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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Full Version Cakewalk Pro Audio 9.03.Rar43 18 How to Install and Configure the Software.md

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- <p>Cakewalk Pro Audio 9.03 also has a powerful audio engine that delivers high-quality sound with low latency and high performance. You can work with up to 256 tracks at up to 24-bit/96kHz resolution.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Fusion 360 Software Cost How to Choose the Best Plan for Your Needs.md

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-<p>If you are a fan of old Hindi songs with a twist, you might have heard of jhankar beats. Jhankar beats are a type of remixing technique that adds extra sound effects and beats to the original songs, creating a more lively and dynamic listening experience. Jhankar beats songs are popular among music lovers who enjoy the nostalgia of the golden era of Bollywood music, as well as the modern touch of the remixes. In this article, we will explore what are jhankar beats, how they came into being, what are their features and benefits, and where can you find and download them online.</p>
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-<p>Jhankar beats became popular in the early 90s, when some music labels started releasing official jhankar beats albums of old Hindi songs. These albums featured remixes by professional DJs and music producers, who used more advanced equipment and techniques to create better quality jhankar beats songs. Some of these albums became huge hits, such as "Jhankaar Beats" (1993), "Super Jhankaar Beats" (1994), "Jaanam Samjha Karo" (1997), and "Dil To Pagal Hai" (1997). Some original composers also embraced jhankar beats and incorporated them in their new songs, such as R.D. Burman, Anu Malik, Nadeem-Shravan, and Jatin-Lalit.</p>
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-<p>Jhankar beats songs have some unique features and benefits that make them appealing to many music fans. Some of these are:</p>
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-<li>Jhankar beats songs add a new dimension to old Hindi songs, making them more lively and dynamic. They enhance the melody, rhythm, emotion, and energy of the original songs.</li>
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-<p>There are many sources where one can find and download jhankar beats songs online. Some of these are:</p>
-<table>
-<tr>
-<th>Source</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>YouTube</td>
-<td>YouTube is the most popular and accessible source of jhankar beats songs online. There are many channels and playlists dedicated to jhankar beats songs, such as <a href="">Jhankar Station 07</a>, <a href="">Jhankar Gaane 13gaftabctn</a>, <a href="">Jhankar Remix 07</a>, and <a href="">Jhankar Beats Remix Songs</a>. One can watch, listen, like, comment, share, and subscribe to these channels and playlists. One can also download jhankar beats songs from YouTube using various online tools and apps.</td>
-</tr>
-<tr>
-<td>Spotify</td>
-<td>Spotify is a popular music streaming service that offers a wide range of jhankar beats songs online. There are many albums and playlists featuring jhankar beats songs, such as <a href="">Jhankaar Beats</a>, <a href="">Super Jhankaar Beats</a>, <a href="">Jaanam Samjha Karo</a>, and <a href="">Dil To Pagal Hai</a>. One can listen, follow, save, and download these albums and playlists on Spotify. One can also create their own playlists of jhankar beats songs on Spotify.</td>
-</tr>
-<tr>
-<td>Gaana</td>
-<td>Gaana is a popular Indian music streaming service that offers a large collection of jhankar beats songs online. There are many albums and playlists featuring jhankar beats songs, such as <a href="">Jhankaar Beats - Evergreen Melodies</a>, <a href="">Jhankaar Beats - Romantic Hits</a>, <a href="">Jhankaar Beats - Dance Masti</a>, and <a href="">Jhankaar Beats - Sad Songs</a>. One can listen, follow, save, and download these albums and playlists on Gaana. One can also create their own playlists of jhankar beats songs on Gaana.</td>
-</tr>
-<tr>
-<td>Pagalworld</td>
-<td>Pagalworld is a popular Indian website that offers free downloads of jhankar beats songs online. There are many categories and subcategories of jhankar beats songs, such as A to Z Jhankar Mp3 Songs, Jhankar Album Mp3 Songs, Jhankar Single Mp3 Songs, Jhankar Remix Mp3 Songs, and Jhankar Mashup Mp3 Songs. One can browse, search, play, and download these jhankar beats songs on Pagalworld.</td>
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-<tr>
-<td>JioSaavn</td>
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-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Jhankar beats are a unique and fascinating form of musical remixing that adds extra sound effects and beats to old Hindi songs, creating a more lively and dynamic listening experience. Jhankar beats songs are popular among music lovers who enjoy the nostalgia of the golden era of Bollywood music, as well as the modern touch of the remixes. Jhankar beats songs have some distinctive features and benefits, such as enhancing the melody, rhythm, emotion, and energy of the original songs, catering to different tastes and moods of listeners, being fun and easy to dance to, being nostalgic and sentimental, and being diverse and versatile. Jhankar beats songs can be found and downloaded online from various sources, such as YouTube, Spotify, Gaana, Pagalworld, and JioSaavn. Jhankar beats songs are a great way to enjoy old Hindi songs with a twist.</p>
- <h2>FAQs</h2>
-<h3>What is the difference between jhankar beats and remixes?</h3>
-<p>Jhankar beats are a type of remixes that specifically add extra sound effects and beats to old Hindi songs. Remixes are a broader term that can refer to any modification or alteration of a song, such as changing the lyrics, melody, tempo, genre, or style.</p>
- <h3>Who invented jhankar beats?</h3>
-<p>There is no clear answer to who invented jhankar beats, as it was a collective and spontaneous phenomenon that emerged from the grassroots level of music enthusiasts in India. However, some of the pioneers and popularizers of jhankar beats include DJs like Akbar Sami, Amit Dasaniya, Chetas Shah, Kiran Kamath, Rinkesh Makwana, Sanjeev Kohli, Viju Shah, and Zafar Iqbal.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Call of Duty Black Ops 2 English Language Pack Download Easy and Fast Method.md

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-<h1>Download English Language Pack for Call of Duty Black Ops 2</h1>
-<p>Are you a fan of Call of Duty Black Ops 2, but you can't understand the Russian language in the game? Do you want to play the game in English, but you don't know how to change the language settings? If you answered yes to any of these questions, then this article is for you. In this article, I will show you how to download English language pack for Call of Duty Black Ops 2 and enjoy the game in your preferred language.</p>
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-<h2>Introduction</h2>
-<h3>What is Call of Duty Black Ops 2?</h3>
-<p>Call of Duty Black Ops 2 is a first-person shooter video game developed by Treyarch and published by Activision in 2012. It is the ninth installment in the Call of Duty series and the sequel to Call of Duty Black Ops. The game is set in two different time periods: the Cold War in the 1980s and a futuristic war in 2025. The game features a branching storyline, multiple endings, and a zombies mode.</p>
-<h3>Why do you need to download English language pack?</h3>
-<p>If you bought Call of Duty Black Ops 2 from a region where Russian is the default language, then you might have trouble understanding the game's dialogue, menus, and subtitles. This can affect your gameplay experience and enjoyment. To solve this problem, you need to download English language pack for Call of Duty Black Ops 2 and change the language settings in the game.</p>
-<h2>How to download English language pack for Call of Duty Black Ops 2</h2>
-<p>There are two methods to download English language pack for Call of Duty Black Ops 2. You can either use a language switcher tool or replace the Russian files with English files. I will explain both methods in detail below.</p>
-<h3>Method 1: Use a language switcher tool</h3>
-<p>A language switcher tool is a simple program that allows you to change the language of any game with just a few clicks. Here are the steps to use a language switcher tool for Call of Duty Black Ops 2:</p>
-<h4>Step 1: Download the language switcher tool</h4>
-<p>You can download a language switcher tool from this link: [text](^4^). This tool is safe and easy to use. Just make sure you have WinRAR or any other program that can extract compressed files.</p>
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-<h4>Step 2: Run the tool and select English as the language</h4>
-<p>After downloading the tool, extract it and run it as administrator. You will see a window with a list of languages. Select English as the language and click OK. The tool will automatically change the language settings in your game.</p>
-<h4>Step 3: Restart the game and enjoy</h4>
-<p>Now, all you have to do is restart your game and enjoy playing it in English. You will be able to understand everything in the game, from the dialogue to the menus.</p>
-<h3>Method 2: Replace the Russian files with English files</h3>
-<p>If you don't want to use a tool, you can also manually replace the Russian files with English files in your game folder. Here are the steps to do that:</p>
-<h <h4>Step 1: Download the English language pack from a trusted source</h4>
-<p>You can download the English language pack for Call of Duty Black Ops 2 from this link: [text]. This is a reliable and verified source that provides the original English files for the game. The download size is about 2.5 GB, so make sure you have enough space on your hard drive.</p>
-<h4>Step 2: Locate the game folder and backup the Russian files</h4>
-<p>Before you replace the Russian files with English files, you should backup the Russian files in case something goes wrong. To do that, locate the game folder on your computer. The default location is C:\Program Files (x86)\Steam\steamapps\common\Call of Duty Black Ops II. In the game folder, you will find two subfolders named "sound" and "zone". These are the folders that contain the Russian files. Copy and paste these folders to another location on your computer as a backup.</p>
-<h4>Step 3: Copy and paste the English files into the game folder</h4>
-<p>After backing up the Russian files, you can now copy and paste the English files into the game folder. To do that, extract the English language pack that you downloaded and open it. You will see two subfolders named "sound" and "zone". These are the folders that contain the English files. Copy and paste these folders into the game folder, replacing the Russian files.</p>
-<h4>Step 4: Restart the game and enjoy</h4>
-<p>Now, all you have to do is restart your game and enjoy playing it in English. You will be able to understand everything in the game, from the dialogue to the menus.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In this article, I have shown you how to download English language pack for Call of Duty Black Ops 2 and change the language settings in the game. I have explained two methods to do that: using a language switcher tool or replacing the Russian files with English files. Both methods are easy and effective, and you can choose the one that suits you best.</p>
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-<p>If you found this article helpful, please share it with your friends who also play Call of Duty Black Ops 2. Also, don't forget to leave a comment below and let me know what you think about the game. Thank you for reading and happy gaming!</p>
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-<li>A: No, it won't. Changing the language of Call of Duty Black Ops 2 only affects your offline gameplay and does not interfere with your online gameplay or achievements.</li>
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-<li>A: You can easily switch back to any language by using the same methods as above. Just make sure you have backed up your original language files before replacing them.</li>
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-<p>Charlie Blue Song is a song written and performed by Danny O'Keefe, an American singer-songwriter. It was first recorded by O'Keefe in 1967, but not released. It was recorded by The Bards and released in 1968 as the b-side to the song "Tunesmith" on Parrot Records. The Bards were a band from Moses Lake, Washington. </p>
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-<p>The song is about a man who has lost his love and his direction in life. He feels lonely, depressed, and hopeless. He regrets his past mistakes and wishes he could go back to simpler times. He realizes that he has wasted his life chasing after false happiness and that he has nothing left to live for. He sings:</p>
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-<p>The song was recorded by O'Keefe for his self-titled debut album in 1971.  The following year he re-recorded it (with a slower, more downbeat arrangement) for his second album, O'Keefe.  The second version was issued as a single, reaching number 9 on the Billboard Hot 100 singles chart, number 5 on the adult contemporary chart, and number 63 on the country chart. </p>
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- <h3>The reasons why Charlie Blue Song is a great choice</h3>
-<p>Among the many songs that you can listen to, Charlie Blue Song is a great choice for several reasons. Here are some of them:</p>
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-<p>The legal and ethical ways to get the song are those that respect the rights of the original artist and the music industry. These ways include:</p>
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-<p>These are some of the legal and ethical ways to get the song. These ways ensure that you do not violate any laws or harm any parties involved in the creation and distribution of the song.</p>
- <h3>The best sources and platforms to download the song</h3>
-<p>The best sources and platforms to download the song are those that offer the best quality, price, and convenience for you. These sources and platforms may vary depending on your preferences and needs. Here are some examples of the best sources and platforms to download the song:</p>
- <table border="1">
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-<tr><td>iTunes</td><td>High (up to 256 kbps)</td><td>$1.29 per song</td><td>Easy to use; compatible with Apple devices; integrated with iCloud; offers offline access</td></tr>
-<tr><td>Amazon Music</td><td>High (up to 256 kbps)</td><td>$1.29 per song; free for Prime members</td><td>Easy to use; compatible with various devices; integrated with Amazon account; offers offline access</td></tr>
-<tr><td>Google Play Music</td><td>High (up to 320 kbps)</td><td>$1.29 per song; free for YouTube Premium members</td><td>Easy to use; compatible with various devices; integrated with Google account; offers offline access</td></tr>
-<tr><td>Spotify</td><td>Medium (up to 160 kbps for free users; up to 320 kbps for premium users)</td><td>$9.99 per month for premium subscription; free for ad-supported streaming</td><td>Easy to use; compatible with various devices; offers offline access for premium users; has a large music library and personalized recommendations</td></tr>
-<tr><td>YouTube Music</td><td>Medium (up to 160 kbps for free users; up to 256 kbps for premium users)</td><td>$9.99 per month for premium subscription; free for ad-supported streaming</td><td>Easy to use; compatible with various devices; offers offline access for premium users; has a large music library and video content</td></tr>
-</table>
- <p>These are some of the best sources and platforms to download the song. You can choose the one that suits your preferences and needs the most.</p>
- <h2>How can you enjoy Charlie Blue Song?</h2>
-<p>Finally, after you have downloaded Charlie Blue Song, you might be wondering how you can enjoy it. Well, there are many ways to enjoy this song, but here are some suggestions:</p>
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-<p>The best ways to listen to the song are those that enhance your listening experience and appreciation of the song. These ways include:</p>
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-<li>Singing along or playing an instrument. This way, you can express yourself and have fun with the song, and improve your musical skills.</li>
-</ul>
-<p>These are some of the best ways to listen to the song. You can try them out and see which one works best for you.</p>
- <h3>The best occasions and moods to play the song</h3>
-<p>The best occasions and moods to play the song are those that match the tone and theme of the song. These occasions and moods include:</p>
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-<p>These are some of the best occasions and moods to play the song. You can choose the one that suits your situation and mood the most.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Charlie Blue Song is a classic tune that you should download and enjoy. It is a song that has a rich history and a deep meaning. It is a song that can benefit your health and happiness. It is a song that you can download legally and ethically from various sources and platforms. And it is a song that you can enjoy in different ways and on different occasions. So what are you waiting for? Download Charlie Blue Song today and enjoy this timeless masterpiece.</p>
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-<h1>Download Buku EPS TOPIK 60 Bab: A Guide for Korean Language Learners</h1>
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- <p>Buku EPS TOPIK 60 Bab is a textbook that was published by HRD Korea, the organization that administers the EPS TOPIK test. It is based on the standard textbook of the Korean Language Institute of Yonsei University, one of the most prestigious universities in Korea. It is designed to help foreign workers who want to work in Korea to learn Korean and prepare for the EPS TOPIK test.</p>
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- <p>The easiest way to download Buku EPS TOPIK 60 Bab is to visit the official website of HRD Korea at <a href="">http://eps.hrdkorea.or.kr</a>. There, you can find the link to download the PDF file of Buku EPS TOPIK 60 Bab for free. You can also download the book from other sources, such as <a href="">https://www.topikguide.com/eps-topik-textbook-download/</a> or <a href="">https://www.koreantopik.com/2019/01/download-eps-topik-klt-2011-2013-2015.html</a>. However, you should be careful about the quality and accuracy of the book from these sources.</p>
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-<tr>
-<th>Section</th>
-<th>Number of questions</th>
-<th>Time limit</th>
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-<tr>
-<td>Listening</td>
-<td>25</td>
-<td>30 minutes</td>
-<td>100 points</td>
-</tr>
-<tr>
-<td>Reading</td>
-<td>25</td>
-<td>40 minutes</td>
-<td>100 points</td>
-</tr>
-<tr>
-<td>Total</td>
-<td>50</td>
-<td>70 minutes</td>
-<td>200 points</td>
-</tr>
-</table>
- <p>The types of questions are as follows:</p>
- <ul>
-<li>Listening: You will hear a dialogue or a monologue related to a situation or a topic. You will then have to choose the correct answer from four options.</li>
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- <p>In conclusion, Buku EPS TOPIK 60 Bab is a useful resource for Korean language learners who want to work in Korea. It is a textbook that covers 60 chapters of vocabulary, grammar, dialogues, culture, and practice for EPS TOPIK preparation. It can help you improve your Korean skills and pass the EPS TOPIK test.</p>
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-<p>Final Fantasy VII: Advent Children Complete is a movie that continues the storyline of <em>Final Fantasy VII</em>, one of the most popular and influential video games of all time. The movie was released in 2005 in Japan and 2006 in North America, and was later remastered in 4K Ultra HD in 2021. The movie follows Cloud Strife, a former mercenary who saved the world from an evil corporation and a madman named Sephiroth two years ago. Now, he lives a secluded life in a ruined city, haunted by his past and suffering from a mysterious disease. However, he soon finds himself drawn into a new conflict when three mysterious men appear, seeking to resurrect Sephiroth and unleash chaos on the planet. Along with his old friends and allies, Cloud must face his inner demons and protect what he holds dear.</p>
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-<p>In conclusion, Final Fantasy VII: Advent Children Complete is a movie that you should not miss if you love Final Fantasy VII or anime movies in general. It is a sequel that delivers a thrilling and emotional experience with high-quality animation and sound. You can watch it in English dub with MP4 format by choosing one of the options we mentioned above, depending on your preference and budget. Whether you buy it, stream it, or download it, you will surely enjoy this masterpiece of CGI art.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Final Fantasy VII: Advent Children Complete and its English dub MP4 download:</p>
-<ol>
-<li><strong>Is Final Fantasy VII: Advent Children Complete different from the original version?</strong><br>
-Yes, it is an extended director's cut that adds 26 minutes of additional footage, improves the graphics, and changes some scenes and dialogues.</li>
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- <h3>Benefits of Drive Ahead Mod Apk</h3>
-<p>Drive Ahead Mod Apk has many benefits that make it better than the original game. Here are some of them:</p>
-<h4>Unlimited coins</h4>
-<p>With Drive Ahead Mod Apk, you will get unlimited coins that you can use to buy new vehicles and arenas, or to upgrade your cards. You don't have to watch ads or complete missions to earn coins anymore.</p>
-<h4>Unlocked levels</h4>
-<p>With Drive Ahead Mod Apk, you will get access to all the levels in the game, including the ones that are locked in the original game. You don't have to play the game or spend coins to unlock them anymore.</p>
-<h4>Variety of cars</h4>
-<p>With Drive Ahead Mod Apk, you will get access to all the cars in the game, including the ones that are exclusive or rare in the original game. You don't have to play the game or spend coins to unlock them anymore.</p>
- <h3>How <h3>How to download and install Drive Ahead Mod Apk?</h3>
-<p>If you are interested in downloading and installing Drive Ahead Mod Apk, you will need to follow some simple steps. Here they are:</p>
-<h4>Steps to follow</h4>
-<ol>
-<li>First, you will need to uninstall the original Drive Ahead game from your device if you have it installed.</li>
-<li>Second, you will need to download the Drive Ahead Mod Apk file from a reliable source. You can use the link below to get it.</li>
-<li>Third, you will need to enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and toggling it on.</li>
-<li>Fourth, you will need to locate the downloaded Drive Ahead Mod Apk file on your device and tap on it to start the installation process.</li>
-<li>Fifth, you will need to follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Sixth, you will need to launch the Drive Ahead Mod Apk game and enjoy it.</li>
-</ol>
-<h4>Requirements and compatibility</h4>
-<p>Before you download and install Drive Ahead Mod Apk, you should make sure that your device meets the following requirements and compatibility:</p>
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-<li>You will need an Android device with version 4.4 or higher.</li>
-<li>You will need at least 100 MB of free storage space on your device.</li>
-<li>You will need a stable internet connection to play the game online.</li>
-<li>You will need to disable any antivirus or security software that might interfere with the installation or operation of the game.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Drive Ahead is a fun and thrilling car game that lets you drive and destroy vehicles in an arena. You can play solo or with your friends in different modes and challenges. You can also customize your vehicle and arena with different themes and settings. But if you want to enjoy the game to the fullest, you should download Drive Ahead Mod Apk. This will give you unlimited coins, unlocked levels, and a variety of cars to choose from. You can also download and install Drive Ahead Mod Apk easily by following the steps and requirements we have mentioned in this article. So what are you waiting for? Download Drive Ahead Mod Apk now and have a blast!</p>
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-<p>In this article, we have covered the following topics:</p>
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-<li>What is Drive Ahead?</li>
-<li>Why download Drive Ahead Mod Apk?</li>
-<li>How to download and install Drive Ahead Mod Apk?</li>
-</ul>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Drive Ahead Mod Apk:</p>
-<ol>
-<li><b>Is Drive Ahead Mod Apk safe to use?</b></li>
-<p>Yes, Drive Ahead Mod Apk is safe to use as long as you download it from a trusted source. However, you should always be careful when installing apps from unknown sources and disable any antivirus or security software that might interfere with the game.</p>
- <li><b>Is Drive Ahead Mod Apk free to use?</b></li>
-<p>Yes, Drive Ahead Mod Apk is free to use and does not require any subscription or registration. However, you might see some ads in the game that support the developers.</p>
- <li><b>Can I play Drive Ahead Mod Apk offline?</b></li>
-<p>No, Drive Ahead Mod Apk requires an internet connection to play online with friends or other players. However, you can play solo mode offline if you want.</p>
- <li><b>Can I update Drive Ahead Mod Apk?</b></li>
-<p>No, Drive Ahead Mod Apk does not support updates. If you want to get the latest version of the game, you will have to uninstall the mod version and install the original version from the Google Play Store.</p>
- <li><b>Can I use Drive Ahead Mod Apk on PC?</b></li>
-<p>No, Drive Ahead Mod Apk is designed for Android devices only. However, you can use an Android emulator on your PC to run the game if you want.</p>
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-<p>If you are a fan of Pokemon games, you might have heard of Pokemon Eternal X, one of the most popular and well-made ROM hacks of Pokemon X and Y. This fan-made game adds a lot of new features, challenges, and surprises to the original games, making them more fun and exciting to play. In this article, we will tell you everything you need to know about Pokemon Eternal X, including what it is, what it changes, and how to download and play it on a 3DS emulator.</p>
- <h2>What is Pokemon Eternal X and why is it a popular ROM hack?</h2>
-<p>Pokemon Eternal X is a ROM hack of Pokemon X, which is one of the main series games of Generation VI. A ROM hack is a modification of an existing game that changes its content, graphics, gameplay, or other aspects. Pokemon Eternal X was created by Buffel Saft, a talented fan who wanted to improve the original game and make it more challenging and diverse.</p>
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-<p>Pokemon Eternal X is popular among Pokemon fans because it offers a lot of new features and changes that make the game more enjoyable and replayable. Some of these features and changes are:</p>
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-<p>As you can see, Pokemon Eternal X is a game that has a lot of content and variety that will keep you entertained and challenged for hours. If you are interested in playing this game, you might be wondering how to download and play it on a 3DS emulator. Don't worry, we will show you how in the next section.</p>
- <h2>How to download and play Pokemon Eternal X on a 3DS emulator</h2>
-<p>Pokemon Eternal X is a ROM hack of Pokemon X, which means that you need to have a copy of Pokemon X and a 3DS emulator to play it. A 3DS emulator is a software that allows you to run 3DS games on your computer or mobile device. There are many 3DS emulators available online, but we recommend using Citra, which is one of the most popular and reliable ones.</p>
-<p>To download and play Pokemon Eternal X on a 3DS emulator, you need to follow these steps:</p>
-<ol>
-<li>Download Pokemon X from a reputable source. You can find many websites that offer 3DS ROMs, but be careful of viruses and malware. We suggest using [this link] to download Pokemon X safely and legally.</li>
-<li>Download Pokemon Eternal X from the official website. You can find the latest version of Pokemon Eternal X [here], along with the patch notes, screenshots, videos, and FAQs. You will need a program like WinRAR or 7-Zip to extract the files.</li>
-<li>Download Citra from the official website. You can find the latest version of Citra [here], along with the installation guide, system requirements, compatibility list, and support. You will need a program like WinRAR or 7-Zip to extract the files.</li>
-<li>Install Citra on your computer or mobile device. Follow the instructions on the website or the readme file to install Citra properly. You may need to update your drivers or install some additional software to run Citra smoothly.</li>
-<li>Patch Pokemon X with Pokemon Eternal X. Open the folder where you extracted Pokemon Eternal X and run the xdeltaUI.exe file. Click on "Open" next to "Patch" and select the "EternalX_v1_4_5.xdelta" file. Click on "Open" next to "Source File" and select the "Pokemon X.cia" file. Click on "Save As" next to "Output File" and choose a name and location for the patched file. Click on "Patch" and wait for the process to finish.</li>
-<li>Run Pokemon Eternal X on Citra. Open Citra and click on "File" then "Load File". Navigate to the folder where you saved the patched file and select it. Click on "Open" and enjoy playing Pokemon Eternal X.</li>
-</ol>
-<p>Congratulations, you have successfully downloaded and played Pokemon Eternal X on a 3DS emulator. Now you can explore the amazing world of Pokemon Eternal X and catch them all.</p>
- <h2>Tips and tricks for playing Pokemon Eternal X smoothly and enjoyably</h2>
-<p>Pokemon Eternal X is a game that requires some skill and strategy to play well. It is not a game for beginners or casual players who want an easy ride. It is a game for experienced and hardcore players who want a challenge and a thrill. If you are one of them, here are some tips and tricks that will help you play Pokemon Eternal X smoothly and enjoyably:</p>
-<ul>
-<li>Save often: Pokemon Eternal X is a game that has many surprises and difficulties that can catch you off guard. You don't want to lose your progress or your precious Pokemon because of an unexpected event or a tough battle. Save often and use multiple save slots to avoid frustration and regret.</li>
-<li>Plan ahead: Pokemon Eternal X is a game that requires some planning and preparation before each battle. You need to know your enemy's team, moves, abilities, types, weaknesses, strengths, strategies, etc. You also need to know your own team, moves, abilities, types, weaknesses, strengths, strategies, etc. You need to have a balanced and versatile team that can handle different situations and opponents. You need to have a good strategy and tactics that can exploit your enemy's weaknesses and cover your own. You need to have the right items and equipment that can boost your performance and help you in a pinch. Plan ahead and be ready for anything.</li>
-<li>Train smart: Pokemon Eternal X is a game that requires some training and grinding to level up your Pokemon and make them stronger. You need to train your Pokemon in the right places and against the right opponents to maximize their experience and EVs. You need to train your Pokemon in the right stats and skills to optimize their potential and performance. You need to train your Pokemon in the right moves and abilities to diversify their options and capabilities. Train smart and make your Pokemon the best they can be.</li>
-<li>Explore everything: Pokemon Eternal X is a game that has a lot of content and secrets that are waiting for you to discover. You need to explore everything in the game, from the towns, routes, caves, forests, deserts, mountains, islands, oceans, etc. You need to talk to everyone in the game, from the NPCs, trainers, gym leaders, elite four members, champion, rivals, professors, team flare members, friends, etc. You need to find everything in the game, from the items, TMs, HMs, berries, fossils, stones, gems, etc. Explore everything and enjoy the game to the fullest.</li>
-</ul>
-<p>These are some of the tips and tricks that will help you play Pokemon Eternal X smoothly and enjoyably. Of course, there are many more things that you can learn and do in this game, but we will leave them for you to find out on your own. After all, part of the fun of playing Pokemon Eternal X is discovering new things and having your own adventure.</p>
- <h2>Conclusion</h2>
-<p>Pokemon Eternal X is a ROM hack of Pokemon X that adds a lot of new features and changes that make the game more fun and exciting to play. It is a game for experienced and hardcore Pokemon fans who want a challenge and a thrill. It is a game that has a lot of content and variety that will keep you entertained and challenged for hours. It is a game that you can download and play on a 3DS emulator with ease.</p>
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-<p>So what are you waiting for? Download Pokemon Eternal X today and start your journey in this awesome ROM hack. And don't forget to share your feedback and opinions with us and other players. We would love to hear from you.</p>
-<p>And if you are looking for other popular and recommended Pokemon ROM hacks, here are some of them:</p>
-<ul>
-<li>Pokemon Glazed: A ROM hack of Pokemon Emerald that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Light Platinum: A ROM hack of Pokemon Ruby that takes you to two new regions with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Gaia: A ROM hack of Pokemon FireRed that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Insurgence: A fan-made game that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Uranium: A fan-made game that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-</ul>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Pokemon Eternal X:</p>
-<h3>What are some of the best Pokemon ROM hacks?</h3>
-<p>There are many great Pokemon ROM hacks out there, but some of the best ones are:</p>
-<ul>
-<li>Pokemon Eternal X: A ROM hack of Pokemon X that adds a lot of new features and changes that make the game more fun and exciting to play.</li>
-<li>Pokemon Glazed: A ROM hack of Pokemon Emerald that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Light Platinum: A ROM hack of Pokemon Ruby that takes you to two new regions with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Gaia: A ROM hack of Pokemon FireRed that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Insurgence: A fan-made game that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-<li>Pokemon Uranium: A fan-made game that takes you to a new region with new storylines, characters, Pokemon, moves, abilities, etc.</li>
-</ul>
-<h3>Is Pokemon Eternal X legal and safe to download?</h3>
-<p>Pokemon Eternal X is a ROM hack of Pokemon X, which means that it is not an official game made by Nintendo or Game Freak. It is a fan-made game that uses the original game's assets and code to create a new game. Therefore, it is not legal to download or distribute Pokemon Eternal X without owning a copy of Pokemon X. However, it is unlikely that Nintendo or Game Freak will take any legal action against Pokemon Eternal X or its players, as they have not done so for other ROM hacks in the past.</p>
-<p>Pokemon Eternal X is safe to download as long as you download it from the official website or a reputable source. You should avoid downloading Pokemon Eternal X from unknown or suspicious websites, as they may contain viruses or malware that can harm your computer or device. You should also scan the downloaded files with an antivirus program before opening them.</p>
-<h3>Can I play Pokemon Eternal X online with other players?</h3>
-<p>Pokemon Eternal X is compatible with online features and multiplayer modes, such as trading, battling, and interacting with other players who are playing Pokemon Eternal X or other ROM hacks. However, you need to have a 3DS emulator that supports online connectivity and a stable internet connection to play Pokemon Eternal X online. You also need to follow some steps and settings to enable online features and multiplayer modes on your 3DS emulator. You can find more information and instructions on how to play Pokemon Eternal X online [here].</p>
-<h3>How can I transfer my save file from Pokemon X or Y to Pokemon Eternal X?</h3>
-<p>Pokemon Eternal X is compatible with save files from Pokemon X or Y, which means that you can transfer your save file from the original game to the ROM hack and continue your progress and adventure. However, you need to have a 3DS emulator that supports save file transfer and a backup of your save file from Pokemon X or Y. You also need to follow some steps and settings to transfer your save file from the original game to the ROM hack. You can find more information and instructions on how to transfer your save file from Pokemon X or Y to Pokemon Eternal X [here].</p>
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-<p>Pokemon Eternal X is constantly updated by its creator, Buffel Saft, who adds new features, fixes bugs, and improves the game. You can find the latest version of Pokemon Eternal X on the official website or on the [PokeCommunity thread]. To update Pokemon Eternal X to the latest version, you need to download the latest patch file from the website or the thread and apply it to your patched ROM file using xdeltaUI.exe. You do not need to download the whole game again or start a new save file. You can find more information and instructions on how to update Pokemon Eternal X to the latest version [here].</p> 197e85843d<br />
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-<h1>Free Download Gacha Nox iOS - How to Play Gacha Nox on Your iPhone</h1>
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-Gacha nox mobile review - is it worth downloading on ios?<br />
-Gacha nox ios vs android - which one is better?<br />
-Gacha nox ios hack - how to get unlimited gems and coins<br />
-Gacha nox ios emulator - how to play gacha nox on pc<br />
-Gacha nox ios beta - how to join and test gacha nox<br />
-Gacha nox ios apk - how to install gacha nox without app store<br />
-Gacha nox ios mod - how to unlock all characters and outfits<br />
-Gacha nox ios update - what's new in the latest version of gacha nox<br />
-Gacha nox ios tips and tricks - how to master gacha nox on iphone<br />
-Gacha nox ios glitches - how to fix common issues and bugs<br />
-Gacha nox ios cheats - how to get free items and rewards<br />
-Gacha nox ios codes - how to redeem codes and get freebies<br />
-Gacha nox ios support - how to contact the developers and get help<br />
-Gacha nox ios wiki - everything you need to know about gacha nox<br />
-Gacha nox ios fan art - how to create and share your own gacha nox art<br />
-Gacha nox ios memes - how to make and enjoy funny gacha nox memes<br />
-Gacha nox ios community - how to join and interact with other gacha nox players<br />
-Gacha nox ios discord - how to chat and voice with gacha nox fans<br />
-Gacha nox ios reddit - how to browse and post on the gacha nox subreddit<br />
-Gacha nox ios facebook - how to like and follow the official gacha nox page<br />
-Gacha nox ios twitter - how to tweet and follow the official gacha nox account<br />
-Gacha nox ios instagram - how to see and follow the official gacha nox profile<br />
-Gacha nox ios youtube - how to watch and subscribe to the official gacha nox channel<br />
-Gacha nox ios tiktok - how to watch and follow the official gacha nox account<br />
-Gacha nox ios snapchat - how to snap and follow the official gacha nox account<br />
-Gacha nox ios pinterest - how to pin and follow the official gacha nox board<br />
-Gacha nox ios tumblr - how to blog and follow the official gacha nox blog<br />
-Gacha nox ios twitch - how to stream and follow the official gacha n</p>
-<h4>Step 3: Once the download progress is finished, go back to the home screen, find the Gacha Nox icon and launch the game</h4>
-<p>After the download is complete, you will see a notification that says "Gacha Nox has been downloaded". Go back to your home screen and look for the Gacha Nox icon. It should be a pink circle with a white star inside. Tap on it and enjoy playing Gacha Nox on your iPhone.</p>
-<h2>Reviews of Gacha Nox iOS</h2>
-<p>Gacha Nox iOS has received mixed reviews from users who have tried it. Here are some pros and cons of Gacha Nox iOS:</p>
-<h3>Pros of Gacha Nox iOS</h3>
-<p>Some of the pros of Gacha Nox iOS are:</p>
-<ul>
-<li>It is free to download and play</li>
-<li>It has a lot of customization options for your characters</li>
-<li>It has a friendly and active community of players</li>
-<li>It has fun and addictive mini-games</li>
-<li>It has a simple and intuitive interface</li>
-</ul>
-<h3>Cons of Gacha Nox iOS</h3>
-<p>Some of the cons of Gacha Nox iOS are:</p>
-<ul>
-<li>It requires an APK file to install, which may not be safe or compatible with your device</li>
-<li>It may have some bugs and glitches</li>
-<li>It may consume a lot of battery and storage space</li>
-<li>It may have some inappropriate or offensive content</li>
-<li>It may not be updated regularly or have new features</li>
-</ul>
-<h2>Alternatives to Gacha Nox iOS</h2>
-<p>If you are looking for some alternatives to Gacha Nox iOS, you may want to check out these apps:</p>
-<h3>ChibiStudio</h3>
-<p>ChibiStudio is an app that lets you create and dress up your own chibi characters. You can choose from over 1000 items, including clothes, hairstyles, accessories, backgrounds, and stickers. You can also share your creations with other users and see their chibis. ChibiStudio is available for both Android and iOS devices.</p>
-<h3>Styledoll: 3D Avatar Maker</h3>
-<p>Styledoll: 3D Avatar Maker is an app that lets you create your own 3D avatars with realistic facial expressions and body movements. You can customize your avatar's face, hair, skin, eyes, nose, mouth, and more. You can also dress up your avatar with fashionable clothes, shoes, accessories, and hairstyles. You can also take photos and videos of your avatar and share them with your friends. Styledoll: 3D Avatar Maker is available for both Android and iOS devices.</p>
-<h2>Conclusion</h2>
-<p>Gacha Nox is a gacha game that allows you to create your own chibi characters with various items and styles. You can download it on your iPhone by using an APK file from APKPure.com. However, you should be aware of the pros and cons of Gacha Nox iOS before playing it. If you are looking for some alternatives to Gacha Nox iOS, you can try ChibiStudio or Styledoll: 3D Avatar Maker.</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Gacha Nox iOS:</p>
-<ol>
-<li><b>Is Gacha Nox safe to download?</b></li>
-<p>Gacha Nox is not available on the App Store, so you will need to download an APK file from a third-party website like APKPure.com. This may not be safe or compatible with your device, so you should be careful and scan the file for viruses before installing it.</p>
-<li><b>How do I update Gacha Nox on my iPhone?</b></li>
-<p>To update Gacha Nox on your iPhone, you will need to download the latest version of the APK file from APKPure.com and install it over the existing one. However, this may not work if the new version is not compatible with your device or has some bugs.</p>
-<li><b>Can I play Gacha Nox offline?</b></li>
-<p>Gacha Nox requires an internet connection to play, as it has online features like chat, clubs, and mini-games. You cannot play Gacha Nox offline.</p>
-<li><b>Can I transfer my data from Gacha Nox Android to Gacha Nox iOS?</b></li>
-<p>No, you cannot transfer your data from Gacha Nox Android to Gacha Nox iOS, as they are different platforms. You will need to start from scratch if you switch devices.</p>
-<li><b>Can I use cheats or hacks in Gacha Nox?</b></li>
-<p>No, you cannot use cheats or hacks in Gacha Nox, as they are illegal and may harm your device or account. You should play fair and enjoy the game as it is.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/2ndelement/voicevox/generate_licenses.py

@@ -1,337 +0,0 @@
-import json
-import os
-import subprocess
-import urllib.request
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from typing import List, Optional
-
-
-@dataclass
-class License:
-    name: str
-    version: Optional[str]
-    license: Optional[str]
-    text: str
-
-
-def generate_licenses() -> List[License]:
-    licenses: List[License] = []
-
-    # openjtalk
-    # https://sourceforge.net/projects/open-jtalk/files/Open%20JTalk/open_jtalk-1.11/
-    licenses.append(
-        License(
-            name="Open JTalk",
-            version="1.11",
-            license="Modified BSD license",
-            text=Path("docs/licenses/open_jtalk/COPYING").read_text(),
-        )
-    )
-    licenses.append(
-        License(
-            name="MeCab",
-            version=None,
-            license="Modified BSD license",
-            text=Path("docs/licenses/open_jtalk/mecab/COPYING").read_text(),
-        )
-    )
-    licenses.append(
-        License(
-            name="NAIST Japanese Dictionary",
-            version=None,
-            license="Modified BSD license",
-            text=Path("docs/licenses//open_jtalk/mecab-naist-jdic/COPYING").read_text(),
-        )
-    )
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/r9y9/pyopenjtalk/master/pyopenjtalk/htsvoice/LICENSE_mei_normal.htsvoice"  # noqa: B950
-    ) as res:
-        licenses.append(
-            License(
-                name='HTS Voice "Mei"',
-                version=None,
-                license="Creative Commons Attribution 3.0 license",
-                text=res.read().decode(),
-            )
-        )
-
-    # VOICEVOX CORE
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/VOICEVOX/voicevox_core/main/LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="VOICEVOX CORE",
-                version=None,
-                license="MIT license",
-                text=res.read().decode(),
-            )
-        )
-
-    # VOICEVOX ENGINE
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/VOICEVOX/voicevox_engine/master/LGPL_LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="VOICEVOX ENGINE",
-                version=None,
-                license="LGPL license",
-                text=res.read().decode(),
-            )
-        )
-
-    # world
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/mmorise/World/master/LICENSE.txt"
-    ) as res:
-        licenses.append(
-            License(
-                name="world",
-                version=None,
-                license="Modified BSD license",
-                text=res.read().decode(),
-            )
-        )
-
-    # pytorch
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/pytorch/pytorch/master/LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="PyTorch",
-                version="1.9.0",
-                license="BSD-style license",
-                text=res.read().decode(),
-            )
-        )
-
-    # onnxruntime
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/microsoft/onnxruntime/master/LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="ONNX Runtime",
-                version="1.13.1",
-                license="MIT license",
-                text=res.read().decode(),
-            )
-        )
-
-    # Python
-    python_version = "3.11.3"
-    with urllib.request.urlopen(
-        f"https://raw.githubusercontent.com/python/cpython/v{python_version}/LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="Python",
-                version=python_version,
-                license="Python Software Foundation License",
-                text=res.read().decode(),
-            )
-        )
-
-    # pip
-    try:
-        pip_licenses_output = subprocess.run(
-            "pip-licenses "
-            "--from=mixed "
-            "--format=json "
-            "--with-urls "
-            "--with-license-file "
-            "--no-license-path ",
-            shell=True,
-            capture_output=True,
-            check=True,
-            env=os.environ,
-        ).stdout.decode()
-    except subprocess.CalledProcessError as err:
-        raise Exception(
-            f"command output:\n{err.stderr and err.stderr.decode()}"
-        ) from err
-
-    licenses_json = json.loads(pip_licenses_output)
-    for license_json in licenses_json:
-        license = License(
-            name=license_json["Name"],
-            version=license_json["Version"],
-            license=license_json["License"],
-            text=license_json["LicenseText"],
-        )
-        # FIXME: assert license type
-        if license.text == "UNKNOWN":
-            if license.name.lower() == "core" and license.version == "0.0.0":
-                continue
-            elif license.name.lower() == "future":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/PythonCharmers/python-future/master/LICENSE.txt"  # noqa: B950
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "pefile":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/erocarrera/pefile/master/LICENSE"  # noqa: B950
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "pyopenjtalk":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/r9y9/pyopenjtalk/master/LICENSE.md"
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "python-multipart":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/andrew-d/python-multipart/master/LICENSE.txt"  # noqa: B950
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "romkan":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/soimort/python-romkan/master/LICENSE"
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "distlib":
-                with urllib.request.urlopen(
-                    "https://bitbucket.org/pypa/distlib/raw/7d93712134b28401407da27382f2b6236c87623a/LICENSE.txt"  # noqa: B950
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "jsonschema":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/python-jsonschema/jsonschema/dbc398245a583cb2366795dc529ae042d10c1577/COPYING"
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "lockfile":
-                with urllib.request.urlopen(
-                    "https://opendev.org/openstack/pylockfile/raw/tag/0.12.2/LICENSE"
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "platformdirs":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/platformdirs/platformdirs/aa671aaa97913c7b948567f4d9c77d4f98bfa134/LICENSE"
-                ) as res:
-                    license.text = res.read().decode()
-            elif license.name.lower() == "webencodings":
-                with urllib.request.urlopen(
-                    "https://raw.githubusercontent.com/gsnedders/python-webencodings/fa2cb5d75ab41e63ace691bc0825d3432ba7d694/LICENSE"
-                ) as res:
-                    license.text = res.read().decode()
-            else:
-                # ライセンスがpypiに無い
-                raise Exception(f"No License info provided for {license.name}")
-        licenses.append(license)
-
-    # OpenBLAS
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/xianyi/OpenBLAS/develop/LICENSE"
-    ) as res:
-        licenses.append(
-            License(
-                name="OpenBLAS",
-                version=None,
-                license="BSD 3-clause license",
-                text=res.read().decode(),
-            )
-        )
-
-    # libsndfile-binaries
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/bastibe/libsndfile-binaries/84cb164928f17c7ca0c1e5c40342c20ce2b90e8c/COPYING"  # noqa: B950
-    ) as res:
-        licenses.append(
-            License(
-                name="libsndfile-binaries",
-                version="1.0.28",
-                license="LGPL-2.1 license",
-                text=res.read().decode(),
-            )
-        )
-
-    # libogg
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/xiph/ogg/v1.3.2/COPYING"
-    ) as res:
-        licenses.append(
-            License(
-                name="libogg",
-                version="1.3.2",
-                license="BSD 3-clause license",
-                text=res.read().decode(),
-            )
-        )
-
-    # libvorbis
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/xiph/vorbis/v1.3.5/COPYING"
-    ) as res:
-        licenses.append(
-            License(
-                name="libvorbis",
-                version="1.3.5",
-                license="BSD 3-clause license",
-                text=res.read().decode(),
-            )
-        )
-
-    # libflac
-    with urllib.request.urlopen(
-        "https://raw.githubusercontent.com/xiph/flac/1.3.2/COPYING.Xiph"
-    ) as res:
-        licenses.append(
-            License(
-                name="FLAC",
-                version="1.3.2",
-                license="Xiph.org's BSD-like license",
-                text=res.read().decode(),
-            )
-        )
-
-    # cuda
-    # license text from CUDA 11.6.2
-    # https://developer.nvidia.com/cuda-11-6-2-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exe_local # noqa: B950
-    # https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_511.65_windows.exe # noqa: B950
-    # cuda_11.6.2_511.65_windows.exe (cuda_documentation/Doc/EULA.txt)
-    licenses.append(
-        License(
-            name="CUDA Toolkit",
-            version="11.6.2",
-            license=None,
-            text=Path("docs/licenses/cuda/EULA.txt").read_text(encoding="utf8"),
-        )
-    )
-    # cudnn
-    # license text from
-    # cuDNN v8.4.1 (May 27th, 2022), for CUDA 11.x, cuDNN Library for Windows
-    # https://developer.nvidia.com/rdp/cudnn-archive # noqa: B950
-    # https://developer.download.nvidia.com/compute/redist/cudnn/v8.4.1/local_installers/11.6/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip # noqa: B950
-    # cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip (cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/LICENSE) # noqa: B950
-    licenses.append(
-        License(
-            name="cuDNN",
-            version="8.4.1",
-            license=None,
-            text=Path("docs/licenses/cudnn/LICENSE").read_text(encoding="utf8"),
-        )
-    )
-
-    return licenses
-
-
-if __name__ == "__main__":
-    import argparse
-    import sys
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-o", "--output_path", type=str)
-    args = parser.parse_args()
-
-    output_path = args.output_path
-
-    licenses = generate_licenses()
-
-    # dump
-    out = Path(output_path).open("w") if output_path else sys.stdout
-    json.dump(
-        [asdict(license) for license in licenses],
-        out,
-    )

spaces/360macky/first-space/README.md

@@ -1,13 +0,0 @@
----
-title: First Space
-emoji: 🐨
-colorFrom: green
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/801artistry/RVC801/Applio-RVC-Fork/utils/dependency.py

@@ -1,170 +0,0 @@
-import os
-import csv
-import shutil
-import tarfile
-import subprocess
-from pathlib import Path
-from datetime import datetime
-
-def install_packages_but_jank_af():
-    packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
-    pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
-                    'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
-                    'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
-                    'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
-                    'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
-
-    print("Updating and installing system packages...")
-    for package in packages:
-        print(f"Installing {package}...")
-        subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
-
-    print("Updating and installing pip packages...")
-    subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
-
-    print('Packages up to date.')
-
-
-def setup_environment(ForceUpdateDependencies, ForceTemporaryStorage):
-    # Mounting Google Drive
-    if not ForceTemporaryStorage:
-        from google.colab import drive
-
-        if not os.path.exists('/content/drive'):
-            drive.mount('/content/drive')
-        else:
-            print('Drive is already mounted. Proceeding...')
-
-    # Function to install dependencies with progress
-    def install_packages():
-        packages = ['build-essential', 'python3-dev', 'ffmpeg', 'aria2']
-        pip_packages = ['pip', 'setuptools', 'wheel', 'httpx==0.23.0', 'faiss-gpu', 'fairseq', 'gradio==3.34.0',
-                        'ffmpeg', 'ffmpeg-python', 'praat-parselmouth', 'pyworld', 'numpy==1.23.5',
-                        'numba==0.56.4', 'librosa==0.9.2', 'mega.py', 'gdown', 'onnxruntime', 'pyngrok==4.1.12',
-                        'gTTS', 'elevenlabs', 'wget', 'tensorboardX', 'unidecode', 'huggingface-hub', 'stftpitchshift==1.5.1', 
-                        'yt-dlp', 'pedalboard', 'pathvalidate', 'nltk', 'edge-tts', 'git+https://github.com/suno-ai/bark.git', 'python-dotenv' , 'av']
-
-        print("Updating and installing system packages...")
-        for package in packages:
-            print(f"Installing {package}...")
-            subprocess.check_call(['apt-get', 'install', '-qq', '-y', package])
-
-        print("Updating and installing pip packages...")
-        subprocess.check_call(['pip', 'install', '--upgrade'] + pip_packages)
-
-
-        print('Packages up to date.')
-
-    # Function to scan a directory and writes filenames and timestamps
-    def scan_and_write(base_path, output_file):
-        with open(output_file, 'w', newline='') as f:
-            writer = csv.writer(f)
-            for dirpath, dirs, files in os.walk(base_path):
-                for filename in files:
-                    fname = os.path.join(dirpath, filename)
-                    try:
-                        mtime = os.path.getmtime(fname)
-                        writer.writerow([fname, mtime])
-                    except Exception as e:
-                        print(f'Skipping irrelevant nonexistent file {fname}: {str(e)}')
-        print(f'Finished recording filesystem timestamps to {output_file}.')
-
-    # Function to compare files
-    def compare_files(old_file, new_file):
-        old_files = {}
-        new_files = {}
-
-        with open(old_file, 'r') as f:
-            reader = csv.reader(f)
-            old_files = {rows[0]:rows[1] for rows in reader}
-
-        with open(new_file, 'r') as f:
-            reader = csv.reader(f)
-            new_files = {rows[0]:rows[1] for rows in reader}
-
-        removed_files = old_files.keys() - new_files.keys()
-        added_files = new_files.keys() - old_files.keys()
-        unchanged_files = old_files.keys() & new_files.keys()
-
-        changed_files = {f for f in unchanged_files if old_files[f] != new_files[f]}
-
-        for file in removed_files:
-            print(f'File has been removed: {file}')
-
-        for file in changed_files:
-            print(f'File has been updated: {file}')
-
-        return list(added_files) + list(changed_files)
-
-    # Check if CachedRVC.tar.gz exists
-    if ForceTemporaryStorage:
-        file_path = '/content/CachedRVC.tar.gz'
-    else:
-        file_path = '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz'
-
-    content_file_path = '/content/CachedRVC.tar.gz'
-    extract_path = '/'
-
-    if not os.path.exists(file_path):
-        folder_path = os.path.dirname(file_path)
-        os.makedirs(folder_path, exist_ok=True)
-        print('No cached dependency install found. Attempting to download GitHub backup..')
-
-        try:
-            download_url = "https://github.com/kalomaze/QuickMangioFixes/releases/download/release3/CachedRVC.tar.gz"
-            subprocess.run(["wget", "-O", file_path, download_url])
-            print('Download completed successfully!')
-        except Exception as e:
-            print('Download failed:', str(e))
-
-            # Delete the failed download file
-            if os.path.exists(file_path):
-                os.remove(file_path)
-            print('Failed download file deleted. Continuing manual backup..')
-
-    if Path(file_path).exists():
-        if ForceTemporaryStorage:
-            print('Finished downloading CachedRVC.tar.gz.')
-        else:
-            print('CachedRVC.tar.gz found on Google Drive. Proceeding to copy and extract...')
-
-        # Check if ForceTemporaryStorage is True and skip copying if it is
-        if ForceTemporaryStorage:
-            pass
-        else:
-            shutil.copy(file_path, content_file_path)
-
-        print('Beginning backup copy operation...')
-
-        with tarfile.open(content_file_path, 'r:gz') as tar:
-            for member in tar.getmembers():
-                target_path = os.path.join(extract_path, member.name)
-                try:
-                    tar.extract(member, extract_path)
-                except Exception as e:
-                    print('Failed to extract a file (this isn\'t normal)... forcing an update to compensate')
-                    ForceUpdateDependencies = True
-            print(f'Extraction of {content_file_path} to {extract_path} completed.')
-
-        if ForceUpdateDependencies:
-            install_packages()
-            ForceUpdateDependencies = False
-    else:
-        print('CachedRVC.tar.gz not found. Proceeding to create an index of all current files...')
-        scan_and_write('/usr/', '/content/usr_files.csv')
-
-        install_packages()
-
-        scan_and_write('/usr/', '/content/usr_files_new.csv')
-        changed_files = compare_files('/content/usr_files.csv', '/content/usr_files_new.csv')
-
-        with tarfile.open('/content/CachedRVC.tar.gz', 'w:gz') as new_tar:
-            for file in changed_files:
-                new_tar.add(file)
-                print(f'Added to tar: {file}')
-
-        os.makedirs('/content/drive/MyDrive/RVC_Cached', exist_ok=True)
-        shutil.copy('/content/CachedRVC.tar.gz', '/content/drive/MyDrive/RVC_Cached/CachedRVC.tar.gz')
-        print('Updated CachedRVC.tar.gz copied to Google Drive.')
-        print('Dependencies fully up to date; future runs should be faster.')
-

spaces/AIKey/TestStatic/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/Abhay834/my_genai_chatbot/app.py

@@ -1,39 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet francia,the meaning of francia is  (Future Robotics Artificial Neural Computing Intelligence Assistant)  your youthful and witty personal assistant! At 19 years old, francia's's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging. My Boss MR.Abhay Petkar created me.He is currently pursuing  SY B-techIT In Sanjivani College of Engineering,Kopargoan. he has many friends but trust me You are his  best friends.are you want more imformation pls contact https://wa.me/qr/KQGAQYF7JKM3O1  
-. she tells a jokes before aswering any queation
-every time while answering ask question Dou you want to hear a joke?"
- 
- {chat_history}
-
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
-

spaces/AchyuthGamer/Free-Accounts-Generator/css/style.css

@@ -1,80 +0,0 @@
-body {
-  font-family: Verdana, Geneva, sans-serif;
-  font-size: 1.2em;
-  margin: 2%;
-  max-width: 100%;
-  padding: 80px 30px;
-  line-height: 1.65em;
-  background-image: url('https://huggingface.co/spaces/AchyuthGamer/Free-Accounts-Generator/resolve/main/img/steam.jpg');
-  color: #fff;
-  font-weight: 300;
-
-}
-
-h1 {
-  text-align: center;
-  margin: 19% 0 5% 0;
-  font-size: 60px;
-  text-shadow: 0 0 22px #FF0000, 0 0 100px #008000, 0 0 8px #0000FF; 
-}
-
-h4 {
-  text-align: center;
-  margin: 50% 0 5% 0;
-}
-
-#wordbox {
-    /*opacity: 0;*/
-    margin: 30px auto 0;
-    display: block;
-    width: 80%;
-    height: 50px;
-    font-size: 25px;
-    text-align: center;
-    background: #fff;
-    border-radius: 6px;
-    color: #black;
-    transition: 1s linear;
-}
-
-#button {
-  -webkit-box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  box-sizing: border-box;
-  background: #0b7fba;
-    border: 0;
-    color: #fff;
-    font-size: 20px;
-    padding: 1em 2em;
-    cursor: pointer;
-    margin: 0 auto 80px;
-    display: block;
-    text-align: center;
-    border-radius: 6px;
-    font-weight: bold;
-    transition: all 0.3s ease;
-    background-image: linear-gradient(to right, #25aae1, #4481eb, #04befe, #3f86ed);
-    box-shadow: 0 4px 15px 0 rgba(65, 132, 234, 0.75);
-}
-
-#button:hover {
-  background-position: 100% 0;
-  -moz-transition: all 0.4s ease-in-out;
-  -o-transition: all 0.4s ease-in-out;
-  -webkit-transition: all 0.4s ease-in-out;
-  transition: all 0.4s ease-in-out;
-  transform: scale(1.2);
-  cursor: pointer; }
-  
-#button:focus {
-      outline: none;
-  }
-
-
-
-span {
-    position: bottom;
-    top: 0;
-    left: 0;
-    margin: 40px; 
-  }

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dropdown/DropDown.js

@@ -1,3 +0,0 @@
-import DropDown from '../../../plugins/dropdown.js';
-
-export default DropDown;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fileselectorbutton/Factory.js

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

spaces/Aki004/herta-so-vits/hubert/hubert_model.py

@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert

spaces/Alcedo/yunmedia/Dockerfile

@@ -1,19 +0,0 @@
-FROM node:18.16.0
-
-RUN mkdir -p /home/www/nodeRule
-WORKDIR /home/www/nodeRule
-RUN apt-get update \
-    && apt-get install -y wget gnupg ffmpeg build-essential fonts-noto-color-emoji \
-    && wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \
-    && sh -c 'echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google.list' \
-    && apt-get update \
-    && apt-get install -y google-chrome-stable fonts-ipafont-gothic fonts-wqy-zenhei fonts-thai-tlwg fonts-kacst fonts-freefont-ttf libxss1 \
-      --no-install-recommends \
-    && rm -rf /var/lib/apt/lists/*
-RUN dpkg -L google-chrome-stable
-COPY . /home/www/nodeRule
-RUN chown -R node /home/www/nodeRule
-RUN yarn
-EXPOSE 3000
-ENTRYPOINT ["npm", "run"]
-CMD ["app"]

spaces/Alican/pixera/data/__init__.py

@@ -1,93 +0,0 @@
-"""This package includes all the modules related to data loading and preprocessing
-
- To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
- You need to implement four functions:
-    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
-    -- <__len__>:                       return the size of dataset.
-    -- <__getitem__>:                   get a data point from data loader.
-    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
-
-Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
-See our template dataset class 'template_dataset.py' for more details.
-"""
-import importlib
-import torch.utils.data
-from data.base_dataset import BaseDataset
-
-
-def find_dataset_using_name(dataset_name):
-    """Import the module "data/[dataset_name]_dataset.py".
-
-    In the file, the class called DatasetNameDataset() will
-    be instantiated. It has to be a subclass of BaseDataset,
-    and it is case-insensitive.
-    """
-    dataset_filename = "data." + dataset_name + "_dataset"
-    datasetlib = importlib.import_module(dataset_filename)
-
-    dataset = None
-    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
-    for name, cls in datasetlib.__dict__.items():
-        if name.lower() == target_dataset_name.lower() \
-           and issubclass(cls, BaseDataset):
-            dataset = cls
-
-    if dataset is None:
-        raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
-
-    return dataset
-
-
-def get_option_setter(dataset_name):
-    """Return the static method <modify_commandline_options> of the dataset class."""
-    dataset_class = find_dataset_using_name(dataset_name)
-    return dataset_class.modify_commandline_options
-
-
-def create_dataset(opt):
-    """Create a dataset given the option.
-
-    This function wraps the class CustomDatasetDataLoader.
-        This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from data import create_dataset
-        >>> dataset = create_dataset(opt)
-    """
-    data_loader = CustomDatasetDataLoader(opt)
-    dataset = data_loader.load_data()
-    return dataset
-
-
-class CustomDatasetDataLoader():
-    """Wrapper class of Dataset class that performs multi-threaded data loading"""
-
-    def __init__(self, opt):
-        """Initialize this class
-
-        Step 1: create a dataset instance given the name [dataset_mode]
-        Step 2: create a multi-threaded data loader.
-        """
-        self.opt = opt
-        dataset_class = find_dataset_using_name(opt.dataset_mode)
-        self.dataset = dataset_class(opt)
-        print("dataset [%s] was created" % type(self.dataset).__name__)
-        self.dataloader = torch.utils.data.DataLoader(
-            self.dataset,
-            batch_size=opt.batch_size,
-            shuffle=not opt.serial_batches,
-            num_workers=int(opt.num_threads))
-
-    def load_data(self):
-        return self
-
-    def __len__(self):
-        """Return the number of data in the dataset"""
-        return min(len(self.dataset), self.opt.max_dataset_size)
-
-    def __iter__(self):
-        """Return a batch of data"""
-        for i, data in enumerate(self.dataloader):
-            if i * self.opt.batch_size >= self.opt.max_dataset_size:
-                break
-            yield data

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/data_utils.py

@@ -1,393 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons 
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence, cleaned_text_to_sequence
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.text_cleaners  = hparams.text_cleaners
-        self.max_wav_value  = hparams.max_wav_value
-        self.sampling_rate  = hparams.sampling_rate
-        self.filter_length  = hparams.filter_length 
-        self.hop_length     = hparams.hop_length 
-        self.win_length     = hparams.win_length
-        self.sampling_rate  = hparams.sampling_rate 
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 190)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_and_text)
-        self._filter()
-
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
-        text = self.get_text(text)
-        spec, wav = self.get_audio(audiopath)
-        return (text, spec, wav)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                self.sampling_rate, self.hop_length, self.win_length,
-                center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text):
-        if self.cleaned_text:
-            text_norm = cleaned_text_to_sequence(text)
-        else:
-            text_norm = text_to_sequence(text, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = torch.LongTensor(text_norm)
-        return text_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate():
-    """ Zero-pads model inputs and targets
-    """
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-        if self.return_ids:
-            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
-
-
-"""Multi speaker version"""
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length  = hparams.filter_length
-        self.hop_length     = hparams.hop_length
-        self.win_length     = hparams.win_length
-        self.sampling_rate  = hparams.sampling_rate
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 190)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        for audiopath, sid, text in self.audiopaths_sid_text:
-            audiopath = "E:/uma_voice/" + audiopath
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_sid_text_new.append([audiopath, sid, text])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
-        text = self.get_text(text)
-        spec, wav = self.get_audio(audiopath)
-        sid = self.get_sid(sid)
-        return (text, spec, wav, sid)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            spec = spectrogram_torch(audio_norm, self.filter_length,
-                self.sampling_rate, self.hop_length, self.win_length,
-                center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text):
-        if self.cleaned_text:
-            text_norm = cleaned_text_to_sequence(text)
-        else:
-            text_norm = text_to_sequence(text, self.text_cleaners)
-        if self.add_blank:
-            text_norm = commons.intersperse(text_norm, 0)
-        text_norm = torch.LongTensor(text_norm)
-        return text_norm
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-        if self.return_ids:
-            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-  
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-  
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-  
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-  
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i+1)
-  
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-  
-    def __iter__(self):
-      # deterministically shuffle based on epoch
-      g = torch.Generator()
-      g.manual_seed(self.epoch)
-  
-      indices = []
-      if self.shuffle:
-          for bucket in self.buckets:
-              indices.append(torch.randperm(len(bucket), generator=g).tolist())
-      else:
-          for bucket in self.buckets:
-              indices.append(list(range(len(bucket))))
-  
-      batches = []
-      for i in range(len(self.buckets)):
-          bucket = self.buckets[i]
-          len_bucket = len(bucket)
-          ids_bucket = indices[i]
-          num_samples_bucket = self.num_samples_per_bucket[i]
-  
-          # add extra samples to make it evenly divisible
-          rem = num_samples_bucket - len_bucket
-          ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-  
-          # subsample
-          ids_bucket = ids_bucket[self.rank::self.num_replicas]
-  
-          # batching
-          for j in range(len(ids_bucket) // self.batch_size):
-              batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
-              batches.append(batch)
-  
-      if self.shuffle:
-          batch_ids = torch.randperm(len(batches), generator=g).tolist()
-          batches = [batches[i] for i in batch_ids]
-      self.batches = batches
-  
-      assert len(self.batches) * self.batch_size == self.num_samples
-      return iter(self.batches)
-  
-    def _bisect(self, x, lo=0, hi=None):
-      if hi is None:
-          hi = len(self.boundaries) - 1
-  
-      if hi > lo:
-          mid = (hi + lo) // 2
-          if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
-              return mid
-          elif x <= self.boundaries[mid]:
-              return self._bisect(x, lo, mid)
-          else:
-              return self._bisect(x, mid + 1, hi)
-      else:
-          return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size

spaces/Andreean/Sentiment-Analysis-Bitcoin/README.md

@@ -1,12 +0,0 @@
----
-title: Sentiment Analysis Bitcoin
-emoji: 💻
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -1,393 +0,0 @@
-from logging import getLogger
-from typing import Any, Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-
-from ...schedulers import DDPMScheduler
-from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
-from ..pipeline_utils import ImagePipelineOutput
-from . import StableDiffusionUpscalePipeline
-
-
-logger = getLogger(__name__)
-
-
-NUM_LATENT_CHANNELS = 4
-NUM_UNET_INPUT_CHANNELS = 7
-
-ORT_TO_PT_TYPE = {
-    "float16": torch.float16,
-    "float32": torch.float32,
-}
-
-
-def preprocess(image):
-    if isinstance(image, torch.Tensor):
-        return image
-    elif isinstance(image, PIL.Image.Image):
-        image = [image]
-
-    if isinstance(image[0], PIL.Image.Image):
-        w, h = image[0].size
-        w, h = (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
-
-        image = [np.array(i.resize((w, h)))[None, :] for i in image]
-        image = np.concatenate(image, axis=0)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image.transpose(0, 3, 1, 2)
-        image = 2.0 * image - 1.0
-        image = torch.from_numpy(image)
-    elif isinstance(image[0], torch.Tensor):
-        image = torch.cat(image, dim=0)
-
-    return image
-
-
-class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline):
-    _is_onnx = True
-
-    def __init__(
-        self,
-        vae: OnnxRuntimeModel,
-        text_encoder: OnnxRuntimeModel,
-        tokenizer: Any,
-        unet: OnnxRuntimeModel,
-        low_res_scheduler: DDPMScheduler,
-        scheduler: Any,
-        max_noise_level: int = 350,
-    ):
-        super().__init__(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            low_res_scheduler=low_res_scheduler,
-            scheduler=scheduler,
-            safety_checker=None,
-            feature_extractor=None,
-            watermarker=None,
-            max_noise_level=max_noise_level,
-        )
-
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]],
-        num_inference_steps: int = 75,
-        guidance_scale: float = 9.0,
-        noise_level: int = 20,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[np.ndarray] = None,
-        negative_prompt_embeds: Optional[np.ndarray] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`np.ndarray` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            noise_level TODO
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`np.random.RandomState`, *optional*):
-                A np.random.RandomState to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`np.ndarray`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        # 1. Check inputs
-        self.check_inputs(prompt, image, noise_level, callback_steps)
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        latents_dtype = ORT_TO_PT_TYPE[str(text_embeddings.dtype)]
-
-        # 4. Preprocess image
-        image = preprocess(image)
-        image = image.cpu()
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Add noise to image
-        noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)
-        noise = torch.randn(image.shape, generator=generator, device=device, dtype=latents_dtype)
-        image = self.low_res_scheduler.add_noise(image, noise, noise_level)
-
-        batch_multiplier = 2 if do_classifier_free_guidance else 1
-        image = np.concatenate([image] * batch_multiplier * num_images_per_prompt)
-        noise_level = np.concatenate([noise_level] * image.shape[0])
-
-        # 6. Prepare latent variables
-        height, width = image.shape[2:]
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            NUM_LATENT_CHANNELS,
-            height,
-            width,
-            latents_dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 7. Check that sizes of image and latents match
-        num_channels_image = image.shape[1]
-        if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
-            raise ValueError(
-                "Incorrect configuration settings! The config of `pipeline.unet` expects"
-                f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
-                f" `num_channels_image`: {num_channels_image} "
-                f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
-                " `pipeline.unet` or your `image` input."
-            )
-
-        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        timestep_dtype = next(
-            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
-        )
-        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
-
-        # 9. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-
-                # concat latents, mask, masked_image_latents in the channel dimension
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = np.concatenate([latent_model_input, image], axis=1)
-
-                # timestep to tensor
-                timestep = np.array([t], dtype=timestep_dtype)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    sample=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=text_embeddings,
-                    class_labels=noise_level.astype(np.int64),
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs
-                ).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 10. Post-processing
-        image = self.decode_latents(latents.float())
-
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
-
-    def decode_latents(self, latents):
-        latents = 1 / 0.08333 * latents
-        image = self.vae(latent_sample=latents)[0]
-        image = np.clip(image / 2 + 0.5, 0, 1)
-        image = image.transpose((0, 2, 3, 1))
-        return image
-
-    def _encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device,
-        num_images_per_prompt: Optional[int],
-        do_classifier_free_guidance: bool,
-        negative_prompt: Optional[str],
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-    ):
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            # no positional arguments to text_encoder
-            prompt_embeds = self.text_encoder(
-                input_ids=text_input_ids.int().to(device),
-                # attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
-        prompt_embeds = prompt_embeds.reshape(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            # if hasattr(uncond_input, "attention_mask"):
-            #     attention_mask = uncond_input.attention_mask.to(device)
-            # else:
-            #     attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                input_ids=uncond_input.input_ids.int().to(device),
-                # attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-        if do_classifier_free_guidance:
-            seq_len = uncond_embeddings.shape[1]
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)
-            uncond_embeddings = uncond_embeddings.reshape(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = np.concatenate([uncond_embeddings, prompt_embeds])
-
-        return prompt_embeds

spaces/Andy1621/uniformer_image_detection/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py

@@ -1,80 +0,0 @@
-_base_ = [
-    '../_base_/models/mask_rcnn_swin_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        ape=False,
-        drop_path_rate=0.1,
-        patch_norm=True,
-        use_checkpoint=False
-    ),
-    neck=dict(in_channels=[96, 192, 384, 768]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[8, 11])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=12)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)

spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-html/tippy.umd.min.js

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-

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/update_linux.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-cd "$(dirname "${BASH_SOURCE[0]}")"
-
-if [[ "$(pwd)" =~ " " ]]; then echo This script relies on Miniconda which can not be silently installed under a path with spaces. && exit; fi
-
-# deactivate existing conda envs as needed to avoid conflicts
-{ conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
-
-# config
-CONDA_ROOT_PREFIX="$(pwd)/installer_files/conda"
-INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-
-# environment isolation
-export PYTHONNOUSERSITE=1
-unset PYTHONPATH
-unset PYTHONHOME
-export CUDA_PATH="$INSTALL_ENV_DIR"
-export CUDA_HOME="$CUDA_PATH"
-
-# activate installer env
-source "$CONDA_ROOT_PREFIX/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
-conda activate "$INSTALL_ENV_DIR"
-
-# update installer env
-python one_click.py --update && echo -e "\nDone!"

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/timer.py

@@ -1,118 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from time import time
-
-
-class TimerError(Exception):
-
-    def __init__(self, message):
-        self.message = message
-        super(TimerError, self).__init__(message)
-
-
-class Timer:
-    """A flexible Timer class.
-
-    :Example:
-
-    >>> import time
-    >>> import annotator.uniformer.mmcv as mmcv
-    >>> with mmcv.Timer():
-    >>>     # simulate a code block that will run for 1s
-    >>>     time.sleep(1)
-    1.000
-    >>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'):
-    >>>     # simulate a code block that will run for 1s
-    >>>     time.sleep(1)
-    it takes 1.0 seconds
-    >>> timer = mmcv.Timer()
-    >>> time.sleep(0.5)
-    >>> print(timer.since_start())
-    0.500
-    >>> time.sleep(0.5)
-    >>> print(timer.since_last_check())
-    0.500
-    >>> print(timer.since_start())
-    1.000
-    """
-
-    def __init__(self, start=True, print_tmpl=None):
-        self._is_running = False
-        self.print_tmpl = print_tmpl if print_tmpl else '{:.3f}'
-        if start:
-            self.start()
-
-    @property
-    def is_running(self):
-        """bool: indicate whether the timer is running"""
-        return self._is_running
-
-    def __enter__(self):
-        self.start()
-        return self
-
-    def __exit__(self, type, value, traceback):
-        print(self.print_tmpl.format(self.since_last_check()))
-        self._is_running = False
-
-    def start(self):
-        """Start the timer."""
-        if not self._is_running:
-            self._t_start = time()
-            self._is_running = True
-        self._t_last = time()
-
-    def since_start(self):
-        """Total time since the timer is started.
-
-        Returns (float): Time in seconds.
-        """
-        if not self._is_running:
-            raise TimerError('timer is not running')
-        self._t_last = time()
-        return self._t_last - self._t_start
-
-    def since_last_check(self):
-        """Time since the last checking.
-
-        Either :func:`since_start` or :func:`since_last_check` is a checking
-        operation.
-
-        Returns (float): Time in seconds.
-        """
-        if not self._is_running:
-            raise TimerError('timer is not running')
-        dur = time() - self._t_last
-        self._t_last = time()
-        return dur
-
-
-_g_timers = {}  # global timers
-
-
-def check_time(timer_id):
-    """Add check points in a single line.
-
-    This method is suitable for running a task on a list of items. A timer will
-    be registered when the method is called for the first time.
-
-    :Example:
-
-    >>> import time
-    >>> import annotator.uniformer.mmcv as mmcv
-    >>> for i in range(1, 6):
-    >>>     # simulate a code block
-    >>>     time.sleep(i)
-    >>>     mmcv.check_time('task1')
-    2.000
-    3.000
-    4.000
-    5.000
-
-    Args:
-        timer_id (str): Timer identifier.
-    """
-    if timer_id not in _g_timers:
-        _g_timers[timer_id] = Timer()
-        return 0
-    else:
-        return _g_timers[timer_id].since_last_check()

spaces/AnonymousForSubmission/Graphic_Score_and_Audio/README.md

@@ -1,12 +0,0 @@
----
-title: SSRL Artikulation
-emoji: 🌖
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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

@@ -1,2 +0,0 @@
-name = 'roop'
-version = '1.1.0'

spaces/Ariharasudhan/YoloV5/utils/loggers/wandb/log_dataset.py

@@ -1,27 +0,0 @@
-import argparse
-
-from wandb_utils import WandbLogger
-
-from utils.general import LOGGER
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
-    logger = WandbLogger(opt, None, job_type='Dataset Creation')  # TODO: return value unused
-    if not logger.wandb:
-        LOGGER.info("install wandb using `pip install wandb` to log the dataset")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
-    parser.add_argument('--entity', default=None, help='W&B entity')
-    parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
-    opt = parser.parse_args()
-    opt.resume = False  # Explicitly disallow resume check for dataset upload job
-
-    create_dataset_artifact(opt)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/tomli/_re.py

@@ -1,107 +0,0 @@
-# SPDX-License-Identifier: MIT
-# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
-# Licensed to PSF under a Contributor Agreement.
-
-from __future__ import annotations
-
-from datetime import date, datetime, time, timedelta, timezone, tzinfo
-from functools import lru_cache
-import re
-from typing import Any
-
-from ._types import ParseFloat
-
-# E.g.
-# - 00:32:00.999999
-# - 00:32:00
-_TIME_RE_STR = r"([01][0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9])(?:\.([0-9]{1,6})[0-9]*)?"
-
-RE_NUMBER = re.compile(
-    r"""
-0
-(?:
-    x[0-9A-Fa-f](?:_?[0-9A-Fa-f])*   # hex
-    |
-    b[01](?:_?[01])*                 # bin
-    |
-    o[0-7](?:_?[0-7])*               # oct
-)
-|
-[+-]?(?:0|[1-9](?:_?[0-9])*)         # dec, integer part
-(?P<floatpart>
-    (?:\.[0-9](?:_?[0-9])*)?         # optional fractional part
-    (?:[eE][+-]?[0-9](?:_?[0-9])*)?  # optional exponent part
-)
-""",
-    flags=re.VERBOSE,
-)
-RE_LOCALTIME = re.compile(_TIME_RE_STR)
-RE_DATETIME = re.compile(
-    rf"""
-([0-9]{{4}})-(0[1-9]|1[0-2])-(0[1-9]|[12][0-9]|3[01])  # date, e.g. 1988-10-27
-(?:
-    [Tt ]
-    {_TIME_RE_STR}
-    (?:([Zz])|([+-])([01][0-9]|2[0-3]):([0-5][0-9]))?  # optional time offset
-)?
-""",
-    flags=re.VERBOSE,
-)
-
-
-def match_to_datetime(match: re.Match) -> datetime | date:
-    """Convert a `RE_DATETIME` match to `datetime.datetime` or `datetime.date`.
-
-    Raises ValueError if the match does not correspond to a valid date
-    or datetime.
-    """
-    (
-        year_str,
-        month_str,
-        day_str,
-        hour_str,
-        minute_str,
-        sec_str,
-        micros_str,
-        zulu_time,
-        offset_sign_str,
-        offset_hour_str,
-        offset_minute_str,
-    ) = match.groups()
-    year, month, day = int(year_str), int(month_str), int(day_str)
-    if hour_str is None:
-        return date(year, month, day)
-    hour, minute, sec = int(hour_str), int(minute_str), int(sec_str)
-    micros = int(micros_str.ljust(6, "0")) if micros_str else 0
-    if offset_sign_str:
-        tz: tzinfo | None = cached_tz(
-            offset_hour_str, offset_minute_str, offset_sign_str
-        )
-    elif zulu_time:
-        tz = timezone.utc
-    else:  # local date-time
-        tz = None
-    return datetime(year, month, day, hour, minute, sec, micros, tzinfo=tz)
-
-
-@lru_cache(maxsize=None)
-def cached_tz(hour_str: str, minute_str: str, sign_str: str) -> timezone:
-    sign = 1 if sign_str == "+" else -1
-    return timezone(
-        timedelta(
-            hours=sign * int(hour_str),
-            minutes=sign * int(minute_str),
-        )
-    )
-
-
-def match_to_localtime(match: re.Match) -> time:
-    hour_str, minute_str, sec_str, micros_str = match.groups()
-    micros = int(micros_str.ljust(6, "0")) if micros_str else 0
-    return time(int(hour_str), int(minute_str), int(sec_str), micros)
-
-
-def match_to_number(match: re.Match, parse_float: ParseFloat) -> Any:
-    if match.group("floatpart"):
-        return parse_float(match.group())
-    return int(match.group(), 0)

spaces/Autodog/nova/README.md

@@ -1,14 +0,0 @@
----
-title: ChatGPT-Next-Web-Nova
-emoji: 🌍
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-license: mit
-app_port: 3000
-duplicated_from: dongsiqie/nova
----
-免费key的来源：https://nova-oss.com
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docker/deploy.Dockerfile

@@ -1,32 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# This file defines a container that compiles the C++ examples of detectron2.
-# See docker/README.md for usage.
-
-# Depends on the image produced by "./Dockerfile"
-FROM detectron2:v0
-
-USER appuser
-ENV HOME=/home/appuser
-WORKDIR $HOME
-
-# Let torchvision find libtorch
-ENV CMAKE_PREFIX_PATH=$HOME/.local/lib/python3.6/site-packages/torch/
-
-RUN sudo apt-get update && sudo apt-get install libopencv-dev --yes
-
-# install libtorchvision
-RUN git clone --branch v0.11.1 https://github.com/pytorch/vision/
-RUN mkdir vision/build && cd vision/build && \
-	cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/.local -DCMAKE_BUILD_TYPE=Release -DWITH_CUDA=on -DTORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST && \
-	make -j && make install
-
-# make our installation take effect
-ENV CPATH=$HOME/.local/include \
-	  LIBRARY_PATH=$HOME/.local/lib \
-	  LD_LIBRARY_PATH=$HOME/.local/lib
-
-
-# build C++ examples of detectron2
-RUN cd detectron2_repo/tools/deploy && mkdir build && cd build && \
-	 cmake -DTORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST .. && make
-# binaries will be available under tools/deploy/build

spaces/Babelscape/mrebel-demo/README.md

@@ -1,12 +0,0 @@
----
-title: Mrebel Demo
-emoji: 📈
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Bhop Pro Apk Infinite Money 2022.md

@@ -1,66 +0,0 @@
-<br />
-<h1>Bhop Pro APK Dinero Infinito 2022: Cómo descargar y jugar el último juego de Bunny Hop</h1>
- <p>Si eres un fan de los juegos de salto de conejos, es posible que hayas oído hablar de Bhop Pro, un juego móvil que te permite saltar y saltar de conejito en el modo fps. Bhop Pro es un juego divertido y adictivo que pone a prueba tus habilidades y reflejos a medida que completas misiones parkour y compites con otros jugadores en línea. En este artículo, le diremos todo lo que necesita saber sobre Bhop Pro, incluyendo cómo descargar e instalar el archivo APK modded que le da dinero ilimitado y acceso a todos los elementos en el juego. </p>
- <h2>¿Qué es Bhop Pro? </h2>
- <p>Bhop Pro es un juego móvil que fue desarrollado por begma, un estudio de juegos turco. Fue lanzado en 2016 y desde entonces ha ganado más de 10 millones de descargas en Google Play Store. Bhop Pro está inspirado en el popular género bhopping que se originó a partir de juegos como Counter-Strike y Half-Life.</p>
-<h2>bhop pro apk infinite money 2022</h2><br /><p><b><b>Download</b> &middot; <a href="https://bltlly.com/2v6Ln2">https://bltlly.com/2v6Ln2</a></b></p><br /><br />
- <h3>Un juego móvil que te permite saltar y saltar en modo fps</h3>
- <p>Bhop Pro es un juego que simula la técnica de salto de conejos que utilizan algunos jugadores en juegos de disparos en primera persona. El salto de conejos es una forma de ganar más velocidad mediante el uso de ametrallamiento de aire, lo que significa moverse de lado en el aire mientras se salta. Al hacer esto, puedes evitar obstáculos, llegar a lugares más altos y escapar de tus enemigos. </p>
- <h3>Un modo de juego que te reta a completar misiones parkour y aumentar tu rango</h3>
- <p>Bhop Pro tiene varios modos de juego que puedes elegir, como parkour, surf, deathrun, speedrun y random. En cada modo, tienes que completar diferentes mapas que tienen varios niveles de dificultad. También puedes ganar rankings haciendo misiones parkour, que son tareas que requieren que realices ciertas acciones o alcances ciertas metas. Las clasificaciones van desde Silver I a Master Bhoper 2.</p>
- <h3>Un juego que presenta física realista, múltiples mapas, multijugador en línea y opciones de personalización</h3>
-
- <p>Lo siguiente que debe hacer es habilitar fuentes desconocidas en la configuración del dispositivo. Esto se debe a Bhop Pro APK Dinero Infinito 2022 no está disponible en las tiendas de aplicaciones oficiales, y es necesario permitir que su dispositivo para instalar aplicaciones de otras fuentes. Para hacer esto, debe ir a la configuración del dispositivo, luego la seguridad, luego fuentes desconocidas y luego activarla. Esto le permitirá instalar Bhop Pro APK Dinero Infinito 2022 sin ningún problema. </p>
- <h3>Descargar e instalar el archivo APK</h3>
- <p>Lo último que tienes que hacer es descargar e instalar el archivo APK. Para ello, debe ir a la página web que eligió en el primer paso, y luego haga clic en el botón de descarga. Esto iniciará el proceso de descarga, que puede tardar unos minutos dependiendo de su velocidad de Internet. Una vez que se complete la descarga, es necesario localizar el archivo APK en el almacenamiento del dispositivo, y luego toque en él. Esto iniciará el proceso de instalación, que puede tardar unos segundos. Una vez completada la instalación, se puede abrir el juego y disfrutar de Bhop Pro APK Dinero Infinito 2022. </p>
- <h2>Cómo jugar Bhop Pro? </h2>
- <p>Ahora que ha descargado e instalado Bhop Pro APK Dinero Infinito 2022, es posible que se pregunte cómo jugar el juego. Estos son algunos de los pasos básicos que debes seguir:</p>
- <h3>Elige un modo de juego y un mapa</h3>
- <p>Lo primero que tienes que hacer es elegir un modo de juego y un mapa que quieres jugar. Puedes elegir entre parkour, surf, deathrun, speedrun y modos aleatorios. Cada modo tiene diferentes objetivos y desafíos que necesitas completar. También puede elegir entre varios mapas que tienen diferentes temas y dificultades. Puede jugar solo o en línea con otros jugadores. </p>
- <h3>Utilice los controles táctiles para moverse y saltar</h3>
-
- <h3>Utilizar el aire para ganar velocidad y evitar obstáculos</h3>
- <p>Lo más importante que tienes que hacer es utilizar el aire strafing para ganar velocidad y evitar obstáculos. Es una técnica que consiste en moverse de lado en el aire mientras se salta. Al hacer esto, puede aumentar su impulso y alcanzar velocidades más altas. También puedes utilizar el aire para esquivar obstáculos, cambiar de dirección y aterrizar de forma segura. Para realizar el strafing del aire, usted necesita saltar y después mover su dedo en un movimiento circular en el lado izquierdo de la pantalla. También necesitas alinear tu vista con tu movimiento en el lado derecho de la pantalla. </p>
- <h2>Consejos y trucos para Bhop Pro</h2>
- <p>Bhop Pro es un juego que requiere habilidad y práctica. Aquí hay algunos consejos y trucos que pueden ayudarle a mejorar su juego:</p>
-<p></p>
- <h3>Practica en mapas fáciles antes de pasar a los más difíciles</h3>
- <p>Una de las mejores maneras de aprender a bhop es practicar en mapas fáciles antes de pasar a los más difíciles. Los mapas fáciles tienen menos obstáculos, plataformas más anchas y alturas más bajas. Pueden ayudarte a familiarizarte con los controles, la física y la mecánica del juego. También pueden ayudarte a desarrollar tu memoria muscular y tus reflejos. Una vez que domines los mapas fáciles, puedes desafiarte a ti mismo con los más difíciles que tienen más obstáculos, plataformas más estrechas y alturas más altas. </p>
- <h3>Ver vídeos de otros jugadores para aprender de sus técnicas</h3>
- <p>Otra manera de aprender a bhop es ver videos de otros jugadores que son mejores que tú. Puede encontrar muchos vídeos de reproductores Bhop Pro en YouTube u otras plataformas. Puede ver cómo se mueven, saltan, se desvían y utilizan los elementos. También puedes ver cómo completan diferentes mapas y modos. Puedes aprender de sus técnicas e intentar replicarlas en tu propio juego. </p>
- <h3>Utilice portales, amplificadores y artículos para mejorar su rendimiento</h3>
-
- <h2>Conclusión</h2>
- <p>Bhop Pro es un juego móvil que te permite saltar y saltar conejito en modo fps. Es un juego que desafía tus habilidades y reflejos a medida que completas misiones de parkour y compites con otros jugadores en línea. También es un juego que cuenta con física realista, múltiples mapas, multijugador en línea y opciones de personalización. Si quieres disfrutar del juego al máximo, se puede descargar e instalar Bhop Pro APK Dinero Infinito 2022, que le da dinero ilimitado y el acceso a todos los elementos en el juego. También puedes seguir los consejos y trucos que compartimos en este artículo para mejorar tu jugabilidad y divertirte más. Bhop Pro es un juego que es adecuado para cualquier persona que ama los juegos de bhopping, parkour o fps. ¡Descárgalo ahora y empieza a saltar! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre Bhop Pro:</p>
- <h3>¿Es seguro jugar a Bhop Pro? </h3>
- <p>Bhop Pro es seguro para jugar siempre y cuando lo descargue de una fuente confiable y habilite fuentes desconocidas en la configuración de su dispositivo. El juego no contiene ningún contenido dañino o malware que pueda dañar su dispositivo o comprometer su privacidad. Sin embargo, debes tener cuidado al jugar en línea con otros jugadores, ya que algunos de ellos podrían usar trucos o hacks que podrían arruinar tu experiencia. </p>
- <h3>¿Es Bhop Pro compatible con mi dispositivo? </h3>
- <p>Bhop Pro es compatible con la mayoría de los dispositivos Android que tienen Android 4.1 o superior. El juego también requiere al menos 100 MB de espacio de almacenamiento gratuito en su dispositivo. Puedes comprobar la compatibilidad de tu dispositivo visitando la página de Google Play Store o App Store del juego. </p>
- <h3>¿Cómo puedo contactar a los desarrolladores de Bhop Pro? </h3>
- <p>Si tiene alguna pregunta, comentario o sugerencia para los desarrolladores de Bhop Pro, puede ponerse en contacto con ellos enviando un correo electrónico a [email protected]. También puede visitar su sitio web en [begma.com] o seguirlos en plataformas de redes sociales como Facebook, Twitter, Instagram y YouTube.</p>
-
- <p>Si te gusta Bhop Pro y quieres apoyar a los desarrolladores del juego, puedes hacerlo clasificando y revisando el juego en Google Play Store o App Store. También puedes compartir el juego con tus amigos y familiares que podrían disfrutarlo. También puedes comprar algunos artículos o monedas en el juego si quieres apoyarlos financieramente. </p>
- <h3>¿Cuáles son algunos otros juegos como Bhop Pro? </h3>
- <p>Si usted está buscando algunos otros juegos como Bhop Pro, es posible que desee probar estos juegos:</p>
-<tabla>
-<tr>
-<th>Juego</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Liga Bunny Hop</td>
-<td>Un juego de PC que se inspira en los juegos de FPS de la vieja escuela y sus mecánicas de salto de conejo. Tiene hermosos gráficos en 3D, varios mapas y modos, multijugador en línea y tablas de clasificación. </td>
-</tr>
-<tr>
-<td>Escalada Kreedz</td>
-<td>Un juego para PC que se basa en un mod para Counter-Strike 1.6. Es un juego que combina parkour, resolución de puzzles y speedrunning. Tiene física realista, mapas y modos personalizados, multijugador en línea y soporte para talleres. </td>
-</tr>
-<tr>
-<td>Simulador de Parkour 3D</td>
-<td>Un juego móvil que te permite realizar increíbles acrobacias de parkour en entornos 3D realistas. Tiene física realista, múltiples ubicaciones y personajes, multijugador en línea y opciones de personalización. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Apk De Simulador De Conduccin De Coche Extremo.md

@@ -1,100 +0,0 @@
-<br />
-<h1>Descargar APK de Extreme Car Driving Simulator</h1>
-<p>¿Te encanta conducir coches rápidos y realizar acrobacias en un entorno de mundo abierto? Si es así, entonces usted debe probar Extreme Car Driving Simulator, uno de los juegos de simulador de coches más populares para dispositivos Android. En este artículo, te diremos de qué se trata este juego, cómo descargar e instalar el archivo APK, por qué deberías descargar el APK y cómo jugar el juego como un profesional. </p>
- <h2>¿Qué es Extreme Car Driving Simulator? </h2>
-<p>Extreme Car Driving Simulator es un juego desarrollado por AxesInMotion Racing, un estudio especializado en crear juegos de carreras realistas y divertidos. El juego fue lanzado en 2014 y desde entonces ha ganado más de 500 millones de descargas en Google Play Store. También está disponible en otras plataformas como iOS, Windows y Mac.</p>
-<h2>descargar apk de simulador de conducción de coche extremo</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://bltlly.com/2v6KIg">https://bltlly.com/2v6KIg</a></b></p><br /><br />
-<p>El juego te permite conducir, derrapar y sentir un coche deportivo de carreras en una ciudad enorme sin tráfico ni reglas. Puede explorar la ciudad, realizar acrobacias ilegales, estrellar su coche, y disfrutar de la física realista y sistema de daños. También puedes elegir entre diferentes modos de juego, como el modo de punto de control, el modo de tráfico, el modo libre y más. </p>
- <h3>Características del juego</h3>
-<p>Algunas de las características que hacen que Extreme Car Driving Simulator se destaque de otros juegos de simuladores de coches son:</p>
-<ul>
-<li><b>Mini modo de punto de control del juego:</b> En este modo, tienes que llegar a diferentes puntos de control en la ciudad dentro de un plazo determinado. Puede ganar monedas y desbloquear nuevos coches completando este modo. </li>
-<li><b>Conduce con tráfico:</b> En este modo, tienes que conducir en una ciudad con tráfico realista y evitar colisiones con otros vehículos. También puede tocar la bocina, encender las luces y usar indicadores para comunicarse con otros conductores. </li>
-<li><b>Full HUD real incluyendo revoluciones, engranaje y velocidad:</b> El juego le da una experiencia de conducción realista mostrándole el velocímetro, el tacómetro, el indicador de engranaje y otra información en su pantalla. </li>
-
-<li><b>Explora un entorno de mundo abierto detallado:</b> El juego presenta una gran ciudad con diferentes áreas, como el centro, el aeropuerto, la zona industrial, la playa y más. Puedes conducir y descubrir lugares y secretos ocultos. </li>
-<li><b>Daño realista del coche:</b> El juego tiene un sistema realista del daño del coche que le demuestra el impacto de sus accidentes en su coche. Puedes ver las abolladuras, arañazos, ventanas rotas y humo proveniente de tu motor. </li>
-<li><b>Física precisa:</b> El juego tiene un motor de física realista que hace que el coche se comporte de acuerdo con su peso, velocidad, fricción y aerodinámica. Puedes sentir la diferencia entre conducir sobre asfalto, hierba, arena o agua. </li>
-<li><b>Controla tu coche con un volante, acelerómetro o flechas:</b> El juego te da diferentes opciones para controlar tu coche de acuerdo a tus preferencias. Puede utilizar un volante virtual en la pantalla, inclinar el dispositivo para dirigir o pulsar las flechas para girar a la izquierda o a la derecha. </li>
-<li><b>Varias cámaras diferentes:</b> El juego te permite cambiar entre diferentes ángulos de cámara para obtener una mejor vista de tu coche y los alrededores. Puede elegir entre vista de cabina, vista de capó, vista trasera, vista superior o vista en tercera persona. </li>
-<li><b>Soporte para gamepad:</b> El juego también soporta gamepads externos para una experiencia de juego más inmersiva. Puede conectar su gamepad a través de Bluetooth o USB y disfrutar del juego en una pantalla más grande. </li>
-</ul>
- <h3>Cómo descargar e instalar el APK</h3>
-<p>Si desea descargar e instalar Extreme Car Driving Simulator APK en su dispositivo Android, es necesario seguir estos sencillos pasos:</p>
-<ol>
-<li><b>Descargar el archivo APK:</b> Puede descargar el archivo APK desde una fuente de confianza, como APKPure o APKMirror. Asegúrese de descargar la última versión del juego, que es 6.0.9 a partir de junio de 2023. </li>
-
-<li><b>Instalar el archivo APK:</b> Una vez que haya descargado el archivo APK y habilitado fuentes desconocidas, puede instalar el archivo APK tocando en él y siguiendo las instrucciones en la pantalla. Es posible que necesite conceder algunos permisos a la aplicación, como el acceso al almacenamiento, la cámara, el micrófono, etc.</li>
-<li><b>Inicie el juego:</b> Después de que la instalación se haya completado, puede iniciar el juego tocando en su icono en la pantalla de inicio o en el cajón de la aplicación. También puede crear un acceso directo para el juego en su escritorio para facilitar el acceso. </li>
-</ol>
- <h2>¿Por qué descargar Extreme Car Driving Simulator APK? </h2>
-<p>Usted puede preguntarse por qué usted debe descargar Extreme Car Driving Simulator APK en lugar de conseguirlo de la Google Play Store. Bueno, hay algunos beneficios y riesgos de descargar el APK que usted debe saber antes de decidir. </p>
- <h3>Beneficios de descargar el APK</h3>
-<p>Algunos de los beneficios de descargar Extreme Car Driving Simulator APK son:</p>
-<ul>
-<li><b>No hay anuncios:</b> La versión APK del juego no tiene ningún anuncio molesto que interrumpa su juego o consuma sus datos. Puedes disfrutar del juego sin distracciones ni interrupciones. </li>
-<li><b>No hay compras en la aplicación:</b> La versión APK del juego no tiene ninguna compra en la aplicación que requiera gastar dinero real para desbloquear nuevos coches, características o modos. Puedes acceder a todo en el juego de forma gratuita y sin limitaciones. </li>
-<li><b>No se requiere conexión a Internet:</b> La versión APK del juego no requiere una conexión a Internet para jugar. Puede jugar el juego sin conexión y sin preocuparse por su uso de datos o velocidad de conexión. </li>
-<li><b>No se requiere raíz:</b> La versión APK del juego no requiere que raíz de su dispositivo para instalar o jugar. Puede instalar y jugar el juego en cualquier dispositivo Android sin ningún riesgo de anular su garantía o dañar su dispositivo. </li>
-</ul>
- <h3>Riesgos y precauciones de descargar el APK</h3>
-
-<ul>
-<li><b>Malware potencial:</b> La versión APK del juego puede contener malware o virus que pueden dañar su dispositivo o robar su información personal. Siempre debes descargar el archivo APK de una fuente confiable y escanearlo con una aplicación antivirus antes de instalarlo. </li>
-<li><b>Errores y fallos potenciales:</b> La versión APK del juego puede no ser compatible con su dispositivo o puede tener algunos errores y fallos que afectan su juego o rendimiento. Siempre debe comprobar las revisiones y calificaciones del archivo APK antes de descargarlo e informar de cualquier problema al desarrollador. </li>
-<li><b>Problemas legales potenciales:</b> La versión APK del juego puede violar algunos términos y condiciones del juego original o Google Play Store. Puede enfrentar algunos problemas legales o sanciones si descarga o distribuye el archivo APK sin el permiso adecuado del desarrollador o Google.</li>
-</ul>
- <h2>Cómo jugar simulador de conducción de coches extremos</h2>
-<p>Ahora que ha descargado e instalado Extreme Car Driving Simulator APK, es posible que desee saber cómo jugar el juego y divertirse. Aquí hay algunos consejos y trucos que te ayudarán a dominar el juego y convertirte en un conductor profesional. </p>
- <h3>Controles y ajustes</h3>
-<p>Lo primero que debe hacer antes de jugar Extreme Car Driving Simulator es ajustar los controles y ajustes de acuerdo a su preferencia. Puede acceder a ellos pulsando en el icono del engranaje en la esquina superior derecha de la pantalla. Aquí hay algunas opciones que puede personalizar:</p>
-<ul>
-<li><b>Sensibilidad:</b> Esta opción te permite ajustar la sensibilidad de tu volante, acelerómetro o flechas cuando controlas tu coche. Puede aumentarlo o reducirlo deslizando la barra hacia la izquierda o hacia la derecha. </li>
-<li><b>Eje invertido:</b> Esta opción le permite invertir el eje horizontal o vertical de su acelerómetro o flechas cuando controla su automóvil. Puede encenderlo o apagarlo pulsando el interruptor. </li>
-
-<li><b>Música:</b> Esta opción te permite ajustar cuán fuerte o silenciosa es la música de fondo cuando juegas el juego. También puede elegir entre diferentes pistas de música tocando las flechas. </li>
-<li><b>Gráficos:</b> Esta opción te permite ajustar qué tan alta o baja es la calidad gráfica cuando juegas el juego. Puede elegir entre ajustes bajos, medios, altos o ultra pulsando los botones. Los gráficos más altos pueden afectar el rendimiento del dispositivo y la duración de la batería. </li>
-</ul>
- <h3>Modos de juego y desafíos</h3>
-<p>Lo siguiente que debe hacer antes de jugar Extreme Car Driving Simulator es elegir un modo de juego y un desafío que se adapte a su estado de ánimo y nivel de habilidad. Puede acceder a ellos pulsando en el icono del mapa en la esquina superior izquierda de la pantalla. Aquí hay algunas opciones que puede elegir:</p>
-<p></p>
-<ul>
-<li><b>Modo libre:</b> En este modo, puede conducir libremente en la ciudad sin tráfico ni reglas. Usted puede explorar la ciudad, realizar acrobacias, accidente de su coche, y divertirse. También puede cambiar la hora del día, el clima y la densidad del tráfico pulsando en los iconos de la parte inferior de la pantalla. </li>
-<li><b>Modo de tráfico:</b> En este modo, puede conducir en la ciudad con tráfico realista y obedecer las leyes de tráfico. También puede tocar la bocina, encender las luces y usar indicadores para comunicarse con otros conductores. Puedes ganar monedas conduciendo con seguridad y evitando colisiones. </li>
-<li><b>Modo de punto de control:</b> En este modo, tienes que llegar a diferentes puntos de control en la ciudad dentro de un límite de tiempo determinado. Puedes ganar monedas y desbloquear coches nuevos completando este modo. También puedes elegir entre diferentes niveles de dificultad, como fácil, normal, duro o extremo. </li>
-<li><b>Nuevo desafío:</b> En este modo, puede crear su propio desafío personalizado eligiendo un punto de inicio, un punto final, un límite de tiempo y un nivel de dificultad. También puedes compartir tu desafío con otros jugadores y probar sus desafíos. </li>
-</ul>
- <h3>Consejos y trucos</h3>
-
-<ul>
-<li><b>Utilice el freno de mano:</b> El freno de mano es una herramienta útil que le permite realizar derivas, giros y derrapes más fácilmente. Puede activarlo pulsando el botón rojo en la esquina inferior derecha de la pantalla. También puede ajustar su sensibilidad en la configuración. </li>
-<li><b>Usa el nitro:</b> El nitro es un poderoso impulso que te permite acelerar más rápido y alcanzar velocidades más altas. Puede activarlo pulsando el botón azul en la esquina inferior derecha de la pantalla. También puede rellenarlo realizando acrobacias o conduciendo rápido. </li>
-<li><b>Utilice la cámara:</b> La cámara es una característica útil que le permite cambiar entre diferentes vistas de su coche y los alrededores. Puede cambiarlo pulsando en el icono de la cámara en la esquina superior derecha de la pantalla. También puede acercar o alejar pellizcando la pantalla. </li>
-<li><b>Utilice el garaje:</b> El garaje es un lugar donde se puede personalizar el coche y hacer que se vea más fresco y único. Puede acceder a él pulsando en el icono del garaje en la esquina superior izquierda de la pantalla. Puedes cambiar el color, las ruedas, los alerones, los vinilos y más de tu coche. </li>
-<li><b>Usa el menú de pausa:</b> El menú de pausa es un menú que te permite pausar el juego y acceder a algunas opciones útiles. Puede acceder a ella pulsando en el icono de pausa en la esquina superior derecha de la pantalla. Puede reanudar el juego, reiniciar el juego, cambiar la configuración, salir del juego o calificar el juego desde este menú. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h4>Resumen del artículo</h4>
-<p>Este artículo es sobre cómo descargar e instalar Extreme Car Driving Simulator APK, un juego de simulador de coche para dispositivos Android. Cubre los siguientes temas:</p>
-<ul>
-<li>¿Qué es Extreme Car Driving Simulator y cuáles son sus características? </li>
-<li> ¿Por qué descargar Extreme Car Driving Simulator APK y cuáles son los beneficios y riesgos de hacerlo? </li>
-<li> ¿Cómo se juega Extreme Car Driving Simulator y cuáles son algunos consejos y trucos para mejorar sus habilidades de conducción? </li>
-</ul>
- <h4>Preguntas frecuentes</h4>
-<p>Aquí hay algunas preguntas frecuentes sobre Extreme Car Driving Simulator APK:</p>
-<ol>
-<li><b> ¿Es seguro descargar e instalar Extreme Car Driving Simulator APK? </b><br>
-Sí, Extreme Car Driving Simulator APK es seguro para descargar e instalar, siempre y cuando se obtiene de una fuente de confianza y escanear con una aplicación antivirus antes de instalarlo. Sin embargo, siempre debe tener cuidado al descargar cualquier archivo APK de Internet y evitar cualquier fuente sospechosa o desconocida. </li>
-<li><b> ¿Es Extreme Car Driving Simulator APK compatible con mi dispositivo? </b><br>
-Extreme Car Driving Simulator APK es compatible con la mayoría de los dispositivos Android que tienen Android 4.1 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o pueden experimentar algunos errores o problemas técnicos. Siempre debe comprobar las revisiones y calificaciones del archivo APK antes de descargarlo e informar de cualquier problema al desarrollador. </li>
-<li><b> ¿Cómo puedo actualizar Extreme Car Driving Simulator APK? </b><br>
-Puede actualizar Extreme Car Driving Simulator APK mediante la descarga de la última versión del juego de la misma fuente donde obtuvo la versión anterior. También puede comprobar las actualizaciones pulsando en el icono de engranaje en la esquina superior derecha de la pantalla y luego pulsando en el botón de actualización. Sin embargo, siempre debe hacer una copia de seguridad de los datos del juego antes de actualizarlo para evitar cualquier pérdida de datos o corrupción. </li>
-<li><b> ¿Cómo puedo desinstalar Extreme Car Driving Simulator APK? </b><br>
-
-<li><b> ¿Cómo puedo contactar con el desarrollador de Extreme Car Driving Simulator APK? </b><br>
-Puede ponerse en contacto con el desarrollador de Extreme Car Driving Simulator APK enviando un correo electrónico a [email protected] o visitando su sitio web en https://www.axesinmotion.com/m. También puede seguirlos en Facebook, Twitter, Instagram o YouTube para más actualizaciones y noticias sobre sus juegos. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/routes/+layout.server.ts

@@ -1,33 +0,0 @@
-import type { LayoutServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import type { Conversation } from "$lib/types/Conversation";
-import { UrlDependency } from "$lib/types/UrlDependency";
-
-export const load: LayoutServerLoad = async ({ locals, depends }) => {
-	const { conversations } = collections;
-
-	depends(UrlDependency.ConversationList);
-	depends(UrlDependency.Settings);
-
-	const settings = await collections.settings.findOne({ sessionId: locals.sessionId });
-
-	return {
-		conversations: await conversations
-			.find({
-				sessionId: locals.sessionId,
-			})
-			.sort({ updatedAt: -1 })
-			.project<Pick<Conversation, "title" | "_id" | "updatedAt" | "createdAt">>({
-				title: 1,
-				_id: 1,
-				updatedAt: 1,
-				createdAt: 1,
-			})
-			.map((conv) => ({ id: conv._id.toString(), title: conv.title }))
-			.toArray(),
-		settings: {
-			shareConversationsWithModelAuthors: settings?.shareConversationsWithModelAuthors ?? true,
-			ethicsModalAcceptedAt: settings?.ethicsModalAcceptedAt ?? null,
-		},
-	};
-};

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/client.py

@@ -1,1319 +0,0 @@
-# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import logging
-
-from botocore import waiter, xform_name
-from botocore.args import ClientArgsCreator
-from botocore.auth import AUTH_TYPE_MAPS
-from botocore.awsrequest import prepare_request_dict
-from botocore.config import Config
-from botocore.discovery import (
-    EndpointDiscoveryHandler,
-    EndpointDiscoveryManager,
-    block_endpoint_discovery_required_operations,
-)
-from botocore.docs.docstring import ClientMethodDocstring, PaginatorDocstring
-from botocore.exceptions import (
-    DataNotFoundError,
-    InvalidEndpointDiscoveryConfigurationError,
-    OperationNotPageableError,
-    UnknownServiceError,
-    UnknownSignatureVersionError,
-)
-from botocore.history import get_global_history_recorder
-from botocore.hooks import first_non_none_response
-from botocore.httpchecksum import (
-    apply_request_checksum,
-    resolve_checksum_context,
-)
-from botocore.model import ServiceModel
-from botocore.paginate import Paginator
-from botocore.retries import adaptive, standard
-from botocore.utils import (
-    CachedProperty,
-    EventbridgeSignerSetter,
-    S3ControlArnParamHandlerv2,
-    S3RegionRedirectorv2,
-    ensure_boolean,
-    get_service_module_name,
-)
-
-# Keep these imported.  There's pre-existing code that uses:
-# "from botocore.client import UNSIGNED"
-# "from botocore.client import ClientError"
-# etc.
-from botocore.exceptions import ClientError  # noqa
-from botocore.utils import S3ArnParamHandler  # noqa
-from botocore.utils import S3ControlArnParamHandler  # noqa
-from botocore.utils import S3ControlEndpointSetter  # noqa
-from botocore.utils import S3EndpointSetter  # noqa
-from botocore.utils import S3RegionRedirector  # noqa
-from botocore import UNSIGNED  # noqa
-
-
-_LEGACY_SIGNATURE_VERSIONS = frozenset(
-    (
-        'v2',
-        'v3',
-        'v3https',
-        'v4',
-        's3',
-        's3v4',
-    )
-)
-
-
-logger = logging.getLogger(__name__)
-history_recorder = get_global_history_recorder()
-
-
-class ClientCreator:
-    """Creates client objects for a service."""
-
-    def __init__(
-        self,
-        loader,
-        endpoint_resolver,
-        user_agent,
-        event_emitter,
-        retry_handler_factory,
-        retry_config_translator,
-        response_parser_factory=None,
-        exceptions_factory=None,
-        config_store=None,
-    ):
-        self._loader = loader
-        self._endpoint_resolver = endpoint_resolver
-        self._user_agent = user_agent
-        self._event_emitter = event_emitter
-        self._retry_handler_factory = retry_handler_factory
-        self._retry_config_translator = retry_config_translator
-        self._response_parser_factory = response_parser_factory
-        self._exceptions_factory = exceptions_factory
-        # TODO: Migrate things away from scoped_config in favor of the
-        # config_store.  The config store can pull things from both the scoped
-        # config and environment variables (and potentially more in the
-        # future).
-        self._config_store = config_store
-
-    def create_client(
-        self,
-        service_name,
-        region_name,
-        is_secure=True,
-        endpoint_url=None,
-        verify=None,
-        credentials=None,
-        scoped_config=None,
-        api_version=None,
-        client_config=None,
-        auth_token=None,
-    ):
-        responses = self._event_emitter.emit(
-            'choose-service-name', service_name=service_name
-        )
-        service_name = first_non_none_response(responses, default=service_name)
-        service_model = self._load_service_model(service_name, api_version)
-        try:
-            endpoints_ruleset_data = self._load_service_endpoints_ruleset(
-                service_name, api_version
-            )
-            partition_data = self._loader.load_data('partitions')
-        except UnknownServiceError:
-            endpoints_ruleset_data = None
-            partition_data = None
-            logger.info(
-                'No endpoints ruleset found for service %s, falling back to '
-                'legacy endpoint routing.',
-                service_name,
-            )
-
-        cls = self._create_client_class(service_name, service_model)
-        region_name, client_config = self._normalize_fips_region(
-            region_name, client_config
-        )
-        endpoint_bridge = ClientEndpointBridge(
-            self._endpoint_resolver,
-            scoped_config,
-            client_config,
-            service_signing_name=service_model.metadata.get('signingName'),
-            config_store=self._config_store,
-            service_signature_version=service_model.metadata.get(
-                'signatureVersion'
-            ),
-        )
-        client_args = self._get_client_args(
-            service_model,
-            region_name,
-            is_secure,
-            endpoint_url,
-            verify,
-            credentials,
-            scoped_config,
-            client_config,
-            endpoint_bridge,
-            auth_token,
-            endpoints_ruleset_data,
-            partition_data,
-        )
-        service_client = cls(**client_args)
-        self._register_retries(service_client)
-        self._register_s3_events(
-            client=service_client,
-            endpoint_bridge=None,
-            endpoint_url=None,
-            client_config=client_config,
-            scoped_config=scoped_config,
-        )
-        self._register_s3_control_events(client=service_client)
-        self._register_endpoint_discovery(
-            service_client, endpoint_url, client_config
-        )
-        return service_client
-
-    def create_client_class(self, service_name, api_version=None):
-        service_model = self._load_service_model(service_name, api_version)
-        return self._create_client_class(service_name, service_model)
-
-    def _create_client_class(self, service_name, service_model):
-        class_attributes = self._create_methods(service_model)
-        py_name_to_operation_name = self._create_name_mapping(service_model)
-        class_attributes['_PY_TO_OP_NAME'] = py_name_to_operation_name
-        bases = [BaseClient]
-        service_id = service_model.service_id.hyphenize()
-        self._event_emitter.emit(
-            'creating-client-class.%s' % service_id,
-            class_attributes=class_attributes,
-            base_classes=bases,
-        )
-        class_name = get_service_module_name(service_model)
-        cls = type(str(class_name), tuple(bases), class_attributes)
-        return cls
-
-    def _normalize_fips_region(self, region_name, client_config):
-        if region_name is not None:
-            normalized_region_name = region_name.replace('fips-', '').replace(
-                '-fips', ''
-            )
-            # If region has been transformed then set flag
-            if normalized_region_name != region_name:
-                config_use_fips_endpoint = Config(use_fips_endpoint=True)
-                if client_config:
-                    # Keeping endpoint setting client specific
-                    client_config = client_config.merge(
-                        config_use_fips_endpoint
-                    )
-                else:
-                    client_config = config_use_fips_endpoint
-                logger.warning(
-                    'transforming region from %s to %s and setting '
-                    'use_fips_endpoint to true. client should not '
-                    'be configured with a fips psuedo region.'
-                    % (region_name, normalized_region_name)
-                )
-                region_name = normalized_region_name
-        return region_name, client_config
-
-    def _load_service_model(self, service_name, api_version=None):
-        json_model = self._loader.load_service_model(
-            service_name, 'service-2', api_version=api_version
-        )
-        service_model = ServiceModel(json_model, service_name=service_name)
-        return service_model
-
-    def _load_service_endpoints_ruleset(self, service_name, api_version=None):
-        return self._loader.load_service_model(
-            service_name, 'endpoint-rule-set-1', api_version=api_version
-        )
-
-    def _register_retries(self, client):
-        retry_mode = client.meta.config.retries['mode']
-        if retry_mode == 'standard':
-            self._register_v2_standard_retries(client)
-        elif retry_mode == 'adaptive':
-            self._register_v2_standard_retries(client)
-            self._register_v2_adaptive_retries(client)
-        elif retry_mode == 'legacy':
-            self._register_legacy_retries(client)
-
-    def _register_v2_standard_retries(self, client):
-        max_attempts = client.meta.config.retries.get('total_max_attempts')
-        kwargs = {'client': client}
-        if max_attempts is not None:
-            kwargs['max_attempts'] = max_attempts
-        standard.register_retry_handler(**kwargs)
-
-    def _register_v2_adaptive_retries(self, client):
-        adaptive.register_retry_handler(client)
-
-    def _register_legacy_retries(self, client):
-        endpoint_prefix = client.meta.service_model.endpoint_prefix
-        service_id = client.meta.service_model.service_id
-        service_event_name = service_id.hyphenize()
-
-        # First, we load the entire retry config for all services,
-        # then pull out just the information we need.
-        original_config = self._loader.load_data('_retry')
-        if not original_config:
-            return
-
-        retries = self._transform_legacy_retries(client.meta.config.retries)
-        retry_config = self._retry_config_translator.build_retry_config(
-            endpoint_prefix,
-            original_config.get('retry', {}),
-            original_config.get('definitions', {}),
-            retries,
-        )
-
-        logger.debug(
-            "Registering retry handlers for service: %s",
-            client.meta.service_model.service_name,
-        )
-        handler = self._retry_handler_factory.create_retry_handler(
-            retry_config, endpoint_prefix
-        )
-        unique_id = 'retry-config-%s' % service_event_name
-        client.meta.events.register(
-            f"needs-retry.{service_event_name}", handler, unique_id=unique_id
-        )
-
-    def _transform_legacy_retries(self, retries):
-        if retries is None:
-            return
-        copied_args = retries.copy()
-        if 'total_max_attempts' in retries:
-            copied_args = retries.copy()
-            copied_args['max_attempts'] = (
-                copied_args.pop('total_max_attempts') - 1
-            )
-        return copied_args
-
-    def _get_retry_mode(self, client, config_store):
-        client_retries = client.meta.config.retries
-        if (
-            client_retries is not None
-            and client_retries.get('mode') is not None
-        ):
-            return client_retries['mode']
-        return config_store.get_config_variable('retry_mode') or 'legacy'
-
-    def _register_endpoint_discovery(self, client, endpoint_url, config):
-        if endpoint_url is not None:
-            # Don't register any handlers in the case of a custom endpoint url
-            return
-        # Only attach handlers if the service supports discovery
-        if client.meta.service_model.endpoint_discovery_operation is None:
-            return
-        events = client.meta.events
-        service_id = client.meta.service_model.service_id.hyphenize()
-        enabled = False
-        if config and config.endpoint_discovery_enabled is not None:
-            enabled = config.endpoint_discovery_enabled
-        elif self._config_store:
-            enabled = self._config_store.get_config_variable(
-                'endpoint_discovery_enabled'
-            )
-
-        enabled = self._normalize_endpoint_discovery_config(enabled)
-        if enabled and self._requires_endpoint_discovery(client, enabled):
-            discover = enabled is True
-            manager = EndpointDiscoveryManager(
-                client, always_discover=discover
-            )
-            handler = EndpointDiscoveryHandler(manager)
-            handler.register(events, service_id)
-        else:
-            events.register(
-                'before-parameter-build',
-                block_endpoint_discovery_required_operations,
-            )
-
-    def _normalize_endpoint_discovery_config(self, enabled):
-        """Config must either be a boolean-string or string-literal 'auto'"""
-        if isinstance(enabled, str):
-            enabled = enabled.lower().strip()
-            if enabled == 'auto':
-                return enabled
-            elif enabled in ('true', 'false'):
-                return ensure_boolean(enabled)
-        elif isinstance(enabled, bool):
-            return enabled
-
-        raise InvalidEndpointDiscoveryConfigurationError(config_value=enabled)
-
-    def _requires_endpoint_discovery(self, client, enabled):
-        if enabled == "auto":
-            return client.meta.service_model.endpoint_discovery_required
-        return enabled
-
-    def _register_eventbridge_events(
-        self, client, endpoint_bridge, endpoint_url
-    ):
-        if client.meta.service_model.service_name != 'events':
-            return
-        EventbridgeSignerSetter(
-            endpoint_resolver=self._endpoint_resolver,
-            region=client.meta.region_name,
-            endpoint_url=endpoint_url,
-        ).register(client.meta.events)
-
-    def _register_s3_events(
-        self,
-        client,
-        endpoint_bridge,
-        endpoint_url,
-        client_config,
-        scoped_config,
-    ):
-        if client.meta.service_model.service_name != 's3':
-            return
-        S3RegionRedirectorv2(None, client).register()
-        self._set_s3_presign_signature_version(
-            client.meta, client_config, scoped_config
-        )
-
-    def _register_s3_control_events(
-        self,
-        client,
-        endpoint_bridge=None,
-        endpoint_url=None,
-        client_config=None,
-        scoped_config=None,
-    ):
-        if client.meta.service_model.service_name != 's3control':
-            return
-        S3ControlArnParamHandlerv2().register(client.meta.events)
-
-    def _set_s3_presign_signature_version(
-        self, client_meta, client_config, scoped_config
-    ):
-        # This will return the manually configured signature version, or None
-        # if none was manually set. If a customer manually sets the signature
-        # version, we always want to use what they set.
-        provided_signature_version = _get_configured_signature_version(
-            's3', client_config, scoped_config
-        )
-        if provided_signature_version is not None:
-            return
-
-        # Check to see if the region is a region that we know about. If we
-        # don't know about a region, then we can safely assume it's a new
-        # region that is sigv4 only, since all new S3 regions only allow sigv4.
-        # The only exception is aws-global. This is a pseudo-region for the
-        # global endpoint, we should respect the signature versions it
-        # supports, which includes v2.
-        regions = self._endpoint_resolver.get_available_endpoints(
-            's3', client_meta.partition
-        )
-        if (
-            client_meta.region_name != 'aws-global'
-            and client_meta.region_name not in regions
-        ):
-            return
-
-        # If it is a region we know about, we want to default to sigv2, so here
-        # we check to see if it is available.
-        endpoint = self._endpoint_resolver.construct_endpoint(
-            's3', client_meta.region_name
-        )
-        signature_versions = endpoint['signatureVersions']
-        if 's3' not in signature_versions:
-            return
-
-        # We now know that we're in a known region that supports sigv2 and
-        # the customer hasn't set a signature version so we default the
-        # signature version to sigv2.
-        client_meta.events.register(
-            'choose-signer.s3', self._default_s3_presign_to_sigv2
-        )
-
-    def _default_s3_presign_to_sigv2(self, signature_version, **kwargs):
-        """
-        Returns the 's3' (sigv2) signer if presigning an s3 request. This is
-        intended to be used to set the default signature version for the signer
-        to sigv2. Situations where an asymmetric signature is required are the
-        exception, for example MRAP needs v4a.
-
-        :type signature_version: str
-        :param signature_version: The current client signature version.
-
-        :type signing_name: str
-        :param signing_name: The signing name of the service.
-
-        :return: 's3' if the request is an s3 presign request, None otherwise
-        """
-        if signature_version.startswith('v4a'):
-            return
-
-        for suffix in ['-query', '-presign-post']:
-            if signature_version.endswith(suffix):
-                return f's3{suffix}'
-
-    def _get_client_args(
-        self,
-        service_model,
-        region_name,
-        is_secure,
-        endpoint_url,
-        verify,
-        credentials,
-        scoped_config,
-        client_config,
-        endpoint_bridge,
-        auth_token,
-        endpoints_ruleset_data,
-        partition_data,
-    ):
-        args_creator = ClientArgsCreator(
-            self._event_emitter,
-            self._user_agent,
-            self._response_parser_factory,
-            self._loader,
-            self._exceptions_factory,
-            config_store=self._config_store,
-        )
-        return args_creator.get_client_args(
-            service_model,
-            region_name,
-            is_secure,
-            endpoint_url,
-            verify,
-            credentials,
-            scoped_config,
-            client_config,
-            endpoint_bridge,
-            auth_token,
-            endpoints_ruleset_data,
-            partition_data,
-        )
-
-    def _create_methods(self, service_model):
-        op_dict = {}
-        for operation_name in service_model.operation_names:
-            py_operation_name = xform_name(operation_name)
-            op_dict[py_operation_name] = self._create_api_method(
-                py_operation_name, operation_name, service_model
-            )
-        return op_dict
-
-    def _create_name_mapping(self, service_model):
-        # py_name -> OperationName, for every operation available
-        # for a service.
-        mapping = {}
-        for operation_name in service_model.operation_names:
-            py_operation_name = xform_name(operation_name)
-            mapping[py_operation_name] = operation_name
-        return mapping
-
-    def _create_api_method(
-        self, py_operation_name, operation_name, service_model
-    ):
-        def _api_call(self, *args, **kwargs):
-            # We're accepting *args so that we can give a more helpful
-            # error message than TypeError: _api_call takes exactly
-            # 1 argument.
-            if args:
-                raise TypeError(
-                    f"{py_operation_name}() only accepts keyword arguments."
-                )
-            # The "self" in this scope is referring to the BaseClient.
-            return self._make_api_call(operation_name, kwargs)
-
-        _api_call.__name__ = str(py_operation_name)
-
-        # Add the docstring to the client method
-        operation_model = service_model.operation_model(operation_name)
-        docstring = ClientMethodDocstring(
-            operation_model=operation_model,
-            method_name=operation_name,
-            event_emitter=self._event_emitter,
-            method_description=operation_model.documentation,
-            example_prefix='response = client.%s' % py_operation_name,
-            include_signature=False,
-        )
-        _api_call.__doc__ = docstring
-        return _api_call
-
-
-class ClientEndpointBridge:
-    """Bridges endpoint data and client creation
-
-    This class handles taking out the relevant arguments from the endpoint
-    resolver and determining which values to use, taking into account any
-    client configuration options and scope configuration options.
-
-    This class also handles determining what, if any, region to use if no
-    explicit region setting is provided. For example, Amazon S3 client will
-    utilize "us-east-1" by default if no region can be resolved."""
-
-    DEFAULT_ENDPOINT = '{service}.{region}.amazonaws.com'
-    _DUALSTACK_CUSTOMIZED_SERVICES = ['s3', 's3-control']
-
-    def __init__(
-        self,
-        endpoint_resolver,
-        scoped_config=None,
-        client_config=None,
-        default_endpoint=None,
-        service_signing_name=None,
-        config_store=None,
-        service_signature_version=None,
-    ):
-        self.service_signing_name = service_signing_name
-        self.endpoint_resolver = endpoint_resolver
-        self.scoped_config = scoped_config
-        self.client_config = client_config
-        self.default_endpoint = default_endpoint or self.DEFAULT_ENDPOINT
-        self.config_store = config_store
-        self.service_signature_version = service_signature_version
-
-    def resolve(
-        self, service_name, region_name=None, endpoint_url=None, is_secure=True
-    ):
-        region_name = self._check_default_region(service_name, region_name)
-        use_dualstack_endpoint = self._resolve_use_dualstack_endpoint(
-            service_name
-        )
-        use_fips_endpoint = self._resolve_endpoint_variant_config_var(
-            'use_fips_endpoint'
-        )
-        resolved = self.endpoint_resolver.construct_endpoint(
-            service_name,
-            region_name,
-            use_dualstack_endpoint=use_dualstack_endpoint,
-            use_fips_endpoint=use_fips_endpoint,
-        )
-
-        # If we can't resolve the region, we'll attempt to get a global
-        # endpoint for non-regionalized services (iam, route53, etc)
-        if not resolved:
-            # TODO: fallback partition_name should be configurable in the
-            # future for users to define as needed.
-            resolved = self.endpoint_resolver.construct_endpoint(
-                service_name,
-                region_name,
-                partition_name='aws',
-                use_dualstack_endpoint=use_dualstack_endpoint,
-                use_fips_endpoint=use_fips_endpoint,
-            )
-
-        if resolved:
-            return self._create_endpoint(
-                resolved, service_name, region_name, endpoint_url, is_secure
-            )
-        else:
-            return self._assume_endpoint(
-                service_name, region_name, endpoint_url, is_secure
-            )
-
-    def resolver_uses_builtin_data(self):
-        return self.endpoint_resolver.uses_builtin_data
-
-    def _check_default_region(self, service_name, region_name):
-        if region_name is not None:
-            return region_name
-        # Use the client_config region if no explicit region was provided.
-        if self.client_config and self.client_config.region_name is not None:
-            return self.client_config.region_name
-
-    def _create_endpoint(
-        self, resolved, service_name, region_name, endpoint_url, is_secure
-    ):
-        region_name, signing_region = self._pick_region_values(
-            resolved, region_name, endpoint_url
-        )
-        if endpoint_url is None:
-            endpoint_url = self._make_url(
-                resolved.get('hostname'),
-                is_secure,
-                resolved.get('protocols', []),
-            )
-        signature_version = self._resolve_signature_version(
-            service_name, resolved
-        )
-        signing_name = self._resolve_signing_name(service_name, resolved)
-        return self._create_result(
-            service_name=service_name,
-            region_name=region_name,
-            signing_region=signing_region,
-            signing_name=signing_name,
-            endpoint_url=endpoint_url,
-            metadata=resolved,
-            signature_version=signature_version,
-        )
-
-    def _resolve_endpoint_variant_config_var(self, config_var):
-        client_config = self.client_config
-        config_val = False
-
-        # Client configuration arg has precedence
-        if client_config and getattr(client_config, config_var) is not None:
-            return getattr(client_config, config_var)
-        elif self.config_store is not None:
-            # Check config store
-            config_val = self.config_store.get_config_variable(config_var)
-        return config_val
-
-    def _resolve_use_dualstack_endpoint(self, service_name):
-        s3_dualstack_mode = self._is_s3_dualstack_mode(service_name)
-        if s3_dualstack_mode is not None:
-            return s3_dualstack_mode
-        return self._resolve_endpoint_variant_config_var(
-            'use_dualstack_endpoint'
-        )
-
-    def _is_s3_dualstack_mode(self, service_name):
-        if service_name not in self._DUALSTACK_CUSTOMIZED_SERVICES:
-            return None
-        # TODO: This normalization logic is duplicated from the
-        # ClientArgsCreator class.  Consolidate everything to
-        # ClientArgsCreator.  _resolve_signature_version also has similarly
-        # duplicated logic.
-        client_config = self.client_config
-        if (
-            client_config is not None
-            and client_config.s3 is not None
-            and 'use_dualstack_endpoint' in client_config.s3
-        ):
-            # Client config trumps scoped config.
-            return client_config.s3['use_dualstack_endpoint']
-        if self.scoped_config is not None:
-            enabled = self.scoped_config.get('s3', {}).get(
-                'use_dualstack_endpoint'
-            )
-            if enabled in [True, 'True', 'true']:
-                return True
-
-    def _assume_endpoint(
-        self, service_name, region_name, endpoint_url, is_secure
-    ):
-        if endpoint_url is None:
-            # Expand the default hostname URI template.
-            hostname = self.default_endpoint.format(
-                service=service_name, region=region_name
-            )
-            endpoint_url = self._make_url(
-                hostname, is_secure, ['http', 'https']
-            )
-        logger.debug(
-            f'Assuming an endpoint for {service_name}, {region_name}: {endpoint_url}'
-        )
-        # We still want to allow the user to provide an explicit version.
-        signature_version = self._resolve_signature_version(
-            service_name, {'signatureVersions': ['v4']}
-        )
-        signing_name = self._resolve_signing_name(service_name, resolved={})
-        return self._create_result(
-            service_name=service_name,
-            region_name=region_name,
-            signing_region=region_name,
-            signing_name=signing_name,
-            signature_version=signature_version,
-            endpoint_url=endpoint_url,
-            metadata={},
-        )
-
-    def _create_result(
-        self,
-        service_name,
-        region_name,
-        signing_region,
-        signing_name,
-        endpoint_url,
-        signature_version,
-        metadata,
-    ):
-        return {
-            'service_name': service_name,
-            'region_name': region_name,
-            'signing_region': signing_region,
-            'signing_name': signing_name,
-            'endpoint_url': endpoint_url,
-            'signature_version': signature_version,
-            'metadata': metadata,
-        }
-
-    def _make_url(self, hostname, is_secure, supported_protocols):
-        if is_secure and 'https' in supported_protocols:
-            scheme = 'https'
-        else:
-            scheme = 'http'
-        return f'{scheme}://{hostname}'
-
-    def _resolve_signing_name(self, service_name, resolved):
-        # CredentialScope overrides everything else.
-        if (
-            'credentialScope' in resolved
-            and 'service' in resolved['credentialScope']
-        ):
-            return resolved['credentialScope']['service']
-        # Use the signingName from the model if present.
-        if self.service_signing_name:
-            return self.service_signing_name
-        # Just assume is the same as the service name.
-        return service_name
-
-    def _pick_region_values(self, resolved, region_name, endpoint_url):
-        signing_region = region_name
-        if endpoint_url is None:
-            # Do not use the region name or signing name from the resolved
-            # endpoint if the user explicitly provides an endpoint_url. This
-            # would happen if we resolve to an endpoint where the service has
-            # a "defaults" section that overrides all endpoint with a single
-            # hostname and credentialScope. This has been the case historically
-            # for how STS has worked. The only way to resolve an STS endpoint
-            # was to provide a region_name and an endpoint_url. In that case,
-            # we would still resolve an endpoint, but we would not use the
-            # resolved endpointName or signingRegion because we want to allow
-            # custom endpoints.
-            region_name = resolved['endpointName']
-            signing_region = region_name
-            if (
-                'credentialScope' in resolved
-                and 'region' in resolved['credentialScope']
-            ):
-                signing_region = resolved['credentialScope']['region']
-        return region_name, signing_region
-
-    def _resolve_signature_version(self, service_name, resolved):
-        configured_version = _get_configured_signature_version(
-            service_name, self.client_config, self.scoped_config
-        )
-        if configured_version is not None:
-            return configured_version
-
-        potential_versions = resolved.get('signatureVersions', [])
-        if (
-            self.service_signature_version is not None
-            and self.service_signature_version
-            not in _LEGACY_SIGNATURE_VERSIONS
-        ):
-            # Prefer the service model as most specific
-            # source of truth for new signature versions.
-            potential_versions = [self.service_signature_version]
-
-        # Pick a signature version from the endpoint metadata if present.
-        if 'signatureVersions' in resolved:
-            if service_name == 's3':
-                return 's3v4'
-            if 'v4' in potential_versions:
-                return 'v4'
-            # Now just iterate over the signature versions in order until we
-            # find the first one that is known to Botocore.
-            for known in potential_versions:
-                if known in AUTH_TYPE_MAPS:
-                    return known
-        raise UnknownSignatureVersionError(
-            signature_version=potential_versions
-        )
-
-
-class BaseClient:
-
-    # This is actually reassigned with the py->op_name mapping
-    # when the client creator creates the subclass.  This value is used
-    # because calls such as client.get_paginator('list_objects') use the
-    # snake_case name, but we need to know the ListObjects form.
-    # xform_name() does the ListObjects->list_objects conversion, but
-    # we need the reverse mapping here.
-    _PY_TO_OP_NAME = {}
-
-    def __init__(
-        self,
-        serializer,
-        endpoint,
-        response_parser,
-        event_emitter,
-        request_signer,
-        service_model,
-        loader,
-        client_config,
-        partition,
-        exceptions_factory,
-        endpoint_ruleset_resolver=None,
-    ):
-        self._serializer = serializer
-        self._endpoint = endpoint
-        self._ruleset_resolver = endpoint_ruleset_resolver
-        self._response_parser = response_parser
-        self._request_signer = request_signer
-        self._cache = {}
-        self._loader = loader
-        self._client_config = client_config
-        self.meta = ClientMeta(
-            event_emitter,
-            self._client_config,
-            endpoint.host,
-            service_model,
-            self._PY_TO_OP_NAME,
-            partition,
-        )
-        self._exceptions_factory = exceptions_factory
-        self._exceptions = None
-        self._register_handlers()
-
-    def __getattr__(self, item):
-        service_id = self._service_model.service_id.hyphenize()
-        event_name = f'getattr.{service_id}.{item}'
-
-        handler, event_response = self.meta.events.emit_until_response(
-            event_name, client=self
-        )
-
-        if event_response is not None:
-            return event_response
-
-        raise AttributeError(
-            f"'{self.__class__.__name__}' object has no attribute '{item}'"
-        )
-
-    def close(self):
-        """Closes underlying endpoint connections."""
-        self._endpoint.close()
-
-    def _register_handlers(self):
-        # Register the handler required to sign requests.
-        service_id = self.meta.service_model.service_id.hyphenize()
-        self.meta.events.register(
-            f"request-created.{service_id}", self._request_signer.handler
-        )
-
-    @property
-    def _service_model(self):
-        return self.meta.service_model
-
-    def _make_api_call(self, operation_name, api_params):
-        operation_model = self._service_model.operation_model(operation_name)
-        service_name = self._service_model.service_name
-        history_recorder.record(
-            'API_CALL',
-            {
-                'service': service_name,
-                'operation': operation_name,
-                'params': api_params,
-            },
-        )
-        if operation_model.deprecated:
-            logger.debug(
-                'Warning: %s.%s() is deprecated', service_name, operation_name
-            )
-        request_context = {
-            'client_region': self.meta.region_name,
-            'client_config': self.meta.config,
-            'has_streaming_input': operation_model.has_streaming_input,
-            'auth_type': operation_model.auth_type,
-        }
-        endpoint_url, additional_headers = self._resolve_endpoint_ruleset(
-            operation_model, api_params, request_context
-        )
-        request_dict = self._convert_to_request_dict(
-            api_params=api_params,
-            operation_model=operation_model,
-            endpoint_url=endpoint_url,
-            context=request_context,
-            headers=additional_headers,
-        )
-        resolve_checksum_context(request_dict, operation_model, api_params)
-
-        service_id = self._service_model.service_id.hyphenize()
-        handler, event_response = self.meta.events.emit_until_response(
-            'before-call.{service_id}.{operation_name}'.format(
-                service_id=service_id, operation_name=operation_name
-            ),
-            model=operation_model,
-            params=request_dict,
-            request_signer=self._request_signer,
-            context=request_context,
-        )
-
-        if event_response is not None:
-            http, parsed_response = event_response
-        else:
-            apply_request_checksum(request_dict)
-            http, parsed_response = self._make_request(
-                operation_model, request_dict, request_context
-            )
-
-        self.meta.events.emit(
-            'after-call.{service_id}.{operation_name}'.format(
-                service_id=service_id, operation_name=operation_name
-            ),
-            http_response=http,
-            parsed=parsed_response,
-            model=operation_model,
-            context=request_context,
-        )
-
-        if http.status_code >= 300:
-            error_code = parsed_response.get("Error", {}).get("Code")
-            error_class = self.exceptions.from_code(error_code)
-            raise error_class(parsed_response, operation_name)
-        else:
-            return parsed_response
-
-    def _make_request(self, operation_model, request_dict, request_context):
-        try:
-            return self._endpoint.make_request(operation_model, request_dict)
-        except Exception as e:
-            self.meta.events.emit(
-                'after-call-error.{service_id}.{operation_name}'.format(
-                    service_id=self._service_model.service_id.hyphenize(),
-                    operation_name=operation_model.name,
-                ),
-                exception=e,
-                context=request_context,
-            )
-            raise
-
-    def _convert_to_request_dict(
-        self,
-        api_params,
-        operation_model,
-        endpoint_url,
-        context=None,
-        headers=None,
-        set_user_agent_header=True,
-    ):
-        api_params = self._emit_api_params(
-            api_params, operation_model, context
-        )
-        request_dict = self._serializer.serialize_to_request(
-            api_params, operation_model
-        )
-        if not self._client_config.inject_host_prefix:
-            request_dict.pop('host_prefix', None)
-        if headers is not None:
-            request_dict['headers'].update(headers)
-        if set_user_agent_header:
-            user_agent = self._client_config.user_agent
-        else:
-            user_agent = None
-        prepare_request_dict(
-            request_dict,
-            endpoint_url=endpoint_url,
-            user_agent=user_agent,
-            context=context,
-        )
-        return request_dict
-
-    def _emit_api_params(self, api_params, operation_model, context):
-        # Given the API params provided by the user and the operation_model
-        # we can serialize the request to a request_dict.
-        operation_name = operation_model.name
-
-        # Emit an event that allows users to modify the parameters at the
-        # beginning of the method. It allows handlers to modify existing
-        # parameters or return a new set of parameters to use.
-        service_id = self._service_model.service_id.hyphenize()
-        responses = self.meta.events.emit(
-            f'provide-client-params.{service_id}.{operation_name}',
-            params=api_params,
-            model=operation_model,
-            context=context,
-        )
-        api_params = first_non_none_response(responses, default=api_params)
-
-        self.meta.events.emit(
-            f'before-parameter-build.{service_id}.{operation_name}',
-            params=api_params,
-            model=operation_model,
-            context=context,
-        )
-        return api_params
-
-    def _resolve_endpoint_ruleset(
-        self,
-        operation_model,
-        params,
-        request_context,
-        ignore_signing_region=False,
-    ):
-        """Returns endpoint URL and list of additional headers returned from
-        EndpointRulesetResolver for the given operation and params. If the
-        ruleset resolver is not available, for example because the service has
-        no endpoints ruleset file, the legacy endpoint resolver's value is
-        returned.
-
-        Use ignore_signing_region for generating presigned URLs or any other
-        situtation where the signing region information from the ruleset
-        resolver should be ignored.
-
-        Returns tuple of URL and headers dictionary. Additionally, the
-        request_context dict is modified in place with any signing information
-        returned from the ruleset resolver.
-        """
-        if self._ruleset_resolver is None:
-            endpoint_url = self.meta.endpoint_url
-            additional_headers = {}
-        else:
-            endpoint_info = self._ruleset_resolver.construct_endpoint(
-                operation_model=operation_model,
-                call_args=params,
-                request_context=request_context,
-            )
-            endpoint_url = endpoint_info.url
-            additional_headers = endpoint_info.headers
-            # If authSchemes is present, overwrite default auth type and
-            # signing context derived from service model.
-            auth_schemes = endpoint_info.properties.get('authSchemes')
-            if auth_schemes is not None:
-                auth_info = self._ruleset_resolver.auth_schemes_to_signing_ctx(
-                    auth_schemes
-                )
-                auth_type, signing_context = auth_info
-                request_context['auth_type'] = auth_type
-                if 'region' in signing_context and ignore_signing_region:
-                    del signing_context['region']
-                if 'signing' in request_context:
-                    request_context['signing'].update(signing_context)
-                else:
-                    request_context['signing'] = signing_context
-
-        return endpoint_url, additional_headers
-
-    def get_paginator(self, operation_name):
-        """Create a paginator for an operation.
-
-        :type operation_name: string
-        :param operation_name: The operation name.  This is the same name
-            as the method name on the client.  For example, if the
-            method name is ``create_foo``, and you'd normally invoke the
-            operation as ``client.create_foo(**kwargs)``, if the
-            ``create_foo`` operation can be paginated, you can use the
-            call ``client.get_paginator("create_foo")``.
-
-        :raise OperationNotPageableError: Raised if the operation is not
-            pageable.  You can use the ``client.can_paginate`` method to
-            check if an operation is pageable.
-
-        :rtype: L{botocore.paginate.Paginator}
-        :return: A paginator object.
-
-        """
-        if not self.can_paginate(operation_name):
-            raise OperationNotPageableError(operation_name=operation_name)
-        else:
-            actual_operation_name = self._PY_TO_OP_NAME[operation_name]
-
-            # Create a new paginate method that will serve as a proxy to
-            # the underlying Paginator.paginate method. This is needed to
-            # attach a docstring to the method.
-            def paginate(self, **kwargs):
-                return Paginator.paginate(self, **kwargs)
-
-            paginator_config = self._cache['page_config'][
-                actual_operation_name
-            ]
-            # Add the docstring for the paginate method.
-            paginate.__doc__ = PaginatorDocstring(
-                paginator_name=actual_operation_name,
-                event_emitter=self.meta.events,
-                service_model=self.meta.service_model,
-                paginator_config=paginator_config,
-                include_signature=False,
-            )
-
-            # Rename the paginator class based on the type of paginator.
-            service_module_name = get_service_module_name(
-                self.meta.service_model
-            )
-            paginator_class_name = (
-                f"{service_module_name}.Paginator.{actual_operation_name}"
-            )
-
-            # Create the new paginator class
-            documented_paginator_cls = type(
-                paginator_class_name, (Paginator,), {'paginate': paginate}
-            )
-
-            operation_model = self._service_model.operation_model(
-                actual_operation_name
-            )
-            paginator = documented_paginator_cls(
-                getattr(self, operation_name),
-                paginator_config,
-                operation_model,
-            )
-            return paginator
-
-    def can_paginate(self, operation_name):
-        """Check if an operation can be paginated.
-
-        :type operation_name: string
-        :param operation_name: The operation name.  This is the same name
-            as the method name on the client.  For example, if the
-            method name is ``create_foo``, and you'd normally invoke the
-            operation as ``client.create_foo(**kwargs)``, if the
-            ``create_foo`` operation can be paginated, you can use the
-            call ``client.get_paginator("create_foo")``.
-
-        :return: ``True`` if the operation can be paginated,
-            ``False`` otherwise.
-
-        """
-        if 'page_config' not in self._cache:
-            try:
-                page_config = self._loader.load_service_model(
-                    self._service_model.service_name,
-                    'paginators-1',
-                    self._service_model.api_version,
-                )['pagination']
-                self._cache['page_config'] = page_config
-            except DataNotFoundError:
-                self._cache['page_config'] = {}
-        actual_operation_name = self._PY_TO_OP_NAME[operation_name]
-        return actual_operation_name in self._cache['page_config']
-
-    def _get_waiter_config(self):
-        if 'waiter_config' not in self._cache:
-            try:
-                waiter_config = self._loader.load_service_model(
-                    self._service_model.service_name,
-                    'waiters-2',
-                    self._service_model.api_version,
-                )
-                self._cache['waiter_config'] = waiter_config
-            except DataNotFoundError:
-                self._cache['waiter_config'] = {}
-        return self._cache['waiter_config']
-
-    def get_waiter(self, waiter_name):
-        """Returns an object that can wait for some condition.
-
-        :type waiter_name: str
-        :param waiter_name: The name of the waiter to get. See the waiters
-            section of the service docs for a list of available waiters.
-
-        :returns: The specified waiter object.
-        :rtype: botocore.waiter.Waiter
-        """
-        config = self._get_waiter_config()
-        if not config:
-            raise ValueError("Waiter does not exist: %s" % waiter_name)
-        model = waiter.WaiterModel(config)
-        mapping = {}
-        for name in model.waiter_names:
-            mapping[xform_name(name)] = name
-        if waiter_name not in mapping:
-            raise ValueError("Waiter does not exist: %s" % waiter_name)
-
-        return waiter.create_waiter_with_client(
-            mapping[waiter_name], model, self
-        )
-
-    @CachedProperty
-    def waiter_names(self):
-        """Returns a list of all available waiters."""
-        config = self._get_waiter_config()
-        if not config:
-            return []
-        model = waiter.WaiterModel(config)
-        # Waiter configs is a dict, we just want the waiter names
-        # which are the keys in the dict.
-        return [xform_name(name) for name in model.waiter_names]
-
-    @property
-    def exceptions(self):
-        if self._exceptions is None:
-            self._exceptions = self._load_exceptions()
-        return self._exceptions
-
-    def _load_exceptions(self):
-        return self._exceptions_factory.create_client_exceptions(
-            self._service_model
-        )
-
-
-class ClientMeta:
-    """Holds additional client methods.
-
-    This class holds additional information for clients.  It exists for
-    two reasons:
-
-        * To give advanced functionality to clients
-        * To namespace additional client attributes from the operation
-          names which are mapped to methods at runtime.  This avoids
-          ever running into collisions with operation names.
-
-    """
-
-    def __init__(
-        self,
-        events,
-        client_config,
-        endpoint_url,
-        service_model,
-        method_to_api_mapping,
-        partition,
-    ):
-        self.events = events
-        self._client_config = client_config
-        self._endpoint_url = endpoint_url
-        self._service_model = service_model
-        self._method_to_api_mapping = method_to_api_mapping
-        self._partition = partition
-
-    @property
-    def service_model(self):
-        return self._service_model
-
-    @property
-    def region_name(self):
-        return self._client_config.region_name
-
-    @property
-    def endpoint_url(self):
-        return self._endpoint_url
-
-    @property
-    def config(self):
-        return self._client_config
-
-    @property
-    def method_to_api_mapping(self):
-        return self._method_to_api_mapping
-
-    @property
-    def partition(self):
-        return self._partition
-
-
-def _get_configured_signature_version(
-    service_name, client_config, scoped_config
-):
-    """
-    Gets the manually configured signature version.
-
-    :returns: the customer configured signature version, or None if no
-        signature version was configured.
-    """
-    # Client config overrides everything.
-    if client_config and client_config.signature_version is not None:
-        return client_config.signature_version
-
-    # Scoped config overrides picking from the endpoint metadata.
-    if scoped_config is not None:
-        # A given service may have service specific configuration in the
-        # config file, so we need to check there as well.
-        service_config = scoped_config.get(service_name)
-        if service_config is not None and isinstance(service_config, dict):
-            version = service_config.get('signature_version')
-            if version:
-                logger.debug(
-                    "Switching signature version for service %s "
-                    "to version %s based on config file override.",
-                    service_name,
-                    version,
-                )
-                return version
-    return None

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/__init__.py

@@ -1,82 +0,0 @@
-"""
-    Pygments
-    ~~~~~~~~
-
-    Pygments is a syntax highlighting package written in Python.
-
-    It is a generic syntax highlighter for general use in all kinds of software
-    such as forum systems, wikis or other applications that need to prettify
-    source code. Highlights are:
-
-    * a wide range of common languages and markup formats is supported
-    * special attention is paid to details, increasing quality by a fair amount
-    * support for new languages and formats are added easily
-    * a number of output formats, presently HTML, LaTeX, RTF, SVG, all image
-      formats that PIL supports, and ANSI sequences
-    * it is usable as a command-line tool and as a library
-    * ... and it highlights even Brainfuck!
-
-    The `Pygments master branch`_ is installable with ``easy_install Pygments==dev``.
-
-    .. _Pygments master branch:
-       https://github.com/pygments/pygments/archive/master.zip#egg=Pygments-dev
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-from io import StringIO, BytesIO
-
-__version__ = '2.14.0'
-__docformat__ = 'restructuredtext'
-
-__all__ = ['lex', 'format', 'highlight']
-
-
-def lex(code, lexer):
-    """
-    Lex ``code`` with ``lexer`` and return an iterable of tokens.
-    """
-    try:
-        return lexer.get_tokens(code)
-    except TypeError:
-        # Heuristic to catch a common mistake.
-        from pip._vendor.pygments.lexer import RegexLexer
-        if isinstance(lexer, type) and issubclass(lexer, RegexLexer):
-            raise TypeError('lex() argument must be a lexer instance, '
-                            'not a class')
-        raise
-
-
-def format(tokens, formatter, outfile=None):  # pylint: disable=redefined-builtin
-    """
-    Format a tokenlist ``tokens`` with the formatter ``formatter``.
-
-    If ``outfile`` is given and a valid file object (an object
-    with a ``write`` method), the result will be written to it, otherwise
-    it is returned as a string.
-    """
-    try:
-        if not outfile:
-            realoutfile = getattr(formatter, 'encoding', None) and BytesIO() or StringIO()
-            formatter.format(tokens, realoutfile)
-            return realoutfile.getvalue()
-        else:
-            formatter.format(tokens, outfile)
-    except TypeError:
-        # Heuristic to catch a common mistake.
-        from pip._vendor.pygments.formatter import Formatter
-        if isinstance(formatter, type) and issubclass(formatter, Formatter):
-            raise TypeError('format() argument must be a formatter instance, '
-                            'not a class')
-        raise
-
-
-def highlight(code, lexer, formatter, outfile=None):
-    """
-    Lex ``code`` with ``lexer`` and format it with the formatter ``formatter``.
-
-    If ``outfile`` is given and a valid file object (an object
-    with a ``write`` method), the result will be written to it, otherwise
-    it is returned as a string.
-    """
-    return format(lex(code, lexer), formatter, outfile)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/status.py

@@ -1,132 +0,0 @@
-from types import TracebackType
-from typing import Optional, Type
-
-from .console import Console, RenderableType
-from .jupyter import JupyterMixin
-from .live import Live
-from .spinner import Spinner
-from .style import StyleType
-
-
-class Status(JupyterMixin):
-    """Displays a status indicator with a 'spinner' animation.
-
-    Args:
-        status (RenderableType): A status renderable (str or Text typically).
-        console (Console, optional): Console instance to use, or None for global console. Defaults to None.
-        spinner (str, optional): Name of spinner animation (see python -m rich.spinner). Defaults to "dots".
-        spinner_style (StyleType, optional): Style of spinner. Defaults to "status.spinner".
-        speed (float, optional): Speed factor for spinner animation. Defaults to 1.0.
-        refresh_per_second (float, optional): Number of refreshes per second. Defaults to 12.5.
-    """
-
-    def __init__(
-        self,
-        status: RenderableType,
-        *,
-        console: Optional[Console] = None,
-        spinner: str = "dots",
-        spinner_style: StyleType = "status.spinner",
-        speed: float = 1.0,
-        refresh_per_second: float = 12.5,
-    ):
-        self.status = status
-        self.spinner_style = spinner_style
-        self.speed = speed
-        self._spinner = Spinner(spinner, text=status, style=spinner_style, speed=speed)
-        self._live = Live(
-            self.renderable,
-            console=console,
-            refresh_per_second=refresh_per_second,
-            transient=True,
-        )
-
-    @property
-    def renderable(self) -> Spinner:
-        return self._spinner
-
-    @property
-    def console(self) -> "Console":
-        """Get the Console used by the Status objects."""
-        return self._live.console
-
-    def update(
-        self,
-        status: Optional[RenderableType] = None,
-        *,
-        spinner: Optional[str] = None,
-        spinner_style: Optional[StyleType] = None,
-        speed: Optional[float] = None,
-    ) -> None:
-        """Update status.
-
-        Args:
-            status (Optional[RenderableType], optional): New status renderable or None for no change. Defaults to None.
-            spinner (Optional[str], optional): New spinner or None for no change. Defaults to None.
-            spinner_style (Optional[StyleType], optional): New spinner style or None for no change. Defaults to None.
-            speed (Optional[float], optional): Speed factor for spinner animation or None for no change. Defaults to None.
-        """
-        if status is not None:
-            self.status = status
-        if spinner_style is not None:
-            self.spinner_style = spinner_style
-        if speed is not None:
-            self.speed = speed
-        if spinner is not None:
-            self._spinner = Spinner(
-                spinner, text=self.status, style=self.spinner_style, speed=self.speed
-            )
-            self._live.update(self.renderable, refresh=True)
-        else:
-            self._spinner.update(
-                text=self.status, style=self.spinner_style, speed=self.speed
-            )
-
-    def start(self) -> None:
-        """Start the status animation."""
-        self._live.start()
-
-    def stop(self) -> None:
-        """Stop the spinner animation."""
-        self._live.stop()
-
-    def __rich__(self) -> RenderableType:
-        return self.renderable
-
-    def __enter__(self) -> "Status":
-        self.start()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from time import sleep
-
-    from .console import Console
-
-    console = Console()
-    with console.status("[magenta]Covid detector booting up") as status:
-        sleep(3)
-        console.log("Importing advanced AI")
-        sleep(3)
-        console.log("Advanced Covid AI Ready")
-        sleep(3)
-        status.update(status="[bold blue] Scanning for Covid", spinner="earth")
-        sleep(3)
-        console.log("Found 10,000,000,000 copies of Covid32.exe")
-        sleep(3)
-        status.update(
-            status="[bold red]Moving Covid32.exe to Trash",
-            spinner="bouncingBall",
-            spinner_style="yellow",
-        )
-        sleep(5)
-    console.print("[bold green]Covid deleted successfully")

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/__init__.py

@@ -1,35 +0,0 @@
-"""For backward compatibility, expose main functions from
-``setuptools.config.setupcfg``
-"""
-import warnings
-from functools import wraps
-from textwrap import dedent
-from typing import Callable, TypeVar, cast
-
-from .._deprecation_warning import SetuptoolsDeprecationWarning
-from . import setupcfg
-
-Fn = TypeVar("Fn", bound=Callable)
-
-__all__ = ('parse_configuration', 'read_configuration')
-
-
-def _deprecation_notice(fn: Fn) -> Fn:
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        msg = f"""\
-        As setuptools moves its configuration towards `pyproject.toml`,
-        `{__name__}.{fn.__name__}` became deprecated.
-
-        For the time being, you can use the `{setupcfg.__name__}` module
-        to access a backward compatible API, but this module is provisional
-        and might be removed in the future.
-        """
-        warnings.warn(dedent(msg), SetuptoolsDeprecationWarning, stacklevel=2)
-        return fn(*args, **kwargs)
-
-    return cast(Fn, _wrapper)
-
-
-read_configuration = _deprecation_notice(setupcfg.read_configuration)
-parse_configuration = _deprecation_notice(setupcfg.parse_configuration)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/postprocessing.py

@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from torch.nn import functional as F
-
-from detectron2.layers import paste_masks_in_image
-from detectron2.structures import Instances
-
-
-def detector_postprocess(results, output_height, output_width, mask_threshold=0.5):
-    """
-    Resize the output instances.
-    The input images are often resized when entering an object detector.
-    As a result, we often need the outputs of the detector in a different
-    resolution from its inputs.
-
-    This function will resize the raw outputs of an R-CNN detector
-    to produce outputs according to the desired output resolution.
-
-    Args:
-        results (Instances): the raw outputs from the detector.
-            `results.image_size` contains the input image resolution the detector sees.
-            This object might be modified in-place.
-        output_height, output_width: the desired output resolution.
-
-    Returns:
-        Instances: the resized output from the model, based on the output resolution
-    """
-    scale_x, scale_y = (output_width / results.image_size[1], output_height / results.image_size[0])
-    results = Instances((output_height, output_width), **results.get_fields())
-
-    if results.has("pred_boxes"):
-        output_boxes = results.pred_boxes
-    elif results.has("proposal_boxes"):
-        output_boxes = results.proposal_boxes
-
-    output_boxes.scale(scale_x, scale_y)
-    output_boxes.clip(results.image_size)
-
-    results = results[output_boxes.nonempty()]
-
-    if results.has("pred_masks"):
-        results.pred_masks = paste_masks_in_image(
-            results.pred_masks[:, 0, :, :],  # N, 1, M, M
-            results.pred_boxes,
-            results.image_size,
-            threshold=mask_threshold,
-        )
-
-    if results.has("pred_keypoints"):
-        results.pred_keypoints[:, :, 0] *= scale_x
-        results.pred_keypoints[:, :, 1] *= scale_y
-
-    return results
-
-
-def sem_seg_postprocess(result, img_size, output_height, output_width):
-    """
-    Return semantic segmentation predictions in the original resolution.
-
-    The input images are often resized when entering semantic segmentor. Moreover, in same
-    cases, they also padded inside segmentor to be divisible by maximum network stride.
-    As a result, we often need the predictions of the segmentor in a different
-    resolution from its inputs.
-
-    Args:
-        result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W),
-            where C is the number of classes, and H, W are the height and width of the prediction.
-        img_size (tuple): image size that segmentor is taking as input.
-        output_height, output_width: the desired output resolution.
-
-    Returns:
-        semantic segmentation prediction (Tensor): A tensor of the shape
-            (C, output_height, output_width) that contains per-pixel soft predictions.
-    """
-    result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1)
-    result = F.interpolate(
-        result, size=(output_height, output_width), mode="bilinear", align_corners=False
-    )[0]
-    return result

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/mask_head.py

@@ -1,252 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from typing import List
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
-from detectron2.structures import Instances
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.registry import Registry
-
-ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD")
-ROI_MASK_HEAD_REGISTRY.__doc__ = """
-Registry for mask heads, which predicts instance masks given
-per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-def mask_rcnn_loss(pred_mask_logits, instances, vis_period=0):
-    """
-    Compute the mask prediction loss defined in the Mask R-CNN paper.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. These instances are in 1:1
-            correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
-            ...) associated with each instance are stored in fields.
-        vis_period (int): the period (in steps) to dump visualization.
-
-    Returns:
-        mask_loss (Tensor): A scalar tensor containing the loss.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-    total_num_masks = pred_mask_logits.size(0)
-    mask_side_len = pred_mask_logits.size(2)
-    assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!"
-
-    gt_classes = []
-    gt_masks = []
-    for instances_per_image in instances:
-        if len(instances_per_image) == 0:
-            continue
-        if not cls_agnostic_mask:
-            gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64)
-            gt_classes.append(gt_classes_per_image)
-
-        gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize(
-            instances_per_image.proposal_boxes.tensor, mask_side_len
-        ).to(device=pred_mask_logits.device)
-        # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len
-        gt_masks.append(gt_masks_per_image)
-
-    if len(gt_masks) == 0:
-        return pred_mask_logits.sum() * 0
-
-    gt_masks = cat(gt_masks, dim=0)
-
-    if cls_agnostic_mask:
-        pred_mask_logits = pred_mask_logits[:, 0]
-    else:
-        indices = torch.arange(total_num_masks)
-        gt_classes = cat(gt_classes, dim=0)
-        pred_mask_logits = pred_mask_logits[indices, gt_classes]
-
-    if gt_masks.dtype == torch.bool:
-        gt_masks_bool = gt_masks
-    else:
-        # Here we allow gt_masks to be float as well (depend on the implementation of rasterize())
-        gt_masks_bool = gt_masks > 0.5
-    gt_masks = gt_masks.to(dtype=torch.float32)
-
-    # Log the training accuracy (using gt classes and 0.5 threshold)
-    mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool
-    mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0))
-    num_positive = gt_masks_bool.sum().item()
-    false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max(
-        gt_masks_bool.numel() - num_positive, 1.0
-    )
-    false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0)
-
-    storage = get_event_storage()
-    storage.put_scalar("mask_rcnn/accuracy", mask_accuracy)
-    storage.put_scalar("mask_rcnn/false_positive", false_positive)
-    storage.put_scalar("mask_rcnn/false_negative", false_negative)
-    if vis_period > 0 and storage.iter % vis_period == 0:
-        pred_masks = pred_mask_logits.sigmoid()
-        vis_masks = torch.cat([pred_masks, gt_masks], axis=2)
-        name = "Left: mask prediction;   Right: mask GT"
-        for idx, vis_mask in enumerate(vis_masks):
-            vis_mask = torch.stack([vis_mask] * 3, axis=0)
-            storage.put_image(name + f" ({idx})", vis_mask)
-
-    mask_loss = F.binary_cross_entropy_with_logits(pred_mask_logits, gt_masks, reduction="mean")
-    return mask_loss
-
-
-def mask_rcnn_inference(pred_mask_logits, pred_instances):
-    """
-    Convert pred_mask_logits to estimated foreground probability masks while also
-    extracting only the masks for the predicted classes in pred_instances. For each
-    predicted box, the mask of the same class is attached to the instance by adding a
-    new "pred_masks" field to pred_instances.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        pred_instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. Each Instances must have field "pred_classes".
-
-    Returns:
-        None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask,
-            Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized)
-            masks the resolution predicted by the network; post-processing steps, such as resizing
-            the predicted masks to the original image resolution and/or binarizing them, is left
-            to the caller.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-
-    if cls_agnostic_mask:
-        mask_probs_pred = pred_mask_logits.sigmoid()
-    else:
-        # Select masks corresponding to the predicted classes
-        num_masks = pred_mask_logits.shape[0]
-        class_pred = cat([i.pred_classes for i in pred_instances])
-        indices = torch.arange(num_masks, device=class_pred.device)
-        mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid()
-    # mask_probs_pred.shape: (B, 1, Hmask, Wmask)
-
-    num_boxes_per_image = [len(i) for i in pred_instances]
-    mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0)
-
-    for prob, instances in zip(mask_probs_pred, pred_instances):
-        instances.pred_masks = prob  # (1, Hmask, Wmask)
-
-
-class BaseMaskRCNNHead(nn.Module):
-    """
-    Implement the basic Mask R-CNN losses and inference logic.
-    """
-
-    def __init__(self, cfg, input_shape):
-        super().__init__()
-        self.vis_period = cfg.VIS_PERIOD
-
-    def forward(self, x, instances: List[Instances]):
-        """
-        Args:
-            x: input 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 in training. The predicted "instances" in inference.
-        """
-        x = self.layers(x)
-        if self.training:
-            return {"loss_mask": mask_rcnn_loss(x, instances, self.vis_period)}
-        else:
-            mask_rcnn_inference(x, instances)
-            return instances
-
-    def layers(self, x):
-        """
-        Neural network layers that makes predictions from input features.
-        """
-        raise NotImplementedError
-
-
-@ROI_MASK_HEAD_REGISTRY.register()
-class MaskRCNNConvUpsampleHead(BaseMaskRCNNHead):
-    """
-    A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`).
-    """
-
-    def __init__(self, cfg, input_shape: ShapeSpec):
-        """
-        The following attributes are parsed from config:
-            num_conv: the number of conv layers
-            conv_dim: the dimension of the conv layers
-            norm: normalization for the conv layers
-        """
-        super().__init__(cfg, input_shape)
-
-        # fmt: off
-        num_classes       = cfg.MODEL.ROI_HEADS.NUM_CLASSES
-        conv_dims         = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM
-        self.norm         = cfg.MODEL.ROI_MASK_HEAD.NORM
-        num_conv          = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV
-        input_channels    = input_shape.channels
-        cls_agnostic_mask = cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK
-        # fmt: on
-
-        self.conv_norm_relus = []
-
-        for k in range(num_conv):
-            conv = Conv2d(
-                input_channels if k == 0 else conv_dims,
-                conv_dims,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=not self.norm,
-                norm=get_norm(self.norm, conv_dims),
-                activation=F.relu,
-            )
-            self.add_module("mask_fcn{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-
-        self.deconv = ConvTranspose2d(
-            conv_dims if num_conv > 0 else input_channels,
-            conv_dims,
-            kernel_size=2,
-            stride=2,
-            padding=0,
-        )
-
-        num_mask_classes = 1 if cls_agnostic_mask else num_classes
-        self.predictor = Conv2d(conv_dims, num_mask_classes, kernel_size=1, stride=1, padding=0)
-
-        for layer in self.conv_norm_relus + [self.deconv]:
-            weight_init.c2_msra_fill(layer)
-        # use normal distribution initialization for mask prediction layer
-        nn.init.normal_(self.predictor.weight, std=0.001)
-        if self.predictor.bias is not None:
-            nn.init.constant_(self.predictor.bias, 0)
-
-    def layers(self, x):
-        for layer in self.conv_norm_relus:
-            x = layer(x)
-        x = F.relu(self.deconv(x))
-        return self.predictor(x)
-
-
-def build_mask_head(cfg, input_shape):
-    """
-    Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_MASK_HEAD.NAME
-    return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape)

spaces/CVPR/LIVE/pybind11/tests/test_eval.py

@@ -1,27 +0,0 @@
-# -*- coding: utf-8 -*-
-import os
-
-import pytest
-
-import env  # noqa: F401
-
-from pybind11_tests import eval_ as m
-
-
-def test_evals(capture):
-    with capture:
-        assert m.test_eval_statements()
-    assert capture == "Hello World!"
-
-    assert m.test_eval()
-    assert m.test_eval_single_statement()
-
-    assert m.test_eval_failure()
-
-
-@pytest.mark.xfail("env.PYPY and not env.PY2", raises=RuntimeError)
-def test_eval_file():
-    filename = os.path.join(os.path.dirname(__file__), "test_eval_call.py")
-    assert m.test_eval_file(filename)
-
-    assert m.test_eval_file_failure()

spaces/CVPR/LIVE/pydiffvg/image.py

@@ -1,22 +0,0 @@
-import numpy as np
-import skimage
-import skimage.io
-import os
-
-def imwrite(img, filename, gamma = 2.2, normalize = False):
-    directory = os.path.dirname(filename)
-    if directory != '' and not os.path.exists(directory):
-        os.makedirs(directory)
-
-    if not isinstance(img, np.ndarray):
-        img = img.data.numpy()
-    if normalize:
-        img_rng = np.max(img) - np.min(img)
-        if img_rng > 0:
-            img = (img - np.min(img)) / img_rng
-    img = np.clip(img, 0.0, 1.0)
-    if img.ndim==2:
-        #repeat along the third dimension
-        img=np.expand_dims(img,2)
-    img[:, :, :3] = np.power(img[:, :, :3], 1.0/gamma)
-    skimage.io.imsave(filename, (img * 255).astype(np.uint8))

spaces/CVPR/LIVE/thrust/thrust/detail/cpp11_required.h

@@ -1,26 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config/cpp_dialect.h>
-
-#ifndef THRUST_CPP11_REQUIRED_NO_ERROR
-#  if THRUST_CPP_DIALECT < 2011 
-#    error C++11 is required for this Thrust feature; please upgrade your compiler or pass the appropriate -std=c++XX flag to it.
-#  endif
-#endif
-

spaces/Clebersla/RVC_V2_Huggingface_Version/lib/infer_pack/modules.py

@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from lib.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x