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-<h1>Download Idol Star Athletics Championships 2022: How to Watch Your Favorite Idols Compete in Sports</h1>
- <p>If you are a fan of K-pop, you probably know about the Idol Star Athletics Championships (ISAC), a popular TV program that features idols competing in various sports events. ISAC is a special program that airs during major holidays in Korea, such as Lunar New Year and Chuseok. It is a great opportunity for fans to see their favorite idols show off their athletic skills, teamwork, and charisma.</p>
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- <h2>What is Idol Star Athletics Championships?</h2>
- <p>Idol Star Athletics Championships (ISAC) is a TV program that was first aired in 2010 by MBC, one of the major broadcasting networks in Korea. It is a special program that invites idols from different agencies and groups to compete in various sports events, such as track and field, archery, futsal, dance sports, and e-sports. The program aims to showcase the idols' talents, personalities, and interactions with each other.</p>
- <h3>A brief history of ISAC</h3>
- <p>ISAC was first aired in 2010 as a Lunar New Year special program. It featured 14 idol groups competing in track and field events. Since then, it has become a regular program that airs during major holidays in Korea, such as Lunar New Year and Chuseok. Over the years, ISAC has expanded its scope and scale, adding more events and categories, inviting more idols, and attracting more viewers. Some of the most memorable moments of ISAC include BTS's Jungkook breaking the record for the 400-meter dash, TWICE's Tzuyu hitting a perfect score in archery, EXO's Kai winning the dance sports event, and NCT's Jaehyun scoring a goal in futsal.</p>
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- <p>ISAC 2022 will feature five main events and categories: track and field, archery, dance sports, futsal, and e-sports. Track and field will include running, jumping, throwing, and relay races. Archery will involve shooting arrows at a target from a distance. Dance sports will consist of ballroom dancing styles such as cha-cha-cha, rumba, jive, paso doble, and samba. Futsal will be a modified version of soccer played on a smaller field with five players on each team. E-sports will be a new event added for the first time this year, where idols will play popular video games such as League of Legends, PUBG Mobile, KartRider Rush+, and Among Us.</p>
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- <p>ISAC has many benefits for both idols and fans. For idols, it is a chance to showcase their athletic abilities, have fun with their fellow idols, and interact with their fans. For fans, it is a chance to see their favorite idols in a different setting, cheer for them, and enjoy their performances. ISAC also helps promote K-pop <p>However, ISAC also has some controversies and criticisms. Some of the common issues are the idols' safety, fairness, and scheduling. Some idols have suffered injuries or accidents during the events, such as sprains, fractures, or concussions. Some fans have complained about the unfairness or bias of the judges, referees, or staff. Some idols have expressed their exhaustion or stress due to the long hours of filming or the tight schedules.</p>
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- <h2>How to download and watch ISAC 2022?</h2>
- <p>If you want to download and watch ISAC 2022, you have several options. Depending on your location, preference, and budget, you can choose the best way to enjoy the program.</p>
- <h3>The official broadcasting channels and platforms of ISAC</h3>
- <p>The official broadcasting channel of ISAC is MBC, which is a terrestrial TV network in Korea. You can watch ISAC live on MBC if you have access to Korean TV channels. You can also watch ISAC online on MBC's official website or app, which require registration and verification. However, these options may not be available or convenient for international fans.</p>
- <p>Another official platform of ISAC is WAVVE, which is a streaming service that offers various Korean content, including dramas, movies, variety shows, and music. You can watch ISAC live or on-demand on WAVVE with a subscription fee. WAVVE is available in Korea and some other countries, such as Thailand, Indonesia, Malaysia, Singapore, Taiwan, Hong Kong, and Macau.</p>
- <h3>The alternative ways to download and watch ISAC online</h3>
- <p>If you cannot access the official channels or platforms of ISAC, you can still download and watch ISAC online through some alternative ways. However, you should be careful and cautious when using these methods, as they may involve illegal or unauthorized sources.</p>
- <p>One of the alternative ways to download and watch ISAC online is to use torrent sites or file-sharing platforms. These sites or platforms allow users to upload and download various files, including videos, audios, subtitles, and images. You can search for ISAC files on these sites or platforms and download them to your device. However, you should be aware of the risks of malware, viruses, or phishing when using these sites or platforms. You should also respect the intellectual property rights of the creators and producers of ISAC.</p>
- <p>Another alternative way to download and watch ISAC online is to use streaming sites or apps. These sites or apps provide links to various online sources that stream ISAC live or on-demand. You can click on these links and watch ISAC on your browser or app. However, you should be aware of the quality, reliability, and security of these sites or apps. You should also avoid clicking on any pop-ups or ads that may appear on these sites or apps.</p>
- <h3>The tips and precautions for downloading and watching ISAC safely</h3>
- <p>If you decide to use any of the alternative ways to download and watch ISAC online, you should follow some tips and precautions to ensure your safety and enjoyment.</p>
- <p>First, you should use a VPN (virtual private network) service when accessing any site or platform that is not official or authorized by MBC or WAVVE. A VPN service can help you hide your IP address and location, encrypt your data, and bypass any geo-restrictions or censorship. This way, you can protect your privacy and security while downloading and watching ISAC online.</p>
- <p>Second, you should use a reputable antivirus software when downloading any file from any site or platform that is not official or authorized by MBC or WAVVE. An antivirus software can help you scan your device for any malware, <p>viruses, or phishing that may harm your device or steal your information. This way, you can prevent any damage or loss while downloading and watching ISAC online.</p>
- <p>Third, you should use a reliable media player when watching any file from any site or platform that is not official or authorized by MBC or WAVVE. A media player can help you play the file smoothly, adjust the quality, add subtitles, and control the speed. This way, you can enjoy the file without any interruption or inconvenience while watching ISAC online.</p>
- <h2>Who are the idols participating in ISAC 2022?</h2>
- <p>Now that you know how to download and watch ISAC 2022, you may be wondering who are the idols participating in ISAC 2022. ISAC 2022 will feature more than 200 idols from more than 50 groups and solo artists. Here are some of the idols who have confirmed their participation in ISAC 2022.</p>
- <h3>The confirmed lineup of idols for ISAC 2022</h3>
- <p>The confirmed lineup of idols for ISAC 2022 is as follows:</p>
- <table>
-<tr>
-<th>Event</th>
-<th>Idols</th>
-</tr>
-<tr>
-<td>Track and field</td>
-<td>BTS, TWICE, EXO, BLACKPINK, NCT, SEVENTEEN, ITZY, TXT, ENHYPEN, aespa, Stray Kids, ATEEZ, (G)I-DLE, MONSTA X, IZ*ONE, THE BOYZ, LOONA, EVERGLOW, CRAVITY, WEi, STAYC, WEKIMEKI, AB6IX, CIX, PENTAGON, SF9, ASTRO, OH MY GIRL, MOMOLAND, GOLDEN CHILD, VERIVERY</td>
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-<tr>
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-<td>BTS, TWICE, EXO, BLACKPINK, NCT, SEVENTEEN, ITZY, TXT, ENHYPEN, aespa, Stray Kids, ATEEZ, (G)I-DLE, MONSTA X, IZ*ONE, THE BOYZ, LOONA, EVERGLOW</td>
-</tr>
-<tr>
-<td>Dance sports</td>
-<td>BTS's Jimin and J-Hope, TWICE's Momo and Sana, EXO's Kai and Sehun, BLACKPINK's Lisa and Rosé, NCT's Taeyong and Ten, SEVENTEEN's Hoshi and Dino, ITZY's Yeji and Chaeryeong, TXT's Yeonjun and Beomgyu, ENHYPEN's Sunoo and Jake, aespa's Karina and Giselle, Stray Kids' Hyunjin and Felix, ATEEZ's San and Wooyoung, (G)I-DLE's Soojin and Miyeon, MONSTA X's Shownu and Hyungwon, IZ*ONE's Chaeyeon and Yena, THE BOYZ's Q and Juyeon, LOONA's Heejin and Olivia Hye, EVERGLOW's Mia and Yiren</td>
-</tr>
-<tr>
-<td>Futsal</td>
-<td>BTS's Jin and Jungkook, TWICE's Nayeon and Dahyun, EXO's Chanyeol and Baekhyun, BLACKPINK's Jennie and Jisoo, NCT's Mark and Haechan, SEVENTEEN's S.Coups and Mingyu, ITZY's Lia and Ryujin, TXT's Soobin and Hueningkai, ENHYPEN's Heeseung and Jay, aespa's Ningning and Winter, Stray Kids' Bang Chan and Lee Know, ATEEZ's Hongjoong and Yunho, (G)I-DLE's Soyeon and Minnie, MONSTA X's Kihyun and Minhyuk, IZ*ONE's Sakura and Eunbi, THE BOYZ's Sangyeon and Younghoon, LOONA's Kim Lip and Chuu, EVERGLOW's Sihyeon and Onda</td>
-</tr>
-<tr>
-<td>E-sports</td>
-<td>BTS's RM and V, TWICE's Jihyo and Tzuyu, EXO's Suho and Chen, BLACKPINK's Jennie and Jisoo, NCT's Jaehyun and Doyoung, SEVENTEEN's Woozi and Vernon, ITZY's Yuna and Ryujin, TXT's Taehyun and Hueningkai, ENHYPEN's Sunghoon and Ni-ki, aespa's Karina and Giselle, Stray Kids' Changbin and I.N, ATEEZ's Seonghwa and Jongho, (G)I-DLE's Yuqi and Shuhua, MONSTA X's Jooheon and I.M, IZ*ONE's Wonyoung and Hyewon, THE BOYZ's Eric and New, LOONA's Yves and Gowon, EVERGLOW's Aisha and E:U</td>
-</tr>
-</table>
- <h3>The expected highlights and performances of ISAC 2022</h3>
- <p>ISAC 2022 is expected to be full of highlights and performances that will impress and entertain the fans. Some of the anticipated moments are:</p>
- <ul>
-<li>The debut of e-sports as a new event, where idols will show their gaming skills and strategies.</li>
-<li>The return of dance sports as a popular event, where idols will dazzle with their elegant and energetic moves.</li>
-<li>The fierce competition of archery as a fan-favorite event, where idols will aim for the bullseye with their accuracy and concentration.</li>
-<li>The exciting action of futsal as a thrilling event, where idols will score goals with their agility and teamwork.</li>
-<li>The record-breaking feats of track and field as a classic event, where idols will run, jump, throw, and relay with their speed, strength, and stamina.</li>
-</ul>
- <h3>The idols to watch out for in ISAC 2022</h3>
- <p>ISAC 2022 will feature many idols who have proven their skills and talents in previous ISACs or other programs. Some of the idols to watch out for are:</p>
- <ul>
-<li>BTS's Jungkook, who holds the record for the 400-meter dash and is known as the golden maknae for his all-around abilities.</li>
-<li>TWICE's Tzuyu, who scored a perfect 10 in archery and is known as the archery goddess for her beauty and grace.</li>
-<li>EXO's Kai, who won the dance sports event with his partner Sehun and is known as the dancing king for his charisma and skill.</li>
-<li>BLACKPINK's Lisa, who is a master of various video games and is known as the gaming queen for her intelligence and strategy.</li>
-<li>NCT's Taeyong, who scored a goal in futsal with his amazing dribbling and shooting skills and is known as the futsal ace for his passion and leadership.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, ISAC 2022 is a must-watch program for K-pop fans who want to see their favorite idols compete in various sports events. ISAC 2022 will feature more than 200 idols from more than 50 groups and solo artists, who will participate in five main events: track and field, archery, dance sports, futsal, and e-sports. You can download and watch ISAC 2022 online through various ways, such as the official channels or platforms of MBC or WAVVE, or the alternative sites or platforms that offer torrent or streaming services. However, you should be careful and cautious when using these methods, as they may involve illegal or unauthorized sources. You should also use a VPN service, an antivirus software, and a reliable media player to ensure your safety and enjoyment while downloading and watching ISAC online.</p>
- <p>If you are excited about ISAC 2022, you should mark your calendar for the airing dates. ISAC 2022 will air on MBC on February 11th and February 12th, 2022, at 5:50 PM KST. You can also watch it on WAVVE with a subscription fee. If you want to download and watch it online, you can use the methods we discussed above, but remember to be safe and respectful.</p>
- <p>We hope this article has helped you learn more about ISAC 2022 and how to download and watch it online. ISAC 2022 is a great way to celebrate the Lunar New Year with your favorite idols and enjoy their sportsmanship and entertainment. Don't miss this chance to see your idols shine in ISAC 2022!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about ISAC 2022:</p>
- <ol>
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-<p>The full name of ISAC 2022 is Idol Star Athletics Championships - New Year Special 2022.</p>
-<li>How many episodes are there in ISAC 2022?</li>
-<p>There are two episodes in ISAC 2022, each lasting for about two hours.</p>
-<li>Who are the hosts of ISAC 2022?</li>
-<p>The hosts of ISAC 2022 are Jun Hyun-moo, Super Junior's Leeteuk, and Apink's Bomi.</p>
-<li>Who are the winners of ISAC 2021?</li>
-<p>The winners of ISAC 2021 were NCT (track and field), TWICE (archery), EXO (dance sports), SEVENTEEN (futsal), and BTS (e-sports).</p>
-<li>Where can I find more information about ISAC 2022?</li>
-<p>You can find more information about ISAC 2022 on MBC's official website or social media accounts, or on WAVVE's official website or app.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Enjoy Modern Combat 4 Zero Hour Mod APK with Full Features and No Ads.md DELETED Viewed

@@ -1,129 +0,0 @@
-<br />
-<h1>Modern Combat 4: Zero Hour Mod APK - The Ultimate Action Game for Android</h1>
-<p>If you are looking for an action-packed first-person shooter (FPS) game that will keep you on the edge of your seat, then you should try Modern Combat 4: Zero Hour. This game is one of the best FPS games for Android devices, with an engaging storyline, stunning graphics, and thrilling multiplayer mode. But what if you want to make it even better? Well, you can do that by downloading and installing Modern Combat 4: Zero Hour Mod APK, which is a modified version of the game that gives you unlimited money, unlocked features, and no ads. In this article, we will tell you everything you need to know about this modded version of the game, including how to download and install it, what are its features, and what are its pros and cons.</p>
- <h2>What is Modern Combat 4: Zero Hour?</h2>
-<p>Modern Combat 4: Zero Hour is a FPS game developed by Gameloft for Android and iOS devices. It is the fourth installment in the Modern Combat series, which is inspired by popular games like Call of Duty and Battlefield. The game has two main modes:</p>
-<h2>modern combat 4 zero hour mod apk</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://jinyurl.com/2uNMMG">https://jinyurl.com/2uNMMG</a></b></p><br /><br />
-<h3>A thrilling FPS game with an immersive storyline and realistic graphics</h3>
-<p>In this mode, you play as a soldier who has to stop a global nuclear war that is triggered by a group of terrorists. You will have to fight your way through various locations around the world, such as Barcelona, Antarctica, Hawaii, and more. You will also have to face different enemies, such as soldiers, snipers, helicopters, tanks, and even drones. You will have access to a wide range of weapons, such as assault rifles, shotguns, pistols, grenades, rocket launchers, and more. You can also customize your weapons with attachments, such as scopes, silencers, magazines, etc. The game has realistic graphics that will make you feel like you are in the middle of a war zone. The game also has a cinematic soundtrack and voice acting that will immerse you in the story. The game has 12 missions that will take you around 5 hours to complete.</p>
-<h3>A multiplayer mode with various modes and maps</h3>
-<p>In this mode, you can play online with or against other players from around the world. You can choose from different modes, such as Team Deathmatch, Capture the Flag, Free for All, Zone Control, and more. You can also choose from different maps, such as Rooftops, Paradise, Blockbuster, and more. You can also create your own custom matches with your own rules and settings. You can also join or create clans and chat with other players. The game has a ranking system that will reward you with experience points, medals, and badges as you play. You can also unlock new weapons, skills, and perks as you level up.</p>
-<h3>A modded version with unlimited money and unlocked features</h3>
-<p>This is where Modern Combat 4: Zero Hour Mod APK comes in. This is a modified version of the game that gives you some extra benefits that are not available in the original version. These include:</p>
-<ul>
-<li>Unlimited money to buy weapons, armor, and upgrades</li>
-<li>Unlocked all levels, modes, and characters</li>
-<li>No ads, no root, no virus</li>
-</ul>
-<p>With these features, you can enjoy the game without any limitations or interruptions. You can have more fun and excitement with more options and customization. You can also save your time and effort by not having to grind or pay for anything.</p>
- <h2>How to download and install Modern Combat 4: Zero Hour Mod APK?</h2>
-<p>If you want to try Modern Combat 4: Zero Hour Mod APK, you will need to follow these simple steps:</p>
-<h3>Download the APK and OBB files from a trusted source</h3>
-<p>The first thing you need to do is to download the APK and OBB files of the modded version of the game from a reliable source. You can find many websites that offer these files, but be careful as some of them may contain malware or viruses that can harm your device. We recommend you to use [this link] to download the files safely and securely.</p>
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-<h3>Enable unknown sources on your device settings</h3>
-<p>The next thing you need to do is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings > security > unknown sources > enable.</p>
-<h3>Install the APK file and extract the OBB file to the Android/obb folder</h3>
-<p>The third thing you need to do is to install the APK file and extract the OBB file to the Android/obb folder on your device storage. To do this, locate the downloaded APK file on your file manager and tap on it to install it. Then, locate the downloaded OBB file on your file manager and extract it using a ZIP extractor app. You should get a folder named com.gameloft.android.ANMP.GloftM4HM. Move this folder to the Android/obb folder on your device storage.</p>
-<h3>Launch the game and enjoy</h3>
-<p>The final thing you need to do is to launch the game and enjoy it. You should see a mod menu on the screen where you can enable or disable the mod features as you wish. You can also access all the levels, modes, and characters without any restrictions. You can also buy any weapons, armor, or upgrades with unlimited money.</p>
- <h2>What are the features of Modern Combat 4: Zero Hour Mod APK?</h2>
-<p>As we mentioned earlier, Modern Combat 4: Zero Hour Mod APK has some amazing features that make it better than the original version of the game. These include:</p>
-<h3>Unlimited money to buy weapons, armor, and upgrades</h3>
-<p>One of the main features of this modded version of the game is that it gives you unlimited money to buy anything you want in the game. You can buy any weapons, armor, or upgrades that suit your style and preference. You can also customize your weapons with attachments, such as scopes, silencers, magazines, etc. You don't have to worry about running out of money or spending real money on in-app purchases.</p>
-<h3>Unlocked all levels, modes, and characters</h3>
-<p>Another feature of this modded version of the game is that it unlocks all the levels, modes, and characters in the game. You can access all the 12 missions in the single-player mode without having to complete them in order. You can also choose from any of the modes and maps in the multiplayer mode without having to unlock them. You can also play as any of the characters in the game, such as Edward Page, Joel Blake, James Walker, and more. You can enjoy the full content of the game without any limitations.</p>
-<h3>No ads, no root, no virus</h3>
-<p>The last feature of this modded version of the game is that it has no ads, no root, and no virus. You don't have to see any annoying ads that pop up on your screen or interrupt your gameplay. You don't have to root your device or risk damaging it to install this modded version of the game. You don't have to worry about any malware or viruses that can infect your device or steal your data. You can play this modded version of the game safely and securely.</p>
- <h2>What are the pros and cons of Modern Combat 4: Zero Hour Mod APK?</h2>
-<p>As with any modded version of a game, Modern Combat 4: Zero Hour Mod APK has its own advantages and disadvantages. Here are some of them:</p>
-<h3>Pros</h3>
-<ul>
-<li>Enhanced gameplay experience with more options and customization</li>
-<p>One of the pros of this modded version of the game is that it enhances your gameplay experience with more options and customization. You can have more fun and excitement with more weapons, armor, upgrades, levels, modes, and characters. You can also customize your weapons with attachments, such as scopes, silencers, magazines, etc. You can also adjust the difficulty level and the graphics quality according to your preference. You can have a better gaming experience than the original version.</p>
-<li>Free to download and play without any restrictions</li>
-<p>Another pro of this modded version of the game is that it is free to download and play without any restrictions. You don't have to pay anything to download or install this modded version of the game. You don't have to spend any real money on in-app purchases or subscriptions. You don't have to complete any surveys or offers to access the mod features. You can play this modded version of the game without any cost or hassle.</p>
-<li>Compatible with most Android devices and versions</li>
-<p>The last pro of this modded version of the game is that it is compatible with most Android devices and versions. You don't need a high-end device or a latest Android version to play this modded version of the game. You can play it on any Android device that has at least 2 GB of RAM and Android 4.0 or higher. You can also play it on devices that are not supported by the original version of the game.</p>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>May not be compatible with some online features or servers</li>
-<p>One of the cons of this modded version of the game is that it may not be compatible with some online features or servers. You may not be able to play online with other players who are using the original version of the game. You may also face some issues with connecting to some servers or modes in the multiplayer mode. You may also get banned or blocked by some servers or players for using a modded version of the game.</p>
-<li>May cause some glitches or bugs in the game</li>
-<p>Another con of this modded version of the game is that it may cause some glitches or bugs in the game. You may encounter some errors or crashes while playing this modded version of the game. You may also experience some lagging or freezing issues while playing this modded version of the game. You may also lose some data or progress while playing this modded version of the game.</p>
-<li>May violate the terms and conditions of the original game developer</li>
-<p>The last con of this modded version of the game is that it may violate the terms and conditions of the original game developer. You may be violating the intellectual property rights or the privacy policy of Gameloft, the developer of Modern Combat 4: Zero Hour. You may also be breaking the rules or the code of conduct of the game. You may face some legal consequences or penalties for using a modded version of the game.</p>
- <h2>Conclusion</h2>
-<p>Modern Combat 4: Zero Hour Mod APK is a great choice for action lovers who want to enjoy a high-quality FPS game on their Android devices. It offers unlimited money, unlocked features, and no ads, making it more fun and exciting than the original version. However, it also has some drawbacks, such as possible compatibility issues, glitches, or legal risks. Therefore, users should download and install it at their own discretion and responsibility.</p>
- <h2>FAQs</h2>
-<h3>Is Modern Combat 4: Zero Hour Mod APK safe to use?</h3>
-<p>Modern Combat 4: Zero Hour Mod APK is generally safe to use, as long as you download it from a trusted source and scan it with an antivirus app before installing it. However, there is no guarantee that it will not cause any harm to your device or data, so you should use it at your own risk.</p>
-<h3>How to update Modern Combat 4: Zero Hour Mod APK?</h3>
-<p>To update Modern Combat 4: Zero Hour Mod APK, you will need to download and install the latest version of the modded version of the game from the same source that you downloaded it from. You will also need to delete the old version of the game and its data before installing the new version. You may also need to backup your progress or data before updating the game.</p>
-<h3>How to fix Modern Combat 4: Zero Hour Mod APK not working?</h3>
-<p>If Modern Combat 4: Zero Hour Mod APK is not working on your device, you may try some of these solutions:</p>
-<ul>
-<li>Check your internet connection and make sure it is stable and fast.</li>
-<li>Clear your cache and data of the game and restart your device.</li>
-<li>Reinstall the game and its data from a trusted source.</li>
-<li>Change your device settings or permissions to allow the game to run properly.</li>
-<li>Contact the mod developer or the original game developer for support or assistance.</li>
-</ul>
-<h3>How to play Modern Combat 4: Zero Hour Mod APK online?</h3>
-<p>To play Modern Combat 4: Zero Hour Mod APK online, you will need to have a stable and fast internet connection and a valid account for the game. You will also need to make sure that you are using a compatible version of the modded version of the game with the online servers or features. You may also need to disable some of the mod features that may interfere with the online gameplay.</p>
-<h3>How to uninstall Modern Combat 4: Zero Hour Mod APK?</h3>
-<p>To uninstall Modern Combat 4: Zero Hour Mod APK, you will need to follow these steps:</p>
-<ol>
-<li>Go to your device settings > apps > Modern Combat 4: Zero Hour > uninstall.</li>
-<li>Delete the com.gameloft.android.ANMP.GloftM4HM folder from your Android/obb folder on your device storage.</li>
-<li>Delete any other files or folders related to the game from your device storage.</li>
-</ol></p> 401be4b1e0<br />
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spaces/1toTree/lora_test/ppdiffusers/experimental/README.md DELETED Viewed

@@ -1,6 +0,0 @@
-# 🧨 PPDiffusers Experimental
-为了使得**PPDiffusers库**能够有更多的应用场景，我们在这里添加了一些**实验性的代码**。
-目前我们支持了以下场景:
-* Reinforcement learning via an implementation of the [PPDiffuser](https://arxiv.org/abs/2205.09991) model.

spaces/2023Liu2023/bingo/src/components/tailwind-indicator.tsx DELETED Viewed

@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
-  if (process.env.NODE_ENV === 'production') return null
-  return (
-    <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white">
-      <div className="block sm:hidden">xs</div>
-      <div className="hidden sm:block md:hidden">sm</div>
-      <div className="hidden md:block lg:hidden">md</div>
-      <div className="hidden lg:block xl:hidden">lg</div>
-      <div className="hidden xl:block 2xl:hidden">xl</div>
-      <div className="hidden 2xl:block">2xl</div>
-    </div>
-  )
-}

spaces/4Taps/SadTalker/src/face3d/options/__init__.py DELETED Viewed

	@@ -1 +0,0 @@
1	- """This package options includes option modules: training options, test options, and basic options (used in both training and test)."""

spaces/AI-Hobbyist/Hoyo-RVC/Dockerfile DELETED Viewed

@@ -1,13 +0,0 @@
-# syntax=docker/dockerfile:1
-FROM python:3.10-bullseye
-EXPOSE 7865
-WORKDIR /app
-COPY . .
-RUN pip3 install -r requirements.txt
-CMD ["python3", "infer-web.py"]

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/multi_window_disc.py DELETED Viewed

@@ -1,136 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-class SingleWindowDisc(nn.Module):
-    def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
-        super().__init__()
-        padding = (kernel[0] // 2, kernel[1] // 2)
-        self.model = nn.ModuleList([
-            nn.Sequential(*[
-                nn.Conv2d(c_in, hidden_size, kernel, (2, 2), padding),
-                nn.LeakyReLU(0.2, inplace=True),
-                nn.Dropout2d(0.25),
-                nn.BatchNorm2d(hidden_size, 0.8)
-            ]),
-            nn.Sequential(*[
-                nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
-                nn.LeakyReLU(0.2, inplace=True),
-                nn.Dropout2d(0.25),
-                nn.BatchNorm2d(hidden_size, 0.8)
-            ]),
-            nn.Sequential(*[
-                nn.Conv2d(hidden_size, hidden_size, kernel, (2, 2), padding),
-                nn.LeakyReLU(0.2, inplace=True),
-                nn.Dropout2d(0.25),
-            ]),
-        ])
-        ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
-        self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
-    def forward(self, x):
-        """
-        :param x: [B, C, T, n_bins]
-        :return: validity: [B, 1], h: List of hiddens
-        """
-        h = []
-        for l in self.model:
-            x = l(x)
-            h.append(x)
-        x = x.view(x.shape[0], -1)
-        validity = self.adv_layer(x)  # [B, 1]
-        return validity, h
-class MultiWindowDiscriminator(nn.Module):
-    def __init__(self, time_lengths, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
-        super(MultiWindowDiscriminator, self).__init__()
-        self.win_lengths = time_lengths
-        self.discriminators = nn.ModuleList()
-        for time_length in time_lengths:
-            self.discriminators += [SingleWindowDisc(time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size)]
-    def forward(self, x, x_len, start_frames_wins=None):
-        '''
-        Args:
-            x (tensor): input mel, (B, c_in, T, n_bins).
-            x_length (tensor): len of per mel. (B,).
-        Returns:
-            tensor : (B).
-        '''
-        validity = []
-        if start_frames_wins is None:
-            start_frames_wins = [None] * len(self.discriminators)
-        h = []
-        for i, start_frames in zip(range(len(self.discriminators)), start_frames_wins):
-            x_clip, start_frames = self.clip(x, x_len, self.win_lengths[i], start_frames)  # (B, win_length, C)
-            start_frames_wins[i] = start_frames
-            if x_clip is None:
-                continue
-            x_clip, h_ = self.discriminators[i](x_clip)
-            h += h_
-            validity.append(x_clip)
-        if len(validity) != len(self.discriminators):
-            return None, start_frames_wins, h
-        validity = sum(validity)  # [B]
-        return validity, start_frames_wins, h
-    def clip(self, x, x_len, win_length, start_frames=None):
-        '''Ramdom clip x to win_length.
-        Args:
-            x (tensor) : (B, c_in, T, n_bins).
-            cond (tensor) : (B, T, H).
-            x_len (tensor) : (B,).
-            win_length (int): target clip length
-        Returns:
-            (tensor) : (B, c_in, win_length, n_bins).
-        '''
-        T_start = 0
-        T_end = x_len.max() - win_length
-        if T_end < 0:
-            return None, None, start_frames
-        T_end = T_end.item()
-        if start_frames is None:
-            start_frame = np.random.randint(low=T_start, high=T_end + 1)
-            start_frames = [start_frame] * x.size(0)
-        else:
-            start_frame = start_frames[0]
-        x_batch = x[:, :, start_frame: start_frame + win_length]
-        return x_batch, start_frames
-class Discriminator(nn.Module):
-    def __init__(self, time_lengths=[32, 64, 128], freq_length=80, kernel=(3, 3), c_in=1,
-                 hidden_size=128):
-        super(Discriminator, self).__init__()
-        self.time_lengths = time_lengths
-        self.discriminator = MultiWindowDiscriminator(
-            freq_length=freq_length,
-            time_lengths=time_lengths,
-            kernel=kernel,
-            c_in=c_in, hidden_size=hidden_size
-        )
-    def forward(self, x, start_frames_wins=None):
-        """
-        :param x: [B, T, 80]
-        :param return_y_only:
-        :return:
-        """
-        if len(x.shape) == 3:
-            x = x[:, None, :, :] # [B,1,T,80]
-        x_len = x.sum([1, -1]).ne(0).int().sum([-1])
-        ret = {'y_c': None, 'y': None}
-        ret['y'], start_frames_wins, ret['h'] = self.discriminator(
-            x, x_len, start_frames_wins=start_frames_wins)
-        ret['start_frames_wins'] = start_frames_wins
-        return ret

spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/data_generator.py DELETED Viewed

@@ -1,421 +0,0 @@
-import numpy as np
-import h5py
-import csv
-import time
-import logging
-from utilities import int16_to_float32
-def read_black_list(black_list_csv):
-    """Read audio names from black list.
-    """
-    with open(black_list_csv, 'r') as fr:
-        reader = csv.reader(fr)
-        lines = list(reader)
-    black_list_names = ['Y{}.wav'.format(line[0]) for line in lines]
-    return black_list_names
-class AudioSetDataset(object):
-    def __init__(self, sample_rate=32000):
-        """This class takes the meta of an audio clip as input, and return
-        the waveform and target of the audio clip. This class is used by DataLoader.
-        """
-        self.sample_rate = sample_rate
-    def __getitem__(self, meta):
-        """Load waveform and target of an audio clip.
-        Args:
-          meta: {
-            'hdf5_path': str,
-            'index_in_hdf5': int}
-        Returns:
-          data_dict: {
-            'audio_name': str,
-            'waveform': (clip_samples,),
-            'target': (classes_num,)}
-        """
-        hdf5_path = meta['hdf5_path']
-        index_in_hdf5 = meta['index_in_hdf5']
-        with h5py.File(hdf5_path, 'r') as hf:
-            audio_name = hf['audio_name'][index_in_hdf5].decode()
-            waveform = int16_to_float32(hf['waveform'][index_in_hdf5])
-            waveform = self.resample(waveform)
-            target = hf['target'][index_in_hdf5].astype(np.float32)
-        data_dict = {
-            'audio_name': audio_name, 'waveform': waveform, 'target': target}
-        return data_dict
-    def resample(self, waveform):
-        """Resample.
-        Args:
-          waveform: (clip_samples,)
-        Returns:
-          (resampled_clip_samples,)
-        """
-        if self.sample_rate == 32000:
-            return waveform
-        elif self.sample_rate == 16000:
-            return waveform[0 :: 2]
-        elif self.sample_rate == 8000:
-            return waveform[0 :: 4]
-        else:
-            raise Exception('Incorrect sample rate!')
-class Base(object):
-    def __init__(self, indexes_hdf5_path, batch_size, black_list_csv, random_seed):
-        """Base class of train sampler.
-        Args:
-          indexes_hdf5_path: string
-          batch_size: int
-          black_list_csv: string
-          random_seed: int
-        """
-        self.batch_size = batch_size
-        self.random_state = np.random.RandomState(random_seed)
-        # Black list
-        if black_list_csv:
-            self.black_list_names = read_black_list(black_list_csv)
-        else:
-            self.black_list_names = []
-        logging.info('Black list samples: {}'.format(len(self.black_list_names)))
-        # Load target
-        load_time = time.time()
-        with h5py.File(indexes_hdf5_path, 'r') as hf:
-            self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
-            self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
-            self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
-            self.targets = hf['target'][:].astype(np.float32)
-        (self.audios_num, self.classes_num) = self.targets.shape
-        logging.info('Training number: {}'.format(self.audios_num))
-        logging.info('Load target time: {:.3f} s'.format(time.time() - load_time))
-class TrainSampler(Base):
-    def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
-        random_seed=1234):
-        """Balanced sampler. Generate batch meta for training.
-        Args:
-          indexes_hdf5_path: string
-          batch_size: int
-          black_list_csv: string
-          random_seed: int
-        """
-        super(TrainSampler, self).__init__(indexes_hdf5_path, batch_size,
-            black_list_csv, random_seed)
-        self.indexes = np.arange(self.audios_num)
-        # Shuffle indexes
-        self.random_state.shuffle(self.indexes)
-        self.pointer = 0
-    def __iter__(self):
-        """Generate batch meta for training.
-        Returns:
-          batch_meta: e.g.: [
-            {'hdf5_path': string, 'index_in_hdf5': int},
-            ...]
-        """
-        batch_size = self.batch_size
-        while True:
-            batch_meta = []
-            i = 0
-            while i < batch_size:
-                index = self.indexes[self.pointer]
-                self.pointer += 1
-                # Shuffle indexes and reset pointer
-                if self.pointer >= self.audios_num:
-                    self.pointer = 0
-                    self.random_state.shuffle(self.indexes)
-                # If audio in black list then continue
-                if self.audio_names[index] in self.black_list_names:
-                    continue
-                else:
-                    batch_meta.append({
-                        'hdf5_path': self.hdf5_paths[index],
-                        'index_in_hdf5': self.indexes_in_hdf5[index]})
-                    i += 1
-            yield batch_meta
-    def state_dict(self):
-        state = {
-            'indexes': self.indexes,
-            'pointer': self.pointer}
-        return state
-    def load_state_dict(self, state):
-        self.indexes = state['indexes']
-        self.pointer = state['pointer']
-class BalancedTrainSampler(Base):
-    def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
-        random_seed=1234):
-        """Balanced sampler. Generate batch meta for training. Data are equally
-        sampled from different sound classes.
-        Args:
-          indexes_hdf5_path: string
-          batch_size: int
-          black_list_csv: string
-          random_seed: int
-        """
-        super(BalancedTrainSampler, self).__init__(indexes_hdf5_path,
-            batch_size, black_list_csv, random_seed)
-        self.samples_num_per_class = np.sum(self.targets, axis=0)
-        logging.info('samples_num_per_class: {}'.format(
-            self.samples_num_per_class.astype(np.int32)))
-        # Training indexes of all sound classes. E.g.:
-        # [[0, 11, 12, ...], [3, 4, 15, 16, ...], [7, 8, ...], ...]
-        self.indexes_per_class = []
-        for k in range(self.classes_num):
-            self.indexes_per_class.append(
-                np.where(self.targets[:, k] == 1)[0])
-        # Shuffle indexes
-        for k in range(self.classes_num):
-            self.random_state.shuffle(self.indexes_per_class[k])
-        self.queue = []
-        self.pointers_of_classes = [0] * self.classes_num
-    def expand_queue(self, queue):
-        classes_set = np.arange(self.classes_num).tolist()
-        self.random_state.shuffle(classes_set)
-        queue += classes_set
-        return queue
-    def __iter__(self):
-        """Generate batch meta for training.
-        Returns:
-          batch_meta: e.g.: [
-            {'hdf5_path': string, 'index_in_hdf5': int},
-            ...]
-        """
-        batch_size = self.batch_size
-        while True:
-            batch_meta = []
-            i = 0
-            while i < batch_size:
-                if len(self.queue) == 0:
-                    self.queue = self.expand_queue(self.queue)
-                class_id = self.queue.pop(0)
-                pointer = self.pointers_of_classes[class_id]
-                self.pointers_of_classes[class_id] += 1
-                index = self.indexes_per_class[class_id][pointer]
-                # When finish one epoch of a sound class, then shuffle its indexes and reset pointer
-                if self.pointers_of_classes[class_id] >= self.samples_num_per_class[class_id]:
-                    self.pointers_of_classes[class_id] = 0
-                    self.random_state.shuffle(self.indexes_per_class[class_id])
-                # If audio in black list then continue
-                if self.audio_names[index] in self.black_list_names:
-                    continue
-                else:
-                    batch_meta.append({
-                        'hdf5_path': self.hdf5_paths[index],
-                        'index_in_hdf5': self.indexes_in_hdf5[index]})
-                    i += 1
-            yield batch_meta
-    def state_dict(self):
-        state = {
-            'indexes_per_class': self.indexes_per_class,
-            'queue': self.queue,
-            'pointers_of_classes': self.pointers_of_classes}
-        return state
-    def load_state_dict(self, state):
-        self.indexes_per_class = state['indexes_per_class']
-        self.queue = state['queue']
-        self.pointers_of_classes = state['pointers_of_classes']
-class AlternateTrainSampler(Base):
-    def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
-        random_seed=1234):
-        """AlternateSampler is a combination of Sampler and Balanced Sampler.
-        AlternateSampler alternately sample data from Sampler and Blanced Sampler.
-        Args:
-          indexes_hdf5_path: string
-          batch_size: int
-          black_list_csv: string
-          random_seed: int
-        """
-        self.sampler1 = TrainSampler(indexes_hdf5_path, batch_size,
-            black_list_csv, random_seed)
-        self.sampler2 = BalancedTrainSampler(indexes_hdf5_path, batch_size,
-            black_list_csv, random_seed)
-        self.batch_size = batch_size
-        self.count = 0
-    def __iter__(self):
-        """Generate batch meta for training.
-        Returns:
-          batch_meta: e.g.: [
-            {'hdf5_path': string, 'index_in_hdf5': int},
-            ...]
-        """
-        batch_size = self.batch_size
-        while True:
-            self.count += 1
-            if self.count % 2 == 0:
-                batch_meta = []
-                i = 0
-                while i < batch_size:
-                    index = self.sampler1.indexes[self.sampler1.pointer]
-                    self.sampler1.pointer += 1
-                    # Shuffle indexes and reset pointer
-                    if self.sampler1.pointer >= self.sampler1.audios_num:
-                        self.sampler1.pointer = 0
-                        self.sampler1.random_state.shuffle(self.sampler1.indexes)
-                    # If audio in black list then continue
-                    if self.sampler1.audio_names[index] in self.sampler1.black_list_names:
-                        continue
-                    else:
-                        batch_meta.append({
-                            'hdf5_path': self.sampler1.hdf5_paths[index],
-                            'index_in_hdf5': self.sampler1.indexes_in_hdf5[index]})
-                        i += 1
-            elif self.count % 2 == 1:
-                batch_meta = []
-                i = 0
-                while i < batch_size:
-                    if len(self.sampler2.queue) == 0:
-                        self.sampler2.queue = self.sampler2.expand_queue(self.sampler2.queue)
-                    class_id = self.sampler2.queue.pop(0)
-                    pointer = self.sampler2.pointers_of_classes[class_id]
-                    self.sampler2.pointers_of_classes[class_id] += 1
-                    index = self.sampler2.indexes_per_class[class_id][pointer]
-                    # When finish one epoch of a sound class, then shuffle its indexes and reset pointer
-                    if self.sampler2.pointers_of_classes[class_id] >= self.sampler2.samples_num_per_class[class_id]:
-                        self.sampler2.pointers_of_classes[class_id] = 0
-                        self.sampler2.random_state.shuffle(self.sampler2.indexes_per_class[class_id])
-                    # If audio in black list then continue
-                    if self.sampler2.audio_names[index] in self.sampler2.black_list_names:
-                        continue
-                    else:
-                        batch_meta.append({
-                            'hdf5_path': self.sampler2.hdf5_paths[index],
-                            'index_in_hdf5': self.sampler2.indexes_in_hdf5[index]})
-                        i += 1
-            yield batch_meta
-    def state_dict(self):
-        state = {
-            'sampler1': self.sampler1.state_dict(),
-            'sampler2': self.sampler2.state_dict()}
-        return state
-    def load_state_dict(self, state):
-        self.sampler1.load_state_dict(state['sampler1'])
-        self.sampler2.load_state_dict(state['sampler2'])
-class EvaluateSampler(object):
-    def __init__(self, indexes_hdf5_path, batch_size):
-        """Evaluate sampler. Generate batch meta for evaluation.
-        Args:
-          indexes_hdf5_path: string
-          batch_size: int
-        """
-        self.batch_size = batch_size
-        with h5py.File(indexes_hdf5_path, 'r') as hf:
-            self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
-            self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
-            self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
-            self.targets = hf['target'][:].astype(np.float32)
-        self.audios_num = len(self.audio_names)
-    def __iter__(self):
-        """Generate batch meta for training.
-        Returns:
-          batch_meta: e.g.: [
-            {'hdf5_path': string,
-             'index_in_hdf5': int}
-            ...]
-        """
-        batch_size = self.batch_size
-        pointer = 0
-        while pointer < self.audios_num:
-            batch_indexes = np.arange(pointer,
-                min(pointer + batch_size, self.audios_num))
-            batch_meta = []
-            for index in batch_indexes:
-                batch_meta.append({
-                    'audio_name': self.audio_names[index],
-                    'hdf5_path': self.hdf5_paths[index],
-                    'index_in_hdf5': self.indexes_in_hdf5[index],
-                    'target': self.targets[index]})
-            pointer += batch_size
-            yield batch_meta
-def collate_fn(list_data_dict):
-    """Collate data.
-    Args:
-      list_data_dict, e.g., [{'audio_name': str, 'waveform': (clip_samples,), ...},
-                             {'audio_name': str, 'waveform': (clip_samples,), ...},
-                             ...]
-    Returns:
-      np_data_dict, dict, e.g.,
-          {'audio_name': (batch_size,), 'waveform': (batch_size, clip_samples), ...}
-    """
-    np_data_dict = {}
-    for key in list_data_dict[0].keys():
-        np_data_dict[key] = np.array([data_dict[key] for data_dict in list_data_dict])
-    return np_data_dict

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/audio/Factory.js DELETED Viewed

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

spaces/Ahmedmewloud/Depplearnig/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Depplearnig
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AixiaGreyatt/QQsign/README.md DELETED Viewed

@@ -1,10 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: pink
-sdk: docker
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Aki004/herta-so-vits/modules/losses.py DELETED Viewed

@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-import modules.commons as commons
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-  return loss * 2
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-  return loss, r_losses, g_losses
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-  return loss, gen_losses
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-  #print(logs_p)
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_transforms.py DELETED Viewed

@@ -1,196 +0,0 @@
-import torch
-from torch.nn import functional as F
-import numpy as np
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-def piecewise_rational_quadratic_transform(inputs,
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None,
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-    if tails == 'linear':
-        #unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        unnormalized_derivatives_ = torch.zeros((1, 1, unnormalized_derivatives.size(2), unnormalized_derivatives.size(3)+2))
-        unnormalized_derivatives_[...,1:-1] = unnormalized_derivatives
-        unnormalized_derivatives = unnormalized_derivatives_
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-    return outputs, logabsdet
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-    num_bins = unnormalized_widths.shape[-1]
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-        return outputs, logabsdet

spaces/Alpaca233/LangchainPDF/app.py DELETED Viewed

@@ -1,64 +0,0 @@
-import gradio as gr
-from langchain.document_loaders import PyMuPDFLoader  # for loading the pdf
-from langchain.embeddings import OpenAIEmbeddings  # for creating embeddings
-from langchain.vectorstores import Chroma  # for the vectorization part
-from langchain.chains import ChatVectorDBChain  # for chatting with the pdf
-from langchain.llms import OpenAI  # the LLM model we'll use (CHatGPT)
-class Chat:
-    def __init__(self, pdf, api_input):
-        self.api = api_input
-        loader = PyMuPDFLoader(pdf)
-        pages = loader.load_and_split()
-        embeddings = OpenAIEmbeddings(openai_api_key=self.api)
-        vectordb = Chroma.from_documents(pages, embedding=embeddings, persist_directory=".")
-        vectordb.persist()
-        self.pdf_qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0.9, model_name="gpt-3.5-turbo",
-                                                        openai_api_key=self.api),
-                                                 vectordb, return_source_documents=True)
-    def question(self, query):
-        result = self.pdf_qa({"question": "请使用中文回答" + query, "chat_history": ""})
-        print("Answer:")
-        print(result["answer"])
-        return result["answer"]
-def analyse(pdf_file, api_input):
-    print(pdf_file.name)
-    session = Chat(pdf_file.name, api_input)
-    return session, "文章分析完成"
-def ask_question(data, question):
-    if data == "":
-        return "Please upload PDF file first!"
-    return data.question(question)
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # ChatPDF based on Langchain
-        """)
-    data = gr.State()
-    with gr.Tab("Upload PDF File"):
-        pdf_input = gr.File(label="PDF File")
-        api_input = gr.Textbox(label="OpenAI API Key")
-        result = gr.Textbox()
-        upload_button = gr.Button("Start Analyse")
-        question_input = gr.Textbox(label="Your Question", placeholder="Authors of this paper?")
-        answer = gr.Textbox(label="Answer")
-        ask_button = gr.Button("Ask")
-    upload_button.click(fn=analyse, inputs=[pdf_input, api_input], outputs=[data, result])
-    ask_button.click(ask_question, inputs=[data, question_input], outputs=answer)
-if __name__ == "__main__":
-    demo.title = "ChatPDF Based on Langchain"
-    demo.launch()

spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/regnet.py DELETED Viewed

@@ -1,325 +0,0 @@
-import numpy as np
-import torch.nn as nn
-from mmcv.cnn import build_conv_layer, build_norm_layer
-from ..builder import BACKBONES
-from .resnet import ResNet
-from .resnext import Bottleneck
-@BACKBONES.register_module()
-class RegNet(ResNet):
-    """RegNet backbone.
-    More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .
-    Args:
-        arch (dict): The parameter of RegNets.
-            - w0 (int): initial width
-            - wa (float): slope of width
-            - wm (float): quantization parameter to quantize the width
-            - depth (int): depth of the backbone
-            - group_w (int): width of group
-            - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        base_channels (int): Base channels after stem layer.
-        in_channels (int): Number of input image channels. Default: 3.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-        zero_init_residual (bool): whether to use zero init for last norm layer
-            in resblocks to let them behave as identity.
-    Example:
-        >>> from mmdet.models import RegNet
-        >>> import torch
-        >>> self = RegNet(
-                arch=dict(
-                    w0=88,
-                    wa=26.31,
-                    wm=2.25,
-                    group_w=48,
-                    depth=25,
-                    bot_mul=1.0))
-        >>> self.eval()
-        >>> inputs = torch.rand(1, 3, 32, 32)
-        >>> level_outputs = self.forward(inputs)
-        >>> for level_out in level_outputs:
-        ...     print(tuple(level_out.shape))
-        (1, 96, 8, 8)
-        (1, 192, 4, 4)
-        (1, 432, 2, 2)
-        (1, 1008, 1, 1)
-    """
-    arch_settings = {
-        'regnetx_400mf':
-        dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0),
-        'regnetx_800mf':
-        dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0),
-        'regnetx_1.6gf':
-        dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0),
-        'regnetx_3.2gf':
-        dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0),
-        'regnetx_4.0gf':
-        dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0),
-        'regnetx_6.4gf':
-        dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0),
-        'regnetx_8.0gf':
-        dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0),
-        'regnetx_12gf':
-        dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0),
-    }
-    def __init__(self,
-                 arch,
-                 in_channels=3,
-                 stem_channels=32,
-                 base_channels=32,
-                 strides=(2, 2, 2, 2),
-                 dilations=(1, 1, 1, 1),
-                 out_indices=(0, 1, 2, 3),
-                 style='pytorch',
-                 deep_stem=False,
-                 avg_down=False,
-                 frozen_stages=-1,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN', requires_grad=True),
-                 norm_eval=True,
-                 dcn=None,
-                 stage_with_dcn=(False, False, False, False),
-                 plugins=None,
-                 with_cp=False,
-                 zero_init_residual=True):
-        super(ResNet, self).__init__()
-        # Generate RegNet parameters first
-        if isinstance(arch, str):
-            assert arch in self.arch_settings, \
-                f'"arch": "{arch}" is not one of the' \
-                ' arch_settings'
-            arch = self.arch_settings[arch]
-        elif not isinstance(arch, dict):
-            raise ValueError('Expect "arch" to be either a string '
-                             f'or a dict, got {type(arch)}')
-        widths, num_stages = self.generate_regnet(
-            arch['w0'],
-            arch['wa'],
-            arch['wm'],
-            arch['depth'],
-        )
-        # Convert to per stage format
-        stage_widths, stage_blocks = self.get_stages_from_blocks(widths)
-        # Generate group widths and bot muls
-        group_widths = [arch['group_w'] for _ in range(num_stages)]
-        self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)]
-        # Adjust the compatibility of stage_widths and group_widths
-        stage_widths, group_widths = self.adjust_width_group(
-            stage_widths, self.bottleneck_ratio, group_widths)
-        # Group params by stage
-        self.stage_widths = stage_widths
-        self.group_widths = group_widths
-        self.depth = sum(stage_blocks)
-        self.stem_channels = stem_channels
-        self.base_channels = base_channels
-        self.num_stages = num_stages
-        assert num_stages >= 1 and num_stages <= 4
-        self.strides = strides
-        self.dilations = dilations
-        assert len(strides) == len(dilations) == num_stages
-        self.out_indices = out_indices
-        assert max(out_indices) < num_stages
-        self.style = style
-        self.deep_stem = deep_stem
-        self.avg_down = avg_down
-        self.frozen_stages = frozen_stages
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.with_cp = with_cp
-        self.norm_eval = norm_eval
-        self.dcn = dcn
-        self.stage_with_dcn = stage_with_dcn
-        if dcn is not None:
-            assert len(stage_with_dcn) == num_stages
-        self.plugins = plugins
-        self.zero_init_residual = zero_init_residual
-        self.block = Bottleneck
-        expansion_bak = self.block.expansion
-        self.block.expansion = 1
-        self.stage_blocks = stage_blocks[:num_stages]
-        self._make_stem_layer(in_channels, stem_channels)
-        self.inplanes = stem_channels
-        self.res_layers = []
-        for i, num_blocks in enumerate(self.stage_blocks):
-            stride = self.strides[i]
-            dilation = self.dilations[i]
-            group_width = self.group_widths[i]
-            width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i]))
-            stage_groups = width // group_width
-            dcn = self.dcn if self.stage_with_dcn[i] else None
-            if self.plugins is not None:
-                stage_plugins = self.make_stage_plugins(self.plugins, i)
-            else:
-                stage_plugins = None
-            res_layer = self.make_res_layer(
-                block=self.block,
-                inplanes=self.inplanes,
-                planes=self.stage_widths[i],
-                num_blocks=num_blocks,
-                stride=stride,
-                dilation=dilation,
-                style=self.style,
-                avg_down=self.avg_down,
-                with_cp=self.with_cp,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                dcn=dcn,
-                plugins=stage_plugins,
-                groups=stage_groups,
-                base_width=group_width,
-                base_channels=self.stage_widths[i])
-            self.inplanes = self.stage_widths[i]
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, res_layer)
-            self.res_layers.append(layer_name)
-        self._freeze_stages()
-        self.feat_dim = stage_widths[-1]
-        self.block.expansion = expansion_bak
-    def _make_stem_layer(self, in_channels, base_channels):
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            in_channels,
-            base_channels,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            bias=False)
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, base_channels, postfix=1)
-        self.add_module(self.norm1_name, norm1)
-        self.relu = nn.ReLU(inplace=True)
-    def generate_regnet(self,
-                        initial_width,
-                        width_slope,
-                        width_parameter,
-                        depth,
-                        divisor=8):
-        """Generates per block width from RegNet parameters.
-        Args:
-            initial_width ([int]): Initial width of the backbone
-            width_slope ([float]): Slope of the quantized linear function
-            width_parameter ([int]): Parameter used to quantize the width.
-            depth ([int]): Depth of the backbone.
-            divisor (int, optional): The divisor of channels. Defaults to 8.
-        Returns:
-            list, int: return a list of widths of each stage and the number \
-                of stages
-        """
-        assert width_slope >= 0
-        assert initial_width > 0
-        assert width_parameter > 1
-        assert initial_width % divisor == 0
-        widths_cont = np.arange(depth) * width_slope + initial_width
-        ks = np.round(
-            np.log(widths_cont / initial_width) / np.log(width_parameter))
-        widths = initial_width * np.power(width_parameter, ks)
-        widths = np.round(np.divide(widths, divisor)) * divisor
-        num_stages = len(np.unique(widths))
-        widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
-        return widths, num_stages
-    @staticmethod
-    def quantize_float(number, divisor):
-        """Converts a float to closest non-zero int divisible by divisor.
-        Args:
-            number (int): Original number to be quantized.
-            divisor (int): Divisor used to quantize the number.
-        Returns:
-            int: quantized number that is divisible by devisor.
-        """
-        return int(round(number / divisor) * divisor)
-    def adjust_width_group(self, widths, bottleneck_ratio, groups):
-        """Adjusts the compatibility of widths and groups.
-        Args:
-            widths (list[int]): Width of each stage.
-            bottleneck_ratio (float): Bottleneck ratio.
-            groups (int): number of groups in each stage
-        Returns:
-            tuple(list): The adjusted widths and groups of each stage.
-        """
-        bottleneck_width = [
-            int(w * b) for w, b in zip(widths, bottleneck_ratio)
-        ]
-        groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)]
-        bottleneck_width = [
-            self.quantize_float(w_bot, g)
-            for w_bot, g in zip(bottleneck_width, groups)
-        ]
-        widths = [
-            int(w_bot / b)
-            for w_bot, b in zip(bottleneck_width, bottleneck_ratio)
-        ]
-        return widths, groups
-    def get_stages_from_blocks(self, widths):
-        """Gets widths/stage_blocks of network at each stage.
-        Args:
-            widths (list[int]): Width in each stage.
-        Returns:
-            tuple(list): width and depth of each stage
-        """
-        width_diff = [
-            width != width_prev
-            for width, width_prev in zip(widths + [0], [0] + widths)
-        ]
-        stage_widths = [
-            width for width, diff in zip(widths, width_diff[:-1]) if diff
-        ]
-        stage_blocks = np.diff([
-            depth for depth, diff in zip(range(len(width_diff)), width_diff)
-            if diff
-        ]).tolist()
-        return stage_widths, stage_blocks
-    def forward(self, x):
-        """Forward function."""
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu(x)
-        outs = []
-        for i, layer_name in enumerate(self.res_layers):
-            res_layer = getattr(self, layer_name)
-            x = res_layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        return tuple(outs)

spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- _base_ = './ann_r50-d8_512x1024_40k_cityscapes.py'
2	- model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py DELETED Viewed

@@ -1,36 +0,0 @@
-_base_ = [
-    '../_base_/models/ocrnet_hr18.py',
-    '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_20k.py'
-]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(decode_head=[
-    dict(
-        type='FCNHead',
-        in_channels=[18, 36, 72, 144],
-        channels=sum([18, 36, 72, 144]),
-        in_index=(0, 1, 2, 3),
-        input_transform='resize_concat',
-        kernel_size=1,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=-1,
-        num_classes=21,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    dict(
-        type='OCRHead',
-        in_channels=[18, 36, 72, 144],
-        in_index=(0, 1, 2, 3),
-        input_transform='resize_concat',
-        channels=512,
-        ocr_channels=256,
-        dropout_ratio=-1,
-        num_classes=21,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-])

spaces/Andy1621/uniformer_image_segmentation/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py DELETED Viewed

@@ -1,9 +0,0 @@
-_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/cp949prober.py DELETED Viewed

@@ -1,49 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-from .chardistribution import EUCKRDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import CP949_SM_MODEL
-class CP949Prober(MultiByteCharSetProber):
-    def __init__(self) -> None:
-        super().__init__()
-        self.coding_sm = CodingStateMachine(CP949_SM_MODEL)
-        # NOTE: CP949 is a superset of EUC-KR, so the distribution should be
-        #       not different.
-        self.distribution_analyzer = EUCKRDistributionAnalysis()
-        self.reset()
-    @property
-    def charset_name(self) -> str:
-        return "CP949"
-    @property
-    def language(self) -> str:
-        return "Korean"

spaces/Bart92/RVC_HF/gui_v1.py DELETED Viewed

@@ -1,708 +0,0 @@
-import os
-import logging
-import sys
-from dotenv import load_dotenv
-load_dotenv()
-os.environ["OMP_NUM_THREADS"] = "4"
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import multiprocessing
-logger = logging.getLogger(__name__)
-class Harvest(multiprocessing.Process):
-    def __init__(self, inp_q, opt_q):
-        multiprocessing.Process.__init__(self)
-        self.inp_q = inp_q
-        self.opt_q = opt_q
-    def run(self):
-        import numpy as np
-        import pyworld
-        while 1:
-            idx, x, res_f0, n_cpu, ts = self.inp_q.get()
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=16000,
-                f0_ceil=1100,
-                f0_floor=50,
-                frame_period=10,
-            )
-            res_f0[idx] = f0
-            if len(res_f0.keys()) >= n_cpu:
-                self.opt_q.put(ts)
-if __name__ == "__main__":
-    import json
-    import multiprocessing
-    import re
-    import threading
-    import time
-    import traceback
-    from multiprocessing import Queue, cpu_count
-    from queue import Empty
-    import librosa
-    from tools.torchgate import TorchGate
-    import numpy as np
-    import PySimpleGUI as sg
-    import sounddevice as sd
-    import torch
-    import torch.nn.functional as F
-    import torchaudio.transforms as tat
-    import tools.rvc_for_realtime as rvc_for_realtime
-    from i18n.i18n import I18nAuto
-    i18n = I18nAuto()
-    device = rvc_for_realtime.config.device
-    # device = torch.device(
-    #     "cuda"
-    #     if torch.cuda.is_available()
-    #     else ("mps" if torch.backends.mps.is_available() else "cpu")
-    # )
-    current_dir = os.getcwd()
-    inp_q = Queue()
-    opt_q = Queue()
-    n_cpu = min(cpu_count(), 8)
-    for _ in range(n_cpu):
-        Harvest(inp_q, opt_q).start()
-    class GUIConfig:
-        def __init__(self) -> None:
-            self.pth_path: str = ""
-            self.index_path: str = ""
-            self.pitch: int = 0
-            self.samplerate: int = 40000
-            self.block_time: float = 1.0  # s
-            self.buffer_num: int = 1
-            self.threhold: int = -60
-            self.crossfade_time: float = 0.04
-            self.extra_time: float = 2.0
-            self.I_noise_reduce = False
-            self.O_noise_reduce = False
-            self.rms_mix_rate = 0.0
-            self.index_rate = 0.3
-            self.n_cpu = min(n_cpu, 6)
-            self.f0method = "harvest"
-            self.sg_input_device = ""
-            self.sg_output_device = ""
-    class GUI:
-        def __init__(self) -> None:
-            self.config = GUIConfig()
-            self.flag_vc = False
-            self.launcher()
-        def load(self):
-            input_devices, output_devices, _, _ = self.get_devices()
-            try:
-                with open("configs/config.json", "r") as j:
-                    data = json.load(j)
-                    data["pm"] = data["f0method"] == "pm"
-                    data["harvest"] = data["f0method"] == "harvest"
-                    data["crepe"] = data["f0method"] == "crepe"
-                    data["rmvpe"] = data["f0method"] == "rmvpe"
-            except:
-                with open("configs/config.json", "w") as j:
-                    data = {
-                        "pth_path": " ",
-                        "index_path": " ",
-                        "sg_input_device": input_devices[sd.default.device[0]],
-                        "sg_output_device": output_devices[sd.default.device[1]],
-                        "threhold": "-60",
-                        "pitch": "0",
-                        "index_rate": "0",
-                        "rms_mix_rate": "0",
-                        "block_time": "0.25",
-                        "crossfade_length": "0.04",
-                        "extra_time": "2",
-                        "f0method": "rmvpe",
-                    }
-                    data["pm"] = data["f0method"] == "pm"
-                    data["harvest"] = data["f0method"] == "harvest"
-                    data["crepe"] = data["f0method"] == "crepe"
-                    data["rmvpe"] = data["f0method"] == "rmvpe"
-            return data
-        def launcher(self):
-            data = self.load()
-            sg.theme("LightBlue3")
-            input_devices, output_devices, _, _ = self.get_devices()
-            layout = [
-                [
-                    sg.Frame(
-                        title=i18n("加载模型"),
-                        layout=[
-                            [
-                                sg.Input(
-                                    default_text=data.get("pth_path", ""),
-                                    key="pth_path",
-                                ),
-                                sg.FileBrowse(
-                                    i18n("选择.pth文件"),
-                                    initial_folder=os.path.join(
-                                        os.getcwd(), "assets/weights"
-                                    ),
-                                    file_types=((". pth"),),
-                                ),
-                            ],
-                            [
-                                sg.Input(
-                                    default_text=data.get("index_path", ""),
-                                    key="index_path",
-                                ),
-                                sg.FileBrowse(
-                                    i18n("选择.index文件"),
-                                    initial_folder=os.path.join(os.getcwd(), "logs"),
-                                    file_types=((". index"),),
-                                ),
-                            ],
-                        ],
-                    )
-                ],
-                [
-                    sg.Frame(
-                        layout=[
-                            [
-                                sg.Text(i18n("输入设备")),
-                                sg.Combo(
-                                    input_devices,
-                                    key="sg_input_device",
-                                    default_value=data.get("sg_input_device", ""),
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("输出设备")),
-                                sg.Combo(
-                                    output_devices,
-                                    key="sg_output_device",
-                                    default_value=data.get("sg_output_device", ""),
-                                ),
-                            ],
-                            [sg.Button(i18n("重载设备列表"), key="reload_devices")],
-                        ],
-                        title=i18n("音频设备(请使用同种类驱动)"),
-                    )
-                ],
-                [
-                    sg.Frame(
-                        layout=[
-                            [
-                                sg.Text(i18n("响应阈值")),
-                                sg.Slider(
-                                    range=(-60, 0),
-                                    key="threhold",
-                                    resolution=1,
-                                    orientation="h",
-                                    default_value=data.get("threhold", "-60"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("音调设置")),
-                                sg.Slider(
-                                    range=(-24, 24),
-                                    key="pitch",
-                                    resolution=1,
-                                    orientation="h",
-                                    default_value=data.get("pitch", "0"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("Index Rate")),
-                                sg.Slider(
-                                    range=(0.0, 1.0),
-                                    key="index_rate",
-                                    resolution=0.01,
-                                    orientation="h",
-                                    default_value=data.get("index_rate", "0"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("响度因子")),
-                                sg.Slider(
-                                    range=(0.0, 1.0),
-                                    key="rms_mix_rate",
-                                    resolution=0.01,
-                                    orientation="h",
-                                    default_value=data.get("rms_mix_rate", "0"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("音高算法")),
-                                sg.Radio(
-                                    "pm",
-                                    "f0method",
-                                    key="pm",
-                                    default=data.get("pm", "") == True,
-                                    enable_events=True,
-                                ),
-                                sg.Radio(
-                                    "harvest",
-                                    "f0method",
-                                    key="harvest",
-                                    default=data.get("harvest", "") == True,
-                                    enable_events=True,
-                                ),
-                                sg.Radio(
-                                    "crepe",
-                                    "f0method",
-                                    key="crepe",
-                                    default=data.get("crepe", "") == True,
-                                    enable_events=True,
-                                ),
-                                sg.Radio(
-                                    "rmvpe",
-                                    "f0method",
-                                    key="rmvpe",
-                                    default=data.get("rmvpe", "") == True,
-                                    enable_events=True,
-                                ),
-                            ],
-                        ],
-                        title=i18n("常规设置"),
-                    ),
-                    sg.Frame(
-                        layout=[
-                            [
-                                sg.Text(i18n("采样长度")),
-                                sg.Slider(
-                                    range=(0.05, 2.4),
-                                    key="block_time",
-                                    resolution=0.01,
-                                    orientation="h",
-                                    default_value=data.get("block_time", "0.25"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("harvest进程数")),
-                                sg.Slider(
-                                    range=(1, n_cpu),
-                                    key="n_cpu",
-                                    resolution=1,
-                                    orientation="h",
-                                    default_value=data.get(
-                                        "n_cpu", min(self.config.n_cpu, n_cpu)
-                                    ),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("淡入淡出长度")),
-                                sg.Slider(
-                                    range=(0.01, 0.15),
-                                    key="crossfade_length",
-                                    resolution=0.01,
-                                    orientation="h",
-                                    default_value=data.get("crossfade_length", "0.04"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Text(i18n("额外推理时长")),
-                                sg.Slider(
-                                    range=(0.05, 5.00),
-                                    key="extra_time",
-                                    resolution=0.01,
-                                    orientation="h",
-                                    default_value=data.get("extra_time", "2.0"),
-                                    enable_events=True,
-                                ),
-                            ],
-                            [
-                                sg.Checkbox(
-                                    i18n("输入降噪"),
-                                    key="I_noise_reduce",
-                                    enable_events=True,
-                                ),
-                                sg.Checkbox(
-                                    i18n("输出降噪"),
-                                    key="O_noise_reduce",
-                                    enable_events=True,
-                                ),
-                            ],
-                        ],
-                        title=i18n("性能设置"),
-                    ),
-                ],
-                [
-                    sg.Button(i18n("开始音频转换"), key="start_vc"),
-                    sg.Button(i18n("停止音频转换"), key="stop_vc"),
-                    sg.Text(i18n("推理时间(ms):")),
-                    sg.Text("0", key="infer_time"),
-                ],
-            ]
-            self.window = sg.Window("RVC - GUI", layout=layout, finalize=True)
-            self.event_handler()
-        def event_handler(self):
-            while True:
-                event, values = self.window.read()
-                if event == sg.WINDOW_CLOSED:
-                    self.flag_vc = False
-                    exit()
-                if event == "reload_devices":
-                    prev_input = self.window["sg_input_device"].get()
-                    prev_output = self.window["sg_output_device"].get()
-                    input_devices, output_devices, _, _ = self.get_devices(update=True)
-                    if prev_input not in input_devices:
-                        self.config.sg_input_device = input_devices[0]
-                    else:
-                        self.config.sg_input_device = prev_input
-                    self.window["sg_input_device"].Update(values=input_devices)
-                    self.window["sg_input_device"].Update(
-                        value=self.config.sg_input_device
-                    )
-                    if prev_output not in output_devices:
-                        self.config.sg_output_device = output_devices[0]
-                    else:
-                        self.config.sg_output_device = prev_output
-                    self.window["sg_output_device"].Update(values=output_devices)
-                    self.window["sg_output_device"].Update(
-                        value=self.config.sg_output_device
-                    )
-                if event == "start_vc" and self.flag_vc == False:
-                    if self.set_values(values) == True:
-                        logger.info("Use CUDA: %s", torch.cuda.is_available())
-                        self.start_vc()
-                        settings = {
-                            "pth_path": values["pth_path"],
-                            "index_path": values["index_path"],
-                            "sg_input_device": values["sg_input_device"],
-                            "sg_output_device": values["sg_output_device"],
-                            "threhold": values["threhold"],
-                            "pitch": values["pitch"],
-                            "rms_mix_rate": values["rms_mix_rate"],
-                            "index_rate": values["index_rate"],
-                            "block_time": values["block_time"],
-                            "crossfade_length": values["crossfade_length"],
-                            "extra_time": values["extra_time"],
-                            "n_cpu": values["n_cpu"],
-                            "f0method": ["pm", "harvest", "crepe", "rmvpe"][
-                                [
-                                    values["pm"],
-                                    values["harvest"],
-                                    values["crepe"],
-                                    values["rmvpe"],
-                                ].index(True)
-                            ],
-                        }
-                        with open("configs/config.json", "w") as j:
-                            json.dump(settings, j)
-                if event == "stop_vc" and self.flag_vc == True:
-                    self.flag_vc = False
-                # Parameter hot update
-                if event == "threhold":
-                    self.config.threhold = values["threhold"]
-                elif event == "pitch":
-                    self.config.pitch = values["pitch"]
-                    if hasattr(self, "rvc"):
-                        self.rvc.change_key(values["pitch"])
-                elif event == "index_rate":
-                    self.config.index_rate = values["index_rate"]
-                    if hasattr(self, "rvc"):
-                        self.rvc.change_index_rate(values["index_rate"])
-                elif event == "rms_mix_rate":
-                    self.config.rms_mix_rate = values["rms_mix_rate"]
-                elif event in ["pm", "harvest", "crepe", "rmvpe"]:
-                    self.config.f0method = event
-                elif event == "I_noise_reduce":
-                    self.config.I_noise_reduce = values["I_noise_reduce"]
-                elif event == "O_noise_reduce":
-                    self.config.O_noise_reduce = values["O_noise_reduce"]
-                elif event != "start_vc" and self.flag_vc == True:
-                    # Other parameters do not support hot update
-                    self.flag_vc = False
-        def set_values(self, values):
-            if len(values["pth_path"].strip()) == 0:
-                sg.popup(i18n("请选择pth文件"))
-                return False
-            if len(values["index_path"].strip()) == 0:
-                sg.popup(i18n("请选择index文件"))
-                return False
-            pattern = re.compile("[^\x00-\x7F]+")
-            if pattern.findall(values["pth_path"]):
-                sg.popup(i18n("pth文件路径不可包含中文"))
-                return False
-            if pattern.findall(values["index_path"]):
-                sg.popup(i18n("index文件路径不可包含中文"))
-                return False
-            self.set_devices(values["sg_input_device"], values["sg_output_device"])
-            self.config.pth_path = values["pth_path"]
-            self.config.index_path = values["index_path"]
-            self.config.threhold = values["threhold"]
-            self.config.pitch = values["pitch"]
-            self.config.block_time = values["block_time"]
-            self.config.crossfade_time = values["crossfade_length"]
-            self.config.extra_time = values["extra_time"]
-            self.config.I_noise_reduce = values["I_noise_reduce"]
-            self.config.O_noise_reduce = values["O_noise_reduce"]
-            self.config.rms_mix_rate = values["rms_mix_rate"]
-            self.config.index_rate = values["index_rate"]
-            self.config.n_cpu = values["n_cpu"]
-            self.config.f0method = ["pm", "harvest", "crepe", "rmvpe"][
-                [
-                    values["pm"],
-                    values["harvest"],
-                    values["crepe"],
-                    values["rmvpe"],
-                ].index(True)
-            ]
-            return True
-        def start_vc(self):
-            torch.cuda.empty_cache()
-            self.flag_vc = True
-            self.rvc = rvc_for_realtime.RVC(
-                self.config.pitch,
-                self.config.pth_path,
-                self.config.index_path,
-                self.config.index_rate,
-                self.config.n_cpu,
-                inp_q,
-                opt_q,
-                device,
-                self.rvc if hasattr(self, "rvc") else None
-            )
-            self.config.samplerate = self.rvc.tgt_sr
-            self.zc = self.rvc.tgt_sr // 100
-            self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
-            self.block_frame_16k = 160 * self.block_frame // self.zc
-            self.crossfade_frame = int(np.round(self.config.crossfade_time * self.config.samplerate / self.zc)) * self.zc
-            self.sola_search_frame = self.zc
-            self.extra_frame = int(np.round(self.config.extra_time * self.config.samplerate / self.zc)) * self.zc
-            self.input_wav: torch.Tensor = torch.zeros(
-                self.extra_frame
-                + self.crossfade_frame
-                + self.sola_search_frame
-                + self.block_frame,
-                device=device,
-                dtype=torch.float32,
-            )
-            self.input_wav_res: torch.Tensor= torch.zeros(160 * self.input_wav.shape[0] // self.zc, device=device,dtype=torch.float32)
-            self.pitch: np.ndarray = np.zeros(
-                self.input_wav.shape[0] // self.zc,
-                dtype="int32",
-            )
-            self.pitchf: np.ndarray = np.zeros(
-                self.input_wav.shape[0] // self.zc,
-                dtype="float64",
-            )
-            self.sola_buffer: torch.Tensor = torch.zeros(
-                self.crossfade_frame, device=device, dtype=torch.float32
-            )
-            self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
-            self.output_buffer: torch.Tensor = self.input_wav.clone()
-            self.res_buffer: torch.Tensor = torch.zeros(2 * self.zc, device=device,dtype=torch.float32)
-            self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0]
-            self.fade_in_window: torch.Tensor = (
-                torch.sin(
-                    0.5
-                    * np.pi
-                    * torch.linspace(
-                        0.0,
-                        1.0,
-                        steps=self.crossfade_frame,
-                        device=device,
-                        dtype=torch.float32,
-                    )
-                )
-                ** 2
-            )
-            self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
-            self.resampler = tat.Resample(
-                orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
-            ).to(device)
-            self.tg = TorchGate(sr=self.config.samplerate, n_fft=4*self.zc, prop_decrease=0.9).to(device)
-            thread_vc = threading.Thread(target=self.soundinput)
-            thread_vc.start()
-        def soundinput(self):
-            """
-            接受音频输入
-            """
-            channels = 1 if sys.platform == "darwin" else 2
-            with sd.Stream(
-                channels=channels,
-                callback=self.audio_callback,
-                blocksize=self.block_frame,
-                samplerate=self.config.samplerate,
-                dtype="float32",
-            ):
-                while self.flag_vc:
-                    time.sleep(self.config.block_time)
-                    logger.debug("Audio block passed.")
-            logger.debug("ENDing VC")
-        def audio_callback(
-            self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
-        ):
-            """
-            音频处理
-            """
-            start_time = time.perf_counter()
-            indata = librosa.to_mono(indata.T)
-            if self.config.threhold > -60:
-                rms = librosa.feature.rms(
-                y=indata, frame_length=4*self.zc, hop_length=self.zc
-                )
-                db_threhold = (
-                    librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
-                )
-                for i in range(db_threhold.shape[0]):
-                    if db_threhold[i]:
-                        indata[i * self.zc : (i + 1) * self.zc] = 0
-            self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
-            self.input_wav[-self.block_frame: ] = torch.from_numpy(indata).to(device)
-            self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
-            # input noise reduction and resampling
-            if self.config.I_noise_reduce:
-                input_wav = self.input_wav[-self.crossfade_frame -self.block_frame-2*self.zc: ]
-                input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2*self.zc:]
-                input_wav[: self.crossfade_frame] *= self.fade_in_window
-                input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window
-                self.nr_buffer[:] = input_wav[-self.crossfade_frame: ]
-                input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame]))
-                self.res_buffer[:] = input_wav[-2*self.zc: ]
-                self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(input_wav)[160: ]
-            else:
-                self.input_wav_res[-self.block_frame_16k-160: ] = self.resampler(self.input_wav[-self.block_frame-2*self.zc: ])[160: ]
-            # infer
-            f0_extractor_frame = self.block_frame_16k + 800
-            if self.config.f0method == 'rmvpe':
-                f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
-            infer_wav = self.rvc.infer(
-                self.input_wav_res,
-                self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
-                self.block_frame_16k,
-                self.valid_rate,
-                self.pitch,
-                self.pitchf,
-                self.config.f0method,
-            )
-            infer_wav = infer_wav[
-                -self.crossfade_frame - self.sola_search_frame - self.block_frame :
-            ]
-            # output noise reduction
-            if self.config.O_noise_reduce:
-                self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone()
-                self.output_buffer[-self.block_frame: ] = infer_wav[-self.block_frame:]
-                infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0)
-            # volume envelop mixing
-            if self.config.rms_mix_rate < 1:
-                rms1 = librosa.feature.rms(
-                y=self.input_wav_res[-160*infer_wav.shape[0]//self.zc :].cpu().numpy(),
-                frame_length=640,
-                hop_length=160,
-                )
-                rms1 = torch.from_numpy(rms1).to(device)
-                rms1 = F.interpolate(
-                    rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True,
-                )[0,0,:-1]
-                rms2 = librosa.feature.rms(
-                y=infer_wav[:].cpu().numpy(), frame_length=4*self.zc, hop_length=self.zc
-                )
-                rms2 = torch.from_numpy(rms2).to(device)
-                rms2 = F.interpolate(
-                    rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear",align_corners=True,
-                )[0,0,:-1]
-                rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
-                infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.config.rms_mix_rate))
-            # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
-            conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
-            cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
-            cor_den = torch.sqrt(
-                F.conv1d(conv_input ** 2, torch.ones(1, 1, self.crossfade_frame, device=device)) + 1e-8)
-            if sys.platform == "darwin":
-                _, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0])
-                sola_offset = sola_offset.item()
-            else:
-                sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
-            logger.debug("sola_offset = %d", int(sola_offset))
-            infer_wav = infer_wav[sola_offset: sola_offset + self.block_frame + self.crossfade_frame]
-            infer_wav[: self.crossfade_frame] *= self.fade_in_window
-            infer_wav[: self.crossfade_frame] += self.sola_buffer *self.fade_out_window
-            self.sola_buffer[:] = infer_wav[-self.crossfade_frame:]
-            if sys.platform == "darwin":
-                outdata[:] = infer_wav[:-self.crossfade_frame].cpu().numpy()[:, np.newaxis]
-            else:
-                outdata[:] = infer_wav[:-self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
-            total_time = time.perf_counter() - start_time
-            self.window["infer_time"].update(int(total_time * 1000))
-            logger.info("Infer time: %.2f", total_time)
-        def get_devices(self, update: bool = True):
-            """获取设备列表"""
-            if update:
-                sd._terminate()
-                sd._initialize()
-            devices = sd.query_devices()
-            hostapis = sd.query_hostapis()
-            for hostapi in hostapis:
-                for device_idx in hostapi["devices"]:
-                    devices[device_idx]["hostapi_name"] = hostapi["name"]
-            input_devices = [
-                f"{d['name']} ({d['hostapi_name']})"
-                for d in devices
-                if d["max_input_channels"] > 0
-            ]
-            output_devices = [
-                f"{d['name']} ({d['hostapi_name']})"
-                for d in devices
-                if d["max_output_channels"] > 0
-            ]
-            input_devices_indices = [
-                d["index"] if "index" in d else d["name"]
-                for d in devices
-                if d["max_input_channels"] > 0
-            ]
-            output_devices_indices = [
-                d["index"] if "index" in d else d["name"]
-                for d in devices
-                if d["max_output_channels"] > 0
-            ]
-            return (
-                input_devices,
-                output_devices,
-                input_devices_indices,
-                output_devices_indices,
-            )
-        def set_devices(self, input_device, output_device):
-            """设置输出设备"""
-            (
-                input_devices,
-                output_devices,
-                input_device_indices,
-                output_device_indices,
-            ) = self.get_devices()
-            sd.default.device[0] = input_device_indices[
-                input_devices.index(input_device)
-            ]
-            sd.default.device[1] = output_device_indices[
-                output_devices.index(output_device)
-            ]
-            logger.info(
-                "Input device: %s:%s", str(sd.default.device[0]), input_device
-            )
-            logger.info(
-                "Output device: %s:%s", str(sd.default.device[1]), output_device
-            )
-    gui = GUI()

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/discriminator/model.py DELETED Viewed

@@ -1,67 +0,0 @@
-import functools
-import torch.nn as nn
-from taming.modules.util import ActNorm
-def weights_init(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.normal_(m.weight.data, 0.0, 0.02)
-    elif classname.find('BatchNorm') != -1:
-        nn.init.normal_(m.weight.data, 1.0, 0.02)
-        nn.init.constant_(m.bias.data, 0)
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator as in Pix2Pix
-        --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
-    """
-    def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if not use_actnorm:
-            norm_layer = nn.BatchNorm2d
-        else:
-            norm_layer = ActNorm
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func != nn.BatchNorm2d
-        else:
-            use_bias = norm_layer != nn.BatchNorm2d
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence += [
-            nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*sequence)
-    def forward(self, input):
-        """Standard forward."""
-        return self.main(input)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/metadata/importlib/_compat.py DELETED Viewed

@@ -1,55 +0,0 @@
-import importlib.metadata
-from typing import Any, Optional, Protocol, cast
-class BadMetadata(ValueError):
-    def __init__(self, dist: importlib.metadata.Distribution, *, reason: str) -> None:
-        self.dist = dist
-        self.reason = reason
-    def __str__(self) -> str:
-        return f"Bad metadata in {self.dist} ({self.reason})"
-class BasePath(Protocol):
-    """A protocol that various path objects conform.
-    This exists because importlib.metadata uses both ``pathlib.Path`` and
-    ``zipfile.Path``, and we need a common base for type hints (Union does not
-    work well since ``zipfile.Path`` is too new for our linter setup).
-    This does not mean to be exhaustive, but only contains things that present
-    in both classes *that we need*.
-    """
-    @property
-    def name(self) -> str:
-        raise NotImplementedError()
-    @property
-    def parent(self) -> "BasePath":
-        raise NotImplementedError()
-def get_info_location(d: importlib.metadata.Distribution) -> Optional[BasePath]:
-    """Find the path to the distribution's metadata directory.
-    HACK: This relies on importlib.metadata's private ``_path`` attribute. Not
-    all distributions exist on disk, so importlib.metadata is correct to not
-    expose the attribute as public. But pip's code base is old and not as clean,
-    so we do this to avoid having to rewrite too many things. Hopefully we can
-    eliminate this some day.
-    """
-    return getattr(d, "_path", None)
-def get_dist_name(dist: importlib.metadata.Distribution) -> str:
-    """Get the distribution's project name.
-    The ``name`` attribute is only available in Python 3.10 or later. We are
-    targeting exactly that, but Mypy does not know this.
-    """
-    name = cast(Any, dist).name
-    if not isinstance(name, str):
-        raise BadMetadata(dist, reason="invalid metadata entry 'name'")
-    return name

spaces/Boadiwaa/Recipes/openai/upload_progress.py DELETED Viewed

@@ -1,52 +0,0 @@
-import io
-class CancelledError(Exception):
-    def __init__(self, msg):
-        self.msg = msg
-        Exception.__init__(self, msg)
-    def __str__(self):
-        return self.msg
-    __repr__ = __str__
-class BufferReader(io.BytesIO):
-    def __init__(self, buf=b"", desc=None):
-        self._len = len(buf)
-        io.BytesIO.__init__(self, buf)
-        self._progress = 0
-        self._callback = progress(len(buf), desc=desc)
-    def __len__(self):
-        return self._len
-    def read(self, n=-1):
-        chunk = io.BytesIO.read(self, n)
-        self._progress += len(chunk)
-        if self._callback:
-            try:
-                self._callback(self._progress)
-            except Exception as e:  # catches exception from the callback
-                raise CancelledError("The upload was cancelled: {}".format(e))
-        return chunk
-def progress(total, desc):
-    import tqdm  # type: ignore
-    meter = tqdm.tqdm(total=total, unit_scale=True, desc=desc)
-    def incr(progress):
-        meter.n = progress
-        if progress == total:
-            meter.close()
-        else:
-            meter.refresh()
-    return incr
-def MB(i):
-    return int(i // 1024 ** 2)

spaces/Brasd99/TTS-Voice-Conversion/app.py DELETED Viewed

@@ -1,72 +0,0 @@
-from TTS.api import TTS
-from bs4 import BeautifulSoup
-import requests
-import streamlit as st
-import tempfile
-import os
-import json
-import datetime
-with open('config.json', 'r') as f:
-    config = json.load(f)
-APP_NAME = config['APP_NAME']
-APP_LOGO = config['APP_LOGO']
-APP_DESCRIPTION = config['APP_DESCRIPTION']
-def contains_only_ascii(input_string):
-    return all(ord(char) < 128 for char in input_string)
-def create_temp_file(input_wav):
-    temp_file = tempfile.NamedTemporaryFile(delete=False)
-    temp_file.write(input_wav.read())
-    return temp_file
-def remove_temp_file(temp_file):
-    temp_file.close()
-    os.remove(temp_file.name)
-def update_progress(percent, text):
-    progress_bar.progress(percent)
-    status_text.text(text)
-st.set_page_config(page_title=APP_NAME)
-st.title(APP_NAME)
-st.image(APP_LOGO, use_column_width=True)
-st.markdown(APP_DESCRIPTION)
-input_wav = st.file_uploader("Upload a WAV file with your voice", type=["wav"])
-clone_wav = st.file_uploader("Upload a WAV file with voice to clone", type=["wav"])
-if input_wav and clone_wav:
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    current_datetime = datetime.datetime.now()
-    formatted_datetime = current_datetime.strftime("%Y-%m-%d_%H%M%S")
-    output_filename = f"recording_{formatted_datetime}.wav"
-    temp_input_file = create_temp_file(input_wav)
-    temp_clone_file = create_temp_file(clone_wav)
-    update_progress(0, 'Loading TTS model...')
-    api = TTS("voice_conversion_models/multilingual/vctk/freevc24")
-    update_progress(50, 'Generating audio...')
-    api.voice_conversion_to_file(
-        source_wav=temp_input_file.name,
-        target_wav=temp_clone_file.name,
-        file_path=output_filename
-    )
-    remove_temp_file(temp_input_file)
-    remove_temp_file(temp_clone_file)
-    audio_file = open(output_filename, 'rb')
-    audio_bytes = audio_file.read()
-    update_progress(100, 'Audio generated successfully!')
-    st.audio(audio_bytes, format='audio/wav')
-    st.download_button('Download WAV', data=audio_bytes, file_name='output.wav')

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

@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .cityscapes import load_cityscapes_instances
-from .coco import load_coco_json, load_sem_seg
-from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta
-from .register_coco import register_coco_instances, register_coco_panoptic_separated
-from . import builtin  # ensure the builtin datasets are registered
-__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")]

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mcan/adapter.py DELETED Viewed

@@ -1,90 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-import torch.nn as nn
-import torch
-from openvqa.core.base_dataset import BaseAdapter
-from openvqa.utils.make_mask import make_mask
-class Adapter(BaseAdapter):
-    def __init__(self, __C):
-        super(Adapter, self).__init__(__C)
-        self.__C = __C
-    def bbox_proc(self, bbox):
-        area = (bbox[:, :, 2] - bbox[:, :, 0]) * (bbox[:, :, 3] - bbox[:, :, 1])
-        return torch.cat((bbox, area.unsqueeze(2)), -1)
-    def vqa_init(self, __C):
-        imgfeat_linear_size = __C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'][1]
-        if __C.USE_BBOX_FEAT:
-            self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
-            imgfeat_linear_size += __C.BBOXFEAT_EMB_SIZE
-        self.frcn_linear = nn.Linear(imgfeat_linear_size, __C.HIDDEN_SIZE)
-    def gqa_init(self, __C):
-        imgfeat_linear_size = __C.FEAT_SIZE['gqa']['FRCN_FEAT_SIZE'][1]
-        if __C.USE_BBOX_FEAT:
-            self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
-            imgfeat_linear_size += __C.BBOXFEAT_EMB_SIZE
-        self.frcn_linear = nn.Linear(imgfeat_linear_size, __C.HIDDEN_SIZE)
-        if __C.USE_AUX_FEAT:
-            self.grid_linear = nn.Linear(__C.FEAT_SIZE['gqa']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-    def clevr_init(self, __C):
-        self.grid_linear = nn.Linear(__C.FEAT_SIZE['clevr']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-    def vqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-        img_feat_mask = make_mask(frcn_feat)
-        if self.__C.USE_BBOX_FEAT:
-            bbox_feat = self.bbox_proc(bbox_feat)
-            bbox_feat = self.bbox_linear(bbox_feat)
-            frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
-        img_feat = self.frcn_linear(frcn_feat)
-        return img_feat, img_feat_mask
-    def gqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-        grid_feat = feat_dict['GRID_FEAT']
-        img_feat_mask = make_mask(frcn_feat)
-        if self.__C.USE_BBOX_FEAT:
-            bbox_feat = self.bbox_proc(bbox_feat)
-            bbox_feat = self.bbox_linear(bbox_feat)
-            frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
-        img_feat = self.frcn_linear(frcn_feat)
-        if self.__C.USE_AUX_FEAT:
-            grid_feat_mask = make_mask(grid_feat)
-            img_feat_mask = torch.cat((img_feat_mask, grid_feat_mask), dim=-1)
-            grid_feat = self.grid_linear(grid_feat)
-            img_feat = torch.cat((img_feat, grid_feat), dim=1)
-        return img_feat, img_feat_mask
-    def clevr_forward(self, feat_dict):
-        grid_feat = feat_dict['GRID_FEAT']
-        img_feat_mask = make_mask(grid_feat)
-        img_feat = self.grid_linear(grid_feat)
-        return img_feat, img_feat_mask

spaces/CarperAI/StableVicuna/app.py DELETED Viewed

@@ -1,138 +0,0 @@
-import os
-import gc
-from string import Template
-from threading import Thread
-import torch
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, BatchEncoding, TextIteratorStreamer
-auth_token = os.environ.get("HUGGINGFACE_TOKEN")
-tokenizer = AutoTokenizer.from_pretrained(
-    "CarperAI/stable-vicuna-13b-fp16",
-    use_auth_token=auth_token if auth_token else True,
-)
-model = AutoModelForCausalLM.from_pretrained(
-    "CarperAI/stable-vicuna-13b-fp16",
-    torch_dtype=torch.float16,
-    low_cpu_mem_usage=True,
-    device_map="auto",
-    use_auth_token=auth_token if auth_token else True,
-)
-model.eval()
-max_context_length = model.config.max_position_embeddings
-max_new_tokens = 768
-prompt_template = Template("""\
-### Human: $human
-### Assistant: $bot\
-""")
-system_prompt = "### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!"
-system_prompt_tokens = tokenizer([f"{system_prompt}\n\n"], return_tensors="pt")
-max_sys_tokens = system_prompt_tokens['input_ids'].size(-1)
-def bot(history):
-    history = history or []
-    # Inject prompt formatting into the history
-    prompt_history = []
-    for human, bot in history:
-        if bot is not None:
-            bot = bot.replace("<br>", "\n")
-            bot = bot.rstrip()
-        prompt_history.append(
-            prompt_template.substitute(
-                human=human, bot=bot if bot is not None else "")
-        )
-    msg_tokens = tokenizer(
-        "\n\n".join(prompt_history).strip(),
-        return_tensors="pt",
-        add_special_tokens=False  # Use <BOS> from the system prompt
-    )
-    # Take only the most recent context up to the max context length and prepend the
-    # system prompt with the messages
-    max_tokens = -max_context_length + max_new_tokens + max_sys_tokens
-    inputs = BatchEncoding({
-        k: torch.concat([system_prompt_tokens[k], msg_tokens[k][:, max_tokens:]], dim=-1)
-        for k in msg_tokens
-    }).to('cuda')
-    # Remove `token_type_ids` b/c it's not yet supported for LLaMA `transformers` models
-    if inputs.get("token_type_ids", None) is not None:
-        inputs.pop("token_type_ids")
-    streamer = TextIteratorStreamer(
-        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
-    )
-    generate_kwargs = dict(
-        inputs,
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        top_p=1.0,
-        temperature=1.0,
-    )
-    thread = Thread(target=model.generate, kwargs=generate_kwargs)
-    thread.start()
-    partial_text = ""
-    for new_text in streamer:
-        # Process out the prompt separator
-        new_text = new_text.replace("<br>", "\n")
-        if "###" in new_text:
-            new_text = new_text.split("###")[0]
-            partial_text += new_text.strip()
-            history[-1][1] = partial_text
-            break
-        else:
-            # Filter empty trailing new lines
-            if new_text == "\n":
-                new_text = new_text.strip()
-            partial_text += new_text
-            history[-1][1] = partial_text
-        yield history
-    return partial_text
-def user(user_message, history):
-    return "", history + [[user_message, None]]
-with gr.Blocks() as demo:
-    gr.Markdown("# StableVicuna by CarperAI")
-    gr.HTML("<a href='https://huggingface.co/CarperAI/stable-vicuna-13b-delta'><code>CarperAI/stable-vicuna-13b-delta</a>")
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/CarperAI/StableVicuna?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''')
-    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=500)
-    state = gr.State([])
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(
-                label="Send a message",
-                placeholder="Send a message",
-                show_label=False
-            ).style(container=False)
-        with gr.Column():
-            with gr.Row():
-                submit = gr.Button("Send")
-                stop = gr.Button("Stop")
-                clear = gr.Button("Clear History")
-    submit_event = msg.submit(user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
-        fn=bot, inputs=[chatbot], outputs=[chatbot], queue=True)
-    submit_click_event = submit.click(user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
-        fn=bot, inputs=[chatbot], outputs=[chatbot], queue=True)
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event, submit_click_event], queue=False)
-    clear.click(lambda: None, None, [chatbot], queue=True)
-demo.queue(max_size=32)
-demo.launch()

spaces/ChandraMohanNayal/AutoGPT/autogpt/__init__.py DELETED Viewed

File without changes

spaces/ChandraMohanNayal/AutoGPT/autogpt/llm_utils.py DELETED Viewed

@@ -1,172 +0,0 @@
-from __future__ import annotations
-import time
-from ast import List
-import openai
-from colorama import Fore, Style
-from openai.error import APIError, RateLimitError
-from autogpt.config import Config
-from autogpt.logs import logger
-CFG = Config()
-openai.api_key = CFG.openai_api_key
-def call_ai_function(
-    function: str, args: list, description: str, model: str | None = None
-) -> str:
-    """Call an AI function
-    This is a magic function that can do anything with no-code. See
-    https://github.com/Torantulino/AI-Functions for more info.
-    Args:
-        function (str): The function to call
-        args (list): The arguments to pass to the function
-        description (str): The description of the function
-        model (str, optional): The model to use. Defaults to None.
-    Returns:
-        str: The response from the function
-    """
-    if model is None:
-        model = CFG.smart_llm_model
-    # For each arg, if any are None, convert to "None":
-    args = [str(arg) if arg is not None else "None" for arg in args]
-    # parse args to comma separated string
-    args = ", ".join(args)
-    messages = [
-        {
-            "role": "system",
-            "content": f"You are now the following python function: ```# {description}"
-            f"\n{function}```\n\nOnly respond with your `return` value.",
-        },
-        {"role": "user", "content": args},
-    ]
-    return create_chat_completion(model=model, messages=messages, temperature=0)
-# Overly simple abstraction until we create something better
-# simple retry mechanism when getting a rate error or a bad gateway
-def create_chat_completion(
-    messages: list,  # type: ignore
-    model: str | None = None,
-    temperature: float = CFG.temperature,
-    max_tokens: int | None = None,
-) -> str:
-    """Create a chat completion using the OpenAI API
-    Args:
-        messages (list[dict[str, str]]): The messages to send to the chat completion
-        model (str, optional): The model to use. Defaults to None.
-        temperature (float, optional): The temperature to use. Defaults to 0.9.
-        max_tokens (int, optional): The max tokens to use. Defaults to None.
-    Returns:
-        str: The response from the chat completion
-    """
-    response = None
-    num_retries = 10
-    warned_user = False
-    if CFG.debug_mode:
-        print(
-            Fore.GREEN
-            + f"Creating chat completion with model {model}, temperature {temperature},"
-            f" max_tokens {max_tokens}" + Fore.RESET
-        )
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                response = openai.ChatCompletion.create(
-                    deployment_id=CFG.get_azure_deployment_id_for_model(model),
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            else:
-                response = openai.ChatCompletion.create(
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            break
-        except RateLimitError:
-            if CFG.debug_mode:
-                print(
-                    Fore.RED + "Error: ",
-                    f"Reached rate limit, passing..." + Fore.RESET,
-                )
-            if not warned_user:
-                logger.double_check(
-                    f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
-                    + f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
-                )
-                warned_user = True
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)
-    if response is None:
-        logger.typewriter_log(
-            "FAILED TO GET RESPONSE FROM OPENAI",
-            Fore.RED,
-            "Auto-GPT has failed to get a response from OpenAI's services. "
-            + f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
-        )
-        logger.double_check()
-        if CFG.debug_mode:
-            raise RuntimeError(f"Failed to get response after {num_retries} retries")
-        else:
-            quit(1)
-    return response.choices[0].message["content"]
-def create_embedding_with_ada(text) -> list:
-    """Create an embedding with text-ada-002 using the OpenAI SDK"""
-    num_retries = 10
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                return openai.Embedding.create(
-                    input=[text],
-                    engine=CFG.get_azure_deployment_id_for_model(
-                        "text-embedding-ada-002"
-                    ),
-                )["data"][0]["embedding"]
-            else:
-                return openai.Embedding.create(
-                    input=[text], model="text-embedding-ada-002"
-                )["data"][0]["embedding"]
-        except RateLimitError:
-            pass
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)

spaces/Chilangosta/text-to-pokemon/app.py DELETED Viewed

@@ -1,204 +0,0 @@
-from contextlib import nullcontext
-import gradio as gr
-import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
-device = "cuda" if torch.cuda.is_available() else "cpu"
-context = autocast if device == "cuda" else nullcontext
-dtype = torch.float16 if device == "cuda" else torch.float32
-pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", torch_dtype=dtype)
-pipe = pipe.to(device)
-# Sometimes the nsfw checker is confused by the Pokémon images, you can disable
-# it at your own risk here
-disable_safety = True
-if disable_safety:
-  def null_safety(images, **kwargs):
-      return images, False
-  pipe.safety_checker = null_safety
-def infer(prompt, n_samples, steps, scale):
-    with context("cuda"):
-        images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images
-    return images
-css = """
-        a {
-            color: inherit;
-            text-decoration: underline;
-        }
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: #9d66e5;
-            background: #9d66e5;
-        }
-        input[type='range'] {
-            accent-color: #9d66e5;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-options {
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .logo{ filter: invert(1); }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-"""
-block = gr.Blocks(css=css)
-examples = [
-    [
-        'Yoda',
-        2,
-        7.5,
-    ],
-    [
-        'Abraham Lincoln',
-        2,
-        7.5,
-    ],
-    [
-        'George Washington',
-        2,
-        7,
-    ],
-]
-with block:
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-              <div>
-                <img class="logo" src="https://lambdalabs.com/hubfs/logos/lambda-logo.svg" alt="Lambda Logo"
-                    style="margin: auto; max-width: 7rem;">
-                <h1 style="font-weight: 900; font-size: 3rem;">
-                  Pokémon text to image
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-              Generate new Pokémon from a text description,
-                <a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">created by Lambda Labs</a>.
-              </p>
-            </div>
-        """
-    )
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Enter your prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-        with gr.Row(elem_id="advanced-options"):
-            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
-            steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=25, step=5)
-            scale = gr.Slider(
-                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
-            )
-        ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
-        ex.dataset.headers = [""]
-        text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery)
-        btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery)
-        gr.HTML(
-            """
-                <div class="footer">
-                    <p> Gradio Demo by 🤗 Hugging Face and Lambda Labs
-                    </p>
-                </div>
-                <div class="acknowledgments">
-                    <p> Put in a text prompt and generate your own Pokémon character, no "prompt engineering" required!
-                    <p>If you want to find out how we made this model read about it in <a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">this blog post</a>.
-                    <p>And if you want to train your own Stable Diffusion variants, see our <a href="https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning">Examples Repo</a>!
-                    <p>Trained by <a href="justinpinkney.com">Justin Pinkney</a> (<a href="https://twitter.com/Buntworthy">@Buntworthy</a>) at <a href="https://lambdalabs.com/">Lambda Labs</a>.</p>
-               </div>
-           """
-        )
-block.launch()

spaces/Cletrason/Cletrason-toad-in-the-mario-movie/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Cletrason Toad In The Mario Movie
-emoji: 💩
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DHEIVER/CoronaryAngioSegment/detect_anomalies.py DELETED Viewed

@@ -1,13 +0,0 @@
-import numpy as np
-import cv2
-def detect_anomalies(mask):
-    # Aplicar um limiar à máscara para identificar as regiões de anomalia
-    threshold = 0.5  # Ajuste o limiar conforme necessário
-    anomalies = (mask > threshold).astype(np.uint8)
-    # Aplicar pós-processamento, como erosão e dilatação, se necessário
-    # anomalies = cv2.erode(anomalies, kernel, iterations=1)
-    # anomalies = cv2.dilate(anomalies, kernel, iterations=1)
-    return anomalies

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/ttCollection.py DELETED Viewed

@@ -1,127 +0,0 @@
-from fontTools.ttLib.ttFont import TTFont
-from fontTools.ttLib.sfnt import readTTCHeader, writeTTCHeader
-from io import BytesIO
-import struct
-import logging
-log = logging.getLogger(__name__)
-class TTCollection(object):
-    """Object representing a TrueType Collection / OpenType Collection.
-    The main API is self.fonts being a list of TTFont instances.
-    If shareTables is True, then different fonts in the collection
-    might point to the same table object if the data for the table was
-    the same in the font file.  Note, however, that this might result
-    in suprises and incorrect behavior if the different fonts involved
-    have different GlyphOrder.  Use only if you know what you are doing.
-    """
-    def __init__(self, file=None, shareTables=False, **kwargs):
-        fonts = self.fonts = []
-        if file is None:
-            return
-        assert "fontNumber" not in kwargs, kwargs
-        closeStream = False
-        if not hasattr(file, "read"):
-            file = open(file, "rb")
-            closeStream = True
-        tableCache = {} if shareTables else None
-        header = readTTCHeader(file)
-        for i in range(header.numFonts):
-            font = TTFont(file, fontNumber=i, _tableCache=tableCache, **kwargs)
-            fonts.append(font)
-        # don't close file if lazy=True, as the TTFont hold a reference to the original
-        # file; the file will be closed once the TTFonts are closed in the
-        # TTCollection.close(). We still want to close the file if lazy is None or
-        # False, because in that case the TTFont no longer need the original file
-        # and we want to avoid 'ResourceWarning: unclosed file'.
-        if not kwargs.get("lazy") and closeStream:
-            file.close()
-    def __enter__(self):
-        return self
-    def __exit__(self, type, value, traceback):
-        self.close()
-    def close(self):
-        for font in self.fonts:
-            font.close()
-    def save(self, file, shareTables=True):
-        """Save the font to disk. Similarly to the constructor,
-        the 'file' argument can be either a pathname or a writable
-        file object.
-        """
-        if not hasattr(file, "write"):
-            final = None
-            file = open(file, "wb")
-        else:
-            # assume "file" is a writable file object
-            # write to a temporary stream to allow saving to unseekable streams
-            final = file
-            file = BytesIO()
-        tableCache = {} if shareTables else None
-        offsets_offset = writeTTCHeader(file, len(self.fonts))
-        offsets = []
-        for font in self.fonts:
-            offsets.append(file.tell())
-            font._save(file, tableCache=tableCache)
-            file.seek(0, 2)
-        file.seek(offsets_offset)
-        file.write(struct.pack(">%dL" % len(self.fonts), *offsets))
-        if final:
-            final.write(file.getvalue())
-        file.close()
-    def saveXML(self, fileOrPath, newlinestr="\n", writeVersion=True, **kwargs):
-        from fontTools.misc import xmlWriter
-        writer = xmlWriter.XMLWriter(fileOrPath, newlinestr=newlinestr)
-        if writeVersion:
-            from fontTools import version
-            version = ".".join(version.split(".")[:2])
-            writer.begintag("ttCollection", ttLibVersion=version)
-        else:
-            writer.begintag("ttCollection")
-        writer.newline()
-        writer.newline()
-        for font in self.fonts:
-            font._saveXML(writer, writeVersion=False, **kwargs)
-            writer.newline()
-        writer.endtag("ttCollection")
-        writer.newline()
-        writer.close()
-    def __getitem__(self, item):
-        return self.fonts[item]
-    def __setitem__(self, item, value):
-        self.fonts[item] = value
-    def __delitem__(self, item):
-        return self.fonts[item]
-    def __len__(self):
-        return len(self.fonts)
-    def __iter__(self):
-        return iter(self.fonts)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/BlockTitle-dee077e8.js DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import{S as h,e as k,s as g,a9 as w,N as $,O as B,m as I,K as d,U as _,p as c,ab as N,ac as S,ad as j,z as r,u as q,v as m,y as v,A as p,k as z,o as A,x as C,P as K,R as O}from"./index-1d65707a.js";import{I as P}from"./Info-7c6961ef.js";import"./Button-f155035a.js";function b(a){let e,l;return e=new P({props:{$$slots:{default:[R]},$$scope:{ctx:a}}}),{c(){z(e.$$.fragment)},m(n,o){A(e,n,o),l=!0},p(n,o){const u={};o&10&&(u.$$scope={dirty:o,ctx:n}),e.$set(u)},i(n){l\|\|(r(e.$$.fragment,n),l=!0)},o(n){m(e.$$.fragment,n),l=!1},d(n){C(e,n)}}}function R(a){let e;return{c(){e=K(a[1])},m(l,n){c(l,e,n)},p(l,n){n&2&&O(e,l[1])},d(l){l&&p(e)}}}function T(a){let e,l,n,o;const u=a[2].default,f=w(u,a,a[3],null);let s=a[1]&&b(a);return{c(){e=$("span"),f&&f.c(),l=B(),s&&s.c(),n=I(),d(e,"data-testid","block-info"),d(e,"class","svelte-1gfkn6j"),_(e,"sr-only",!a[0]),_(e,"hide",!a[0]),_(e,"has-info",a[1]!=null)},m(t,i){c(t,e,i),f&&f.m(e,null),c(t,l,i),s&&s.m(t,i),c(t,n,i),o=!0},p(t,[i]){f&&f.p&&(!o\|\|i&8)&&N(f,u,t,t[3],o?j(u,t[3],i,null):S(t[3]),null),(!o\|\|i&1)&&_(e,"sr-only",!t[0]),(!o\|\|i&1)&&_(e,"hide",!t[0]),(!o\|\|i&2)&&_(e,"has-info",t[1]!=null),t[1]?s?(s.p(t,i),i&2&&r(s,1)):(s=b(t),s.c(),r(s,1),s.m(n.parentNode,n)):s&&(q(),m(s,1,1,()=>{s=null}),v())},i(t){o\|\|(r(f,t),r(s),o=!0)},o(t){m(f,t),m(s),o=!1},d(t){t&&(p(e),p(l),p(n)),f&&f.d(t),s&&s.d(t)}}}function U(a,e,l){let{$$slots:n={},$$scope:o}=e,{show_label:u=!0}=e,{info:f=void 0}=e;return a.$$set=s=>{"show_label"in s&&l(0,u=s.show_label),"info"in s&&l(1,f=s.info),"$$scope"in s&&l(3,o=s.$$scope)},[u,f,n,o]}class G extends h{constructor(e){super(),k(this,e,U,T,g,{show_label:0,info:1})}}export{G as B};
2	- //# sourceMappingURL=BlockTitle-dee077e8.js.map

spaces/DataScienceEngineering/6-TreemapAndSunburst/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: 🧠Visualization Plotly Sunbursts Treemaps WebGL🩺
-emoji: 6-Vis🧠
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Datatrooper/boston_housing/app.py DELETED Viewed

@@ -1,85 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import gradio as gr
-#import joblib
-from sklearn.linear_model import LinearRegression
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import StratifiedShuffleSplit
-from sklearn.impute import SimpleImputer
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler
-from sklearn.preprocessing import OneHotEncoder
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import cross_val_score
-from sklearn.model_selection import RandomizedSearchCV
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.model_selection import train_test_split
-df = pd.read_csv('Housing.csv')
-cat_columns = ['mainroad',
-       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',
-        'prefarea']
-def binary_mapping(x):
-  return x.map({'yes': 1, "no": 0})
-df[cat_columns] = df[cat_columns].apply(binary_mapping)
-ohe = OneHotEncoder(sparse=False, handle_unknown='error', drop='first')
-ohe_df = pd.DataFrame(ohe.fit_transform(df[['furnishingstatus']]))
-ohe_df.columns = ohe.get_feature_names(['status'])
-df = pd.concat([df,ohe_df], axis=1)
-df.drop(['furnishingstatus'], axis = 1, inplace = True)
-df.head()
-df_new = df.copy(deep=True)
-num_columns = ['area', 'bedrooms', 'bathrooms', 'stories','parking']
-scaler = MinMaxScaler().fit(df_new[num_columns])
-df_new[num_columns] = scaler.transform(df_new[num_columns])
-y = df_new.pop('price')
-x = df_new
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)
-model = RandomForestRegressor()
-model.fit(x_train, y_train)
-def prediction(properties):
-    print(properties)
-    df = pd.DataFrame(properties, columns=x_test.columns)
-    print(df)
-    df = df[x_test.columns].iloc[0].to_frame().T
-    df[num_columns] = scaler.transform(df[num_columns])
-    return model.predict(df)
-example = pd.DataFrame([7420, 4, 2, 3, 1, 0, 0, 0, 1, 2, 1, 0, 0]).T
-example.columns = x_test.columns
-demo = gr.Interface(
-    prediction,
-    [
-        gr.Dataframe(
-            headers=['area', 'bedrooms', 'bathrooms', 'stories', 'mainroad', 'guestroom',
-                      'basement', 'hotwaterheating', 'airconditioning', 'parking', 'prefarea',
-                      'status_semi-furnished', 'status_unfurnished'],
-            datatype=["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"],
-        )
-    ],
-    "number",
-    description="Enter The Properties Of The Home",
-    title="California Housing Prices Prediction",
-    examples=[example],
-)
-demo.launch()

spaces/DiamondYin/AnewGame/Build/WaliwebGLgameFPS.loader.js DELETED Viewed

@@ -1 +0,0 @@

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spaces/EPFL-VILAB/MultiMAE/utils/layers/drop.py DELETED Viewed

@@ -1,176 +0,0 @@
-# --------------------------------------------------------
-# Based on timm and MAE-priv code bases
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/BUPT-PRIV/MAE-priv
-# --------------------------------------------------------
-""" DropBlock, DropPath
-PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
-Papers:
-DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
-Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
-Code:
-DropBlock impl inspired by two Tensorflow impl that I liked:
- - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-def drop_block_2d(
-        x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
-        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
-    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
-    runs with success, but needs further validation and possibly optimization for lower runtime impact.
-    """
-    B, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    # seed_drop_rate, the gamma parameter
-    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
-            (W - block_size + 1) * (H - block_size + 1))
-    # Forces the block to be inside the feature map.
-    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
-    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
-                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
-    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
-    else:
-        uniform_noise = torch.rand_like(x)
-    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
-    block_mask = -F.max_pool2d(
-        -block_mask,
-        kernel_size=clipped_block_size,  # block_size,
-        stride=1,
-        padding=clipped_block_size // 2)
-    if with_noise:
-        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
-        if inplace:
-            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
-        else:
-            x = x * block_mask + normal_noise * (1 - block_mask)
-    else:
-        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-def drop_block_fast_2d(
-        x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
-        gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
-    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
-    block mask at edges.
-    """
-    B, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
-            (W - block_size + 1) * (H - block_size + 1))
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
-    else:
-        # mask per batch element
-        block_mask = torch.rand_like(x) < gamma
-    block_mask = F.max_pool2d(
-        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
-    if with_noise:
-        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
-        if inplace:
-            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
-        else:
-            x = x * (1. - block_mask) + normal_noise * block_mask
-    else:
-        block_mask = 1 - block_mask
-        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-class DropBlock2d(nn.Module):
-    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-    """
-    def __init__(self,
-                 drop_prob=0.1,
-                 block_size=7,
-                 gamma_scale=1.0,
-                 with_noise=False,
-                 inplace=False,
-                 batchwise=False,
-                 fast=True):
-        super(DropBlock2d, self).__init__()
-        self.drop_prob = drop_prob
-        self.gamma_scale = gamma_scale
-        self.block_size = block_size
-        self.with_noise = with_noise
-        self.inplace = inplace
-        self.batchwise = batchwise
-        self.fast = fast  # FIXME finish comparisons of fast vs not
-    def forward(self, x):
-        if not self.training or not self.drop_prob:
-            return x
-        if self.fast:
-            return drop_block_fast_2d(
-                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
-        else:
-            return drop_block_2d(
-                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
-def drop_path(x, drop_prob: float = 0., training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-    """
-    if drop_prob == 0. or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)

spaces/Fengbinbin/gpt-academic/crazy_functions/解析JupyterNotebook.py DELETED Viewed

@@ -1,145 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = True
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(
-            enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-                    file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(
-                        self.file_paths[index] + f".part-{j}.txt")
-def parseNotebook(filename, enable_markdown=1):
-    import json
-    CodeBlocks = []
-    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
-        notebook = json.load(f)
-    for cell in notebook['cells']:
-        if cell['cell_type'] == 'code' and cell['source']:
-            # remove blank lines
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("".join(cell['source']))
-        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("Markdown:"+"".join(cell['source']))
-    Code = ""
-    for idx, code in enumerate(CodeBlocks):
-        Code += f"This is {idx+1}th code block: \n"
-        Code += code+"\n"
-    return Code
-def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    enable_markdown = plugin_kwargs.get("advanced_arg", "1")
-    try:
-        enable_markdown = int(enable_markdown)
-    except ValueError:
-        enable_markdown = 1
-    pfg = PaperFileGroup()
-    for fp in file_manifest:
-        file_content = parseNotebook(fp, enable_markdown=enable_markdown)
-        pfg.file_paths.append(fp)
-        pfg.file_contents.append(file_content)
-    #  <-------- 拆分过长的IPynb文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-    inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
-                    r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
-                    r"Start a new line for a block and block num use Chinese." +
-                    f"\n\n{frag}" for frag in pfg.sp_file_contents]
-    inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
-    sys_prompt_array = ["You are a professional programmer."] * n_split
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len=80
-    )
-    #  <-------- 整理结果，退出 ---------->
-    block_result = "  \n".join(gpt_response_collection)
-    chatbot.append(("解析的结果如下", block_result))
-    history.extend(["解析的结果如下", block_result])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-    #  <-------- 写入文件，退出 ---------->
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-@CatchException
-def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    chatbot.append([
-        "函数插件功能？",
-        "对IPynb文件进行解析。Contributor: codycjy."])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-    history = []    # 清空历史
-    import glob
-    import os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    if txt.endswith('.ipynb'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(
-            f'{project_folder}/**/*.ipynb', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )

spaces/Frantz103/CaptionQuest/app.py DELETED Viewed

@@ -1,230 +0,0 @@
-from transformers import pipeline
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
-import torch
-from torchvision import transforms
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.decomposition import LatentDirichletAllocation
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import textstat
-import spacy
-import re
-# Initialize the processor and model for the large COCO model
-processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
-model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
-detection_pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
-classification_pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-large-patch14")
-# Initialize the pipeline for the VIT model
-vit_pipeline = pipeline(task="image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-# Move the COCO model to the device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-def generate_text_and_caption(image):
-    # Define the preprocessing pipeline for the image
-    preprocess = transforms.Compose([
-        transforms.Resize((256, 256)),  # Resize to 256x256, change this to match the required dimensions
-        transforms.CenterCrop(224),  # Center crop to 224x224, change this to match the required dimensions
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Normalize with ImageNet mean and std
-    ])
-    # Apply the preprocessing pipeline to the image
-    preprocessed_image = preprocess(image).unsqueeze(0).to(device)  # unsqueeze to add batch dimension
-    # For large COCO model
-    generated_ids = model.generate(pixel_values=preprocessed_image, max_length=20)
-    caption1 = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    # For VIT model
-    vit_output = vit_pipeline(image)
-    caption2_info = vit_output[0] if vit_output else {"generated_text": "N/A"}
-    caption2 = caption2_info.get('generated_text', 'N/A')
-    return caption1, caption2
-def get_unique_refined_labels(image):
-    original_output = detection_pipe(image)
-    filtered_output = [item for item in original_output if item['score'] >= 0.95]
-    unique_refined_labels = {}
-    for item in filtered_output:
-        box = item['box']
-        label = item['label']
-        xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
-        cropped_image = image.crop((xmin, ymin, xmax, ymax))
-        predictions = classification_pipe(cropped_image, candidate_labels=[label])
-        if predictions:
-            top_prediction = sorted(predictions, key=lambda x: x['score'], reverse=True)[0]
-            top_label = top_prediction['label']
-            top_score = top_prediction['score']
-            if top_label not in unique_refined_labels or unique_refined_labels[top_label] < top_score:
-                unique_refined_labels[top_label] = top_score
-    return unique_refined_labels, original_output, filtered_output
-# Load NLP model for entity extraction
-nlp = spacy.load("en_core_web_sm")
-def extract_main_words(text):
-    doc = nlp(text)
-    main_words = [token.lemma_ for token in doc if token.pos_ == 'NOUN']
-    return main_words
-def get_topics(text):
-    # Vectorize the text
-    vectorizer = CountVectorizer()
-    text_vec = vectorizer.fit_transform([text])
-    # Fit LDA model to get topics
-    lda = LatentDirichletAllocation(n_components=1, random_state=0)
-    lda.fit(text_vec)
-    # Get the top words per topic (assuming one topic for simplicity)
-    feature_names = vectorizer.get_feature_names_out()
-    top_words = [feature_names[i] for i in lda.components_[0].argsort()[:-10 - 1:-1]]
-    return top_words
-def check_readability(caption):
-    # Compute the Flesch Reading Ease score of the caption
-    reading_ease_score = textstat.flesch_reading_ease(caption)
-    return reading_ease_score
-def compute_similarity(caption1, caption2):
-    vectorizer = TfidfVectorizer().fit_transform([caption1, caption2])
-    vectors = vectorizer.toarray()
-    cosine_sim = cosine_similarity(vectors)
-    # The similarity between the captions is the off-diagonal value of the cosine_sim matrix
-    similarity_score = cosine_sim[0, 1]
-    return similarity_score
-def evaluate_caption(image, caption1, caption2, unique_refined_labels):
-    # Scores initialization
-    score_caption1 = 0
-    score_caption2 = 0
-    # Initialize object presence scores
-    object_presence_score1 = 0
-    object_presence_score2 = 0
-     # Assume you have a function to extract main words
-    main_words_caption1 = extract_main_words(caption1)
-    main_words_caption2 = extract_main_words(caption2)
-    # Check for object presence using unique_refined_labels
-    object_presence_score1 += sum([1 for word in main_words_caption1 if word in unique_refined_labels.keys()])
-    object_presence_score2 += sum([1 for word in main_words_caption2 if word in unique_refined_labels.keys()])
-    # Entity Extraction
-    entities_caption1 = [ent.text for ent in nlp(caption1).ents]
-    entities_caption2 = [ent.text for ent in nlp(caption2).ents]
-    # Check for object presence using unique_refined_labels
-    score_caption1 += sum([1 for entity in entities_caption1 if entity in unique_refined_labels.keys()])
-    score_caption2 += sum([1 for entity in entities_caption2 if entity in unique_refined_labels.keys()])
-    # Topic Modeling
-    topics_caption1 = get_topics(caption1)
-    topics_caption2 = get_topics(caption2)
-    # Check for topic relevance using unique_refined_labels
-    score_caption1 += sum([1 for topic in topics_caption1 if topic in unique_refined_labels.keys()])
-    score_caption2 += sum([1 for topic in topics_caption2 if topic in unique_refined_labels.keys()])
-    # Implement custom rules
-    def custom_rules(caption):
-        score = 0
-        # Rule for starting with a capital letter
-        if not caption[0].isupper():
-            score -= 1
-        # Rule for ending with punctuation
-        if caption[-1] not in ['.', '!', '?']:
-            score -= 1
-        return score
-    # Custom rule scores
-    custom_score1 = custom_rules(caption1)
-    custom_score2 = custom_rules(caption2)
-    # Update scores based on custom rules
-    score_caption1 += custom_score1  # Note: if these were errors, you'd subtract instead
-    score_caption2 += custom_score2
-    # Check length
-    length_caption1 = len(caption1.split())
-    length_caption2 = len(caption2.split())
-    if length_caption1 < 3:  # assuming a reasonable caption should have at least 3 words
-        score_caption1 -= 3  # arbitrary penalty
-    if length_caption2 < 3:
-        score_caption2 -= 3  # arbitrary penalty
-    #Define similarity threshold
-    similarity_score = compute_similarity(caption1, caption2)
-    similarity_threshold = 0.9  # Replace this with whatever you consider "close enough"
-    score_difference = abs(score_caption1 - score_caption2)
-    score_threshold = 2  # Replace this with whatever you consider "close enough"
-    if score_difference <= score_threshold:
-        if similarity_score > similarity_threshold:
-            readability_score_caption1 = check_readability(caption1)
-            readability_score_caption2 = check_readability(caption2)
-            return caption1 if readability_score_caption1 > readability_score_caption2 else caption2
-    else:
-        return caption1 if score_caption1 > score_caption2 else caption2
-    # Fallback return statement
-    return caption2 if score_caption2 > score_caption2 else caption1
-# Define the post_process_caption function
-def post_process_caption(caption):
-    # Remove [unusedX] tokens, where X is any number
-    cleaned_caption = re.sub(r'\[\s*unused\d+\s*\](, )? ?', '', caption)
-    return cleaned_caption
-def process_image(image_path):
-    image = Image.open(image_path).convert("RGB")
-    caption1, caption2 = generate_text_and_caption(image)
-    unique_refined_labels, _, _ = get_unique_refined_labels(image)
-    # Update return values for caption1
-    caption1 = post_process_caption(caption1)
-    # evealuate the captions
-    better_caption = evaluate_caption(image, caption1, caption2, unique_refined_labels)
-    return caption1, caption2, better_caption
-import gradio as gr
-img_cap_ui = gr.Interface(
-    fn=process_image,
-    title="Image Captioning with Automatic Evaluation",
-    description="Caution: this is a research experiment for personal use, please review the captions before using.",
-    inputs=gr.inputs.Image(type="filepath",label="Add your image"),
-    outputs=[gr.Textbox(label="Caption from the git-coco model", show_copy_button=True),
-    gr.Textbox(label="Caption from the nlp-connect model", show_copy_button=True),
-    gr.Textbox(label="Suggested caption after automatic evaluation", show_copy_button=True)],
-    examples=["image_31.jpg","image_41.jpg","image_48.jpg", "image_50.jpg"],
-    article="The caption evaluation method use a simple voting scheme from outputs of 2 additional models. This is an experiment, please make edit if you use the generated caption.",
-    theme=gr.themes.Soft()
-)
-img_cap_ui.launch()

spaces/GT4SD/protein_properties/app.py DELETED Viewed

@@ -1,83 +0,0 @@
-import logging
-import pathlib
-import gradio as gr
-import numpy as np
-import pandas as pd
-from gt4sd.properties.proteins import PROTEIN_PROPERTY_PREDICTOR_FACTORY
-from utils import draw_grid_predict
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.NullHandler())
-AMIDE_FNS = ["protein_weight", "charge", "charge_density", "isoelectric_point"]
-PH_FNS = ["charge", "charge_density", "isoelectric_point"]
-def main(property: str, seq: str, seq_file: str, amide: bool, ph: float):
-    prop_name = property.lower()
-    algo, config = PROTEIN_PROPERTY_PREDICTOR_FACTORY[prop_name]
-    # Pass hyperparameters if applicable
-    kwargs = {}
-    if prop_name in AMIDE_FNS:
-        kwargs["amide"] = amide
-    if prop_name in PH_FNS:
-        kwargs["ph"] = ph
-    model = algo(config(**kwargs))
-    # Read and parse data
-    if seq != "" and seq_file is not None:
-        raise ValueError("Pass either smiles or seq_file, not both.")
-    elif seq != "":
-        seqs = [seq]
-    elif seq_file is not None:
-        seqs = pd.read_csv(seq_file.name, header=None, sep="\t")[0].tolist()
-    props = np.array(list(map(model, seqs))).round(2)
-    # Expand to 2D array if needed
-    if len(props.shape) == 1:
-        props = np.expand_dims(np.array(props), -1)
-    return draw_grid_predict(seqs, props, property_names=[property], domain="Proteins")
-if __name__ == "__main__":
-    # Preparation (retrieve all available algorithms)
-    properties = list(PROTEIN_PROPERTY_PREDICTOR_FACTORY.keys())[::-1]
-    properties = list(map(lambda x: x.capitalize(), properties))
-    # Load metadata
-    metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
-    examples = [
-        ["Aliphaticity", "", metadata_root.joinpath("examples.smi"), False, 7],
-        ["Isoelectric_point", "KFLIYQMECSTMIFGL", None, False, 7],
-        ["Charge", "KFLIYQMECSTMIFGL", None, True, 12],
-    ]
-    with open(metadata_root.joinpath("article.md"), "r") as f:
-        article = f.read()
-    with open(metadata_root.joinpath("description.md"), "r") as f:
-        description = f.read()
-    demo = gr.Interface(
-        fn=main,
-        title="Protein properties",
-        inputs=[
-            gr.Dropdown(properties, label="Property", value="Instability"),
-            gr.Textbox(
-                label="Single Protein sequence", placeholder="KFLIYQMECSTMIFGL", lines=1
-            ),
-            gr.File(file_types=[".smi"], label="One AAS per line"),
-            gr.Radio(choices=[True, False], label="Amide", value=True),
-            gr.Slider(minimum=0, maximum=14, value=7, label="pH", description="Blub"),
-        ],
-        outputs=gr.HTML(label="Output"),
-        article=article,
-        description=description,
-        examples=examples,
-    )
-    demo.launch(debug=True, show_error=True)

spaces/GaenKoki/voicevox/test/test_preset.py DELETED Viewed

@@ -1,303 +0,0 @@
-from os import remove
-from pathlib import Path
-from shutil import copyfile
-from tempfile import TemporaryDirectory
-from unittest import TestCase
-from voicevox_engine.preset import Preset, PresetError, PresetManager
-class TestPresetManager(TestCase):
-    def setUp(self):
-        self.tmp_dir = TemporaryDirectory()
-        self.tmp_dir_path = Path(self.tmp_dir.name)
-    def tearDown(self):
-        self.tmp_dir.cleanup()
-    def test_validation(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-1.yaml"))
-        presets = preset_manager.load_presets()
-        self.assertFalse(presets is None)
-    def test_validation_same(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-1.yaml"))
-        presets = preset_manager.load_presets()
-        presets2 = preset_manager.load_presets()
-        self.assertFalse(presets is None)
-        self.assertEqual(presets, presets2)
-    def test_validation_2(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-2.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルにミスがあります"):
-            preset_manager.load_presets()
-    def test_preset_id(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-3.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットのidに重複があります"):
-            preset_manager.load_presets()
-    def test_empty_file(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-4.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルが空の内容です"):
-            preset_manager.load_presets()
-    def test_not_exist_file(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-dummy.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルが見つかりません"):
-            preset_manager.load_presets()
-    def test_add_preset(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 10,
-                "name": "test10",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        id = preset_manager.add_preset(preset)
-        self.assertEqual(id, 10)
-        self.assertEqual(len(preset_manager.presets), 3)
-        for _preset in preset_manager.presets:
-            if _preset.id == id:
-                self.assertEqual(_preset, preset)
-        remove(temp_path)
-    def test_add_preset_load_failure(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-2.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルにミスがあります"):
-            preset_manager.add_preset(
-                Preset(
-                    **{
-                        "id": 1,
-                        "name": "",
-                        "speaker_uuid": "",
-                        "style_id": 0,
-                        "speedScale": 0,
-                        "pitchScale": 0,
-                        "intonationScale": 0,
-                        "volumeScale": 0,
-                        "prePhonemeLength": 0,
-                        "postPhonemeLength": 0,
-                    }
-                )
-            )
-    def test_add_preset_conflict_id(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 2,
-                "name": "test3",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        id = preset_manager.add_preset(preset)
-        self.assertEqual(id, 3)
-        self.assertEqual(len(preset_manager.presets), 3)
-        for _preset in preset_manager.presets:
-            if _preset.id == id:
-                self.assertEqual(_preset, preset)
-        remove(temp_path)
-    def test_add_preset_conflict_id2(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": -1,
-                "name": "test3",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        id = preset_manager.add_preset(preset)
-        self.assertEqual(id, 3)
-        self.assertEqual(len(preset_manager.presets), 3)
-        for _preset in preset_manager.presets:
-            if _preset.id == id:
-                self.assertEqual(_preset, preset)
-        remove(temp_path)
-    def test_add_preset_write_failure(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 10,
-                "name": "test10",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        preset_manager.load_presets()
-        preset_manager.load_presets = lambda: []
-        preset_manager.preset_path = ""
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルに書き込み失敗しました"):
-            preset_manager.add_preset(preset)
-        self.assertEqual(len(preset_manager.presets), 2)
-        remove(temp_path)
-    def test_update_preset(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 1,
-                "name": "test1 new",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        id = preset_manager.update_preset(preset)
-        self.assertEqual(id, 1)
-        self.assertEqual(len(preset_manager.presets), 2)
-        for _preset in preset_manager.presets:
-            if _preset.id == id:
-                self.assertEqual(_preset, preset)
-        remove(temp_path)
-    def test_update_preset_load_failure(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-2.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルにミスがあります"):
-            preset_manager.update_preset(
-                Preset(
-                    **{
-                        "id": 1,
-                        "name": "",
-                        "speaker_uuid": "",
-                        "style_id": 0,
-                        "speedScale": 0,
-                        "pitchScale": 0,
-                        "intonationScale": 0,
-                        "volumeScale": 0,
-                        "prePhonemeLength": 0,
-                        "postPhonemeLength": 0,
-                    }
-                )
-            )
-    def test_update_preset_not_found(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 10,
-                "name": "test1 new",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        with self.assertRaises(PresetError, msg="更新先のプリセットが存在しません"):
-            preset_manager.update_preset(preset)
-        self.assertEqual(len(preset_manager.presets), 2)
-        remove(temp_path)
-    def test_update_preset_write_failure(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset = Preset(
-            **{
-                "id": 1,
-                "name": "test1 new",
-                "speaker_uuid": "7ffcb7ce-00ec-4bdc-82cd-45a8889e43ff",
-                "style_id": 2,
-                "speedScale": 1,
-                "pitchScale": 1,
-                "intonationScale": 0.5,
-                "volumeScale": 1,
-                "prePhonemeLength": 0.1,
-                "postPhonemeLength": 0.1,
-            }
-        )
-        preset_manager.load_presets()
-        preset_manager.load_presets = lambda: []
-        preset_manager.preset_path = ""
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルに書き込み失敗しました"):
-            preset_manager.update_preset(preset)
-        self.assertEqual(len(preset_manager.presets), 2)
-        self.assertEqual(preset_manager.presets[0].name, "test")
-        remove(temp_path)
-    def test_delete_preset(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        id = preset_manager.delete_preset(1)
-        self.assertEqual(id, 1)
-        self.assertEqual(len(preset_manager.presets), 1)
-        remove(temp_path)
-    def test_delete_preset_load_failure(self):
-        preset_manager = PresetManager(preset_path=Path("test/presets-test-2.yaml"))
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルにミスがあります"):
-            preset_manager.delete_preset(10)
-    def test_delete_preset_not_found(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        with self.assertRaises(PresetError, msg="削除対象のプリセットが存在しません"):
-            preset_manager.delete_preset(10)
-        self.assertEqual(len(preset_manager.presets), 2)
-        remove(temp_path)
-    def test_delete_preset_write_failure(self):
-        temp_path = self.tmp_dir_path / "presets-test-temp.yaml"
-        copyfile(Path("test/presets-test-1.yaml"), temp_path)
-        preset_manager = PresetManager(preset_path=temp_path)
-        preset_manager.load_presets()
-        preset_manager.load_presets = lambda: []
-        preset_manager.preset_path = ""
-        with self.assertRaises(PresetError, msg="プリセットの設定ファイルに書き込み失敗しました"):
-            preset_manager.delete_preset(1)
-        self.assertEqual(len(preset_manager.presets), 2)
-        remove(temp_path)

spaces/Gen-Sim/Gen-Sim/cliport/models/clip_ling.py DELETED Viewed

@@ -1,97 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-import cliport.utils.utils as utils
-from cliport.models.resnet import IdentityBlock, ConvBlock
-from cliport.models.core.unet import Up
-from cliport.models.core import fusion
-from cliport.models.clip_lingunet_lat import CLIPLingUNetLat
-class CLIPLing(CLIPLingUNetLat):
-    """ CLIP RN50 with U-Net skip connections """
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super().__init__(input_shape, output_dim, cfg, device, preprocess)
-    # def _build_decoder(self):
-    #     # language
-    #     self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2)
-    #     self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4)
-    #     self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8)
-    #     self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024
-    #     self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024)
-    #     self.lang_proj2 = nn.Linear(self.proj_input_dim, 512)
-    #     self.lang_proj3 = nn.Linear(self.proj_input_dim, 256)
-    #     # vision
-    #     self.conv1 = nn.Sequential(
-    #         nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False),
-    #         nn.ReLU(True)
-    #     )
-    #     self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear)
-    #     self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
-    #     self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
-    #     self.layer1 = nn.Sequential(
-    #         ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         nn.UpsamplingBilinear2d(scale_factor=2),
-    #     )
-    #     self.layer2 = nn.Sequential(
-    #         ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         nn.UpsamplingBilinear2d(scale_factor=2),
-    #     )
-    #     self.layer3 = nn.Sequential(
-    #         ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-    #         nn.UpsamplingBilinear2d(scale_factor=2),
-    #     )
-        del self.lang_fuser2, self.lang_fuser1, self.lang_proj1, self.lang_proj2, self.layer2, self.layer1, self.layer3
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(128, self.output_dim, kernel_size=1)
-        )
-    def forward(self, x, lat, l):
-        x = self.preprocess(x, dist='clip')
-        in_type = x.dtype
-        in_shape = x.shape
-        x = x[:,:3]  # select RGB
-        x, im = self.encode_image(x)
-        x = x.to(in_type)
-        l_enc, l_emb, l_mask = self.encode_text(l)
-        l_input = l_emb if 'word' in self.lang_fusion_type else l_enc
-        l_input = l_input.to(dtype=x.dtype)
-        assert x.shape[1] == self.input_dim
-        x = self.conv1(x)
-        # x = self.lang_fuser1(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj1)
-        # x = self.up1(x, im[-2])
-        # x = self.lat_fusion1(x, lat[-6])
-        # x = self.lang_fuser2(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj2)
-        # x = self.up2(x, im[-3])
-        # x = self.lat_fusion2(x, lat[-5])
-        x = self.lang_fuser3(x, l_input, x2_mask=l_mask, x2_proj=self.lang_proj3)
-        x = self.up3(x, im[-4])
-        x = self.lat_fusion3(x, lat[1])
-        x = self.conv2(x)
-        x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
-        return x

spaces/Gradio-Blocks/uniformer_image_detection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py DELETED Viewed

@@ -1,39 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w32',
-    backbone=dict(
-        _delete_=True,
-        type='HRNet',
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(32, 64)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(32, 64, 128)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(32, 64, 128, 256)))),
-    neck=dict(
-        _delete_=True,
-        type='HRFPN',
-        in_channels=[32, 64, 128, 256],
-        out_channels=256))
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)