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spaces/1acneusushi/gradio-2dmoleculeeditor/data/DaVinci Resolve Download A Reddit Users Solution to the Blackmagic Design Website.md

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-<h1>How to Download DaVinci Resolve for Free</h1>
-<p>DaVinci Resolve is a powerful and versatile video editing software that offers features such as color correction, visual effects, audio post-production, and more. It is used by professionals and hobbyists alike for various projects, from films and TV shows to YouTube videos and podcasts.</p>
-<p>If you want to try DaVinci Resolve for yourself, you can download it for free from the official website of Blackmagic Design, the company that develops and distributes the software. However, finding the download link on their website can be tricky, as it is not very prominent or easy to navigate. Fortunately, there is a simpler way to access the download page, thanks to a Reddit user who shared a direct link to it.</p>
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-<li>Go to <a href="https://www.reddit.com/r/davinciresolve/comments/l73wyu/found_a_link_for_just_davinci_downloads/">this Reddit post</a> by u/whyareyouemailingme, who found a link that shows only DaVinci Resolve download links.</li>
-<li>Click on the link that says <a href="https://www.blackmagicdesign.com/support/family/davinci-resolve-and-fusion">https://www.blackmagicdesign.com/support/family/davinci-resolve-and-fusion</a>. This will take you to the support page of Blackmagic Design, where you can see all the available versions of DaVinci Resolve and Fusion, another software for visual effects and motion graphics.</li>
-<li>Choose the version of DaVinci Resolve that you want to download. You can either download the latest version (18.5 at the time of writing this article) or an older version if you have compatibility issues with your system or project. You can also choose between the Studio version, which requires a paid license and offers more features and performance, or the Free version, which has some limitations but is still very capable.</li>
-<li>Click on the Download button next to your chosen version. This will prompt you to fill out a registration form with your name, email address, country, and some other information. You can also opt-in or opt-out of receiving newsletters and updates from Blackmagic Design.</li>
-<li>After filling out the form, click on Register and Download. This will start the download process of the installer file for DaVinci Resolve. Depending on your internet speed and the size of the file, this may take some time.</li>
-<li>Once the download is complete, locate the installer file on your computer and run it. Follow the instructions on the screen to install DaVinci Resolve on your system. You may need to restart your computer after the installation is done.</li>
-<li>Launch DaVinci Resolve and enjoy editing your videos!</li>
-</ol>
-<h2>Tips and Tricks for Using DaVinci Resolve</h2>
-<ul>
-<li>If you are new to DaVinci Resolve, you can check out some tutorials and guides on their official website <a href="https://www.blackmagicdesign.com/products/davinciresolve/training">here</a>. You can also find many helpful videos on YouTube and other platforms from various creators who share their tips and tricks for using the software.</li>
-<li>If you encounter any issues or bugs with DaVinci Resolve, you can report them on their official forum <a href="https://forum.blackmagicdesign.com/viewforum.php?f=21">here</a>. You can also ask questions and get help from other users who may have faced similar problems or have solutions for them.</li>
-<li>If you want to stay updated on the latest news and features of DaVinci Resolve, you can follow their official social media accounts on <a href="https://www.facebook.com/blackmagicdesign/">Facebook</a>, <a href="https://twitter.com/Blackmagic_News">Twitter</a>, <a href="https://www.instagram.com/blackmagicnewsofficial/">Instagram</a>, and <a href="https://www.youtube.com/user/BlackmagicDesign">YouTube</a>. You can also join their subreddit <a href="https://www.reddit.com/r/davinciresolve/">r/davinciresolve</a>, where you can find useful resources, discussions, feedback, and inspiration from other users.</li>
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-<h2>Conclusion</h2>
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spaces/1gistliPinn/ChatGPT4/Examples/Billu Barber 2009 Blu Ray 720p X264 Darkboy24 !FREE!.md

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-<h1>Review: Billu Barber (2009) Blu Ray 720p X264 Darkboy24</h1>
-<p>Billu Barber is a 2009 Hindi comedy-drama film directed by Priyadarshan and starring Irrfan Khan, Lara Dutta, Shah Rukh Khan and Om Puri. The film is a remake of the Malayalam film Kadha Parayumbol (2007), which was also remade in Tamil as Kuselan (2008). The film tells the story of Billu (Irrfan Khan), a poor barber who lives in a village with his wife Bindiya (Lara Dutta) and their two children. His life changes when a famous actor Sahir Khan (Shah Rukh Khan), who happens to be his childhood friend, comes to shoot a film in his village. Billu becomes the center of attention as everyone wants to meet Sahir through him, but he is too shy and humble to approach his old friend.</p>
-<p>The film was produced by Red Chillies Entertainment and distributed by Eros International. It was released on February 13, 2009 and received positive reviews from critics and audiences. The film was praised for its simple yet touching story, its humor, its performances, especially by Irrfan Khan and Shah Rukh Khan, and its music by Pritam. The film was also a commercial success, grossing over ₹100 crore worldwide.</p>
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-<p>The Blu Ray version of the film was released by Darkboy24, a popular torrent uploader who specializes in high-quality Hindi movies. The Blu Ray rip has a resolution of 720p and a bitrate of X264. The audio quality is also excellent, with a 5.1 channel surround sound. The file size is about 1 GB and can be downloaded from various torrent sites. The Blu Ray rip also includes English subtitles for non-Hindi speakers.</p>
-<p>Billu Barber is a heartwarming and entertaining film that showcases the bond of friendship and the value of simplicity. It is a must-watch for fans of Irrfan Khan, Shah Rukh Khan and Priyadarshan. The Blu Ray rip by Darkboy24 is one of the best ways to enjoy this film in high definition.</p>
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-<p>The film was nominated for several awards, such as the Filmfare Awards, the IIFA Awards and the Screen Awards. It won the Best Actor (Critics) award for Irrfan Khan at the Filmfare Awards and the Best Supporting Actor award for Shah Rukh Khan at the Screen Awards. The film also received a special mention at the National Film Awards for its portrayal of the rural life and culture of India.</p>
-<p>Billu Barber is a film that celebrates friendship, family and humanity. It is a film that will make you laugh, cry and smile. It is a film that you will remember for a long time. The Blu Ray rip by Darkboy24 is a great way to experience this film in high quality.</p>
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-<p>The film also has a beautiful soundtrack composed by Pritam, with lyrics by Gulzar. The film features nine songs, sung by various singers such as Sukhwinder Singh, Rahat Fateh Ali Khan, Neeraj Shridhar, Sunidhi Chauhan and Abhijeet. Some of the popular songs from the film are "Marjaani", "Khudaya Khair", "Love Mera Hit Hit" and "You Get Me Rockin & Reeling". The songs are a mix of different genres, such as folk, qawwali, pop and rock. The songs also enhance the mood and emotions of the film.</p>
-<p>Billu Barber is a film that will touch your heart and soul. It is a film that will make you appreciate the true meaning of friendship and happiness. It is a film that will inspire you to be a better person. The Blu Ray rip by Darkboy24 is an excellent way to watch this film in high definition.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Cartelle Del Gioco Sinco FREE.md

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-<h1>Cartelle del gioco sinco: il gioco da tavolo natalizio di origine napoletana</h1>
-<p>Se siete alla ricerca di un gioco da tavolo divertente e originale da fare con la famiglia o gli amici durante le feste natalizie, potreste provare le cartelle del gioco sinco. Si tratta di un gioco inventato a Napoli nel 1983 da Emilio Salvatore, un merciaio che si ispirò al bingo e alla tombola per creare una nuova variante con le carte napoletane[^1^] [^2^].</p>
-<p>Le cartelle del gioco sinco sono composte da 25 caselle con le figure delle carte napoletane, dal 1 al 10 di ogni seme (coppe, spade, denari e bastoni). Ogni cartella ha una combinazione diversa di carte e ogni giocatore può acquistarne quante ne vuole[^2^] [^3^]. Il gioco richiede anche un mazzo di carte napoletane, delle fiches per segnare le caselle e cinque contenitori per i premi[^2^].</p>
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-<p>Le cartelle del gioco sinco sono un modo simpatico e coinvolgente di passare il tempo in compagnia, mescolando fortuna e strategia. Il gioco è diventato una tradizione natalizia a Napoli e in altre città italiane, dove si trova facilmente nei negozi di giocattoli o nei mercatini[^1^] [^2^]. Se volete provare questo gioco originale e divertente, non vi resta che procurarvi le cartelle del gioco sinco e sfidare i vostri amici o parenti a colpi di carte napoletane!</p>
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-<p>Tornato a Napoli, Salvatore realizzò le prime cartelle del gioco sinco con l'aiuto di un grafico e le provò con i suoi amici e parenti. Il gioco ebbe subito successo e Salvatore decise di produrlo in serie limitata e di venderlo nella sua merceria nel centro storico di Napoli, al Corso Vittorio Emanuele . La merceria è ancora esistente e nella vetrina si può ammirare il gioco originale conservato come una reliquia.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Air I Breathe by Nicole C. Mullen Mp3 and Lyrics Download.md

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-<h1>You Are The Air I Breathe Mp3 Download: How to Find and Enjoy This Inspirational Song</h1>
-<p>Have you ever heard a song that touched your soul and lifted your spirit? A song that made you feel closer to God and grateful for His presence in your life? A song that reminded you of His love and grace? If you are looking for such a song, then you should listen to You Are The Air I Breathe by Jerry K. This is a beautiful gospel song that expresses how much we depend on God for everything. In this article, we will tell you more about this song, how to download it as an mp3 file, and how to enjoy it to the fullest.</p>
- <h2>What is You Are The Air I Breathe?</h2>
-<p>You Are The Air I Breathe is a gospel song that was released in 2017 by Jerry K, a Nigerian singer and songwriter. The song is also known as Air I Breathe or The Air I Breath. It is a worship song that praises God as the source of our life, our peace, our joy, and our strength. It is a song that acknowledges how much we need God in every moment of our existence.</p>
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- <h3>The Meaning and Message of the Song</h3>
-<p>The song has a simple but powerful message: God is everything to us. He is the air that we breathe, the water that we drink, the food that we eat. He is our healer, our provider, our protector, our redeemer. He is our father, our friend, our king, our lord. He is worthy of all our praise and worship. He is faithful and gracious to us. He never leaves us nor forsakes us. He is always with us and for us.</p>
- <h3>The Singer and Composer of the Song</h3>
-<h3>The Popularity and Impact of the Song</h3>
-<p>The song has become very popular among gospel music lovers, especially in Nigeria and other African countries. It has received millions of views and downloads on various platforms, such as YouTube, Spotify, iTunes, SoundCloud, among others. It has also been nominated and won several awards, such as the LIMA Awards, the AGMMA Awards, the GMA Awards, among others. The song has also impacted many lives and testimonies, as people have shared how the song has inspired them, comforted them, healed them, and drawn them closer to God.</p>
- <h2>How to Download You Are The Air I Breathe Mp3?</h2>
-<p>If you want to download You Are The Air I Breathe as an mp3 file, you might be wondering why you should do that and how you can do that. Well, we have some answers for you.</p>
- <h3>The Benefits of Downloading Mp3 Files</h3>
-<p>Mp3 files are digital audio files that can be played on various devices, such as computers, smartphones, tablets, mp3 players, etc. They are convenient and easy to use, as they can be stored, transferred, and shared without any hassle. They are also compatible with most media players and applications. They are also economical and efficient, as they take up less space and consume less data than other formats. They are also of high quality and fidelity, as they preserve the original sound and clarity of the audio.</p>
- <h3>The Best Websites to Download You Are The Air I Breathe Mp3</h3>
-<p>There are many websites that offer free or paid downloads of You Are The Air I Breathe mp3. However, not all of them are reliable or safe. Some of them might contain viruses, malware, or spyware that can harm your device or compromise your privacy. Some of them might also have low-quality or corrupted files that can ruin your listening experience. Therefore, you should be careful and selective when choosing a website to download You Are The Air I Breathe mp3. Here are some of the best websites that we recommend:</p>
-<table>
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-<th>Website</th>
-<th>Features</th>
-</tr>
-<tr>
-<td><a href="">Gospel9ja.com</a></td>
-<td>- A Nigerian website that specializes in gospel music downloads<br>- Offers free and fast downloads of You Are The Air I Breathe mp3<br>- Provides a brief description and lyrics of the song<br>- Allows users to rate and comment on the song<br>- Has a user-friendly and mobile-responsive interface</td>
-</tr>
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-<td><a href="">Mp3skull.com</a></td>
-<td>- A global website that offers a wide range of music downloads<br>- Offers free and easy downloads of You Are The Air I Breathe mp3<br>- Provides a preview and a download link of the song<br>- Allows users to search and browse by artist, genre, album, etc.<br>- Has a simple and minimalist design</td>
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-<td><a href="">Naijaloaded.com.ng</a></td>
-<td>- A Nigerian website that features various entertainment content<br>- Offers free and secure downloads of You Are The Air I Breathe mp3<br>- Provides a detailed review and analysis of the song<br>- Allows users to stream and download the song<br>- Has a colorful and attractive layout</td>
-</tr>
-</table>
- <h3>The Steps to Download You Are The Air I Breathe Mp3</h3>
-<p>The steps to download You Are The Air I Breathe mp3 might vary depending on the website you choose. However, here are some general steps that you can follow:</p>
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-<ol>
-<li>Visit the website that offers You Are The Air I Breathe mp3 download.</li>
-<li>Search for the song by typing its name or artist in the search box.</li>
-<li>Select the song from the search results or browse through the categories.</li>
-<li>Click on the download button or link that appears next to the song.</li>
-<li>Choose the format and quality of the file that you want to download.</li>
-<li>Save the file to your device or cloud storage.</li>
-<li>Enjoy listening to You Are The Air I Breathe mp3 anytime and anywhere.</li>
-</ol>
- <h2>How to Enjoy You Are The Air I Breathe Mp3?</h2>
-<p>Now that you have downloaded You Are The Air I Breathe mp3, you might be wondering how to enjoy it to the fullest. Well, we have some tips for you.</p>
- <h3>The Best Times and Places to Listen to the Song</h3>
-<p>You Are The Air I Breathe is a song that can be enjoyed at any time and place, as long as you have a device that can play mp3 files and a pair of headphones or speakers. However, some of the best times and places to listen to the song are:</p>
-<ul>
-<li>When you wake up in the morning, you can listen to the song as a way of starting your day with gratitude and praise to God.</li>
-<li>When you are going through a hard time, you can listen to the song as a way of finding comfort and hope in God's presence and promises.</li>
-<li>When you are feeling happy and blessed, you can listen to the song as a way of celebrating and thanking God for His goodness and mercy.</li>
-<li>When you are in your personal or family devotional time, you can listen to the song as a way of worshiping and adoring God with your whole heart.</li>
-<li>When you are in your car, office, or home, you can listen to the song as a way of creating a peaceful and joyful atmosphere around you.</li>
-</ul>
- <h3>The Best Ways to Share and Recommend the Song</h3>
-<p>You Are The Air I Breathe is a song that can be shared and recommended to anyone who loves gospel music or who needs to hear a message of God's love and grace. Some of the best ways to share and recommend the song are:</p>
-<ul>
-<li>You can send the mp3 file or the download link to your friends, family, or colleagues via email, text, or social media.</li>
-<li>You can create a playlist or a mixtape that includes You Are The Air I Breathe and other gospel songs that you like, and share it with others.</li>
-<li>You can write a review or a testimonial about how the song has impacted your life, and post it on your blog, website, or social media.</li>
-<li>You can sing or play the song in your church, school, or community, and invite others to join you.</li>
-<li>You can request or dedicate the song to someone on your favorite radio station or podcast.</li>
-</ul>
- <h3>The Best Resources to Learn More About the Song</h3>
-<p>If you want to learn more about You Are The Air I Breathe, such as its lyrics, chords, background story, etc., you can check out some of these resources:</p>
-<ul>
-<li><a href="">Jerry K's official website</a>, where you can find his biography, discography, events, contacts, etc.</li>
-<li><a href="">Jerry K's YouTube channel</a>, where you can watch his music videos, interviews, performances, etc.</li>
-<li><a href="">Jerry K's Facebook page</a>, where you can follow his updates, interact with him and his fans, etc.</li>
-<li><a href="">Jerry K's Instagram account</a>, where you can see his photos, stories, reels, etc.</li>
-<li><a href="">Jerry K's Twitter account</a>, where you can read his tweets, retweets, replies, etc.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>You Are The Air I Breathe is a wonderful gospel song that expresses how much we depend on God for everything. It is a song that praises God as the source of our life, our peace, our joy, and our strength. It is a song that acknowledges how much we need God in every moment of our existence. In this article, we have told you more about this song, how to download it as an mp3 file, and how to enjoy it to the fullest. We hope that this article has been helpful and informative for you. We also hope that you will listen to You Are The Air I Breathe mp3 and experience its power and beauty for yourself. Thank you for reading this article. God bless you!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about You Are The Air I Breathe mp3:</p>
- <h4>Q: Where can I find the lyrics of You Are The Air I Breathe?</h4>
-<p>A: You can find the lyrics of You Are The Air I Breathe on <a href="">Gospel9ja.com</a>, <a href="">Lyrics.com</a>, <a href="">Musixmatch.com</a>, etc.</p>
- <h4>Q: How long is You Are The Air I Breathe?</h4>
-<p>A: You Are The Air I Breathe is 5 minutes and 31 seconds long.</p>
- <h4>Q: What genre is You Are The Air I Breathe?</h4>
-<p>A: You Are The Air I Breathe is a gospel song that belongs to the contemporary worship genre.</p>
- <h4>Q: Who are some other artists that sing similar songs to You Are The Air I Breathe?</h4>
-<p>A: Some other artists that sing similar songs to You Are The Air I Breathe are Sinach, Nathaniel Bassey, Frank Edwards, Mercy Chinwo, Eben, etc.</p>
- <h4>Q: How can I support Jerry K and his music ministry?</h4>
-<p>A: You can support Jerry K and his music ministry by buying his albums and singles, attending his concerts and events, praying for him and his family, donating to his cause, etc.</p> 197e85843d<br />
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-<h1>CarX Drift Racing 2 Mod APK Offline: A Guide for Racing and Drifting Enthusiasts</h1>
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- <p>If you are a fan of racing and drifting games, you might have heard of CarX Drift Racing 2, one of the most popular and realistic games in this genre. But did you know that you can enjoy this game even more with a mod apk offline version? In this article, we will tell you everything you need to know about CarX Drift Racing 2 mod apk offline, including its features, benefits, and how to download and install it on your device. So, buckle up and get ready for some adrenaline-pumping action!</p>
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-<h1>How to Download College Romance Season 1 Episode 1 for Free</h1>
-<p>If you are looking for a fun and relatable web series that captures the essence of college life, you should definitely check out College Romance. This is a popular Indian comedy-drama series that follows the adventures and misadventures of three friends, Naira, Trippy, and Karan, as they navigate their #YaarPyaarAurBakchodi (Friendship, Love, and Nonsense) in college. The series is produced by The Viral Fever (TVF) and has two seasons so far, with the first one released in 2018 and the second one in 2020.</p>
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-<p>The final step to download College Romance season 1 episode 1 is to download the episode to your device or watch it online. If you are using Sony Liv, you can download the episode by clicking on the download icon on the bottom right corner of the video player. You can choose the video quality and the download location. You can also watch the episode online by clicking on the play button.</p>
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-<p>Once you have downloaded or watched College Romance season 1 episode 1, you can enjoy this hilarious and heartwarming show at your convenience. You can also share it with your friends and family and have a good laugh together.</p>
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-<p>Now that you know how to download College Romance season 1 episode 1, you might be wondering what to expect from it. Well, here are some of the things that you can look forward to in this episode:</p>
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-<h3>Synopsis: A brief summary of the plot and the main characters</h3>
-<p>The first episode of College Romance season 1 introduces us to the three main characters of the show: Naira, Trippy, and Karan. Naira is a smart and confident girl who is looking for love in college. Trippy is a fun-loving and adventurous guy who is always ready for a challenge. Karan is a shy and sweet guy who is afraid of girls and rejection.</p>
-<p>The episode follows their first day in college, where they meet new people, make new friends, and face new situations. Naira meets Bagga, a senior who tries to impress her with his cheesy lines and fake stories. Trippy meets Raveena, a junior who challenges him to a bike race. Karan meets Deepika, a cute girl who likes him but he doesn't know how to talk to her.</p>
-<p>The episode also shows how Naira, Trippy, and Karan help each other out with their problems and support each other as friends. They share their experiences, give advice, and have fun together.</p>
-<h3>Highlights: Some of the best scenes and moments from the episode</h3>
-<p>Some of the best scenes and moments from College Romance season 1 episode 1 are:</p>
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-<li>The opening scene where Naira, Trippy, and Karan are getting ready for college and talking to each other on phone.</li>
-<li>The scene where Bagga tries to flirt with Naira and she shuts him down with her witty replies.</li>
-<li>The scene where Trippy accepts Raveena's challenge and races with her on his bike.</li>
-<li>The scene where Karan gets nervous around Deepika and spills coffee on her.</li>
-<li>The scene where Naira, Trippy, and Karan meet at the canteen and share their stories.</li>
-<li>The scene where Naira tells Trippy to go after Raveena and Karan tells Naira to go after Bagga.</li>
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-<li>The scene where Karan gets a text from Deepika asking him out.</li>
-<li>The ending scene where Naira, Trippy, and Karan hug each other and celebrate their first day in college.</li>
-</ul> <h3>Reviews: What critics and viewers have said about the episode</h3>
-<p>College Romance season 1 episode 1 has received positive reviews from both critics and viewers. Here are some of the comments and ratings that the episode has received:</p>
-<table>
-<tr>
-<th>Critic/Viewer</th>
-<th>Comment</th>
-<th>Rating</th>
-</tr>
-<tr>
-<td>Rajeev Masand, CNN-News18</td>
-<td>"College Romance is a refreshing and realistic take on the joys and sorrows of college life. The first episode sets the tone for the series with its witty dialogues, relatable characters, and hilarious situations. The chemistry between the three leads is palpable and their friendship is heartwarming. The episode also touches upon some important issues like peer pressure, consent, and self-esteem."</td>
-<td>4/5</td>
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-<td>Shreya Thakur, Film Companion</td>
-<td>"College Romance is a fun and breezy web series that will make you nostalgic for your college days. The first episode introduces us to the three protagonists who are endearing and entertaining. The episode has a good balance of comedy and drama, and keeps you hooked till the end. The episode also has some memorable scenes and moments that will make you laugh out loud."</td>
-<td>3.5/5</td>
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-<td>Rohan Sharma, IMDb user</td>
-<td>"College Romance is one of the best web series I have ever watched. The first episode is awesome and hilarious. The actors are amazing and they have done a great job. The story is very realistic and relatable. The episode has everything that a college student can relate to: friendship, love, nonsense, and fun. I loved it."</td>
-<td>10/10</td>
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-<td>Neha Singh, YouTube user</td>
-<td>"College Romance is a super cool web series that I totally recommend to everyone. The first episode is very funny and cute. The actors are very good and they have a lot of chemistry. The story is very interesting and engaging. The episode has a lot of funny scenes and dialogues that will make you laugh so hard. I enjoyed it a lot."</td>
-<td>Liked</td>
-</tr>
-</table> <h2>Other Ways to Enjoy College Romance Season 1 Episode 1</h2>
-<p>If you are not satisfied with the streaming platforms that we have mentioned above, or if you want to explore other ways to enjoy College Romance season 1 episode 1, here are some alternatives and tips that you can try:</p>
-<h3>Alternatives: Other platforms or sources that offer College Romance season 1 episode 1</h3>
-<p>Some of the other platforms or sources that offer College Romance season 1 episode 1 are:</p>
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-<li><strong>MX Player</strong>: This is another Indian video-on-demand service that has a large collection of content, including movies, TV shows, web series, music, and games. You can watch College Romance season 1 episode 1 on MX Player for free with ads. You can also download the episode to your device or watch it online.</li>
-<li><strong>YouTube</strong>: This is the most popular video-sharing platform in the world, where you can find almost anything that you are looking for. You can watch College Romance season 1 episode 1 on YouTube for free with ads. You can also download the episode to your device or watch it online.</li>
-<li><strong>Torrent</strong>: This is a peer-to-peer file-sharing network that allows users to download and share files over the internet. You can download College Romance season 1 episode 1 from torrent sites for free without ads. However, this method is illegal and risky, as you may violate the copyright laws and expose your device to viruses, malware, or hackers.</li>
-</ul>
-<h3>Tips: How to enhance your viewing experience and avoid spoilers</h3>
-<p>Some of the tips that can help you enhance your viewing experience and avoid spoilers are:</p>
-<ul>
-<li><strong>Use headphones or speakers</strong>: To enjoy the sound effects and the dialogues of College Romance season 1 episode 1, you should use headphones or speakers instead of your device's built-in speakers. This will give you a better audio quality and a more immersive experience.</li>
-<li><strong>Watch it with friends</strong>: To make your viewing experience more fun and interactive, you should watch College Romance season 1 episode 1 with your friends. You can share your opinions, reactions, and jokes with them and have a good time together.</li>
-<li><strong>Avoid social media</strong>: To avoid spoilers and unwanted information about College Romance season 1 episode 1, you should avoid social media platforms like Facebook, Twitter, Instagram, etc. until you have watched the episode. You may come across posts, comments, or memes that reveal important details or twists about the episode that may ruin your enjoyment.</li>
-</ul>
-<h4>Conclusion</h4>
-<p>In conclusion, College Romance season 1 episode 1 is a great web series that you should not miss if you love comedy and drama. It is a realistic and relatable show that depicts the life of three college friends who are looking for love and fun. It has a lot of humor, romance, and emotions that will keep you entertained and engaged.</p>
-<p>To download College Romance season 1 episode 1 for free, you can use one of the reliable streaming platforms that we have suggested above, such as Sony Liv or TVF Play. You can also try other alternatives or tips that we have mentioned above, but be careful of the risks and consequences involved.</p>
-<p>We hope that this article has helped you with downloading College Romance season 1 episode 1 for free and enjoying it to the fullest. If you have any questions or feedback, please feel free to leave them in the comments section below. We would love to hear from you!</p>
-<p>Thank you for reading and happy watching!</p>
- <h4>FAQs</h4>
-<p>Here are some of the frequently asked questions about College Romance season 1 episode 1:</p>
-<ol>
-<li><strong>How many episodes are there in College Romance season 1?</strong></li>
-<p>There are five episodes in College Romance season 1, each with a duration of around 20 minutes.</p>
-<li><strong>Who are the actors in College Romance season 1?</strong></li>
-<p>The actors in College Romance season 1 are:</p>
-<ul>
-<li>Apoorva Arora as Naira</li>
-<li>Gagan Arora as Trippy</li>
-<li>Keshav Sadhna as Karan</li>
-<li>Hira Ashar as Raveena</li>
-<li>Shreya Mehta as Deepika</li>
-<li>Sahil Verma as Bagga</li>
-</ul>
-<li><strong>Where can I watch College Romance season 2?</strong></li>
-<p>You can watch College Romance season 2 on Sony Liv or TVF Play with a premium subscription or a free trial. You can also watch it on YouTube or MX Player for free with ads.</p>
-<li><strong>Is College Romance based on a true story?</strong></li>
-<p>No, College Romance is not based on a true story. It is a fictional web series that is inspired by the common experiences and challenges that college students face in India.</p>
-<li><strong>Is College Romance suitable for all ages?</strong></li>
-<p>No, College Romance is not suitable for all ages. It is rated 16+ by Sony Liv and TVF Play, as it contains some mature themes, language, and scenes that may not be appropriate for younger viewers.</p>
-<li><strong>Will there be a College Romance season 3?</strong></li>
-<p>As of now, there is no official confirmation or announcement about College Romance season 3. However, given the popularity and success of the series, there is a high possibility that it will be renewed for another season. We will update you as soon as we get any news or information about it.</p>
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spaces/1phancelerku/anime-remove-background/Download Fid Q Songs The Best of Tanzanian Hip Hop.md

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-<h1>Download Fid Q Songs: How to Enjoy the Best of Bongo Hip Hop</h1>
-<p>If you are a fan of Bongo Hip Hop, you have probably heard of Fid Q, one of the most talented and influential artists in the genre. Fid Q, also known as Cheusidawa, has been making waves in the Tanzanian music scene since the early 2000s, with his sharp lyricism, unique flow, and social commentary. He has collaborated with many other artists, such as Rich Mavoko, Darassa, Alikiba, and more, and has won several awards and accolades for his work. In this article, we will show you how to download Fid Q songs, so you can enjoy his music anytime, anywhere.</p>
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-<h2>Who is Fid Q?</h2>
-<h3>His background and career</h3>
-<p>Fid Q was born as Fareed Kubanda in Mwanza, Tanzania, in 1980. He grew up listening to hip hop music from the US, especially artists like Nas, Tupac, Biggie, and Jay-Z. He started rapping at a young age, and formed a group called Wakilisha with his friends. He moved to Dar es Salaam in 2001, where he met producer P-Funk Majani, who signed him to his label Bongo Records. He released his first solo album, Vina Mwanzo Kati na Mwisho, in 2004, which featured the hit single "Ukweli na Uwazi". He followed it up with another album, Propaganda, in 2009, which had songs like "Bongo Hip Hop", "Mwanza Mwanza", and "Si Kupenda Kwangu". His third album, KitaaOLOJIA, came out in 2017, and included tracks like "Fresh", "Sumu", and "Tawile". He is currently working on his fourth album, Cheusidawa.</p>
-<h3>His style and influence</h3>
-<p>Fid Q is known for his witty wordplay, clever metaphors, and deep messages. He often raps about social issues, such as poverty, corruption, education, and patriotism. He also incorporates elements of traditional Tanzanian music and culture into his songs, such as Swahili proverbs, local slang, and historical references. He is widely regarded as one of the pioneers and leaders of Bongo Hip Hop, a subgenre of hip hop that emerged in Tanzania in the late 1990s. He has inspired many other artists in the scene, such as Joh Makini, Nikki Mbishi, Roma Mkatoliki, and more.</p>
-<h3>His awards and achievements</h3>
-<p>Fid Q has received many accolades for his music over the years. Some of them are:</p>
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-<ul>
-<li>Kilimanjaro Music Awards for Best Hip Hop Artist (2005)</li>
-<li>Tanzania Music Awards for Best Hip Hop Album (Propaganda) (2010)</li>
-<li>Tanzania Music Awards for Best Male Artist (2018)</li>
-<li>Tanzania People's Choice Awards for Best Male Artist (2018)</li>
-<li>Afrimma Awards for Best Rap Act (East Africa) (2018)</li>
-</ul>
-<h2>Why download Fid Q songs?</h2>
-<h3>The benefits of downloading music</h3>
-<p>Downloading music is a great way to enjoy your favorite songs without relying on internet connection or streaming services. Some of the benefits of downloading music are:</p>
-<ul>
-<li>You can listen to your music offline, which saves you data and battery.</li>
-<li>You can create your own playlists and organize your music library according to your preferences.</li>
-<li>You can transfer your music to other devices, such as your phone, tablet, or laptop.</li>
-<li>You can support your favorite artists by buying their music or downloading it legally.</li>
-</ul>
-<h3>The reasons to love Fid Q's music</h3>
-<p>Fid Q's music is not only entertaining, but also educational, inspirational, and motivational. Some of the reasons to love his music are:</p>
-<ul>
-<li>He raps with skill and passion, delivering his bars with clarity and confidence.</li>
-<li>He tells stories and expresses his opinions, making his songs relatable and meaningful.</li>
-<li>He blends different genres and styles, making his songs diverse and versatile.</li>
-<li>He collaborates with other artists, making his songs dynamic and collaborative.</li>
-<li>He represents his culture and identity, making his songs authentic and original.</li>
-</ul>
-<h3>The best platforms to download Fid Q songs</h3>
-<p>There are many platforms where you can download Fid Q songs, but some of the best ones are:</p>
-<ul>
-<li><a href="">Boomplay</a>: This is a popular music streaming and downloading app in Africa, where you can find Fid Q's albums and singles. You can also access other features, such as lyrics, videos, podcasts, and more.</li>
-<li><a href="">Mdundo</a>: This is another leading music platform in Africa, where you can download Fid Q's songs for free. You can also discover new music, create playlists, and share your favorites with others.</li>
-<li><a href="">iTunes</a>: This is a well-known music store and player, where you can buy and download Fid Q's songs. You can also sync your music with your Apple devices and enjoy other benefits, such as iCloud Music Library, Apple Music, and more.</li>
-</ul> <h2>How to download Fid Q songs?</h2>
-<h3>The steps to follow</h3>
-<p>Downloading Fid Q songs is easy and fast, if you follow these simple steps:</p>
-<ol>
-<li>Choose the platform that you want to use, such as Boomplay, Mdundo, or iTunes.</li>
-<li>Search for Fid Q's name or the song that you want to download.</li>
-<li>Select the song and click on the download button or icon.</li>
-<li>Wait for the download to complete and enjoy your music.</li>
-</ol>
-<h3>The tips and tricks to optimize your experience</h3>
-<p>To make the most out of your music downloading experience, here are some tips and tricks that you can use:</p>
-<ul>
-<li>Check the quality and size of the song before downloading it, to ensure that it meets your expectations and device capacity.</li>
-<li>Use a reliable and secure internet connection, to avoid interruptions and errors during the download process.</li>
-<li>Use a good music player, to enhance the sound and performance of your music.</li>
-<li>Update your music library regularly, to keep track of your downloads and discover new songs.</li>
-</ul>
-<h3>The challenges and solutions to downloading Fid Q songs</h3>
-<p>Downloading Fid Q songs may not always be smooth and easy, as you may encounter some challenges along the way. Some of them are:</p>
-<ul>
-<li>Limited access: Some platforms may not be available in your region or device, or may require a subscription or payment to download Fid Q songs. To solve this, you can use a VPN service, a proxy server, or an alternative platform that offers free or affordable downloads.</li>
-<li>Legal issues: Some platforms may not have the rights or permission to distribute Fid Q songs, or may violate the intellectual property laws of the artist or the label. To solve this, you can use a platform that has a license or agreement with Fid Q or his management, or respect his terms and conditions of use.</li>
-<li>Technical problems: Some platforms may have bugs, glitches, or errors that prevent you from downloading Fid Q songs, or may damage your device or data. To solve this, you can use a platform that has a good reputation, a high rating, and a positive feedback from other users, or contact their customer support for assistance.</li>
-</ul> <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In this article, we have learned how to download Fid Q songs, so we can enjoy the best of Bongo Hip Hop. We have also learned more about Fid Q, his background, his style, and his achievements. We have explored the benefits of downloading music, the reasons to love Fid Q's music, and the best platforms to download his songs. We have also shared the steps to follow, the tips and tricks to optimize our experience, and the challenges and solutions to downloading his songs.</p>
-<h3>Call to action and recommendation</h3>
-<p>Now that you know how to download Fid Q songs, what are you waiting for? Go ahead and download your favorite songs from his albums and singles, and enjoy his music on your device. You can also share his music with your friends and family, and support him on his social media platforms. If you like Fid Q's music, you may also like other Bongo Hip Hop artists, such as Professor Jay, G Nako, Young Killer, and more. You can find their songs on the same platforms that we have mentioned above. Thank you for reading this article, and we hope you have a great time listening to Fid Q's music.</p>
-<h2>FAQs</h2>
-<h4>Q: How can I contact Fid Q?</h4>
-<p>A: You can contact Fid Q through his official email address ([email protected]), his Instagram account (@fidqcheusidawa), his Twitter account (@fidqcheusidawa), or his Facebook page (Fid Q).</p>
-<h4>Q: How can I buy Fid Q's merchandise?</h4>
-<p>A: You can buy Fid Q's merchandise, such as T-shirts, caps, hoodies, and more, from his online store (https://fidqstore.com/). You can also find his merchandise at some physical stores in Tanzania.</p>
-<h4>Q: How can I watch Fid Q's videos?</h4>
-<p>A: You can watch Fid Q's videos on his YouTube channel (https://www.youtube.com/user/fidqcheusidawa), where he uploads his official music videos, behind the scenes footage, interviews, and more.</p>
-<h4>Q: How can I support Fid Q's projects?</h4>
-<p>A: You can support Fid Q's projects by buying his music, streaming his songs, downloading his songs legally, sharing his music with others, following him on social media, subscribing to his YouTube channel, buying his merchandise, attending his shows, and giving him feedback.</p>
-<h4>Q: How can I learn more about Bongo Hip Hop?</h4>
-<p>A: You can learn more about Bongo Hip Hop by listening to more artists in the genre, reading articles and blogs about it, watching documentaries and shows about it, joining online forums and groups about it, and visiting Tanzania and experiencing it firsthand.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/ForYou Pakistan - TikTok The Ultimate App for Viral Content Creators.md

@@ -1,131 +0,0 @@
-
-<h1>Pakistan TikTok APK: What You Need to Know</h1>
-<p>TikTok is one of the most popular social media platforms in the world, with over one billion users. However, in Pakistan, the app has faced some difficulties due to its content and regulations. In this article, we will explain what TikTok is, why it is banned in Pakistan, what are the alternatives, and how to download TikTok APK for Android devices.</p>
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- <h2>What is TikTok and why is it popular?</h2>
-<p>TikTok is a video-sharing app that allows users to create and share short-form videos on any topic. Users can add music, effects, filters, stickers, voiceovers, and more to their videos. They can also watch videos from other users, follow their favorite creators, comment, like, and share. TikTok has a variety of categories and genres, such as comedy, gaming, DIY, food, sports, memes, pets, and more.</p>
-<p>TikTok has several features and benefits that make it entertaining, creative, and engaging. Some of these features are:</p>
-<ul>
-<li>A personalized video feed based on what you watch, like, and share</li>
-<li>An endless stream of short videos that are exciting, spontaneous, and genuine</li>
-<li>A global community of creators that showcase their incredible skills and everyday life</li>
-<li>A platform that encourages innovation and expression</li>
-<li>An easy-to-use interface and editing tools</li>
-<li>A huge library of music clips and sounds</li>
-<li>A way to reuse content from other videos by remixing or adding your own touch</li>
-</ul>
- <h2>Why is TikTok banned in Pakistan and what are the alternatives?</h2>
-<p>TikTok has been banned in Pakistan multiple times due to complaints about immoral and indecent content. The Pakistan Telecommunication Authority (PTA) has issued orders to block access to the app after receiving petitions from different segments of society. The PTA has also said that TikTok has not complied with its requests to moderate unlawful content according to local laws.</p>
-<p>TikTok users in Pakistan can use other apps that offer similar or different features as alternatives. Some of these apps are:</p>
-<ul>
-<li><strong>Instagram Reels:</strong> A feature within Instagram that lets users create short videos with music and effects. Users can also discover reels from other users on the Explore tab.</li>
-<li><strong>Triller:</strong> An app similar to TikTok that allows users to create short videos with music and filters. Users can also collaborate with other creators and join challenges.</li>
-<li><strong>YouTube Shorts:</strong> A feature within YouTube that lets users create short vertical videos with music and effects. Users can also browse shorts from other users on the Shorts tab.</li> Continuing the article: <li><strong>Chingari:</strong> An app similar to TikTok that allows users to create short videos with music and filters. Users can also watch videos from different categories, such as comedy, news, sports, and more .</li>
-<li><strong>Dubsmash:</strong> An app similar to TikTok that allows users to create short videos with audio clips from famous songs, movie scenes, quotes, and more. Users can also watch videos from other users and chat with them  .</li>
-</ul>
- <h2>How to download TikTok APK for Android devices?</h2>
-<p>TikTok APK is a file that allows users to install the app on their Android devices without using the Google Play Store. This can be useful for users who cannot access the app from the official store or want to use an older or modified version of the app.</p>
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-<p>Users can download TikTok APK from various sources, such as APKPure, Uptodown, or WizCase. However, users should be careful and only download the APK files from trusted and verified sources, as some files may contain malware or viruses that can harm their devices. Users should also enable the option to install apps from unknown sources in their device settings before installing the APK files.</p>
-<p>Here are the steps to download TikTok APK from APKPure:</p>
-<ol>
-<li>Go to <a href="(^1^)">https://apkpure.com/tiktok/com.zhiliaoapp.musically</a> on your browser.</li>
-<li>Click on the green Download APK button and wait for the file to be downloaded.</li>
-<li>Open the file manager on your device and locate the downloaded file.</li>
-<li>Tap on the file and follow the instructions to install the app.</li>
-<li>Enjoy TikTok on your device.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>TikTok is a fun and popular app that has faced some challenges in Pakistan due to its content. Users can still enjoy TikTok or its alternatives by downloading the APK files from reliable sources. However, users should be aware of the risks and responsibilities of using these apps and respect the local laws and norms.</p>
- <h2>FAQs</h2>
-<h3>What are the advantages and disadvantages of TikTok?</h3>
-<p>TikTok has many advantages, such as:</p>
-<ul>
-<li>It is a platform for creativity and expression</li>
-<li>It is a source of entertainment and education</li>
-<li>It is a way to connect with people and cultures</li>
-<li>It is a tool for marketing and promotion</li>
-</ul>
-<p>TikTok also has some disadvantages, such as:</p>
-<ul>
-<li>It can be addictive and time-consuming</li>
-<li>It can expose users to inappropriate or harmful content</li>
-<li>It can violate users' privacy and security</li>
-<li>It can cause legal or ethical issues</li>
-</ul>
- <h3>What does TikTok mean and where did it come from?</h3>
-<p>TikTok is a combination of two words: "tick" and "tock", which are the sounds of a clock. The name suggests that the app is about capturing moments in time. TikTok was launched in 2016 by ByteDance, a Chinese internet company. It was originally called Douyin in China, but was rebranded as TikTok for the international market in 2017. In 2018, TikTok merged with Musical.ly, another popular video-sharing app.</p>
- <h3>How can I watch TikTok videos without downloading the app?</h3>
-<p>You can watch TikTok videos without downloading the app by using a web browser. You can go to <a href="(^2^)">https://www.tiktok.com/</a> and browse through different categories and hashtags. You can also search for specific users or videos by using the search bar. However, you will not be able to create or upload videos, comment, like, or share without an account or the app.</p>
- <h3>How can I make a successful video on TikTok?</h3>
-<p>To make a successful video on TikTok, you should follow some tips, such as:</p>
-<ul>
-<li>Pick a niche or theme that suits your personality and interests</li>
-<li>Use catchy music, effects, filters, and stickers to enhance your video</li>
-<li>Add relevant hashtags, captions, and keywords to your video</li>
-<li>Follow the trends and challenges on TikTok and join them</li>
-<li>Collaborate with other creators and influencers on TikTok</li>
-<li>Engage with your audience and respond to their comments</li> Continuing the article: <li>Post regularly and at the best times for your audience</li>
-<li>Analyze your performance and improve your strategy</li>
-</ul>
- <h3>How can I use TikTok for business promotion?</h3>
-<p>TikTok can be a powerful tool for business promotion, as it can help you reach a large and diverse audience, increase your brand awareness, showcase your products or services, and drive traffic to your website or store. To use TikTok for business promotion, you should follow some steps, such as:</p>
-<ol>
-<li>Create a business account on TikTok and optimize your profile</li>
-<li>Define your target audience and goals</li>
-<li>Create engaging and relevant content that showcases your brand personality and value proposition</li>
-<li>Use hashtags, keywords, and calls to action to increase your visibility and conversions</li>
-<li>Partner with influencers or celebrities that match your brand image and audience</li>
-<li>Run paid ads or sponsored campaigns on TikTok to reach more potential customers</li>
-<li>Measure your results and adjust your strategy accordingly</li>
-</ol></p> 401be4b1e0<br />
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spaces/4Taps/SadTalker/src/audio2pose_models/audio_encoder.py

@@ -1,64 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        return self.act(out)
-
-class AudioEncoder(nn.Module):
-    def __init__(self, wav2lip_checkpoint):
-        super(AudioEncoder, self).__init__()
-
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
-
-        #### load the pre-trained audio_encoder\
-        wav2lip_state_dict = torch.load(wav2lip_checkpoint)['state_dict']
-        state_dict = self.audio_encoder.state_dict()
-
-        for k,v in wav2lip_state_dict.items():
-            if 'audio_encoder' in k:
-                state_dict[k.replace('module.audio_encoder.', '')] = v
-        self.audio_encoder.load_state_dict(state_dict)
-
-
-    def forward(self, audio_sequences):
-        # audio_sequences = (B, T, 1, 80, 16)
-        B = audio_sequences.size(0)
-
-        audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
-
-        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
-        dim = audio_embedding.shape[1]
-        audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1))
-
-        return audio_embedding.squeeze(-1).squeeze(-1) #B seq_len+1 512 

spaces/52Hz/SRMNet_real_world_denoising/main_test_SRMNet.py

@@ -1,86 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-from collections import OrderedDict
-from skimage import img_as_ubyte
-import os
-import torch
-import requests
-from PIL import Image
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from natsort import natsorted
-from model.SRMNet import SRMNet
-
-def main():
-    parser = argparse.ArgumentParser(description='Demo Image Denoising')
-    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
-    parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')
-    parser.add_argument('--weights',
-                        default='experiments/pretrained_models/real_denoising_SRMNet.pth', type=str,
-                        help='Path to weights')
-
-    args = parser.parse_args()
-
-    inp_dir = args.input_dir
-    out_dir = args.result_dir
-
-    os.makedirs(out_dir, exist_ok=True)
-
-    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
-
-    if len(files) == 0:
-        raise Exception(f"No files found at {inp_dir}")
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Load corresponding models architecture and weights
-    model = SRMNet()
-    model = model.to(device)
-    model.eval()
-    load_checkpoint(model, args.weights)
-    
-
-    mul = 16
-    for file_ in files:
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-
-        # Pad the input if not_multiple_of 8
-        h, w = input_.shape[2], input_.shape[3]
-        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-        padh = H - h if h % mul != 0 else 0
-        padw = W - w if w % mul != 0 else 0
-        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-        with torch.no_grad():
-            restored = model(input_)
-
-        restored = torch.clamp(restored, 0, 1)
-        restored = restored[:, :, :h, :w]
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(out_dir, f + '.png')), restored)
-
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-
-
-if __name__ == '__main__':
-    main()

spaces/7hao/bingo/src/components/chat-message.tsx

@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
-  message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
-  useEffect(() => {
-    if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
-      window.scrollBy(0, 200)
-    }
-  }, [message.text])
-
-  return message.text ? (
-    <div
-      className={cn('text-message', message.author)}
-      {...props}
-    >
-      <div className="text-message-content">
-        <MemoizedReactMarkdown
-          linkTarget="_blank"
-          className="prose break-words dark:prose-invert prose-p:leading-relaxed prose-pre:p-0"
-          remarkPlugins={[remarkGfm, remarkMath, supersub, remarkBreaks]}
-          components={{
-            img(obj) {
-              try {
-                const uri = new URL(obj.src!)
-                const w = uri.searchParams.get('w')
-                const h = uri.searchParams.get('h')
-                if (w && h) {
-                  uri.searchParams.delete('w')
-                  uri.searchParams.delete('h')
-                  return <a style={{ float: 'left', maxWidth: '50%' }} href={uri.toString()} target="_blank" rel="noopener noreferrer"><img src={obj.src} alt={obj.alt} width={w!} height={h!}/></a>
-                }
-              } catch (e) {
-              }
-              return <img src={obj.src} alt={obj.alt} title={obj.title} />
-            },
-            p({ children }) {
-              return <p className="mb-2">{children}</p>
-            },
-            code({ node, inline, className, children, ...props }) {
-              if (children.length) {
-                if (children[0] == '▍') {
-                  return (
-                    <span className="mt-1 animate-pulse cursor-default">▍</span>
-                  )
-                }
-
-                children[0] = (children[0] as string).replace('`▍`', '▍')
-              }
-
-              const match = /language-(\w+)/.exec(className || '')
-
-              if (inline) {
-                return (
-                  <code className={className} {...props}>
-                    {children}
-                  </code>
-                )
-              }
-
-              return (
-                <CodeBlock
-                  key={Math.random()}
-                  language={(match && match[1]) || ''}
-                  value={String(children).replace(/\n$/, '')}
-                  {...props}
-                />
-              )
-            }
-          }}
-        >
-          {message.text}
-        </MemoizedReactMarkdown>
-      </div>
-      <div className="text-message-footer">
-        {message.author === 'bot' && <LearnMore sourceAttributions={message.sourceAttributions} />}
-        {message.author === 'bot' && <TurnCounter throttling={message.throttling} />}
-      </div>
-    </div>
-  ) : null
-}

spaces/A666sxr/Genshin_TTS/pqmf.py

@@ -1,116 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Pseudo QMF modules."""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from scipy.signal import kaiser
-
-
-def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
-    """Design prototype filter for PQMF.
-    This method is based on `A Kaiser window approach for the design of prototype
-    filters of cosine modulated filterbanks`_.
-    Args:
-        taps (int): The number of filter taps.
-        cutoff_ratio (float): Cut-off frequency ratio.
-        beta (float): Beta coefficient for kaiser window.
-    Returns:
-        ndarray: Impluse response of prototype filter (taps + 1,).
-    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
-        https://ieeexplore.ieee.org/abstract/document/681427
-    """
-    # check the arguments are valid
-    assert taps % 2 == 0, "The number of taps mush be even number."
-    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
-
-    # make initial filter
-    omega_c = np.pi * cutoff_ratio
-    with np.errstate(invalid='ignore'):
-        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
-            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
-    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
-
-    # apply kaiser window
-    w = kaiser(taps + 1, beta)
-    h = h_i * w
-
-    return h
-
-
-class PQMF(torch.nn.Module):
-    """PQMF module.
-    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
-    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
-        https://ieeexplore.ieee.org/document/258122
-    """
-
-    def __init__(self, device, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
-        """Initilize PQMF module.
-        Args:
-            subbands (int): The number of subbands.
-            taps (int): The number of filter taps.
-            cutoff_ratio (float): Cut-off frequency ratio.
-            beta (float): Beta coefficient for kaiser window.
-        """
-        super(PQMF, self).__init__()
-
-        # define filter coefficient
-        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
-        h_analysis = np.zeros((subbands, len(h_proto)))
-        h_synthesis = np.zeros((subbands, len(h_proto)))
-        for k in range(subbands):
-            h_analysis[k] = 2 * h_proto * np.cos(
-                (2 * k + 1) * (np.pi / (2 * subbands)) *
-                (np.arange(taps + 1) - ((taps - 1) / 2)) +
-                (-1) ** k * np.pi / 4)
-            h_synthesis[k] = 2 * h_proto * np.cos(
-                (2 * k + 1) * (np.pi / (2 * subbands)) *
-                (np.arange(taps + 1) - ((taps - 1) / 2)) -
-                (-1) ** k * np.pi / 4)
-
-        # convert to tensor
-        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)
-        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)
-
-        # register coefficients as beffer
-        self.register_buffer("analysis_filter", analysis_filter)
-        self.register_buffer("synthesis_filter", synthesis_filter)
-
-        # filter for downsampling & upsampling
-        updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)
-        for k in range(subbands):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.subbands = subbands
-
-        # keep padding info
-        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
-
-    def analysis(self, x):
-        """Analysis with PQMF.
-        Args:
-            x (Tensor): Input tensor (B, 1, T).
-        Returns:
-            Tensor: Output tensor (B, subbands, T // subbands).
-        """
-        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
-        return F.conv1d(x, self.updown_filter, stride=self.subbands)
-
-    def synthesis(self, x):
-        """Synthesis with PQMF.
-        Args:
-            x (Tensor): Input tensor (B, subbands, T // subbands).
-        Returns:
-            Tensor: Output tensor (B, 1, T).
-        """
-        # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
-        #   Not sure this is the correct way, it is better to check again.
-        # TODO(kan-bayashi): Understand the reconstruction procedure
-        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
-        return F.conv1d(self.pad_fn(x), self.synthesis_filter)

spaces/AIConsultant/MusicGen/tests/losses/__init__.py

@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.

spaces/AIFILMS/StyleGANEX/scripts/train.py

@@ -1,32 +0,0 @@
-"""
-This file runs the main training/val loop
-"""
-import os
-import json
-import sys
-import pprint
-
-sys.path.append(".")
-sys.path.append("..")
-
-from options.train_options import TrainOptions
-from training.coach import Coach
-
-
-def main():
-	opts = TrainOptions().parse()
-	if os.path.exists(opts.exp_dir):
-		raise Exception('Oops... {} already exists'.format(opts.exp_dir))
-	os.makedirs(opts.exp_dir)
-
-	opts_dict = vars(opts)
-	pprint.pprint(opts_dict)
-	with open(os.path.join(opts.exp_dir, 'opt.json'), 'w') as f:
-		json.dump(opts_dict, f, indent=4, sort_keys=True)
-
-	coach = Coach(opts)
-	coach.train()
-
-
-if __name__ == '__main__':
-	main()

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/hifigan/models.py

@@ -1,174 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-LRELU_SLOPE = 0.1
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-class ResBlock(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.conv_pre = weight_norm(
-            Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)
-        )
-        resblock = ResBlock
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        h.upsample_initial_channel // (2**i),
-                        h.upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        # print("Removing weight norm...")
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/midas_net.py

@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)

spaces/AILab-CVC/EvalCrafter/src/auto_leaderboard/model_metadata_type.py

@@ -1,30 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-import glob
-import json
-import os
-from typing import Dict, List
-
-from ..utils_display import AutoEvalColumn
-
-@dataclass
-class ModelInfo:
-    name: str
-    symbol: str # emoji
-
-model_type_symbols = {
-    "LLM": "🟢",
-    "ImageLLM": "🔶",
-    "VideoLLM": "⭕",
-    "Other": "🟦",
-}
-
-class ModelType(Enum):
-    PT = ModelInfo(name="LLM", symbol="🟢")
-    FT = ModelInfo(name="ImageLLM", symbol="🔶")
-    IFT = ModelInfo(name="VideoLLM", symbol="⭕")
-    RL = ModelInfo(name="Other", symbol="🟦")
-
-    def to_str(self, separator = " "):
-        return f"{self.value.symbol}{separator}{self.value.name}" 
-

spaces/AIZ2H/06-Streamlit-NLP-Image-Semantic-Search-Images/README.md

@@ -1,13 +0,0 @@
----
-title: 🖼️StreamlitNLUImageSemanticSearch
-emoji: 🔍
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIZ2H/Gradio-Multilingual-ImageToOCR/app.py

@@ -1,54 +0,0 @@
-import pandas as pd
-import PIL
-from PIL import Image
-from PIL import ImageDraw
-import gradio as gr
-import torch
-import easyocr
-
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'english.png')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg')
-torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg')
-
-def draw_boxes(image, bounds, color='yellow', width=2):
-    draw = ImageDraw.Draw(image)
-    for bound in bounds:
-        p0, p1, p2, p3 = bound[0]
-        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
-    return image
-
-def inference(img, lang):
-    reader = easyocr.Reader(lang)
-    bounds = reader.readtext(img.name)
-    im = PIL.Image.open(img.name)
-    draw_boxes(im, bounds)
-    im.save('result.jpg')
-    return ['result.jpg', pd.DataFrame(bounds).iloc[: , 1:]]
-
-title = 'Image To Optical Character Recognition'
-description = 'Multilingual OCR which works conveniently on all devices in multiple languages.'
-article = "<p style='text-align: center'></p>"
-examples = [['english.png',['en']],['chinese.jpg',['ch_sim', 'en']],['japanese.jpg',['ja', 'en']],['Hindi.jpeg',['hi', 'en']]]
-css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
-choices = [
-    "ch_sim",
-    "ch_tra",
-    "de",
-    "en",
-    "es",
-    "ja",
-    "hi",
-    "ru"
-]
-gr.Interface(
-    inference,
-    [gr.inputs.Image(type='file', label='Input'),gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='language')],
-    [gr.outputs.Image(type='file', label='Output'), gr.outputs.Dataframe(headers=['text', 'confidence'])],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    css=css,
-    enable_queue=True
-    ).launch(debug=True)

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

@@ -1,40 +0,0 @@
-import os
-import requests
-from ...typing import get_type_hints
-
-url = "https://aiservice.vercel.app/api/chat/answer"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    base = ''
-    for message in messages:
-        base += '%s: %s\n' % (message['role'], message['content'])
-    base += 'assistant:'
-
-    headers = {
-        "accept": "*/*",
-        "content-type": "text/plain;charset=UTF-8",
-        "sec-fetch-dest": "empty",
-        "sec-fetch-mode": "cors",
-        "sec-fetch-site": "same-origin",
-        "Referer": "https://aiservice.vercel.app/chat",
-    }
-    data = {
-        "input": base
-    }
-    response = requests.post(url, headers=headers, json=data)
-    if response.status_code == 200:
-        _json = response.json()
-        yield _json['data']
-    else:
-        print(f"Error Occurred::{response.status_code}")
-        return None
-    
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinputbase/Factory.d.ts

@@ -1,5 +0,0 @@
-import ColorInputBase from './ColorInputBase';
-
-export default function (
-    config?: ColorInputBase.IConfig
-): ColorInputBase;

spaces/Aki004/herta-so-vits/resample.py

@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    # speaker 's5', 'p280', 'p315' are excluded,
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=None)
-        wav, _ = librosa.effects.trim(wav, top_db=20)
-        peak = np.abs(wav).max()
-        if peak > 1.0:
-            wav = 0.98 * wav / peak
-        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
-        wav2 /= max(wav2.max(), -wav2.min())
-        save_name = wav_name
-        save_path2 = os.path.join(args.out_dir2, speaker, save_name)
-        wavfile.write(
-            save_path2,
-            args.sr2,
-            (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-        )
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
-    parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
-    args = parser.parse_args()
-    processs = 30 if cpu_count() > 60 else (cpu_count()-2 if cpu_count() > 4 else 1)
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass

spaces/AlexWang/lama/saicinpainting/training/modules/squeeze_excitation.py

@@ -1,20 +0,0 @@
-import torch.nn as nn
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        res = x * y.expand_as(x)
-        return res

spaces/AmrElsayeh/Interior_style_detector/README.md

@@ -1,13 +0,0 @@
----
-title: Interior Style Detector
-emoji: 👀
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Amrrs/DragGan-Inversion/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py

@@ -1,1007 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Network architectures from the paper
-"Analyzing and Improving the Image Quality of StyleGAN".
-Matches the original implementation of configs E-F by Karras et al. at
-https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
-
-import numpy as np
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
-    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
-    # Input tensor of shape [batch_size, in_channels, in_height, in_width].
-    x,
-    # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
-    weight,
-    # Modulation coefficients of shape [batch_size, in_channels].
-    styles,
-    noise=None,     # Optional noise tensor to add to the output activations.
-    up=1,        # Integer upsampling factor.
-    down=1,        # Integer downsampling factor.
-    padding=0,        # Padding with respect to the upsampled image.
-    # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
-    resample_filter=None,
-    demodulate=True,     # Apply weight demodulation?
-    # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
-    flip_weight=True,
-    # Perform modulation, convolution, and demodulation as a single fused operation?
-    fused_modconv=True,
-):
-    batch_size = x.shape[0]
-    out_channels, in_channels, kh, kw = weight.shape
-    misc.assert_shape(weight, [out_channels, in_channels, kh, kw])  # [OIkk]
-    misc.assert_shape(x, [batch_size, in_channels, None, None])  # [NIHW]
-    misc.assert_shape(styles, [batch_size, in_channels])  # [NI]
-
-    # Pre-normalize inputs to avoid FP16 overflow.
-    if x.dtype == torch.float16 and demodulate:
-        weight = weight * (1 / np.sqrt(in_channels * kh * kw) /
-                           weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True))  # max_Ikk
-        styles = styles / \
-            styles.norm(float('inf'), dim=1, keepdim=True)  # max_I
-
-    # Calculate per-sample weights and demodulation coefficients.
-    w = None
-    dcoefs = None
-    if demodulate or fused_modconv:
-        w = weight.unsqueeze(0)  # [NOIkk]
-        w = w * styles.reshape(batch_size, 1, -1, 1, 1)  # [NOIkk]
-    if demodulate:
-        dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt()  # [NO]
-    if demodulate and fused_modconv:
-        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1)  # [NOIkk]
-
-    # Execute by scaling the activations before and after the convolution.
-    if not fused_modconv:
-        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        x = conv2d_resample.conv2d_resample(x=x, w=weight.to(
-            x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
-        if demodulate and noise is not None:
-            x = fma.fma(x, dcoefs.to(x.dtype).reshape(
-                batch_size, -1, 1, 1), noise.to(x.dtype))
-        elif demodulate:
-            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        elif noise is not None:
-            x = x.add_(noise.to(x.dtype))
-        return x
-
-    # Execute as one fused op using grouped convolution.
-    with misc.suppress_tracer_warnings():  # this value will be treated as a constant
-        batch_size = int(batch_size)
-    misc.assert_shape(x, [batch_size, in_channels, None, None])
-    x = x.reshape(1, -1, *x.shape[2:])
-    w = w.reshape(-1, in_channels, kh, kw)
-    x = conv2d_resample.conv2d_resample(x=x, w=w.to(
-        x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
-    x = x.reshape(batch_size, -1, *x.shape[2:])
-    if noise is not None:
-        x = x.add_(noise)
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
-    def __init__(self,
-                 in_features,                # Number of input features.
-                 out_features,               # Number of output features.
-                 bias=True,     # Apply additive bias before the activation function?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='linear',
-                 lr_multiplier=1,        # Learning rate multiplier.
-                 bias_init=0,        # Initial value for the additive bias.
-                 ):
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.activation = activation
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_features, in_features]) / lr_multiplier)
-        self.bias = torch.nn.Parameter(torch.full(
-            [out_features], np.float32(bias_init))) if bias else None
-        self.weight_gain = lr_multiplier / np.sqrt(in_features)
-        self.bias_gain = lr_multiplier
-
-    def forward(self, x):
-        w = self.weight.to(x.dtype) * self.weight_gain
-        b = self.bias
-        if b is not None:
-            b = b.to(x.dtype)
-            if self.bias_gain != 1:
-                b = b * self.bias_gain
-
-        if self.activation == 'linear' and b is not None:
-            x = torch.addmm(b.unsqueeze(0), x, w.t())
-        else:
-            x = x.matmul(w.t())
-            x = bias_act.bias_act(x, b, act=self.activation)
-        return x
-
-    def extra_repr(self):
-        return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 out_channels,                   # Number of output channels.
-                 # Width and height of the convolution kernel.
-                 kernel_size,
-                 bias=True,         # Apply additive bias before the activation function?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='linear',
-                 up=1,            # Integer upsampling factor.
-                 down=1,            # Integer downsampling factor.
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 channels_last=False,        # Expect the input to have memory_format=channels_last?
-                 trainable=True,         # Update the weights of this layer during training?
-                 ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.activation = activation
-        self.up = up
-        self.down = down
-        self.conv_clamp = conv_clamp
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
-            memory_format=memory_format)
-        bias = torch.zeros([out_channels]) if bias else None
-        if trainable:
-            self.weight = torch.nn.Parameter(weight)
-            self.bias = torch.nn.Parameter(bias) if bias is not None else None
-        else:
-            self.register_buffer('weight', weight)
-            if bias is not None:
-                self.register_buffer('bias', bias)
-            else:
-                self.bias = None
-
-    def forward(self, x, gain=1):
-        w = self.weight * self.weight_gain
-        b = self.bias.to(x.dtype) if self.bias is not None else None
-        flip_weight = (self.up == 1)  # slightly faster
-        x = conv2d_resample.conv2d_resample(x=x, w=w.to(
-            x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, b, act=self.activation,
-                              gain=act_gain, clamp=act_clamp)
-        return x
-
-    def extra_repr(self):
-        return ' '.join([
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
-            f'up={self.up}, down={self.down}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
-    def __init__(self,
-                 # Input latent (Z) dimensionality, 0 = no latent.
-                 z_dim,
-                 # Conditioning label (C) dimensionality, 0 = no label.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 # Number of intermediate latents to output, None = do not broadcast.
-                 num_ws,
-                 num_layers=8,        # Number of mapping layers.
-                 # Label embedding dimensionality, None = same as w_dim.
-                 embed_features=None,
-                 # Number of intermediate features in the mapping layers, None = same as w_dim.
-                 layer_features=None,
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Learning rate multiplier for the mapping layers.
-                 lr_multiplier=0.01,
-                 # Decay for tracking the moving average of W during training, None = do not track.
-                 w_avg_beta=0.998,
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.num_ws = num_ws
-        self.num_layers = num_layers
-        self.w_avg_beta = w_avg_beta
-
-        if embed_features is None:
-            embed_features = w_dim
-        if c_dim == 0:
-            embed_features = 0
-        if layer_features is None:
-            layer_features = w_dim
-        features_list = [z_dim + embed_features] + \
-            [layer_features] * (num_layers - 1) + [w_dim]
-
-        if c_dim > 0:
-            self.embed = FullyConnectedLayer(c_dim, embed_features)
-        for idx in range(num_layers):
-            in_features = features_list[idx]
-            out_features = features_list[idx + 1]
-            layer = FullyConnectedLayer(
-                in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
-            setattr(self, f'fc{idx}', layer)
-
-        if num_ws is not None and w_avg_beta is not None:
-            self.register_buffer('w_avg', torch.zeros([w_dim]))
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
-        # Embed, normalize, and concat inputs.
-        x = None
-        with torch.autograd.profiler.record_function('input'):
-            if self.z_dim > 0:
-                misc.assert_shape(z, [None, self.z_dim])
-                x = normalize_2nd_moment(z.to(torch.float32))
-            if self.c_dim > 0:
-                misc.assert_shape(c, [None, self.c_dim])
-                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
-                x = torch.cat([x, y], dim=1) if x is not None else y
-
-        # Main layers.
-        for idx in range(self.num_layers):
-            layer = getattr(self, f'fc{idx}')
-            x = layer(x)
-
-        # Update moving average of W.
-        if update_emas and self.w_avg_beta is not None:
-            with torch.autograd.profiler.record_function('update_w_avg'):
-                self.w_avg.copy_(x.detach().mean(
-                    dim=0).lerp(self.w_avg, self.w_avg_beta))
-
-        # Broadcast.
-        if self.num_ws is not None:
-            with torch.autograd.profiler.record_function('broadcast'):
-                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
-        # Apply truncation.
-        if truncation_psi != 1:
-            with torch.autograd.profiler.record_function('truncate'):
-                assert self.w_avg_beta is not None
-                if self.num_ws is None or truncation_cutoff is None:
-                    x = self.w_avg.lerp(x, truncation_psi)
-                else:
-                    x[:, :truncation_cutoff] = self.w_avg.lerp(
-                        x[:, :truncation_cutoff], truncation_psi)
-        return x
-
-    def extra_repr(self):
-        return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 out_channels,                   # Number of output channels.
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 resolution,                     # Resolution of this layer.
-                 kernel_size=3,            # Convolution kernel size.
-                 up=1,            # Integer upsampling factor.
-                 use_noise=True,         # Enable noise input?
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 channels_last=False,        # Use channels_last format for the weights?
-                 square=False,        # default if for rectangle images
-                 ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.w_dim = w_dim
-        self.resolution = resolution
-        self.up = up
-        self.use_noise = use_noise
-        self.activation = activation
-        self.conv_clamp = conv_clamp
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-        self.square = square
-
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        if use_noise:
-            if self.square:
-                self.register_buffer(
-                    'noise_const', torch.randn([resolution, resolution]))
-            else:
-                self.register_buffer('noise_const', torch.randn(
-                    [resolution, resolution // 2]))
-            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
-    def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
-        assert noise_mode in ['random', 'const', 'none']
-        in_resolution = self.resolution // self.up
-        if self.square:
-            misc.assert_shape(
-                x, [None, self.weight.shape[1], in_resolution, in_resolution])
-        else:
-            misc.assert_shape(
-                x, [None, self.weight.shape[1], in_resolution, in_resolution // 2])
-        styles = self.affine(w)
-
-        noise = None
-        if self.use_noise and noise_mode == 'random':
-            if self.square:
-                noise = torch.randn(
-                    [x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
-            else:
-                noise = torch.randn(
-                    [x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength
-        if self.use_noise and noise_mode == 'const':
-            noise = self.noise_const * self.noise_strength
-
-        flip_weight = (self.up == 1)  # slightly faster
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
-                             padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, self.bias.to(
-            x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
-        return x
-
-    def extra_repr(self):
-        return ' '.join([
-            f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
-            f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.w_dim = w_dim
-        self.conv_clamp = conv_clamp
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn(
-            [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
-    def forward(self, x, w, fused_modconv=True):
-        styles = self.affine(w) * self.weight_gain
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles,
-                             demodulate=False, fused_modconv=fused_modconv)
-        x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
-        return x
-
-    def extra_repr(self):
-        return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
-    def __init__(self,
-                 # Number of input channels, 0 = first block.
-                 in_channels,
-                 # Number of output channels.
-                 out_channels,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 # Resolution of this block.
-                 resolution,
-                 # Number of output color channels.
-                 img_channels,
-                 is_last,                                # Is this the last block?
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='skip',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=256,
-                 use_fp16=False,        # Use FP16 for this block?
-                 fp16_channels_last=False,        # Use channels-last memory format with FP16?
-                 square=False,        # default is for rectangle images
-                 # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
-                 fused_modconv_default=True,
-                 # Arguments for SynthesisLayer.
-                 **layer_kwargs,
-                 ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.w_dim = w_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.is_last = is_last
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.fused_modconv_default = fused_modconv_default
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.num_conv = 0
-        self.num_torgb = 0
-        self.square = square
-
-        if in_channels == 0:
-            if self.square:
-                self.const = torch.nn.Parameter(torch.randn(
-                    [out_channels, resolution, resolution]))
-            else:  # rectangle
-                self.const = torch.nn.Parameter(torch.randn(
-                    [out_channels, resolution, resolution // 2]))
-
-        if in_channels != 0:
-            self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
-                                        resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last,  square=square, **layer_kwargs)
-            self.num_conv += 1
-
-        self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
-                                    conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs)
-        self.num_conv += 1
-
-        if is_last or architecture == 'skip':
-            self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
-                                    conv_clamp=conv_clamp, channels_last=self.channels_last)
-            self.num_torgb += 1
-
-        if in_channels != 0 and architecture == 'resnet':
-            self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
-                                    resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
-        _ = update_emas  # unused
-        misc.assert_shape(
-            ws, [None, self.num_conv + self.num_torgb, self.w_dim])
-        w_iter = iter(ws.unbind(dim=1))
-        if ws.device.type != 'cuda':
-            force_fp32 = True
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-        if fused_modconv is None:
-            fused_modconv = self.fused_modconv_default
-        if fused_modconv == 'inference_only':
-            fused_modconv = (not self.training)
-
-        # Input.
-        if self.in_channels == 0:
-            x = self.const.to(dtype=dtype, memory_format=memory_format)
-            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
-        else:
-            if self.square:
-                misc.assert_shape(
-                    x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
-            else:  # rectangle
-                misc.assert_shape(
-                    x, [None, self.in_channels, self.resolution // 2, self.resolution // 4])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # Main layers.
-        if self.in_channels == 0:
-            x = self.conv1(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-        elif self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv,
-                           gain=np.sqrt(0.5), **layer_kwargs)
-            x = y.add_(x)
-        else:
-            x = self.conv0(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter),
-                           fused_modconv=fused_modconv, **layer_kwargs)
-
-        # ToRGB.
-        if img is not None:
-            if self.square:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
-            else:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution // 2, self.resolution // 4])
-            img = upfirdn2d.upsample2d(img, self.resample_filter)
-        if self.is_last or self.architecture == 'skip':
-            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
-            y = y.to(dtype=torch.float32,
-                     memory_format=torch.contiguous_format)
-            img = img.add_(y) if img is not None else y
-
-        assert x.dtype == dtype
-        assert img is None or img.dtype == torch.float32
-        return x, img
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
-    def __init__(self,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 img_resolution,             # Output image resolution.
-                 img_channels,               # Number of color channels.
-                 square,
-                 # Overall multiplier for the number of channels.
-                 channel_base=32768,
-                 # Maximum number of channels in any layer.
-                 channel_max=512,
-                 # Use FP16 for the N highest resolutions.
-                 num_fp16_res=4,
-                 **block_kwargs,             # Arguments for SynthesisBlock.
-                 ):
-        assert img_resolution >= 4 and img_resolution & (
-            img_resolution - 1) == 0
-        super().__init__()
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.square = square
-        self.num_fp16_res = num_fp16_res
-        self.block_resolutions = [
-            2 ** i for i in range(2, self.img_resolution_log2 + 1)]
-        channels_dict = {res: min(channel_base // res, channel_max)
-                         for res in self.block_resolutions}
-        fp16_resolution = max(
-            2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        self.num_ws = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res // 2] if res > 4 else 0
-            out_channels = channels_dict[res]
-            use_fp16 = (res >= fp16_resolution)
-            is_last = (res == self.img_resolution)
-            block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
-                                   img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square, **block_kwargs)
-            self.num_ws += block.num_conv
-            if is_last:
-                self.num_ws += block.num_torgb
-            setattr(self, f'b{res}', block)
-
-    def forward(self, ws, **block_kwargs):
-        block_ws = []
-        with torch.autograd.profiler.record_function('split_ws'):
-            misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
-            ws = ws.to(torch.float32)
-            w_idx = 0
-            for res in self.block_resolutions:
-                block = getattr(self, f'b{res}')
-                block_ws.append(
-                    ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
-                w_idx += block.num_conv
-
-        x = img = None
-        for res, cur_ws in zip(self.block_resolutions, block_ws):
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, cur_ws, **block_kwargs)
-        return img
-
-    def extra_repr(self):
-        return ' '.join([
-            f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
-            f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
-            f'num_fp16_res={self.num_fp16_res:d}'])
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
-    def __init__(self,
-                 z_dim,                      # Input latent (Z) dimensionality.
-                 # Conditioning label (C) dimensionality.
-                 c_dim,
-                 # Intermediate latent (W) dimensionality.
-                 w_dim,
-                 square,
-                 img_resolution,             # Output resolution.
-                 img_channels,               # Number of output color channels.
-                 mapping_kwargs={},   # Arguments for MappingNetwork.
-                 **synthesis_kwargs,         # Arguments for SynthesisNetwork.
-                 ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.square = square
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.synthesis = SynthesisNetwork(
-            w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs)
-        self.num_ws = self.synthesis.num_ws
-        self.mapping = MappingNetwork(
-            z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
-        ws = self.mapping(z, c, truncation_psi=truncation_psi,
-                          truncation_cutoff=truncation_cutoff, update_emas=update_emas)
-        img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
-        return img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
-    def __init__(self,
-                 # Number of input channels, 0 = first block.
-                 in_channels,
-                 # Number of intermediate channels.
-                 tmp_channels,
-                 # Number of output channels.
-                 out_channels,
-                 # Resolution of this block.
-                 resolution,
-                 # Number of input color channels.
-                 img_channels,
-                 # Index of the first layer.
-                 first_layer_idx,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Low-pass filter to apply when resampling activations.
-                 resample_filter=[1, 3, 3, 1],
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 use_fp16=False,        # Use FP16 for this block?
-                 fp16_channels_last=False,        # Use channels-last memory format with FP16?
-                 # Freeze-D: Number of layers to freeze.
-                 freeze_layers=0,
-                 square=False,
-                 ):
-        assert in_channels in [0, tmp_channels]
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.first_layer_idx = first_layer_idx
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.register_buffer(
-            'resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.square = square
-
-        self.num_layers = 0
-
-        def trainable_gen():
-            while True:
-                layer_idx = self.first_layer_idx + self.num_layers
-                trainable = (layer_idx >= freeze_layers)
-                self.num_layers += 1
-                yield trainable
-        trainable_iter = trainable_gen()
-
-        if in_channels == 0 or architecture == 'skip':
-            self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
-                                       trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
-                                 trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
-                                 trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        if architecture == 'resnet':
-            self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
-                                    trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, force_fp32=False):
-        if (x if x is not None else img).device.type != 'cuda':
-            force_fp32 = True
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
-        # Input.
-        if x is not None:
-            if self.square:
-                misc.assert_shape(
-                    x, [None, self.in_channels, self.resolution, self.resolution])
-            else:
-                misc.assert_shape(
-                    x, [None, self.in_channels, self.resolution, self.resolution // 2])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # FromRGB.
-        if self.in_channels == 0 or self.architecture == 'skip':
-            if self.square:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution, self.resolution])
-            else:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution, self.resolution // 2])
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            y = self.fromrgb(img)
-            x = x + y if x is not None else y
-            img = upfirdn2d.downsample2d(
-                img, self.resample_filter) if self.architecture == 'skip' else None
-
-        # Main layers.
-        if self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x)
-            x = self.conv1(x, gain=np.sqrt(0.5))
-            x = y.add_(x)
-        else:
-            x = self.conv0(x)
-            x = self.conv1(x)
-
-        assert x.dtype == dtype
-        return x, img
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
-    def __init__(self, group_size, num_channels=1):
-        super().__init__()
-        self.group_size = group_size
-        self.num_channels = num_channels
-
-    def forward(self, x):
-        N, C, H, W = x.shape
-        with misc.suppress_tracer_warnings():  # as_tensor results are registered as constants
-            G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(
-                N)) if self.group_size is not None else N
-        F = self.num_channels
-        c = C // F
-
-        # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
-        y = x.reshape(G, -1, F, c, H, W)
-        # [GnFcHW] Subtract mean over group.
-        y = y - y.mean(dim=0)
-        # [nFcHW]  Calc variance over group.
-        y = y.square().mean(dim=0)
-        y = (y + 1e-8).sqrt()               # [nFcHW]  Calc stddev over group.
-        # [nF]     Take average over channels and pixels.
-        y = y.mean(dim=[2, 3, 4])
-        y = y.reshape(-1, F, 1, 1)          # [nF11]   Add missing dimensions.
-        # [NFHW]   Replicate over group and pixels.
-        y = y.repeat(G, 1, H, W)
-        # [NCHW]   Append to input as new channels.
-        x = torch.cat([x, y], dim=1)
-        return x
-
-    def extra_repr(self):
-        return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
-    def __init__(self,
-                 in_channels,                    # Number of input channels.
-                 # Dimensionality of mapped conditioning label, 0 = no label.
-                 cmap_dim,
-                 resolution,                     # Resolution of this block.
-                 # Number of input color channels.
-                 img_channels,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Group size for the minibatch standard deviation layer, None = entire minibatch.
-                 mbstd_group_size=4,
-                 # Number of features for the minibatch standard deviation layer, 0 = disable.
-                 mbstd_num_channels=1,
-                 # Activation function: 'relu', 'lrelu', etc.
-                 activation='lrelu',
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=None,
-                 square=False,
-                 ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.cmap_dim = cmap_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.architecture = architecture
-        self.square = square
-
-        if architecture == 'skip':
-            self.fromrgb = Conv2dLayer(
-                img_channels, in_channels, kernel_size=1, activation=activation)
-        self.mbstd = MinibatchStdLayer(
-            group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
-        self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels,
-                                kernel_size=3, activation=activation, conv_clamp=conv_clamp)
-
-        if self.square:
-            self.fc = FullyConnectedLayer(
-                in_channels * (resolution ** 2), in_channels, activation=activation)
-        else:
-            self.fc = FullyConnectedLayer(
-                in_channels * (resolution ** 2 // 2), in_channels, activation=activation)
-
-        self.out = FullyConnectedLayer(
-            in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
-    def forward(self, x, img, cmap, force_fp32=False):
-        if self.square:
-            misc.assert_shape(x, [None, self.in_channels,
-                              self.resolution, self.resolution])
-        else:
-            misc.assert_shape(
-                x, [None, self.in_channels, self.resolution, self.resolution // 2])  # [NCHW]
-
-        _ = force_fp32  # unused
-        dtype = torch.float32
-        memory_format = torch.contiguous_format
-
-        # FromRGB.
-        x = x.to(dtype=dtype, memory_format=memory_format)
-        if self.architecture == 'skip':
-            if self.square:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution, self.resolution])
-            else:
-                misc.assert_shape(
-                    img, [None, self.img_channels, self.resolution, self.resolution // 2])
-
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            x = x + self.fromrgb(img)
-
-        # Main layers.
-        if self.mbstd is not None:
-            x = self.mbstd(x)
-        x = self.conv(x)
-        x = self.fc(x.flatten(1))
-        x = self.out(x)
-
-        # Conditioning.
-        if self.cmap_dim > 0:
-            misc.assert_shape(cmap, [None, self.cmap_dim])
-            x = (x * cmap).sum(dim=1, keepdim=True) * \
-                (1 / np.sqrt(self.cmap_dim))
-
-        assert x.dtype == dtype
-        return x
-
-    def extra_repr(self):
-        return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
-    def __init__(self,
-                 # Conditioning label (C) dimensionality.
-                 c_dim,
-                 img_resolution,                 # Input resolution.
-                 # Number of input color channels.
-                 img_channels,
-                 # Architecture: 'orig', 'skip', 'resnet'.
-                 architecture='resnet',
-                 # Overall multiplier for the number of channels.
-                 channel_base=32768,
-                 # Maximum number of channels in any layer.
-                 channel_max=512,
-                 # Use FP16 for the N highest resolutions.
-                 num_fp16_res=4,
-                 # Clamp the output of convolution layers to +-X, None = disable clamping.
-                 conv_clamp=256,
-                 # Dimensionality of mapped conditioning label, None = default.
-                 cmap_dim=None,
-                 square=False,    # default for rectangle images
-                 block_kwargs={},       # Arguments for DiscriminatorBlock.
-                 mapping_kwargs={},       # Arguments for MappingNetwork.
-                 # Arguments for DiscriminatorEpilogue.
-                 epilogue_kwargs={},
-                 ):
-        super().__init__()
-        self.c_dim = c_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.square = square
-        self.block_resolutions = [
-            2 ** i for i in range(self.img_resolution_log2, 2, -1)]
-        channels_dict = {res: min(channel_base // res, channel_max)
-                         for res in self.block_resolutions + [4]}
-        fp16_resolution = max(
-            2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        if cmap_dim is None:
-            cmap_dim = channels_dict[4]
-        if c_dim == 0:
-            cmap_dim = 0
-
-        common_kwargs = dict(img_channels=img_channels,
-                             architecture=architecture, conv_clamp=conv_clamp)
-        cur_layer_idx = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res] if res < img_resolution else 0
-            tmp_channels = channels_dict[res]
-            out_channels = channels_dict[res // 2]
-            use_fp16 = (res >= fp16_resolution)
-            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
-                                       first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs)
-            setattr(self, f'b{res}', block)
-            cur_layer_idx += block.num_layers
-        if c_dim > 0:
-            self.mapping = MappingNetwork(
-                z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
-        self.b4 = DiscriminatorEpilogue(
-            channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs)
-
-    def forward(self, img, c, update_emas=False, **block_kwargs):
-        _ = update_emas  # unused
-        x = None
-        for res in self.block_resolutions:
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, **block_kwargs)
-
-        cmap = None
-        if self.c_dim > 0:
-            cmap = self.mapping(None, c)
-        x = self.b4(x, img, cmap)
-        return x
-
-    def extra_repr(self):
-        return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
-
-# ----------------------------------------------------------------------------

spaces/Andreean/Sentiment-Analysis-Bitcoin/app.py

@@ -1,34 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-from tensorflow import keras
-import pandas as pd
-import numpy as np
-from PIL import Image
-
-
-from tensorflow.keras.models import load_model
-
-st.set_page_config(page_title = 'Sentiment Analysis Bitcoin',
-                  initial_sidebar_state = "expanded",
-                  menu_items = {
-                      'About' : 'Milestone 2 Fase 2'
-                  })
-
-image = Image.open('bitcoin.png')
-
-# load model
-model = keras.models.load_model("model_bitcoin")
-
-
-label = ['Negative', 'Neutral', 'Positive']
-
-st.title("Sentiment Analysis Bitcoin")
-st.image(image)
-
-news_title = st.text_input('Enter a Tweet Bitcoin')
-new_data = pd.DataFrame([news_title])
-res = model.predict(new_data)
-res = res.argmax()
-press = st.button('Predict')
-if press:
-   st.title(label[res])

spaces/Anustup/NS_AI_LABS/README.md

@@ -1,66 +0,0 @@
----
-title: Whisper Webui
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Running Locally
-
-To run this program locally, first install Python 3.9+ and Git. Then install Pytorch 10.1+ and all the other dependencies:
-```
-pip install -r requirements.txt
-```
-
-Finally, run the full version (no audio length restrictions) of the app:
-```
-python app-full.py
-```
-
-You can also run the CLI interface, which is similar to Whisper's own CLI but also supports the following additional arguments:
-```
-python cli.py \
-[--vad {none,silero-vad,silero-vad-skip-gaps,silero-vad-expand-into-gaps,periodic-vad}] \
-[--vad_merge_window VAD_MERGE_WINDOW] \
-[--vad_max_merge_size VAD_MAX_MERGE_SIZE] \
-[--vad_padding VAD_PADDING] \
-[--vad_prompt_window VAD_PROMPT_WINDOW]
-```
-In addition, you may also use URL's in addition to file paths as input.
-```
-python cli.py --model large --vad silero-vad --language Japanese "https://www.youtube.com/watch?v=4cICErqqRSM"
-```
-
-# Docker
-
-To run it in Docker, first install Docker and optionally the NVIDIA Container Toolkit in order to use the GPU. Then 
-check out this repository and build an image:
-```
-sudo docker build -t whisper-webui:1 .
-```
-
-You can then start the WebUI with GPU support like so:
-```
-sudo docker run -d --gpus=all -p 7860:7860 whisper-webui:1
-```
-
-Leave out "--gpus=all" if you don't have access to a GPU with enough memory, and are fine with running it on the CPU only:
-```
-sudo docker run -d -p 7860:7860 whisper-webui:1
-```
-
-## Caching
-
-Note that the models themselves are currently not included in the Docker images, and will be downloaded on the demand.
-To avoid this, bind the directory /root/.cache/whisper to some directory on the host (for instance /home/administrator/.cache/whisper), where you can (optionally) 
-prepopulate the directory with the different Whisper models. 
-```
-sudo docker run -d --gpus=all -p 7860:7860 --mount type=bind,source=/home/administrator/.cache/whisper,target=/root/.cache/whisper whisper-webui:1
-```

spaces/Apex-X/Tm/README.md

@@ -1,14 +0,0 @@
----
-title: Roop
-emoji: 📈
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: agpl-3.0
-duplicated_from: ezioruan/roop
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Apex-X/nono/.github/ISSUE_TEMPLATE/installation.md

@@ -1,12 +0,0 @@
----
-name: Installation
-about: Platform and installation issues
-title: '[Installation]'
-labels: 'installation'
-
----
-
-Please **DO NOT OPEN** platform and installation issues!
-
-- Check the [troubleshooting](https://github.com/s0md3v/roop/wiki/4.-Troubleshooting) that covers many issues.
-- Join our helpful community on [Discord](https://discord.gg/Y9p4ZQ2sB9) for instant help.

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/install.py

@@ -1,775 +0,0 @@
-import errno
-import json
-import operator
-import os
-import shutil
-import site
-from optparse import SUPPRESS_HELP, Values
-from typing import List, Optional
-
-from pip._vendor.rich import print_json
-
-from pip._internal.cache import WheelCache
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.cmdoptions import make_target_python
-from pip._internal.cli.req_command import (
-    RequirementCommand,
-    warn_if_run_as_root,
-    with_cleanup,
-)
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.exceptions import CommandError, InstallationError
-from pip._internal.locations import get_scheme
-from pip._internal.metadata import get_environment
-from pip._internal.models.installation_report import InstallationReport
-from pip._internal.operations.build.build_tracker import get_build_tracker
-from pip._internal.operations.check import ConflictDetails, check_install_conflicts
-from pip._internal.req import install_given_reqs
-from pip._internal.req.req_install import (
-    InstallRequirement,
-    check_legacy_setup_py_options,
-)
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.filesystem import test_writable_dir
-from pip._internal.utils.logging import getLogger
-from pip._internal.utils.misc import (
-    check_externally_managed,
-    ensure_dir,
-    get_pip_version,
-    protect_pip_from_modification_on_windows,
-    write_output,
-)
-from pip._internal.utils.temp_dir import TempDirectory
-from pip._internal.utils.virtualenv import (
-    running_under_virtualenv,
-    virtualenv_no_global,
-)
-from pip._internal.wheel_builder import build, should_build_for_install_command
-
-logger = getLogger(__name__)
-
-
-class InstallCommand(RequirementCommand):
-    """
-    Install packages from:
-
-    - PyPI (and other indexes) using requirement specifiers.
-    - VCS project urls.
-    - Local project directories.
-    - Local or remote source archives.
-
-    pip also supports installing from "requirements files", which provide
-    an easy way to specify a whole environment to be installed.
-    """
-
-    usage = """
-      %prog [options] <requirement specifier> [package-index-options] ...
-      %prog [options] -r <requirements file> [package-index-options] ...
-      %prog [options] [-e] <vcs project url> ...
-      %prog [options] [-e] <local project path> ...
-      %prog [options] <archive url/path> ..."""
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(cmdoptions.requirements())
-        self.cmd_opts.add_option(cmdoptions.constraints())
-        self.cmd_opts.add_option(cmdoptions.no_deps())
-        self.cmd_opts.add_option(cmdoptions.pre())
-
-        self.cmd_opts.add_option(cmdoptions.editable())
-        self.cmd_opts.add_option(
-            "--dry-run",
-            action="store_true",
-            dest="dry_run",
-            default=False,
-            help=(
-                "Don't actually install anything, just print what would be. "
-                "Can be used in combination with --ignore-installed "
-                "to 'resolve' the requirements."
-            ),
-        )
-        self.cmd_opts.add_option(
-            "-t",
-            "--target",
-            dest="target_dir",
-            metavar="dir",
-            default=None,
-            help=(
-                "Install packages into <dir>. "
-                "By default this will not replace existing files/folders in "
-                "<dir>. Use --upgrade to replace existing packages in <dir> "
-                "with new versions."
-            ),
-        )
-        cmdoptions.add_target_python_options(self.cmd_opts)
-
-        self.cmd_opts.add_option(
-            "--user",
-            dest="use_user_site",
-            action="store_true",
-            help=(
-                "Install to the Python user install directory for your "
-                "platform. Typically ~/.local/, or %APPDATA%\\Python on "
-                "Windows. (See the Python documentation for site.USER_BASE "
-                "for full details.)"
-            ),
-        )
-        self.cmd_opts.add_option(
-            "--no-user",
-            dest="use_user_site",
-            action="store_false",
-            help=SUPPRESS_HELP,
-        )
-        self.cmd_opts.add_option(
-            "--root",
-            dest="root_path",
-            metavar="dir",
-            default=None,
-            help="Install everything relative to this alternate root directory.",
-        )
-        self.cmd_opts.add_option(
-            "--prefix",
-            dest="prefix_path",
-            metavar="dir",
-            default=None,
-            help=(
-                "Installation prefix where lib, bin and other top-level "
-                "folders are placed. Note that the resulting installation may "
-                "contain scripts and other resources which reference the "
-                "Python interpreter of pip, and not that of ``--prefix``. "
-                "See also the ``--python`` option if the intention is to "
-                "install packages into another (possibly pip-free) "
-                "environment."
-            ),
-        )
-
-        self.cmd_opts.add_option(cmdoptions.src())
-
-        self.cmd_opts.add_option(
-            "-U",
-            "--upgrade",
-            dest="upgrade",
-            action="store_true",
-            help=(
-                "Upgrade all specified packages to the newest available "
-                "version. The handling of dependencies depends on the "
-                "upgrade-strategy used."
-            ),
-        )
-
-        self.cmd_opts.add_option(
-            "--upgrade-strategy",
-            dest="upgrade_strategy",
-            default="only-if-needed",
-            choices=["only-if-needed", "eager"],
-            help=(
-                "Determines how dependency upgrading should be handled "
-                "[default: %default]. "
-                '"eager" - dependencies are upgraded regardless of '
-                "whether the currently installed version satisfies the "
-                "requirements of the upgraded package(s). "
-                '"only-if-needed" -  are upgraded only when they do not '
-                "satisfy the requirements of the upgraded package(s)."
-            ),
-        )
-
-        self.cmd_opts.add_option(
-            "--force-reinstall",
-            dest="force_reinstall",
-            action="store_true",
-            help="Reinstall all packages even if they are already up-to-date.",
-        )
-
-        self.cmd_opts.add_option(
-            "-I",
-            "--ignore-installed",
-            dest="ignore_installed",
-            action="store_true",
-            help=(
-                "Ignore the installed packages, overwriting them. "
-                "This can break your system if the existing package "
-                "is of a different version or was installed "
-                "with a different package manager!"
-            ),
-        )
-
-        self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
-        self.cmd_opts.add_option(cmdoptions.no_build_isolation())
-        self.cmd_opts.add_option(cmdoptions.use_pep517())
-        self.cmd_opts.add_option(cmdoptions.no_use_pep517())
-        self.cmd_opts.add_option(cmdoptions.check_build_deps())
-        self.cmd_opts.add_option(cmdoptions.override_externally_managed())
-
-        self.cmd_opts.add_option(cmdoptions.config_settings())
-        self.cmd_opts.add_option(cmdoptions.global_options())
-
-        self.cmd_opts.add_option(
-            "--compile",
-            action="store_true",
-            dest="compile",
-            default=True,
-            help="Compile Python source files to bytecode",
-        )
-
-        self.cmd_opts.add_option(
-            "--no-compile",
-            action="store_false",
-            dest="compile",
-            help="Do not compile Python source files to bytecode",
-        )
-
-        self.cmd_opts.add_option(
-            "--no-warn-script-location",
-            action="store_false",
-            dest="warn_script_location",
-            default=True,
-            help="Do not warn when installing scripts outside PATH",
-        )
-        self.cmd_opts.add_option(
-            "--no-warn-conflicts",
-            action="store_false",
-            dest="warn_about_conflicts",
-            default=True,
-            help="Do not warn about broken dependencies",
-        )
-        self.cmd_opts.add_option(cmdoptions.no_binary())
-        self.cmd_opts.add_option(cmdoptions.only_binary())
-        self.cmd_opts.add_option(cmdoptions.prefer_binary())
-        self.cmd_opts.add_option(cmdoptions.require_hashes())
-        self.cmd_opts.add_option(cmdoptions.progress_bar())
-        self.cmd_opts.add_option(cmdoptions.root_user_action())
-
-        index_opts = cmdoptions.make_option_group(
-            cmdoptions.index_group,
-            self.parser,
-        )
-
-        self.parser.insert_option_group(0, index_opts)
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-        self.cmd_opts.add_option(
-            "--report",
-            dest="json_report_file",
-            metavar="file",
-            default=None,
-            help=(
-                "Generate a JSON file describing what pip did to install "
-                "the provided requirements. "
-                "Can be used in combination with --dry-run and --ignore-installed "
-                "to 'resolve' the requirements. "
-                "When - is used as file name it writes to stdout. "
-                "When writing to stdout, please combine with the --quiet option "
-                "to avoid mixing pip logging output with JSON output."
-            ),
-        )
-
-    @with_cleanup
-    def run(self, options: Values, args: List[str]) -> int:
-        if options.use_user_site and options.target_dir is not None:
-            raise CommandError("Can not combine '--user' and '--target'")
-
-        # Check whether the environment we're installing into is externally
-        # managed, as specified in PEP 668. Specifying --root, --target, or
-        # --prefix disables the check, since there's no reliable way to locate
-        # the EXTERNALLY-MANAGED file for those cases. An exception is also
-        # made specifically for "--dry-run --report" for convenience.
-        installing_into_current_environment = (
-            not (options.dry_run and options.json_report_file)
-            and options.root_path is None
-            and options.target_dir is None
-            and options.prefix_path is None
-        )
-        if (
-            installing_into_current_environment
-            and not options.override_externally_managed
-        ):
-            check_externally_managed()
-
-        upgrade_strategy = "to-satisfy-only"
-        if options.upgrade:
-            upgrade_strategy = options.upgrade_strategy
-
-        cmdoptions.check_dist_restriction(options, check_target=True)
-
-        logger.verbose("Using %s", get_pip_version())
-        options.use_user_site = decide_user_install(
-            options.use_user_site,
-            prefix_path=options.prefix_path,
-            target_dir=options.target_dir,
-            root_path=options.root_path,
-            isolated_mode=options.isolated_mode,
-        )
-
-        target_temp_dir: Optional[TempDirectory] = None
-        target_temp_dir_path: Optional[str] = None
-        if options.target_dir:
-            options.ignore_installed = True
-            options.target_dir = os.path.abspath(options.target_dir)
-            if (
-                # fmt: off
-                os.path.exists(options.target_dir) and
-                not os.path.isdir(options.target_dir)
-                # fmt: on
-            ):
-                raise CommandError(
-                    "Target path exists but is not a directory, will not continue."
-                )
-
-            # Create a target directory for using with the target option
-            target_temp_dir = TempDirectory(kind="target")
-            target_temp_dir_path = target_temp_dir.path
-            self.enter_context(target_temp_dir)
-
-        global_options = options.global_options or []
-
-        session = self.get_default_session(options)
-
-        target_python = make_target_python(options)
-        finder = self._build_package_finder(
-            options=options,
-            session=session,
-            target_python=target_python,
-            ignore_requires_python=options.ignore_requires_python,
-        )
-        build_tracker = self.enter_context(get_build_tracker())
-
-        directory = TempDirectory(
-            delete=not options.no_clean,
-            kind="install",
-            globally_managed=True,
-        )
-
-        try:
-            reqs = self.get_requirements(args, options, finder, session)
-            check_legacy_setup_py_options(options, reqs)
-
-            wheel_cache = WheelCache(options.cache_dir)
-
-            # Only when installing is it permitted to use PEP 660.
-            # In other circumstances (pip wheel, pip download) we generate
-            # regular (i.e. non editable) metadata and wheels.
-            for req in reqs:
-                req.permit_editable_wheels = True
-
-            preparer = self.make_requirement_preparer(
-                temp_build_dir=directory,
-                options=options,
-                build_tracker=build_tracker,
-                session=session,
-                finder=finder,
-                use_user_site=options.use_user_site,
-                verbosity=self.verbosity,
-            )
-            resolver = self.make_resolver(
-                preparer=preparer,
-                finder=finder,
-                options=options,
-                wheel_cache=wheel_cache,
-                use_user_site=options.use_user_site,
-                ignore_installed=options.ignore_installed,
-                ignore_requires_python=options.ignore_requires_python,
-                force_reinstall=options.force_reinstall,
-                upgrade_strategy=upgrade_strategy,
-                use_pep517=options.use_pep517,
-            )
-
-            self.trace_basic_info(finder)
-
-            requirement_set = resolver.resolve(
-                reqs, check_supported_wheels=not options.target_dir
-            )
-
-            if options.json_report_file:
-                report = InstallationReport(requirement_set.requirements_to_install)
-                if options.json_report_file == "-":
-                    print_json(data=report.to_dict())
-                else:
-                    with open(options.json_report_file, "w", encoding="utf-8") as f:
-                        json.dump(report.to_dict(), f, indent=2, ensure_ascii=False)
-
-            if options.dry_run:
-                would_install_items = sorted(
-                    (r.metadata["name"], r.metadata["version"])
-                    for r in requirement_set.requirements_to_install
-                )
-                if would_install_items:
-                    write_output(
-                        "Would install %s",
-                        " ".join("-".join(item) for item in would_install_items),
-                    )
-                return SUCCESS
-
-            try:
-                pip_req = requirement_set.get_requirement("pip")
-            except KeyError:
-                modifying_pip = False
-            else:
-                # If we're not replacing an already installed pip,
-                # we're not modifying it.
-                modifying_pip = pip_req.satisfied_by is None
-            protect_pip_from_modification_on_windows(modifying_pip=modifying_pip)
-
-            reqs_to_build = [
-                r
-                for r in requirement_set.requirements.values()
-                if should_build_for_install_command(r)
-            ]
-
-            _, build_failures = build(
-                reqs_to_build,
-                wheel_cache=wheel_cache,
-                verify=True,
-                build_options=[],
-                global_options=global_options,
-            )
-
-            if build_failures:
-                raise InstallationError(
-                    "Could not build wheels for {}, which is required to "
-                    "install pyproject.toml-based projects".format(
-                        ", ".join(r.name for r in build_failures)  # type: ignore
-                    )
-                )
-
-            to_install = resolver.get_installation_order(requirement_set)
-
-            # Check for conflicts in the package set we're installing.
-            conflicts: Optional[ConflictDetails] = None
-            should_warn_about_conflicts = (
-                not options.ignore_dependencies and options.warn_about_conflicts
-            )
-            if should_warn_about_conflicts:
-                conflicts = self._determine_conflicts(to_install)
-
-            # Don't warn about script install locations if
-            # --target or --prefix has been specified
-            warn_script_location = options.warn_script_location
-            if options.target_dir or options.prefix_path:
-                warn_script_location = False
-
-            installed = install_given_reqs(
-                to_install,
-                global_options,
-                root=options.root_path,
-                home=target_temp_dir_path,
-                prefix=options.prefix_path,
-                warn_script_location=warn_script_location,
-                use_user_site=options.use_user_site,
-                pycompile=options.compile,
-            )
-
-            lib_locations = get_lib_location_guesses(
-                user=options.use_user_site,
-                home=target_temp_dir_path,
-                root=options.root_path,
-                prefix=options.prefix_path,
-                isolated=options.isolated_mode,
-            )
-            env = get_environment(lib_locations)
-
-            installed.sort(key=operator.attrgetter("name"))
-            items = []
-            for result in installed:
-                item = result.name
-                try:
-                    installed_dist = env.get_distribution(item)
-                    if installed_dist is not None:
-                        item = f"{item}-{installed_dist.version}"
-                except Exception:
-                    pass
-                items.append(item)
-
-            if conflicts is not None:
-                self._warn_about_conflicts(
-                    conflicts,
-                    resolver_variant=self.determine_resolver_variant(options),
-                )
-
-            installed_desc = " ".join(items)
-            if installed_desc:
-                write_output(
-                    "Successfully installed %s",
-                    installed_desc,
-                )
-        except OSError as error:
-            show_traceback = self.verbosity >= 1
-
-            message = create_os_error_message(
-                error,
-                show_traceback,
-                options.use_user_site,
-            )
-            logger.error(message, exc_info=show_traceback)  # noqa
-
-            return ERROR
-
-        if options.target_dir:
-            assert target_temp_dir
-            self._handle_target_dir(
-                options.target_dir, target_temp_dir, options.upgrade
-            )
-        if options.root_user_action == "warn":
-            warn_if_run_as_root()
-        return SUCCESS
-
-    def _handle_target_dir(
-        self, target_dir: str, target_temp_dir: TempDirectory, upgrade: bool
-    ) -> None:
-        ensure_dir(target_dir)
-
-        # Checking both purelib and platlib directories for installed
-        # packages to be moved to target directory
-        lib_dir_list = []
-
-        # Checking both purelib and platlib directories for installed
-        # packages to be moved to target directory
-        scheme = get_scheme("", home=target_temp_dir.path)
-        purelib_dir = scheme.purelib
-        platlib_dir = scheme.platlib
-        data_dir = scheme.data
-
-        if os.path.exists(purelib_dir):
-            lib_dir_list.append(purelib_dir)
-        if os.path.exists(platlib_dir) and platlib_dir != purelib_dir:
-            lib_dir_list.append(platlib_dir)
-        if os.path.exists(data_dir):
-            lib_dir_list.append(data_dir)
-
-        for lib_dir in lib_dir_list:
-            for item in os.listdir(lib_dir):
-                if lib_dir == data_dir:
-                    ddir = os.path.join(data_dir, item)
-                    if any(s.startswith(ddir) for s in lib_dir_list[:-1]):
-                        continue
-                target_item_dir = os.path.join(target_dir, item)
-                if os.path.exists(target_item_dir):
-                    if not upgrade:
-                        logger.warning(
-                            "Target directory %s already exists. Specify "
-                            "--upgrade to force replacement.",
-                            target_item_dir,
-                        )
-                        continue
-                    if os.path.islink(target_item_dir):
-                        logger.warning(
-                            "Target directory %s already exists and is "
-                            "a link. pip will not automatically replace "
-                            "links, please remove if replacement is "
-                            "desired.",
-                            target_item_dir,
-                        )
-                        continue
-                    if os.path.isdir(target_item_dir):
-                        shutil.rmtree(target_item_dir)
-                    else:
-                        os.remove(target_item_dir)
-
-                shutil.move(os.path.join(lib_dir, item), target_item_dir)
-
-    def _determine_conflicts(
-        self, to_install: List[InstallRequirement]
-    ) -> Optional[ConflictDetails]:
-        try:
-            return check_install_conflicts(to_install)
-        except Exception:
-            logger.exception(
-                "Error while checking for conflicts. Please file an issue on "
-                "pip's issue tracker: https://github.com/pypa/pip/issues/new"
-            )
-            return None
-
-    def _warn_about_conflicts(
-        self, conflict_details: ConflictDetails, resolver_variant: str
-    ) -> None:
-        package_set, (missing, conflicting) = conflict_details
-        if not missing and not conflicting:
-            return
-
-        parts: List[str] = []
-        if resolver_variant == "legacy":
-            parts.append(
-                "pip's legacy dependency resolver does not consider dependency "
-                "conflicts when selecting packages. This behaviour is the "
-                "source of the following dependency conflicts."
-            )
-        else:
-            assert resolver_variant == "2020-resolver"
-            parts.append(
-                "pip's dependency resolver does not currently take into account "
-                "all the packages that are installed. This behaviour is the "
-                "source of the following dependency conflicts."
-            )
-
-        # NOTE: There is some duplication here, with commands/check.py
-        for project_name in missing:
-            version = package_set[project_name][0]
-            for dependency in missing[project_name]:
-                message = (
-                    "{name} {version} requires {requirement}, "
-                    "which is not installed."
-                ).format(
-                    name=project_name,
-                    version=version,
-                    requirement=dependency[1],
-                )
-                parts.append(message)
-
-        for project_name in conflicting:
-            version = package_set[project_name][0]
-            for dep_name, dep_version, req in conflicting[project_name]:
-                message = (
-                    "{name} {version} requires {requirement}, but {you} have "
-                    "{dep_name} {dep_version} which is incompatible."
-                ).format(
-                    name=project_name,
-                    version=version,
-                    requirement=req,
-                    dep_name=dep_name,
-                    dep_version=dep_version,
-                    you=("you" if resolver_variant == "2020-resolver" else "you'll"),
-                )
-                parts.append(message)
-
-        logger.critical("\n".join(parts))
-
-
-def get_lib_location_guesses(
-    user: bool = False,
-    home: Optional[str] = None,
-    root: Optional[str] = None,
-    isolated: bool = False,
-    prefix: Optional[str] = None,
-) -> List[str]:
-    scheme = get_scheme(
-        "",
-        user=user,
-        home=home,
-        root=root,
-        isolated=isolated,
-        prefix=prefix,
-    )
-    return [scheme.purelib, scheme.platlib]
-
-
-def site_packages_writable(root: Optional[str], isolated: bool) -> bool:
-    return all(
-        test_writable_dir(d)
-        for d in set(get_lib_location_guesses(root=root, isolated=isolated))
-    )
-
-
-def decide_user_install(
-    use_user_site: Optional[bool],
-    prefix_path: Optional[str] = None,
-    target_dir: Optional[str] = None,
-    root_path: Optional[str] = None,
-    isolated_mode: bool = False,
-) -> bool:
-    """Determine whether to do a user install based on the input options.
-
-    If use_user_site is False, no additional checks are done.
-    If use_user_site is True, it is checked for compatibility with other
-    options.
-    If use_user_site is None, the default behaviour depends on the environment,
-    which is provided by the other arguments.
-    """
-    # In some cases (config from tox), use_user_site can be set to an integer
-    # rather than a bool, which 'use_user_site is False' wouldn't catch.
-    if (use_user_site is not None) and (not use_user_site):
-        logger.debug("Non-user install by explicit request")
-        return False
-
-    if use_user_site:
-        if prefix_path:
-            raise CommandError(
-                "Can not combine '--user' and '--prefix' as they imply "
-                "different installation locations"
-            )
-        if virtualenv_no_global():
-            raise InstallationError(
-                "Can not perform a '--user' install. User site-packages "
-                "are not visible in this virtualenv."
-            )
-        logger.debug("User install by explicit request")
-        return True
-
-    # If we are here, user installs have not been explicitly requested/avoided
-    assert use_user_site is None
-
-    # user install incompatible with --prefix/--target
-    if prefix_path or target_dir:
-        logger.debug("Non-user install due to --prefix or --target option")
-        return False
-
-    # If user installs are not enabled, choose a non-user install
-    if not site.ENABLE_USER_SITE:
-        logger.debug("Non-user install because user site-packages disabled")
-        return False
-
-    # If we have permission for a non-user install, do that,
-    # otherwise do a user install.
-    if site_packages_writable(root=root_path, isolated=isolated_mode):
-        logger.debug("Non-user install because site-packages writeable")
-        return False
-
-    logger.info(
-        "Defaulting to user installation because normal site-packages "
-        "is not writeable"
-    )
-    return True
-
-
-def create_os_error_message(
-    error: OSError, show_traceback: bool, using_user_site: bool
-) -> str:
-    """Format an error message for an OSError
-
-    It may occur anytime during the execution of the install command.
-    """
-    parts = []
-
-    # Mention the error if we are not going to show a traceback
-    parts.append("Could not install packages due to an OSError")
-    if not show_traceback:
-        parts.append(": ")
-        parts.append(str(error))
-    else:
-        parts.append(".")
-
-    # Spilt the error indication from a helper message (if any)
-    parts[-1] += "\n"
-
-    # Suggest useful actions to the user:
-    #  (1) using user site-packages or (2) verifying the permissions
-    if error.errno == errno.EACCES:
-        user_option_part = "Consider using the `--user` option"
-        permissions_part = "Check the permissions"
-
-        if not running_under_virtualenv() and not using_user_site:
-            parts.extend(
-                [
-                    user_option_part,
-                    " or ",
-                    permissions_part.lower(),
-                ]
-            )
-        else:
-            parts.append(permissions_part)
-        parts.append(".\n")
-
-    # Suggest the user to enable Long Paths if path length is
-    # more than 260
-    if (
-        WINDOWS
-        and error.errno == errno.ENOENT
-        and error.filename
-        and len(error.filename) > 260
-    ):
-        parts.append(
-            "HINT: This error might have occurred since "
-            "this system does not have Windows Long Path "
-            "support enabled. You can find information on "
-            "how to enable this at "
-            "https://pip.pypa.io/warnings/enable-long-paths\n"
-        )
-
-    return "".join(parts).strip() + "\n"

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/req/__init__.py

@@ -1,92 +0,0 @@
-import collections
-import logging
-from typing import Generator, List, Optional, Sequence, Tuple
-
-from pip._internal.utils.logging import indent_log
-
-from .req_file import parse_requirements
-from .req_install import InstallRequirement
-from .req_set import RequirementSet
-
-__all__ = [
-    "RequirementSet",
-    "InstallRequirement",
-    "parse_requirements",
-    "install_given_reqs",
-]
-
-logger = logging.getLogger(__name__)
-
-
-class InstallationResult:
-    def __init__(self, name: str) -> None:
-        self.name = name
-
-    def __repr__(self) -> str:
-        return f"InstallationResult(name={self.name!r})"
-
-
-def _validate_requirements(
-    requirements: List[InstallRequirement],
-) -> Generator[Tuple[str, InstallRequirement], None, None]:
-    for req in requirements:
-        assert req.name, f"invalid to-be-installed requirement: {req}"
-        yield req.name, req
-
-
-def install_given_reqs(
-    requirements: List[InstallRequirement],
-    global_options: Sequence[str],
-    root: Optional[str],
-    home: Optional[str],
-    prefix: Optional[str],
-    warn_script_location: bool,
-    use_user_site: bool,
-    pycompile: bool,
-) -> List[InstallationResult]:
-    """
-    Install everything in the given list.
-
-    (to be called after having downloaded and unpacked the packages)
-    """
-    to_install = collections.OrderedDict(_validate_requirements(requirements))
-
-    if to_install:
-        logger.info(
-            "Installing collected packages: %s",
-            ", ".join(to_install.keys()),
-        )
-
-    installed = []
-
-    with indent_log():
-        for req_name, requirement in to_install.items():
-            if requirement.should_reinstall:
-                logger.info("Attempting uninstall: %s", req_name)
-                with indent_log():
-                    uninstalled_pathset = requirement.uninstall(auto_confirm=True)
-            else:
-                uninstalled_pathset = None
-
-            try:
-                requirement.install(
-                    global_options,
-                    root=root,
-                    home=home,
-                    prefix=prefix,
-                    warn_script_location=warn_script_location,
-                    use_user_site=use_user_site,
-                    pycompile=pycompile,
-                )
-            except Exception:
-                # if install did not succeed, rollback previous uninstall
-                if uninstalled_pathset and not requirement.install_succeeded:
-                    uninstalled_pathset.rollback()
-                raise
-            else:
-                if uninstalled_pathset and requirement.install_succeeded:
-                    uninstalled_pathset.commit()
-
-            installed.append(InstallationResult(req_name))
-
-    return installed

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/unpacking.py

@@ -1,257 +0,0 @@
-"""Utilities related archives.
-"""
-
-import logging
-import os
-import shutil
-import stat
-import tarfile
-import zipfile
-from typing import Iterable, List, Optional
-from zipfile import ZipInfo
-
-from pip._internal.exceptions import InstallationError
-from pip._internal.utils.filetypes import (
-    BZ2_EXTENSIONS,
-    TAR_EXTENSIONS,
-    XZ_EXTENSIONS,
-    ZIP_EXTENSIONS,
-)
-from pip._internal.utils.misc import ensure_dir
-
-logger = logging.getLogger(__name__)
-
-
-SUPPORTED_EXTENSIONS = ZIP_EXTENSIONS + TAR_EXTENSIONS
-
-try:
-    import bz2  # noqa
-
-    SUPPORTED_EXTENSIONS += BZ2_EXTENSIONS
-except ImportError:
-    logger.debug("bz2 module is not available")
-
-try:
-    # Only for Python 3.3+
-    import lzma  # noqa
-
-    SUPPORTED_EXTENSIONS += XZ_EXTENSIONS
-except ImportError:
-    logger.debug("lzma module is not available")
-
-
-def current_umask() -> int:
-    """Get the current umask which involves having to set it temporarily."""
-    mask = os.umask(0)
-    os.umask(mask)
-    return mask
-
-
-def split_leading_dir(path: str) -> List[str]:
-    path = path.lstrip("/").lstrip("\\")
-    if "/" in path and (
-        ("\\" in path and path.find("/") < path.find("\\")) or "\\" not in path
-    ):
-        return path.split("/", 1)
-    elif "\\" in path:
-        return path.split("\\", 1)
-    else:
-        return [path, ""]
-
-
-def has_leading_dir(paths: Iterable[str]) -> bool:
-    """Returns true if all the paths have the same leading path name
-    (i.e., everything is in one subdirectory in an archive)"""
-    common_prefix = None
-    for path in paths:
-        prefix, rest = split_leading_dir(path)
-        if not prefix:
-            return False
-        elif common_prefix is None:
-            common_prefix = prefix
-        elif prefix != common_prefix:
-            return False
-    return True
-
-
-def is_within_directory(directory: str, target: str) -> bool:
-    """
-    Return true if the absolute path of target is within the directory
-    """
-    abs_directory = os.path.abspath(directory)
-    abs_target = os.path.abspath(target)
-
-    prefix = os.path.commonprefix([abs_directory, abs_target])
-    return prefix == abs_directory
-
-
-def set_extracted_file_to_default_mode_plus_executable(path: str) -> None:
-    """
-    Make file present at path have execute for user/group/world
-    (chmod +x) is no-op on windows per python docs
-    """
-    os.chmod(path, (0o777 & ~current_umask() | 0o111))
-
-
-def zip_item_is_executable(info: ZipInfo) -> bool:
-    mode = info.external_attr >> 16
-    # if mode and regular file and any execute permissions for
-    # user/group/world?
-    return bool(mode and stat.S_ISREG(mode) and mode & 0o111)
-
-
-def unzip_file(filename: str, location: str, flatten: bool = True) -> None:
-    """
-    Unzip the file (with path `filename`) to the destination `location`.  All
-    files are written based on system defaults and umask (i.e. permissions are
-    not preserved), except that regular file members with any execute
-    permissions (user, group, or world) have "chmod +x" applied after being
-    written. Note that for windows, any execute changes using os.chmod are
-    no-ops per the python docs.
-    """
-    ensure_dir(location)
-    zipfp = open(filename, "rb")
-    try:
-        zip = zipfile.ZipFile(zipfp, allowZip64=True)
-        leading = has_leading_dir(zip.namelist()) and flatten
-        for info in zip.infolist():
-            name = info.filename
-            fn = name
-            if leading:
-                fn = split_leading_dir(name)[1]
-            fn = os.path.join(location, fn)
-            dir = os.path.dirname(fn)
-            if not is_within_directory(location, fn):
-                message = (
-                    "The zip file ({}) has a file ({}) trying to install "
-                    "outside target directory ({})"
-                )
-                raise InstallationError(message.format(filename, fn, location))
-            if fn.endswith("/") or fn.endswith("\\"):
-                # A directory
-                ensure_dir(fn)
-            else:
-                ensure_dir(dir)
-                # Don't use read() to avoid allocating an arbitrarily large
-                # chunk of memory for the file's content
-                fp = zip.open(name)
-                try:
-                    with open(fn, "wb") as destfp:
-                        shutil.copyfileobj(fp, destfp)
-                finally:
-                    fp.close()
-                    if zip_item_is_executable(info):
-                        set_extracted_file_to_default_mode_plus_executable(fn)
-    finally:
-        zipfp.close()
-
-
-def untar_file(filename: str, location: str) -> None:
-    """
-    Untar the file (with path `filename`) to the destination `location`.
-    All files are written based on system defaults and umask (i.e. permissions
-    are not preserved), except that regular file members with any execute
-    permissions (user, group, or world) have "chmod +x" applied after being
-    written.  Note that for windows, any execute changes using os.chmod are
-    no-ops per the python docs.
-    """
-    ensure_dir(location)
-    if filename.lower().endswith(".gz") or filename.lower().endswith(".tgz"):
-        mode = "r:gz"
-    elif filename.lower().endswith(BZ2_EXTENSIONS):
-        mode = "r:bz2"
-    elif filename.lower().endswith(XZ_EXTENSIONS):
-        mode = "r:xz"
-    elif filename.lower().endswith(".tar"):
-        mode = "r"
-    else:
-        logger.warning(
-            "Cannot determine compression type for file %s",
-            filename,
-        )
-        mode = "r:*"
-    tar = tarfile.open(filename, mode, encoding="utf-8")
-    try:
-        leading = has_leading_dir([member.name for member in tar.getmembers()])
-        for member in tar.getmembers():
-            fn = member.name
-            if leading:
-                fn = split_leading_dir(fn)[1]
-            path = os.path.join(location, fn)
-            if not is_within_directory(location, path):
-                message = (
-                    "The tar file ({}) has a file ({}) trying to install "
-                    "outside target directory ({})"
-                )
-                raise InstallationError(message.format(filename, path, location))
-            if member.isdir():
-                ensure_dir(path)
-            elif member.issym():
-                try:
-                    tar._extract_member(member, path)
-                except Exception as exc:
-                    # Some corrupt tar files seem to produce this
-                    # (specifically bad symlinks)
-                    logger.warning(
-                        "In the tar file %s the member %s is invalid: %s",
-                        filename,
-                        member.name,
-                        exc,
-                    )
-                    continue
-            else:
-                try:
-                    fp = tar.extractfile(member)
-                except (KeyError, AttributeError) as exc:
-                    # Some corrupt tar files seem to produce this
-                    # (specifically bad symlinks)
-                    logger.warning(
-                        "In the tar file %s the member %s is invalid: %s",
-                        filename,
-                        member.name,
-                        exc,
-                    )
-                    continue
-                ensure_dir(os.path.dirname(path))
-                assert fp is not None
-                with open(path, "wb") as destfp:
-                    shutil.copyfileobj(fp, destfp)
-                fp.close()
-                # Update the timestamp (useful for cython compiled files)
-                tar.utime(member, path)
-                # member have any execute permissions for user/group/world?
-                if member.mode & 0o111:
-                    set_extracted_file_to_default_mode_plus_executable(path)
-    finally:
-        tar.close()
-
-
-def unpack_file(
-    filename: str,
-    location: str,
-    content_type: Optional[str] = None,
-) -> None:
-    filename = os.path.realpath(filename)
-    if (
-        content_type == "application/zip"
-        or filename.lower().endswith(ZIP_EXTENSIONS)
-        or zipfile.is_zipfile(filename)
-    ):
-        unzip_file(filename, location, flatten=not filename.endswith(".whl"))
-    elif (
-        content_type == "application/x-gzip"
-        or tarfile.is_tarfile(filename)
-        or filename.lower().endswith(TAR_EXTENSIONS + BZ2_EXTENSIONS + XZ_EXTENSIONS)
-    ):
-        untar_file(filename, location)
-    else:
-        # FIXME: handle?
-        # FIXME: magic signatures?
-        logger.critical(
-            "Cannot unpack file %s (downloaded from %s, content-type: %s); "
-            "cannot detect archive format",
-            filename,
-            location,
-            content_type,
-        )
-        raise InstallationError(f"Cannot determine archive format of {location}")

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-Detection/retinanet_R_50_FPN_1x.py

@@ -1,11 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.retinanet import model
-from ..common.train import train
-
-dataloader.train.mapper.use_instance_mask = False
-model.backbone.bottom_up.freeze_at = 2
-optimizer.lr = 0.01
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py

@@ -1,14 +0,0 @@
-from .mask_rcnn_R_50_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter *= 2  # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter

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

@@ -1,47 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from torch.utils.data.sampler import BatchSampler, Sampler
-
-
-class GroupedBatchSampler(BatchSampler):
-    """
-    Wraps another sampler to yield a mini-batch of indices.
-    It enforces that the batch only contain elements from the same group.
-    It also tries to provide mini-batches which follows an ordering which is
-    as close as possible to the ordering from the original sampler.
-    """
-
-    def __init__(self, sampler, group_ids, batch_size):
-        """
-        Args:
-            sampler (Sampler): Base sampler.
-            group_ids (list[int]): If the sampler produces indices in range [0, N),
-                `group_ids` must be a list of `N` ints which contains the group id of each sample.
-                The group ids must be a set of integers in the range [0, num_groups).
-            batch_size (int): Size of mini-batch.
-        """
-        if not isinstance(sampler, Sampler):
-            raise ValueError(
-                "sampler should be an instance of "
-                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
-            )
-        self.sampler = sampler
-        self.group_ids = np.asarray(group_ids)
-        assert self.group_ids.ndim == 1
-        self.batch_size = batch_size
-        groups = np.unique(self.group_ids).tolist()
-
-        # buffer the indices of each group until batch size is reached
-        self.buffer_per_group = {k: [] for k in groups}
-
-    def __iter__(self):
-        for idx in self.sampler:
-            group_id = self.group_ids[idx]
-            group_buffer = self.buffer_per_group[group_id]
-            group_buffer.append(idx)
-            if len(group_buffer) == self.batch_size:
-                yield group_buffer[:]  # yield a copy of the list
-                del group_buffer[:]
-
-    def __len__(self):
-        raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.")

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/data/test_sampler.py

@@ -1,111 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import math
-import operator
-import unittest
-import torch
-from torch.utils import data
-from torch.utils.data.sampler import SequentialSampler
-
-from detectron2.data.build import worker_init_reset_seed
-from detectron2.data.common import DatasetFromList, ToIterableDataset
-from detectron2.data.samplers import (
-    GroupedBatchSampler,
-    InferenceSampler,
-    RepeatFactorTrainingSampler,
-    TrainingSampler,
-)
-from detectron2.utils.env import seed_all_rng
-
-
-class TestGroupedBatchSampler(unittest.TestCase):
-    def test_missing_group_id(self):
-        sampler = SequentialSampler(list(range(100)))
-        group_ids = [1] * 100
-        samples = GroupedBatchSampler(sampler, group_ids, 2)
-
-        for mini_batch in samples:
-            self.assertEqual(len(mini_batch), 2)
-
-    def test_groups(self):
-        sampler = SequentialSampler(list(range(100)))
-        group_ids = [1, 0] * 50
-        samples = GroupedBatchSampler(sampler, group_ids, 2)
-
-        for mini_batch in samples:
-            self.assertEqual((mini_batch[0] + mini_batch[1]) % 2, 0)
-
-
-class TestSamplerDeterministic(unittest.TestCase):
-    def test_to_iterable(self):
-        sampler = TrainingSampler(100, seed=10)
-        gt_output = list(itertools.islice(sampler, 100))
-        self.assertEqual(set(gt_output), set(range(100)))
-
-        dataset = DatasetFromList(list(range(100)))
-        dataset = ToIterableDataset(dataset, sampler)
-        data_loader = data.DataLoader(dataset, num_workers=0, collate_fn=operator.itemgetter(0))
-
-        output = list(itertools.islice(data_loader, 100))
-        self.assertEqual(output, gt_output)
-
-        data_loader = data.DataLoader(
-            dataset,
-            num_workers=2,
-            collate_fn=operator.itemgetter(0),
-            worker_init_fn=worker_init_reset_seed,
-            # reset seed should not affect behavior of TrainingSampler
-        )
-        output = list(itertools.islice(data_loader, 100))
-        # multiple workers should not lead to duplicate or different data
-        self.assertEqual(output, gt_output)
-
-    def test_training_sampler_seed(self):
-        seed_all_rng(42)
-        sampler = TrainingSampler(30)
-        data = list(itertools.islice(sampler, 65))
-
-        seed_all_rng(42)
-        sampler = TrainingSampler(30)
-        seed_all_rng(999)  # should be ineffective
-        data2 = list(itertools.islice(sampler, 65))
-        self.assertEqual(data, data2)
-
-
-class TestRepeatFactorTrainingSampler(unittest.TestCase):
-    def test_repeat_factors_from_category_frequency(self):
-        repeat_thresh = 0.5
-
-        dataset_dicts = [
-            {"annotations": [{"category_id": 0}, {"category_id": 1}]},
-            {"annotations": [{"category_id": 0}]},
-            {"annotations": []},
-        ]
-
-        rep_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
-            dataset_dicts, repeat_thresh
-        )
-
-        expected_rep_factors = torch.tensor([math.sqrt(3 / 2), 1.0, 1.0])
-        self.assertTrue(torch.allclose(rep_factors, expected_rep_factors))
-
-
-class TestInferenceSampler(unittest.TestCase):
-    def test_local_indices(self):
-        sizes = [0, 16, 2, 42]
-        world_sizes = [5, 2, 3, 4]
-
-        expected_results = [
-            [range(0) for _ in range(5)],
-            [range(8), range(8, 16)],
-            [range(1), range(1, 2), range(0)],
-            [range(11), range(11, 22), range(22, 32), range(32, 42)],
-        ]
-
-        for size, world_size, expected_result in zip(sizes, world_sizes, expected_results):
-            with self.subTest(f"size={size}, world_size={world_size}"):
-                local_indices = [
-                    InferenceSampler._get_local_indices(size, world_size, r)
-                    for r in range(world_size)
-                ]
-                self.assertEqual(local_indices, expected_result)

spaces/BaiyuS/Real-CUGAN-YZ/upcunet_v3.py

@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分���一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)

spaces/BertChristiaens/youtube-dl/app.py

@@ -1,72 +0,0 @@
-"""This is the main module of the streamlit app that allows the user to download youtube videos as mp3 files."""
-import streamlit as st
-from yt_dlp import YoutubeDL
-import os
-from io import BytesIO
-from datetime import datetime
-
-URLS = ['https://www.youtube.com/watch?v=BaW_jenozKc']
-
-
-ydl_opts = {
-    'format': 'bestaudio/best',
-    'postprocessors': [{
-        'key': 'FFmpegExtractAudio',
-        'preferredcodec': 'mp3',
-        'preferredquality': '192',
-    }],
-    'outtmpl': 'audio'
-}
-
-def download_video(url):
-    with YoutubeDL(ydl_opts) as ydl:
-        print(url)
-        error_code = ydl.download([url])
-        info = ydl.extract_info(url, download=False)
-        print(error_code)
-    return error_code, info
-
-def clean_files():
-    if os.path.isfile('audio'):
-        os.remove('audio')
-    if os.path.isfile('audio.mp3'):
-        os.remove('audio.mp3')
-
-
-def main():
-    """This method has a text input field, radio button and a button for downloading the video as mp3."""
-    st.title('Youtube to mp3')
-    st.write('Enter the url of the youtube video you want to download')
-    url = st.text_input('URL')
-
-    if st.button('Download video'):
-        with st.spinner('Downloading video'):
-            clean_files()
-            
-            error_code, info = download_video(url)
-
-            st.session_state['latest_video'] = url
-            st.session_state['latest_title'] = info['fulltitle']
-
-            if error_code:
-                st.error('Error downloading video')
-            else:
-                st.success('Downloaded video')
-    
-    if os.path.isfile('audio.mp3') and st.session_state.get('latest_video'):
-        video_url = st.session_state.get('latest_video', '/')
-        video_title = st.session_state.get('latest_title', '/')
-
-        st.write(f"Last downloaded video is: {video_title} with url {video_url}")
-        st.audio('audio.mp3')
-        buffer = BytesIO()
-        with open('audio.mp3', 'rb') as f:
-            buffer.write(f.read())
-        timestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-        st.download_button(label='Download mp3',
-                           data=buffer.getvalue(),
-                           file_name=f"{video_title.replace(' ', '-')}.mp3",
-                           mime="audio/mp3")
-
-if __name__ == '__main__':
-    main()

spaces/BetterAPI/BetterChat/src/lib/shareConversation.ts

@@ -1,27 +0,0 @@
-import { base } from "$app/paths";
-import { ERROR_MESSAGES, error } from "$lib/stores/errors";
-import { share } from "./utils/share";
-
-export async function shareConversation(id: string, title: string) {
-	try {
-		const res = await fetch(`${base}/conversation/${id}/share`, {
-			method: "POST",
-			headers: {
-				"Content-Type": "application/json",
-			},
-		});
-
-		if (!res.ok) {
-			error.set("Error while sharing conversation, try again.");
-			console.error("Error while sharing conversation: " + (await res.text()));
-			return;
-		}
-
-		const { url } = await res.json();
-
-		share(url, title);
-	} catch (err) {
-		error.set(ERROR_MESSAGES.default);
-		console.error(err);
-	}
-}

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

@@ -1,56 +0,0 @@
-import re
-from typing import Iterable, List, Tuple
-
-from ._loop import loop_last
-from .cells import cell_len, chop_cells
-
-re_word = re.compile(r"\s*\S+\s*")
-
-
-def words(text: str) -> Iterable[Tuple[int, int, str]]:
-    position = 0
-    word_match = re_word.match(text, position)
-    while word_match is not None:
-        start, end = word_match.span()
-        word = word_match.group(0)
-        yield start, end, word
-        word_match = re_word.match(text, end)
-
-
-def divide_line(text: str, width: int, fold: bool = True) -> List[int]:
-    divides: List[int] = []
-    append = divides.append
-    line_position = 0
-    _cell_len = cell_len
-    for start, _end, word in words(text):
-        word_length = _cell_len(word.rstrip())
-        if line_position + word_length > width:
-            if word_length > width:
-                if fold:
-                    chopped_words = chop_cells(word, max_size=width, position=0)
-                    for last, line in loop_last(chopped_words):
-                        if start:
-                            append(start)
-
-                        if last:
-                            line_position = _cell_len(line)
-                        else:
-                            start += len(line)
-                else:
-                    if start:
-                        append(start)
-                    line_position = _cell_len(word)
-            elif line_position and start:
-                append(start)
-                line_position = _cell_len(word)
-        else:
-            line_position += _cell_len(word)
-    return divides
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from .console import Console
-
-    console = Console(width=10)
-    console.print("12345 abcdefghijklmnopqrstuvwyxzABCDEFGHIJKLMNOPQRSTUVWXYZ 12345")
-    print(chop_cells("abcdefghijklmnopqrstuvwxyz", 10, position=2))

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

@@ -1,94 +0,0 @@
-from typing import Optional, Union
-
-from .color import Color
-from .console import Console, ConsoleOptions, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style
-
-# There are left-aligned characters for 1/8 to 7/8, but
-# the right-aligned characters exist only for 1/8 and 4/8.
-BEGIN_BLOCK_ELEMENTS = ["█", "█", "█", "▐", "▐", "▐", "▕", "▕"]
-END_BLOCK_ELEMENTS = [" ", "▏", "▎", "▍", "▌", "▋", "▊", "▉"]
-FULL_BLOCK = "█"
-
-
-class Bar(JupyterMixin):
-    """Renders a solid block bar.
-
-    Args:
-        size (float): Value for the end of the bar.
-        begin (float): Begin point (between 0 and size, inclusive).
-        end (float): End point (between 0 and size, inclusive).
-        width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None.
-        color (Union[Color, str], optional): Color of the bar. Defaults to "default".
-        bgcolor (Union[Color, str], optional): Color of bar background. Defaults to "default".
-    """
-
-    def __init__(
-        self,
-        size: float,
-        begin: float,
-        end: float,
-        *,
-        width: Optional[int] = None,
-        color: Union[Color, str] = "default",
-        bgcolor: Union[Color, str] = "default",
-    ):
-        self.size = size
-        self.begin = max(begin, 0)
-        self.end = min(end, size)
-        self.width = width
-        self.style = Style(color=color, bgcolor=bgcolor)
-
-    def __repr__(self) -> str:
-        return f"Bar({self.size}, {self.begin}, {self.end})"
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-
-        width = min(
-            self.width if self.width is not None else options.max_width,
-            options.max_width,
-        )
-
-        if self.begin >= self.end:
-            yield Segment(" " * width, self.style)
-            yield Segment.line()
-            return
-
-        prefix_complete_eights = int(width * 8 * self.begin / self.size)
-        prefix_bar_count = prefix_complete_eights // 8
-        prefix_eights_count = prefix_complete_eights % 8
-
-        body_complete_eights = int(width * 8 * self.end / self.size)
-        body_bar_count = body_complete_eights // 8
-        body_eights_count = body_complete_eights % 8
-
-        # When start and end fall into the same cell, we ideally should render
-        # a symbol that's "center-aligned", but there is no good symbol in Unicode.
-        # In this case, we fall back to right-aligned block symbol for simplicity.
-
-        prefix = " " * prefix_bar_count
-        if prefix_eights_count:
-            prefix += BEGIN_BLOCK_ELEMENTS[prefix_eights_count]
-
-        body = FULL_BLOCK * body_bar_count
-        if body_eights_count:
-            body += END_BLOCK_ELEMENTS[body_eights_count]
-
-        suffix = " " * (width - len(body))
-
-        yield Segment(prefix + body[len(prefix) :] + suffix, self.style)
-        yield Segment.line()
-
-    def __rich_measure__(
-        self, console: Console, options: ConsoleOptions
-    ) -> Measurement:
-        return (
-            Measurement(self.width, self.width)
-            if self.width is not None
-            else Measurement(4, options.max_width)
-        )

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/exceptions.py

@@ -1,323 +0,0 @@
-from __future__ import absolute_import
-
-from .packages.six.moves.http_client import IncompleteRead as httplib_IncompleteRead
-
-# Base Exceptions
-
-
-class HTTPError(Exception):
-    """Base exception used by this module."""
-
-    pass
-
-
-class HTTPWarning(Warning):
-    """Base warning used by this module."""
-
-    pass
-
-
-class PoolError(HTTPError):
-    """Base exception for errors caused within a pool."""
-
-    def __init__(self, pool, message):
-        self.pool = pool
-        HTTPError.__init__(self, "%s: %s" % (pool, message))
-
-    def __reduce__(self):
-        # For pickling purposes.
-        return self.__class__, (None, None)
-
-
-class RequestError(PoolError):
-    """Base exception for PoolErrors that have associated URLs."""
-
-    def __init__(self, pool, url, message):
-        self.url = url
-        PoolError.__init__(self, pool, message)
-
-    def __reduce__(self):
-        # For pickling purposes.
-        return self.__class__, (None, self.url, None)
-
-
-class SSLError(HTTPError):
-    """Raised when SSL certificate fails in an HTTPS connection."""
-
-    pass
-
-
-class ProxyError(HTTPError):
-    """Raised when the connection to a proxy fails."""
-
-    def __init__(self, message, error, *args):
-        super(ProxyError, self).__init__(message, error, *args)
-        self.original_error = error
-
-
-class DecodeError(HTTPError):
-    """Raised when automatic decoding based on Content-Type fails."""
-
-    pass
-
-
-class ProtocolError(HTTPError):
-    """Raised when something unexpected happens mid-request/response."""
-
-    pass
-
-
-#: Renamed to ProtocolError but aliased for backwards compatibility.
-ConnectionError = ProtocolError
-
-
-# Leaf Exceptions
-
-
-class MaxRetryError(RequestError):
-    """Raised when the maximum number of retries is exceeded.
-
-    :param pool: The connection pool
-    :type pool: :class:`~urllib3.connectionpool.HTTPConnectionPool`
-    :param string url: The requested Url
-    :param exceptions.Exception reason: The underlying error
-
-    """
-
-    def __init__(self, pool, url, reason=None):
-        self.reason = reason
-
-        message = "Max retries exceeded with url: %s (Caused by %r)" % (url, reason)
-
-        RequestError.__init__(self, pool, url, message)
-
-
-class HostChangedError(RequestError):
-    """Raised when an existing pool gets a request for a foreign host."""
-
-    def __init__(self, pool, url, retries=3):
-        message = "Tried to open a foreign host with url: %s" % url
-        RequestError.__init__(self, pool, url, message)
-        self.retries = retries
-
-
-class TimeoutStateError(HTTPError):
-    """Raised when passing an invalid state to a timeout"""
-
-    pass
-
-
-class TimeoutError(HTTPError):
-    """Raised when a socket timeout error occurs.
-
-    Catching this error will catch both :exc:`ReadTimeoutErrors
-    <ReadTimeoutError>` and :exc:`ConnectTimeoutErrors <ConnectTimeoutError>`.
-    """
-
-    pass
-
-
-class ReadTimeoutError(TimeoutError, RequestError):
-    """Raised when a socket timeout occurs while receiving data from a server"""
-
-    pass
-
-
-# This timeout error does not have a URL attached and needs to inherit from the
-# base HTTPError
-class ConnectTimeoutError(TimeoutError):
-    """Raised when a socket timeout occurs while connecting to a server"""
-
-    pass
-
-
-class NewConnectionError(ConnectTimeoutError, PoolError):
-    """Raised when we fail to establish a new connection. Usually ECONNREFUSED."""
-
-    pass
-
-
-class EmptyPoolError(PoolError):
-    """Raised when a pool runs out of connections and no more are allowed."""
-
-    pass
-
-
-class ClosedPoolError(PoolError):
-    """Raised when a request enters a pool after the pool has been closed."""
-
-    pass
-
-
-class LocationValueError(ValueError, HTTPError):
-    """Raised when there is something wrong with a given URL input."""
-
-    pass
-
-
-class LocationParseError(LocationValueError):
-    """Raised when get_host or similar fails to parse the URL input."""
-
-    def __init__(self, location):
-        message = "Failed to parse: %s" % location
-        HTTPError.__init__(self, message)
-
-        self.location = location
-
-
-class URLSchemeUnknown(LocationValueError):
-    """Raised when a URL input has an unsupported scheme."""
-
-    def __init__(self, scheme):
-        message = "Not supported URL scheme %s" % scheme
-        super(URLSchemeUnknown, self).__init__(message)
-
-        self.scheme = scheme
-
-
-class ResponseError(HTTPError):
-    """Used as a container for an error reason supplied in a MaxRetryError."""
-
-    GENERIC_ERROR = "too many error responses"
-    SPECIFIC_ERROR = "too many {status_code} error responses"
-
-
-class SecurityWarning(HTTPWarning):
-    """Warned when performing security reducing actions"""
-
-    pass
-
-
-class SubjectAltNameWarning(SecurityWarning):
-    """Warned when connecting to a host with a certificate missing a SAN."""
-
-    pass
-
-
-class InsecureRequestWarning(SecurityWarning):
-    """Warned when making an unverified HTTPS request."""
-
-    pass
-
-
-class SystemTimeWarning(SecurityWarning):
-    """Warned when system time is suspected to be wrong"""
-
-    pass
-
-
-class InsecurePlatformWarning(SecurityWarning):
-    """Warned when certain TLS/SSL configuration is not available on a platform."""
-
-    pass
-
-
-class SNIMissingWarning(HTTPWarning):
-    """Warned when making a HTTPS request without SNI available."""
-
-    pass
-
-
-class DependencyWarning(HTTPWarning):
-    """
-    Warned when an attempt is made to import a module with missing optional
-    dependencies.
-    """
-
-    pass
-
-
-class ResponseNotChunked(ProtocolError, ValueError):
-    """Response needs to be chunked in order to read it as chunks."""
-
-    pass
-
-
-class BodyNotHttplibCompatible(HTTPError):
-    """
-    Body should be :class:`http.client.HTTPResponse` like
-    (have an fp attribute which returns raw chunks) for read_chunked().
-    """
-
-    pass
-
-
-class IncompleteRead(HTTPError, httplib_IncompleteRead):
-    """
-    Response length doesn't match expected Content-Length
-
-    Subclass of :class:`http.client.IncompleteRead` to allow int value
-    for ``partial`` to avoid creating large objects on streamed reads.
-    """
-
-    def __init__(self, partial, expected):
-        super(IncompleteRead, self).__init__(partial, expected)
-
-    def __repr__(self):
-        return "IncompleteRead(%i bytes read, %i more expected)" % (
-            self.partial,
-            self.expected,
-        )
-
-
-class InvalidChunkLength(HTTPError, httplib_IncompleteRead):
-    """Invalid chunk length in a chunked response."""
-
-    def __init__(self, response, length):
-        super(InvalidChunkLength, self).__init__(
-            response.tell(), response.length_remaining
-        )
-        self.response = response
-        self.length = length
-
-    def __repr__(self):
-        return "InvalidChunkLength(got length %r, %i bytes read)" % (
-            self.length,
-            self.partial,
-        )
-
-
-class InvalidHeader(HTTPError):
-    """The header provided was somehow invalid."""
-
-    pass
-
-
-class ProxySchemeUnknown(AssertionError, URLSchemeUnknown):
-    """ProxyManager does not support the supplied scheme"""
-
-    # TODO(t-8ch): Stop inheriting from AssertionError in v2.0.
-
-    def __init__(self, scheme):
-        # 'localhost' is here because our URL parser parses
-        # localhost:8080 -> scheme=localhost, remove if we fix this.
-        if scheme == "localhost":
-            scheme = None
-        if scheme is None:
-            message = "Proxy URL had no scheme, should start with http:// or https://"
-        else:
-            message = (
-                "Proxy URL had unsupported scheme %s, should use http:// or https://"
-                % scheme
-            )
-        super(ProxySchemeUnknown, self).__init__(message)
-
-
-class ProxySchemeUnsupported(ValueError):
-    """Fetching HTTPS resources through HTTPS proxies is unsupported"""
-
-    pass
-
-
-class HeaderParsingError(HTTPError):
-    """Raised by assert_header_parsing, but we convert it to a log.warning statement."""
-
-    def __init__(self, defects, unparsed_data):
-        message = "%s, unparsed data: %r" % (defects or "Unknown", unparsed_data)
-        super(HeaderParsingError, self).__init__(message)
-
-
-class UnrewindableBodyError(HTTPError):
-    """urllib3 encountered an error when trying to rewind a body"""
-
-    pass

spaces/Boadiwaa/Recipes/openai/api_resources/fine_tune.py

@@ -1,87 +0,0 @@
-from urllib.parse import quote_plus
-
-from openai import api_requestor, util, error
-from openai.api_resources.abstract import (
-    CreateableAPIResource,
-    ListableAPIResource,
-    nested_resource_class_methods,
-)
-from openai.api_resources.abstract.deletable_api_resource import DeletableAPIResource
-from openai.openai_response import OpenAIResponse
-from openai.util import ApiType
-
-
-@nested_resource_class_methods("event", operations=["list"])
-class FineTune(ListableAPIResource, CreateableAPIResource, DeletableAPIResource):
-    OBJECT_NAME = "fine-tunes"
-
-    @classmethod
-    def cancel(
-        cls,
-        id,
-        api_key=None,
-        api_type=None,
-        request_id=None,
-        api_version=None,
-        **params
-    ):
-        base = cls.class_url()
-        extn = quote_plus(id)
-
-        typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
-        if typed_api_type == ApiType.AZURE:
-            url = "/%s%s/%s/cancel?api-version=%s" % (cls.azure_api_prefix, base, extn, api_version)
-        elif typed_api_type == ApiType.OPEN_AI:
-            url = "%s/%s/cancel" % (base, extn)
-        else:
-            raise error.InvalidAPIType('Unsupported API type %s' % api_type)            
-
-        instance = cls(id, api_key, **params)
-        return instance.request("post", url, request_id=request_id)
-
-    @classmethod
-    def stream_events(
-        cls,
-        id,
-        api_key=None,
-        api_base=None,
-        api_type=None,
-        request_id=None,
-        api_version=None,
-        organization=None,
-        **params,
-    ):
-        base = cls.class_url()
-        extn = quote_plus(id)
-
-        requestor = api_requestor.APIRequestor(
-            api_key,
-            api_base=api_base,
-            api_type=api_type,
-            api_version=api_version,
-            organization=organization,
-        )
-
-        typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
-
-        if typed_api_type == ApiType.AZURE:
-            url = "/%s%s/%s/events?stream=true&api-version=%s" % (cls.azure_api_prefix, base, extn, api_version)
-        elif typed_api_type == ApiType.OPEN_AI:
-            url = "%s/%s/events?stream=true" % (base, extn)
-        else:
-            raise error.InvalidAPIType('Unsupported API type %s' % api_type)            
-        
-        response, _, api_key = requestor.request(
-            "get", url, params, stream=True, request_id=request_id
-        )
-
-        assert not isinstance(response, OpenAIResponse)  # must be an iterator
-        return (
-            util.convert_to_openai_object(
-                line,
-                api_key,
-                api_version,
-                organization,
-            )
-            for line in response
-        )

spaces/BuBBLe1q/anything-v3.0/README.md

@@ -1,13 +0,0 @@
----
-title: Anything V3.0
-emoji: 🏃
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/anything-v3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/thrust/thrust/detail/config/device_system.h

@@ -1,61 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-// reserve 0 for undefined
-#define THRUST_DEVICE_SYSTEM_CUDA    1
-#define THRUST_DEVICE_SYSTEM_OMP     2
-#define THRUST_DEVICE_SYSTEM_TBB     3
-#define THRUST_DEVICE_SYSTEM_CPP     4
-
-#ifndef THRUST_DEVICE_SYSTEM
-#define THRUST_DEVICE_SYSTEM THRUST_DEVICE_SYSTEM_CUDA
-#endif // THRUST_DEVICE_SYSTEM
-
-// XXX make the use of THRUST_DEVICE_BACKEND an error in Thrust 1.7
-// XXX eliminate the following in Thrust 1.7
-
-#define THRUST_DEVICE_BACKEND_CUDA THRUST_DEVICE_SYSTEM_CUDA
-#define THRUST_DEVICE_BACKEND_OMP  THRUST_DEVICE_SYSTEM_OMP
-#define THRUST_DEVICE_BACKEND_TBB  THRUST_DEVICE_SYSTEM_TBB
-
-#ifdef THRUST_DEVICE_BACKEND
-#  if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC
-#    pragma message("----------------------------------------------------------------------------------")
-#    pragma message("| WARNING: THRUST_DEVICE_BACKEND is deprecated; use THRUST_DEVICE_SYSTEM instead |")
-#    pragma message("----------------------------------------------------------------------------------")
-#  else
-#    warning ----------------------------------------------------------------------------------
-#    warning | WARNING: THRUST_DEVICE_BACKEND is deprecated; use THRUST_DEVICE_SYSTEM instead |
-#    warning ----------------------------------------------------------------------------------
-#  endif // THRUST_HOST_COMPILER
-#  undef THRUST_DEVICE_SYSTEM
-#  define THRUST_DEVICE_SYSTEM THRUST_DEVICE_BACKEND
-#endif // THRUST_DEVICE_BACKEND
-
-#if THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_CUDA
-#define __THRUST_DEVICE_SYSTEM_NAMESPACE cuda
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_OMP
-#define __THRUST_DEVICE_SYSTEM_NAMESPACE omp
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_TBB
-#define __THRUST_DEVICE_SYSTEM_NAMESPACE tbb
-#elif THRUST_DEVICE_SYSTEM == THRUST_DEVICE_SYSTEM_CPP
-#define __THRUST_DEVICE_SYSTEM_NAMESPACE cpp
-#endif
-
-#define __THRUST_DEVICE_SYSTEM_ROOT thrust/system/__THRUST_DEVICE_SYSTEM_NAMESPACE
-

spaces/CVPR/LIVE/thrust/thrust/random/discard_block_engine.h

@@ -1,252 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file discard_block_engine.h
- *  \brief A random number engine which adapts a base engine and produces
- *         numbers by discarding all but a contiguous blocks of its values.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-#include <thrust/detail/config.h>
-#include <iostream>
-#include <thrust/detail/cstdint.h>
-#include <thrust/random/detail/random_core_access.h>
-
-namespace thrust
-{
-
-namespace random
-{
-
-/*! \addtogroup random_number_engine_adaptors Random Number Engine Adaptor Class Templates
- *  \ingroup random
- *  \{
- */
-
-/*! \class discard_block_engine
- *  \brief A \p discard_block_engine adapts an existing base random number engine and produces
- *         random values by discarding some of the values returned by its base engine.
- *         Each cycle of the compound engine begins by returning \c r values successively produced
- *         by the base engine and ends by discarding <tt>p-r</tt> such values. The engine's state
- *         is the state of its base engine followed by the number of calls to <tt>operator()</tt>
- *         that have occurred since the beginning of the current cycle.
- *
- *  \tparam Engine The type of the base random number engine to adapt.
- *  \tparam p The discard cycle length.
- *  \tparam r The number of values to return of the base engine. Because <tt>p-r</tt> will be
- *            discarded, <tt>r <= p</tt>.
- *
- *  The following code snippet shows an example of using a \p discard_block_engine instance:
- *
- *  \code
- *  #include <thrust/random/linear_congruential_engine.h>
- *  #include <thrust/random/discard_block_engine.h>
- *  #include <iostream>
- *
- *  int main(void)
- *  {
- *    // create a discard_block_engine from minstd_rand, with a cycle length of 13
- *    // keep every first 10 values, and discard the next 3
- *    thrust::discard_block_engine<thrust::minstd_rand, 13, 10> rng;
- *
- *    // print a random number to standard output
- *    std::cout << rng() << std::endl;
- *
- *    return 0;
- *  }
- *  \endcode
- */         
-template<typename Engine, size_t p, size_t r>
-  class discard_block_engine
-{
-  public:
-    // types
-
-    /*! \typedef base_type
-     *  \brief The type of the adapted base random number engine.
-     */
-    typedef Engine base_type;
-
-    /*! \typedef result_type
-     *  \brief The type of the unsigned integer produced by this \p linear_congruential_engine.
-     */
-    typedef typename base_type::result_type result_type;
-
-    // engine characteristics
-
-    /*! The length of the production cycle.
-     */
-    static const size_t block_size = p;
-
-    /*! The number of used numbers per production cycle.
-     */
-    static const size_t used_block = r;
-
-    /*! The smallest value this \p discard_block_engine may potentially produce.
-     */
-    static const result_type min = base_type::min;
-
-    /*! The largest value this \p discard_block_engine may potentially produce.
-     */
-    static const result_type max = base_type::max;
-
-    // constructors and seeding functions
-
-    /*! This constructor constructs a new \p discard_block_engine and constructs
-     *  its \p base_type engine using its null constructor.
-     */
-    __host__ __device__
-    discard_block_engine();
-
-    /*! This constructor constructs a new \p discard_block_engine using
-     *  a given \p base_type engine to initialize its adapted base engine.
-     *
-     *  \param urng A \p base_type to use to initialize this \p discard_block_engine's
-     *         adapted base engine.
-     */
-    __host__ __device__
-    explicit discard_block_engine(const base_type &urng);
-
-    /*! This constructor initializes a new \p discard_block_engine with a given seed.
-     *  
-     *  \param s The seed used to intialize this \p discard_block_engine's adapted base engine.
-     */
-    __host__ __device__
-    explicit discard_block_engine(result_type s);
-
-    /*! This method initializes the state of this \p discard_block_engine's adapted base engine
-     *  by using its \p default_seed value.
-     */
-    __host__ __device__
-    void seed(void);
-
-    /*! This method initializes the state of this \p discard_block_engine's adapted base engine
-     *  by using the given seed.
-     *
-     *  \param s The seed with which to intialize this \p discard_block_engine's adapted base engine.
-     */
-    __host__ __device__
-    void seed(result_type s);
-
-    // generating functions
-    
-    /*! This member function produces a new random value and updates this \p discard_block_engine's state.
-     *  \return A new random number.
-     */
-    __host__ __device__
-    result_type operator()(void);
-
-    /*! This member function advances this \p discard_block_engine's state a given number of times
-     *  and discards the results.
-     *
-     *  \param z The number of random values to discard.
-     *  \note This function is provided because an implementation may be able to accelerate it.
-     */
-    __host__ __device__
-    void discard(unsigned long long z);
-
-    // property functions
-
-    /*! This member function returns a const reference to this \p discard_block_engine's
-     *  adapted base engine.
-     *
-     *  \return A const reference to the base engine this \p discard_block_engine adapts.
-     */
-    __host__ __device__
-    const base_type &base(void) const;
-
-    /*! \cond
-     */
-  private:
-    base_type m_e;
-    unsigned int m_n;
-
-    friend struct thrust::random::detail::random_core_access;
-
-    __host__ __device__
-    bool equal(const discard_block_engine &rhs) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_ostream<CharT,Traits>& stream_out(std::basic_ostream<CharT,Traits> &os) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_istream<CharT,Traits>& stream_in(std::basic_istream<CharT,Traits> &is);
-    /*! \endcond
-     */
-}; // end discard_block_engine
-
-
-/*! This function checks two \p discard_block_engines for equality.
- *  \param lhs The first \p discard_block_engine to test.
- *  \param rhs The second \p discard_block_engine to test.
- *  \return \c true if \p lhs is equal to \p rhs; \c false, otherwise.
- */
-template<typename Engine, size_t p, size_t r>
-__host__ __device__
-bool operator==(const discard_block_engine<Engine,p,r> &lhs,
-                const discard_block_engine<Engine,p,r> &rhs);
-
-
-/*! This function checks two \p discard_block_engines for inequality.
- *  \param lhs The first \p discard_block_engine to test.
- *  \param rhs The second \p discard_block_engine to test.
- *  \return \c true if \p lhs is not equal to \p rhs; \c false, otherwise.
- */
-template<typename Engine, size_t p, size_t r>
-__host__ __device__
-bool operator!=(const discard_block_engine<Engine,p,r> &lhs,
-                const discard_block_engine<Engine,p,r> &rhs);
-
-
-/*! This function streams a discard_block_engine to a \p std::basic_ostream.
- *  \param os The \p basic_ostream to stream out to.
- *  \param e The \p discard_block_engine to stream out.
- *  \return \p os
- */
-template<typename Engine, size_t p, size_t r,
-         typename CharT, typename Traits>
-std::basic_ostream<CharT,Traits>&
-operator<<(std::basic_ostream<CharT,Traits> &os,
-           const discard_block_engine<Engine,p,r> &e);
-
-
-/*! This function streams a discard_block_engine in from a std::basic_istream.
- *  \param is The \p basic_istream to stream from.
- *  \param e The \p discard_block_engine to stream in.
- *  \return \p is
- */
-template<typename Engine, size_t p, size_t r,
-         typename CharT, typename Traits>
-std::basic_istream<CharT,Traits>&
-operator>>(std::basic_istream<CharT,Traits> &is,
-           discard_block_engine<Engine,p,r> &e);
-
-/*! \} // end random_number_engine_adaptors
- */
-
-} // end random
-
-// import names into thrust::
-using random::discard_block_engine;
-
-} // end thrust
-
-#include <thrust/random/detail/discard_block_engine.inl>
-

spaces/CVPR/WALT/mmdet/apis/train.py

@@ -1,185 +0,0 @@
-import random
-import warnings
-
-import numpy as np
-import torch
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
-                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner)
-from mmcv.utils import build_from_cfg
-
-from mmdet.core import DistEvalHook, EvalHook
-from mmdet.datasets import (build_dataloader, build_dataset,
-                            replace_ImageToTensor)
-from mmdet.utils import get_root_logger
-from mmcv_custom.runner import EpochBasedRunnerAmp
-try:
-    import apex
-except:
-    print('apex is not installed')
-
-
-def set_random_seed(seed, deterministic=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-
-
-def train_detector(model,
-                   dataset,
-                   cfg,
-                   distributed=False,
-                   validate=False,
-                   timestamp=None,
-                   meta=None):
-    logger = get_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    if 'imgs_per_gpu' in cfg.data:
-        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
-                       'Please use "samples_per_gpu" instead')
-        if 'samples_per_gpu' in cfg.data:
-            logger.warning(
-                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
-                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
-                f'={cfg.data.imgs_per_gpu} is used in this experiments')
-        else:
-            logger.warning(
-                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
-                f'{cfg.data.imgs_per_gpu} in this experiments')
-        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
-
-    data_loaders = [
-        build_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed) for ds in dataset
-    ]
-
-    # build optimizer
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    # use apex fp16 optimizer
-    if cfg.optimizer_config.get("type", None) and cfg.optimizer_config["type"] == "DistOptimizerHook":
-        if cfg.optimizer_config.get("use_fp16", False):
-            model, optimizer = apex.amp.initialize(
-                model.cuda(), optimizer, opt_level="O1")
-            for m in model.modules():
-                if hasattr(m, "fp16_enabled"):
-                    m.fp16_enabled = True
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    if 'runner' not in cfg:
-        cfg.runner = {
-            'type': 'EpochBasedRunner',
-            'max_epochs': cfg.total_epochs
-        }
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-    else:
-        if 'total_epochs' in cfg:
-            assert cfg.total_epochs == cfg.runner.max_epochs
-
-    # build runner
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # an ugly workaround to make .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # fp16 setting
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        optimizer_config = Fp16OptimizerHook(
-            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
-    elif distributed and 'type' not in cfg.optimizer_config:
-        optimizer_config = OptimizerHook(**cfg.optimizer_config)
-    else:
-        optimizer_config = cfg.optimizer_config
-
-    # register hooks
-    runner.register_training_hooks(cfg.lr_config, optimizer_config,
-                                   cfg.checkpoint_config, cfg.log_config,
-                                   cfg.get('momentum_config', None))
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
-
-    # register eval hooks
-    if validate:
-        # Support batch_size > 1 in validation
-        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
-        if val_samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.val.pipeline = replace_ImageToTensor(
-                cfg.data.val.pipeline)
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_dataloader(
-            val_dataset,
-            samples_per_gpu=val_samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = DistEvalHook if distributed else EvalHook
-        runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
-
-    # user-defined hooks
-    if cfg.get('custom_hooks', None):
-        custom_hooks = cfg.custom_hooks
-        assert isinstance(custom_hooks, list), \
-            f'custom_hooks expect list type, but got {type(custom_hooks)}'
-        for hook_cfg in cfg.custom_hooks:
-            assert isinstance(hook_cfg, dict), \
-                'Each item in custom_hooks expects dict type, but got ' \
-                f'{type(hook_cfg)}'
-            hook_cfg = hook_cfg.copy()
-            priority = hook_cfg.pop('priority', 'NORMAL')
-            hook = build_from_cfg(hook_cfg, HOOKS)
-            runner.register_hook(hook, priority=priority)
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)

spaces/CVPR/regionclip-demo/config.py

@@ -1,245 +0,0 @@
-# --------------------------------------------------------
-# Unified Contrastive Learning (UniCL)
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Jianwei Yang ([email protected])
-# Based on Swin Transformer written by Zhe Liu
-# --------------------------------------------------------
-
-import os
-import yaml
-from yacs.config import CfgNode as CN
-
-_C = CN()
-_C.VERBOSE = False
-
-# Base config files
-_C.BASE = ['']
-
-# -----------------------------------------------------------------------------
-# Data settings
-# -----------------------------------------------------------------------------
-_C.DATA = CN()
-# Batch size for a single GPU, could be overwritten by command line argument
-_C.DATA.BATCH_SIZE = 128
-# Path to dataset, could be overwritten by command line argument
-_C.DATA.DATA_PATH = ''
-# Dataset name
-_C.DATA.DATASET = 'imagenet'
-# Input image size
-_C.DATA.IMG_SIZE = 224
-# Interpolation to resize image (random, bilinear, bicubic)
-_C.DATA.INTERPOLATION = 'bicubic'
-# Use zipped dataset instead of folder dataset
-# could be overwritten by command line argument
-_C.DATA.ZIP_MODE = False
-# Cache Data in Memory, could be overwritten by command line argument
-_C.DATA.CACHE_MODE = 'part'
-# Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
-_C.DATA.PIN_MEMORY = True
-# Number of data loading threads
-_C.DATA.NUM_WORKERS = 8
-
-# -----------------------------------------------------------------------------
-# Model settings
-# -----------------------------------------------------------------------------
-_C.MODEL = CN()
-# Model name
-_C.MODEL.NAME = ''
-# Checkpoint to resume, could be overwritten by command line argument
-_C.MODEL.RESUME = ''
-# Number of classes, overwritten in data preparation
-_C.MODEL.NUM_CLASSES = 0
-# Label Smoothing
-_C.MODEL.LABEL_SMOOTHING = 0.1
-# Whether load pretrained model
-_C.MODEL.PRETRAINED = ''
-# Projection dimension
-_C.MODEL.DIM_PROJECTION = 512
-# Mode specific
-_C.MODEL.SPEC = CN(new_allowed=True)
-# -----------------------------------------------------------------------------
-# Build Image Encoder
-# -----------------------------------------------------------------------------
-_C.MODEL.IMAGE_ENCODER = CN()
-# Image encoder type
-_C.MODEL.IMAGE_ENCODER.TYPE = 'swin'
-# Input image size
-_C.MODEL.IMAGE_ENCODER.IMG_SIZE = 224
-# Dropout rate
-_C.MODEL.IMAGE_ENCODER.DROP_RATE = 0.0
-# Drop path rate
-_C.MODEL.IMAGE_ENCODER.DROP_PATH_RATE = 0.1
-
-# Swin Transformer parameters
-_C.MODEL.IMAGE_ENCODER.SWIN = CN()
-_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_SIZE = 4
-_C.MODEL.IMAGE_ENCODER.SWIN.IN_CHANS = 3
-_C.MODEL.IMAGE_ENCODER.SWIN.EMBED_DIM = 96
-_C.MODEL.IMAGE_ENCODER.SWIN.DEPTHS = [2, 2, 6, 2]
-_C.MODEL.IMAGE_ENCODER.SWIN.NUM_HEADS = [3, 6, 12, 24]
-_C.MODEL.IMAGE_ENCODER.SWIN.WINDOW_SIZE = 7
-_C.MODEL.IMAGE_ENCODER.SWIN.MLP_RATIO = 4.
-_C.MODEL.IMAGE_ENCODER.SWIN.QKV_BIAS = True
-_C.MODEL.IMAGE_ENCODER.SWIN.QK_SCALE = None
-_C.MODEL.IMAGE_ENCODER.SWIN.APE = False
-_C.MODEL.IMAGE_ENCODER.SWIN.PATCH_NORM = True
-
-# FocalNet parameters
-_C.MODEL.IMAGE_ENCODER.FOCAL = CN()
-_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_SIZE = 4
-_C.MODEL.IMAGE_ENCODER.FOCAL.IN_CHANS = 3
-_C.MODEL.IMAGE_ENCODER.FOCAL.EMBED_DIM = 96
-_C.MODEL.IMAGE_ENCODER.FOCAL.DEPTHS = [2, 2, 6, 2]
-_C.MODEL.IMAGE_ENCODER.FOCAL.MLP_RATIO = 4.
-_C.MODEL.IMAGE_ENCODER.FOCAL.PATCH_NORM = True
-_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_LEVELS = [2, 2, 2, 2]
-_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_WINDOWS = [3, 3, 3, 3]
-_C.MODEL.IMAGE_ENCODER.FOCAL.FOCAL_FACTORS = [2, 2, 2, 2]
-_C.MODEL.IMAGE_ENCODER.FOCAL.USE_CONV_EMBED = False
-_C.MODEL.IMAGE_ENCODER.FOCAL.USE_LAYERSCALE = False
-_C.MODEL.IMAGE_ENCODER.FOCAL.USE_POSTLN = False
-
-# -----------------------------------------------------------------------------
-# Build Text Encoder
-# -----------------------------------------------------------------------------
-_C.MODEL.TEXT_ENCODER = CN()
-
-_C.MODEL.TEXT_ENCODER.NAME = 'transformer'
-_C.MODEL.TEXT_ENCODER.LOAD_PRETRAINED = False
-_C.MODEL.TEXT_ENCODER.PRETRAINED = ''
-_C.MODEL.TEXT_ENCODER.TOKENIZER = 'clip'
-_C.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
-_C.MODEL.TEXT_ENCODER.WIDTH = 1024
-_C.MODEL.TEXT_ENCODER.HEADS = 16
-_C.MODEL.TEXT_ENCODER.LAYERS = 12
-_C.MODEL.TEXT_ENCODER.AUTOGRESSIVE = True
-
-# -----------------------------------------------------------------------------
-# Training settings
-# -----------------------------------------------------------------------------
-_C.TRAIN = CN()
-_C.TRAIN.START_EPOCH = 0
-_C.TRAIN.EPOCHS = 32
-_C.TRAIN.WARMUP_EPOCHS = 5
-_C.TRAIN.WEIGHT_DECAY = 0.1
-_C.TRAIN.BASE_LR = 5e-4
-_C.TRAIN.WARMUP_LR = 5e-7
-_C.TRAIN.MIN_LR = 5e-6
-# Clip gradient norm
-_C.TRAIN.CLIP_GRAD = 5.0
-# Auto resume from latest checkpoint
-_C.TRAIN.AUTO_RESUME = True
-# Gradient accumulation steps
-# could be overwritten by command line argument
-_C.TRAIN.ACCUMULATION_STEPS = 0
-# Whether to use gradient checkpointing to save memory
-# could be overwritten by command line argument
-_C.TRAIN.USE_CHECKPOINT = False
-
-# LR scheduler
-_C.TRAIN.LR_SCHEDULER = CN()
-_C.TRAIN.LR_SCHEDULER.NAME = 'cosine'
-# Epoch interval to decay LR, used in StepLRScheduler
-_C.TRAIN.LR_SCHEDULER.DECAY_EPOCHS = 30
-# LR decay rate, used in StepLRScheduler
-_C.TRAIN.LR_SCHEDULER.DECAY_RATE = 0.1
-
-# Optimizer
-_C.TRAIN.OPTIMIZER = CN()
-_C.TRAIN.OPTIMIZER.NAME = 'adamw'
-# Optimizer Epsilon
-_C.TRAIN.OPTIMIZER.EPS = 1e-8
-# Optimizer Betas
-_C.TRAIN.OPTIMIZER.BETAS = (0.9, 0.999)
-# SGD momentum
-_C.TRAIN.OPTIMIZER.MOMENTUM = 0.9
-
-# -----------------------------------------------------------------------------
-# Augmentation settings
-# -----------------------------------------------------------------------------
-_C.AUG = CN()
-# Color jitter factor
-_C.AUG.COLOR_JITTER = 0.4
-# Use AutoAugment policy. "v0" or "original"
-_C.AUG.AUTO_AUGMENT = 'rand-m9-mstd0.5-inc1'
-# Random erase prob
-_C.AUG.REPROB = 0.25
-# Random erase mode
-_C.AUG.REMODE = 'pixel'
-# Random erase count
-_C.AUG.RECOUNT = 1
-# Mixup alpha, mixup enabled if > 0
-_C.AUG.MIXUP = 0.8
-# Cutmix alpha, cutmix enabled if > 0
-_C.AUG.CUTMIX = 1.0
-# Cutmix min/max ratio, overrides alpha and enables cutmix if set
-_C.AUG.CUTMIX_MINMAX = None
-# Probability of performing mixup or cutmix when either/both is enabled
-_C.AUG.MIXUP_PROB = 1.0
-# Probability of switching to cutmix when both mixup and cutmix enabled
-_C.AUG.MIXUP_SWITCH_PROB = 0.5
-# How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
-_C.AUG.MIXUP_MODE = 'batch'
-
-# -----------------------------------------------------------------------------
-# Testing settings
-# -----------------------------------------------------------------------------
-_C.TEST = CN()
-# Whether to use center crop when testing
-_C.TEST.CROP = True
-
-# -----------------------------------------------------------------------------
-# Misc
-# -----------------------------------------------------------------------------
-# Mixed precision opt level, if O0, no amp is used ('O0', 'O1', 'O2')
-# overwritten by command line argument
-_C.AMP_OPT_LEVEL = ''
-# Path to output folder, overwritten by command line argument
-_C.OUTPUT = ''
-# Tag of experiment, overwritten by command line argument
-_C.TAG = 'default'
-# Frequency to save checkpoint
-_C.SAVE_FREQ = 1
-# Frequency to logging info
-_C.PRINT_FREQ = 100
-# Fixed random seed
-_C.SEED = 0
-# Perform evaluation only, overwritten by command line argument
-_C.EVAL_MODE = False
-# Test throughput only, overwritten by command line argument
-_C.THROUGHPUT_MODE = False
-# Debug only so that skip dataloader initialization, overwritten by command line argument
-_C.DEBUG_MODE = False
-# local rank for DistributedDataParallel, given by command line argument
-_C.LOCAL_RANK = 0
-
-
-def _update_config_from_file(config, cfg_file):
-    config.defrost()
-    with open(cfg_file, 'r') as f:
-        yaml_cfg = yaml.load(f, Loader=yaml.FullLoader)
-
-    for cfg in yaml_cfg.setdefault('BASE', ['']):
-        if cfg:
-            _update_config_from_file(
-                config, os.path.join(os.path.dirname(cfg_file), cfg)
-            )
-    print('=> merge config from {}'.format(cfg_file))
-    config.merge_from_file(cfg_file)
-    config.freeze()
-
-
-def update_config(config, args):
-    _update_config_from_file(config, args.cfg)
-    config.freeze()
-
-
-def get_config(args):
-    """Get a yacs CfgNode object with default values."""
-    # Return a clone so that the defaults will not be altered
-    # This is for the "local variable" use pattern
-    config = _C.clone()
-    update_config(config, args)
-
-    return config

spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/web_requests.py

@@ -1,190 +0,0 @@
-"""Browse a webpage and summarize it using the LLM model"""
-from __future__ import annotations
-
-from urllib.parse import urljoin, urlparse
-
-import requests
-from bs4 import BeautifulSoup
-from requests import Response
-from requests.compat import urljoin
-
-from autogpt.config import Config
-from autogpt.memory import get_memory
-from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-
-CFG = Config()
-memory = get_memory(CFG)
-
-session = requests.Session()
-session.headers.update({"User-Agent": CFG.user_agent})
-
-
-def is_valid_url(url: str) -> bool:
-    """Check if the URL is valid
-
-    Args:
-        url (str): The URL to check
-
-    Returns:
-        bool: True if the URL is valid, False otherwise
-    """
-    try:
-        result = urlparse(url)
-        return all([result.scheme, result.netloc])
-    except ValueError:
-        return False
-
-
-def sanitize_url(url: str) -> str:
-    """Sanitize the URL
-
-    Args:
-        url (str): The URL to sanitize
-
-    Returns:
-        str: The sanitized URL
-    """
-    return urljoin(url, urlparse(url).path)
-
-
-def check_local_file_access(url: str) -> bool:
-    """Check if the URL is a local file
-
-    Args:
-        url (str): The URL to check
-
-    Returns:
-        bool: True if the URL is a local file, False otherwise
-    """
-    local_prefixes = [
-        "file:///",
-        "file://localhost/",
-        "file://localhost",
-        "http://localhost",
-        "http://localhost/",
-        "https://localhost",
-        "https://localhost/",
-        "http://2130706433",
-        "http://2130706433/",
-        "https://2130706433",
-        "https://2130706433/",
-        "http://127.0.0.1/",
-        "http://127.0.0.1",
-        "https://127.0.0.1/",
-        "https://127.0.0.1",
-        "https://0.0.0.0/",
-        "https://0.0.0.0",
-        "http://0.0.0.0/",
-        "http://0.0.0.0",
-        "http://0000",
-        "http://0000/",
-        "https://0000",
-        "https://0000/",
-    ]
-    return any(url.startswith(prefix) for prefix in local_prefixes)
-
-
-def get_response(
-    url: str, timeout: int = 10
-) -> tuple[None, str] | tuple[Response, None]:
-    """Get the response from a URL
-
-    Args:
-        url (str): The URL to get the response from
-        timeout (int): The timeout for the HTTP request
-
-    Returns:
-        tuple[None, str] | tuple[Response, None]: The response and error message
-
-    Raises:
-        ValueError: If the URL is invalid
-        requests.exceptions.RequestException: If the HTTP request fails
-    """
-    try:
-        # Restrict access to local files
-        if check_local_file_access(url):
-            raise ValueError("Access to local files is restricted")
-
-        # Most basic check if the URL is valid:
-        if not url.startswith("http://") and not url.startswith("https://"):
-            raise ValueError("Invalid URL format")
-
-        sanitized_url = sanitize_url(url)
-
-        response = session.get(sanitized_url, timeout=timeout)
-
-        # Check if the response contains an HTTP error
-        if response.status_code >= 400:
-            return None, f"Error: HTTP {str(response.status_code)} error"
-
-        return response, None
-    except ValueError as ve:
-        # Handle invalid URL format
-        return None, f"Error: {str(ve)}"
-
-    except requests.exceptions.RequestException as re:
-        # Handle exceptions related to the HTTP request
-        #  (e.g., connection errors, timeouts, etc.)
-        return None, f"Error: {str(re)}"
-
-
-def scrape_text(url: str) -> str:
-    """Scrape text from a webpage
-
-    Args:
-        url (str): The URL to scrape text from
-
-    Returns:
-        str: The scraped text
-    """
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-    if not response:
-        return "Error: Could not get response"
-
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    text = soup.get_text()
-    lines = (line.strip() for line in text.splitlines())
-    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = "\n".join(chunk for chunk in chunks if chunk)
-
-    return text
-
-
-def scrape_links(url: str) -> str | list[str]:
-    """Scrape links from a webpage
-
-    Args:
-        url (str): The URL to scrape links from
-
-    Returns:
-       str | list[str]: The scraped links
-    """
-    response, error_message = get_response(url)
-    if error_message:
-        return error_message
-    if not response:
-        return "Error: Could not get response"
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    for script in soup(["script", "style"]):
-        script.extract()
-
-    hyperlinks = extract_hyperlinks(soup, url)
-
-    return format_hyperlinks(hyperlinks)
-
-
-def create_message(chunk, question):
-    """Create a message for the user to summarize a chunk of text"""
-    return {
-        "role": "user",
-        "content": f'"""{chunk}""" Using the above text, answer the following'
-        f' question: "{question}" -- if the question cannot be answered using the'
-        " text, summarize the text.",
-    }