, remove_stopwords
-import spacy
-from langdetect import detect
-import pickle
-import gzip
-nltk.download('stopwords')
-#function definitions
-#strips values out of encoded stream lists
-def text_col_cleaner(frame, cols, pattern):
-    pattern = re.compile(pattern)
-    for col in cols:
-      frame[col] = frame[col].map(lambda x: [re.findall(pattern,val)[0].strip() for val in x], na_action='ignore')
-    return frame
-#converts specified columns to one-hot
-def encode_columns(frame):
-    targets = list(frame.columns)
-    for t in targets:
-        one_hot = pd.get_dummies(frame[t].apply(pd.Series).stack(),prefix=t).groupby(level=0).sum()
-        frame = pd.concat([frame,one_hot],axis=1)
-    return frame
-#custom text processor for tokenizing descriptions by Kuan Chen & Nick Canu
-def doc_text_preprocessing(ser):
-  nlp=spacy.load("en_core_web_sm", exclude=['parser','ner','textcat'])
-  """text processing steps"""
-  stop_words=set(stopwords.words('english'))
-  stop_words.update(['game','player','players','games', 'also',
-                     'description','publisher'])
-  single_letter_replace=lambda c: re.sub("\s+\w{1}\s+|\n|-|—",'',c)
-  to_lower_func=lambda c: c.lower()
-  lemma_text=[preprocess_string(
-      ' '.join([token.lemma_ for token in desc]
-          ),[remove_stopwords,strip_numeric,strip_punctuation,strip_tags,
-             strip_multiple_whitespaces,single_letter_replace,to_lower_func]
-             ) for desc in ser.apply(lambda x: nlp(x))]
-  tokenize_text=[[word for word in string if word not in stop_words] for string in lemma_text]
-  return tokenize_text
-#performs english language detection on the descriptions w/langdetect then additionally drops games using non-english characters in the name
-def lang_cleanup(frame):
-  nlp=spacy.load("en_core_web_sm")
-  frame['description']=frame['description'].fillna('no words')
-  frame = frame[frame['description']!='no words']
-  frame['cleaned_descriptions']=doc_text_preprocessing(frame['description'])
-  detected_lang = []
-  for word in frame.cleaned_descriptions:
-    word=', '.join(word)
-    detected_lang.append(detect(word))
-  frame['lang'] = detected_lang
-  frame = frame[frame['lang']=='en']
-  non_eng_title_filter = frame['name'].str.contains('[^\x00-\x7f]', flags=re.IGNORECASE)
-  return frame[~non_eng_title_filter]
-#column name stripper for creating key values
-def column_fixer(frame,targ):
-  return [col.replace(targ, "").strip('"') for col in frame.columns if col.startswith(targ)]
-#creates key list for defining web app lists & nlp tokens of the same unknown input search
-def key_collator(frame):
-  nlp=spacy.load("en_core_web_sm")
-  fam = column_fixer(frame,'family_')
-  gt = column_fixer(frame,'game_type_')
-  mec = column_fixer(frame,'mechanic_')
-  cat = column_fixer(frame,'category_')
-  current_keys = (['cooperative'],gt,mec,cat,fam)
-  fam_keys = [nlp(w) for w in fam]
-  gt_keys = [nlp(w) for w in gt]
-  mec_keys = [nlp(w) for w in mec]
-  cat_keys = [nlp(w) for w in cat]
-  search_tokens = (gt_keys,mec_keys,cat_keys,fam_keys)
-  return current_keys, search_tokens
-#-----------
-#reading in raw file & removing unranked and compilation game items
-df = pd.read_json(r'./bgg_GameItem.jl', lines=True)
-df['rank'] = df['rank'].fillna(0).astype(int)
-df = df[(df['rank']>0) & (df['compilation']!=1)]
-#separating and cleaning the one-hot target columns
-in_df = text_col_cleaner(frame = df[['game_type','mechanic','category','family']],
-                    cols = ['game_type','mechanic','category','family'],
-                    pattern = re.compile("([\S ]+)(?=:)"))
-print('Text has been cleaned, now encoding one-hot columns')
-#encoding one-hot columns and rejoining to features for output
-proc_df = encode_columns(in_df)
-step = df[['name','description','cooperative']]
-join_df = pd.concat([step,proc_df.drop(['game_type','mechanic','category','family',
-    'game_type_Amiga','game_type_Arcade','game_type_Atari ST',
-    'game_type_Commodore 64'],axis=1)],axis=1)
-print('Columns encoded, now performing english language detection and cleanup')
-#english language detection steps & first data save
-eng_df = lang_cleanup(join_df)
-eng_df = eng_df.loc[:,~eng_df.columns.duplicated()].copy().reset_index(drop=True).fillna(0)
-print('Creating vector-only dataframe & saving output')
-#vector only data for operations
-vector_df = eng_df.copy().drop(['name','description','cleaned_descriptions','lang'],axis=1)
-eng_df.to_parquet('game_data.parquet.gzip',compression='gzip')
-vector_df.to_parquet('game_vectors.parquet.gzip',compression='gzip')
-print('Creating key lists')
-#creating key lists - 1. string list of values by feature class for defining input selections & 2. nlp processed list for unknown input search
-keys, search_toks = key_collator(vector_df)
-with gzip.open("current_keys.gz", "wb") as f:
-    pickle.dump(keys, f)
-f.close()
-with gzip.open("key_search_tokens.gz", "wb") as f:
-    pickle.dump(search_toks, f)
-f.close()
-print('File creation is complete')

spaces/Benson/text-generation/Examples/Descargar Burbuja Bruja Saga 3 Apk.md DELETED Viewed

@@ -1,62 +0,0 @@
-<h1>Descargar MU Origin 3 APK: Una guía para los usuarios de Android</h1>
-<p>Si eres un fan de los MMORPG, es posible que hayas oído hablar de MU Origin 3, un popular juego móvil que ofrece un mundo de fantasía rico e inmersivo. En este artículo, le diremos todo lo que necesita saber sobre MU Origin 3, y cómo descargar su archivo APK para su dispositivo Android. </p>
-<h2>descargar burbuja bruja saga 3 apk</h2><br /><p><b><b>DOWNLOAD</b> &#127775; <a href="https://bltlly.com/2v6J8I">https://bltlly.com/2v6J8I</a></b></p><br /><br />
- <h2>¿Qué es MU Origin 3?</h2>
-<p>MU Origin 3 es un MMORPG móvil que se basa en el clásico juego de PC MU Online. Es desarrollado por Fingerfun y publicado por Webzen, la misma compañía que creó la franquicia original de MU. MU Origin 3 es la tercera entrega de la serie MU Origin, y presenta gráficos, jugabilidad y contenido mejorados. </p>
- <h3>Características de MU Origin 3</h3>
-<p>MU Origin 3 tiene muchas características que lo hacen destacar de otros MMORPG móviles. Aquí están algunas de ellas:</p>
- <h4>Impresionantes gráficos y efectos</h4>
-<p>MU Origin 3 cuenta con gráficos y efectos de próxima generación que te harán sentir como si estuvieras en un mundo de fantasía real. Podrás disfrutar de iluminación realista, sombras, reflejos y animaciones, así como de ciclos dinámicos de día y noche. También puedes personalizar la apariencia de tu personaje con varios disfraces, alas, monturas y mascotas. </p>
- <h4>Exploración del mundo abierto</h4>
-<p>MU Origin 3 ofrece un vasto y diverso mundo abierto que puedes explorar libremente. Puede viajar por tierra, mar o aire y descubrir diferentes regiones, como el Cañón Congelado, la Atlántida y el Bosque Oscuro. También puedes interactuar con NPCs, completar misiones, recopilar recursos y encontrar secretos ocultos. </p>
-<p></p>
- <h4>Diversas clases y habilidades</h4>
-<p>MU Origin 3 tiene seis clases para elegir: Caballero Oscuro, Mago Oscuro, Elfo, Gladiador Mágico, Señor Oscuro y Invocador. Cada clase tiene sus propias habilidades y habilidades únicas que puedes actualizar y personalizar. También puedes cambiar entre diferentes conjuntos de habilidades dependiendo de la situación. </p>
- <h4>Mazmorras épicas y redadas</h4>
- <h4>Modos PvP competitivos</h4>
-<p>MU Origin 3 tiene varios modos PvP que puedes disfrutar con otros jugadores. Puedes competir en batallas de arena, duelos, partidos de equipo, guerras entre servidores y más. También puedes posicionarte en las tablas de clasificación y ganar fama y gloria. </p>
- <h2>¿Por qué descargar MU Origin 3 APK? </h2>
-<p>Si usted está interesado en jugar MU Origin 3 en su dispositivo Android, es posible que desee descargar su archivo APK en lugar de instalarlo desde la Google Play Store. Aquí hay algunas razones por las que:</p>
- <h3>Beneficios de descargar MU Origin 3 APK</h3>
-<p>Descarga de MU Origin 3 APK tiene varios beneficios que usted no puede obtener de la versión de Google Play Store. Aquí están algunos de ellos:</p>
- <h4>Libre para jugar</h4>
-<p>M <p>MU Origin 3 es un juego gratuito que no requiere que gastes dinero para disfrutarlo. Puedes acceder a todas las funciones y contenidos sin pagar nada. Sin embargo, si quieres apoyar a los desarrolladores y obtener algunos beneficios adicionales, también puedes comprar algunos artículos opcionales con dinero real. </p>
- <h4>No hay anuncios ni compras en la aplicación</h4>
-<p>MU Origin 3 no tiene ningún anuncio molesto o ventanas emergentes que interrumpirán su experiencia de juego. Puedes jugar el juego sin distracciones ni interrupciones. Además, MU Origin 3 no tiene compras en la aplicación que den ventajas injustas a algunos jugadores. Puedes progresar en el juego por tus propias habilidades y esfuerzos. </p>
- <h4>Últimas actualizaciones y parches</h4>
-<p>MU Origin 3 es constantemente actualizado y parcheado por los desarrolladores para corregir errores, mejorar el rendimiento y agregar nuevo contenido. Al descargar el archivo APK, puede obtener la última versión del juego tan pronto como se libera. No tienes que esperar a que Google Play Store apruebe y distribuya las actualizaciones. </p>
- <h4>Compatible con la mayoría de dispositivos Android</h4>
- <h2>Cómo descargar MU Origin 3 APK? </h2>
-<p>Si está convencido de que la descarga de MU Origin 3 APK es una buena idea, es posible que se pregunte cómo hacerlo. No se preocupe, es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
- <h3> Pasos para descargar MU Origin 3 APK</h3>
-<p>Aquí están los pasos para descargar MU Origin 3 APK para su dispositivo Android:</p>
- <h4>Visita el sitio web oficial o Google Play Store</h4>
-<p>El primer paso es visitar el sitio web oficial de MU Origin 3 o la página de Google Play Store del juego. Puede utilizar cualquier navegador de su dispositivo para hacer esto. También puede escanear el código QR a continuación para ir directamente a la página de descarga. </p>
- <img src="https://www.qrcode-monkey.com/img/default-preview-qr.svg" alt="QR code for MU Origin 3 download page" width="200" height=">
- <h4>Toque en el botón de descarga</h4>
-<p>El siguiente paso es tocar el botón de descarga en el sitio web o la página de Google Play Store. Esto comenzará a descargar el archivo APK de MU Origin 3 en su dispositivo. Es posible que vea un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo". No se preocupe, esto es solo un mensaje estándar para todos los archivos APK. Solo toca "Aceptar" o "Descargar de todos modos" para continuar. </p>
- <h4>Permitir fuentes desconocidas en la configuración</h4>
-<p>El tercer paso es permitir fuentes desconocidas en su configuración. Esto es necesario porque los dispositivos Android normalmente bloquean la instalación de aplicaciones desde fuentes distintas de Google Play Store. Para permitir fuentes desconocidas, ve a la configuración del dispositivo, luego a la seguridad y luego a fuentes desconocidas. Activa el interruptor o marca la casilla que dice "Permitir la instalación de aplicaciones desde fuentes desconocidas". Es posible que vea un mensaje de confirmación que dice "Su teléfono y los datos personales son más vulnerables a los ataques de aplicaciones de fuentes desconocidas". Solo toca "Aceptar" o "Permitir" para continuar. </p>
- <h4>Instalar el archivo APK y lanzar el juego</h4>
- <h2>Conclusión</h2>
-<p>MU Origin 3 es un MMORPG móvil que ofrece un mundo de fantasía rico e inmersivo con gráficos impresionantes, exploración del mundo abierto, diversas clases y habilidades, mazmorras y redadas épicas y modos PvP competitivos. Es gratis para jugar, no tiene anuncios o compras en la aplicación, tiene las últimas actualizaciones y parches, y es compatible con la mayoría de los dispositivos Android. Para descargar MU Origin 3 APK para su dispositivo Android, basta con visitar el sitio web oficial o Google Play Store página del juego, toque en el botón de descarga, permitir fuentes desconocidas en la configuración, instalar el archivo APK y lanzar el juego. </p>
- <p>Esperamos que este artículo te haya ayudado a aprender más sobre MU Origin 3 y cómo descargar su archivo APK para tu dispositivo Android. Si usted tiene alguna pregunta o retroalimentación, por favor no dude en dejar un comentario a continuación. Gracias por leer! </ <p>Como bono, aquí hay algunas preguntas y respuestas frecuentes sobre MU Origin 3 y su archivo APK:</p>
- <h3>Preguntas frecuentes</h3>
-<ol>
-<li> ¿Es seguro descargar y jugar MU Origin 3? </li>
-<p>Sí, MU Origin 3 es seguro para descargar y jugar. Es desarrollado y publicado por empresas de renombre, y no contiene ningún virus, malware o spyware. Sin embargo, siempre debe descargar el archivo APK desde el sitio web oficial o la página de Google Play Store del juego, y no desde fuentes de terceros que podrían ser poco fiables o maliciosos. </p>
-<li> ¿Cuánto espacio de almacenamiento requiere MU Origin 3? </li>
-<p>MU Origin 3 requiere aproximadamente 2 GB de espacio de almacenamiento en su dispositivo. También debe tener un poco de espacio adicional para las actualizaciones y parches que podrían ser liberados en el futuro. Puede comprobar la capacidad de almacenamiento de su dispositivo y liberar algo de espacio mediante la eliminación de archivos o aplicaciones no deseados. </p>
-<li>¿Puedo jugar MU Origin 3 sin conexión? </li>
-<li>¿Puedo jugar MU Origin 3 con otros jugadores? </li>
-<p>Sí, MU Origin 3 es un juego multijugador que te permite jugar con otros jugadores de todo el mundo. Puedes chatear, intercambiar, formar equipo, luchar y competir con otros jugadores en varios modos y eventos. También puedes unirte a gremios y hacer amigos con otros jugadores que compartan tus intereses y objetivos. </p>
-<li> ¿Puedo transferir mi progreso de MU Origin o MU Origin 2 a MU Origin 3?</li>
-<p>No, MU Origin 3 es un juego separado de MU Origin y MU Origin 2, y no admite transferencia de datos ni sincronización. Tendrás que empezar desde cero y crear un nuevo personaje en MU Origin 3. Sin embargo, puedes mantener tus personajes antiguos en MU Origin y MU Origin 2, y reproducirlos por separado si quieres. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/lib/utils/deepestChild.ts DELETED Viewed

@@ -1,7 +0,0 @@
-export function deepestChild(el: HTMLElement) {
-	let newEl = el;
-	while (newEl.hasChildNodes()) {
-		newEl = newEl.lastElementChild as HTMLElement;
-	}
-	return newEl;
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/collection.py DELETED Viewed

@@ -1,312 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import os
-from botocore import xform_name
-from botocore.docs.bcdoc.restdoc import DocumentStructure
-from botocore.docs.method import get_instance_public_methods
-from botocore.docs.utils import DocumentedShape
-from boto3.docs.base import NestedDocumenter
-from boto3.docs.method import document_model_driven_resource_method
-from boto3.docs.utils import (
-    add_resource_type_overview,
-    get_resource_ignore_params,
-)
-class CollectionDocumenter(NestedDocumenter):
-    def document_collections(self, section):
-        collections = self._resource.meta.resource_model.collections
-        collections_list = []
-        add_resource_type_overview(
-            section=section,
-            resource_type='Collections',
-            description=(
-                'Collections provide an interface to iterate over and '
-                'manipulate groups of resources. '
-            ),
-            intro_link='guide_collections',
-        )
-        self.member_map['collections'] = collections_list
-        for collection in collections:
-            collections_list.append(collection.name)
-            # Create a new DocumentStructure for each collection and add contents.
-            collection_doc = DocumentStructure(collection.name, target='html')
-            breadcrumb_section = collection_doc.add_new_section('breadcrumb')
-            breadcrumb_section.style.ref(self._resource_class_name, 'index')
-            breadcrumb_section.write(f' / Collection / {collection.name}')
-            collection_doc.add_title_section(collection.name)
-            collection_section = collection_doc.add_new_section(
-                collection.name,
-                context={'qualifier': f'{self.class_name}.'},
-            )
-            self._document_collection(collection_section, collection)
-            # Write collections in individual/nested files.
-            # Path: <root>/reference/services/<service>/<resource_name>/<collection_name>.rst
-            collections_dir_path = os.path.join(
-                self._root_docs_path,
-                f'{self._service_name}',
-                f'{self._resource_sub_path}',
-            )
-            collection_doc.write_to_file(collections_dir_path, collection.name)
-    def _document_collection(self, section, collection):
-        methods = get_instance_public_methods(
-            getattr(self._resource, collection.name)
-        )
-        document_collection_object(section, collection)
-        batch_actions = {}
-        for batch_action in collection.batch_actions:
-            batch_actions[batch_action.name] = batch_action
-        for method in sorted(methods):
-            method_section = section.add_new_section(method)
-            if method in batch_actions:
-                document_batch_action(
-                    section=method_section,
-                    resource_name=self._resource_name,
-                    event_emitter=self._resource.meta.client.meta.events,
-                    batch_action_model=batch_actions[method],
-                    collection_model=collection,
-                    service_model=self._resource.meta.client.meta.service_model,
-                )
-            else:
-                document_collection_method(
-                    section=method_section,
-                    resource_name=self._resource_name,
-                    action_name=method,
-                    event_emitter=self._resource.meta.client.meta.events,
-                    collection_model=collection,
-                    service_model=self._resource.meta.client.meta.service_model,
-                )
-def document_collection_object(
-    section,
-    collection_model,
-    include_signature=True,
-):
-    """Documents a collection resource object
-    :param section: The section to write to
-    :param collection_model: The model of the collection
-    :param include_signature: Whether or not to include the signature.
-        It is useful for generating docstrings.
-    """
-    if include_signature:
-        full_collection_name = (
-            f"{section.context.get('qualifier', '')}{collection_model.name}"
-        )
-        section.style.start_sphinx_py_attr(full_collection_name)
-    section.include_doc_string(
-        f'A collection of {collection_model.resource.type} resources.'
-    )
-    section.include_doc_string(
-        f'A {collection_model.resource.type} Collection will include all '
-        f'resources by default, and extreme caution should be taken when '
-        f'performing actions on all resources.'
-    )
-def document_batch_action(
-    section,
-    resource_name,
-    event_emitter,
-    batch_action_model,
-    service_model,
-    collection_model,
-    include_signature=True,
-):
-    """Documents a collection's batch action
-    :param section: The section to write to
-    :param resource_name: The name of the resource
-    :param action_name: The name of collection action. Currently only
-        can be all, filter, limit, or page_size
-    :param event_emitter: The event emitter to use to emit events
-    :param batch_action_model: The model of the batch action
-    :param collection_model: The model of the collection
-    :param service_model: The model of the service
-    :param include_signature: Whether or not to include the signature.
-        It is useful for generating docstrings.
-    """
-    operation_model = service_model.operation_model(
-        batch_action_model.request.operation
-    )
-    ignore_params = get_resource_ignore_params(
-        batch_action_model.request.params
-    )
-    example_return_value = 'response'
-    if batch_action_model.resource:
-        example_return_value = xform_name(batch_action_model.resource.type)
-    example_resource_name = xform_name(resource_name)
-    if service_model.service_name == resource_name:
-        example_resource_name = resource_name
-    example_prefix = '{} = {}.{}.{}'.format(
-        example_return_value,
-        example_resource_name,
-        collection_model.name,
-        batch_action_model.name,
-    )
-    document_model_driven_resource_method(
-        section=section,
-        method_name=batch_action_model.name,
-        operation_model=operation_model,
-        event_emitter=event_emitter,
-        method_description=operation_model.documentation,
-        example_prefix=example_prefix,
-        exclude_input=ignore_params,
-        resource_action_model=batch_action_model,
-        include_signature=include_signature,
-    )
-def document_collection_method(
-    section,
-    resource_name,
-    action_name,
-    event_emitter,
-    collection_model,
-    service_model,
-    include_signature=True,
-):
-    """Documents a collection method
-    :param section: The section to write to
-    :param resource_name: The name of the resource
-    :param action_name: The name of collection action. Currently only
-        can be all, filter, limit, or page_size
-    :param event_emitter: The event emitter to use to emit events
-    :param collection_model: The model of the collection
-    :param service_model: The model of the service
-    :param include_signature: Whether or not to include the signature.
-        It is useful for generating docstrings.
-    """
-    operation_model = service_model.operation_model(
-        collection_model.request.operation
-    )
-    underlying_operation_members = []
-    if operation_model.input_shape:
-        underlying_operation_members = operation_model.input_shape.members
-    example_resource_name = xform_name(resource_name)
-    if service_model.service_name == resource_name:
-        example_resource_name = resource_name
-    custom_action_info_dict = {
-        'all': {
-            'method_description': (
-                f'Creates an iterable of all {collection_model.resource.type} '
-                f'resources in the collection.'
-            ),
-            'example_prefix': '{}_iterator = {}.{}.all'.format(
-                xform_name(collection_model.resource.type),
-                example_resource_name,
-                collection_model.name,
-            ),
-            'exclude_input': underlying_operation_members,
-        },
-        'filter': {
-            'method_description': (
-                f'Creates an iterable of all {collection_model.resource.type} '
-                f'resources in the collection filtered by kwargs passed to '
-                f'method. A {collection_model.resource.type} collection will '
-                f'include all resources by default if no filters are provided, '
-                f'and extreme caution should be taken when performing actions '
-                f'on all resources.'
-            ),
-            'example_prefix': '{}_iterator = {}.{}.filter'.format(
-                xform_name(collection_model.resource.type),
-                example_resource_name,
-                collection_model.name,
-            ),
-            'exclude_input': get_resource_ignore_params(
-                collection_model.request.params
-            ),
-        },
-        'limit': {
-            'method_description': (
-                f'Creates an iterable up to a specified amount of '
-                f'{collection_model.resource.type} resources in the collection.'
-            ),
-            'example_prefix': '{}_iterator = {}.{}.limit'.format(
-                xform_name(collection_model.resource.type),
-                example_resource_name,
-                collection_model.name,
-            ),
-            'include_input': [
-                DocumentedShape(
-                    name='count',
-                    type_name='integer',
-                    documentation=(
-                        'The limit to the number of resources '
-                        'in the iterable.'
-                    ),
-                )
-            ],
-            'exclude_input': underlying_operation_members,
-        },
-        'page_size': {
-            'method_description': (
-                f'Creates an iterable of all {collection_model.resource.type} '
-                f'resources in the collection, but limits the number of '
-                f'items returned by each service call by the specified amount.'
-            ),
-            'example_prefix': '{}_iterator = {}.{}.page_size'.format(
-                xform_name(collection_model.resource.type),
-                example_resource_name,
-                collection_model.name,
-            ),
-            'include_input': [
-                DocumentedShape(
-                    name='count',
-                    type_name='integer',
-                    documentation=(
-                        'The number of items returned by each ' 'service call'
-                    ),
-                )
-            ],
-            'exclude_input': underlying_operation_members,
-        },
-    }
-    if action_name in custom_action_info_dict:
-        action_info = custom_action_info_dict[action_name]
-        document_model_driven_resource_method(
-            section=section,
-            method_name=action_name,
-            operation_model=operation_model,
-            event_emitter=event_emitter,
-            resource_action_model=collection_model,
-            include_signature=include_signature,
-            **action_info,
-        )

spaces/Big-Web/MMSD/env/Lib/site-packages/jmespath/parser.py DELETED Viewed

@@ -1,527 +0,0 @@
-"""Top down operator precedence parser.
-This is an implementation of Vaughan R. Pratt's
-"Top Down Operator Precedence" parser.
-(http://dl.acm.org/citation.cfm?doid=512927.512931).
-These are some additional resources that help explain the
-general idea behind a Pratt parser:
-* http://effbot.org/zone/simple-top-down-parsing.htm
-* http://javascript.crockford.com/tdop/tdop.html
-A few notes on the implementation.
-* All the nud/led tokens are on the Parser class itself, and are dispatched
-  using getattr().  This keeps all the parsing logic contained to a single
-  class.
-* We use two passes through the data.  One to create a list of token,
-  then one pass through the tokens to create the AST.  While the lexer actually
-  yields tokens, we convert it to a list so we can easily implement two tokens
-  of lookahead.  A previous implementation used a fixed circular buffer, but it
-  was significantly slower.  Also, the average jmespath expression typically
-  does not have a large amount of token so this is not an issue.  And
-  interestingly enough, creating a token list first is actually faster than
-  consuming from the token iterator one token at a time.
-"""
-import random
-from jmespath import lexer
-from jmespath.compat import with_repr_method
-from jmespath import ast
-from jmespath import exceptions
-from jmespath import visitor
-class Parser(object):
-    BINDING_POWER = {
-        'eof': 0,
-        'unquoted_identifier': 0,
-        'quoted_identifier': 0,
-        'literal': 0,
-        'rbracket': 0,
-        'rparen': 0,
-        'comma': 0,
-        'rbrace': 0,
-        'number': 0,
-        'current': 0,
-        'expref': 0,
-        'colon': 0,
-        'pipe': 1,
-        'or': 2,
-        'and': 3,
-        'eq': 5,
-        'gt': 5,
-        'lt': 5,
-        'gte': 5,
-        'lte': 5,
-        'ne': 5,
-        'flatten': 9,
-        # Everything above stops a projection.
-        'star': 20,
-        'filter': 21,
-        'dot': 40,
-        'not': 45,
-        'lbrace': 50,
-        'lbracket': 55,
-        'lparen': 60,
-    }
-    # The maximum binding power for a token that can stop
-    # a projection.
-    _PROJECTION_STOP = 10
-    # The _MAX_SIZE most recent expressions are cached in
-    # _CACHE dict.
-    _CACHE = {}
-    _MAX_SIZE = 128
-    def __init__(self, lookahead=2):
-        self.tokenizer = None
-        self._tokens = [None] * lookahead
-        self._buffer_size = lookahead
-        self._index = 0
-    def parse(self, expression):
-        cached = self._CACHE.get(expression)
-        if cached is not None:
-            return cached
-        parsed_result = self._do_parse(expression)
-        self._CACHE[expression] = parsed_result
-        if len(self._CACHE) > self._MAX_SIZE:
-            self._free_cache_entries()
-        return parsed_result
-    def _do_parse(self, expression):
-        try:
-            return self._parse(expression)
-        except exceptions.LexerError as e:
-            e.expression = expression
-            raise
-        except exceptions.IncompleteExpressionError as e:
-            e.set_expression(expression)
-            raise
-        except exceptions.ParseError as e:
-            e.expression = expression
-            raise
-    def _parse(self, expression):
-        self.tokenizer = lexer.Lexer().tokenize(expression)
-        self._tokens = list(self.tokenizer)
-        self._index = 0
-        parsed = self._expression(binding_power=0)
-        if not self._current_token() == 'eof':
-            t = self._lookahead_token(0)
-            raise exceptions.ParseError(t['start'], t['value'], t['type'],
-                                        "Unexpected token: %s" % t['value'])
-        return ParsedResult(expression, parsed)
-    def _expression(self, binding_power=0):
-        left_token = self._lookahead_token(0)
-        self._advance()
-        nud_function = getattr(
-            self, '_token_nud_%s' % left_token['type'],
-            self._error_nud_token)
-        left = nud_function(left_token)
-        current_token = self._current_token()
-        while binding_power < self.BINDING_POWER[current_token]:
-            led = getattr(self, '_token_led_%s' % current_token, None)
-            if led is None:
-                error_token = self._lookahead_token(0)
-                self._error_led_token(error_token)
-            else:
-                self._advance()
-                left = led(left)
-                current_token = self._current_token()
-        return left
-    def _token_nud_literal(self, token):
-        return ast.literal(token['value'])
-    def _token_nud_unquoted_identifier(self, token):
-        return ast.field(token['value'])
-    def _token_nud_quoted_identifier(self, token):
-        field = ast.field(token['value'])
-        # You can't have a quoted identifier as a function
-        # name.
-        if self._current_token() == 'lparen':
-            t = self._lookahead_token(0)
-            raise exceptions.ParseError(
-                0, t['value'], t['type'],
-                'Quoted identifier not allowed for function names.')
-        return field
-    def _token_nud_star(self, token):
-        left = ast.identity()
-        if self._current_token() == 'rbracket':
-            right = ast.identity()
-        else:
-            right = self._parse_projection_rhs(self.BINDING_POWER['star'])
-        return ast.value_projection(left, right)
-    def _token_nud_filter(self, token):
-        return self._token_led_filter(ast.identity())
-    def _token_nud_lbrace(self, token):
-        return self._parse_multi_select_hash()
-    def _token_nud_lparen(self, token):
-        expression = self._expression()
-        self._match('rparen')
-        return expression
-    def _token_nud_flatten(self, token):
-        left = ast.flatten(ast.identity())
-        right = self._parse_projection_rhs(
-            self.BINDING_POWER['flatten'])
-        return ast.projection(left, right)
-    def _token_nud_not(self, token):
-        expr = self._expression(self.BINDING_POWER['not'])
-        return ast.not_expression(expr)
-    def _token_nud_lbracket(self, token):
-        if self._current_token() in ['number', 'colon']:
-            right = self._parse_index_expression()
-            # We could optimize this and remove the identity() node.
-            # We don't really need an index_expression node, we can
-            # just use emit an index node here if we're not dealing
-            # with a slice.
-            return self._project_if_slice(ast.identity(), right)
-        elif self._current_token() == 'star' and \
-                self._lookahead(1) == 'rbracket':
-            self._advance()
-            self._advance()
-            right = self._parse_projection_rhs(self.BINDING_POWER['star'])
-            return ast.projection(ast.identity(), right)
-        else:
-            return self._parse_multi_select_list()
-    def _parse_index_expression(self):
-        # We're here:
-        # [<current>
-        #  ^
-        #  | current token
-        if (self._lookahead(0) == 'colon' or
-                self._lookahead(1) == 'colon'):
-            return self._parse_slice_expression()
-        else:
-            # Parse the syntax [number]
-            node = ast.index(self._lookahead_token(0)['value'])
-            self._advance()
-            self._match('rbracket')
-            return node
-    def _parse_slice_expression(self):
-        # [start:end:step]
-        # Where start, end, and step are optional.
-        # The last colon is optional as well.
-        parts = [None, None, None]
-        index = 0
-        current_token = self._current_token()
-        while not current_token == 'rbracket' and index < 3:
-            if current_token == 'colon':
-                index += 1
-                if index == 3:
-                    self._raise_parse_error_for_token(
-                        self._lookahead_token(0), 'syntax error')
-                self._advance()
-            elif current_token == 'number':
-                parts[index] = self._lookahead_token(0)['value']
-                self._advance()
-            else:
-                self._raise_parse_error_for_token(
-                    self._lookahead_token(0), 'syntax error')
-            current_token = self._current_token()
-        self._match('rbracket')
-        return ast.slice(*parts)
-    def _token_nud_current(self, token):
-        return ast.current_node()
-    def _token_nud_expref(self, token):
-        expression = self._expression(self.BINDING_POWER['expref'])
-        return ast.expref(expression)
-    def _token_led_dot(self, left):
-        if not self._current_token() == 'star':
-            right = self._parse_dot_rhs(self.BINDING_POWER['dot'])
-            if left['type'] == 'subexpression':
-                left['children'].append(right)
-                return left
-            else:
-                return ast.subexpression([left, right])
-        else:
-            # We're creating a projection.
-            self._advance()
-            right = self._parse_projection_rhs(
-                self.BINDING_POWER['dot'])
-            return ast.value_projection(left, right)
-    def _token_led_pipe(self, left):
-        right = self._expression(self.BINDING_POWER['pipe'])
-        return ast.pipe(left, right)
-    def _token_led_or(self, left):
-        right = self._expression(self.BINDING_POWER['or'])
-        return ast.or_expression(left, right)
-    def _token_led_and(self, left):
-        right = self._expression(self.BINDING_POWER['and'])
-        return ast.and_expression(left, right)
-    def _token_led_lparen(self, left):
-        if left['type'] != 'field':
-            #  0 - first func arg or closing paren.
-            # -1 - '(' token
-            # -2 - invalid function "name".
-            prev_t = self._lookahead_token(-2)
-            raise exceptions.ParseError(
-                prev_t['start'], prev_t['value'], prev_t['type'],
-                "Invalid function name '%s'" % prev_t['value'])
-        name = left['value']
-        args = []
-        while not self._current_token() == 'rparen':
-            expression = self._expression()
-            if self._current_token() == 'comma':
-                self._match('comma')
-            args.append(expression)
-        self._match('rparen')
-        function_node = ast.function_expression(name, args)
-        return function_node
-    def _token_led_filter(self, left):
-        # Filters are projections.
-        condition = self._expression(0)
-        self._match('rbracket')
-        if self._current_token() == 'flatten':
-            right = ast.identity()
-        else:
-            right = self._parse_projection_rhs(self.BINDING_POWER['filter'])
-        return ast.filter_projection(left, right, condition)
-    def _token_led_eq(self, left):
-        return self._parse_comparator(left, 'eq')
-    def _token_led_ne(self, left):
-        return self._parse_comparator(left, 'ne')
-    def _token_led_gt(self, left):
-        return self._parse_comparator(left, 'gt')
-    def _token_led_gte(self, left):
-        return self._parse_comparator(left, 'gte')
-    def _token_led_lt(self, left):
-        return self._parse_comparator(left, 'lt')
-    def _token_led_lte(self, left):
-        return self._parse_comparator(left, 'lte')
-    def _token_led_flatten(self, left):
-        left = ast.flatten(left)
-        right = self._parse_projection_rhs(
-            self.BINDING_POWER['flatten'])
-        return ast.projection(left, right)
-    def _token_led_lbracket(self, left):
-        token = self._lookahead_token(0)
-        if token['type'] in ['number', 'colon']:
-            right = self._parse_index_expression()
-            if left['type'] == 'index_expression':
-                # Optimization: if the left node is an index expr,
-                # we can avoid creating another node and instead just add
-                # the right node as a child of the left.
-                left['children'].append(right)
-                return left
-            else:
-                return self._project_if_slice(left, right)
-        else:
-            # We have a projection
-            self._match('star')
-            self._match('rbracket')
-            right = self._parse_projection_rhs(self.BINDING_POWER['star'])
-            return ast.projection(left, right)
-    def _project_if_slice(self, left, right):
-        index_expr = ast.index_expression([left, right])
-        if right['type'] == 'slice':
-            return ast.projection(
-                index_expr,
-                self._parse_projection_rhs(self.BINDING_POWER['star']))
-        else:
-            return index_expr
-    def _parse_comparator(self, left, comparator):
-        right = self._expression(self.BINDING_POWER[comparator])
-        return ast.comparator(comparator, left, right)
-    def _parse_multi_select_list(self):
-        expressions = []
-        while True:
-            expression = self._expression()
-            expressions.append(expression)
-            if self._current_token() == 'rbracket':
-                break
-            else:
-                self._match('comma')
-        self._match('rbracket')
-        return ast.multi_select_list(expressions)
-    def _parse_multi_select_hash(self):
-        pairs = []
-        while True:
-            key_token = self._lookahead_token(0)
-            # Before getting the token value, verify it's
-            # an identifier.
-            self._match_multiple_tokens(
-                token_types=['quoted_identifier', 'unquoted_identifier'])
-            key_name = key_token['value']
-            self._match('colon')
-            value = self._expression(0)
-            node = ast.key_val_pair(key_name=key_name, node=value)
-            pairs.append(node)
-            if self._current_token() == 'comma':
-                self._match('comma')
-            elif self._current_token() == 'rbrace':
-                self._match('rbrace')
-                break
-        return ast.multi_select_dict(nodes=pairs)
-    def _parse_projection_rhs(self, binding_power):
-        # Parse the right hand side of the projection.
-        if self.BINDING_POWER[self._current_token()] < self._PROJECTION_STOP:
-            # BP of 10 are all the tokens that stop a projection.
-            right = ast.identity()
-        elif self._current_token() == 'lbracket':
-            right = self._expression(binding_power)
-        elif self._current_token() == 'filter':
-            right = self._expression(binding_power)
-        elif self._current_token() == 'dot':
-            self._match('dot')
-            right = self._parse_dot_rhs(binding_power)
-        else:
-            self._raise_parse_error_for_token(self._lookahead_token(0),
-                                              'syntax error')
-        return right
-    def _parse_dot_rhs(self, binding_power):
-        # From the grammar:
-        # expression '.' ( identifier /
-        #                  multi-select-list /
-        #                  multi-select-hash /
-        #                  function-expression /
-        #                  *
-        # In terms of tokens that means that after a '.',
-        # you can have:
-        lookahead = self._current_token()
-        # Common case "foo.bar", so first check for an identifier.
-        if lookahead in ['quoted_identifier', 'unquoted_identifier', 'star']:
-            return self._expression(binding_power)
-        elif lookahead == 'lbracket':
-            self._match('lbracket')
-            return self._parse_multi_select_list()
-        elif lookahead == 'lbrace':
-            self._match('lbrace')
-            return self._parse_multi_select_hash()
-        else:
-            t = self._lookahead_token(0)
-            allowed = ['quoted_identifier', 'unquoted_identifier',
-                       'lbracket', 'lbrace']
-            msg = (
-                "Expecting: %s, got: %s" % (allowed, t['type'])
-            )
-            self._raise_parse_error_for_token(t, msg)
-    def _error_nud_token(self, token):
-        if token['type'] == 'eof':
-            raise exceptions.IncompleteExpressionError(
-                token['start'], token['value'], token['type'])
-        self._raise_parse_error_for_token(token, 'invalid token')
-    def _error_led_token(self, token):
-        self._raise_parse_error_for_token(token, 'invalid token')
-    def _match(self, token_type=None):
-        # inline'd self._current_token()
-        if self._current_token() == token_type:
-            # inline'd self._advance()
-            self._advance()
-        else:
-            self._raise_parse_error_maybe_eof(
-                token_type, self._lookahead_token(0))
-    def _match_multiple_tokens(self, token_types):
-        if self._current_token() not in token_types:
-            self._raise_parse_error_maybe_eof(
-                token_types, self._lookahead_token(0))
-        self._advance()
-    def _advance(self):
-        self._index += 1
-    def _current_token(self):
-        return self._tokens[self._index]['type']
-    def _lookahead(self, number):
-        return self._tokens[self._index + number]['type']
-    def _lookahead_token(self, number):
-        return self._tokens[self._index + number]
-    def _raise_parse_error_for_token(self, token, reason):
-        lex_position = token['start']
-        actual_value = token['value']
-        actual_type = token['type']
-        raise exceptions.ParseError(lex_position, actual_value,
-                                    actual_type, reason)
-    def _raise_parse_error_maybe_eof(self, expected_type, token):
-        lex_position = token['start']
-        actual_value = token['value']
-        actual_type = token['type']
-        if actual_type == 'eof':
-            raise exceptions.IncompleteExpressionError(
-                lex_position, actual_value, actual_type)
-        message = 'Expecting: %s, got: %s' % (expected_type,
-                                              actual_type)
-        raise exceptions.ParseError(
-            lex_position, actual_value, actual_type, message)
-    def _free_cache_entries(self):
-        for key in random.sample(list(self._CACHE.keys()), int(self._MAX_SIZE / 2)):
-            self._CACHE.pop(key, None)
-    @classmethod
-    def purge(cls):
-        """Clear the expression compilation cache."""
-        cls._CACHE.clear()
-@with_repr_method
-class ParsedResult(object):
-    def __init__(self, expression, parsed):
-        self.expression = expression
-        self.parsed = parsed
-    def search(self, value, options=None):
-        interpreter = visitor.TreeInterpreter(options)
-        result = interpreter.visit(self.parsed, value)
-        return result
-    def _render_dot_file(self):
-        """Render the parsed AST as a dot file.
-        Note that this is marked as an internal method because
-        the AST is an implementation detail and is subject
-        to change.  This method can be used to help troubleshoot
-        or for development purposes, but is not considered part
-        of the public supported API.  Use at your own risk.
-        """
-        renderer = visitor.GraphvizVisitor()
-        contents = renderer.visit(self.parsed)
-        return contents
-    def __repr__(self):
-        return repr(self.parsed)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/x_user_defined.py DELETED Viewed

@@ -1,325 +0,0 @@
-# coding: utf-8
-"""
-    webencodings.x_user_defined
-    ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-    An implementation of the x-user-defined encoding.
-    :copyright: Copyright 2012 by Simon Sapin
-    :license: BSD, see LICENSE for details.
-"""
-from __future__ import unicode_literals
-import codecs
-### Codec APIs
-class Codec(codecs.Codec):
-    def encode(self, input, errors='strict'):
-        return codecs.charmap_encode(input, errors, encoding_table)
-    def decode(self, input, errors='strict'):
-        return codecs.charmap_decode(input, errors, decoding_table)
-class IncrementalEncoder(codecs.IncrementalEncoder):
-    def encode(self, input, final=False):
-        return codecs.charmap_encode(input, self.errors, encoding_table)[0]
-class IncrementalDecoder(codecs.IncrementalDecoder):
-    def decode(self, input, final=False):
-        return codecs.charmap_decode(input, self.errors, decoding_table)[0]
-class StreamWriter(Codec, codecs.StreamWriter):
-    pass
-class StreamReader(Codec, codecs.StreamReader):
-    pass
-### encodings module API
-codec_info = codecs.CodecInfo(
-    name='x-user-defined',
-    encode=Codec().encode,
-    decode=Codec().decode,
-    incrementalencoder=IncrementalEncoder,
-    incrementaldecoder=IncrementalDecoder,
-    streamreader=StreamReader,
-    streamwriter=StreamWriter,
-)
-### Decoding Table
-# Python 3:
-# for c in range(256): print('    %r' % chr(c if c < 128 else c + 0xF700))
-decoding_table = (
-    '\x00'
-    '\x01'
-    '\x02'
-    '\x03'
-    '\x04'
-    '\x05'
-    '\x06'
-    '\x07'
-    '\x08'
-    '\t'
-    '\n'
-    '\x0b'
-    '\x0c'
-    '\r'
-    '\x0e'
-    '\x0f'
-    '\x10'
-    '\x11'
-    '\x12'
-    '\x13'
-    '\x14'
-    '\x15'
-    '\x16'
-    '\x17'
-    '\x18'
-    '\x19'
-    '\x1a'
-    '\x1b'
-    '\x1c'
-    '\x1d'
-    '\x1e'
-    '\x1f'
-    ' '
-    '!'
-    '"'
-    '#'
-    '$'
-    '%'
-    '&'
-    "'"
-    '('
-    ')'
-    '*'
-    '+'
-    ','
-    '-'
-    '.'
-    '/'
-    '0'
-    '1'
-    '2'
-    '3'
-    '4'
-    '5'
-    '6'
-    '7'
-    '8'
-    '9'
-    ':'
-    ';'
-    '<'
-    '='
-    '>'
-    '?'
-    '@'
-    'A'
-    'B'
-    'C'
-    'D'
-    'E'
-    'F'
-    'G'
-    'H'
-    'I'
-    'J'
-    'K'
-    'L'
-    'M'
-    'N'
-    'O'
-    'P'
-    'Q'
-    'R'
-    'S'
-    'T'
-    'U'
-    'V'
-    'W'
-    'X'
-    'Y'
-    'Z'
-    '['
-    '\\'
-    ']'
-    '^'
-    '_'
-    '`'
-    'a'
-    'b'
-    'c'
-    'd'
-    'e'
-    'f'
-    'g'
-    'h'
-    'i'
-    'j'
-    'k'
-    'l'
-    'm'
-    'n'
-    'o'
-    'p'
-    'q'
-    'r'
-    's'
-    't'
-    'u'
-    'v'
-    'w'
-    'x'
-    'y'
-    'z'
-    '{'
-    '|'
-    '}'
-    '~'
-    '\x7f'
-    '\uf780'
-    '\uf781'
-    '\uf782'
-    '\uf783'
-    '\uf784'
-    '\uf785'
-    '\uf786'
-    '\uf787'
-    '\uf788'
-    '\uf789'
-    '\uf78a'
-    '\uf78b'
-    '\uf78c'
-    '\uf78d'
-    '\uf78e'
-    '\uf78f'
-    '\uf790'
-    '\uf791'
-    '\uf792'
-    '\uf793'
-    '\uf794'
-    '\uf795'
-    '\uf796'
-    '\uf797'
-    '\uf798'
-    '\uf799'
-    '\uf79a'
-    '\uf79b'
-    '\uf79c'
-    '\uf79d'
-    '\uf79e'
-    '\uf79f'
-    '\uf7a0'
-    '\uf7a1'
-    '\uf7a2'
-    '\uf7a3'
-    '\uf7a4'
-    '\uf7a5'
-    '\uf7a6'
-    '\uf7a7'
-    '\uf7a8'
-    '\uf7a9'
-    '\uf7aa'
-    '\uf7ab'
-    '\uf7ac'
-    '\uf7ad'
-    '\uf7ae'
-    '\uf7af'
-    '\uf7b0'
-    '\uf7b1'
-    '\uf7b2'
-    '\uf7b3'
-    '\uf7b4'
-    '\uf7b5'
-    '\uf7b6'
-    '\uf7b7'
-    '\uf7b8'
-    '\uf7b9'
-    '\uf7ba'
-    '\uf7bb'
-    '\uf7bc'
-    '\uf7bd'
-    '\uf7be'
-    '\uf7bf'
-    '\uf7c0'
-    '\uf7c1'
-    '\uf7c2'
-    '\uf7c3'
-    '\uf7c4'
-    '\uf7c5'
-    '\uf7c6'
-    '\uf7c7'
-    '\uf7c8'
-    '\uf7c9'
-    '\uf7ca'
-    '\uf7cb'
-    '\uf7cc'
-    '\uf7cd'
-    '\uf7ce'
-    '\uf7cf'
-    '\uf7d0'
-    '\uf7d1'
-    '\uf7d2'
-    '\uf7d3'
-    '\uf7d4'
-    '\uf7d5'
-    '\uf7d6'
-    '\uf7d7'
-    '\uf7d8'
-    '\uf7d9'
-    '\uf7da'
-    '\uf7db'
-    '\uf7dc'
-    '\uf7dd'
-    '\uf7de'
-    '\uf7df'
-    '\uf7e0'
-    '\uf7e1'
-    '\uf7e2'
-    '\uf7e3'
-    '\uf7e4'
-    '\uf7e5'
-    '\uf7e6'
-    '\uf7e7'
-    '\uf7e8'
-    '\uf7e9'
-    '\uf7ea'
-    '\uf7eb'
-    '\uf7ec'
-    '\uf7ed'
-    '\uf7ee'
-    '\uf7ef'
-    '\uf7f0'
-    '\uf7f1'
-    '\uf7f2'
-    '\uf7f3'
-    '\uf7f4'
-    '\uf7f5'
-    '\uf7f6'
-    '\uf7f7'
-    '\uf7f8'
-    '\uf7f9'
-    '\uf7fa'
-    '\uf7fb'
-    '\uf7fc'
-    '\uf7fd'
-    '\uf7fe'
-    '\uf7ff'
-)
-### Encoding table
-encoding_table = codecs.charmap_build(decoding_table)

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/__init__.py DELETED Viewed

The diff for this file is too large to render. See raw diff

spaces/BigSalmon/MASKK/README.md DELETED Viewed

@@ -1,37 +0,0 @@
----
-title: MASKK
-emoji: 🏢
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-# Configuration
-`title`: _string_
-Display title for the Space
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Boadiwaa/Recipes/openai/api_resources/classification.py DELETED Viewed

@@ -1,12 +0,0 @@
-from openai.openai_object import OpenAIObject
-class Classification(OpenAIObject):
-    @classmethod
-    def get_url(self):
-        return "/classifications"
-    @classmethod
-    def create(cls, **params):
-        instance = cls()
-        return instance.request("post", cls.get_url(), params)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/__init__.py DELETED Viewed

	@@ -1 +0,0 @@
1	- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

spaces/CVPR/LIVE/thrust/examples/cmake/add_subdir/dummy.cpp DELETED Viewed

@@ -1,32 +0,0 @@
-#include <thrust/detail/config.h>
-#include <iostream>
-int main()
-{
-  std::cout << "Hello from Thrust version " << THRUST_VERSION << ":\n"
-            << "Host system: "
-#if THRUST_HOST_SYSTEM == THRUST_HOST_SYSTEM_CPP
-            << "CPP\n"
-#elif THRUST_HOST_SYSTEM == THRUST_HOST_SYSTEM_OMP
-            << "OMP\n"
-#elif THRUST_HOST_SYSTEM == THRUST_HOST_SYSTEM_TBB
-            << "TBB\n"
-#else
-            << "Unknown\n"
-#endif
-            << "Device system: "
-#if THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_CPP
-            << "CPP\n";
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_CUDA
-            << "CUDA\n";
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_OMP
-            << "OMP\n";
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_TBB
-            << "TBB\n";
-#else
-            << "Unknown\n";
-#endif
-}

spaces/CVPR/LIVE/thrust/thrust/detail/overlapped_copy.h DELETED Viewed

@@ -1,131 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-#pragma once
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/iterator/detail/minimum_system.h>
-#include <thrust/detail/copy.h>
-#include <thrust/detail/temporary_array.h>
-#include <thrust/system/cpp/detail/execution_policy.h>
-namespace thrust
-{
-namespace detail
-{
-template<typename InputIterator,
-         typename OutputIterator>
-  OutputIterator sequential_copy(InputIterator first,
-                                 InputIterator last,
-                                 OutputIterator result)
-{
-  for(; first != last; ++first, ++result)
-  {
-    *result = *first;
-  } // end for
-  return result;
-} // end sequential_copy()
-template<typename BidirectionalIterator1,
-         typename BidirectionalIterator2>
-  BidirectionalIterator2 sequential_copy_backward(BidirectionalIterator1 first,
-                                                  BidirectionalIterator1 last,
-                                                  BidirectionalIterator2 result)
-{
-  // yes, we preincrement
-  // the ranges are open on the right, i.e. [first, last)
-  while(first != last)
-  {
-    *--result = *--last;
-  } // end while
-  return result;
-} // end sequential_copy_backward()
-namespace dispatch
-{
-template<typename DerivedPolicy,
-         typename RandomAccessIterator1,
-         typename RandomAccessIterator2>
-  RandomAccessIterator2 overlapped_copy(thrust::system::cpp::detail::execution_policy<DerivedPolicy> &,
-                                        RandomAccessIterator1 first,
-                                        RandomAccessIterator1 last,
-                                        RandomAccessIterator2 result)
-{
-  if(first < last && first <= result && result < last)
-  {
-    // result lies in [first, last)
-    // it's safe to use std::copy_backward here
-    thrust::detail::sequential_copy_backward(first, last, result + (last - first));
-    result += (last - first);
-  } // end if
-  else
-  {
-    // result + (last - first) lies in [first, last)
-    // it's safe to use sequential_copy here
-    result = thrust::detail::sequential_copy(first, last, result);
-  } // end else
-  return result;
-} // end overlapped_copy()
-template<typename DerivedPolicy,
-         typename RandomAccessIterator1,
-         typename RandomAccessIterator2>
-  RandomAccessIterator2 overlapped_copy(thrust::execution_policy<DerivedPolicy> &exec,
-                                        RandomAccessIterator1 first,
-                                        RandomAccessIterator1 last,
-                                        RandomAccessIterator2 result)
-{
-  typedef typename thrust::iterator_value<RandomAccessIterator1>::type value_type;
-  // make a temporary copy of [first,last), and copy into it first
-  thrust::detail::temporary_array<value_type, DerivedPolicy> temp(exec, first, last);
-  return thrust::copy(exec, temp.begin(), temp.end(), result);
-} // end overlapped_copy()
-} // end dispatch
-template<typename RandomAccessIterator1,
-         typename RandomAccessIterator2>
-  RandomAccessIterator2 overlapped_copy(RandomAccessIterator1 first,
-                                        RandomAccessIterator1 last,
-                                        RandomAccessIterator2 result)
-{
-  typedef typename thrust::iterator_system<RandomAccessIterator2>::type System1;
-  typedef typename thrust::iterator_system<RandomAccessIterator2>::type System2;
-  typedef typename thrust::detail::minimum_system<System1, System2>::type System;
-  // XXX presumes System is default constructible
-  System system;
-  return thrust::detail::dispatch::overlapped_copy(system, first, last, result);
-} // end overlapped_copy()
-} // end detail
-} // end thrust

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/transform.h DELETED Viewed

@@ -1,106 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-#pragma once
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator,
-         typename UnaryFunction>
-__host__ __device__
-  OutputIterator transform(thrust::execution_policy<DerivedPolicy> &exec,
-                           InputIterator first,
-                           InputIterator last,
-                           OutputIterator result,
-                           UnaryFunction op);
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename BinaryFunction>
-__host__ __device__
-  OutputIterator transform(thrust::execution_policy<DerivedPolicy> &exec,
-                           InputIterator1 first1,
-                           InputIterator1 last1,
-                           InputIterator2 first2,
-                           OutputIterator result,
-                           BinaryFunction op);
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename ForwardIterator,
-         typename UnaryFunction,
-         typename Predicate>
-__host__ __device__
-  ForwardIterator transform_if(thrust::execution_policy<DerivedPolicy> &exec,
-                               InputIterator first,
-                               InputIterator last,
-                               ForwardIterator result,
-                               UnaryFunction unary_op,
-                               Predicate pred);
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename ForwardIterator,
-         typename UnaryFunction,
-         typename Predicate>
-__host__ __device__
-  ForwardIterator transform_if(thrust::execution_policy<DerivedPolicy> &exec,
-                               InputIterator1 first,
-                               InputIterator1 last,
-                               InputIterator2 stencil,
-                               ForwardIterator result,
-                               UnaryFunction unary_op,
-                               Predicate pred);
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename InputIterator3,
-         typename ForwardIterator,
-         typename BinaryFunction,
-         typename Predicate>
-__host__ __device__
-  ForwardIterator transform_if(thrust::execution_policy<DerivedPolicy> &exec,
-                               InputIterator1 first1,
-                               InputIterator1 last1,
-                               InputIterator2 first2,
-                               InputIterator3 stencil,
-                               ForwardIterator result,
-                               BinaryFunction binary_op,
-                               Predicate pred);
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-#include <thrust/system/detail/generic/transform.inl>

spaces/CVPR/regionclip-demo/detectron2/layers/csrc/cocoeval/cocoeval.h DELETED Viewed

@@ -1,88 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
-#include <pybind11/stl_bind.h>
-#include <vector>
-namespace py = pybind11;
-namespace detectron2 {
-namespace COCOeval {
-// Annotation data for a single object instance in an image
-struct InstanceAnnotation {
-  InstanceAnnotation(
-      uint64_t id,
-      double score,
-      double area,
-      bool is_crowd,
-      bool ignore)
-      : id{id}, score{score}, area{area}, is_crowd{is_crowd}, ignore{ignore} {}
-  uint64_t id;
-  double score = 0.;
-  double area = 0.;
-  bool is_crowd = false;
-  bool ignore = false;
-};
-// Stores intermediate results for evaluating detection results for a single
-// image that has D detected instances and G ground truth instances. This stores
-// matches between detected and ground truth instances
-struct ImageEvaluation {
-  // For each of the D detected instances, the id of the matched ground truth
-  // instance, or 0 if unmatched
-  std::vector<uint64_t> detection_matches;
-  // The detection score of each of the D detected instances
-  std::vector<double> detection_scores;
-  // Marks whether or not each of G instances was ignored from evaluation (e.g.,
-  // because it's outside area_range)
-  std::vector<bool> ground_truth_ignores;
-  // Marks whether or not each of D instances was ignored from evaluation (e.g.,
-  // because it's outside aRng)
-  std::vector<bool> detection_ignores;
-};
-template <class T>
-using ImageCategoryInstances = std::vector<std::vector<std::vector<T>>>;
-// C++ implementation of COCO API cocoeval.py::COCOeval.evaluateImg().  For each
-// combination of image, category, area range settings, and IOU thresholds to
-// evaluate, it matches detected instances to ground truth instances and stores
-// the results into a vector of ImageEvaluation results, which will be
-// interpreted by the COCOeval::Accumulate() function to produce precion-recall
-// curves.  The parameters of nested vectors have the following semantics:
-//   image_category_ious[i][c][d][g] is the intersection over union of the d'th
-//     detected instance and g'th ground truth instance of
-//     category category_ids[c] in image image_ids[i]
-//   image_category_ground_truth_instances[i][c] is a vector of ground truth
-//     instances in image image_ids[i] of category category_ids[c]
-//   image_category_detection_instances[i][c] is a vector of detected
-//     instances in image image_ids[i] of category category_ids[c]
-std::vector<ImageEvaluation> EvaluateImages(
-    const std::vector<std::array<double, 2>>& area_ranges, // vector of 2-tuples
-    int max_detections,
-    const std::vector<double>& iou_thresholds,
-    const ImageCategoryInstances<std::vector<double>>& image_category_ious,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_ground_truth_instances,
-    const ImageCategoryInstances<InstanceAnnotation>&
-        image_category_detection_instances);
-// C++ implementation of COCOeval.accumulate(), which generates precision
-// recall curves for each set of category, IOU threshold, detection area range,
-// and max number of detections parameters.  It is assumed that the parameter
-// evaluations is the return value of the functon COCOeval::EvaluateImages(),
-// which was called with the same parameter settings params
-py::dict Accumulate(
-    const py::object& params,
-    const std::vector<ImageEvaluation>& evalutations);
-} // namespace COCOeval
-} // namespace detectron2

spaces/CVPR/regionclip-demo/detectron2/modeling/roi_heads/clip_roi_heads.py DELETED Viewed

@@ -1,747 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import inspect
-import logging
-import numpy as np
-from typing import Dict, List, Optional, Tuple
-import torch
-from torch import nn
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, nonzero_tuple
-from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-from ..backbone.resnet import BottleneckBlock, ResNet
-from ..matcher import Matcher
-from ..poolers import ROIPooler
-from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals
-from ..sampling import subsample_labels
-from .box_head import build_box_head
-from .fast_rcnn import FastRCNNOutputLayers
-from .keypoint_head import build_keypoint_head
-from .mask_head import build_mask_head
-from .roi_heads import ROI_HEADS_REGISTRY, select_foreground_proposals, ROIHeads
-@ROI_HEADS_REGISTRY.register()
-class CLIPRes5ROIHeads(ROIHeads):
-    """
-    Created for CLIP ResNet. This head uses the last resnet layer from backbone.
-    The ROIHeads in a typical "C4" R-CNN model, where
-    the box and mask head share the cropping and
-    the per-region feature computation by a Res5 block.
-    See :paper:`ResNet` Appendix A.
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        in_features: List[str],
-        pooler: ROIPooler,
-        res5: None,
-        box_predictor: nn.Module,
-        mask_head: Optional[nn.Module] = None,
-        **kwargs,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            in_features (list[str]): list of backbone feature map names to use for
-                feature extraction
-            pooler (ROIPooler): pooler to extra region features from backbone
-            res5 (nn.Sequential): a CNN to compute per-region features, to be used by
-                ``box_predictor`` and ``mask_head``. Typically this is a "res5"
-                block from a ResNet.
-            box_predictor (nn.Module): make box predictions from the feature.
-                Should have the same interface as :class:`FastRCNNOutputLayers`.
-            mask_head (nn.Module): transform features to make mask predictions
-        """
-        super().__init__(**kwargs)
-        self.in_features = in_features
-        self.pooler = pooler
-        # if isinstance(res5, (list, tuple)):
-        #     res5 = nn.Sequential(*res5)
-        self.res5 = res5  #  None, this head uses the res5 from backbone
-        self.box_predictor = box_predictor
-        self.mask_on = mask_head is not None
-        if self.mask_on:
-            self.mask_head = mask_head
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # fmt: off
-        ret = super().from_config(cfg)
-        in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        pooler_scales     = (1.0 / input_shape[in_features[0]].stride, )
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        mask_on           = cfg.MODEL.MASK_ON
-        # fmt: on
-        assert not cfg.MODEL.KEYPOINT_ON
-        assert len(in_features) == 1
-        ret["pooler"] = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        # Compatbility with old moco code. Might be useful.
-        # See notes in StandardROIHeads.from_config
-        # if not inspect.ismethod(cls._build_res5_block):
-        #     logger.warning(
-        #         "The behavior of _build_res5_block may change. "
-        #         "Please do not depend on private methods."
-        #     )
-        #     cls._build_res5_block = classmethod(cls._build_res5_block)
-        ret["res5"], out_channels = None, cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * 8 # cls._build_res5_block(cfg)
-        ret["box_predictor"] = FastRCNNOutputLayers(
-            cfg, ShapeSpec(channels=out_channels, height=1, width=1)
-        )
-        if mask_on:
-            ret["mask_head"] = build_mask_head(
-                cfg,
-                ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution),
-            )
-        return ret
-    def _shared_roi_transform(self, features, boxes, backbone_res5):
-        x = self.pooler(features, boxes)
-        return backbone_res5(x)
-    def forward(self, images, features, proposals, queries, targets=None,
-            res5=None, ds=None, norm=None, vision_projection=None, attnpool=None):
-        """
-        See :meth:`ROIHeads.forward`.
-        """
-        del images
-        if self.training:
-            assert targets
-            proposals = self.label_and_sample_proposals(proposals, targets)
-        del targets
-        proposal_boxes = [x.proposal_boxes for x in proposals]
-        box_features = self._shared_roi_transform(
-            [features[f] for f in self.in_features], proposal_boxes, res5
-        )
-        if attnpool:  # att pooling
-            att_feats = attnpool(box_features)
-            predictions = self.box_predictor(att_feats, queries)
-        else: # mean pooling
-            predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
-        if self.training:
-            del features
-            losses = self.box_predictor.losses(predictions, proposals)
-            if self.mask_on:
-                proposals, fg_selection_masks = select_foreground_proposals(
-                    proposals, self.num_classes
-                )
-                # Since the ROI feature transform is shared between boxes and masks,
-                # we don't need to recompute features. The mask loss is only defined
-                # on foreground proposals, so we need to select out the foreground
-                # features.
-                mask_features = box_features[torch.cat(fg_selection_masks, dim=0)]
-                del box_features
-                losses.update(self.mask_head(mask_features, proposals))
-            return [], losses
-        else:
-            pred_instances, _ = self.box_predictor.inference(predictions, proposals)
-            pred_instances = self.forward_with_given_boxes(features, pred_instances, res5)
-            return pred_instances, {}
-    def forward_with_given_boxes(self, features, instances, res5=None):
-        """
-        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
-        Args:
-            features: same as in `forward()`
-            instances (list[Instances]): instances to predict other outputs. Expect the keys
-                "pred_boxes" and "pred_classes" to exist.
-        Returns:
-            instances (Instances):
-                the same `Instances` object, with extra
-                fields such as `pred_masks` or `pred_keypoints`.
-        """
-        assert not self.training
-        assert instances[0].has("pred_boxes") and instances[0].has("pred_classes")
-        if self.mask_on:
-            features = [features[f] for f in self.in_features]
-            x = self._shared_roi_transform(features, [x.pred_boxes for x in instances], res5)
-            return self.mask_head(x, instances)
-        else:
-            return instances
-@ROI_HEADS_REGISTRY.register()
-class CLIPSwinROIHeads(ROIHeads):
-    """
-    Created for CLIP ResNet. This head uses the last resnet layer from backbone.
-    The ROIHeads in a typical "C4" R-CNN model, where
-    the box and mask head share the cropping and
-    the per-region feature computation by a Res5 block.
-    See :paper:`ResNet` Appendix A.
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        in_features: List[str],
-        pooler: ROIPooler,
-        res5: None,
-        box_predictor: nn.Module,
-        mask_head: Optional[nn.Module] = None,
-        **kwargs,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            in_features (list[str]): list of backbone feature map names to use for
-                feature extraction
-            pooler (ROIPooler): pooler to extra region features from backbone
-            res5 (nn.Sequential): a CNN to compute per-region features, to be used by
-                ``box_predictor`` and ``mask_head``. Typically this is a "res5"
-                block from a ResNet.
-            box_predictor (nn.Module): make box predictions from the feature.
-                Should have the same interface as :class:`FastRCNNOutputLayers`.
-            mask_head (nn.Module): transform features to make mask predictions
-        """
-        super().__init__(**kwargs)
-        self.in_features = in_features
-        self.pooler = pooler
-        # if isinstance(res5, (list, tuple)):
-        #     res5 = nn.Sequential(*res5)
-        self.res5 = res5  #  None, this head uses the res5 from backbone
-        self.box_predictor = box_predictor
-        self.mask_on = mask_head is not None
-        if self.mask_on:
-            self.mask_head = mask_head
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # fmt: off
-        ret = super().from_config(cfg)
-        in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        pooler_scales     = (1.0 / input_shape[in_features[0]].stride, )
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        mask_on           = cfg.MODEL.MASK_ON
-        # fmt: on
-        assert not cfg.MODEL.KEYPOINT_ON
-        assert len(in_features) == 1
-        ret["pooler"] = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        # Compatbility with old moco code. Might be useful.
-        # See notes in StandardROIHeads.from_config
-        # if not inspect.ismethod(cls._build_res5_block):
-        #     logger.warning(
-        #         "The behavior of _build_res5_block may change. "
-        #         "Please do not depend on private methods."
-        #     )
-        #     cls._build_res5_block = classmethod(cls._build_res5_block)
-        ret["res5"], out_channels = None, cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * 8 # cls._build_res5_block(cfg)
-        ret["box_predictor"] = FastRCNNOutputLayers(
-            cfg, ShapeSpec(channels=out_channels, height=1, width=1)
-        )
-        if mask_on:
-            ret["mask_head"] = build_mask_head(
-                cfg,
-                ShapeSpec(channels=out_channels, width=pooler_resolution, height=pooler_resolution),
-            )
-        return ret
-    def _shared_roi_transform(self, features, boxes, backbone_res5, backbone_ds):
-        x = self.pooler(features, boxes)
-        if backbone_ds:
-            x_flattened = x.flatten(2).transpose(1, 2)
-            x_ds = backbone_ds(x_flattened, x.shape[2], x.shape[3])
-            return backbone_res5(x_ds, x.shape[2] // 2, x.shape[3] // 2)
-        else:
-            return backbone_res5(x)
-    def forward(self, images, features, proposals, queries, targets=None,
-            res5=None, ds=None, norm=None, vision_projection=None, attnpool=None):
-        """
-        See :meth:`ROIHeads.forward`.
-        """
-        del images
-        if self.training:
-            assert targets
-            proposals = self.label_and_sample_proposals(proposals, targets)
-        del targets
-        proposal_boxes = [x.proposal_boxes for x in proposals]
-        box_features = self._shared_roi_transform(
-            [features[f] for f in self.in_features], proposal_boxes, res5, ds,
-        )
-        if isinstance(box_features, tuple):
-            box_features = norm(box_features[0]).mean(1)
-            box_features = box_features @ vision_projection
-            box_features = box_features / box_features.norm(dim=-1, keepdim=True)
-        if attnpool:  # att pooling
-            att_feats = attnpool(box_features)
-            predictions = self.box_predictor(att_feats)
-        else: # mean pooling
-            predictions = self.box_predictor(box_features, queries)
-        if self.training:
-            del features
-            losses = self.box_predictor.losses(predictions, proposals)
-            if self.mask_on:
-                proposals, fg_selection_masks = select_foreground_proposals(
-                    proposals, self.num_classes
-                )
-                # Since the ROI feature transform is shared between boxes and masks,
-                # we don't need to recompute features. The mask loss is only defined
-                # on foreground proposals, so we need to select out the foreground
-                # features.
-                mask_features = box_features[torch.cat(fg_selection_masks, dim=0)]
-                del box_features
-                losses.update(self.mask_head(mask_features, proposals))
-            return [], losses
-        else:
-            pred_instances, _ = self.box_predictor.inference(predictions, proposals)
-            # pred_instances = self.forward_with_given_boxes(features, pred_instances, res5)
-            return pred_instances, {}
-    def forward_with_given_boxes(self, features, instances, res5=None):
-        """
-        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
-        Args:
-            features: same as in `forward()`
-            instances (list[Instances]): instances to predict other outputs. Expect the keys
-                "pred_boxes" and "pred_classes" to exist.
-        Returns:
-            instances (Instances):
-                the same `Instances` object, with extra
-                fields such as `pred_masks` or `pred_keypoints`.
-        """
-        assert not self.training
-        assert instances[0].has("pred_boxes") and instances[0].has("pred_classes")
-        if self.mask_on:
-            features = [features[f] for f in self.in_features]
-            x = self._shared_roi_transform(features, [x.pred_boxes for x in instances], res5)
-            return self.mask_head(x, instances)
-        else:
-            return instances
-@ROI_HEADS_REGISTRY.register()
-class PretrainRes5ROIHeads(ROIHeads):
-    """
-    Created for pretraining CLIP ResNet without box_predictor. This head uses the last resnet layer from backbone.
-    The ROIHeads in a typical "C4" R-CNN model, where
-    the box and mask head share the cropping and
-    the per-region feature computation by a Res5 block.
-    See :paper:`ResNet` Appendix A.
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        in_features: List[str],
-        pooler: ROIPooler,
-        res5: None,
-        box_predictor: Optional[nn.Module] = None,
-        mask_head: Optional[nn.Module] = None,
-        **kwargs,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            in_features (list[str]): list of backbone feature map names to use for
-                feature extraction
-            pooler (ROIPooler): pooler to extra region features from backbone
-            res5 (nn.Sequential): a CNN to compute per-region features, to be used by
-                ``box_predictor`` and ``mask_head``. Typically this is a "res5"
-                block from a ResNet.
-            box_predictor (nn.Module): make box predictions from the feature.
-                Should have the same interface as :class:`FastRCNNOutputLayers`.
-            mask_head (nn.Module): transform features to make mask predictions
-        """
-        super().__init__(**kwargs)
-        self.in_features = in_features
-        self.pooler = pooler
-        # if isinstance(res5, (list, tuple)):
-        #     res5 = nn.Sequential(*res5)
-        self.res5 = res5  #  None, this head uses the res5 from backbone
-        self.box_predictor = None
-        self.mask_on = None
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        # fmt: off
-        ret = super().from_config(cfg)
-        in_features = ret["in_features"] = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        pooler_scales     = (1.0 / input_shape[in_features[0]].stride, )
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        mask_on           = cfg.MODEL.MASK_ON
-        # fmt: on
-        assert not cfg.MODEL.KEYPOINT_ON
-        assert len(in_features) == 1
-        ret["pooler"] = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        ret["res5"], out_channels = None, cfg.MODEL.RESNETS.RES2_OUT_CHANNELS * 8 # cls._build_res5_block(cfg)
-        ret["box_predictor"] = None
-        ret["mask_head"] = None
-        return ret
-    def _shared_roi_transform(self, features, boxes, backbone_res5, backbone_ds):
-        x = self.pooler(features, boxes)
-        if backbone_ds:
-            return backbone_res5(backbone_ds(x))
-        else:
-            return backbone_res5(x)
-    def forward(self, images, features, proposals, targets=None, res5=None, ds=None, attnpool=None):
-        """
-        See :meth:`ROIHeads.forward`.
-        """
-        # if self.training:
-        #     assert targets
-        #     proposals = self.label_and_sample_proposals(proposals, targets)
-        # del targets
-        if isinstance(proposals[0], Boxes): # grid boxes
-            proposal_boxes = proposals
-        else:  # object proposals
-            proposal_boxes = [x.proposal_boxes for x in proposals]
-        box_features = self._shared_roi_transform(
-            [features[f] for f in self.in_features], proposal_boxes, res5
-        )
-        if attnpool:  # att pooling
-            att_feats = attnpool(box_features)
-            region_feats = att_feats # self.box_predictor(att_feats)
-        else: # mean pooling
-            region_feats = box_features.mean(dim=[2, 3]) # self.box_predictor(box_features.mean(dim=[2, 3]))
-        return region_feats
-    def forward_with_given_boxes(self, features, instances, res5=None):
-        """
-        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
-        Args:
-            features: same as in `forward()`
-            instances (list[Instances]): instances to predict other outputs. Expect the keys
-                "pred_boxes" and "pred_classes" to exist.
-        Returns:
-            instances (Instances):
-                the same `Instances` object, with extra
-                fields such as `pred_masks` or `pred_keypoints`.
-        """
-        assert not self.training
-        assert instances[0].has("pred_boxes") and instances[0].has("pred_classes")
-        return instances
-@ROI_HEADS_REGISTRY.register()
-class CLIPStandardROIHeads(ROIHeads):
-    """
-    Created for CLIP ResNet. This head uses the attention pool layers from backbone.
-    It's "standard" in a sense that there is no ROI transform sharing
-    or feature sharing between tasks.
-    Each head independently processes the input features by each head's
-    own pooler and head.
-    This class is used by most models, such as FPN and C5.
-    To implement more models, you can subclass it and implement a different
-    :meth:`forward()` or a head.
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        box_in_features: List[str],
-        box_pooler: ROIPooler,
-        box_head: nn.Module,
-        box_predictor: nn.Module,
-        mask_in_features: Optional[List[str]] = None,
-        mask_pooler: Optional[ROIPooler] = None,
-        mask_head: Optional[nn.Module] = None,
-        train_on_pred_boxes: bool = False,
-        **kwargs,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            box_in_features (list[str]): list of feature names to use for the box head.
-            box_pooler (ROIPooler): pooler to extra region features for box head
-            box_head (nn.Module): transform features to make box predictions
-            box_predictor (nn.Module): make box predictions from the feature.
-                Should have the same interface as :class:`FastRCNNOutputLayers`.
-            mask_in_features (list[str]): list of feature names to use for the mask
-                pooler or mask head. None if not using mask head.
-            mask_pooler (ROIPooler): pooler to extract region features from image features.
-                The mask head will then take region features to make predictions.
-                If None, the mask head will directly take the dict of image features
-                defined by `mask_in_features`
-            mask_head (nn.Module): transform features to make mask predictions
-            keypoint_in_features, keypoint_pooler, keypoint_head: similar to ``mask_*``.
-            train_on_pred_boxes (bool): whether to use proposal boxes or
-                predicted boxes from the box head to train other heads.
-        """
-        super().__init__(**kwargs)
-        # keep self.in_features for backward compatibility
-        self.in_features = self.box_in_features = box_in_features
-        self.box_pooler = box_pooler
-        self.box_head = box_head
-        self.box_predictor = box_predictor
-        self.mask_on = mask_in_features is not None
-        if self.mask_on:
-            self.mask_in_features = mask_in_features
-            self.mask_pooler = mask_pooler
-            self.mask_head = mask_head
-        self.train_on_pred_boxes = train_on_pred_boxes
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg)
-        ret["train_on_pred_boxes"] = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES
-        # Subclasses that have not been updated to use from_config style construction
-        # may have overridden _init_*_head methods. In this case, those overridden methods
-        # will not be classmethods and we need to avoid trying to call them here.
-        # We test for this with ismethod which only returns True for bound methods of cls.
-        # Such subclasses will need to handle calling their overridden _init_*_head methods.
-        if inspect.ismethod(cls._init_box_head):
-            ret.update(cls._init_box_head(cfg, input_shape))
-        if inspect.ismethod(cls._init_mask_head):
-            ret.update(cls._init_mask_head(cfg, input_shape))
-        return ret
-    @classmethod
-    def _init_box_head(cls, cfg, input_shape):
-        # fmt: off
-        in_features       = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_scales     = tuple(1.0 / input_shape[k].stride for k in in_features)
-        sampling_ratio    = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type       = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        # fmt: on
-        # If StandardROIHeads is applied on multiple feature maps (as in FPN),
-        # then we share the same predictors and therefore the channel counts must be the same
-        in_channels = [input_shape[f].channels for f in in_features]
-        # Check all channel counts are equal
-        assert len(set(in_channels)) == 1, in_channels
-        in_channels = in_channels[0]
-        box_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        # Here we split "box head" and "box predictor", which is mainly due to historical reasons.
-        # They are used together so the "box predictor" layers should be part of the "box head".
-        # New subclasses of ROIHeads do not need "box predictor"s.
-        box_head = None if cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER else build_box_head(
-            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)
-        )
-        box_head_output_shape = 1024
-        box_predictor = FastRCNNOutputLayers(cfg, box_head_output_shape)
-        return {
-            "box_in_features": in_features,
-            "box_pooler": box_pooler,
-            "box_head": box_head,
-            "box_predictor": box_predictor,
-        }
-    @classmethod
-    def _init_mask_head(cls, cfg, input_shape):
-        if not cfg.MODEL.MASK_ON:
-            return {}
-        # fmt: off
-        in_features       = cfg.MODEL.ROI_HEADS.IN_FEATURES
-        pooler_resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
-        pooler_scales     = tuple(1.0 / input_shape[k].stride for k in in_features)
-        sampling_ratio    = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type       = cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE
-        # fmt: on
-        in_channels = [input_shape[f].channels for f in in_features][0]
-        ret = {"mask_in_features": in_features}
-        ret["mask_pooler"] = (
-            ROIPooler(
-                output_size=pooler_resolution,
-                scales=pooler_scales,
-                sampling_ratio=sampling_ratio,
-                pooler_type=pooler_type,
-            )
-            if pooler_type
-            else None
-        )
-        if pooler_type:
-            shape = ShapeSpec(
-                channels=in_channels, width=pooler_resolution, height=pooler_resolution
-            )
-        else:
-            shape = {f: input_shape[f] for f in in_features}
-        ret["mask_head"] = build_mask_head(cfg, shape)
-        return ret
-    def forward(
-        self,
-        images: ImageList,
-        features: Dict[str, torch.Tensor],
-        proposals: List[Instances],
-        targets: Optional[List[Instances]] = None,
-        attnpool=None,
-    ) -> Tuple[List[Instances], Dict[str, torch.Tensor]]:
-        """
-        See :class:`ROIHeads.forward`.
-        """
-        del images
-        if self.training:
-            assert targets, "'targets' argument is required during training"
-            proposals = self.label_and_sample_proposals(proposals, targets)
-        del targets
-        if self.training:
-            losses = self._forward_box(features, proposals, attnpool=attnpool)
-            # Usually the original proposals used by the box head are used by the mask, keypoint
-            # heads. But when `self.train_on_pred_boxes is True`, proposals will contain boxes
-            # predicted by the box head.
-            losses.update(self._forward_mask(features, proposals))
-            return proposals, losses
-        else:
-            pred_instances = self._forward_box(features, proposals, attnpool=attnpool)
-            # During inference cascaded prediction is used: the mask and keypoints heads are only
-            # applied to the top scoring box detections.
-            pred_instances = self.forward_with_given_boxes(features, pred_instances)
-            return pred_instances, {}
-    def forward_with_given_boxes(
-        self, features: Dict[str, torch.Tensor], instances: List[Instances]
-    ) -> List[Instances]:
-        """
-        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
-        This is useful for downstream tasks where a box is known, but need to obtain
-        other attributes (outputs of other heads).
-        Test-time augmentation also uses this.
-        Args:
-            features: same as in `forward()`
-            instances (list[Instances]): instances to predict other outputs. Expect the keys
-                "pred_boxes" and "pred_classes" to exist.
-        Returns:
-            list[Instances]:
-                the same `Instances` objects, with extra
-                fields such as `pred_masks` or `pred_keypoints`.
-        """
-        assert not self.training
-        assert instances[0].has("pred_boxes") and instances[0].has("pred_classes")
-        instances = self._forward_mask(features, instances)
-        return instances
-    def _forward_box(self, features: Dict[str, torch.Tensor], proposals: List[Instances], attnpool=None):
-        """
-        Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`,
-            the function puts predicted boxes in the `proposal_boxes` field of `proposals` argument.
-        Args:
-            features (dict[str, Tensor]): mapping from feature map names to tensor.
-                Same as in :meth:`ROIHeads.forward`.
-            proposals (list[Instances]): the per-image object proposals with
-                their matching ground truth.
-                Each has fields "proposal_boxes", and "objectness_logits",
-                "gt_classes", "gt_boxes".
-        Returns:
-            In training, a dict of losses.
-            In inference, a list of `Instances`, the predicted instances.
-        """
-        features = [features[f] for f in self.box_in_features]
-        box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
-        if attnpool: # att pooling
-            box_features = attnpool(box_features)
-        else: # default FPN pooling (FastRCNNConvFCHead)
-            box_features = self.box_head(box_features)
-        predictions = self.box_predictor(box_features)
-        del box_features
-        if self.training:
-            losses = self.box_predictor.losses(predictions, proposals)
-            # proposals is modified in-place below, so losses must be computed first.
-            if self.train_on_pred_boxes:
-                with torch.no_grad():
-                    pred_boxes = self.box_predictor.predict_boxes_for_gt_classes(
-                        predictions, proposals
-                    )
-                    for proposals_per_image, pred_boxes_per_image in zip(proposals, pred_boxes):
-                        proposals_per_image.proposal_boxes = Boxes(pred_boxes_per_image)
-            return losses
-        else:
-            pred_instances, _ = self.box_predictor.inference(predictions, proposals)
-            return pred_instances
-    def _forward_mask(self, features: Dict[str, torch.Tensor], instances: List[Instances]):
-        """
-        Forward logic of the mask prediction branch.
-        Args:
-            features (dict[str, Tensor]): mapping from feature map names to tensor.
-                Same as in :meth:`ROIHeads.forward`.
-            instances (list[Instances]): the per-image instances to train/predict masks.
-                In training, they can be the proposals.
-                In inference, they can be the boxes predicted by R-CNN box head.
-        Returns:
-            In training, a dict of losses.
-            In inference, update `instances` with new fields "pred_masks" and return it.
-        """
-        if not self.mask_on:
-            return {} if self.training else instances
-        if self.training:
-            # head is only trained on positive proposals.
-            instances, _ = select_foreground_proposals(instances, self.num_classes)
-        if self.mask_pooler is not None:
-            features = [features[f] for f in self.mask_in_features]
-            boxes = [x.proposal_boxes if self.training else x.pred_boxes for x in instances]
-            features = self.mask_pooler(features, boxes)
-        else:
-            features = {f: features[f] for f in self.mask_in_features}
-        return self.mask_head(features, instances)

spaces/Chris4K/llms_compare/Tachosoft 23.1 Download.md DELETED Viewed

@@ -1,66 +0,0 @@
-## Tachosoft 23.1 Download
- ![Tachosoft 23.1 Download](https://myimages.onl/images/2015/03/24/tachosoft.png)
-**DOWNLOAD ===> [https://urluso.com/2tBNDY](https://urluso.com/2tBNDY)**
-# Tachosoft 23.1: A Powerful Tool for Odometer Correction
-If you are looking for a reliable and easy-to-use software for odometer correction, you might want to check out Tachosoft 23.1. This software is one of the world's largest digital odometer calculators, covering more than 2246 vehicle models from various manufacturers[^1^] [^2^]. It can help you adjust the mileage of your car's dashboard by reading and modifying the data stored in the memory chip.
-Tachosoft 23.1 is compatible with Windows operating system and supports various languages. You can download it from the official website or from some trusted online sources[^1^] [^3^]. However, you need to be careful when using this software, as it may be illegal in some countries to tamper with the odometer reading. You should only use it for personal or educational purposes, and not for fraud or deception.
-To use Tachosoft 23.1, you need to have a programmer device that can read and write the memory chip of your dashboard. You also need to know the type and model of your chip, as well as the location and format of the mileage data. Tachosoft 23.1 can provide you with some useful tips and pinouts for different chips and vehicles[^1^] [^4^]. You can also refer to the user manual or online tutorials for more guidance.
-Once you have read the dump file from the chip, you can open it with Tachosoft 23.1 and enter the desired mileage value. The software will calculate the new checksum and display the modified dump file. You can then write it back to the chip using your programmer device. After that, you can reinstall the dashboard and enjoy your new mileage reading.
-Tachosoft 23.1 is a powerful tool for odometer correction that can save you time and money. It is easy to use for everyone, from beginner to professional. However, you should always be responsible and ethical when using this software, and respect the laws of your country.
-Tachosoft 23.1 is not only a mileage calculator, but also a data provider. It can show you the mileage data in the position of the computer program, so you can easily change it yourself. It can also help you find the correct chip type and pinout for your dashboard, as well as the offset and swap values for the mileage data. You can also use the search function to find the vehicle model and chip type that you need.
-Tachosoft 23.1 supports a wide range of vehicles, including cars, trucks, motorcycles, and boats. It can work with various dashboards and memory chips, such as 93c46, 93c56, 93c66, 93c86, 24c01, 24c02, 24c04, 24c08, 24c16, 24c32, 24c64, 95xxx, ST6249, NEC, and many more . It can also handle different calculation methods and algorithms used by different manufacturers and dashboards.
-Tachosoft 23.1 is constantly updated with new models and features. The latest version is 23.1, released in 2013. It added more than 200 new models to the database, such as Alfa Romeo Giulietta, Audi A1/A6/A7/A8/Q3/Q5/Q7/S5/TT/TTS/TTRS/R8/RS3/RS4/RS5/RS6/RS7/S8/SQ5/V8/V10/V12/R18/R20/R21/R22/R23/R24/R25/R26/R27/R28/R29/R30/R31/R32/R33/R34/R35/R36/R37/R38/R39/R40/R41/R42/R43/R44/R45/R46/R47/R48/R49/, BMW F01/F02/F07/F10/F11/F12/F13/F20/F21/F25/F30/F31/F32/F33/F34/F35/F36/F80/F82/F83/X1/X3/X4/X5/X6/Z4/M3/M4/M5/M6/ActiveHybrid3/ActiveHybrid5/ActiveHybrid7/i3/i8/1M/2M/3M/4M/5M/6M/7M/8M/9M/10M/11M/12M/13M/14M/15M/16M/17M/18M/19M/, Chevrolet Aveo/Camaro/Captiva/Cobalt/Cruze/Equinox/HHR/Impala/Lacetti/Malibu/Matiz/Niva/Silverado/Sonic/Suburban/Tahoe/Tavera/Volt/Zafira/ZR1/Z06/Z07/Z08/Z09/Z10/Z11/Z12/Z13/Z14/Z15/Z16/Z17/Z18/Z19/, Citroen Berlingo/C1/C2/C3/C4/C5/C6/C8/C-Crosser/C-Elysee/C-Zero/Ds3/Ds4/Ds5/Evasion/Jumper/Jumpy/Nemo/Picasso/Saxo/Xantia/Xsara/Xsara Picasso/, Fiat 500/Barchetta/Doblo/Ducato/Idea/Linea/Marea/Multipla/Palio/Panda/Punto/Stilo/Ulysse/, Ford B-Max/C-Max/Ecosport/Escort/Fiesta/Focus/Fusion/Galaxy/Ka/Kuga/Mondeo/Mustang/S-Max/Taurus/Tourneo Connect/Tourneo Custom/Transit Connect/Transit Custom/, Honda Accord/Airwave/Civic/City/Crosstour/Cr-V/Cr-Z/Elysion/Fit/Jazz/Hr-V/Legend/Odyssey/Pilot/S2000/Shuttle/, Hyundai Accent/Azera/Coupe/Elantra/Eon/Equus/Galloper/Gensis Coupe/Gensis Sedan/Gensis/Gets/I10/I20/I30/I40/Ix20/Ix35/Ix55/Lantra/Santa Fe/Sonata/Terracan/Tiburon/Trajet/Tucson/Veloster/Verna/Xg300/Xg350/, Kia Carens/Carnival/Ceed/Cerato/Koup/Optima/Picanto/Pride/Rio/Sorento/Soul/Sportage/Venga/, Land Rover Defender/Discovery/Evoque/Freelander/Lr2/Lr3/Lr4/Range Rover Sport/, Mazda
- 145887f19f

spaces/Cognomen/CatCon-Controlnet-WD-1-5-b2/app.py DELETED Viewed

@@ -1,114 +0,0 @@
-import gradio as gr
-import jax.numpy as jnp
-from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
-from diffusers import FlaxScoreSdeVeScheduler, FlaxDPMSolverMultistepScheduler
-import torch
-torch.backends.cuda.matmul.allow_tf32 = True
-import torchvision
-import torchvision.transforms as T
-from flax.jax_utils import replicate
-from flax.training.common_utils import shard
-#from torchvision.transforms import v2 as T2
-import cv2
-import PIL
-from PIL import Image
-import numpy as np
-import jax
-import torchvision.transforms.functional as F
-output_res = (768,768)
-conditioning_image_transforms = T.Compose(
-    [
-        #T2.ScaleJitter(target_size=output_res, scale_range=(0.5, 3.0))),
-        T.RandomCrop(size=output_res, pad_if_needed=True, padding_mode="symmetric"),
-        T.ToTensor(),
-        #T.Normalize([0.5], [0.5]),
-    ]
-)
-cnet, cnet_params = FlaxControlNetModel.from_pretrained("./models/catcon-controlnet-wd", dtype=jnp.bfloat16, from_flax=True)
-pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-        "./models/wd-1-5-b2-flax",
-        controlnet=cnet,
-        revision="flax",
-        dtype=jnp.bfloat16,
-        )
-#scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
-#    "./models/wd-1-5-b2-flax",
-#    subfolder="scheduler"
-#)
-#params["scheduler"] = scheduler_state
-#scheduler = FlaxDPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-#pipe.enable_model_cpu_offload()
-#pipe.enable_xformers_memory_efficient_attention()
-def get_random(seed):
-    return jax.random.PRNGKey(seed)
-# inference function takes prompt, negative prompt and image
-def infer(prompt, negative_prompt, image):
-    # implement your inference function here
-    params["controlnet"] = cnet_params
-    num_samples = 1
-    inp = Image.fromarray(image)
-    cond_input = conditioning_image_transforms(inp)
-    cond_input = T.ToPILImage()(cond_input)
-    cond_img_in = pipe.prepare_image_inputs([cond_input] * num_samples)
-    cond_img_in = shard(cond_img_in)
-    prompt_in = pipe.prepare_text_inputs([prompt] * num_samples)
-    prompt_in = shard(prompt_in)
-    n_prompt_in = pipe.prepare_text_inputs([negative_prompt] * num_samples)
-    n_prompt_in = shard(n_prompt_in)
-    rng = get_random(0)
-    rng = jax.random.split(rng, jax.device_count())
-    p_params = replicate(params)
-    output = pipe(
-        prompt_ids=prompt_in,
-        image=cond_img_in,
-        params=p_params,
-        prng_seed=rng,
-        num_inference_steps=70,
-        neg_prompt_ids=n_prompt_in,
-        jit=True,
-            ).images
-    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-    return output_images
-gr.Interface(
-    infer,
-    inputs=[
-        gr.Textbox(
-            label="Enter prompt",
-            max_lines=1,
-            placeholder="1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck",
-        ),
-        gr.Textbox(
-            label="Enter negative prompt",
-            max_lines=1,
-            placeholder="low quality",
-        ),
-        gr.Image(),
-    ],
-    outputs=gr.Gallery().style(grid=[2], height="auto"),
-    title="Generate controlled outputs with Categorical Conditioning on Waifu Diffusion 1.5 beta 2.",
-    description="This Space uses image examples as style conditioning.",
-    examples=[
-        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_1.png"],
-        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_2.png"],
-        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_3.png"]
-        ],
-    allow_flagging=False,
-).launch(enable_queue=True)

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/box_coder.py DELETED Viewed

@@ -1,193 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import math
-import torch
-import pandas as pd
-from maskrcnn_benchmark.data.datasets.evaluation.word import io_
-class BoxCoder(object):
-    """
-    This class encodes and decodes a set of bounding boxes into the representation used for training the regressors.
-    """
-    def __init__(self, weights, bbox_xform_clip=math.log(1000. / 16)):
-        """
-        Arguments:
-            weights (4-element tuple)
-            bbox_xform_clip (float)
-        """
-        self.weights = weights
-        self.bbox_xform_clip = bbox_xform_clip
-    def encode(self, reference_boxes, proposals):
-        """
-        Encode a set of proposals with respect to some
-        reference boxes
-        Arguments:
-            reference_boxes (Tensor): reference boxes
-            proposals (Tensor): boxes to be encoded
-        """
-        TO_REMOVE = 1  # TODO remove
-        ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE
-        ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE
-        ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths
-        ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights
-        gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE
-        gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE
-        gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths
-        gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights
-        wx, wy, ww, wh = self.weights
-        targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
-        targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
-        targets_dw = ww * torch.log(gt_widths / ex_widths)
-        targets_dh = wh * torch.log(gt_heights / ex_heights)
-        targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
-        return targets
-    def encode_iou(self, reference_boxes, proposals):
-        """
-        Encode a set of proposals with respect to some
-        reference boxes
-        Arguments:
-            reference_boxes (Tensor): reference boxes
-            proposals (Tensor): boxes to be encoded
-        """
-        TO_REMOVE = 1  # TODO remove
-        ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE
-        ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE
-        ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths
-        ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights
-        gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE
-        gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE
-        gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths
-        gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights
-        wx, wy, ww, wh = self.weights
-        targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
-        targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
-        targets_dw = ww * torch.log(gt_widths / ex_widths)
-        targets_dh = wh * torch.log(gt_heights / ex_heights)
-        targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
-        return targets
-    def decode(self, rel_codes, boxes):
-        """
-        From a set of original boxes and encoded relative box offsets,
-        get the decoded boxes.
-        Arguments:
-            rel_codes (Tensor): encoded boxes   # predict  [2, 12000, 4]
-            boxes (Tensor): reference boxes.   # anchor  [2, 12000, 4]  xmin0 ymin1 xmax2 ymax3
-        """
-        boxes = boxes.to(rel_codes.dtype)
-        TO_REMOVE = 1  # TODO remove
-        widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
-        heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
-        ctr_x = boxes[:, 0] + 0.5 * widths
-        ctr_y = boxes[:, 1] + 0.5 * heights
-        wx, wy, ww, wh = self.weights
-        dx = rel_codes[:, 0::4] / wx
-        dy = rel_codes[:, 1::4] / wy
-        dw = rel_codes[:, 2::4] / ww
-        dh = rel_codes[:, 3::4] / wh
-        dw = torch.clamp(dw, max=self.bbox_xform_clip)
-        dh = torch.clamp(dh, max=self.bbox_xform_clip)
-        pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
-        pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
-        pred_w = torch.exp(dw) * widths[:, None]
-        pred_h = torch.exp(dh) * heights[:, None]
-        ##############################
-        pred_boxes = torch.zeros_like(rel_codes)
-        pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
-        pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
-        pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
-        pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1
-        return pred_boxes
-    def decode_iou(self, rel_codes, boxes, num_p = 8):
-        """
-        From a set of original boxes and encoded relative box offsets,
-        get the decoded boxes.
-        Arguments:
-            rel_codes (Tensor): encoded boxes   # predict  [2, 12000, 4]
-            boxes (Tensor): reference boxes.   # anchor  [2, 12000, 4]  xmin0 ymin1 xmax2 ymax3
-        """
-        boxes = boxes.to(rel_codes.dtype)
-        TO_REMOVE = 1  # TODO remove
-        widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
-        heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
-        ctr_x = boxes[:, 0] + 0.5 * widths
-        ctr_y = boxes[:, 1] + 0.5 * heights
-        # 123
-        # 8#4
-        # 765
-        if num_p == 8:  # 8 boundary points
-            x_1 = boxes[:, 0] + widths * rel_codes[:, 0]
-            y_1 = boxes[:, 1] + heights * rel_codes[:, 1]
-            x_2 = ctr_x + widths * rel_codes[:, 2]
-            y_2 = boxes[:, 1] + heights * rel_codes[:, 3]
-            x_3 = boxes[:, 2] + widths * rel_codes[:, 4]
-            y_3 = boxes[:, 1] + heights * rel_codes[:, 5]
-            x_4 = boxes[:, 2] + widths * rel_codes[:, 6]
-            y_4 = ctr_y + heights * rel_codes[:, 7]
-            x_5 = boxes[:, 2] + widths * rel_codes[:, 8]
-            y_5 = boxes[:, 3] + heights * rel_codes[:, 9]
-            x_6 = ctr_x + widths * rel_codes[:, 10]
-            y_6 = boxes[:, 3] + heights * rel_codes[:, 11]
-            x_7 = boxes[:, 0] + widths * rel_codes[:, 12]
-            y_7 = boxes[:, 3] + heights * rel_codes[:, 13]
-            x_8 = boxes[:, 0] + widths * rel_codes[:, 14]
-            y_8 = ctr_y + heights * rel_codes[:, 15]
-            x_total = torch.stack([x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8], 0)
-            y_total = torch.stack([y_1, y_2, y_3, y_4, y_5, y_6, y_7, y_8], 0)
-        x_min = torch.min(x_total, 0, keepdim=True)  # [1, N]
-        x_max = torch.max(x_total, 0, keepdim=True)
-        y_min = torch.min(y_total, 0, keepdim=True)
-        y_max = torch.max(y_total, 0, keepdim=True)
-        N1, N2 = x_min[0].shape
-        x_min = x_min[0].view([N2])
-        x_max = x_max[0].view([N2])
-        y_min = y_min[0].view([N2])
-        y_max = y_max[0].view([N2])
-        x_min = torch.stack([x_min, ctr_x], 0)
-        x_max = torch.stack([x_max, ctr_x], 0)
-        y_min = torch.stack([y_min, ctr_y], 0)
-        y_max = torch.stack([y_max, ctr_y], 0)
-        x_min = torch.min(x_min, 0, keepdim=True)  # [1, N]
-        x_max = torch.max(x_max, 0, keepdim=True)
-        y_min = torch.min(y_min, 0, keepdim=True)
-        y_max = torch.max(y_max, 0, keepdim=True)
-        pred_boxes = torch.zeros_like(boxes)
-        pred_boxes[:, 0] = x_min[0][0, :]
-        pred_boxes[:, 1] = y_min[0][0, :]
-        pred_boxes[:, 2] = x_max[0][0, :]
-        pred_boxes[:, 3] = y_max[0][0, :]
-        return pred_boxes