Update parquet files (step 42 of 397)

spaces/101-5/gpt4free/testing/readme_table.py

@@ -1,52 +0,0 @@
-from g4f.Provider import (
-    Ails,
-    You,
-    Bing,
-    Yqcloud,
-    Theb,
-    Aichat,
-    Bard,
-    Vercel,
-    Forefront,
-    Lockchat,
-    Liaobots,
-    H2o,
-    ChatgptLogin,
-    DeepAi,
-    GetGpt
-)
-
-from urllib.parse import urlparse
-
-providers = [
-    Ails,
-    You,
-    Bing,
-    Yqcloud,
-    Theb,
-    Aichat,
-    Bard,
-    Vercel,
-    Forefront,
-    Lockchat,
-    Liaobots,
-    H2o,
-    ChatgptLogin,
-    DeepAi,
-    GetGpt
-]
-
-# | Website| Provider| gpt-3.5-turbo | gpt-4 | Supports Stream | Status | Needs Auth |
-print('| Website| Provider| gpt-3.5 | gpt-4 | Streaming | Status | Auth |')
-print('| --- | --- | --- | --- | --- | --- | --- |')
-
-for provider in providers:
-    parsed_url = urlparse(provider.url)
-    name = f"`g4f.Provider{provider.__name__.split('.')[-1]}`"
-    url = f'[{parsed_url.netloc}]({provider.url})'
-    has_gpt4 = '✔️' if 'gpt-4' in provider.model else '❌'
-    has_gpt3_5 = '✔️' if 'gpt-3.5-turbo' in provider.model else '❌'
-    streaming = '✔️' if provider.supports_stream else '❌'
-    needs_auth = '✔️' if provider.needs_auth else '❌'
-    
-    print(f'| {url} | {name} | {has_gpt3_5} | {has_gpt4} | {streaming} | ![Active](https://img.shields.io/badge/Active-brightgreen) | {needs_auth} |')

spaces/1acneusushi/gradio-2dmoleculeeditor/Skoog Analysis Instrumental Pdf Download !!EXCLUSIVE!!.md

@@ -1,60 +0,0 @@
-## Skoog Analysis Instrumental Pdf Download
-
-
-
- 
-
-  
-
- 
-
-**Download ✦✦✦ [https://jinyurl.com/2tzZVK](https://jinyurl.com/2tzZVK)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Principles of Instrumental Analysis by Skoog et al.
-
- 
-
-Principles of Instrumental Analysis is a textbook that covers the theory and applications of various analytical techniques, such as spectroscopy, chromatography, electrochemistry, and thermal methods. The book was written by Douglas A. Skoog, F. James Holler, and Stanley R. Crouch, and was first published in 1985. The latest edition, the seventh, was published in 2016.
-
- 
-
-The book is divided into four sections: Measurement Basics, Atomic Spectroscopy, Molecular Spectroscopy, and Separation Methods. Each section contains several chapters that introduce the principles, instrumentation, and applications of different methods of analysis. The book also includes questions and problems at the end of each chapter, as well as case studies that illustrate the use of instrumental analysis in real-world situations.
-
- 
-
-Principles of Instrumental Analysis is intended for undergraduate and graduate students who are studying analytical chemistry or related fields. It is also a useful reference for researchers and professionals who need to use instrumental methods in their work.
-
- 
-
-If you are interested in learning more about this book, you can find it in various online platforms, such as Internet Archive[^1^] [^3^] or Vdoc[^2^]. However, please note that downloading or accessing the book may require permission from the authors or publishers.
-
-
-
-The book has received positive feedback from students and instructors who have used it in their courses. According to one user review on Google Books[^1^], the book is "10/10" and "very helpful" for learning about instrumental analysis. The reviewer also praised the book for being "clear" and "easy to understand". Another user review on Vdoc[^2^] described the book as "the best" and "very comprehensive". The reviewer also appreciated the "numerous examples" and "detailed explanations" in the book.
-
- 
-
-However, the book also has some limitations and drawbacks that may affect its usefulness for some readers. For instance, some users have complained that the book is too expensive and that the ebook version is not available in some regions. Some users have also reported that the book contains some errors and typos that need to be corrected. Moreover, some users have suggested that the book could be improved by adding more exercises, problems, and case studies, as well as more coverage of recent developments and applications of instrumental analysis.
-
- 
-
-Overall, Principles of Instrumental Analysis is a well-written and authoritative textbook that covers a wide range of topics and methods in analytical chemistry. It is suitable for students who want to learn the fundamentals and applications of modern analytical instruments, as well as for instructors who want to teach them. However, the book may not be accessible or affordable for everyone, and it may not reflect the latest advances and trends in the field. Therefore, readers may need to supplement the book with other sources of information and practice.
-
- 145887f19f
-
- 
-
- 
-

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ciel Gestion Commerciale 2013 Torrent !FULL!.md

@@ -1,182 +0,0 @@
-
-<h1>Ciel Gestion Commerciale 2013 Torrent: What You Need to Know</h1>
-<p>If you are looking for a comprehensive and easy-to-use software for managing your business activities, you might have heard of Ciel Gestion Commerciale 2013. This software is designed for small and medium-sized businesses that need to handle various aspects of their commercial operations, such as invoicing, accounting, stock management, customer relations, etc.</p>
-<h2>ciel gestion commerciale 2013 torrent</h2><br /><p><b><b>Download Zip</b> &#128504; <a href="https://byltly.com/2uKvoM">https://byltly.com/2uKvoM</a></b></p><br /><br />
-<p>Ciel Gestion Commerciale 2013 is popular among French-speaking users because it is adapted to the specificities of the French market and legislation. It also offers a user-friendly interface, a wide range of features and options, and a reliable technical support. However, it also has some drawbacks, such as its high price, its limited compatibility with other systems, and its potential security risks.</p>
-<p>In this article, we will tell you everything you need to know about Ciel Gestion Commerciale 2013 torrent: how to download it, how to use it, and what are some alternatives to it. We will also give you some tips and advice on how to make the most of this software or its competitors.</p>
- <h2>How to Download Ciel Gestion Commerciale 2013 Torrent</h2>
-<p>One of the reasons why some people choose to download Ciel Gestion Commerciale 2013 torrent is because they want to save money on buying the original software. However, this also comes with some risks and challenges that you need to be aware of before proceeding.</p>
-<p>First of all, you need to make sure that your computer meets the minimum requirements for running Ciel Gestion Commerciale 2013. According to the official website, you need at least:</p>
-<ul>
-<li>Windows XP SP3 or later</li>
-<li>Intel Pentium IV processor or equivalent</li>
-<li>1 GB of RAM</li>
-<li>4 GB of free disk space</li>
-<li>Internet connection</li>
-<li>CD-ROM drive</li>
-</ul>
-<p>Secondly, you need to be careful about where you download Ciel Gestion Commerciale 2013 torrent from. There are many websites that claim to offer this software for free, but some of them may contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. Therefore, you should always use a reputable and trusted source for finding torrents, such as The Pirate Bay, Kickass Torrents, or Torrentz. You should also use a reliable antivirus software and a VPN service to protect your device and your identity online.</p>
-<p>Thirdly, you need to know how to install and activate Ciel Gestion Commerciale 2013 after downloading it. Usually, you will need a torrent client such as uTorrent, BitTorrent, or Vuze to open and download the torrent file. Then, you will need a file extractor such as WinRAR, 7-Zip, or PeaZip to unzip the compressed folder that contains the software files. Finally, you will need a crack or a keygen program such as KeyGenius, Serials.ws, or Smart Serials to generate a valid serial number or activation code for Ciel Gestion Commerciale 2013. However, be careful not to use any illegal or fraudulent codes that may cause problems with your software or expose you to legal consequences.</p>
- <h2>How to Use Ciel Gestion Commerciale 2013</h2>
-<p>Once you have successfully installed and activated Ciel Gestion Commerciale 2013 on your computer, you can start using it for managing your business activities. Here are some of the main features and functions that this software offers:</p>
-<p>ciel gestion commerciale 2013 crack<br />
-ciel gestion commerciale 2013 download<br />
-ciel gestion commerciale 2013 keygen<br />
-ciel gestion commerciale 2013 serial<br />
-ciel gestion commerciale 2013 activation<br />
-ciel gestion commerciale 2013 full version<br />
-ciel gestion commerciale 2013 gratuit<br />
-ciel gestion commerciale 2013 mega<br />
-ciel gestion commerciale 2013 uptobox<br />
-ciel gestion commerciale 2013 free<br />
-ciel gestion commerciale 2013 iso<br />
-ciel gestion commerciale 2013 rar<br />
-ciel gestion commerciale 2013 zip<br />
-ciel gestion commerciale 2013 patch<br />
-ciel gestion commerciale 2013 license<br />
-ciel gestion commerciale 2013 french<br />
-ciel gestion commerciale 2013 english<br />
-ciel gestion commerciale 2013 windows<br />
-ciel gestion commerciale 2013 mac<br />
-ciel gestion commerciale 2013 linux<br />
-ciel gestion commerciale 2013 software<br />
-ciel gestion commerciale 2013 program<br />
-ciel gestion commerciale 2013 application<br />
-ciel gestion commerciale 2013 tool<br />
-ciel gestion commerciale 2013 solution<br />
-ciel gestion commerciale 2013 system<br />
-ciel gestion commerciale 2013 product<br />
-ciel gestion commerciale 2013 edition<br />
-ciel gestion commerciale 2013 update<br />
-ciel gestion commerciale 2013 upgrade<br />
-ciel gestion commerciale 2013 review<br />
-ciel gestion commerciale 2013 tutorial<br />
-ciel gestion commerciale 2013 guide<br />
-ciel gestion commerciale 2013 manual<br />
-ciel gestion commerciale 2013 video<br />
-ciel gestion commerciale 2013 online<br />
-ciel gestion commerciale 2013 offline<br />
-ciel gestion comerciale 2021 torrent (misspelled)<br />
-telecharger ciel gestion comerciale torrent (misspelled)<br />
-logiciel de facturation et devis torrent (related)<br />
-logiciel de comptabilité torrent (related)<br />
-logiciel de paie torrent (related)<br />
-logiciel de stock torrent (related)<br />
-logiciel de caisse torrent (related)<br />
-logiciel de fiscalité torrent (related)<br />
-logiciel de e-commerce torrent (related)<br />
-logiciel de CRM torrent (related)<br />
-logiciel de ERP torrent (related)<br />
-logiciel de business plan torrent (related)<br />
-logiciel de cloud torrent (related)</p>
-<ul>
-<li>Create and manage invoices, quotes, orders, delivery notes, credit notes, etc.</li>
-<li>Manage your customers, suppliers, contacts, prospects, etc.</li>
-<li>Manage your stock levels, purchases, sales, returns, etc.</li>
-<li>Generate various reports and statistics on your business performance.</li>
-<li>Synchronize your data with Ciel Compta, Ciel Paye, or other compatible software.</li>
-<li>Customize your documents with your logo, colors, fonts, etc.</li>
-<li>Export your data in various formats such as PDF, Excel, Word, etc.</li>
-<li>Access your data online via Ciel Cloud or Ciel Mobile.</li>
-</ul>
-<p>To use these features and functions effectively,</p><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code>, you need to follow these steps:</p>
- <ol>
-<li>Create a new company file or open an existing one.</li>
-<li>Enter your company information such as name,</p><address>, phone number,</address>, email,</p>, VAT number,</p>, etc.</li>
-<li>Create your products or services catalog by entering their names,</p>, prices,</p>, descriptions,</p>, categories,</p>, etc.</li>
-<li>Create your customers or suppliers database by entering their names,</p>, addresses,</p>, phone numbers,</p>, emails,</p>, payment terms,</p>, etc.</li>
-<li>Create your documents by selecting a template,</p>, adding products or services,</p>, applying discounts or taxes,</p>, adding notes or comments,</p>, etc.</li>
-<li>Print or send your documents by email,</p>, fax,</p>, SMS,</p>, etc.</li>
-<li>Track your payments by recording receipts,</p>, issuing reminders,</p>, managing disputes,</p>, etc.</li>
-<li>Manage your stock by recording movements,</p>, adjusting quantities,</p>, setting alerts,</p>, etc.</li>
-<li>Analyze your data by generating reports such as turnover,</p>, margin,</p>, profitability,</p>, best sellers,</p>, etc.</li>
-<li>Synchronize your data with other software by exporting or importing files in various formats such as CSV, XML, TXT, etc.</li>
-</ol>
- <h2>Alternatives to Ciel Gestion Commerciale 2013 Torrent</h2>
- <p>If you are not satisfied with Ciel Gestion Commerciale 2013 torrent or if you want to try something different, you can consider some of the alternatives that are available on the market. Here are some of the most popular and reputable ones that you can compare with Ciel Gestion Commerciale 2013:</p>
- <h3>Solune Ipso</h3>
-<p>Solune Ipso is a software that specializes in managing projects and contracts. It is suitable for businesses that work on specific orders or services, such as engineering, consulting, construction, etc. It allows you to create and manage invoices, quotes, orders, delivery notes, credit notes, etc. based on your project specifications. It also offers features such as non-conformity management, quality documents management, equipment management, etc. Solune Ipso can interface with standard accounting software and can also be used as a precursor to a more advanced production management software (GPAO).</p>
-<p>Some of the advantages of Solune Ipso are:</p>
-<ul>
-<li>It is adapted to the specific needs of project-based businesses.</li>
-<li>It offers a user-friendly and customizable interface.</li>
-<li>It provides a comprehensive and reliable technical support.</li>
-</ul>
-<p>Some of the drawbacks of Solune Ipso are:</p>
-<ul>
-<li>It is more expensive than Ciel Gestion Commerciale 2013.</li>
-<li>It is not compatible with other systems such as Ciel Compta or Ciel Paye.</li>
-<li>It does not offer online access or mobile app.</li>
-</ul>
- <h3>LYNEADE</h3>
-<p>LYNEADE is an ERP software developed and marketed by LV Informatique. It is designed for small and medium-sized businesses that need to handle various aspects of their business operations, such as accounting, sales, purchases, stocks, production, etc. It offers a modular and scalable solution that can be adapted to different sectors and activities. It also offers features such as CRM, reporting, planning, workflow management, etc. LYNEADE can integrate with other applications such as Microsoft Office or Outlook.</p>
-<p>Some of the advantages of LYNEADE are:</p>
-<ul>
-<li>It is a flexible and versatile solution that can suit different business needs.</li>
-<li>It offers a cloud-based option that allows online access and data backup.</li>
-<li>It provides a free trial and a personalized demonstration.</li>
-</ul>
-<p>Some of the drawbacks of LYNEADE are:</p>
-<ul>
-<li>It is not very well-known or widely used in the market.</li>
-<li>It does not have a mobile app or a web interface.</li>
-<li>It does not have a lot of user reviews or feedback available online.</li>
-</ul>
- <h3>Gestion-360</h3>
-<p>Gestion-360 is an online ERP software designed for very small businesses that need to manage their daily activities in a simple and efficient way. It allows you to create and manage invoices, quotes, orders, stocks, customers, suppliers, etc. It also offers features such as accounting, reporting, statistics, planning, etc. Gestion-360 can export your data in various formats such as PDF, Excel, Word, etc. It also offers a mobile app that allows you to access your data from anywhere.</p>
-<p>Some of the advantages of Gestion-360 are:</p>
-<ul>
-<li>It is a low-cost and easy-to-use solution that does not require any installation or maintenance.</li>
-<li>It offers a cloud-based option that ensures data security and availability.</li>
-<li>It provides a mobile app that allows you to work on the go.</li>
-</ul>
-<p>Some of the drawbacks of Gestion-360 are:</p>
-<ul>
-<li>It is not very comprehensive or advanced in terms of features and functions.</li>
-<li>It is not compatible with other systems such as Ciel Compta or Ciel Paye.</li>
-<li>It does not have a lot of user reviews or feedback available online.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion,</p><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre><code><pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code></pre></code>, Ciel Gestion Commerciale 2013 torrent is a popular software for managing your business activities. However,</p>, it also has some drawbacks,</p>, such as its high price,</p>, its limited compatibility,</p>, and its potential security risks.</p>. Therefore,</p>, you may want to consider some alternatives,</p>, such as Solune Ipso,</p>, LYNEADE,</p>, or Gestion-360,</p>. These software offer different features,</p>, prices,</p>, and benefits,</p>, depending on your business needs,</p>, preferences,</p>, and budget.</p>
- <p>We hope that this article has helped you to understand more about Ciel Gestion Commerciale 2013 torrent and its alternatives. If you want to learn more about these software or other similar ones,</p>, you can visit our website or contact us for more information.</p>
- <h2>Frequently Asked Questions</h2>
- <ol type="1">
-<li>What is Ciel Gestion Commerciale 2013?</li>
- <p>Ciel Gestion Commerciale 2013 is a software for managing your business activities,</p>, such as invoicing,</p>, accounting,</p>, stock management,</p>, customer relations,</p>, etc. It is designed for small and medium-sized businesses that operate in the French market and legislation.</p>
- <li>How to download Ciel Gestion Commerciale 2013 torrent?</li>
- <p>To download Ciel Gestion Commerciale 2013 torrent,</p>, you need to have a torrent client,</p>, a file extractor,</p>, and a crack or a keygen program. You also need to find a reputable and trusted source for finding torrents,</p>, such as The Pirate Bay,</p>, Kickass Torrents,</p>, or Torrentz. You should also use an antivirus software and a VPN service to protect your device and your identity online.</p>
- <li>What are the benefits and drawbacks of using Ciel Gestion Commerciale 2013 torrent?</li>
- <p>The benefits of using Ciel Gestion Commerciale 2013 torrent are:</p>
- <ul type="disc">
-<li>You can save money on buying the original software.</li>
-<li>You can access a wide range of features and options for managing your business activities.</li>
-<li>You can customize and optimize the software for your business needs.</li>
-</ul>
- <p>The drawbacks of using Ciel Gestion Commerciale 2013 torrent are:</p>
- <ul type="disc">
-<li>You may expose yourself to legal consequences for using illegal or fraudulent codes.</li>
-<li>You may encounter viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information.</li>
-<li>You may face compatibility issues with other systems such as Ciel Compta or Ciel Paye.</li>
-<li>You may not receive any technical support or updates from the official provider.</li>
-</ul>
- <li>What are some alternatives to Ciel Gestion Commerciale 2013 torrent?</li>
- <p>Some alternatives to Ciel Gestion Commerciale 2013 torrent are:</p>
- <ul type="disc">
-<li>Solune Ipso: a software that specializes in managing projects and contracts.</li>
-<li>LYNEADE: an ERP software that offers a modular and scalable solution for various business operations.</li>
-<li>Gestion-360: an online ERP software that offers a low-cost and easy-to-use solution for very small businesses.</li>
-</ul>
- <li>How to choose the best alternative to Ciel Gestion Commerciale 2013 torrent?</li>
- <p>To choose the best alternative to Ciel Gestion Commerciale 2013 torrent,</p>, you need to consider several factors,</p>, such as:</p>
- <ul type="disc">
-<li>Your business size,</p>, sector,</p>, and activity.</li>
-<li>Your business needs,</p>, preferences,</p>, and budget.</li>
-<li>The features,</p>, functions,</p>, and benefits of each software.</li>
-<li>The price,</p>, quality,</p>, security,</p>, and support of each software.</li>
-<li>The user reviews,</p>, feedback,</p>, and ratings of each software.</li>
-</ul>
- <p>You can also use our website or contact us for more guidance and assistance on choosing the best alternative to Ciel Gestion Commerciale 2013 torrent.</p>
- <h2>ed</h2>
- <p>Thank you for reading this article. I hope you have found it useful and informative. If you have any questions or comments,</p>, please feel free to leave them below or contact me directly. I would love to hear from you and help you with your content writing needs.</p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Ride 2 Limited Edition Bikes Pack for Windows 8 and Transform Your Garage into a Temple of Motorcycles.md

@@ -1,111 +0,0 @@
-
-<h1>Ride 2 Limited Edition Bikes Pack: A Dream Come True for Motorcycle Lovers</h1>
- <p>Do you love motorcycles? Do you enjoy racing simulation games? Do you want to experience riding some of the most iconic bikes ever made? If you answered yes to any of these questions, then you should definitely check out Ride 2 Limited Edition Bikes Pack, a downloadable content (DLC) for the game Ride 2 that will make your dreams come true.</p>
-<h2>Ride 2 Limited Edition Bikes Pack Download Windows 8</h2><br /><p><b><b>DOWNLOAD</b> > <a href="https://byltly.com/2uKwhn">https://byltly.com/2uKwhn</a></b></p><br /><br />
- <h2>What is Ride 2 Limited Edition Bikes Pack?</h2>
- <h3>A DLC for the racing simulation game Ride 2</h3>
- <p>Ride 2 is a racing simulation game developed by Milestone S.r.l. and released in 2016. It features over 200 bikes from different categories, such as naked, sport, superbike, endurance, motard, cafe racer, and more. It also offers over 30 tracks from around the world, such as Nurburgring, Laguna Seca, Donington Park, and more. You can customize your bike and rider with various options, such as liveries, helmets, suits, gloves, boots, etc. You can also compete with other players online or offline in various modes, such as quick race, time trial, championship, team vs team, etc.</p>
- <h3>A collection of six iconic bikes from different manufacturers and eras</h3>
- <p>Ride 2 Limited Edition Bikes Pack is a DLC that adds six of motorcycling's all-time most iconic bikes to the game. These are bikes that have made history in terms of performance, technology, design, and rarity. They are:</p>
- <ul>
-<li>Aprilia RSV4 Factory APRC ABS 2014</li>
-<li>Bimota SB8K-2000</li>
-<li>Suzuki GSX-R1000 Concept 2016</li>
-<li>Yamaha MT09 Street Rally 2014</li>
-<li>Suter 500 mmx 2016</li>
-<li>MV Agusta F4CC 2006</li>
-</ul>
- <p>Each bike has its own characteristics and features that make it unique and special. You can ride them on any track and challenge yourself and others with these amazing machines.</p>
- <h2>What are the features of Ride 2 Limited Edition Bikes Pack?</h2>
- <h3>High-quality graphics and realistic physics</h3>
- <p>One of the main features of Ride 2 Limited Edition Bikes Pack is the high-quality graphics that make the bikes look stunning and realistic. The developers have paid attention to every detail, such as the shapes, colors, textures, reflections, shadows, etc. The bikes look like they are taken from real life and placed into the game.</p>
-<p>Ride 2 LE Bikes Pack for Windows 8 PC<br />
-How to download Ride 2 Limited Edition Bikes Pack on Windows 8<br />
-Ride 2 Windows 8 compatible bikes pack<br />
-Ride 2 LE Bikes Pack free download for Windows 8<br />
-Best bikes pack for Ride 2 on Windows 8<br />
-Ride 2 Limited Edition Bikes Pack features and reviews<br />
-Ride 2 LE Bikes Pack installation guide for Windows 8<br />
-Ride 2 Windows 8 bike pack download link<br />
-Ride 2 LE Bikes Pack system requirements for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack gameplay and screenshots<br />
-Ride 2 LE Bikes Pack discount and coupon codes for Windows 8<br />
-Ride 2 Windows 8 bike pack troubleshooting and support<br />
-Ride 2 LE Bikes Pack update and patch notes for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack mods and customizations<br />
-Ride 2 LE Bikes Pack DLC and expansions for Windows 8<br />
-Ride 2 Windows 8 bike pack comparison and alternatives<br />
-Ride 2 LE Bikes Pack ratings and feedback for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack online and multiplayer modes<br />
-Ride 2 LE Bikes Pack tips and tricks for Windows 8<br />
-Ride 2 Windows 8 bike pack achievements and trophies<br />
-Ride 2 LE Bikes Pack soundtrack and music for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack videos and trailers<br />
-Ride 2 LE Bikes Pack wallpapers and themes for Windows 8<br />
-Ride 2 Windows 8 bike pack FAQs and guides<br />
-Ride 2 LE Bikes Pack cheats and hacks for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack release date and price<br />
-Ride 2 LE Bikes Pack developer and publisher information<br />
-Ride 2 Windows 8 bike pack genre and category<br />
-Ride 2 LE Bikes Pack languages and subtitles for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack controller and keyboard settings<br />
-Ride 2 LE Bikes Pack graphics and performance options for Windows 8<br />
-Ride 2 Windows 8 bike pack size and download time<br />
-Ride 2 LE Bikes Pack refund and return policy for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack news and updates<br />
-Ride 2 LE Bikes Pack forum and community for Windows 8<br />
-Ride 2 Windows 8 bike pack recommendations and suggestions<br />
-Ride 2 LE Bikes Pack fun facts and trivia for Windows 8<br />
-Ride 2 Limited Edition Bikes Pack history and background<br />
-Ride 2 LE Bikes Pack awards and nominations for Windows 8<br />
-Ride 2 Windows 8 bike pack merchandise and collectibles<br />
-Ride 2 LE Bikes Pack challenges and contests for Windows</p>
- <p>Another feature is the realistic physics that make the bikes behave like they would in real life. The developers have used advanced technologies and algorithms to simulate the dynamics of the bikes, such as the weight distribution, the suspension system, the traction control, the braking system, etc. The bikes react to your inputs and to the environment in a natural and believable way.</p>
- <h3>Customization options and online multiplayer mode</h3>
- <p>the brakes, the suspension, etc. You can also change various aspects of your rider, such as the helmet, the suit, the gloves, etc. You can create your own style and show it off to others.</p>
- <p>A fourth feature is the online multiplayer mode that allows you to compete with other players from around the world who have also downloaded Ride 2 Limited Edition Bikes Pack. You can join or create online races with up to 12 players and see who is the fastest and most skilled rider among them. You can also chat with other players and make new friends who share your passion for motorcycles.</p>
- <h2>What are the bikes included in Ride 2 Limited Edition Bikes Pack?</h2>
- <h3>Aprilia RSV4 Factory APRC ABS 2014</h3>
- <p>The Aprilia RSV4 Factory APRC ABS is a superbike produced by Aprilia since 2009. It is one of the best bikes in terms of performance and technology. It has a V4 engine that produces 170 hp at 11,500 rpm and a top speed of over 300 km/h. It also has an advanced electronic system that includes traction control, wheelie control, launch control, quick shift, etc. It has won several championships and awards in various competitions.</p>
- <h3>Bimota SB8K-2000</h3>
- <p>The Bimota SB8K-2000 is a street superbike produced by Bimota in only 150 models. It is an incomparable, top-of-the-range bike that combines Italian design and Japanese technology. It has a V-twin engine derived from Suzuki that produces over 160 hp at 10,000 rpm and a top speed of over 280 km/h. It also has a carbon fiber frame and fairing that make it light and agile.</p>
- <h3>Suzuki GSX-R1000 Concept 2016</h3>
- <p>The Suzuki GSX-R1000 Concept is a prototype of a superbike presented by Suzuki in 2015. It is expected to be released in <p>hp at 13,000 rpm and a top speed of over 310 km/h. It also has a carbon fiber frame and swingarm, a magnesium alloy wheels, a Brembo braking system, and an Ohlins suspension system. It is a rare and exclusive bike that only a few lucky riders can own.</p>
- <h3>MV Agusta F4CC 2006</h3>
- <p>The MV Agusta F4CC is one of the most expensive and exclusive bikes ever made. It was produced in only 100 models, each with a price tag of $120,000. It has a four-cylinder engine that produces 200 hp at 12,200 rpm and a top speed of over 315 km/h. It also has a titanium exhaust system and a full-carbon fairing. It is a masterpiece of design and engineering that represents the essence of MV Agusta.</p>
- <h2>How to download and install Ride 2 Limited Edition Bikes Pack on Windows 8?</h2>
- <h3>Requirements and steps for downloading and installing the DLC</h3>
- <p>To download and install Ride 2 Limited Edition Bikes Pack on Windows 8, you need to have the following requirements:</p>
- <ul>
-<li>A PC with Windows 8 or higher operating system</li>
-<li>A Steam account and the Steam client installed on your PC</li>
-<li>The base game Ride 2 installed on your PC</li>
-<li>An internet connection</li>
-<li>$5.99 to purchase the DLC</li>
-</ul>
- <p>Once you have these requirements, you can follow these steps to download and install the DLC:</p>
- <ol>
-<li>Open the Steam client on your PC and log in to your Steam account</li>
-<li>Go to the Store tab and search for Ride 2 Limited Edition Bikes Pack</li>
-<li>Click on the Add to Cart button and proceed to checkout</li>
-<li>Complete the payment process and confirm your purchase</li>
-<li>Go to the Library tab and select Ride 2 from your games list</li>
-<li>Click on the Install button and wait for the DLC to download and install</li>
-<li>Launch Ride 2 and enjoy riding the six iconic bikes included in the DLC</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Ride 2 Limited Edition Bikes Pack is a DLC that adds six of motorcycling's all-time most iconic bikes to the game Ride 2. It is a dream come true for motorcycle lovers who want to experience riding these amazing machines on various tracks around the world. It is a DLC that offers high-quality graphics, realistic physics, customization options, and online multiplayer mode. It is a DLC that is worth every penny for anyone who loves motorcycles and racing simulation games.</p>
- <h2>FAQs</h2>
- <h3>Q: How many bikes are included in Ride 2 Limited Edition Bikes Pack?</h3>
-<p>A: Ride 2 Limited Edition Bikes Pack includes six bikes: Aprilia RSV4 Factory APRC ABS 2014, Bimota SB8K-2000, Suzuki GSX-R1000 Concept 2016, Yamaha MT09 Street Rally 2014, Suter 500 mmx 2016, and MV Agusta F4CC 2006.</p>
- <h3>Q: How much does Ride 2 Limited Edition Bikes Pack cost?</h3>
-<p>A: Ride 2 Limited Edition Bikes Pack costs $5.99 on Steam.</p>
- <h3>Q: Do I need to have the base game Ride 2 to play Ride 2 Limited Edition Bikes Pack?</h3>
-<p>A: Yes, you need to have the base game Ride 2 installed on your PC in order to play Ride 2 Limited Edition Bikes Pack.</p>
- <h3>Q: Can I play Ride 2 Limited Edition Bikes Pack offline?</h3>
-<p>A: Yes, you can play Ride 2 Limited Edition Bikes Pack offline in single-player mode.</p>
- <h3>Q: Can I play Ride 2 Limited Edition Bikes Pack online?</h3>
-<p>A: Yes, you can play Ride 2 Limited Edition Bikes Pack online in multiplayer mode with up to 12 players.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />

spaces/1line/AutoGPT/autogpt/config/__init__.py

@@ -1,14 +0,0 @@
-"""
-This module contains the configuration classes for AutoGPT.
-"""
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config, check_openai_api_key
-from autogpt.config.singleton import AbstractSingleton, Singleton
-
-__all__ = [
-    "check_openai_api_key",
-    "AbstractSingleton",
-    "AIConfig",
-    "Config",
-    "Singleton",
-]

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/vocoder_infer/__init__.py

@@ -1,2 +0,0 @@
-from . import hifigan
-from . import pwg

spaces/AIGC-Audio/Make_An_Audio/ldm/util.py

@@ -1,136 +0,0 @@
-import importlib
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-import hashlib
-import requests
-import os
-
-URL_MAP = {
-    'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt',
-}
-
-CKPT_MAP = {
-    'vggishish_lpaps': 'vggishish16.pt',
-    'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt',
-    'melception': 'melception-21-05-10T09-28-40.pt',
-}
-
-MD5_MAP = {
-    'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd',
-    'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625',
-    'melception': 'a71a41041e945b457c7d3d814bbcf72d',
-}
-
-
-def download(url, local_path, chunk_size=1024):
-    os.makedirs(os.path.split(local_path)[0], exist_ok=True)
-    with requests.get(url, stream=True) as r:
-        total_size = int(r.headers.get("content-length", 0))
-        with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
-            with open(local_path, "wb") as f:
-                for data in r.iter_content(chunk_size=chunk_size):
-                    if data:
-                        f.write(data)
-                        pbar.update(chunk_size)
-
-
-def md5_hash(path):
-    with open(path, "rb") as f:
-        content = f.read()
-    return hashlib.md5(content).hexdigest()
-
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x,torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config,reload=False):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-def get_ckpt_path(name, root, check=False):
-    assert name in URL_MAP
-    path = os.path.join(root, CKPT_MAP[name])
-    if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
-        print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
-        download(URL_MAP[name], path)
-        md5 = md5_hash(path)
-        assert md5 == MD5_MAP[name], md5
-    return path

spaces/AIZ2H/07-GraphViz-PyDeck-Map-AIUIUX-Demo/README.md

@@ -1,13 +0,0 @@
----
-title: 07 GraphViz PyDeck Map AIUIUX Demo
-emoji: 🕸️📊
-colorFrom: green
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AdithyaSNair/Medical_price_prediction/app.py

@@ -1,84 +0,0 @@
-import pandas as pd 
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import gradio as gr
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import r2_score
-from sklearn.model_selection import KFold
-from sklearn.model_selection import cross_val_score
-from sklearn.preprocessing import StandardScaler
-def main(Age,Sex,BMI,No_of_Children,Smoker,Region):
-  url="https://raw.githubusercontent.com/ADITHYASNAIR2021/Dataset-cart/main/insurance.csv"
-  data = pd.read_csv(url)
-  label_data = data.copy()
-  s = (data.dtypes =="object")
-  object_cols = list(s[s].index)
-  label_encoder = LabelEncoder()
-  for col in object_cols:
-      label_data[col] = label_encoder.fit_transform(label_data[col])
-  X= label_data.drop(["expenses"],axis =1)
-  y= label_data["expenses"]
-  X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size = 0.25, random_state = 42)
-  X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size = 0.5, random_state = 42)
-  X_train = StandardScaler().fit_transform(X_train)
-  X_test = StandardScaler().fit_transform(X_test)
-
-  Rand_reg=RandomForestRegressor()
-  Rand_reg.fit(X_train,y_train)
-
-  Lin_reg = LinearRegression()
-  Lin_reg.fit(X_train,y_train)
-  
-  data = {'age':Age,'sex':Sex,'bmi':BMI,'children':No_of_Children,'smoker':Smoker,'region':Region}
-  index = [0]
-  cust_df = pd.DataFrame(data, index)
-
-  costpredRand = Rand_reg.predict(cust_df)
-  costpredLin = Lin_reg.predict(cust_df)
-
-  large=[costpredLin,costpredRand]
-  #large.sort(reverse=True)
-  if large[0] <= 0 and large[1]<=0:
-    return 'No values found, 404 error'
-  if large[0] <= 0 and large[1]>0:
-    return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
-  if large[0] >0  and large[1] <= 0:
-    return f"The amount to be paid at the hospital for the mentioned patient is- {large[0]}"
-  if large[0] >=0  and large[1] >=0:
-    return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
-
-iface = gr.Interface(fn = main,
-                     
-inputs =['number','number','number','number','number','number'],
-
-outputs =['text'],
-
-title=" 🩺 Medical cost prediction ",
-
-description ='''    Description
-
-          Age: age of the primary beneficiary
-
-          Sex: insurance contractor gender, female = 0, male = 1
-
-          BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height,
-                objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9
-
-          No_of_Children: Number of children covered by health insurance / Number of dependents
-
-          Smoker: Smoking (Yes(1)/No(0))
-
-          Region: the beneficiary's residential area in the US, northeast =0, northwest =1, southeast = 2, and southwest = 3
-
-''',
-                     article='''
-                     🩺 Medical Cost Prediction
-A regression model that predicts medical cost with an accuracy above 85% 
-''',
-examples=[[19,0,27.9,0,1,3],[32,1,28.9,0,0,1]])
-
-iface.launch(debug =True)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogresscanvas/LineProgressCanvas.js

@@ -1,2 +0,0 @@
-import LineProgressCanvas from '../../../plugins/lineprogresscanvas.js';
-export default LineProgressCanvas;

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

@@ -1,17 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import Knob from '../../knob/Knob.js';
-import CreateChild from './utils/CreateChild.js';
-
-var CreateKnob = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    // Replace data by child game object
-    CreateChild(scene, data, 'background', view, styles, customBuilders);
-    CreateChild(scene, data, 'text', view, styles, customBuilders);
-
-    var gameObject = new Knob(scene, data);
-    scene.add.existing(gameObject);
-    return gameObject;
-};
-
-export default CreateKnob;

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

@@ -1,42 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import Menu from '../../menu/Menu.js';
-import Make from '../Make.js';
-import DeepClone from '../../../../plugins/utils/object/DeepClone.js';
-
-var CreateMenu = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var backgroundConfig = data.background;
-    delete data.background;
-    if (backgroundConfig) {
-        data.createBackgroundCallback = function (items) {
-            var scene = items.scene;
-            var gameObject = Make(scene, DeepClone(backgroundConfig), view, styles, customBuilders);
-            return gameObject;
-        }
-    }
-
-    data.createButtonCallback = function (item, index, items) {
-        // Don't deep-clone scene and $next properties
-        var scene = item.scene;
-        var $next = item.$next;
-        delete item.scene;
-        delete item.$next;
-
-        var gameObject = Make(scene, DeepClone(item), view, styles, customBuilders);
-
-        // Add scene, $next properties back
-        item.scene = scene;
-        item.$next = $next;
-
-        return gameObject;
-    }
-
-    data.childrenKey = '$next';
-
-    var gameObject = new Menu(scene, data);
-    scene.add.existing(gameObject);
-    return gameObject;
-};
-
-export default CreateMenu;

spaces/AiMimicry/sovits-models/modules/mel_processing.py

@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/latex/attention/parameter_attention.tex

@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
-    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
-    A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/semantic_stable_diffusion.md

@@ -1,35 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Semantic Guidance
-
-Semantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Diffusion using Semantic Dimensions](https://huggingface.co/papers/2301.12247) and provides strong semantic control over image generation.
-Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition.
-
-The abstract from the paper is:
-
-*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on a variety of tasks and provide evidence for its versatility and flexibility.*
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## SemanticStableDiffusionPipeline
-[[autodoc]] SemanticStableDiffusionPipeline
-	- all
-	- __call__
-
-## StableDiffusionSafePipelineOutput
-[[autodoc]] pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput
-	- all

spaces/Andy1621/uniformer_image_detection/configs/detectors/htc_r50_rfp_1x_coco.py

@@ -1,24 +0,0 @@
-_base_ = '../htc/htc_r50_fpn_1x_coco.py'
-
-model = dict(
-    backbone=dict(
-        type='DetectoRS_ResNet',
-        conv_cfg=dict(type='ConvAWS'),
-        output_img=True),
-    neck=dict(
-        type='RFP',
-        rfp_steps=2,
-        aspp_out_channels=64,
-        aspp_dilations=(1, 3, 6, 1),
-        rfp_backbone=dict(
-            rfp_inplanes=256,
-            type='DetectoRS_ResNet',
-            depth=50,
-            num_stages=4,
-            out_indices=(0, 1, 2, 3),
-            frozen_stages=1,
-            norm_cfg=dict(type='BN', requires_grad=True),
-            norm_eval=True,
-            conv_cfg=dict(type='ConvAWS'),
-            pretrained='torchvision://resnet50',
-            style='pytorch')))

spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/README.md

@@ -1,43 +0,0 @@
-# Mask R-CNN
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@article{He_2017,
-   title={Mask R-CNN},
-   journal={2017 IEEE International Conference on Computer Vision (ICCV)},
-   publisher={IEEE},
-   author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
-   year={2017},
-   month={Oct}
-}
-```
-
-## Results and models
-
-|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
-|    R-50-FPN     |  caffe  |   1x    | 4.3      |                | 38.0   | 34.4    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) |
-|    R-50-FPN     | pytorch |   1x    | 4.4      | 16.1           | 38.2   | 34.7    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) |
-|    R-50-FPN     | pytorch |   2x    | -        | -              | 39.2   | 35.4    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json)  |
-|    R-101-FPN    |  caffe  |   1x    |          |                | 40.4   | 36.4    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)|
-|    R-101-FPN    | pytorch |   1x    | 6.4      | 13.5           | 40.0   | 36.1    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) |
-|    R-101-FPN    | pytorch |   2x    | -        | -              | 40.8   | 36.6    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json)  |
-| X-101-32x4d-FPN | pytorch |   1x    | 7.6      | 11.3           | 41.9   | 37.5    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) |
-| X-101-32x4d-FPN | pytorch |   2x    | -        | -              | 42.2   | 37.8    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json)  |
-| X-101-64x4d-FPN | pytorch |   1x    | 10.7     | 8.0            | 42.8   | 38.4    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) |
-| X-101-64x4d-FPN | pytorch |   2x    |  -       |   -            |  42.7  |  38.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)|
-| X-101-32x8d-FPN | pytorch |   1x    |  -       |   -            |  42.8  |  38.3   | |
-
-## Pre-trained Models
-
-We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
-
-|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
-| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
-|    [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py)     |  caffe  |   2x    | 4.3      |                | 40.3   | 36.5    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json)
-|    [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py)     |  caffe  |   3x    | 4.3      |                | 40.8   | 37.0    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json)
-|    [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py)     |  pytorch  |   1x    | -     |       | 43.6 | 39.0 |
-|    [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py)     |  pytorch  |   3x    | -     |       | 44.0 | 39.3 |

spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/ae_loss.py

@@ -1,102 +0,0 @@
-import mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def ae_loss_per_image(tl_preds, br_preds, match):
-    """Associative Embedding Loss in one image.
-
-    Associative Embedding Loss including two parts: pull loss and push loss.
-    Pull loss makes embedding vectors from same object closer to each other.
-    Push loss distinguish embedding vector from different objects, and makes
-        the gap between them is large enough.
-
-    During computing, usually there are 3 cases:
-        - no object in image: both pull loss and push loss will be 0.
-        - one object in image: push loss will be 0 and pull loss is computed
-            by the two corner of the only object.
-        - more than one objects in image: pull loss is computed by corner pairs
-            from each object, push loss is computed by each object with all
-            other objects. We use confusion matrix with 0 in diagonal to
-            compute the push loss.
-
-    Args:
-        tl_preds (tensor): Embedding feature map of left-top corner.
-        br_preds (tensor): Embedding feature map of bottim-right corner.
-        match (list): Downsampled coordinates pair of each ground truth box.
-    """
-
-    tl_list, br_list, me_list = [], [], []
-    if len(match) == 0:  # no object in image
-        pull_loss = tl_preds.sum() * 0.
-        push_loss = tl_preds.sum() * 0.
-    else:
-        for m in match:
-            [tl_y, tl_x], [br_y, br_x] = m
-            tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1)
-            br_e = br_preds[:, br_y, br_x].view(-1, 1)
-            tl_list.append(tl_e)
-            br_list.append(br_e)
-            me_list.append((tl_e + br_e) / 2.0)
-
-        tl_list = torch.cat(tl_list)
-        br_list = torch.cat(br_list)
-        me_list = torch.cat(me_list)
-
-        assert tl_list.size() == br_list.size()
-
-        # N is object number in image, M is dimension of embedding vector
-        N, M = tl_list.size()
-
-        pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2)
-        pull_loss = pull_loss.sum() / N
-
-        margin = 1  # exp setting of CornerNet, details in section 3.3 of paper
-
-        # confusion matrix of push loss
-        conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list
-        conf_weight = 1 - torch.eye(N).type_as(me_list)
-        conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs())
-
-        if N > 1:  # more than one object in current image
-            push_loss = F.relu(conf_mat).sum() / (N * (N - 1))
-        else:
-            push_loss = tl_preds.sum() * 0.
-
-    return pull_loss, push_loss
-
-
-@LOSSES.register_module()
-class AssociativeEmbeddingLoss(nn.Module):
-    """Associative Embedding Loss.
-
-    More details can be found in
-    `Associative Embedding <https://arxiv.org/abs/1611.05424>`_ and
-    `CornerNet <https://arxiv.org/abs/1808.01244>`_ .
-    Code is modified from `kp_utils.py <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L180>`_  # noqa: E501
-
-    Args:
-        pull_weight (float): Loss weight for corners from same object.
-        push_weight (float): Loss weight for corners from different object.
-    """
-
-    def __init__(self, pull_weight=0.25, push_weight=0.25):
-        super(AssociativeEmbeddingLoss, self).__init__()
-        self.pull_weight = pull_weight
-        self.push_weight = push_weight
-
-    def forward(self, pred, target, match):
-        """Forward function."""
-        batch = pred.size(0)
-        pull_all, push_all = 0.0, 0.0
-        for i in range(batch):
-            pull, push = ae_loss_per_image(pred[i], target[i], match[i])
-
-            pull_all += self.pull_weight * pull
-            push_all += self.push_weight * push
-
-        return pull_all, push_all

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_r50-d8.py

@@ -1,45 +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, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        num_convs=2,
-        concat_input=True,
-        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)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Andy1621/uniformer_image_segmentation/configs/encnet/README.md

@@ -1,39 +0,0 @@
-# Context Encoding for Semantic Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@InProceedings{Zhang_2018_CVPR,
-author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
-title = {Context Encoding for Semantic Segmentation},
-booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-month = {June},
-year = {2018}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| encnet | R-50-D8  | 512x1024  |   40000 | 8.6      | 4.58           | 75.67 |         77.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json)     |
-| encnet | R-101-D8 | 512x1024  |   40000 | 12.1     | 2.66           | 75.81 |         77.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) |
-| encnet | R-50-D8  | 769x769   |   40000 | 9.8      | 1.82           | 76.24 |         77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json)         |
-| encnet | R-101-D8 | 769x769   |   40000 | 13.7     | 1.26           | 74.25 |         76.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json)     |
-| encnet | R-50-D8  | 512x1024  |   80000 | -        | -              | 77.94 |         79.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json)     |
-| encnet | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.55 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) |
-| encnet | R-50-D8  | 769x769   |   80000 | -        | -              | 77.44 |         78.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json)         |
-| encnet | R-101-D8 | 769x769   |   80000 | -        | -              | 76.10 |         76.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| encnet | R-50-D8  | 512x512   |   80000 | 10.1     | 22.81          | 39.53 |         41.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json)         |
-| encnet | R-101-D8 | 512x512   |   80000 | 13.6     | 14.87          | 42.11 |         43.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json)     |
-| encnet | R-50-D8  | 512x512   |  160000 | -        | -              | 40.10 |         41.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json)     |
-| encnet | R-101-D8 | 512x512   |  160000 | -        | -              | 42.61 |         44.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_512x512_80k_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/Ankit6396/100-Free-ChatGPT4/README.md

@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Arthur678/vits-uma-genshin-honkai/app.py

@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
-    len(hps_ms.symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 500:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        speaker_id = LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    app.queue(concurrency_count=1).launch()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/versionpredicate.py

@@ -1,175 +0,0 @@
-"""Module for parsing and testing package version predicate strings.
-"""
-import re
-import distutils.version
-import operator
-
-
-re_validPackage = re.compile(r"(?i)^\s*([a-z_]\w*(?:\.[a-z_]\w*)*)(.*)", re.ASCII)
-# (package) (rest)
-
-re_paren = re.compile(r"^\s*\((.*)\)\s*$")  # (list) inside of parentheses
-re_splitComparison = re.compile(r"^\s*(<=|>=|<|>|!=|==)\s*([^\s,]+)\s*$")
-# (comp) (version)
-
-
-def splitUp(pred):
-    """Parse a single version comparison.
-
-    Return (comparison string, StrictVersion)
-    """
-    res = re_splitComparison.match(pred)
-    if not res:
-        raise ValueError("bad package restriction syntax: %r" % pred)
-    comp, verStr = res.groups()
-    with distutils.version.suppress_known_deprecation():
-        other = distutils.version.StrictVersion(verStr)
-    return (comp, other)
-
-
-compmap = {
-    "<": operator.lt,
-    "<=": operator.le,
-    "==": operator.eq,
-    ">": operator.gt,
-    ">=": operator.ge,
-    "!=": operator.ne,
-}
-
-
-class VersionPredicate:
-    """Parse and test package version predicates.
-
-    >>> v = VersionPredicate('pyepat.abc (>1.0, <3333.3a1, !=1555.1b3)')
-
-    The `name` attribute provides the full dotted name that is given::
-
-    >>> v.name
-    'pyepat.abc'
-
-    The str() of a `VersionPredicate` provides a normalized
-    human-readable version of the expression::
-
-    >>> print(v)
-    pyepat.abc (> 1.0, < 3333.3a1, != 1555.1b3)
-
-    The `satisfied_by()` method can be used to determine with a given
-    version number is included in the set described by the version
-    restrictions::
-
-    >>> v.satisfied_by('1.1')
-    True
-    >>> v.satisfied_by('1.4')
-    True
-    >>> v.satisfied_by('1.0')
-    False
-    >>> v.satisfied_by('4444.4')
-    False
-    >>> v.satisfied_by('1555.1b3')
-    False
-
-    `VersionPredicate` is flexible in accepting extra whitespace::
-
-    >>> v = VersionPredicate(' pat( ==  0.1  )  ')
-    >>> v.name
-    'pat'
-    >>> v.satisfied_by('0.1')
-    True
-    >>> v.satisfied_by('0.2')
-    False
-
-    If any version numbers passed in do not conform to the
-    restrictions of `StrictVersion`, a `ValueError` is raised::
-
-    >>> v = VersionPredicate('p1.p2.p3.p4(>=1.0, <=1.3a1, !=1.2zb3)')
-    Traceback (most recent call last):
-      ...
-    ValueError: invalid version number '1.2zb3'
-
-    It the module or package name given does not conform to what's
-    allowed as a legal module or package name, `ValueError` is
-    raised::
-
-    >>> v = VersionPredicate('foo-bar')
-    Traceback (most recent call last):
-      ...
-    ValueError: expected parenthesized list: '-bar'
-
-    >>> v = VersionPredicate('foo bar (12.21)')
-    Traceback (most recent call last):
-      ...
-    ValueError: expected parenthesized list: 'bar (12.21)'
-
-    """
-
-    def __init__(self, versionPredicateStr):
-        """Parse a version predicate string."""
-        # Fields:
-        #    name:  package name
-        #    pred:  list of (comparison string, StrictVersion)
-
-        versionPredicateStr = versionPredicateStr.strip()
-        if not versionPredicateStr:
-            raise ValueError("empty package restriction")
-        match = re_validPackage.match(versionPredicateStr)
-        if not match:
-            raise ValueError("bad package name in %r" % versionPredicateStr)
-        self.name, paren = match.groups()
-        paren = paren.strip()
-        if paren:
-            match = re_paren.match(paren)
-            if not match:
-                raise ValueError("expected parenthesized list: %r" % paren)
-            str = match.groups()[0]
-            self.pred = [splitUp(aPred) for aPred in str.split(",")]
-            if not self.pred:
-                raise ValueError("empty parenthesized list in %r" % versionPredicateStr)
-        else:
-            self.pred = []
-
-    def __str__(self):
-        if self.pred:
-            seq = [cond + " " + str(ver) for cond, ver in self.pred]
-            return self.name + " (" + ", ".join(seq) + ")"
-        else:
-            return self.name
-
-    def satisfied_by(self, version):
-        """True if version is compatible with all the predicates in self.
-        The parameter version must be acceptable to the StrictVersion
-        constructor.  It may be either a string or StrictVersion.
-        """
-        for cond, ver in self.pred:
-            if not compmap[cond](version, ver):
-                return False
-        return True
-
-
-_provision_rx = None
-
-
-def split_provision(value):
-    """Return the name and optional version number of a provision.
-
-    The version number, if given, will be returned as a `StrictVersion`
-    instance, otherwise it will be `None`.
-
-    >>> split_provision('mypkg')
-    ('mypkg', None)
-    >>> split_provision(' mypkg( 1.2 ) ')
-    ('mypkg', StrictVersion ('1.2'))
-    """
-    global _provision_rx
-    if _provision_rx is None:
-        _provision_rx = re.compile(
-            r"([a-zA-Z_]\w*(?:\.[a-zA-Z_]\w*)*)(?:\s*\(\s*([^)\s]+)\s*\))?$", re.ASCII
-        )
-    value = value.strip()
-    m = _provision_rx.match(value)
-    if not m:
-        raise ValueError("illegal provides specification: %r" % value)
-    ver = m.group(2) or None
-    if ver:
-        with distutils.version.suppress_known_deprecation():
-            ver = distutils.version.StrictVersion(ver)
-    return m.group(1), ver

spaces/AutoGeneralAI/ChatGPT/app.py

@@ -1,35 +0,0 @@
-import openai
-import gradio as gr
-
-openai.api_key = "sk-" # Replace this with your API key: https://beta.openai.com/docs/quickstart/add-your-api-key
-
-# openai
-def openai_chat(prompt):
-    completions = openai.Completion.create(
-        engine="text-davinci-003",
-        prompt=prompt,
-        max_tokens=1024,
-        n=1,
-        temperature=0.5,
-    )
-
-    message = completions.choices[0].text
-    return message.strip()
-
-# gradio
-def chatbot(key, input, history=[]):
-    openai.api_key = key
-    output = openai_chat(input)
-    history.append((input, output))
-    return history, history
-
-keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"Your API-key...",
-                        type="password",
-                        visible=True,
-                        label="API-Key",
-                    )
-gr.Interface(fn = chatbot,
-             inputs = [keyTxt,"text",'state'],
-             outputs = ["chatbot",'state']).launch(debug = True)

spaces/Bart92/RVC_HF/tools/dlmodels.sh

@@ -1,566 +0,0 @@
-#!/bin/bash
-
-echo working dir is $(pwd)
-echo downloading requirement aria2 check.
-
-if command -v aria2c &> /dev/null
-then
-    echo "aria2c command found"
-else
-    echo failed. please install aria2
-    sleep 5
-    exit 1
-fi
-
-d32="f0D32k.pth"
-d40="f0D40k.pth"
-d48="f0D48k.pth"
-g32="f0G32k.pth"
-g40="f0G40k.pth"
-g48="f0G48k.pth"
-
-d40v2="f0D40k.pth"
-g40v2="f0G40k.pth"
-
-dld32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth"
-dld40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth"
-dld48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth"
-dlg32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth"
-dlg40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth"
-dlg48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth"
-
-dld40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth"
-dlg40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth"
-
-hp2_all="HP2_all_vocals.pth"
-hp3_all="HP3_all_vocals.pth"
-hp5_only="HP5_only_main_vocal.pth"
-VR_DeEchoAggressive="VR-DeEchoAggressive.pth"
-VR_DeEchoDeReverb="VR-DeEchoDeReverb.pth"
-VR_DeEchoNormal="VR-DeEchoNormal.pth"
-onnx_dereverb="vocals.onnx"
-rmvpe="rmvpe.pt"
-
-dlhp2_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth"
-dlhp3_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth"
-dlhp5_only="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth"
-dlVR_DeEchoAggressive="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth"
-dlVR_DeEchoDeReverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth"
-dlVR_DeEchoNormal="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth"
-dlonnx_dereverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx"
-dlrmvpe="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt"
-
-hb="hubert_base.pt"
-
-dlhb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt"
-
-echo dir check start.
-
-if [ -d "./assets/pretrained" ]; then
-    echo dir ./assets/pretrained checked.
-else
-    echo failed. generating dir ./assets/pretrained.
-    mkdir pretrained
-fi
-
-if [ -d "./assets/pretrained_v2" ]; then
-    echo dir ./assets/pretrained_v2 checked.
-else
-    echo failed. generating dir ./assets/pretrained_v2.
-    mkdir pretrained_v2
-fi
-
-if [ -d "./assets/uvr5_weights" ]; then
-    echo dir ./assets/uvr5_weights checked.
-else
-    echo failed. generating dir ./assets/uvr5_weights.
-    mkdir uvr5_weights
-fi
-
-if [ -d "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy" ]; then
-    echo dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
-    echo failed. generating dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy.
-    mkdir uvr5_weights/onnx_dereverb_By_FoxJoy
-fi
-
-echo dir check finished.
-
-echo required files check start.
-
-echo checking D32k.pth
-if [ -f "./assets/pretrained/D32k.pth" ]; then
-    echo D32k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d ./assets/pretrained -o D32k.pth
-        if [ -f "./assets/pretrained/D32k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained/D40k.pth" ]; then
-    echo D40k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d ./assets/pretrained -o D40k.pth
-        if [ -f "./assets/pretrained/D40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
-    echo D40k.pth in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d ./assets/pretrained_v2 -o D40k.pth
-        if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D48k.pth
-if [ -f "./assets/pretrained/D48k.pth" ]; then
-    echo D48k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d ./assets/pretrained -o D48k.pth
-        if [ -f "./assets/pretrained/D48k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G32k.pth
-if [ -f "./assets/pretrained/G32k.pth" ]; then
-    echo G32k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d ./assets/pretrained -o G32k.pth
-        if [ -f "./assets/pretrained/G32k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained/G40k.pth" ]; then
-    echo G40k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d ./assets/pretrained -o G40k.pth
-        if [ -f "./assets/pretrained/G40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
-    echo G40k.pth in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d ./assets/pretrained_v2 -o G40k.pth
-        if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G48k.pth
-if [ -f "./assets/pretrained/G48k.pth" ]; then
-    echo G48k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d ./assets/pretrained -o G48k.pth
-        if [ -f "./assets/pretrained/G48k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d32
-if [ -f "./assets/pretrained/$d32" ]; then
-    echo $d32 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld32 -d ./assets/pretrained -o $d32
-        if [ -f "./assets/pretrained/$d32" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d40
-if [ -f "./assets/pretrained/$d40" ]; then
-    echo $d40 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40 -d ./assets/pretrained -o $d40
-        if [ -f "./assets/pretrained/$d40" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d40v2
-if [ -f "./assets/pretrained_v2/$d40v2" ]; then
-    echo $d40v2 in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40v2 -d ./assets/pretrained_v2 -o $d40v2
-        if [ -f "./assets/pretrained_v2/$d40v2" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d48
-if [ -f "./assets/pretrained/$d48" ]; then
-    echo $d48 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld48 -d ./assets/pretrained -o $d48
-        if [ -f "./assets/pretrained/$d48" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g32
-if [ -f "./assets/pretrained/$g32" ]; then
-    echo $g32 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg32 -d ./assets/pretrained -o $g32
-        if [ -f "./assets/pretrained/$g32" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g40
-if [ -f "./assets/pretrained/$g40" ]; then
-    echo $g40 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40 -d ./assets/pretrained -o $g40
-        if [ -f "./assets/pretrained/$g40" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g40v2
-if [ -f "./assets/pretrained_v2/$g40v2" ]; then
-    echo $g40v2 in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40v2 -d ./assets/pretrained_v2 -o $g40v2
-        if [ -f "./assets/pretrained_v2/$g40v2" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g48
-if [ -f "./assets/pretrained/$g48" ]; then
-    echo $g48 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg48 -d ./assets/pretrained -o $g48
-        if [ -f "./assets/pretrained/$g48" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp2_all
-if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
-    echo $hp2_all in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp2_all -d ./assets/uvr5_weights -o $hp2_all
-        if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp3_all
-if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
-    echo $hp3_all in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp3_all -d ./assets/uvr5_weights -o $hp3_all
-        if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp5_only
-if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
-    echo $hp5_only in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp5_only -d ./assets/uvr5_weights -o $hp5_only
-        if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoAggressive
-if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
-    echo $VR_DeEchoAggressive in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoAggressive -d ./assets/uvr5_weights -o $VR_DeEchoAggressive
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoDeReverb
-if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
-    echo $VR_DeEchoDeReverb in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoDeReverb -d ./assets/uvr5_weights -o $VR_DeEchoDeReverb
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoNormal
-if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
-    echo $VR_DeEchoNormal in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoNormal -d ./assets/uvr5_weights -o $VR_DeEchoNormal
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $onnx_dereverb
-if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
-    echo $onnx_dereverb in ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlonnx_dereverb -d ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy -o $onnx_dereverb
-        if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $rmvpe
-if [ -f "./assets/rmvpe/$rmvpe" ]; then
-    echo $rmvpe in ./assets/rmvpe checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlrmvpe -d ./assets/rmvpe -o $rmvpe
-        if [ -f "./assets/rmvpe/$rmvpe" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hb
-if [ -f "./assets/hubert/$hb" ]; then
-    echo $hb in ./assets/hubert/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhb -d ./assets/hubert/ -o $hb
-        if [ -f "./assets/hubert/$hb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo required files check finished.

spaces/Benson/text-generation/Examples/Apkcome.md

@@ -1,87 +0,0 @@
-
-<h1>Apkcome: Un destino de ventanilla única para Android APK Descargas</h1>
-<p>Si eres un usuario de Android que le encanta jugar y usar aplicaciones en su dispositivo, es posible que haya oído hablar de los archivos APK. APK significa Android Package Kit, y es el formato de archivo que Android utiliza para distribuir e instalar aplicaciones. Los archivos APK se pueden descargar de varias fuentes, como Google Play Store, sitios web de terceros o directamente de los desarrolladores. Sin embargo, no todas las fuentes son confiables, seguras o convenientes. Es por eso que necesitas Apkcome, un sitio web que ofrece descargas de APK para juegos y aplicaciones Android de una manera simple y rápida. </p>
-<h2>apkcome</h2><br /><p><b><b>DOWNLOAD</b> &#128279; <a href="https://bltlly.com/2v6MLj">https://bltlly.com/2v6MLj</a></b></p><br /><br />
-<h2>¿Qué es Apkcome? </h2>
-<h3>Un sitio web que ofrece archivos APK para juegos y aplicaciones Android</h3>
-<p>Apkcome es un sitio web que permite a los usuarios descargar archivos APK para juegos y aplicaciones Android. Tiene una gran colección de juegos y aplicaciones de varias categorías, tales como acción, aventura, juego de roles, árcade, casual, estrategia, deportes, simulación, carreras, rompecabezas, tarjeta, música, tablero, educativo, trivia, palabra, herramientas, entretenimiento, comunicación, social, música y audio, fotografía, reproductores de video y editores, personalización, productividad, deportes, educación, estilo de vida, libros y referencia, compras, negocios, salud y fitness, viajes y locales, alimentos y bebidas, mapas y navegación, belleza, cómics, citas, eventos, finanzas, noticias y revistas, tiempo, casa y hogar, bibliotecas y demostración, médica, crianza, arte y diseño, automóviles y vehículos, y más. </p>
-<h3>Los beneficios de usar Apkcome</h3>
-<h4>Acceso a las últimas versiones, versiones antiguas y versiones beta</h4>
-
-<h4>Soporte para varios dispositivos y arquitecturas</h4>
-<p>Otro beneficio de usar Apkcome es que soporta varios dispositivos y arquitecturas. Puede descargar archivos APK para tabletas Android, televisores inteligentes Android, ropa Android, armeabi-v7a, arm64-v8a, x86, x86_x64, y más. Puede elegir la versión y la arquitectura que mejor se adapte a su dispositivo y disfrutar del rendimiento óptimo de los juegos y aplicaciones. </p>
-<h4>Descargas gratuitas y rápidas</h4>
-<p>El último pero no menos importante beneficio de usar Apkcome es que ofrece descargas gratuitas y rápidas. No tienes que pagar nada para descargar juegos y aplicaciones de Apkcome. Tampoco tiene que registrarse o registrarse para usar el sitio web o proporcionar información personal. Simplemente puede buscar el juego o aplicación que desee y descargarlo con un solo clic. Las descargas son rápidas y seguras, ya que Apkcome utiliza servidores en la nube y cifrado SSL para garantizar la seguridad y velocidad de los archivos. </p>
-<h2>¿Cómo usar Apkcome? </h2>
-<h3>Buscar el juego o aplicación que desea</h3>
-<p>Usar Apkcome es muy fácil y sencillo. El primer paso es buscar el juego o la aplicación que desea descargar. Puede utilizar la barra de búsqueda en la parte superior del sitio web para escribir el nombre del juego o aplicación, o puede navegar a través de las categorías y subcategorías en el lado izquierdo del sitio web para encontrar lo que está buscando. También puedes usar los filtros y las opciones de clasificación en el lado derecho del sitio web para reducir tus resultados de búsqueda por popularidad, calificación, fecha, tamaño y más. </p>
-<p></p>
-<h3>Elija la versión y la arquitectura que necesita</h3>
-
-<h3>Descargar el archivo APK e instalarlo en su dispositivo</h3>
-<p>El tercer y último paso es descargar el archivo APK e instalarlo en su dispositivo. Después de seleccionar la versión y la arquitectura que necesita, puede hacer clic en el botón de descarga para comenzar a descargar el archivo APK. La descarga se iniciará automáticamente y se guardará en la carpeta de descarga del dispositivo. Una vez completada la descarga, puede abrir el archivo APK y seguir las instrucciones para instalarlo en su dispositivo. Es posible que necesite habilitar fuentes desconocidas en la configuración de su dispositivo para permitir la instalación de archivos APK desde Apkcome.</p>
-<h2>¿Cuáles son algunos juegos y aplicaciones populares en Apkcome? </h2>
-<h3>Una tabla con algunos ejemplos de juegos y aplicaciones en Apkcome</h3>
-<tabla>
-<tr>
-<th>Juego/Aplicación</th>
-<th>Categoría</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Minecraft</td>
-<td>árcade</td>
-<td>Un juego sandbox donde puedes crear y explorar un mundo pixelado con recursos ilimitados. </td>
-</tr>
-<tr>
-<td>WhatsApp Messenger</td>
-<td>Comunicación</td>
-<td>Una aplicación de mensajería que te permite enviar mensajes de texto, voz, vídeo e imágenes a tus contactos de forma gratuita. </td>
-</tr>
-<tr>
-<td>PUBG Mobile</td>
-<td>Acción</td>
-<td>Un juego de batalla real donde tienes que sobrevivir contra otros 99 jugadores en una isla. </td>
-</tr>
-<tr>
-<td>Spotify</td>
-<td>Música y audio</td>
-<td>Una aplicación de streaming de música que te permite escuchar millones de canciones y podcasts online o offline. </td>
-</tr>
-<tr>
-<td>TikTok</td>
-<td>Social</td>
-<td>Una aplicación para compartir videos que te permite crear y ver videos cortos con varios efectos y filtros. </td>
-</tr> </table>
-<h2>Conclusión</h2>
-
-<p>Si quieres descargar archivos APK para juegos y aplicaciones Android, visita Apkcome hoy y disfruta de la mejor experiencia Android. </p>
-<h2>Preguntas frecuentes</h2>
-<h3>Q1. ¿Es Apkcome seguro y legal? </h3>
-<p>A1. Apkcome es seguro y legal de usar. Todos los archivos APK en Apkcome son escaneados en busca de virus y malware, y son verificados por los desarrolladores. Apkcome no alberga ningún contenido ilegal o pirata, y respeta los derechos de propiedad intelectual de los desarrolladores. Sin embargo, siempre debe tener cuidado al descargar archivos APK de cualquier fuente, ya que algunos de ellos pueden contener contenido dañino o no deseado. También debes comprobar los permisos y revisiones de los juegos y aplicaciones antes de instalarlos en tu dispositivo. </p>
-<h3>Q2. ¿Cuáles son las ventajas de descargar archivos APK sobre Google Play Store? </h3>
-<p>A2. Hay varias ventajas de descargar archivos APK sobre Google Play Store, como:</p>
-<ul>
-<li>Puede descargar juegos y aplicaciones que no están disponibles en su región o país. </li>
-<li> Puede descargar juegos y aplicaciones que no son compatibles con su dispositivo o sistema operativo. </li>
-<li> Puede descargar juegos y aplicaciones que se eliminan o se prohíben de Google Play Store.</li>
-<li>Puede descargar juegos y aplicaciones que se actualizan más rápido que Google Play Store.</li>
-<li> Puede descargar versiones antiguas o versiones beta de juegos y aplicaciones que prefiera o necesite. </li>
-<li>Puede descargar juegos y aplicaciones sin restricciones o limitaciones. </li>
-</ul>
-<h3>Q3. ¿Cómo puedo actualizar los juegos y aplicaciones que descargué de Apkcome? </h3>
-<p>A3. Puede actualizar los juegos y aplicaciones que descargó de Apkcome visitando el sitio web de nuevo y descargando la última versión del juego o aplicación que desea actualizar. También puedes habilitar las notificaciones en Apkcome para recibir notificaciones cuando una nueva versión de un juego o aplicación esté disponible. A continuación, puede instalar la nueva versión sobre la versión anterior sin perder ningún dato o progreso. </p>
-
-<p>A4. Si encuentra un error o un error al usar Apkcome, puede probar las siguientes soluciones:</p>
-<ul>
-<li>Compruebe su conexión a Internet y asegúrese de que es estable y rápido. </li>
-<li>Borra la caché de tu navegador y las cookies y recarga el sitio web. </li>
-<li>Deshabilita cualquier bloqueador de anuncios o VPN que puedan interferir con el sitio web. </li>
-<li>Descargue el archivo APK de nuevo y asegúrese de que está completo y no está dañado. </li>
-<li>Habilitar fuentes desconocidas en la configuración del dispositivo para permitir la instalación de archivos APK desde Apkcome.</li>
-<li>Póngase en contacto con el servicio de atención al cliente de Apkcome o deje un comentario en el sitio web para informar del problema. </li>
-</ul>
-<h3>Q5. ¿Cómo puedo contactar a Apkcome o dar retroalimentación? </h3>
-<p>A5. Puede ponerse en contacto con Apkcome o dar su opinión utilizando el formulario de contacto en el sitio web o enviando un correo electrónico a [email protected]. También puedes seguir Apkcome en Facebook, Twitter, Instagram, YouTube, Pinterest, LinkedIn, Reddit, Tumblr, Telegram, Quora, Medium, Blogger, WordPress, VK, Digg, StumbleUpon, Mix, Flipboard, Pocket, Feedly, RSS, Email, WhatsApp, Messenger, Skype, Viber, Line, Chat, Snapchat, y más para obtener actualizaciones, noticias, consejos, trucos y más sobre los juegos y aplicaciones de Android. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Arca Supervivencia Evolucionado Mod Apk Libre Dios Consola.md

@@ -1,92 +0,0 @@
-<br />
-<h1> </h1> para encabezados, <b> </b> para negrita, etc. También puede usar atributos para modificar sus etiquetas, como <p align="center"> </p>
-<h2>arca supervivencia evolucionado mod apk libre dios consola</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash; <a href="https://bltlly.com/2v6JQD">https://bltlly.com/2v6JQD</a></b></p><br /><br /> para centrar su texto, <img src="url" alt="text> para insertar imágenes, etc. Aquí hay un ejemplo de cómo crear su artículo con formato HTML basado en el esquema anterior: <h1>Ark: Survival Evolved Mod APK Free God Console</h1>
-<h2>Introducción</h2>
-<p>Ark: Survival Evolved es un juego con muchas complejidades pero puedes tomarlo con calma al principio con algunos consejos para principiantes. Por Joey Carr el 16 de junio de 2022 a las 6:31AM PDT Comentarios Dónde comprar ARK: Survival Evolved $17.99 en Amazon $19.99 en GameStop $46.99 en Best Buy Recently, Ark: Survival Evolved ha experimentado un aumento de popularidad como nunca antes. El juego de supervivencia de mundo abierto superó su cuenta de jugador concurrente de todos los tiempos en Steam gracias a un período libre después del Xbox Games Showcase. Como el juego está atrayendo una horda de nuevos jugadores, va a haber una plétora de aventureros con preguntas sobre cómo funciona Ark. </p>
-<p>En este artículo, vamos a explicar lo que es Ark: Survival Evolved, lo que es un mod APK, lo que es la consola gratuita de Dios, cómo descargar e instalar, cómo usarlo, y cuáles son sus pros y sus contras. Si estás interesado en mejorar tu experiencia de juego con algunas características adicionales y trucos, sigue leyendo. </p>
-<h2>¿Qué es Ark: Survival Evolved? </h2>
-<p>Ark: Survival Evolved es un juego de supervivencia de acción y aventura ambientado en un entorno de mundo abierto con un dinámico ciclo día-noche y jugado desde una perspectiva en tercera persona o en primera persona. El juego cuenta con modos para un jugador y multijugador, con la opción de jugar en línea o fuera de línea. El juego también es altamente personalizable, con varios ajustes, opciones y mods para mejorar la experiencia de juego. </p>
-<h2>¿Qué es un mod APK? </h2>
-
-<p>Los APK Mod generalmente se descargan de sitios web o fuentes de terceros, en lugar de la Google Play Store oficial. Esto significa que no están verificados o aprobados por Google o el desarrollador original de la aplicación, y pueden contener malware, virus u otros componentes dañinos. Por lo tanto, descargar e instalar mod APKs requiere precaución y discreción, así como algunos conocimientos técnicos y habilidades. </p>
-<h2>¿Qué es la consola libre de Dios? </h2>
-<p>La consola gratuita de Dios es una característica exclusiva de la versión mod APK de Ark: Survival Evolved. Es una función para un solo jugador que permite al jugador acceder y usar varios trucos y comandos que normalmente no están disponibles o restringidos en la aplicación oficial. La consola gratuita de Dios se puede activar tocando un icono en la esquina superior derecha de la pantalla, que abre un menú con diferentes opciones y botones. </p>
-<p></p>
-<p>Algunas de las características y trucos que ofrece la consola gratuita de Dios son:</p>
-<ul>
-<li>Recursos - Toque en un icono de recurso para generar una pila de cualquier recurso en el juego, como madera, metal, piedra, fibra, etc.</li>
-<li>Invulnerable - Activar este truco hace que su sobreviviente sea inmune al daño de cualquier fuente, como enemigos, peligros ambientales, hambre, sed, etc.</li>
-<li>Estadísticas infinitas - Mientras este truco está encendido, su sobreviviente tendrá resistencia infinita, salud, oxígeno y peso de carga. </li>
-<li>Fly - Este truco permite a su sobreviviente volar alrededor de la isla con facilidad y velocidad. También puede alternar entre los modos de caminar y volar tocando el botón de volar de nuevo. </li>
-<li>Creación instantánea - Con este truco activado, todos los engramas arte al instante sin ningún tiempo de espera o costo de recursos. </li>
-<li>Noche brillante - Este truco aumenta la visibilidad en la noche haciendo todo más brillante y más claro. </li>
-<li>Infinite Ammo - Este truco le da munición ilimitada para cualquier arma o herramienta que utiliza munición, tales como armas, arcos, lanzas, etc.</li>
-
-<li>Sanar a todos - Este botón cura a su sobreviviente, sus criaturas domadas, su armadura, sus escudos, y todas sus estructuras a la salud completa en un instante. </li>
-<li>Velocidad - Esta característica le permite cambiar la velocidad del tiempo en la isla. Puedes usar esto para acelerar o ralentizar el ciclo día-noche, los cambios climáticos, el proceso de domesticación, el proceso de reproducción, etc.</li>
-<li>Suicidio - Este botón mata a tu sobreviviente al instante y te lleva de vuelta al menú de reaparición. Puedes usar esto si estás atrapado en una situación difícil o quieres empezar de nuevo desde una ubicación diferente. </li>
-<li>Ocultar interfaz de usuario - Este botón oculta todos los elementos de la interfaz de usuario en la pantalla, como la barra de mantenimiento, el menú de inventario, el mapa, etc. Puede usar esto para tomar capturas de pantalla o disfrutar de una vista más inmersiva del mundo del juego. </li>
-<li>Teleport - Este botón le permite teletransportarse a diferentes lugares de la isla al instante. Puede elegir entre una lista de ubicaciones preestablecidas como entradas a cuevas o ubicaciones personalizadas que haya guardado antes. </li>
-</ul>
-<h2>Cómo descargar e instalar el mod APK</h2>
-<p>Si quieres probar la versión mod APK de Ark: Survival Evolved, debes seguir algunos pasos para descargarla e instalarla en tu dispositivo Android. Estos son los requisitos y precauciones que debe tomar antes de continuar:</p>
-<h3>Requisitos y precauciones</h3>
-<ul>
-<li> El dispositivo debe tener Android 7.0 o superior y al menos 3 GB de RAM.</li>
-<li>Necesita tener suficiente espacio de almacenamiento para descargar e instalar el archivo APK mod (aproximadamente 2.4 GB) y el archivo de datos OBB (aproximadamente 2.1 GB). </li>
-<li>Es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li> Es necesario desactivar cualquier antivirus o aplicaciones de seguridad que pueden interferir con el proceso de instalación. </li>
-
-<li>Usted necesita ser consciente de que el uso del mod APK puede violar los términos de servicio del juego y resultar en una prohibición o suspensión de su cuenta. Úsalo bajo tu propio riesgo y discreción. </li>
-</ul>
-<h3>Descargar enlaces e instrucciones</h3>
-<p>Una vez que haya cumplido con los requisitos y tomado las precauciones, puede proceder a descargar e instalar el mod APK siguiendo estos pasos:</p>
-<ol>
-<li>Descargar el archivo mod APK desde este enlace: </li>
-<li>Descargar el archivo de datos OBB desde este enlace: </li>
-<li>Localice los archivos descargados en su dispositivo usando una aplicación de administrador de archivos. </li>
-<li>Toque en el archivo APK mod y siga las instrucciones de instalación. No abra la aplicación todavía. </li>
-<li>Extraiga el archivo de datos OBB usando una aplicación extractora ZIP. Debería obtener una carpeta llamada com.studiowildcard.wardrumstudios.ark. </li>
-<li>Mueva la carpeta extraída a esta ubicación: Almacenamiento interno > Android > OBB. Si no hay carpeta OBB, cree una. </li>
-<li>Iniciar la aplicación y disfrutar del juego con la función de consola de Dios gratis. </li>
-</ol>
-<h2>Cómo usar la consola libre de Dios</h2>
-<p>Para usar la función de consola gratuita de Dios, debes seguir estos pasos:</p>
-<ol>
-<li>Crear un nuevo juego para un jugador o cargar uno existente. </li>
-<li>Toque en el icono en la esquina superior derecha de la pantalla que parece un rayo. Esto abrirá el menú de la consola de Dios libre. </li>
-<li>Seleccione la función o truco que desea utilizar pulsando en su botón. También puede ajustar algunos ajustes como velocidad, brillo, etc. deslizando las barras. </li>
-<li> Para cerrar el menú, pulse sobre el icono de nuevo o en cualquier lugar fuera de él. </li>
-</ol>
-<p>Tenga en cuenta que algunas características o trucos pueden no funcionar correctamente o causar problemas técnicos en algunas situaciones. Úselos con precaución y bajo su propio riesgo. </p>
- <h2>Pros y contras de usar el mod APK</h2>
- <p>Usando la versión mod APK de Ark: Survival Evolved tiene sus pros y sus contras. Aquí están algunos de ellos:</p>
- <h3>Pros</h3>
- <ul>
-
- <li>Puedes volar alrededor de la isla, teletransportarte a diferentes lugares, acelerar o ralentizar el tiempo, y explorar el mundo del juego sin limitaciones o restricciones. </li>
- <li>Puedes domar, criar, sanar, subir de nivel y personalizar cualquier criatura en el juego con facilidad y comodidad. </li>
- <li> Puede ocultar los elementos de la interfaz de usuario y tomar capturas de pantalla o videos de su juego con una vista clara e inmersiva. </li>
- </ul>
- <h3>Contras</h3>
- <ul>
- <li>Puedes perder parte del desafío, la emoción y la satisfacción que surgen de jugar el juego normalmente y superar sus dificultades. </li>
- <li> Puede encontrar errores, errores, fallos o problemas de compatibilidad que pueden afectar su experiencia de juego o dañar su dispositivo. </li>
- <li>Usted puede obtener prohibido o suspendido de jugar en línea o acceder a los servidores oficiales si se detecta el mod APK.</li>
- <li>Puede perderse actualizaciones, parches, nuevas características, eventos o contenido que se publican para la versión oficial de la aplicación. </li>
- </ul>
- <h2>Conclusión</h2>
-
- <p>Aquí hay algunas preguntas frecuentes sobre Ark: Survival Evolved y el mod APK:</p>
- <h3>Q: ¿Es Ark: Survival Evolved libre para jugar? </h3>
-<p>A: No, Ark: Survival Evolved es un juego pagado que cuesta $4.99 en Google Play Store. Sin embargo, puede descargar y reproducir la versión mod APK de forma gratuita desde fuentes de terceros. </p>
- <h3>Q: ¿Es el mod APK seguro de usar? </h3>
-<p>A: El mod APK no está verificado o aprobado por Google o el desarrollador de la aplicación original, y puede contener malware, virus u otros componentes dañinos. Por lo tanto, el uso del mod APK requiere precaución y discreción, así como algunos conocimientos técnicos y habilidades. También debe desactivar cualquier antivirus o aplicaciones de seguridad que puedan interferir con el proceso de instalación. </p>
- <h3>Q: ¿Voy a ser prohibido por usar el mod APK? </h3>
-<p>A: El uso del mod APK puede violar los términos de servicio del juego y resultar en una prohibición o suspensión de su cuenta. Úsalo bajo tu propio riesgo y discreción. También debes evitar jugar online o acceder a servidores oficiales si estás usando el mod APK.</p>
- <h3>Q: ¿Cómo puedo actualizar el mod APK? </h3>
-<p>A: El mod APK no puede recibir actualizaciones, parches, nuevas características, eventos o contenido que se publican para la versión oficial de la aplicación. Para actualizar el mod APK, es necesario descargar e instalar la última versión de una fuente confiable. También es posible que tenga que desinstalar la versión anterior y eliminar los archivos sobrantes antes de instalar el nuevo. </p>
- <h3>Q: ¿Puedo jugar con mis amigos usando el mod APK? </h3>
-<p>A: El mod APK es una característica de un solo jugador que no admite modos multijugador o juego en línea. Solo puedes jugar con tus amigos si también están usando el mod APK en sus dispositivos. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gangstar Nueva York Para Pc.md

@@ -1,73 +0,0 @@
-
-<h1>Gangstar New York: Cómo descargar y jugar el Open Alpha en PC</h1>
-<p>¿Estás buscando un nuevo y emocionante juego para jugar en tu PC? ¿Quieres experimentar la emoción de ser un gángster en un futuro cercano en Nueva York? Si es así, deberías echar un vistazo a Gangstar New York, un juego de disparos en tercera persona gratuito que te permite competir con otros jugadores en una competencia criminal en toda la ciudad. En este artículo, te diremos qué es Gangstar New York, por qué deberías jugarlo y cómo descargarlo y reproducirlo en tu PC. También te daremos algunos consejos y trucos para jugar el juego de manera efectiva. Así que, ¡empecemos! </p>
- <h2>¿Qué es Gangstar Nueva York? </h2>
-<p>Gangstar New York es un juego multijugador en línea desarrollado por Gameloft que se encuentra actualmente en fase alfa abierta. El juego se desarrolla en un futuro cercano en la ciudad de Nueva York que está bajo la vigilancia de Aurora Inc., un conglomerado tecnológico masivo que utiliza una red algorítmica para detener los crímenes antes de que comiencen. Sin embargo, Aurora Inc. también apaga las cámaras durante intervalos de 20 minutos cuando necesitan una negación plausible para probar "iniciativas tecnológicas experimentales" en los ciudadanos. </p>
-<h2>descargar gangstar nueva york para pc</h2><br /><p><b><b>Download</b> &#11088; <a href="https://bltlly.com/2v6LRr">https://bltlly.com/2v6LRr</a></b></p><br /><br />
-<p>Durante estos intervalos, 20 aspirantes a gángsters compiten en una competencia criminal en toda la ciudad para conseguir tanto dinero como sea posible. El ganador de cada ronda es coronado Gangstar, y está colmado de fama y elogios. El juego cuenta con un mundo abierto lleno de acción PVPVE donde se puede explorar, parkour, tirolina, jetpack, robar, tener batallas aéreas y correr alrededor de Nueva York. También puedes personalizar tu personaje con diferentes skins inspirados en varias tipologías de gángsters. </p>
- <h2>¿Por qué deberías jugar Gangstar New York? </h2>
-<p>Gangstar New York es un juego que ofrece mucha diversión y emoción para cualquiera que ame los juegos de disparos, juegos de mundo abierto o juegos de gángsters. Estas son algunas de las razones por las que deberías jugar a Gangstar New York:</p>
- <ul>
-
-<li>El juego es justo y equilibrado y no da ninguna ventaja a los jugadores que pagan o juegan más. Puedes competir con otros jugadores en igualdad de condiciones, independientemente de tu nivel, equipo o habilidades. </li>
-<li>El juego es dinámico e impredecible y no sigue ningún script o patrón fijo. Puedes experimentar diferentes escenarios y resultados cada vez que juegas, dependiendo de los experimentos que Aurora Inc. desata en la ciudad. </li>
-<li>El juego es divertido e inmersivo y no te aburre o frustra con tareas tediosas o misiones repetitivas. Puede explorar la ciudad a su propio ritmo, participar en diversas actividades e interactuar con otros jugadores en un entorno animado y realista. </li>
-</ul>
- <p>Así que, si usted está buscando un juego que le mantendrá entretenido y desafiado durante horas, Gangstar New York es el juego para usted! </p>
- <h2>Cómo descargar y jugar Gangstar Nueva York en PC? </h2>
-<p>Ahora que sabes lo que es Gangstar New York y por qué deberías jugarlo, te estarás preguntando cómo descargarlo y reproducirlo en tu PC. Bueno, no te preocupes, porque te tenemos cubierto. Estos son los pasos para descargar y jugar Gangstar New York en PC:</p>
- <h3>Paso 1: Ir a la página de la tienda de vapor de Gangstar Nueva York</h3>
-<p>Lo primero que tienes que hacer es ir a la página de Steam Store de Gangstar New York. Puedes hacer esto haciendo clic en este enlace: [Gangstar New York on Steam]. Alternativamente, puedes abrir tu cliente de Steam, ir a la pestaña Tienda y buscar "Gangstar New York" en la barra de búsqueda. </p>
-<p>Una vez que estés en la página de la tienda, verás información sobre el juego, como su descripción, capturas de pantalla, vídeos, reseñas y requisitos del sistema. También verás un botón verde que dice "Jugar Juego". Antes de hacer clic en ese botón, le recomendamos que agregue el juego a su lista de deseos haciendo clic en el botón "+WISHLIST" debajo de él. De esta manera, se le notificará de cualquier actualización o noticia sobre el juego en el futuro. </p>
-
-<p>Después de haber agregado el juego a su lista de deseos, puede hacer clic en el botón "Jugar juego" para comenzar a descargar e instalar el juego en su PC. Verá una ventana emergente que le pide que confirme su elección. Haga clic en el botón "Sí" para continuar. </p>
-<p></p>
-<p>El juego se añadirá a tu biblioteca de Steam en la pestaña "Juegos". Verás una barra de progreso que te muestra cuánto del juego se ha descargado e instalado. El tamaño del juego es de aproximadamente 4 GB, por lo que dependiendo de su velocidad de Internet y el rendimiento del PC, podría tomar algún tiempo para completar. Puede comprobar el tiempo estimado restante pasando por encima de la barra de progreso. </p>
- <h3>Paso 3: Inicia el juego y crea tu cuenta</h3>
-<p>Una vez que el juego ha sido descargado e instalado, puede iniciarlo desde su biblioteca de Steam haciendo doble clic en su nombre o haciendo clic derecho en él y seleccionando "Jugar". Verás una pantalla de bienvenida que muestra el logotipo del juego y algunos mensajes de carga. </p>
-<p>Después de unos segundos, serás llevado al menú principal del juego. Aquí, verá algunas opciones como "Reproducir", "Configuración", "Créditos" y "Salir". Antes de hacer clic en "Jugar", es necesario crear su cuenta de Gangstar haciendo clic en el "Crear cuenta" botón en la esquina inferior derecha de la pantalla. </p>
-<p>A continuación, se le pedirá que introduzca su dirección de correo electrónico, contraseña, nombre de usuario y país. Asegúrese de introducir información válida y segura que pueda recordar más tarde. También deberá aceptar los términos del servicio y la política de privacidad del juego marcando las casillas a continuación. Después de eso, haga clic en el botón "Crear cuenta" nuevamente para confirmar su registro. </p>
- <h3>Paso 4: Personaliza tu personaje y únete a un partido</h3>
-
-<p>A continuación, verá una vista previa de su personaje y una lista de skins que puede seleccionar. Algunas pieles se desbloquean de forma predeterminada, mientras que otras requieren efectivo o gemas para desbloquear. El efectivo y las gemas son las monedas del juego que puedes ganar jugando o comprándolas con dinero real. También puedes cambiar tu piel durante el juego encontrando y usando un dispositivo cambiador de piel. </p>
-<p>Después de haber elegido su piel, puede unirse a un partido haciendo clic en el botón "Reproducir" en la esquina superior izquierda de la pantalla. A continuación, verá una lista de coincidencias disponibles a las que puede unirse, o puede crear su propia coincidencia haciendo clic en el botón "Crear coincidencia". Cada partido puede tener hasta 20 jugadores y dura 20 minutos. También puede elegir el mapa, el modo y la dificultad del partido. </p>
-<p>Una vez que te hayas unido o creado un partido, serás llevado al lobby donde podrás ver a los otros jugadores y chatear con ellos. También puedes invitar a tus amigos a unirse a tu partido haciendo clic en el botón "Invitar amigos" en la esquina inferior izquierda de la pantalla. Cuando el partido esté listo para comenzar, verá un temporizador de cuenta atrás y un botón "Listo". Haga clic en el botón "Listo" para confirmar que está listo para jugar. </p>
- <h2>Consejos y trucos para jugar Gangstar Nueva York en PC</h2>
-<p>Ahora que sabes cómo descargar y jugar Gangstar Nueva York en PC, es posible que se pregunte cómo jugar bien y ganar el juego. Bueno, no te preocupes, porque tenemos algunos consejos y trucos para ti que te ayudarán a mejorar tus habilidades y rendimiento en el juego. Estos son algunos de ellos:</p>
- <h3>Consejo 1: Utilice el mapa y el mini-mapa para navegar por la ciudad</h3>
-
-<p>El mini-mapa es una vista a pequeña escala de su entorno inmediato que le muestra su posición, dirección y puntos de interés cercanos. Puedes ver el mini-mapa en la esquina inferior derecha de la pantalla. También puedes acercar y alejar el mini-mapa usando la rueda del ratón o haciendo clic en los botones más y menos. </p>
-<p>Usar el mapa y el mini-mapa te ayudará a planificar tu ruta, evitar el peligro, localizar objetivos y completar objetivos. También puede marcar ubicaciones en el mapa haciendo clic derecho sobre ellas o presionando la tecla "F" en su teclado. Esto creará un waypoint que te guiará a la ubicación marcada. </p>
- <h3>Consejo 2: Recoge dinero y armas de varias fuentes</h3>
-<p>Otra cosa importante que hacer en Gangstar Nueva York es recoger dinero en efectivo y armas de varias fuentes. El efectivo es la moneda principal en el juego que puedes usar para comprar armas, pieles y otros artículos. También puedes usar efectivo para ganar puntos y ganar el juego. Las armas son las herramientas que puedes usar para luchar, defender y sobrevivir en el juego. También puedes usar armas para ganar puntos. </p>
-<p>Puedes recoger dinero y armas de varias fuentes, como:</p>
- <ul>
-<li>Saquear edificios: Puedes entrar y saquear cualquier edificio de la ciudad rompiendo la puerta o la ventana. Puede encontrar dinero en efectivo, armas, municiones, kits de salud y otros artículos dentro de los edificios. Sin embargo, tenga cuidado, ya que algunos edificios pueden tener alarmas, trampas o enemigos dentro. </li>
-<li>Robar vehículos: Puedes robar cualquier vehículo en la ciudad acercándote a él y presionando la tecla "E" en tu teclado o haciendo clic en el icono del vehículo. A continuación, puede conducir o volar el vehículo por la ciudad. También puede encontrar dinero en efectivo, armas, municiones, kits de salud y otros artículos dentro de los vehículos. Sin embargo, tenga cuidado, ya que algunos vehículos pueden tener bloqueos, alarmas o enemigos dentro. </li>
-
-</ul>
- <p>Recoger dinero en efectivo y armas de varias fuentes le ayudará a aumentar su arsenal, mejorar su equipo, y aumentar su puntuación. </p>
- <h3>Consejo 3: Adaptarse a los experimentos y utilizarlos para su ventaja</h3>
-<p>Una de las características más singulares y emocionantes de Gangstar New York son los experimentos que Aurora Inc. lleva a cabo en la ciudad durante cada partido. Estos experimentos son eventos aleatorios que afectan los parámetros del juego, como la gravedad, el clima, la física, la iluminación, el sonido y más. Algunos ejemplos de experimentos son la gravedad lunar, suelo eléctrico, niebla pesada, noche silenciosa, apocalipsis zombi, y más. </p>
-<p>Estos experimentos pueden tener efectos positivos o negativos en su juego, dependiendo de cómo se adapte a ellos y utilizarlos para su ventaja. Por ejemplo, la gravedad lunar puede hacerte saltar más alto y más lejos, traicionar a otros jugadores puede ayudarte a ganar dinero más rápido, obtener objetos más fáciles y eliminar enemigos mejor. </li>
-<li>Robar: Puedes robar a otros jugadores tomando su dinero o objetos sin su consentimiento o conocimiento. También puedes robar a otros jugadores hackeando sus vehículos o dispositivos. Robar a otros jugadores puede ayudarte a aumentar tu puntuación más rápido, mejorar tu equipo más fácilmente y debilitar mejor a tus enemigos. </li>
-</ul>
- <p>Trabajar con o contra otros jugadores te ayudará a crear tu propio estilo de juego, tomar tus propias decisiones y enfrentar tus propias consecuencias. </p>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre Gangstar Nueva York:</p>
- <ol>
-<li><b>¿Gangstar New York está disponible en otras plataformas? </b></li>
-<p>Sí, Gangstar New York también está disponible en dispositivos Android e iOS. Puedes descargar el juego desde la Google Play Store o la App Store respectivamente. </p>
-<li><b>¿Gangstar New York es un juego de un jugador o multijugador? </b></li>
-<p>Gangstar New York es principalmente un juego multijugador que requiere una conexión a Internet para jugar. Sin embargo, también puedes jugar el juego en modo para un solo jugador creando una partida privada y jugando solo o con bots. </p>
-<li><b>¿Cuánto dura la etapa alfa abierta de Gangstar Nueva York? </b></li>
-<p>Se espera que la etapa alfa abierta de Gangstar New York dure varios meses, durante los cuales los desarrolladores recopilarán comentarios, corregirán errores y agregarán nuevas características al juego. La fecha exacta del final de la etapa alfa abierta aún no se ha anunciado. </p>
-<li><b>¿Cómo puedo reportar un error o un problema en Gangstar New York? </b></li>
-<p>Puedes reportar un error o un problema en Gangstar New York usando el sistema de retroalimentación del juego o contactando al equipo de atención al cliente a través de correo electrónico o redes sociales. Puedes encontrar más información sobre cómo denunciar un error o un problema en el sitio web oficial del juego. </p>
-<li><b>¿Cómo puedo obtener más dinero o gemas en Gangstar Nueva York? </b></li>
-<p>Puedes obtener más dinero o gemas en Gangstar Nueva York jugando el juego regularmente, completando objetivos, matando enemigos, saqueando edificios, robando vehículos y ganando partidas. También puedes obtener más dinero o gemas comprándolas con dinero real a través de la tienda del juego. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/__pip-runner__.py

@@ -1,50 +0,0 @@
-"""Execute exactly this copy of pip, within a different environment.
-
-This file is named as it is, to ensure that this module can't be imported via
-an import statement.
-"""
-
-# /!\ This version compatibility check section must be Python 2 compatible. /!\
-
-import sys
-
-# Copied from setup.py
-PYTHON_REQUIRES = (3, 7)
-
-
-def version_str(version):  # type: ignore
-    return ".".join(str(v) for v in version)
-
-
-if sys.version_info[:2] < PYTHON_REQUIRES:
-    raise SystemExit(
-        "This version of pip does not support python {} (requires >={}).".format(
-            version_str(sys.version_info[:2]), version_str(PYTHON_REQUIRES)
-        )
-    )
-
-# From here on, we can use Python 3 features, but the syntax must remain
-# Python 2 compatible.
-
-import runpy  # noqa: E402
-from importlib.machinery import PathFinder  # noqa: E402
-from os.path import dirname  # noqa: E402
-
-PIP_SOURCES_ROOT = dirname(dirname(__file__))
-
-
-class PipImportRedirectingFinder:
-    @classmethod
-    def find_spec(self, fullname, path=None, target=None):  # type: ignore
-        if fullname != "pip":
-            return None
-
-        spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
-        assert spec, (PIP_SOURCES_ROOT, fullname)
-        return spec
-
-
-sys.meta_path.insert(0, PipImportRedirectingFinder())
-
-assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
-runpy.run_module("pip", run_name="__main__", alter_sys=True)

spaces/BigSalmon/MaskSeveralAtOnce/README.md

@@ -1,37 +0,0 @@
----
-title: MASK2
-emoji: 📈
-colorFrom: green
-colorTo: red
-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/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/export/__init__.py

@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from .api import *
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/configs.md

@@ -1,45 +0,0 @@
-# Use Configs
-
-Detectron2's config system uses yaml and [yacs](https://github.com/rbgirshick/yacs).
-In addition to the basic operations that access and update a config, we provide
-the following extra functionalities:
-
-1. The config can have `_BASE_: base.yaml` field, which will load a base config first.
-   Values in the base config will be overwritten in sub-configs, if there are any conflicts.
-   We provided several base configs for standard model architectures.
-2. We provide config versioning, for backward compatibility.
-   If your config file is versioned with a config line like `VERSION: 2`,
-   detectron2 will still recognize it even if we rename some keys in the future.
-
-### Use Configs
-
-Some basic usage of the `CfgNode` object is shown below:
-```python
-from detectron2.config import get_cfg
-cfg = get_cfg()    # obtain detectron2's default config
-cfg.xxx = yyy      # add new configs for your own custom components
-cfg.merge_from_file("my_cfg.yaml")   # load values from a file
-
-cfg.merge_from_list(["MODEL.WEIGHTS", "weights.pth"])   # can also load values from a list of str
-print(cfg.dump())  # print formatted configs
-```
-
-To see a list of available configs in detectron2, see [Config References](../modules/config.html#config-references)
-
-
-### Best Practice with Configs
-
-1. Treat the configs you write as "code": avoid copying them or duplicating them; use `_BASE_`
-   to share common parts between configs.
-
-2. Keep the configs you write simple: don't include keys that do not affect the experimental setting.
-
-3. Keep a version number in your configs (or the base config), e.g., `VERSION: 2`,
-   for backward compatibility.
-	 We print a warning when reading a config without version number.
-   The official configs do not include version number because they are meant to
-   be always up-to-date.
-
-4. Save a full config together with a trained model, and use it to run inference.
-   This is more robust to changes that may happen to the config definition
-   (e.g., if a default value changed), although we will try to avoid such changes.

spaces/CVPR/LIVE/pydiffvg_tensorflow/__init__.py

@@ -1,24 +0,0 @@
-import tensorflow as tf
-try:
-    import diffvg
-except ImportError:
-    print("Warning: diffvg is not installed when you import pydiffvg_tensorflow.")
-from .device import *
-from .shape import *
-from .pixel_filter import *
-from .render_tensorflow import *
-from .image import *
-from .color import *
-import os.path
-
-print(os.path.dirname(diffvg.__file__))
-
-if tf.__cxx11_abi_flag__ == 0:
-    __data_ptr_module = tf.load_op_library(os.path.join(os.path.dirname(diffvg.__file__), 'libdiffvg_tf_data_ptr_no_cxx11_abi.so'))
-else:
-    assert(tf.__cxx11_abi_flag__ == 1)
-    __data_ptr_module = tf.load_op_library(os.path.join(os.path.dirname(diffvg.__file__), 'libdiffvg_tf_data_ptr_cxx11_abi.so'))
-
-def data_ptr(tensor):    
-    addr_as_uint64 = __data_ptr_module.data_ptr(tensor)
-    return int(addr_as_uint64)

spaces/CVPR/LIVE/thrust/internal/reverse_rename_cub_namespace.sh

@@ -1,7 +0,0 @@
-#! /bin/bash
-
-# Run this in //sw/gpgpu/thrust/thrust/system/cuda/detail/cub to undo the
-# renaming of CUB's namespace macro.
-
-sed -i -e 's|THRUST_CUB_NS_P|CUB_NS_P|g' `find . -type f`
-

spaces/CVPR/lama-example/README.md

@@ -1,46 +0,0 @@
----
-title: Lama Example
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# 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`, `streamlit`, or `static`
-
-`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, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/CVPR/regionclip-demo/detectron2/modeling/roi_heads/keypoint_head.py

@@ -1,272 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import List
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ConvTranspose2d, cat, interpolate
-from detectron2.structures import Instances, heatmaps_to_keypoints
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-
-_TOTAL_SKIPPED = 0
-
-
-__all__ = [
-    "ROI_KEYPOINT_HEAD_REGISTRY",
-    "build_keypoint_head",
-    "BaseKeypointRCNNHead",
-    "KRCNNConvDeconvUpsampleHead",
-]
-
-
-ROI_KEYPOINT_HEAD_REGISTRY = Registry("ROI_KEYPOINT_HEAD")
-ROI_KEYPOINT_HEAD_REGISTRY.__doc__ = """
-Registry for keypoint heads, which make keypoint predictions from per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-def build_keypoint_head(cfg, input_shape):
-    """
-    Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME
-    return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape)
-
-
-def keypoint_rcnn_loss(pred_keypoint_logits, instances, normalizer):
-    """
-    Arguments:
-        pred_keypoint_logits (Tensor): A tensor of shape (N, K, S, S) where N is the total number
-            of instances in the batch, K is the number of keypoints, and S is the side length
-            of the keypoint heatmap. The values are spatial logits.
-        instances (list[Instances]): A list of M Instances, where M is the batch size.
-            These instances are predictions from the model
-            that are in 1:1 correspondence with pred_keypoint_logits.
-            Each Instances should contain a `gt_keypoints` field containing a `structures.Keypoint`
-            instance.
-        normalizer (float): Normalize the loss by this amount.
-            If not specified, we normalize by the number of visible keypoints in the minibatch.
-
-    Returns a scalar tensor containing the loss.
-    """
-    heatmaps = []
-    valid = []
-
-    keypoint_side_len = pred_keypoint_logits.shape[2]
-    for instances_per_image in instances:
-        if len(instances_per_image) == 0:
-            continue
-        keypoints = instances_per_image.gt_keypoints
-        heatmaps_per_image, valid_per_image = keypoints.to_heatmap(
-            instances_per_image.proposal_boxes.tensor, keypoint_side_len
-        )
-        heatmaps.append(heatmaps_per_image.view(-1))
-        valid.append(valid_per_image.view(-1))
-
-    if len(heatmaps):
-        keypoint_targets = cat(heatmaps, dim=0)
-        valid = cat(valid, dim=0).to(dtype=torch.uint8)
-        valid = torch.nonzero(valid).squeeze(1)
-
-    # torch.mean (in binary_cross_entropy_with_logits) doesn't
-    # accept empty tensors, so handle it separately
-    if len(heatmaps) == 0 or valid.numel() == 0:
-        global _TOTAL_SKIPPED
-        _TOTAL_SKIPPED += 1
-        storage = get_event_storage()
-        storage.put_scalar("kpts_num_skipped_batches", _TOTAL_SKIPPED, smoothing_hint=False)
-        return pred_keypoint_logits.sum() * 0
-
-    N, K, H, W = pred_keypoint_logits.shape
-    pred_keypoint_logits = pred_keypoint_logits.view(N * K, H * W)
-
-    keypoint_loss = F.cross_entropy(
-        pred_keypoint_logits[valid], keypoint_targets[valid], reduction="sum"
-    )
-
-    # If a normalizer isn't specified, normalize by the number of visible keypoints in the minibatch
-    if normalizer is None:
-        normalizer = valid.numel()
-    keypoint_loss /= normalizer
-
-    return keypoint_loss
-
-
-def keypoint_rcnn_inference(pred_keypoint_logits: torch.Tensor, pred_instances: List[Instances]):
-    """
-    Post process each predicted keypoint heatmap in `pred_keypoint_logits` into (x, y, score)
-        and add it to the `pred_instances` as a `pred_keypoints` field.
-
-    Args:
-        pred_keypoint_logits (Tensor): A tensor of shape (R, K, S, S) where R is the total number
-           of instances in the batch, K is the number of keypoints, and S is the side length of
-           the keypoint heatmap. The values are spatial logits.
-        pred_instances (list[Instances]): A list of N Instances, where N is the number of images.
-
-    Returns:
-        None. Each element in pred_instances will contain extra "pred_keypoints" and
-            "pred_keypoint_heatmaps" fields. "pred_keypoints" is a tensor of shape
-            (#instance, K, 3) where the last dimension corresponds to (x, y, score).
-            The scores are larger than 0. "pred_keypoint_heatmaps" contains the raw
-            keypoint logits as passed to this function.
-    """
-    # flatten all bboxes from all images together (list[Boxes] -> Rx4 tensor)
-    bboxes_flat = cat([b.pred_boxes.tensor for b in pred_instances], dim=0)
-
-    pred_keypoint_logits = pred_keypoint_logits.detach()
-    keypoint_results = heatmaps_to_keypoints(pred_keypoint_logits, bboxes_flat.detach())
-    num_instances_per_image = [len(i) for i in pred_instances]
-    keypoint_results = keypoint_results[:, :, [0, 1, 3]].split(num_instances_per_image, dim=0)
-    heatmap_results = pred_keypoint_logits.split(num_instances_per_image, dim=0)
-
-    for keypoint_results_per_image, heatmap_results_per_image, instances_per_image in zip(
-        keypoint_results, heatmap_results, pred_instances
-    ):
-        # keypoint_results_per_image is (num instances)x(num keypoints)x(x, y, score)
-        # heatmap_results_per_image is (num instances)x(num keypoints)x(side)x(side)
-        instances_per_image.pred_keypoints = keypoint_results_per_image
-        instances_per_image.pred_keypoint_heatmaps = heatmap_results_per_image
-
-
-class BaseKeypointRCNNHead(nn.Module):
-    """
-    Implement the basic Keypoint R-CNN losses and inference logic described in
-    Sec. 5 of :paper:`Mask R-CNN`.
-    """
-
-    @configurable
-    def __init__(self, *, num_keypoints, loss_weight=1.0, loss_normalizer=1.0):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            num_keypoints (int): number of keypoints to predict
-            loss_weight (float): weight to multiple on the keypoint loss
-            loss_normalizer (float or str):
-                If float, divide the loss by `loss_normalizer * #images`.
-                If 'visible', the loss is normalized by the total number of
-                visible keypoints across images.
-        """
-        super().__init__()
-        self.num_keypoints = num_keypoints
-        self.loss_weight = loss_weight
-        assert loss_normalizer == "visible" or isinstance(loss_normalizer, float), loss_normalizer
-        self.loss_normalizer = loss_normalizer
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = {
-            "loss_weight": cfg.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT,
-            "num_keypoints": cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS,
-        }
-        normalize_by_visible = (
-            cfg.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS
-        )  # noqa
-        if not normalize_by_visible:
-            batch_size_per_image = cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE
-            positive_sample_fraction = cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION
-            ret["loss_normalizer"] = (
-                ret["num_keypoints"] * batch_size_per_image * positive_sample_fraction
-            )
-        else:
-            ret["loss_normalizer"] = "visible"
-        return ret
-
-    def forward(self, x, instances: List[Instances]):
-        """
-        Args:
-            x: input 4D region feature(s) provided by :class:`ROIHeads`.
-            instances (list[Instances]): contains the boxes & labels corresponding
-                to the input features.
-                Exact format is up to its caller to decide.
-                Typically, this is the foreground instances in training, with
-                "proposal_boxes" field and other gt annotations.
-                In inference, it contains boxes that are already predicted.
-
-        Returns:
-            A dict of losses if in training. The predicted "instances" if in inference.
-        """
-        x = self.layers(x)
-        if self.training:
-            num_images = len(instances)
-            normalizer = (
-                None if self.loss_normalizer == "visible" else num_images * self.loss_normalizer
-            )
-            return {
-                "loss_keypoint": keypoint_rcnn_loss(x, instances, normalizer=normalizer)
-                * self.loss_weight
-            }
-        else:
-            keypoint_rcnn_inference(x, instances)
-            return instances
-
-    def layers(self, x):
-        """
-        Neural network layers that makes predictions from regional input features.
-        """
-        raise NotImplementedError
-
-
-# To get torchscript support, we make the head a subclass of `nn.Sequential`.
-# Therefore, to add new layers in this head class, please make sure they are
-# added in the order they will be used in forward().
-@ROI_KEYPOINT_HEAD_REGISTRY.register()
-class KRCNNConvDeconvUpsampleHead(BaseKeypointRCNNHead, nn.Sequential):
-    """
-    A standard keypoint head containing a series of 3x3 convs, followed by
-    a transpose convolution and bilinear interpolation for upsampling.
-    It is described in Sec. 5 of :paper:`Mask R-CNN`.
-    """
-
-    @configurable
-    def __init__(self, input_shape, *, num_keypoints, conv_dims, **kwargs):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature
-            conv_dims: an iterable of output channel counts for each conv in the head
-                         e.g. (512, 512, 512) for three convs outputting 512 channels.
-        """
-        super().__init__(num_keypoints=num_keypoints, **kwargs)
-
-        # default up_scale to 2.0 (this can be made an option)
-        up_scale = 2.0
-        in_channels = input_shape.channels
-
-        for idx, layer_channels in enumerate(conv_dims, 1):
-            module = Conv2d(in_channels, layer_channels, 3, stride=1, padding=1)
-            self.add_module("conv_fcn{}".format(idx), module)
-            self.add_module("conv_fcn_relu{}".format(idx), nn.ReLU())
-            in_channels = layer_channels
-
-        deconv_kernel = 4
-        self.score_lowres = ConvTranspose2d(
-            in_channels, num_keypoints, deconv_kernel, stride=2, padding=deconv_kernel // 2 - 1
-        )
-        self.up_scale = up_scale
-
-        for name, param in self.named_parameters():
-            if "bias" in name:
-                nn.init.constant_(param, 0)
-            elif "weight" in name:
-                # Caffe2 implementation uses MSRAFill, which in fact
-                # corresponds to kaiming_normal_ in PyTorch
-                nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu")
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg, input_shape)
-        ret["input_shape"] = input_shape
-        ret["conv_dims"] = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS
-        return ret
-
-    def layers(self, x):
-        for layer in self:
-            x = layer(x)
-        x = interpolate(x, scale_factor=self.up_scale, mode="bilinear", align_corners=False)
-        return x

spaces/Caoyunkang/Segment-Any-Anomaly/SAA/hybrid_prompts.py

@@ -1,23 +0,0 @@
-from .prompts.general_prompts import build_general_prompts
-from .prompts import visa_parameters
-from .prompts import mvtec_parameters
-from .prompts import ksdd2_parameters
-from .prompts import mtd_parameters
-
-
-manul_prompts = {
-    'visa_public': visa_parameters.manual_prompts,
-    'visa_challenge': visa_parameters.manual_prompts,
-    'mvtec': mvtec_parameters.manual_prompts,
-    'ksdd2': ksdd2_parameters.manual_prompts,
-    'mtd': mtd_parameters.manual_prompts,
-
-}
-
-property_prompts = {
-    'visa_public': visa_parameters.property_prompts,
-    'visa_challenge': visa_parameters.property_prompts,
-    'mvtec': mvtec_parameters.property_prompts,
-    'ksdd2': ksdd2_parameters.property_prompts,
-    'mtd': mtd_parameters.property_prompts,
-}

spaces/Chakri1997/ChatGPT-prompt-generator/app.py

@@ -1,18 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import gradio as gr
-
-tokenizer = AutoTokenizer.from_pretrained("merve/chatgpt-prompts-bart-long")
-model = AutoModelForSeq2SeqLM.from_pretrained("merve/chatgpt-prompts-bart-long", from_tf=True)
-
-def generate(prompt):
-
-    batch = tokenizer(prompt, return_tensors="pt")
-    generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
-    output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-    return output[0]
-
-input_component = gr.Textbox(label = "Input a persona, e.g. photographer", value = "photographer")
-output_component = gr.Textbox(label = "Prompt")
-examples = [["photographer"], ["developer"]]
-description = "This app generates ChatGPT prompts, it's based on a BART model trained on [this dataset](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts). 📓 Simply enter a persona that you want the prompt to be generated based on. 🧙🏻🧑🏻‍🚀🧑🏻‍🎨🧑🏻‍🔬🧑🏻‍💻🧑🏼‍🏫🧑🏽‍🌾"
-gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "👨🏻‍🎤 ChatGPT Prompt Generator 👨🏻‍🎤", description=description).launch()

spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/mysite/andrew_alpha/views.py

@@ -1,240 +0,0 @@
-from io import BytesIO
-from django.shortcuts import render
-from django.http import JsonResponse, HttpResponse
-from django.views.decorators.csrf import csrf_exempt
-
-import os
-import random
-import hashlib
-import cv2
-import torch
-import numpy as np
-from groundingdino.util.inference import load_model, load_image, predict, annotate
-from PIL import Image
-from math import ceil
-# from fastai.learner import load_learner
-from huggingface_hub import from_pretrained_fastai
-# from huggingface_hub import hf_hub_download
-from torchvision.transforms import GaussianBlur
-from torchvision.ops import box_convert
-
-
-# Define a custom transform for Gaussian blur
-def gaussian_blur(
-    x,
-    p=0.5,
-    kernel_size_min=3,
-    kernel_size_max=20,
-    sigma_min=0.1,
-    sigma_max=3):
-    if x.ndim == 4:
-        for i in range(x.shape[0]):
-            if random.random() < p:
-                kernel_size = random.randrange(
-                    kernel_size_min,
-                    kernel_size_max + 1, 2)
-                sigma = random.uniform(sigma_min, sigma_max)
-                x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
-    return x
-
-
-# load model
-learn = from_pretrained_fastai("ChristopherMarais/beetle-model", )
-# learn = load_learner(
-#     hf_hub_download(
-#       'ChristopherMarais/Andrew_AI-BB_classification-beta',
-#       filename="model.pkl")
-#     )
-
-# get class names
-labels = np.append(np.array(learn.dls.vocab), "Unknown")
-
-
-# this function only describes how much a singular value in al ist stands out.
-# if all values in the lsit are high or low this is 1
-# the smaller the proportiopn of number of disimilar vlaues are to other more
-# similar values the lower this number
-# the larger the gap between the dissimilar numbers and the simialr number the
-# smaller this number
-# only able to interpret probabilities or values between 0 and 1
-# this function outputs an estimate an inverse of the classification
-# confidence based on the probabilities of all the classes.
-# the wedge threshold splits the data on a threshold with a magnitude of a
-# positive int to force a ledge/peak in the data
-def unkown_prob_calc(
-    probs,
-    wedge_threshold,
-    wedge_magnitude=1,
-    wedge='strict'):
-    if wedge == 'strict':
-        increase_var = (1/(wedge_magnitude))
-        decrease_var = (wedge_magnitude)
-    # this allows points that are furhter from the threshold ot be moved less
-    # and points clsoer to be moved more
-    if wedge == 'dynamic':
-        increase_var = (
-            1/(wedge_magnitude*((1-np.abs(probs-wedge_threshold))))
-            )
-        decrease_var = (wedge_magnitude*((1-np.abs(probs-wedge_threshold))))
-    else:
-        print("Error: use 'strict' (default) or 'dynamic' as options for the wedge parameter!")
-    probs = np.where(probs >= wedge_threshold, probs**increase_var, probs)
-    probs = np.where(probs <= wedge_threshold, probs**decrease_var, probs)
-    diff_matrix = np.abs(probs[:, np.newaxis] - probs)
-    diff_matrix_sum = np.sum(diff_matrix)
-    probs_sum = np.sum(probs)
-    class_val = (diff_matrix_sum/probs_sum)
-    max_class_val = ((len(probs)-1)*2)
-    kown_prob = class_val/max_class_val
-    unknown_prob = 1-kown_prob
-    return (unknown_prob)
-
-
-def detect_objects(og_image_path):
-
-    TEXT_PROMPT = "bug"
-    BOX_TRESHOLD = 0.35
-    TEXT_TRESHOLD = 0.25
-    DEVICE = 'cpu'  # cuda or cpu
-    MODEL_PATH = "./mysite/andrew_alpha/0_object_detection_model/GroundingDINO_SwinT_OGC.cfg.py"
-    MODEL_CONFIG_PATH = "./mysite/andrew_alpha/0_object_detection_model/groundingdino_swint_ogc.pth"
-    # MODEL_PATH = "./mysite/andrew_alpha/0_object_detection_model/GroundingDINO_SwinB.cfg.py"
-    # MODEL_CONFIG_PATH = "./mysite/andrew_alpha/0_object_detection_model/groundingdino_swinb_cogcoor.pth"
-
-    model = load_model(
-        MODEL_PATH,
-        MODEL_CONFIG_PATH)
-
-    image_source, image = load_image(og_image_path)
-
-    boxes, logits, phrases = predict(
-        model=model,
-        image=image,
-        caption=TEXT_PROMPT,
-        box_threshold=BOX_TRESHOLD,
-        text_threshold=TEXT_TRESHOLD,
-        device=DEVICE
-    )
-
-    # # replace phrases
-    # na_phrase = ("OD score:-"*len(phrases)).split("-")
-    # annotated_frame = annotate(
-    #     image_source=image_source,
-    #     boxes=boxes,
-    #     logits=logits,
-    #     phrases=na_phrase)
-    # im_col = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-    # od_image_obj = Image.fromarray(im_col, 'RGB')
-
-    # create a list of all the identified object images
-    # Crop the image using PIL
-    og_image_obj = Image.open(og_image_path)
-    height, width = og_image_obj.size
-
-    boxes_norm = boxes * torch.Tensor([width, height, width, height])
-
-    xywh = box_convert(
-        boxes=boxes_norm,
-        in_fmt="cxcywh",
-        out_fmt="xywh").numpy()
-
-    img_lst = []
-    for i in range(len(boxes_norm)):
-        left = int(xywh[i][0])
-        top = int(xywh[i][1])
-        right = ceil(xywh[i][0])+ceil(xywh[i][2])
-        bottom = ceil(xywh[i][1])+ceil(xywh[i][3])
-        crop_img = og_image_obj.crop((left, top, right, bottom))
-        img_lst.append(crop_img)
-
-    # return (od_image_obj, img_lst, boxes)
-    return (image_source, img_lst, boxes)
-
-
-def bark_beetle_predict(og_image_path):
-    # Detect objects in image
-    # crop into list of smaller images
-    # processed_image, image_lst, boxes = detect_objects(og_image_path)
-    image_source, image_lst, boxes = detect_objects(og_image_path)
-    # get predictions for all segments
-    conf_dict_lst = []
-    img_cnt = len(image_lst)
-
-    for i in range(0, img_cnt):
-        prob_ar = np.array(learn.predict(image_lst[i])[2])
-        unkown_prob = unkown_prob_calc(
-            probs=prob_ar,
-            wedge_threshold=0.85,
-            wedge_magnitude=5,
-            wedge='dynamic')
-        prob_ar = np.append(prob_ar, unkown_prob)
-        # prob_ar = np.around(prob_ar*100, decimals=1)
-
-        conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
-        conf_dict = dict(sorted(
-            conf_dict.items(),
-            key=lambda item: item[1],
-            reverse=True))
-        conf_dict_lst.append(conf_dict)  # str(conf_dict)
-        result = list(zip(image_lst, conf_dict_lst))
-
-    # only get max class and confidence
-    max_conf_lst = []
-    max_class_lst = []
-    for i in range(len(result)):
-        temp_dict = result[i][1]
-        max_conf = list(temp_dict.values())[0]
-        # comment out before the first + to get only top candidate
-        max_class = list(temp_dict.keys())[0]
-        if max_class == "Unknown":
-            max_class = max_class+" ("+str(list(temp_dict.keys())[1])+"?)"
-        max_conf_lst.append(float(max_conf))
-        max_class_lst.append(max_class) 
-
-    # annotate image with classification results
-    annotated_frame = annotate(
-        image_source=image_source,
-        boxes=boxes,
-        logits=max_conf_lst,
-        phrases=max_class_lst)
-    im_col = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-    od_image_obj = Image.fromarray(im_col, 'RGB')
-
-    return (od_image_obj, result)
-
-
-def andrew_alpha(request):
-    return render(request, 'andrew_alpha.html', {})
-
-
-@csrf_exempt
-def process_uploaded_image(request):
-
-    if request.method == 'POST':
-        # Get uploaded file data
-        image_data = request.FILES['image'].read()
-
-        # Open image and make a copy to avoid read-after-close error
-        img = Image.open(BytesIO(image_data))
-        img = img.convert('RGB')
-        processed_img = img.copy()
-
-        # hash the image for a unique name
-        md5hash = hashlib.md5(processed_img.tobytes())
-        image_path = f"./mysite/andrew_alpha/2_submitted_images/{md5hash.hexdigest()}.png"
-
-        # save uploaded image
-        processed_img.save(image_path)
-
-        # Process image
-        processed_img, result = bark_beetle_predict(
-            og_image_path=image_path
-            )
-
-        # Save processed image to BytesIO in memory
-        buffer = BytesIO()
-        processed_img.save(buffer, 'JPEG')
-        img_data = buffer.getvalue()
-        return HttpResponse(img_data, content_type='image/jpeg')
-
-    return JsonResponse({"message": "No image received"})

spaces/ChristopherMarais/Andrew_AI-BB_classification-beta/mysite/mysite/settings.py

@@ -1,127 +0,0 @@
-"""
-Django settings for mysite project.
-
-Generated by 'django-admin startproject' using Django 4.2.5.
-
-For more information on this file, see
-https://docs.djangoproject.com/en/4.2/topics/settings/
-
-For the full list of settings and their values, see
-https://docs.djangoproject.com/en/4.2/ref/settings/
-"""
-
-from pathlib import Path
-
-# Build paths inside the project like this: BASE_DIR / 'subdir'.
-BASE_DIR = Path(__file__).resolve().parent.parent
-
-
-# Quick-start development settings - unsuitable for production
-# See https://docs.djangoproject.com/en/4.2/howto/deployment/checklist/
-
-# SECURITY WARNING: keep the secret key used in production secret!
-SECRET_KEY = 'django-insecure-o0r2ji%*=$9obwzeq9cz3n59a^q74!=ryy2pb7nvky-le8ik!r'
-
-# SECURITY WARNING: don't run with debug turned on in production!
-DEBUG = True
-
-ALLOWED_HOSTS = [
-    'christophermarais-andrew-ai-bb-classification-beta.hf.space',
-    '127.0.0.1',
-]
-
-
-# Application definition
-
-INSTALLED_APPS = [
-    'django.contrib.admin',
-    'django.contrib.auth',
-    'django.contrib.contenttypes',
-    'django.contrib.sessions',
-    'django.contrib.messages',
-    'django.contrib.staticfiles',
-    'andrew_alpha',
-]
-
-MIDDLEWARE = [
-    'django.middleware.security.SecurityMiddleware',
-    'django.contrib.sessions.middleware.SessionMiddleware',
-    'django.middleware.common.CommonMiddleware',
-    'django.middleware.csrf.CsrfViewMiddleware',
-    'django.contrib.auth.middleware.AuthenticationMiddleware',
-    'django.contrib.messages.middleware.MessageMiddleware',
-    'django.middleware.clickjacking.XFrameOptionsMiddleware',
-]
-
-ROOT_URLCONF = 'mysite.urls'
-
-TEMPLATES = [
-    {
-        'BACKEND': 'django.template.backends.django.DjangoTemplates',
-        'DIRS': [],
-        'APP_DIRS': True,
-        'OPTIONS': {
-            'context_processors': [
-                'django.template.context_processors.debug',
-                'django.template.context_processors.request',
-                'django.contrib.auth.context_processors.auth',
-                'django.contrib.messages.context_processors.messages',
-            ],
-        },
-    },
-]
-
-WSGI_APPLICATION = 'mysite.wsgi.application'
-
-
-# Database
-# https://docs.djangoproject.com/en/4.2/ref/settings/#databases
-
-DATABASES = {
-    'default': {
-        'ENGINE': 'django.db.backends.sqlite3',
-        'NAME': BASE_DIR / 'db.sqlite3',
-    }
-}
-
-
-# Password validation
-# https://docs.djangoproject.com/en/4.2/ref/settings/#auth-password-validators
-
-AUTH_PASSWORD_VALIDATORS = [
-    {
-        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
-    },
-]
-
-
-# Internationalization
-# https://docs.djangoproject.com/en/4.2/topics/i18n/
-
-LANGUAGE_CODE = 'en-us'
-
-TIME_ZONE = 'UTC'
-
-USE_I18N = True
-
-USE_TZ = True
-
-
-# Static files (CSS, JavaScript, Images)
-# https://docs.djangoproject.com/en/4.2/howto/static-files/
-
-STATIC_URL = 'static/'
-
-# Default primary key field type
-# https://docs.djangoproject.com/en/4.2/ref/settings/#default-auto-field
-
-DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

spaces/Cloudfaith/anon8231489123-gpt4-x-alpaca-13b-native-4bit-128g/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g").launch()

spaces/DEBO-PROJECT/DEBO-V1/bots/judgement_bot.py

@@ -1,30 +0,0 @@
-from modules.gpt_modules import gpt_call
-from langchain.prompts import PromptTemplate
-
-def debate_judgement(
-        user_debate_history, 
-        bot_debate_history
-        ):
-    
-    if len(user_debate_history.split()) < 100:
-        bot_response = "Under the 100 words, evaluation is not possible."
-    else:
-        judgement_prompt_preset = "\n".join([
-            "!!Instruction!",
-            "You are now the judge of this debate. Evaluate the debate according to the rules below.",
-            "Rule 1. Summarize the debate as a whole and each said.",
-            "Rule 2. Explain what was persuasive and what made the differnce between winning and losing.",
-        ])
-
-        judgement_prompt = "\n".join([
-                judgement_prompt_preset,
-                "User: " + user_debate_history,
-                "Judgement must be logical with paragraphs.",
-                "Do not show Rule",
-                "Write judgement below.",
-                "Judgement: "
-                ])
-
-        bot_response = gpt_call(judgement_prompt)
-    
-    return bot_response

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/certifi/__main__.py

@@ -1,12 +0,0 @@
-import argparse
-
-from certifi import contents, where
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-c", "--contents", action="store_true")
-args = parser.parse_args()
-
-if args.contents:
-    print(contents())
-else:
-    print(where())

spaces/Dewa/Text-Summurisation/README.md

@@ -1,11 +0,0 @@
----
-title: Summariser
-emoji: 😄
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-licenspie: cc
----

spaces/Dinoking/Guccio-AI-Designer/netdissect/dissect.html

@@ -1,399 +0,0 @@
-<!doctype html>
-<html>
-<head>
-<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
-<script src="https://code.jquery.com/jquery-3.3.1.js" integrity="sha256-2Kok7MbOyxpgUVvAk/HJ2jigOSYS2auK4Pfzbm7uH60=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script>
-<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/vue/2.5.16/vue.js" integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ=" crossorigin="anonymous"></script>
-<script src="https://cdn.jsdelivr.net/npm/[email protected]/lodash.js" integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28=" crossorigin="anonymous"></script>
-<style>
-[v-cloak] {
-  display: none;
-}
-.unitviz, .unitviz .modal-header, .unitviz .modal-body, .unitviz .modal-footer {
-  font-family: Arial;
-  font-size: 15px;
-}
-.unitgrid {
-  text-align: center;
-  border-spacing: 5px;
-  border-collapse: separate;
-}
-.unitgrid .info {
-  text-align: left;
-}
-.unitgrid .layername {
-  display: none;
-}
-.unitlabel {
-  font-weight: bold;
-  font-size: 150%;
-  text-align: center;
-  line-height: 1;
-}
-.lowscore .unitlabel {
-   color: silver;
-}
-.thumbcrop {
-  overflow: hidden;
-  width: 288px;
-  height: 72px;
-}
-.thumbcrop img, .img-scroller img {
-  image-rendering: pixelated;
-}
-.unit {
-  display: inline-block;
-  background: white;
-  padding: 3px;
-  margin: 2px;
-  box-shadow: 0 5px 12px grey;
-}
-.iou {
-  display: inline-block;
-  float: right;
-  margin-left: 5px;
-}
-.modal .big-modal {
-  width:auto;
-  max-width:90%;
-  max-height:80%;
-}
-.modal-title {
-  display: inline-block;
-}
-.footer-caption {
-  float: left;
-  width: 100%;
-}
-.histogram {
-  text-align: center;
-  margin-top: 3px;
-}
-.img-wrapper {
-  text-align: center;
-  position: relative;
-}
-.img-mask, .img-seg {
-  position: absolute;
-  top: 0;
-  left: 0;
-  z-index: 0;
-  visibility: hidden;
-}
-input.hidden-toggle {
-  display: none;
-}
-#show-seg:checked ~ .img-wrapper .img-seg,
-#show-mask:checked ~ .img-wrapper .img-mask {
-  visibility: visible;
-}
-.img-controls {
-  text-align: right;
-}
-.img-controls label {
-  display: inline-block;
-  background: silver;
-  padding: 10px;
-  margin-top: 0;
-  -webkit-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-}
-.seginfo {
-  display: inline-block;
-  padding: 10px;
-  float: left;
-}
-.img-mask {
-  pointer-events: none;
-}
-.colorsample {
-  display: inline-block;
-  height: 42px;
-  width: 42px;
-  float: left;
-}
-#show-seg:checked ~ .img-controls .toggle-seg,
-#show-mask:checked ~ .img-controls .toggle-mask {
-  background: navy;
-  color: white;
-}
-.big-modal img {
-  max-height: 60vh;
-}
-.img-scroller {
-  overflow-x: scroll;
-}
-.img-scroller .img-fluid {
-  max-width: initial;
-}
-.gridheader {
-  font-size: 12px;
-  margin-bottom: 10px;
-  margin-left: 30px;
-  margin-right: 30px;
-}
-.gridheader:after {
-  content: '';
-  display: table;
-  clear: both;
-}
-.sortheader {
-  float: right;
-  cursor: default;
-}
-.layerinfo {
-  float: left;
-}
-.sortby {
-  text-decoration: underline;
-  cursor: pointer;
-}
-.sortby.currentsort {
-  text-decoration: none;
-  font-weight: bold;
-  cursor: default;
-}
-.bg-inverse {
-  background: #021B54;
-}
-.dropmenu {
-  display: inline-block;
-  vertical-align: top;
-  position: relative;
-}
-.dropmenulist {
-  pointer-events: auto;
-  visibility: hidden;
-  transition: visiblity 1s;
-  position: absolute;
-  z-index: 1;
-  background: white;
-  right: 0;
-  text-align: right;
-  white-space: nowrap;
-}
-.dropmenu:focus {
-  pointer-events: none;
-}
-.dropmenu:focus .dropmenulist {
-  visibility: visible;
-}
-</style>
-</head>
-<body class="unitviz">
-<div id="app" v-if="dissect" v-cloak>
-
-<nav class="navbar navbar-expand navbar-dark bg-inverse">
-<span class="navbar-brand">{{ dissect.netname || 'Dissection' }}</span>
-<ul class="navbar-nav mr-auto">
-<li :class="{'nav-item': true, active: lindex == selected_layer}"
-    v-for="(lrec, lindex) in dissect.layers">
- <a class="nav-link" :href="'#' + lindex"
-    >{{lrec.layer}}</a>
-</li>
-</ul>
-<ul class="navbar-nav ml-auto" v-if="dissect.meta">
-  <li class="navbar-text ml-2" v-for="(v, k) in dissect.meta">
-    {{k}}={{v | fixed(3, true)}}
-  </li>
-</ul>
-</nav>
-
-<div v-for="lrec in [dissect.layers[selected_layer]]">
-<div v-if="'bargraph' in lrec" class="histogram">
-<a data-toggle="lightbox" :href="lrec.dirname + '/bargraph.svg?'+Math.random()"
-   :data-title="'Summary of ' + (dissect.netname || 'labels')
-          + ' at ' + lrec.layer">
-<img class="img-fluid"
-   :src="lrec.dirname + '/' + lrec.bargraph + '?'+Math.random()">
-</a>
-</div>
-
-<div class="gridheader">
-<div class="layerinfo">
-<span v-if="'interpretable' in lrec"
->{{lrec.interpretable}}/</span
->{{lrec.units.length}} units
-<span v-if="'labels' in lrec">
-covering {{lrec.labels.length}} concepts
-with IoU &ge; {{dissect.iou_threshold}}
-</span>
-</div>
-
-<div class="sortheader">
-sort by
-<span v-for="rank in lrec['rankings']" v-if="!rank.metric">
-<span :class="{sortby: true, currentsort: sort_order == rank.name}"
-      :data-ranking="rank.name"
-      v-on:click="sort_order = $event.currentTarget.dataset.ranking"
-      >{{rank.name}}</span>
-<span> </span>
-</span>
-<span v-for="metric in _.filter(_.uniq(lrec.rankings.map(x => x.metric)))">
-  <div class="dropmenu sortby" tabindex="0">
-    <div class="dropmenutop">
-      *-{{ metric }}
-    </div>
-    <div class="dropmenulist">
-        <div v-for="rank in lrec['rankings']" v-if="rank.metric == metric">
-        <span :class="{sortby: true, currentsort: sort_order == rank.name}"
-              :data-ranking="rank.name"
-              v-on:click="sort_order = $event.currentTarget.dataset.ranking"
-              >{{rank.name}}</span>
-        </div>
-    </div>
-  </div>
-  <span> </span>
-</span>
-
-</div>
-
-</div>
-<div class="unitgrid"
-      v-for="lk in [_.find(lrec.rankings, x=>x.name == sort_order)
-                     .metric || 'iou']"
-><div :class="{unit: true, lowscore: lk == 'iou' && !urec.interp}"
-      v-for="urec in _.find(lrec.rankings, x=>x.name == sort_order)
-                      .ranking.map(x=>lrec.units[x])">
-<div v-if="lk+'_label' in urec" class="unitlabel">{{urec[lk+'_label']}}</div>
-<div class="info"
- ><span class="layername">{{lrec.layer}}</span
- > <span class="unitnum">unit {{urec.unit}}</span
- > <span v-if="lk+'_cat' in urec" class="category">({{urec[lk+'_cat']}})</span
- > <span v-if="lk+'_iou' in urec" class="iou"
-         >iou {{urec[lk + '_iou'] | fixed(2)}}</span
- > <span v-if="lk in urec" class="iou"
-         >{{lk}} {{urec[lk] | fixed(2)}}</span></div>
-<div class="thumbcrop" v-for="imprefix in [lrec['image_prefix_' + lk] || '']"
-><a data-toggle="lightbox"
- :href="lrec.dirname + '/' + imprefix + 'image/' + urec.unit + '-top.jpg'"
-><img
- :src="lrec.dirname + '/' + imprefix + 'image/' + urec.unit + '-top.jpg'"
- height="72"></a></div>
-</div></div> <!-- end unit -->
-
-</div> <!-- end unit grid -->
-
-</div> <!-- end container -->
-
-</div> <!-- end app -->
-
-<div class="modal" id="lightbox">
-  <div class="modal-dialog big-modal" role="document">
-    <div class="modal-content">
-      <div class="modal-header">
-        <h5 class="modal-title"></h5>
-        <button type="button" class="close"
-             data-dismiss="modal" aria-label="Close">
-          <span aria-hidden="true">&times;</span>
-        </button>
-      </div>
-      <div class="modal-body">
-        <input id="show-seg" class="hidden-toggle" type="checkbox">
-        <input id="show-mask" class="hidden-toggle" type="checkbox" checked>
-        <div class="img-wrapper img-scroller">
-          <img class="fullsize img-fluid img-orig">
-          <img class="fullsize img-fluid img-seg">
-          <img class="fullsize img-fluid img-mask">
-        </div>
-        <div class="img-controls">
-          <canvas class="colorsample" height=1 width=1></canvas>
-          <div class="seginfo">
-          </div>
-          <label for="show-seg" class="toggle-seg">segmentation</label>
-          <label for="show-mask" class="toggle-mask">mask</label>
-        </div>
-      </div>
-      <div class="modal-footer">
-        <div class="footer-caption">
-        </div>
-      </div>
-    </div>
-  </div>
-</div>
-<script>
-$(document).on('click', '[data-toggle=lightbox]', function(event) {
-    if ($(this).attr('href').match(/-top/)) {
-        $('#lightbox img.img-orig').attr('src',
-            $(this).attr('href').replace(/-top.jpg/, '-orig.jpg'));
-        $('#lightbox img.img-seg').attr('src',
-            $(this).attr('href').replace(/-top.jpg/, '-seg.png'));
-        $('#lightbox img.img-mask').attr('src',
-            $(this).attr('href').replace(/-top.jpg/, '-mask.png'));
-        $('#lightbox .img-seg, #lightbox .img-mask, .img-controls').show();
-    } else {
-        $('#lightbox img.img-orig').attr('src', $(this).attr('href'));
-        $('#lightbox .img-seg, #lightbox .img-mask, .img-controls').hide();
-    }
-    $('#lightbox .modal-title').text($(this).data('title') ||
-       $(this).closest('.unit').find('.unitlabel').text());
-    $('#lightbox .footer-caption').text($(this).data('footer') ||
-       $(this).closest('.unit').find('.info').text());
-    $('#lightbox .segcolors').text('');
-    event.preventDefault();
-    $('#lightbox').modal();
-    $('#lightbox img').closest('div').scrollLeft(0);
-});
-$(document).on('click', '#lightbox img.img-seg', function(event) {
-  var elt_pos = $(this).offset();
-  var img_x = event.pageX - elt_pos.left;
-  var img_y = event.pageY - elt_pos.top;
-  var canvas = $('#lightbox .colorsample').get(0);
-  canvas.getContext('2d').drawImage(this, img_x, img_y, 1, 1, 0, 0, 1, 1);
-  var pixelData = canvas.getContext('2d').getImageData(0, 0, 1, 1).data;
-  var colorkey = pixelData[0] + ',' + pixelData[1] + ',' + pixelData[2];
-  var meaning = theapp.dissect.segcolors[colorkey];
-  $('#lightbox .seginfo').text(meaning);
-});
-
-var theapp = new Vue({
-  el: '#app',
-  data: {
-    sort_order: 'unit',
-    sort_fields: {
-      label: [[], []],
-      score: [['iou'], ['desc']],
-      unit:  [['unit'], ['asc']],
-    },
-    selected_layer: null,
-    dissect: null
-  },
-  created: function() {
-    var self = this;
-    $.getJSON('dissect.json?' + Math.random(), function(d) {
-      self.dissect = d;
-      for (var layer of d.layers) {
-        // Preprocess ranking records to sort them.
-        for (var rank of layer.rankings) {
-          if (!('ranking' in rank)) {
-            rank.ranking = rank.score.map((score, index) => [score, index])
-                .sort(([score1], [score2]) => score1 - score2)
-                .map(([, index]) => index);
-          }
-        }
-      }
-      self.sort_order = d.default_ranking;
-      self.hashchange();
-    });
-    $(window).on('hashchange', function() { self.hashchange(); });
-  },
-  methods: {
-    hashchange: function() {
-      this.selected_layer = +window.location.hash.substr(1) || 0;
-    },
-  },
-  filters: {
-    fixed: function(value, digits, truncate) {
-       if (typeof value != 'number') return value;
-       var fixed = value.toFixed(digits);
-       return truncate ? +fixed : fixed;
-    }
-  }
-});
-</script>
-</body>
-</html>