Update parquet files (step 95 of 249)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Create Realistic and Immersive Environments with World Creator 2 - Download for Free.md

@@ -1,45 +0,0 @@
-<br />
-<h1>How to Download World Creator 2 for Free and Create Stunning Terrains</h1>
-
-<p>World Creator 2 is a powerful terrain and landscape generator that allows you to create realistic and immersive environments in real-time. Whether you are a game developer, a filmmaker, or an artist, World Creator 2 can help you bring your vision to life with its advanced features and tools.</p>
-
-<p>In this article, we will show you how to download World Creator 2 for free and how to use it to create amazing terrains using real-world data from MapTiler Cloud.</p>
-<h2>world creator 2 free download crack</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://byltly.com/2uKuY0">https://byltly.com/2uKuY0</a></b></p><br /><br />
-
-<h2>What is World Creator 2?</h2>
-
-<p>World Creator 2 is the world's first real-time terrain and landscape generator that performs all its generation and design processes entirely on the GPU using thousands of cores. It offers a highly optimized and improved workflow with more tools and features than its predecessor, World Creator 1.</p>
-
-<p>Some of the key features of World Creator 2 are:</p>
-
-- Real-time terrain generation: You can create terrains from scratch by hand, use existing terrains to stamp your world, or combine both workflows to get what you want. There are no design limitations - everything is possible, and you are in complete control.
-- Outstanding procedural power: You can apply and combine many different kinds of filters to modify the terrain you created or imported from another source. You can erode, create rivers and lakes, apply sediments, transform, stylize, simulate water flow and sediment transport as well as sediment deposit, and much more entirely in real-time.
-- Powerful design capabilities: You can draw anything, anytime, anywhere on your terrain. You can create roads, rivers, lakes, plateaus, terraces, raise mountains, and more - or just draw the shape you want by hand or use custom height-maps and real-world data to stamp your terrain.
-- Real-world maps integration: You can use real-world 3D DEM data from MapTiler Cloud to create realistic terrains based on any location on Earth. MapTiler Cloud provides high-quality elevation data for the whole world that you can easily import into World Creator 2.
-
-<h2>How to Download World Creator 2 for Free?</h2>
-
-<p>World Creator 2 is a commercial software that requires a license to use. However, there is a way to download World Creator 2 for free and use it without any limitations. Here are the steps:</p>
-
-- Go to the official website of World Creator 2: https://www.world-creator.com/
-- Click on the "Download" button at the top right corner of the page.
-- Choose the version that suits your operating system (Windows or Mac) and click on the "Download" button again.
-- You will be redirected to a page where you can enter your email address and name to receive a download link.
-- Check your email inbox for an email from World Creator with the subject "World Creator Download Link".
-- Click on the link in the email to start downloading World Creator 2.
-- Once the download is complete, unzip the file and run the installer.
-- Follow the instructions on the screen to install World Creator 2 on your computer.
-- When the installation is finished, launch World Creator 2 from your desktop or start menu.
-- You will be asked to enter a license key or activate a trial version. Choose the trial version option and click on "Activate".
-- You will be able to use World Creator 2 for free for 30 days with all its features unlocked.
-
-<h2>How to Use World Creator 2 to Create Stunning Terrains?</h2>
-
-<p>Now that you have downloaded World Creator 2 for free, you can start creating amazing terrains with it. Here are some basic steps to get you started:</p>
-
-- Open World Creator 2 and choose a project template or create a new project from scratch.
-- In the project settings panel, you can adjust various parameters such as terrain size, resolution, seed, biome type, etc.
-- In the terrain editor panel, you can use different tools and filters to sculpt and modify your terrain. You can also import height-maps or real-world data from MapTiler Cloud to stamp your terrain with realistic features.
-- In the texture editor panel, you can apply different materials and textures to your terrain. You can also blend multiple textures using</p> ddb901b051<br />
-<br />
-<br />

spaces/1gistliPinn/ChatGPT4/Examples/Blackfridaybookhussainzaidipdffreedownload.md

@@ -1,6 +0,0 @@
-<h2>blackfridaybookhussainzaidipdffreedownload</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://imgfil.com/2uxZvh">https://imgfil.com/2uxZvh</a></b></p><br /><br />
-<br />
-Black Friday Book Hussain Zaidi Pdf Free 33 - Yola Black Friday The True Story Of Bombay Bomb Blasts S ... How to Read a Bomb: Scenes ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>

spaces/1gistliPinn/ChatGPT4/Examples/Blu Hey Bro 1080p Telugu Movies The Best Comedy of the Year.md

@@ -1,6 +0,0 @@
-<h2>Blu Hey Bro 1080p Telugu Movies</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://imgfil.com/2uxYLo">https://imgfil.com/2uxYLo</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>

spaces/1gistliPinn/ChatGPT4/Examples/Buzzsaw 2011 32 Bit Keygen Free The Easiest Way to Install and Activate Buzzsaw.md

@@ -1,8 +0,0 @@
-<br />
-<p>Since 2011 winter season, frost depths have been measured as an outreach program in Hokkaido, northern part of Japan, where seasonal ground freezing occurs in winter. Frost depths were measured in elementary, junior high and high schools in order to emphasis their interest for earth sciences. At schools, using simple frost tube, measurements were conducted directly once a week by students or teacher during ground freezing under no snow-removal condition. A lecture was made in class and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. In 2011 winter season, we started measurements at three schools, and the number of school extended to 32 in 2016 season, 26 elementary schools, 5 junior high schools and one high school. We visited schools in summer time or just before frost season to talk about the method of measurement, and measurements by students started just after ground freezing. After the end of frozen period, we visited schools again to explain results of each school or another schools in Japan, Alaska, Canada or Russia. The measured frost depths in Hokkaido ranged widely, from only a few centimeter to more than 50 cm. However, some schools had no frost depth due to heavy snow. We confirmed that the frost depth strongly depends on air temperature and snow depth. The lecture was made to student why the frost depth ranged widely, and the effect of snow was explained by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at the same elementary school. At the end of December, depths had no significant difference between these conditions, and the difference went to 14 cm after one month, with about 30 cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth.</p>
-<p>In order to emphasis their interest for earth sciences, an outreach program through measurements of frost depth is conducting in Japan since 2011. This program is made at elementary, junior high and high schools in Hokkaido, northern part of Japan where seasonal ground freezing occurs in winter. At schools, a lecture was made and a frost tube was set at schoolyard, as the same tube and protocol as UAF's Permafrost Outreach Program, using clear tube with blue-colored water. Frost depth was measured directly once a week at each school by students during ground freezing under no snow-removal condition. In 2011 season, we started this program at three schools, and the number of participated school is extended to 29 schools in 2014 winter season, 23 elementary schools, 5 junior high schools and one high school. We visited schools summer time and just before frost season to talk about the method of measurement. After the end of measured period, we also visited schools to explain measured results by each school and the other schools in Japan, Alaska, Canada and Russia. The measured values of frost depth in Hokkaido were ranged between 0cm and more than 50cm. We found that the frost depth depends on air temperature and snow depth. We discussed with student why the frost depth ranged widely and explained the effect of snow by using the example of igloo. In order to validate the effect of snow and to compare frost depths, we tried to measure frost depths under snow-removal and no snow-removal conditions at one elementary school. At the end of December, depths had no significant difference between these conditions, 11cm and 10cm, and the difference went to 14cm, 27cm and 13cm after one month, with about 30cm of snow depth. After these measurements and lectures, students noticed snow has a role as insulator and affects the frost depth. The network of this program will be expected to expand, finally more than a hundred schools.</p>
-<h2>Buzzsaw 2011 32 Bit Keygen Free</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://imgfil.com/2uxX3j">https://imgfil.com/2uxX3j</a></b></p><br /><br />
-<p>Spring frost can be a limiting factor in sweet cherry ( Prunus avium L.) production. Rising temperatures in spring force the development of buds, whereby their vulnerability to freezing temperatures continuously increases. With the beginning of blossom, flowers can resist only light frosts without any significant damage. In this study, we investigated the risk of spring frost damages during cherry blossom for historical and future climate conditions at two different sites in NE (Berlin) and SW Germany (Geisenheim). Two phenological models, developed on the basis of phenological observations at the experimental sweet cherry orchard in Berlin-Dahlem and validated for endodormancy release and for warmer climate conditions (already published), were used to calculate the beginning of cherry blossom in Geisenheim, 1951-2015 (external model validation). Afterwards, on the basis of a statistical regionalisation model WETTREG (RCP 8.5), the frequency of frost during cherry blossom was calculated at both sites for historical (1971-2000) and future climate conditions (2011-2100). From these data, we derived the final flower damage, defined as the percentage of frozen flowers due to single or multiple frost events during blossom. The results showed that rising temperatures in this century can premature the beginning of cherry blossom up to 17 days at both sites, independent of the used phenological model. The frequency and strength of frost was characterised by a high temporal and local variability. For both sites, no significant increase in frost frequency and frost damage during blossom was found. In Geisenheim, frost damages significantly decreased from the middle of the twenty-first century. This study additionally emphasises the importance of reliable phenological models which not only work for current but also for changed climate conditions and at different sites. The date of endodormancy release should always be a known parameter in chilling/forcing models.</p>
-<p>Spring frost can be a limiting factor in sweet cherry (Prunus avium L.) production. Rising temperatures in spring force the development of buds, whereby their vulnerability to freezing temperatures continuously increases. With the beginning of blossom, flowers can resist only light frosts without any significant damage. In this study, we investigated the risk of spring frost damages during cherry blossom for historical and future climate conditions at two different sites in NE (Berlin) and SW Germany (Geisenheim). Two phenological models, developed on the basis of phenological observations at the experimental sweet cherry orchard in Berlin-Dahlem and validated for endodormancy release and for warmer climate conditions (already published), were used to calculate the beginning of cherry blossom in Geisenheim, 1951-2015 (external model validation). Afterwards, on the basis of a statistical regionalisation model WETTREG (RCP 8.5), the frequency of frost during cherry blossom was calculated at both sites for historical (1971-2000) and future climate conditions (2011-2100). From these data, we derived the final flower damage, defined as the percentage of frozen flowers due to single or multiple frost events during blossom. The results showed that rising temperatures in this century can premature the beginning of cherry blossom up to 17 days at both sites, independent of the used phenological model. The frequency and strength of frost was characterised by a high temporal and local variability. For both sites, no significant increase in frost frequency and frost damage during blossom was found. In Geisenheim, frost damages significantly decreased from the middle of the twenty-first century. This study additionally emphasises the importance of reliable phenological models which not only work for current but also for changed climate conditions and at different sites. The date of endodormancy release should always be a known parameter in chilling/forcing models.</p> aaccfb2cb3<br />
-<br />
-<br />

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Barbie Dreamhouse Adventures APK MOD VIP Unlocked 2022.md

@@ -1,119 +0,0 @@
-<br />
-<h1>Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK: How to Download and Play</h1>
- <p>If you are a fan of Barbie and her fabulous lifestyle, you might want to try out Barbie Dreamhouse Adventures, a fun simulation game for girls. In this game, you can create your own dreamhouse, design your own fashion, and join Barbie and her friends in various adventures. However, if you want to enjoy all the features and items in the game, you might need to spend some real money or watch ads. That's why some people prefer to use Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK, a modified version of the game that gives you everything for free. In this article, we will show you how to download and play this APK on your Android device.</p>
-<h2>barbie dreamhouse adventures tudo desbloqueado 2022 apk</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://urlin.us/2uT0o0">https://urlin.us/2uT0o0</a></b></p><br /><br />
- <h2>What is Barbie Dreamhouse Adventures?</h2>
- <h3>A fun simulation game for girls</h3>
- <p>Barbie Dreamhouse Adventures is a game developed by Budge Studios, a company that specializes in creating games for kids. The game is based on the popular animated series of the same name, which follows Barbie and her friends as they live in a glamorous dreamhouse. The game allows you to create your own dreamhouse, decorate it with furniture and accessories, and explore different rooms. You can also dress up Barbie and her friends with hundreds of outfits, hairstyles, and accessories. You can even design your own fashion and share it with other players.</p>
- <h3>Features of the game</h3>
- <p>Some of the features of Barbie Dreamhouse Adventures are:</p>
-<ul>
-<li>You can customize your dreamhouse with wallpapers, furniture, decorations, and more.</li>
-<li>You can join Barbie and her friends in various activities, such as baking, dancing, pool parties, pet care, and more.</li>
-<li>You can unlock new items and characters as you progress in the game.</li>
-<li>You can interact with other players and visit their dreamhouses.</li>
-<li>You can watch episodes from the animated series and get inspired by them.</li>
-</ul>
- <h2>What is Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK?</h2>
- <h3>A modified version of the game with everything unlocked</h3>
- <p>Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK is a modified version of the original game that gives you access to everything without paying or watching ads. This means that you can enjoy all the features and items in the game without any limitations. You can unlock all the rooms, furniture, outfits, accessories, characters, and activities in the game for free. You can also get unlimited coins and gems to buy anything you want.</p>
-<p>barbie dreamhouse adventures mod apk vip unlocked 2022<br />
-barbie dreamhouse adventures hack apk tudo liberado 2022<br />
-barbie dreamhouse adventures download apk full unlocked 2022<br />
-barbie dreamhouse adventures apk premium tudo gratis 2022<br />
-barbie dreamhouse adventures latest version apk tudo infinito 2022<br />
-barbie dreamhouse adventures free vip apk tudo ilimitado 2022<br />
-barbie dreamhouse adventures cracked apk tudo sem limites 2022<br />
-barbie dreamhouse adventures unlimited vip apk tudo de graça 2022<br />
-barbie dreamhouse adventures pro apk tudo completo 2022<br />
-barbie dreamhouse adventures modded apk tudo atualizado 2022<br />
-barbie dreamhouse adventures vip hack apk tudo funcionando 2022<br />
-barbie dreamhouse adventures unlocked apk tudo pronto 2022<br />
-barbie dreamhouse adventures premium mod apk tudo desbloqueado 2022<br />
-barbie dreamhouse adventures hack mod apk tudo liberado 2022<br />
-barbie dreamhouse adventures full unlocked apk tudo gratis 2022<br />
-barbie dreamhouse adventures latest mod apk tudo infinito 2022<br />
-barbie dreamhouse adventures free premium apk tudo ilimitado 2022<br />
-barbie dreamhouse adventures cracked mod apk tudo sem limites 2022<br />
-barbie dreamhouse adventures unlimited premium apk tudo de graça 2022<br />
-barbie dreamhouse adventures pro mod apk tudo completo 2022<br />
-barbie dreamhouse adventures modded vip apk tudo atualizado 2022<br />
-barbie dreamhouse adventures vip unlocked mod apk tudo funcionando 2022<br />
-barbie dreamhouse adventures unlocked vip apk tudo pronto 2022<br />
-barbie dreamhouse adventures vip mod apk download 2022<br />
-barbie dreamhouse adventures hack apk download 2022<br />
-barbie dreamhouse adventures download mod apk 2022<br />
-barbie dreamhouse adventures apk download vip unlocked 2022<br />
-barbie dreamhouse adventures premium apk download 2022<br />
-barbie dreamhouse adventures latest version apk download 2022<br />
-barbie dreamhouse adventures free vip apk download 2022<br />
-barbie dreamhouse adventures cracked apk download 2022<br />
-barbie dreamhouse adventures unlimited vip apk download 2022<br />
-barbie dreamhouse adventures pro apk download 2022<br />
-barbie dreamhouse adventures modded apk download 2022<br />
-barbie dreamhouse adventures vip hack apk download 2022<br />
-barbie dreamhouse adventures unlocked apk download 2022<br />
-how to get vip unlocked in barbie dreamhouse adventures apk 2022<br />
-how to hack barbie dreamhouse adventures apk tudo liberado 2022<br />
-how to download barbie dreamhouse adventures full unlocked apk 2022<br />
-how to get premium for free in barbie dreamhouse adventures apk 2022<br />
-how to update barbie dreamhouse adventures to latest version apk 2022<br />
-how to get free vip in barbie dreamhouse adventures hack apk 2022<br />
-how to crack barbie dreamhouse adventures vip unlocked apk 2022<br />
-how to get unlimited vip in barbie dreamhouse adventures modded apk 2022<br />
-how to install barbie dreamhouse adventures pro unlocked apk 2022<br />
-how to play barbie dreamhouse adventures with vip modded apk 2022<br />
-how to use vip features in barbie dreamhouse adventures hacked apk 2022<br />
-how to unlock everything in barbie dreamhouse adventures premium modded apk 2022</p>
- <h3>Benefits of using the APK</h3>
- <p>Some of the benefits of using Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK are:</p>
-<ul>
-<li>You can save your time and money by not having to watch ads or make in-app purchases.</li>
-<li>You can have more fun and creativity by having access to everything in the game.</li>
-<li>You can experience the full potential of the game without any restrictions.</li>
-<li>You can play offline without needing an internet connection.</li>
-</ul>
- <h2>How to download and install Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK?</h2>
- <h3>Steps to follow</h3>
- <p>To download and install Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK on your Android device, you need to follow these steps:</p <p>- Step 1: Go to a trusted website that provides the APK file, such as [APKPure] or [APKCombo].</p>
-<p>- Step 2: Search for Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK and click on the download button.</p>
-<p>- Step 3: Wait for the download to finish and then open the APK file.</p>
-<p>- Step 4: If you see a warning message that says "Install unknown apps", you need to enable the option to allow installation from unknown sources. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
-<p>- Step 5: After that, you can proceed with the installation by following the instructions on the screen.</p>
-<p>- Step 6: Once the installation is complete, you can launch the game and enjoy it.</p>
- <h3>Tips and warnings</h3>
- <p>Here are some tips and warnings that you should keep in mind when using Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK:</p>
-<ul>
-<li>Make sure that you download the APK file from a reliable and safe website. Avoid downloading from unknown or suspicious sources that might contain malware or viruses.</li>
-<li>Before installing the APK file, you should uninstall the original game if you have it on your device. This will prevent any conflicts or errors that might occur.</li>
-<li>Be careful not to update the game from the Google Play Store or any other source. This will overwrite the APK file and remove all the unlocked features and items.</li>
-<li>Backup your data before installing the APK file. This will help you restore your progress in case something goes wrong.</li>
-</ul>
- <h2>How to play Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK?</h2>
- <h3>Explore the dreamhouse and customize it</h3>
- <p>One of the main attractions of Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK is that you can explore and customize your own dreamhouse. You can choose from different rooms, such as the kitchen, the living room, the bedroom, the bathroom, and more. You can also decorate them with various wallpapers, furniture, decorations, and more. You can even change the color and style of each item. You can also unlock new rooms and items as you play. You can create your own dreamhouse according to your taste and imagination.</p>
- <h3>Join Barbie and her friends in various activities</h3>
- <p>Another fun aspect of Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK is that you can join Barbie and her friends in various activities. You can bake delicious cakes, dance to catchy music, have pool parties, take care of cute pets, and more. You can also dress up Barbie and her friends with hundreds of outfits, hairstyles, and accessories. You can even design your own fashion and share it with other players. You can also watch episodes from the animated series and get inspired by them. You can have a lot of fun and adventure with Barbie and her friends.</p>
- <h2>Conclusion</h2>
- <p>Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK is a great game for girls who love Barbie and her fabulous lifestyle. It allows you to create your own dreamhouse, design your own fashion, and join Barbie and her friends in various adventures. It also gives you access to everything in the game without paying or watching ads. You can download and install this APK on your Android device by following the steps and tips we have provided in this article. We hope you enjoy playing this game and have a wonderful time.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK:</p>
-<ul>
-<li><b>Q: Is Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK safe to use?</b></li>
-<li>A: Yes, as long as you download it from a trusted website that provides the original and unmodified APK file. However, you should always be careful when installing apps from unknown sources and scan them for any malware or viruses.</li>
-<li><b>Q: Is Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK compatible with my device?</b></li>
-<li>A: The APK file should work on most Android devices that support Android 4.4 or higher. However, some devices might have compatibility issues or performance problems depending on their specifications.</li>
-<li><b>Q: How can I contact the developer of Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK?</b></li>
-<li>A: The developer of this APK is not affiliated with Budge Studios, the official developer of Barbie Dreamhouse Adventures - The game. You can contact them through their website or email address, which you can find on the APK file or the website where you downloaded it.</li>
-<li><b>Q: Can I play Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK with my friends?</b></li>
-<li>A: Yes, you can play this game with your friends online. You can visit their dreamhouses, chat with them, and join them in activities. You can also invite them to your dreamhouse and show them your creations.</li>
-<li><b>Q: What are some alternatives to Barbie Dreamhouse Adventures Tudo Desbloqueado 2022 APK?</b></li>
-<li>A: If you are looking for other games that are similar to Barbie Dreamhouse Adventures, you might want to check out these games:</li>
-<ul>
-<li>Barbie Fashion Closet: A game where you can dress up Barbie and her friends with different outfits and accessories.</li>
-<li>Barbie Magical Fashion: A game where you can transform Barbie into a princess, a mermaid, a fairy, or a hero.</li>
-<li>Barbie Dreamtopia: A game where you can explore the magical worlds of Dreamtopia with Barbie and her sister Chelsea.</li>
-</ul>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Enjoy Brawlhalla on Your Mobile Device with the 32bit APK File.md

@@ -1,111 +0,0 @@
-
-<h1>Brawlhalla 32bit APK: How to Download and Play the Free Platform Fighting Game on Android</h1>
-<p>If you are looking for a fun and exciting fighting game that you can play on your mobile device, you should check out Brawlhalla. Brawlhalla is a free platform fighting game that supports up to 8 players online or local, with full cross-play across different platforms. You can choose from over 50 unique characters, each with their own weapons and abilities, and compete in various modes and maps. In this article, we will show you how to download and play Brawlhalla 32bit APK on your Android device.</p>
-<h2>What is Brawlhalla?</h2>
-<p>Brawlhalla is a game created and developed by Blue Mammoth Games and published by Ubisoft Entertainment. It was released in 2017 for PC, PS4, Xbox One, and Nintendo Switch, and in 2020 for iOS and Android. It has over 80 million players worldwide and is one of the most popular fighting games on Steam.</p>
-<h2>brawlhalla 32bit apk</h2><br /><p><b><b>Download File</b> &#10003;&#10003;&#10003; <a href="https://jinyurl.com/2uNUlx">https://jinyurl.com/2uNUlx</a></b></p><br /><br />
-<h3>A brief introduction to the game's features, modes, and characters</h3>
-<p>Brawlhalla features simple controls and one-button special moves that make it easy for anyone to pick up and play. You can also customize your controls and settings according to your preference. The game has many features that make it fun and engaging, such as:</p>
-<ul>
-<li>Online Ranked 1v1 & 2v2 - Climb the ranked ladder from Tin up to Platinum and beyond by fighting against players near your skill level.</li>
-<li>4 Player Online Free for All - Casual matches where four fighters enter, but only one can win.</li>
-<li>Cross-play Custom Rooms - Invite up to 8 friends on all platforms to a huge variety of custom matches, such as 4v4s, 1v3, 2v2, FFA, and more.</li>
-<li>Many Game Modes - Mix things up with Brawlball, Bombsketball, Capture the Flag, Kung-Foot, and many more fun party game modes.</li>
-<li>The Training Room - Practice combos and setups inside the Training Room. Look at detailed frame data, hitboxes, hurtboxes, and sharpen your skills.</li>
-<li>Weekly Rotation - Every week, there is a new Legend Rotation of eight free characters that you can play. You can also earn gold to unlock more Legends by playing any online game mode.</li>
-<li>Battle Pass - Every season, there is a new Battle Pass that offers exclusive rewards such as skins, colors, avatars, emotes, sidekicks, KO effects, podiums, and more.</li>
-<li>Crossovers - Brawlhalla features crossover events with other popular franchises such as Adventure Time, WWE, Steven Universe, Ben 10, The Walking Dead, Tomb Raider, Hellboy, Shovel Knight, Rayman, and more.</li>
-</ul>
-<p>Brawlhalla has a diverse roster of over 50 Legends that you can choose from. Each Legend has their own stats (Strength, Dexterity, Defense, Speed), two weapons (Sword, Hammer, Spear, Axe, Rocket Lance, Katars, Blaster, Bow, Gauntlets, Scythe, Cannon, Orb, Greatsword), <h2>How to Download Brawlhalla 32bit APK on Android</h2>
-<p>Brawlhalla is available for free on the Google Play Store for Android devices. However, some older devices may not support the game or run it smoothly. If you have a 32-bit Android device, you may need to download the Brawlhalla 32bit APK file from a trusted source and install it manually. Here are the steps to do that:</p>
-<ol>
-<li>Go to a reputable website that offers APK files, such as APKPure, APKMirror, or Uptodown. Search for Brawlhalla and download the latest version of the 32bit APK file.</li>
-<li>Before installing the APK file, you need to enable the installation of apps from unknown sources on your device. To do that, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Locate the downloaded APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Once the installation is done, you can launch Brawlhalla from your app drawer and enjoy the game.</li>
-</ol>
-<p>Note: Make sure you have enough storage space on your device before downloading and installing the APK file. Also, be careful when downloading APK files from third-party sources, as some of them may contain malware or viruses. Always scan the files with a reliable antivirus app before installing them.</p>
-<h2>How to Play Brawlhalla on Android</h2>
-<p>Brawlhalla is a game that requires quick reflexes, strategic thinking, and skillful execution. Whether you are playing online or offline, you need to know how to control your character and use your weapons effectively. Here are some basic tips and tricks to help you play Brawlhalla on Android:</p>
-<h3>The basic controls and mechanics of the game</h3>
-<p>Brawlhalla has a simple and intuitive control scheme that you can customize according to your preference. You can also choose between different control modes, such as touch screen, virtual joystick, or external controller. The default touch screen controls are as follows:</p>
-<ul>
-<li>Tap anywhere on the left side of the screen to move your character left or right.</li>
-<li>Swipe up or down on the left side of the screen to jump or drop down from a platform.</li>
-<li>Tap on the right side of the screen to perform a light attack with your weapon.</li>
-<li>Swipe in any direction on the right side of the screen to perform a heavy attack or a signature move with your weapon.</li>
-<li>Tap on the weapon icon on the bottom right corner of the screen to pick up or throw a weapon.</li>
-<li>Tap on the dodge icon on the bottom left corner of the screen to dodge an incoming attack or perform a recovery move in mid-air.</li>
-</ul>
-<p>The basic mechanics of Brawlhalla are similar to other platform fighting games, such as Super Smash Bros. The goal is to knock out your opponents by dealing enough damage to them and sending them flying off the stage. You can see how much damage you have taken by looking at your character's color and percentage. The more damage you take, the redder your character becomes and the higher your percentage goes. The higher your percentage, the farther you fly when hit by an attack.</p>
-<p>brawlhalla android game free download<br />
-brawlhalla cross-play platform fighting<br />
-brawlhalla 50 unique characters<br />
-brawlhalla online ranked matches<br />
-brawlhalla custom rooms with friends<br />
-brawlhalla fun party game modes<br />
-brawlhalla training room combos<br />
-brawlhalla best-in-class spectating<br />
-brawlhalla match recording and replay<br />
-brawlhalla dozens of maps<br />
-brawlhalla single player tournament mode<br />
-brawlhalla online brawl-of-the-week<br />
-brawlhalla experimental mode<br />
-brawlhalla regional servers for low-latency<br />
-brawlhalla frequent updates and events<br />
-brawlhalla career history and rewards<br />
-brawlhalla ranked seasons and ladder<br />
-brawlhalla friendly devs and support<br />
-brawlhalla fair free-to-play model<br />
-brawlhalla all legends pack unlock<br />
-brawlhalla crossover characters and skins<br />
-brawlhalla valhalla legends and lore<br />
-brawlhalla smash and fight among legends<br />
-brawlhalla free-for-all casual matches<br />
-brawlhalla 1v1 and 2v2 competitive modes<br />
-brawlhalla 4v4 and 8 player battles<br />
-brawlhalla kung-foot and bombsketball modes<br />
-brawlhalla capture the flag and brawlball modes<br />
-brawlhalla frame data and hitboxes<br />
-brawlhalla hurtboxes and setups<br />
-brawlhalla esports tournaments and prizes<br />
-brawlhalla community colors and codes<br />
-brawlhalla mammoth coins and gold<br />
-brawlhalla battle pass and missions<br />
-brawlhalla patch notes and balance changes<br />
-brawlhalla tips and tricks for beginners<br />
-brawlhalla guides and tutorials for advanced players<br />
-brawlhalla best legends and weapons for each mode<br />
-brawlhalla combos and strings for each weapon<br />
-brawlhalla sigs and stats for each legend<br />
-brawlhalla taunts and emotes for each legend<br />
-brawlhalla podiums and sidekicks for each legend<br />
-brawlhalla avatars and banners for each legend<br />
-brawlhalla KO effects and weapon skins for each legend<br />
-brawlhalla chest rotations and sales</p>
-<p>You can use different weapons and items to deal damage and knock out your opponents. Weapons spawn randomly on the stage and can be picked up by any player. Each weapon has its own moveset and signature moves that vary depending on which Legend you are using. Items such as bombs, mines, spike balls, and horns can also be thrown at your opponents to damage them or disrupt their movement.</p>
-<h3>The tips and tricks to improve your skills and win more matches</h3>
-<p>Brawlhalla is a game that rewards skill, practice, and creativity. There are many ways to improve your skills and win more matches, such as:</p>
-<ul>
-<li>Learn how to use each weapon and Legend effectively. Experiment with different combinations of weapons and Legends and find out what suits your playstyle best. You can also watch tutorials, guides, and gameplay videos from other players online to learn from them.</li>
-<li>Practice your combos and setups in the Training Room. You can use the Training Room to practice your moves, combos, setups, edgeguards, recoveries, and more. You can also adjust various settings such as gravity, damage, hitboxes, hurtboxes, frame data, etc., to help you analyze and improve your gameplay.</li>
-<ul>
-<li>Play online with other players of different skill levels. Playing online with other players is one of the best ways to improve your skills and learn from your mistakes. You can play online ranked matches to climb the ladder and earn rewards, or play online casual matches to have fun and experiment with different strategies. You can also join custom rooms with your friends or other players and play various game modes and settings.</li>
-<li>Watch replays of your matches and analyze your performance. You can watch replays of your matches and see what you did right and what you did wrong. You can also pause, rewind, fast-forward, and slow down the replay to see every detail of the match. You can use replays to identify your strengths and weaknesses, learn from your opponents, and improve your decision-making and execution.</li>
-<li>Have fun and enjoy the game. Brawlhalla is a game that is meant to be fun and enjoyable for everyone. Don't get too frustrated or angry if you lose or make mistakes. Instead, use them as opportunities to grow and improve. Don't be afraid to try new things and experiment with different weapons and Legends. Don't be too hard on yourself or others, and don't forget to have fun.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Brawlhalla is a free platform fighting game that you can play on your Android device with the Brawlhalla 32bit APK file. It is a game that is easy to learn but hard to master, with many features, modes, characters, and items to choose from. It is a game that is fun and exciting for both casual and competitive players, with full cross-play support across different platforms. If you are looking for a game that will keep you entertained for hours, you should definitely give Brawlhalla a try.</p>
-<h2>FAQs</h2>
-<h3>Is Brawlhalla free to play?</h3>
-<p>Yes, Brawlhalla is free to play on all platforms. You can download it from the Google Play Store for Android devices, or from the official website for PC, PS4, Xbox One, Nintendo Switch, iOS devices. You can also download the Brawlhalla 32bit APK file from a trusted source if you have a 32-bit Android device.</p>
-<h3>Is Brawlhalla safe to download?</h3>
-<p>Yes, Brawlhalla is safe to download from the official sources mentioned above. However, if you are downloading the Brawlhalla 32bit APK file from a third-party source, you should be careful and scan the file with a reliable antivirus app before installing it. Some APK files may contain malware or viruses that can harm your device or compromise your privacy.</p>
-<h3>How do I update Brawlhalla on Android?</h3>
-<p>If you have downloaded Brawlhalla from the Google Play Store, you can update it automatically or manually from there. If you have downloaded the Brawlhalla 32bit APK file from a third-party source, you will need to download the latest version of the APK file from the same source and install it over the existing one.</p>
-<h3>How do I get more gold in Brawlhalla?</h3>
-<p>You can get more gold in Brawlhalla by playing any online game mode, such as ranked, free for all, custom rooms, etc. You can also get more gold by completing daily missions and weekly challenges, or by leveling up your account or your Legends.</p>
-<h3>How do I get more skins in Brawlhalla?</h3>
-<p>You can get more skins in Brawlhalla by purchasing them with Mammoth Coins, which are the premium currency of the game. You can buy Mammoth Coins with real money through in-app purchases or through official partner websites. You can also get some skins for free by participating in events, promotions, giveaways, tournaments, etc.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/FNAF 4 Download Free The Ultimate Guide to Install and Play the Game.md

@@ -1,132 +0,0 @@
-
-<h1>How to Download and Play Five Nights at Freddy's 4 for Free</h1>
-<p>If you are a fan of horror games, you might have heard of Five Nights at Freddy's, a popular series of survival horror games that have terrified millions of players around the world. The fourth installment of the series, Five Nights at Freddy's 4, is arguably the most terrifying and challenging one yet. In this article, we will tell you what Five Nights at Freddy's 4 is, why you should play it, and how you can download and play it for free on your PC or mobile device.</p>
- <h2>What is Five Nights at Freddy's 4?</h2>
-<p>Five Nights at Freddy's 4, originally named Five Nights at Freddy's: The Final Chapter in development, is an indie point-and-click survival horror game developed and published by Scott Cawthon, and the fourth installment of the Five Nights at Freddy's series. The game is a prequel to Five Nights at Freddy's 2, and takes place in 1983, chronologically being the first game in the series.</p>
-<h2>fnaf 4 download free</h2><br /><p><b><b>Download Zip</b> &#10004;&#10004;&#10004; <a href="https://jinyurl.com/2uNU7g">https://jinyurl.com/2uNU7g</a></b></p><br /><br />
-<p>The game takes place in the bedroom of a child, where the player must avoid attack by nightmarish animatronics that stalk them. Instead of having a monitor to ward away the animatronics, the player must instead check the doors, closet, and the bed and utilize a flashlight to ward away any nightmare animatronics outside the room, relying on environmental noises to know if something is approaching or about to attack.</p>
- <h2>Why should you play Five Nights at Freddy's 4?</h2>
-<p>Five Nights at Freddy's 4 is a game that will test your nerves, reflexes, and patience. It is not a game for the faint-hearted, as it features some of the most terrifying jumpscares and sound effects in gaming history. The game also has a deep and mysterious lore that will keep you hooked and intrigued. The game has received mostly positive reviews from critics and players alike, praising its horror, suspense, and challenge.</p>
-<p>If you are looking for a game that will make you scream, sweat, and jump out of your seat, then Five Nights at Freddy's 4 is the game for you. It is a game that will make you feel like you are in a nightmare that you can't wake up from. It is a game that will make you question your sanity and reality. It is a game that will make you experience fear like never before.</p>
- <h2>How to download Five Nights at Freddy's 4 for free on PC?</h2>
-<p>If you want to play Five Nights at Freddy's 4 on your PC for free, you can use BlueStacks, an Android emulator that allows you to run Android apps and games on your PC. Here are the steps to download and play Five Nights at Freddy's 4 for free on PC using BlueStacks:</p>
-<ol>
-<li>Download BlueStacks from [here](^1^) and install it on your PC.</li>
-<li>Launch BlueStacks and sign in with your Google account.</li>
-<li>Search for Five Nights at Freddy's 4 in the Google Play Store app and install it.</li>
-<li>Open Five Nights at Freddy's 4 and enjoy playing it on your PC.</li>
-</ol>
-<p>You can also customize the settings, controls, and graphics of the game according to your preference using BlueStacks. You can also record and stream your gameplay using BlueStacks' built-in features.</p>
-<p>fnaf 4 full game free download<br />
-fnaf 4 pc download free<br />
-fnaf 4 android download free<br />
-fnaf 4 apk download free<br />
-fnaf 4 demo download free<br />
-fnaf 4 online free no download<br />
-fnaf 4 free download windows 10<br />
-fnaf 4 free download steam<br />
-fnaf 4 free download mac<br />
-fnaf 4 free download ios<br />
-fnaf 4 free download for laptop<br />
-fnaf 4 free download unblocked<br />
-fnaf 4 free download ocean of games<br />
-fnaf 4 free download mega<br />
-fnaf 4 free download mediafire<br />
-fnaf 4 free download softonic<br />
-fnaf 4 free download gamejolt<br />
-fnaf 4 free download uptodown<br />
-fnaf 4 free download apkpure<br />
-fnaf 4 free download android apk<br />
-fnaf 4 free download android no virus<br />
-fnaf 4 free download android full version<br />
-fnaf 4 free download android aptoide<br />
-fnaf 4 free download android mob.org<br />
-fnaf 4 free download android mod apk<br />
-fnaf 4 free download pc full version<br />
-fnaf 4 free download pc no virus<br />
-fnaf 4 free download pc windows 7<br />
-fnaf 4 free download pc windows xp<br />
-fnaf 4 free download pc rar<br />
-fnaf 4 free download pc zip file<br />
-fnaf 4 free download pc highly compressed<br />
-fnaf 4 free download pc crack<br />
-fnaf 4 free download pc setup.exe<br />
-fnaf 4 free download pc without steam<br />
-how to get fnaf 4 for free on pc<br />
-how to get fnaf 4 for free on android<br />
-how to get fnaf 4 for free on ios<br />
-how to get fnaf 4 for free on mac<br />
-how to get fnaf 4 for free on steam<br />
-how to play fnaf 4 for free online no download<br />
-how to play fnaf 4 for free on pc without downloading it<br />
-how to play fnaf 4 for free on android without downloading it<br />
-how to play fnaf 4 for free on ios without downloading it<br />
-how to play fnaf 4 for free on mac without downloading it</p>
- <h2>How to download Five Nights at Freddy's 4 for free on mobile?</h2>
-<p>If you want to play Five Nights at Freddy's 4 on your mobile device for free, you can use Google Play or App Store to download the game on your Android or iOS device. Here are the steps to download and play Five Nights at Freddy's 4 for free on mobile using Google Play or App Store:</p>
-<ol>
-<li>Open Google Play or App Store on your device and search for Five Nights at Freddy's 4.</li>
-<li>Tap on the game and install it on your device.</li>
-<li>Open Five Nights at Freddy's 4 and enjoy playing it on your mobile device.</li>
-</ol>
-<p>You can also adjust the settings, controls, and sound of the game according to your preference using the game's menu. You can also use headphones or earphones to enhance the immersion and horror of the game.</p>
- <h2>How to play Five Nights at Freddy's 4 effectively?</h2>
-<p>Five Nights at Freddy's 4 is a game that requires skill, strategy, and concentration. It is not a game that you can play casually or mindlessly. It is a game that will challenge you and make you think fast. Here are some gameplay tips and strategies to help you play Five Nights at Freddy's 4 effectively:</p>
-<ul>
-<li>Listen carefully to the sounds. The sounds are your main source of information in the game. You need to listen to the breathing, footsteps, laughter, and other noises that indicate the presence and location of the nightmare animatronics. If you hear breathing at the door, close it until you hear them leave. If you hear footsteps or laughter, flash your light at the door or closet to scare them away. If you hear nothing, check the bed or the closet for any plushies or animatronics.</li>
-<li>Use your flashlight wisely. Your flashlight is your only weapon in the game, but it also consumes power and attracts attention. You need to use it sparingly and strategically. You need to flash it at the door or closet to check for any animatronics or plushies, but only for a brief moment. If you flash it too long or too often, you will run out of power or attract more animatronics. You also need to avoid flashing it when you hear breathing, as that will trigger a jumpscare.</li>
-<li>Manage your time and power. The game lasts from 12 AM to 6 AM, which is equivalent to about 8 minutes in real time. You need to survive each night without running out of power or getting jumpscared by the animatronics. You need to balance your time and power between checking the doors, closet, bed, and hallway. You need to prioritize the most dangerous animatronics, such as Nightmare Fredbear and Nightmare, who can appear from any direction and require quick reactions.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Five Nights at Freddy's 4 is a game that will make you experience horror like never before. It is a game that will make you scream, sweat, and jump out of your seat. It is a game that will make you question your sanity and reality. It is a game that will make you feel like you are in a nightmare that you can't wake up from.</p>
-<p>If you are brave enough to face your fears, then download and play Five Nights at Freddy's 4 for free on your PC or mobile device today. You can use BlueStacks, Google Play, or App Store to download and play the game easily and conveniently. You can also use our gameplay tips and strategies to help you survive the nights and avoid the jumpscares.</p>
-<p>Are you ready to face the nightmare? Are you ready to play Five Nights at Freddy's 4?</p>
- <h2>FAQs</h2>
-<h3>What is the story of Five Nights at Freddy's 4?</h3>
-<p>The story of Five Nights at Freddy's 4 is told through minigames that occur between each night. The minigames reveal that the game takes place in 1983, and follows a young boy who is tormented by his older brother and his friends who wear masks of Freddy Fazbear and his friends. The boy is also terrified of Fredbear's Family Diner, a restaurant that features animatronic mascots that entertain children during the day. On his birthday, his brother and his friends force him to get close to Fredbear, who bites his head, causing the infamous Bite of '83. The boy is then hospitalized and suffers from nightmares of the animatronics, which are the gameplay segments of the game. The boy eventually dies from his injuries, and is comforted by a voice that tells him that he will put him back together.</p>
- <h3>Who are the nightmare animatronics in Five Nights at Freddy's 4?</h3>
-<p>The nightmare animatronics in Five Nights at Freddy's 4 are twisted and monstrous versions of the original animatronics from the previous games. They are the manifestations of the boy's fear and trauma, and they include:</p>
-<ul>
-<li>Nightmare Freddy: A dark brown bear with three smaller Freddles on his body. He can appear from the bed or the right hall.</li>
-<li>Nightmare Bonnie: A dark blue rabbit with sharp teeth and claws. He can appear from the left hall or the closet.</li>
-<li>Nightmare Chica: A dark yellow chicken with a cupcake on a plate. She can appear from the right hall or the closet.</li>
-<li>Nightmare Foxy: A dark red fox with a hook and an eye patch. He can appear from the closet or the left hall.</li>
-<li>Nightmare Fredbear: A golden bear with purple hat and bow tie. He can appear from any direction and replaces all other animatronics on Night 5 and 6.</li>
-<li>Nightmare: A black and transparent version of Nightmare Fredbear with white eyes and teeth. He can appear from any direction and replaces Nightmare Fredbear on Night 7 and 8.</li>
-<li>Plushtrap: A small green rabbit with a spring-loaded mechanism. He can appear in a separate minigame called Fun with Plushtrap, where the player must stop him on an X mark using a flashlight.</li>
-<li>Nightmarionne: A black and white puppet with long arms and legs. He can appear in the Halloween Edition of the game, where he replaces Plushtrap.</li>
-<li>Nightmare Mangle: A mangled version of Foxy with multiple heads and limbs. He can appear in the Halloween Edition of the game, where he replaces Nightmare Foxy.</li>
-<li>Jack-O-Bonnie: A dark orange rabbit with a jack-o-lantern head. He can appear in the Halloween Edition of the game, where he replaces Nightmare Bonnie.</li>
-<li>Jack-O-Chica: A dark orange chicken with a jack-o-lantern head and a pumpkin on a plate. She can appear in the Halloween Edition of the game, where she replaces Nightmare Chica.</li>
-</ul>
- <h3>What are the secrets and easter eggs in Five Nights at Freddy's 4?</h3>
-<p>Five Nights at Freddy's 4 is a game that is full of secrets and easter eggs that add to its lore and mystery. Some of the secrets and easter eggs in Five Nights at Freddy's 4 are:</p>
-<ul>
-<li>The clock ending: If the player collects four keys hidden in various minigames, they can unlock a secret ending where they play as Fredbear plushie and guide the crying child to a locked box that contains "the pieces put together". However, the box never opens, leaving its contents unknown.</li>
-<li>The newspaper clippings: If the player looks closely at some of the newspapers on the walls of the minigames, they can see some references to previous games, such as "Fredbear's Family Diner to close after tragedy", "Local pizzeria threatened with shutdown over sanitation", and "Local pizzeria said to close by year's end".</li>
-<li>The IV drip, flowers, and pills: If the player looks closely at some of the objects in the bedroom, they can see an IV drip, flowers, and pills that appear and disappear randomly. These objects imply that the child is in a coma and is being treated in a hospital.</li>
-<li>The phone call: If the player listens carefully to the background noise of Night 1, they can hear a distorted version of the phone call from Five Nights at Freddy's 1, where Phone Guy mentions the Bite of '87. This suggests that the game is connected to the first game and that the Bite of '83 and the Bite of '87 are two separate incidents.</li>
-<li>The purple guy: If the player completes the Night 3 minigame, they can see a brief glimpse of a man in a purple uniform putting a Spring Bonnie suit on an employee. This man is implied to be William Afton, the main antagonist of the series and the killer of the children who possess the animatronics.</li>
-</ul>
- <h3>Is Five Nights at Freddy's 4 the last game in the series?</h3>
-<p>No, Five Nights at Freddy's 4 is not the last game in the series. Although it was originally intended to be the final chapter of the original story, Scott Cawthon later announced that he would continue to make more games in the series, as well as spin-offs, novels, and movies. Some of the games that have been released after Five Nights at Freddy's 4 are:</p>
-<ul>
-<li>Five Nights at Freddy's: Sister Location: A game that takes place in a sister location of Freddy Fazbear's Pizza, where the player must survive against new animatronics called Circus Baby, Ballora, Funtime Freddy, and Funtime Foxy.</li>
-<li>Freddy Fazbear's Pizzeria Simulator: A game that combines a tycoon simulator and a survival horror game, where the player must manage their own pizzeria and deal with salvaged animatronics that try to kill them.</li>
-<li>Ultimate Custom Night: A game that features 50 selectable animatronics from previous games, where the player can customize their difficulty and challenge themselves to survive as long as possible.</li>
-<li>Five Nights at Freddy's VR: Help Wanted: A game that features virtual reality versions of classic and original minigames set in the Five Nights at Freddy's universe.</li>
-<li>Five Nights at Freddy's AR: Special Delivery: A game that uses augmented reality to bring animatronics to life in the real world, where the player must collect, repair, and fight them.</li>
-<li>Five Nights at Freddy's: Security Breach: A game that is set to be released in late 2021, where the player will explore a new location called Freddy Fazbear's Mega Pizza Plex, and face new animatronics such as Glamrock Freddy, Glamrock Chica, Montgomery Gator, Roxanne Wolf, and Vanny.</li>
-</ul>
- <h3>Where can I find more information about Five Nights at Freddy's 4?</h3>
-<p>If you want to find more information about Five Nights at Freddy's 4, you can visit some of these websites:</p>
-<table>
-<tr><th>Website</th><th>Description</th></tr>
-<tr><td>[Five Nights at Freddy's Wiki]</td><td>A comprehensive wiki that contains information about the characters, locations, gameplay, lore, and secrets of Five Nights at Freddy's 4 and other games in the series.</td></tr>
-<tr><td>[Scott Games]</td><td>The official website of Scott Cawthon, the creator of Five Nights at Freddy's 4 and other games in the series. The website features teasers, updates, and announcements about his projects.</td></tr>
-<tr><td>[Steam]</td><td>The official store page of Five Nights at Freddy's 4 on Steam, where you can buy the game, read reviews, and join discussions.</td></tr>
-<tr><td>[YouTube]</td><td>A popular video-sharing platform where you can watch gameplay videos, trailers, theories, and reactions of Five Nights at Freddy's 4 and other games in the series.</td></tr>
-<tr><td>[Reddit]</td><td>A popular online community where you can join subreddits, such as r/fivenightsatfreddys, r/fnaf, and r/fnaf4, to share your thoughts, opinions, fan art, memes, and questions about Five Nights at Freddy's 4 and other games in the series.</td></tr>
-</table></p> 197e85843d<br />
-<br />
-<br />

spaces/2023Liu2023/bingo/src/components/user-menu.tsx

@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}

spaces/2ndelement/voicevox/voicevox_engine/__init__.py

@@ -1 +0,0 @@
-__version__ = "latest"

spaces/AIFILMS/ControlNet-Video/README.md

@@ -1,14 +0,0 @@
----
-title: ControlNet-Video
-emoji: 🕹
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-python_version: 3.10.9
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/ControlNet-Video
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/commons/conformer/layers.py

@@ -1,260 +0,0 @@
-from torch import nn
-import torch
-
-from text_to_speech.modules.commons.layers import LayerNorm
-
-
-class ConvolutionModule(nn.Module):
-    """ConvolutionModule in Conformer model.
-    Args:
-        channels (int): The number of channels of conv layers.
-        kernel_size (int): Kernerl size of conv layers.
-    """
-
-    def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
-        """Construct an ConvolutionModule object."""
-        super(ConvolutionModule, self).__init__()
-        # kernerl_size should be a odd number for 'SAME' padding
-        assert (kernel_size - 1) % 2 == 0
-
-        self.pointwise_conv1 = nn.Conv1d(
-            channels,
-            2 * channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=bias,
-        )
-        self.depthwise_conv = nn.Conv1d(
-            channels,
-            channels,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-            groups=channels,
-            bias=bias,
-        )
-        self.norm = nn.BatchNorm1d(channels)
-        self.pointwise_conv2 = nn.Conv1d(
-            channels,
-            channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=bias,
-        )
-        self.activation = activation
-
-    def forward(self, x):
-        """Compute convolution module.
-        Args:
-            x (torch.Tensor): Input tensor (#batch, time, channels).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time, channels).
-        """
-        # exchange the temporal dimension and the feature dimension
-        x = x.transpose(1, 2)
-
-        # GLU mechanism
-        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
-        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
-
-        # 1D Depthwise Conv
-        x = self.depthwise_conv(x)
-        x = self.activation(self.norm(x))
-
-        x = self.pointwise_conv2(x)
-
-        return x.transpose(1, 2)
-
-
-class MultiLayeredConv1d(torch.nn.Module):
-    """Multi-layered conv1d for Transformer block.
-    This is a module of multi-leyered conv1d designed
-    to replace positionwise feed-forward network
-    in Transforner block, which is introduced in
-    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
-    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
-        https://arxiv.org/pdf/1905.09263.pdf
-    """
-
-    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
-        """Initialize MultiLayeredConv1d module.
-        Args:
-            in_chans (int): Number of input channels.
-            hidden_chans (int): Number of hidden channels.
-            kernel_size (int): Kernel size of conv1d.
-            dropout_rate (float): Dropout rate.
-        """
-        super(MultiLayeredConv1d, self).__init__()
-        self.w_1 = torch.nn.Conv1d(
-            in_chans,
-            hidden_chans,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-        )
-        self.w_2 = torch.nn.Conv1d(
-            hidden_chans,
-            in_chans,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-        )
-        self.dropout = torch.nn.Dropout(dropout_rate)
-
-    def forward(self, x):
-        """Calculate forward propagation.
-        Args:
-            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
-        Returns:
-            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
-        """
-        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
-        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
-
-
-class Swish(torch.nn.Module):
-    """Construct an Swish object."""
-
-    def forward(self, x):
-        """Return Swich activation function."""
-        return x * torch.sigmoid(x)
-
-
-class EncoderLayer(nn.Module):
-    """Encoder layer module.
-    Args:
-        size (int): Input dimension.
-        self_attn (torch.nn.Module): Self-attention module instance.
-            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
-            can be used as the argument.
-        feed_forward (torch.nn.Module): Feed-forward module instance.
-            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
-            can be used as the argument.
-        feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
-            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
-            can be used as the argument.
-        conv_module (torch.nn.Module): Convolution module instance.
-            `ConvlutionModule` instance can be used as the argument.
-        dropout_rate (float): Dropout rate.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
-            if True, additional linear will be applied.
-            i.e. x -> x + linear(concat(x, att(x)))
-            if False, no additional linear will be applied. i.e. x -> x + att(x)
-    """
-
-    def __init__(
-            self,
-            size,
-            self_attn,
-            feed_forward,
-            feed_forward_macaron,
-            conv_module,
-            dropout_rate,
-            normalize_before=True,
-            concat_after=False,
-    ):
-        """Construct an EncoderLayer object."""
-        super(EncoderLayer, self).__init__()
-        self.self_attn = self_attn
-        self.feed_forward = feed_forward
-        self.feed_forward_macaron = feed_forward_macaron
-        self.conv_module = conv_module
-        self.norm_ff = LayerNorm(size)  # for the FNN module
-        self.norm_mha = LayerNorm(size)  # for the MHA module
-        if feed_forward_macaron is not None:
-            self.norm_ff_macaron = LayerNorm(size)
-            self.ff_scale = 0.5
-        else:
-            self.ff_scale = 1.0
-        if self.conv_module is not None:
-            self.norm_conv = LayerNorm(size)  # for the CNN module
-            self.norm_final = LayerNorm(size)  # for the final output of the block
-        self.dropout = nn.Dropout(dropout_rate)
-        self.size = size
-        self.normalize_before = normalize_before
-        self.concat_after = concat_after
-        if self.concat_after:
-            self.concat_linear = nn.Linear(size + size, size)
-
-    def forward(self, x_input, mask, cache=None):
-        """Compute encoded features.
-        Args:
-            x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
-                - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
-                - w/o pos emb: Tensor (#batch, time, size).
-            mask (torch.Tensor): Mask tensor for the input (#batch, time).
-            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
-        Returns:
-            torch.Tensor: Output tensor (#batch, time, size).
-            torch.Tensor: Mask tensor (#batch, time).
-        """
-        if isinstance(x_input, tuple):
-            x, pos_emb = x_input[0], x_input[1]
-        else:
-            x, pos_emb = x_input, None
-
-        # whether to use macaron style
-        if self.feed_forward_macaron is not None:
-            residual = x
-            if self.normalize_before:
-                x = self.norm_ff_macaron(x)
-            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
-            if not self.normalize_before:
-                x = self.norm_ff_macaron(x)
-
-        # multi-headed self-attention module
-        residual = x
-        if self.normalize_before:
-            x = self.norm_mha(x)
-
-        if cache is None:
-            x_q = x
-        else:
-            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
-            x_q = x[:, -1:, :]
-            residual = residual[:, -1:, :]
-            mask = None if mask is None else mask[:, -1:, :]
-
-        if pos_emb is not None:
-            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
-        else:
-            x_att = self.self_attn(x_q, x, x, mask)
-
-        if self.concat_after:
-            x_concat = torch.cat((x, x_att), dim=-1)
-            x = residual + self.concat_linear(x_concat)
-        else:
-            x = residual + self.dropout(x_att)
-        if not self.normalize_before:
-            x = self.norm_mha(x)
-
-        # convolution module
-        if self.conv_module is not None:
-            residual = x
-            if self.normalize_before:
-                x = self.norm_conv(x)
-            x = residual + self.dropout(self.conv_module(x))
-            if not self.normalize_before:
-                x = self.norm_conv(x)
-
-        # feed forward module
-        residual = x
-        if self.normalize_before:
-            x = self.norm_ff(x)
-        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
-        if not self.normalize_before:
-            x = self.norm_ff(x)
-
-        if self.conv_module is not None:
-            x = self.norm_final(x)
-
-        if cache is not None:
-            x = torch.cat([cache, x], dim=1)
-
-        if pos_emb is not None:
-            return (x, pos_emb), mask
-
-        return x, mask

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/updater/__init__.py

@@ -1,9 +0,0 @@
-from agentverse.registry import Registry
-
-updater_registry = Registry(name="UpdaterRegistry")
-
-from .base import BaseUpdater
-from .basic import BasicUpdater
-from .classroom import ClassroomUpdater
-from .sde_team import SdeTeamUpdater
-from .pokemon import PokemonUpdater

spaces/Ahmadjaved/Genaispeech/README.md

@@ -1,12 +0,0 @@
----
-title: Genaispeech
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/saicinpainting/training/trainers/base.py

@@ -1,291 +0,0 @@
-import copy
-import logging
-from typing import Dict, Tuple
-
-import pandas as pd
-import pytorch_lightning as ptl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DistributedSampler
-
-from saicinpainting.evaluation import make_evaluator
-from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
-from saicinpainting.training.losses.adversarial import make_discrim_loss
-from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
-from saicinpainting.training.modules import make_generator, make_discriminator
-from saicinpainting.training.visualizers import make_visualizer
-from saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \
-    get_has_ddp_rank
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_optimizer(parameters, kind='adamw', **kwargs):
-    if kind == 'adam':
-        optimizer_class = torch.optim.Adam
-    elif kind == 'adamw':
-        optimizer_class = torch.optim.AdamW
-    else:
-        raise ValueError(f'Unknown optimizer kind {kind}')
-    return optimizer_class(parameters, **kwargs)
-
-
-def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999):
-    with torch.no_grad():
-        res_params = dict(result.named_parameters())
-        new_params = dict(new_iterate_model.named_parameters())
-
-        for k in res_params.keys():
-            res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay)
-
-
-def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'):
-    batch_size, _, height, width = base_tensor.shape
-    cur_height, cur_width = height, width
-    result = []
-    align_corners = False if scale_mode in ('bilinear', 'bicubic') else None
-    for _ in range(scales):
-        cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device)
-        cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners)
-        result.append(cur_sample_scaled)
-        cur_height //= 2
-        cur_width //= 2
-    return torch.cat(result, dim=1)
-
-
-class BaseInpaintingTrainingModule(ptl.LightningModule):
-    def __init__(self, config, use_ddp, *args,  predict_only=False, visualize_each_iters=100,
-                 average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000,
-                 average_generator_period=10, store_discr_outputs_for_vis=False,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        LOGGER.info('BaseInpaintingTrainingModule init called')
-
-        self.config = config
-
-        self.generator = make_generator(config, **self.config.generator)
-        self.use_ddp = use_ddp
-
-        if not get_has_ddp_rank():
-            LOGGER.info(f'Generator\n{self.generator}')
-
-        if not predict_only:
-            self.save_hyperparameters(self.config)
-            self.discriminator = make_discriminator(**self.config.discriminator)
-            self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial)
-            self.visualizer = make_visualizer(**self.config.visualizer)
-            self.val_evaluator = make_evaluator(**self.config.evaluator)
-            self.test_evaluator = make_evaluator(**self.config.evaluator)
-
-            if not get_has_ddp_rank():
-                LOGGER.info(f'Discriminator\n{self.discriminator}')
-
-            extra_val = self.config.data.get('extra_val', ())
-            if extra_val:
-                self.extra_val_titles = list(extra_val)
-                self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator)
-                                                       for k in extra_val})
-            else:
-                self.extra_evaluators = {}
-
-            self.average_generator = average_generator
-            self.generator_avg_beta = generator_avg_beta
-            self.average_generator_start_step = average_generator_start_step
-            self.average_generator_period = average_generator_period
-            self.generator_average = None
-            self.last_generator_averaging_step = -1
-            self.store_discr_outputs_for_vis = store_discr_outputs_for_vis
-
-            if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0:
-                self.loss_l1 = nn.L1Loss(reduction='none')
-
-            if self.config.losses.get("mse", {"weight": 0})['weight'] > 0:
-                self.loss_mse = nn.MSELoss(reduction='none')
-            
-            if self.config.losses.perceptual.weight > 0:
-                self.loss_pl = PerceptualLoss()
-
-            if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0:
-                self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl)
-            else:
-                self.loss_resnet_pl = None
-
-        self.visualize_each_iters = visualize_each_iters
-        LOGGER.info('BaseInpaintingTrainingModule init done')
-
-    def configure_optimizers(self):
-        discriminator_params = list(self.discriminator.parameters())
-        return [
-            dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)),
-            dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)),
-        ]
-
-    def train_dataloader(self):
-        kwargs = dict(self.config.data.train)
-        if self.use_ddp:
-            kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
-                                        rank=self.trainer.global_rank,
-                                        shuffle=True)
-        dataloader = make_default_train_dataloader(**self.config.data.train)
-        return dataloader
-
-    def val_dataloader(self):
-        res = [make_default_val_dataloader(**self.config.data.val)]
-
-        if self.config.data.visual_test is not None:
-            res = res + [make_default_val_dataloader(**self.config.data.visual_test)]
-        else:
-            res = res + res
-
-        extra_val = self.config.data.get('extra_val', ())
-        if extra_val:
-            res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles]
-
-        return res
-
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
-        self._is_training_step = True
-        return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx)
-
-    def validation_step(self, batch, batch_idx, dataloader_idx):
-        extra_val_key = None
-        if dataloader_idx == 0:
-            mode = 'val'
-        elif dataloader_idx == 1:
-            mode = 'test'
-        else:
-            mode = 'extra_val'
-            extra_val_key = self.extra_val_titles[dataloader_idx - 2]
-        self._is_training_step = False
-        return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)
-
-    def training_step_end(self, batch_parts_outputs):
-        if self.training and self.average_generator \
-                and self.global_step >= self.average_generator_start_step \
-                and self.global_step >= self.last_generator_averaging_step + self.average_generator_period:
-            if self.generator_average is None:
-                self.generator_average = copy.deepcopy(self.generator)
-            else:
-                update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta)
-            self.last_generator_averaging_step = self.global_step
-
-        full_loss = (batch_parts_outputs['loss'].mean()
-                     if torch.is_tensor(batch_parts_outputs['loss'])  # loss is not tensor when no discriminator used
-                     else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True))
-        log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()}
-        self.log_dict(log_info, on_step=True, on_epoch=False)
-        return full_loss
-
-    def validation_epoch_end(self, outputs):
-        outputs = [step_out for out_group in outputs for step_out in out_group]
-        averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs)
-        self.log_dict({k: v.mean() for k, v in averaged_logs.items()})
-
-        pd.set_option('display.max_columns', 500)
-        pd.set_option('display.width', 1000)
-
-        # standard validation
-        val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s]
-        val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states)
-        val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0)
-        val_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{val_evaluator_res_df}')
-
-        for k, v in flatten_dict(val_evaluator_res).items():
-            self.log(f'val_{k}', v)
-
-        # standard visual test
-        test_evaluator_states = [s['test_evaluator_state'] for s in outputs
-                                 if 'test_evaluator_state' in s]
-        test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states)
-        test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0)
-        test_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{test_evaluator_res_df}')
-
-        for k, v in flatten_dict(test_evaluator_res).items():
-            self.log(f'test_{k}', v)
-
-        # extra validations
-        if self.extra_evaluators:
-            for cur_eval_title, cur_evaluator in self.extra_evaluators.items():
-                cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state'
-                cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s]
-                cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states)
-                cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0)
-                cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-                LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, '
-                            f'total {self.global_step} iterations:\n{cur_evaluator_res_df}')
-                for k, v in flatten_dict(cur_evaluator_res).items():
-                    self.log(f'extra_val_{cur_eval_title}_{k}', v)
-
-    def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None):
-        if optimizer_idx == 0:  # step for generator
-            set_requires_grad(self.generator, True)
-            set_requires_grad(self.discriminator, False)
-        elif optimizer_idx == 1:  # step for discriminator
-            set_requires_grad(self.generator, False)
-            set_requires_grad(self.discriminator, True)
-
-        batch = self(batch)
-
-        total_loss = 0
-        metrics = {}
-
-        if optimizer_idx is None or optimizer_idx == 0:  # step for generator
-            total_loss, metrics = self.generator_loss(batch)
-
-        elif optimizer_idx is None or optimizer_idx == 1:  # step for discriminator
-            if self.config.losses.adversarial.weight > 0:
-                total_loss, metrics = self.discriminator_loss(batch)
-
-        if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'):
-            if self.config.losses.adversarial.weight > 0:
-                if self.store_discr_outputs_for_vis:
-                    with torch.no_grad():
-                        self.store_discr_outputs(batch)
-            vis_suffix = f'_{mode}'
-            if mode == 'extra_val':
-                vis_suffix += f'_{extra_val_key}'
-            self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix)
-
-        metrics_prefix = f'{mode}_'
-        if mode == 'extra_val':
-            metrics_prefix += f'{extra_val_key}_'
-        result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix))
-        if mode == 'val':
-            result['val_evaluator_state'] = self.val_evaluator.process_batch(batch)
-        elif mode == 'test':
-            result['test_evaluator_state'] = self.test_evaluator.process_batch(batch)
-        elif mode == 'extra_val':
-            result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch)
-
-        return result
-
-    def get_current_generator(self, no_average=False):
-        if not no_average and not self.training and self.average_generator and self.generator_average is not None:
-            return self.generator_average
-        return self.generator
-
-    def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-        """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys"""
-        raise NotImplementedError()
-
-    def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def store_discr_outputs(self, batch):
-        out_size = batch['image'].shape[2:]
-        discr_real_out, _ = self.discriminator(batch['image'])
-        discr_fake_out, _ = self.discriminator(batch['predicted_image'])
-        batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest')
-        batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest')
-        batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake']
-
-    def get_ddp_rank(self):
-        return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/__init__.py

@@ -1,13 +0,0 @@
-from .base_bbox_coder import BaseBBoxCoder
-from .bucketing_bbox_coder import BucketingBBoxCoder
-from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
-from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
-from .pseudo_bbox_coder import PseudoBBoxCoder
-from .tblr_bbox_coder import TBLRBBoxCoder
-from .yolo_bbox_coder import YOLOBBoxCoder
-
-__all__ = [
-    'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
-    'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
-    'BucketingBBoxCoder'
-]

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py

@@ -1,172 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init, xavier_init
-
-from mmdet.models.backbones.resnet import Bottleneck
-from mmdet.models.builder import HEADS
-from .bbox_head import BBoxHead
-
-
-class BasicResBlock(nn.Module):
-    """Basic residual block.
-
-    This block is a little different from the block in the ResNet backbone.
-    The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        out_channels (int): Channels of the output feature map.
-        conv_cfg (dict): The config dict for convolution layers.
-        norm_cfg (dict): The config dict for normalization layers.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN')):
-        super(BasicResBlock, self).__init__()
-
-        # main path
-        self.conv1 = ConvModule(
-            in_channels,
-            in_channels,
-            kernel_size=3,
-            padding=1,
-            bias=False,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg)
-        self.conv2 = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=1,
-            bias=False,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        # identity path
-        self.conv_identity = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        identity = x
-
-        x = self.conv1(x)
-        x = self.conv2(x)
-
-        identity = self.conv_identity(identity)
-        out = x + identity
-
-        out = self.relu(out)
-        return out
-
-
-@HEADS.register_module()
-class DoubleConvFCBBoxHead(BBoxHead):
-    r"""Bbox head used in Double-Head R-CNN
-
-    .. code-block:: none
-
-                                          /-> cls
-                      /-> shared convs ->
-                                          \-> reg
-        roi features
-                                          /-> cls
-                      \-> shared fc    ->
-                                          \-> reg
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_convs=0,
-                 num_fcs=0,
-                 conv_out_channels=1024,
-                 fc_out_channels=1024,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 **kwargs):
-        kwargs.setdefault('with_avg_pool', True)
-        super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
-        assert self.with_avg_pool
-        assert num_convs > 0
-        assert num_fcs > 0
-        self.num_convs = num_convs
-        self.num_fcs = num_fcs
-        self.conv_out_channels = conv_out_channels
-        self.fc_out_channels = fc_out_channels
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-
-        # increase the channel of input features
-        self.res_block = BasicResBlock(self.in_channels,
-                                       self.conv_out_channels)
-
-        # add conv heads
-        self.conv_branch = self._add_conv_branch()
-        # add fc heads
-        self.fc_branch = self._add_fc_branch()
-
-        out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
-        self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)
-
-        self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
-        self.relu = nn.ReLU(inplace=True)
-
-    def _add_conv_branch(self):
-        """Add the fc branch which consists of a sequential of conv layers."""
-        branch_convs = nn.ModuleList()
-        for i in range(self.num_convs):
-            branch_convs.append(
-                Bottleneck(
-                    inplanes=self.conv_out_channels,
-                    planes=self.conv_out_channels // 4,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        return branch_convs
-
-    def _add_fc_branch(self):
-        """Add the fc branch which consists of a sequential of fc layers."""
-        branch_fcs = nn.ModuleList()
-        for i in range(self.num_fcs):
-            fc_in_channels = (
-                self.in_channels *
-                self.roi_feat_area if i == 0 else self.fc_out_channels)
-            branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
-        return branch_fcs
-
-    def init_weights(self):
-        # conv layers are already initialized by ConvModule
-        normal_init(self.fc_cls, std=0.01)
-        normal_init(self.fc_reg, std=0.001)
-
-        for m in self.fc_branch.modules():
-            if isinstance(m, nn.Linear):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, x_cls, x_reg):
-        # conv head
-        x_conv = self.res_block(x_reg)
-
-        for conv in self.conv_branch:
-            x_conv = conv(x_conv)
-
-        if self.with_avg_pool:
-            x_conv = self.avg_pool(x_conv)
-
-        x_conv = x_conv.view(x_conv.size(0), -1)
-        bbox_pred = self.fc_reg(x_conv)
-
-        # fc head
-        x_fc = x_cls.view(x_cls.size(0), -1)
-        for fc in self.fc_branch:
-            x_fc = self.relu(fc(x_fc))
-
-        cls_score = self.fc_cls(x_fc)
-
-        return cls_score, bbox_pred

spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/gui/ui_draw.py

@@ -1,189 +0,0 @@
-from PyQt5 import QtGui, QtCore, QtWidgets
-
-
-#######################################################################################################
-# painter function
-#######################################################################################################
-class painter(QtWidgets.QWidget):
-    """the class for a painter"""
-    def __init__(self, parent, image=None):
-        super(painter, self).__init__()
-        if image is None:
-            w = h = 256
-        else:
-            w, h = image.size().width(), image.size().height()
-        self.ParentLink = parent
-        self.setPalette(QtGui.QPalette(QtCore.Qt.white))
-        self.setAutoFillBackground(True)
-        self.setMaximumSize(w, h)
-        self.map = QtGui.QImage(w, h, QtGui.QImage.Format_RGB32)
-        self.map.fill(QtCore.Qt.black)
-        self.image = image
-        self.shape = self.ParentLink.shape
-        self.CurrentWidth = self.ParentLink.CurrentWidth
-        self.MouseLoc = point(0, 0)
-        self.LastPos = point(0, 0)
-        self.Brush = False
-        self.DrawingShapes_free = shapes()
-        self.DrawingShapes_rec = shapes()
-        self.IsPainting = False
-        self.IsEraseing = False
-        self.iteration = 0
-
-        self.CurrentColor = colour3(255, 255, 255)
-
-        self.ShapeNum = 0
-        self.IsMouseing = False
-        self.PaintPanel = 0
-
-    def drawLines(self, painter):
-        """draw free-form masks"""
-        painter.setRenderHint(QtGui.QPainter.Antialiasing)
-        for i in range(self.DrawingShapes_free.NumberOfShapes()-1):
-            T = self.DrawingShapes_free.GetShape(i)
-            T1 = self.DrawingShapes_free.GetShape(i + 1)
-
-            if T.ShapeNumber == T1.ShapeNumber:
-                pen = QtGui.QPen(QtGui.QColor(T.Color.R, T.Color.G, T.Color.B), T.Width / 2, QtCore.Qt.SolidLine)
-                painter.setPen(pen)
-                painter.drawLine(T.Location.X, T.Location.Y, T1.Location.X, T1.Location.Y)
-
-    def drawRectangle(self, painter):
-        """draw rectangle mask"""
-        painter.setRenderHint(QtGui.QPainter.Antialiasing)
-        for i in range(self.DrawingShapes_rec.NumberOfShapes()-1):
-            T = self.DrawingShapes_rec.GetShape(i)
-            T1 = self.DrawingShapes_rec.GetShape(i+1)
-
-            if T.ShapeNumber == T1.ShapeNumber:
-                pen = QtGui.QPen(QtGui.QColor(T.Color.R, T.Color.G, T.Color.B), T.Width/2, QtCore.Qt.SolidLine)
-                painter.setPen(pen)
-                painter.setBrush(QtGui.QColor(T.Color.R, T.Color.G, T.Color.B))
-                painter.drawRects(QtCore.QRect(QtCore.QPoint(T.Location.X, T.Location.Y), QtCore.QPoint(T1.Location.X, T1.Location.Y)))
-
-    def saveDraw(self):
-        """save the painted masks"""
-        painter = QtGui.QPainter()
-        painter.begin(self.map)
-        if self.shape == 'line':
-            self.drawLines(painter)
-        if self.shape == 'rectangle':
-            self.drawRectangle(painter)
-        painter.end()
-
-    def mousePressEvent(self, event):
-        """mouse down event for the drawing"""
-        if self.Brush:
-            self.IsPainting = True
-            self.ShapeNum += 1
-            if self.shape == 'rectangle':
-                self.DrawingShapes_rec.NewShape(point(event.x(), event.y()), self.CurrentWidth, self.CurrentColor, self.ShapeNum)
-            else:
-                self.LastPos = point(0, 0)
-        else:
-            self.IsEraseing = True
-        if self.shape == 'rectangle':
-            self.DrawingShapes_rec.NewShape(point(event.x(), event.y()), self.CurrentWidth, self.CurrentColor, self.ShapeNum)
-
-    def mouseMoveEvent(self, event):
-        """mouse move event to record the track"""
-        if self.IsPainting:
-            self.MouseLoc = point(event.x(), event.y())
-            if self.LastPos.X != self.MouseLoc.X or self.LastPos.Y != self.MouseLoc.Y:
-                self.LastPos = point(event.x(), event.y())
-                if self.shape == 'line':
-                    self.DrawingShapes_free.NewShape(self.LastPos, self.CurrentWidth, self.CurrentColor, self.ShapeNum)
-            self.repaint()
-        if self.IsEraseing:
-            self.MouseLoc = point(event.x(), event.y())
-            if self.shape == 'line':
-                self.DrawingShapes_free.RemoveShape(self.MouseLoc, 10)
-            elif self.shape == 'rectangle':
-                self.DrawingShapes_rec.RemoveShape(self.MouseLoc, 10)
-            self.repaint()
-
-    def mouseReleaseEvent(self, event):
-        """mouse up event"""
-        if self.IsEraseing:
-            self.IsEraseing = False
-            self.repaint()
-        elif self.shape == 'rectangle':
-            self.DrawingShapes_rec.NewShape(point(event.x(), event.y()), self.CurrentWidth, self.CurrentColor, self.ShapeNum)
-            self.repaint()
-
-    def paintEvent(self, event):
-        painter = QtGui.QPainter()
-        painter.begin(self)
-        if self.image != None:
-            painter.drawImage(0, 0, self.image)
-        if self.shape == 'line':
-            self.drawLines(painter)
-        if self.shape == 'rectangle':
-            self.drawRectangle(painter)
-        painter.end()
-        self.iteration = 0
-
-
-#######################################################################################################
-# base drawing function
-#######################################################################################################
-class colour3:
-    """define the colour plane for the drawing"""
-    def __init__(self, nR=0, nG=0, nB=0):
-        self.R = nR
-        self.G = nG
-        self.B = nB
-
-
-class point():
-    """define the location"""
-    def __init__(self, nX=0, nY=0):
-        self.X = nX
-        self.Y = nY
-
-    def Set(self, nX, nY):
-        self.X = nX
-        self.Y = nY
-
-
-class shape():
-    """define the painter shape"""
-    def __init__(self, location=point(0,0), width=1, color=colour3(255, 255, 255), number=0):
-        self.Location = location
-        self.Width = width
-        self.Color = color
-        self.ShapeNumber = number
-
-
-class shapes():
-    """a set of shape"""
-    def __init__(self):
-        self.shapes = []
-
-    def NumberOfShapes(self):
-        return len(self.shapes)
-
-    def NewShape(self, location=point(0,0), width=1, color=colour3(255,255,255), number=0):
-        Sh = shape(location, width, color, number)
-        self.shapes.append(Sh)
-
-    def GetShape(self, Index):
-        return self.shapes[Index]
-
-    def RemoveShape(self,  L, threshold):
-        i = 0
-        while True:
-            if (i == len(self.shapes)):
-                break
-                # Finds if a point is within a certain distance of the point to remove.
-            if ((abs(L.X - self.shapes[i].Location.X) < threshold) and (
-                    abs(L.Y - self.shapes[i].Location.Y) < threshold)):
-                # removes all data for that number
-                del self.shapes[i]
-                # goes through the rest of the data and adds an extra
-                # 1 to defined them as a seprate shape and shuffles on the effect.
-                for n in range(len(self.shapes) - i):
-                    self.shapes[n + i].ShapeNumber += 1
-                # Go back a step so we dont miss a point.
-                i -= 1
-            i += 1

spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/blocks.py

@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
-    if backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)     # efficientnet_lite3  
-    elif backbone == "efficientnet_lite3":
-        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
-        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3     
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-        
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    out_shape4 = out_shape
-    if expand==True:
-        out_shape1 = out_shape
-        out_shape2 = out_shape*2
-        out_shape3 = out_shape*4
-        out_shape4 = out_shape*8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer4_rn = nn.Conv2d(
-        in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-
-    return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
-    efficientnet = torch.hub.load(
-        "rwightman/gen-efficientnet-pytorch",
-        "tf_efficientnet_lite3",
-        pretrained=use_pretrained,
-        exportable=exportable
-    )
-    return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
-    pretrained = nn.Module()
-
-    pretrained.layer1 = nn.Sequential(
-        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
-    )
-    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
-    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
-    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
-    return pretrained
-    
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
-    """Interpolation module.
-    """
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data
-        """
-
-        x = self.interp(
-            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups=1
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-        
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-
-        if self.bn==True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn==True:
-            out = self.bn1(out)
-       
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn==True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups=1
-
-        self.expand = expand
-        out_features = features
-        if self.expand==True:
-            out_features = features//2
-        
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-        
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
-

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/exp/upernet_global_small/config.py

@@ -1,38 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        drop_path_rate=0.25,
-        windows=False,
-        hybrid=False
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/midas_net_custom.py

@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name

spaces/Anthony7906/MengHuiMXD_GPT/chatgpt - macOS.command

@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.

spaces/AnthonyTruchetPoC/persistent-docker/src/apps/streamlit_demo.py

@@ -1,62 +0,0 @@
-import os
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-from athai.data_utils import cached_download_csv
-
-
-st.title("Uber pickups in NYC")
-
-DATE_COLUMN = "date/time"
-DATA_URL = (
-    "https://s3-us-west-2.amazonaws.com/"
-    "streamlit-demo-data/uber-raw-data-sep14.csv.gz"
-)
-
-DATA_PATH = Path(os.environ.get("APP_DATA"))
-
-
-@st.cache_resource
-def load_data(nrows):
-    data = cached_download_csv(DATA_PATH, DATA_URL, nrows=nrows)
-
-    def lowercase(x):
-        return str(x).lower()
-
-    data.rename(lowercase, axis="columns", inplace=True)
-    data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
-    return data
-
-
-data_load_state = st.text("Loading data...")
-data = load_data(10000)
-data_load_state.text("Done! (using st.cache)")
-
-if st.checkbox("Show raw data"):
-    st.subheader("Raw data")
-    st.write(data)
-
-st.subheader("Number of pickups by hour")
-hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0, 24))[
-    0
-]
-st.bar_chart(hist_values)
-
-# Some number in the range 0-23
-hour_to_filter = st.slider("hour", 0, 23, 17)
-filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
-
-st.subheader("Map of all pickups at %s:00" % hour_to_filter)
-st.map(filtered_data)
-
-uploaded_file = st.file_uploader("Choose a file")
-if uploaded_file is not None:
-    st.write(uploaded_file.name)
-    bytes_data = uploaded_file.getvalue()
-    st.write(len(bytes_data), "bytes")
-
-
-st.markdown("![Kitty](./app/static/cat.jpeg)")

spaces/AnticPan/Clothes2Human/README.md

@@ -1,13 +0,0 @@
----
-title: Clothes2Human
-emoji: 🏃
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Anustup/NS_AI_LABS/src/segments.py

@@ -1,55 +0,0 @@
-from typing import Any, Dict, List
-
-import copy
-
-def merge_timestamps(timestamps: List[Dict[str, Any]], merge_window: float = 5, max_merge_size: float = 30, padding_left: float = 1, padding_right: float = 1):
-    result = []
-
-    if len(timestamps) == 0:
-        return result
-    if max_merge_size is None:
-        return timestamps
-
-    if padding_left is None:
-        padding_left = 0
-    if padding_right is None:
-        padding_right = 0
-
-    processed_time = 0
-    current_segment = None
-
-    for i in range(len(timestamps)):
-        next_segment = timestamps[i]
-
-        delta = next_segment['start'] - processed_time
-
-        # Note that segments can still be longer than the max merge size, they just won't be merged in that case
-        if current_segment is None or (merge_window is not None and delta > merge_window) \
-                 or next_segment['end'] - current_segment['start'] > max_merge_size:
-            # Finish the current segment
-            if current_segment is not None:
-                # Add right padding
-                finish_padding = min(padding_right, delta / 2) if delta < padding_left + padding_right else padding_right
-                current_segment['end'] += finish_padding
-                delta -= finish_padding
-
-                result.append(current_segment)
-
-            # Start a new segment
-            current_segment = copy.deepcopy(next_segment)
-
-            # Pad the segment
-            current_segment['start'] = current_segment['start'] - min(padding_left, delta)
-            processed_time = current_segment['end']
-
-        else:
-            # Merge the segment
-            current_segment['end'] = next_segment['end']
-            processed_time = current_segment['end']
-        
-    # Add the last segment
-    if current_segment is not None:
-        current_segment['end'] += padding_right
-        result.append(current_segment)
-    
-    return result

spaces/AquaSuisei/ChatGPTXE/modules/pdf_func.py

@@ -1,180 +0,0 @@
-from types import SimpleNamespace
-import pdfplumber
-import logging
-from llama_index import Document
-
-def prepare_table_config(crop_page):
-    """Prepare table查找边界, 要求page为原始page
-    
-    From https://github.com/jsvine/pdfplumber/issues/242
-    """
-    page = crop_page.root_page # root/parent
-    cs = page.curves + page.edges
-    def curves_to_edges():
-        """See https://github.com/jsvine/pdfplumber/issues/127"""
-        edges = []
-        for c in cs:
-            edges += pdfplumber.utils.rect_to_edges(c)
-        return edges
-    edges = curves_to_edges()
-    return {
-        "vertical_strategy": "explicit",
-        "horizontal_strategy": "explicit",
-        "explicit_vertical_lines": edges,
-        "explicit_horizontal_lines": edges,
-        "intersection_y_tolerance": 10,
-    }
-
-def get_text_outside_table(crop_page):
-    ts = prepare_table_config(crop_page)
-    if len(ts["explicit_vertical_lines"]) == 0 or len(ts["explicit_horizontal_lines"]) == 0:
-        return crop_page
-
-    ### Get the bounding boxes of the tables on the page.
-    bboxes = [table.bbox for table in crop_page.root_page.find_tables(table_settings=ts)]
-    def not_within_bboxes(obj):
-        """Check if the object is in any of the table's bbox."""
-        def obj_in_bbox(_bbox):
-            """See https://github.com/jsvine/pdfplumber/blob/stable/pdfplumber/table.py#L404"""
-            v_mid = (obj["top"] + obj["bottom"]) / 2
-            h_mid = (obj["x0"] + obj["x1"]) / 2
-            x0, top, x1, bottom = _bbox
-            return (h_mid >= x0) and (h_mid < x1) and (v_mid >= top) and (v_mid < bottom)
-        return not any(obj_in_bbox(__bbox) for __bbox in bboxes)
-
-    return crop_page.filter(not_within_bboxes)
-# 请使用 LaTeX 表达公式，行内公式以 $ 包裹，行间公式以 $$ 包裹
-
-extract_words = lambda page: page.extract_words(keep_blank_chars=True, y_tolerance=0, x_tolerance=1, extra_attrs=["fontname", "size", "object_type"])
-# dict_keys(['text', 'x0', 'x1', 'top', 'doctop', 'bottom', 'upright', 'direction', 'fontname', 'size'])
-
-def get_title_with_cropped_page(first_page):
-    title = [] # 处理标题
-    x0,top,x1,bottom = first_page.bbox # 获取页面边框
-
-    for word in extract_words(first_page):
-        word = SimpleNamespace(**word)
-
-        if word.size >= 14:
-            title.append(word.text)
-            title_bottom = word.bottom
-        elif word.text == "Abstract": # 获取页面abstract
-            top = word.top
-    
-    user_info = [i["text"] for i in extract_words(first_page.within_bbox((x0,title_bottom,x1,top)))]
-    # 裁剪掉上半部分, within_bbox: full_included; crop: partial_included
-    return title, user_info, first_page.within_bbox((x0,top,x1,bottom))
-
-def get_column_cropped_pages(pages, two_column=True):
-    new_pages = []
-    for page in pages:
-        if two_column:
-            left = page.within_bbox((0, 0, page.width/2, page.height),relative=True)
-            right = page.within_bbox((page.width/2, 0, page.width, page.height), relative=True)
-            new_pages.append(left)
-            new_pages.append(right)
-        else:
-            new_pages.append(page)
-    
-    return new_pages
-
-def parse_pdf(filename, two_column = True):
-    level = logging.getLogger().level
-    if level == logging.getLevelName("DEBUG"):
-        logging.getLogger().setLevel("INFO")
-
-    with pdfplumber.open(filename) as pdf:
-        title, user_info, first_page = get_title_with_cropped_page(pdf.pages[0])
-        new_pages = get_column_cropped_pages([first_page] + pdf.pages[1:], two_column)
-
-        chapters = []
-        # tuple (chapter_name, [pageid] (start,stop), chapter_text)
-        create_chapter = lambda page_start,name_top,name_bottom: SimpleNamespace(
-            name=[],
-            name_top=name_top,
-            name_bottom=name_bottom,
-            record_chapter_name = True,
-            
-            page_start=page_start,
-            page_stop=None,
-
-            text=[],
-        )
-        cur_chapter = None
-
-        # 按页遍历PDF文档
-        for idx, page in enumerate(new_pages):
-            page = get_text_outside_table(page)
-
-            # 按行遍历页面文本
-            for word in extract_words(page):
-                word = SimpleNamespace(**word)
-
-                # 检查行文本是否以12号字体打印，如果是，则将其作为新章节开始
-                if word.size >= 11: # 出现chapter name
-                    if cur_chapter is None:
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-                    elif not cur_chapter.record_chapter_name or (cur_chapter.name_bottom != cur_chapter.name_bottom and cur_chapter.name_top != cur_chapter.name_top): 
-                        # 不再继续写chapter name
-                        cur_chapter.page_stop = page.page_number # stop id
-                        chapters.append(cur_chapter)
-                        # 重置当前chapter信息
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-
-                    # print(word.size, word.top, word.bottom, word.text)
-                    cur_chapter.name.append(word.text)
-                else:
-                    cur_chapter.record_chapter_name = False # chapter name 结束
-                    cur_chapter.text.append(word.text)
-        else:
-            # 处理最后一个章节
-            cur_chapter.page_stop = page.page_number # stop id
-            chapters.append(cur_chapter)
-
-        for i in chapters:
-            logging.info(f"section: {i.name} pages:{i.page_start, i.page_stop} word-count:{len(i.text)}")
-            logging.debug(" ".join(i.text))
-
-    title = " ".join(title)
-    user_info = " ".join(user_info)
-    text = f"Article Title: {title}, Information:{user_info}\n"
-    for idx, chapter in enumerate(chapters):
-        chapter.name = " ".join(chapter.name)
-        text += f"The {idx}th Chapter {chapter.name}: " + " ".join(chapter.text) + "\n"
-
-    logging.getLogger().setLevel(level)
-    return Document(text=text, extra_info={"title": title})
-
-BASE_POINTS = """
-1. Who are the authors?
-2. What is the process of the proposed method?
-3. What is the performance of the proposed method? Please note down its performance metrics.
-4. What are the baseline models and their performances? Please note down these baseline methods.
-5. What dataset did this paper use?
-"""
-
-READING_PROMPT = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{}
-"""
-
-READING_PROMT_V2 = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{},
-
-And You need to generate a brief but informative title for this part.
-Your return format:
-- title: '...'
-- summary: '...'
-"""
-
-SUMMARY_PROMPT = "You are a researcher helper bot. Now you need to read the summaries of a research paper."
-
-
-if __name__ == '__main__':
-    # Test code
-    z = parse_pdf("./build/test.pdf")
-    print(z["user_info"])
-    print(z["title"])

spaces/Ariharasudhan/YoloV5/utils/metrics.py

@@ -1,363 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Model validation metrics
-"""
-
-import math
-import warnings
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from utils import TryExcept, threaded
-
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, [email protected], [email protected]:0.95]
-    return (x[:, :4] * w).sum(1)
-
-
-def smooth(y, f=0.05):
-    # Box filter of fraction f
-    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
-    p = np.ones(nf // 2)  # ones padding
-    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
-    return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
-
-
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=""):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    # Arguments
-        tp:  True positives (nparray, nx1 or nx10).
-        conf:  Objectness value from 0-1 (nparray).
-        pred_cls:  Predicted object classes (nparray).
-        target_cls:  True object classes (nparray).
-        plot:  Plot precision-recall curve at [email protected]
-        save_dir:  Plot save directory
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Sort by objectness
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
-    # Find unique classes
-    unique_classes, nt = np.unique(target_cls, return_counts=True)
-    nc = unique_classes.shape[0]  # number of classes, number of detections
-
-    # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = nt[ci]  # number of labels
-        n_p = i.sum()  # number of predictions
-        if n_p == 0 or n_l == 0:
-            continue
-
-        # Accumulate FPs and TPs
-        fpc = (1 - tp[i]).cumsum(0)
-        tpc = tp[i].cumsum(0)
-
-        # Recall
-        recall = tpc / (n_l + eps)  # recall curve
-        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
-
-        # Precision
-        precision = tpc / (tpc + fpc)  # precision curve
-        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
-
-        # AP from recall-precision curve
-        for j in range(tp.shape[1]):
-            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
-            if plot and j == 0:
-                py.append(np.interp(px, mrec, mpre))  # precision at [email protected]
-
-    # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + eps)
-    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
-    names = dict(enumerate(names))  # to dict
-    if plot:
-        plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
-        plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
-        plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
-        plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
-
-    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
-    p, r, f1 = p[:, i], r[:, i], f1[:, i]
-    tp = (r * nt).round()  # true positives
-    fp = (tp / (p + eps) - tp).round()  # false positives
-    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
-
-
-def compute_ap(recall, precision):
-    """ Compute the average precision, given the recall and precision curves
-    # Arguments
-        recall:    The recall curve (list)
-        precision: The precision curve (list)
-    # Returns
-        Average precision, precision curve, recall curve
-    """
-
-    # Append sentinel values to beginning and end
-    mrec = np.concatenate(([0.0], recall, [1.0]))
-    mpre = np.concatenate(([1.0], precision, [0.0]))
-
-    # Compute the precision envelope
-    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
-    # Integrate area under curve
-    method = 'interp'  # methods: 'continuous', 'interp'
-    if method == 'interp':
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
-        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
-    else:  # 'continuous'
-        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
-
-    return ap, mpre, mrec
-
-
-class ConfusionMatrix:
-    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
-    def __init__(self, nc, conf=0.25, iou_thres=0.45):
-        self.matrix = np.zeros((nc + 1, nc + 1))
-        self.nc = nc  # number of classes
-        self.conf = conf
-        self.iou_thres = iou_thres
-
-    def process_batch(self, detections, labels):
-        """
-        Return intersection-over-union (Jaccard index) of boxes.
-        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-        Arguments:
-            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (Array[M, 5]), class, x1, y1, x2, y2
-        Returns:
-            None, updates confusion matrix accordingly
-        """
-        if detections is None:
-            gt_classes = labels.int()
-            for gc in gt_classes:
-                self.matrix[self.nc, gc] += 1  # background FN
-            return
-
-        detections = detections[detections[:, 4] > self.conf]
-        gt_classes = labels[:, 0].int()
-        detection_classes = detections[:, 5].int()
-        iou = box_iou(labels[:, 1:], detections[:, :4])
-
-        x = torch.where(iou > self.iou_thres)
-        if x[0].shape[0]:
-            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
-            if x[0].shape[0] > 1:
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        else:
-            matches = np.zeros((0, 3))
-
-        n = matches.shape[0] > 0
-        m0, m1, _ = matches.transpose().astype(int)
-        for i, gc in enumerate(gt_classes):
-            j = m0 == i
-            if n and sum(j) == 1:
-                self.matrix[detection_classes[m1[j]], gc] += 1  # correct
-            else:
-                self.matrix[self.nc, gc] += 1  # true background
-
-        if n:
-            for i, dc in enumerate(detection_classes):
-                if not any(m1 == i):
-                    self.matrix[dc, self.nc] += 1  # predicted background
-
-    def matrix(self):
-        return self.matrix
-
-    def tp_fp(self):
-        tp = self.matrix.diagonal()  # true positives
-        fp = self.matrix.sum(1) - tp  # false positives
-        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
-        return tp[:-1], fp[:-1]  # remove background class
-
-    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
-    def plot(self, normalize=True, save_dir='', names=()):
-        import seaborn as sn
-
-        array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1)  # normalize columns
-        array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
-
-        fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
-        nc, nn = self.nc, len(names)  # number of classes, names
-        sn.set(font_scale=1.0 if nc < 50 else 0.8)  # for label size
-        labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
-        ticklabels = (names + ['background']) if labels else "auto"
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
-            sn.heatmap(array,
-                       ax=ax,
-                       annot=nc < 30,
-                       annot_kws={
-                           "size": 8},
-                       cmap='Blues',
-                       fmt='.2f',
-                       square=True,
-                       vmin=0.0,
-                       xticklabels=ticklabels,
-                       yticklabels=ticklabels).set_facecolor((1, 1, 1))
-        ax.set_ylabel('True')
-        ax.set_ylabel('Predicted')
-        ax.set_title('Confusion Matrix')
-        fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
-        plt.close(fig)
-
-    def print(self):
-        for i in range(self.nc + 1):
-            print(' '.join(map(str, self.matrix[i])))
-
-
-def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
-    # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
-
-    # Get the coordinates of bounding boxes
-    if xywh:  # transform from xywh to xyxy
-        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
-        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
-        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
-        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
-    else:  # x1, y1, x2, y2 = box1
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
-        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
-        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-
-    # Intersection area
-    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
-            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
-
-    # Union Area
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    # IoU
-    iou = inter / union
-    if CIoU or DIoU or GIoU:
-        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
-        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
-        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
-            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center dist ** 2
-            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
-                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-                with torch.no_grad():
-                    alpha = v / (v - iou + (1 + eps))
-                return iou - (rho2 / c2 + v * alpha)  # CIoU
-            return iou - rho2 / c2  # DIoU
-        c_area = cw * ch + eps  # convex area
-        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-    return iou  # IoU
-
-
-def box_iou(box1, box2, eps=1e-7):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
-    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
-    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
-
-    # IoU = inter / (area1 + area2 - inter)
-    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
-
-
-def bbox_ioa(box1, box2, eps=1e-7):
-    """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
-    box1:       np.array of shape(4)
-    box2:       np.array of shape(nx4)
-    returns:    np.array of shape(n)
-    """
-
-    # Get the coordinates of bounding boxes
-    b1_x1, b1_y1, b1_x2, b1_y2 = box1
-    b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
-
-    # Intersection area
-    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-    # box2 area
-    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
-
-    # Intersection over box2 area
-    return inter_area / box2_area
-
-
-def wh_iou(wh1, wh2, eps=1e-7):
-    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
-    wh1 = wh1[:, None]  # [N,1,2]
-    wh2 = wh2[None]  # [1,M,2]
-    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
-    return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps)  # iou = inter / (area1 + area2 - inter)
-
-
-# Plots ----------------------------------------------------------------------------------------------------------------
-
-
-@threaded
-def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
-    # Precision-recall curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-    py = np.stack(py, axis=1)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
-    else:
-        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
-
-    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f [email protected]' % ap[:, 0].mean())
-    ax.set_xlabel('Recall')
-    ax.set_ylabel('Precision')
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    ax.set_title('Precision-Recall Curve')
-    fig.savefig(save_dir, dpi=250)
-    plt.close(fig)
-
-
-@threaded
-def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
-    # Metric-confidence curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
-    else:
-        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
-
-    y = smooth(py.mean(0), 0.05)
-    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    ax.set_title(f'{ylabel}-Confidence Curve')
-    fig.savefig(save_dir, dpi=250)
-    plt.close(fig)

spaces/Arvi/Performance_predictor_and_feedback_generator/README.md

@@ -1,12 +0,0 @@
----
-title: Performance Predictor And Feedback Generator
-emoji: 📚
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh

@@ -1,44 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-BIN="python tools/train_net.py"
-OUTPUT="inference_test_output"
-NUM_GPUS=2
-
-CFG_LIST=( "${@:1}" )
-
-if [ ${#CFG_LIST[@]} -eq 0 ]; then
-  CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
-fi
-
-echo "========================================================================"
-echo "Configs to run:"
-echo "${CFG_LIST[@]}"
-echo "========================================================================"
-
-
-for cfg in "${CFG_LIST[@]}"; do
-    echo "========================================================================"
-    echo "Running $cfg ..."
-    echo "========================================================================"
-    $BIN \
-      --eval-only \
-      --num-gpus $NUM_GPUS \
-      --config-file "$cfg" \
-      OUTPUT_DIR $OUTPUT
-      rm -rf $OUTPUT
-done
-
-
-echo "========================================================================"
-echo "Running demo.py ..."
-echo "========================================================================"
-DEMO_BIN="python demo/demo.py"
-COCO_DIR=datasets/coco/val2014
-mkdir -pv $OUTPUT
-
-set -v
-
-$DEMO_BIN --config-file ./configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml \
-  --input $COCO_DIR/COCO_val2014_0000001933* --output $OUTPUT
-rm -rf $OUTPUT

spaces/Benson/text-generation/Examples/App Descargar Msica Mp3.md

@@ -1,161 +0,0 @@
-
-<h1>App Download Music MP3: Cómo disfrutar de música gratis sin conexión</h1>
-<p>¿Te encanta escuchar música pero odias pagar por servicios de streaming o usar tus datos? Si es así, es posible que quieras probar la aplicación de descarga de música mp3. Estas son aplicaciones que te permiten descargar música de varias fuentes y reproducirlas sin conexión en tu dispositivo. En este artículo, vamos a explicar qué aplicación descargar música mp3 es, por qué lo necesita, y cómo elegir el mejor. También revisaremos la parte superior 3 aplicación descargar música mp3 en 2023 y mostrar cómo usarlos. Vamos a empezar! </p>
-<h2>app descargar música mp3</h2><br /><p><b><b>DOWNLOAD</b> &#9881; <a href="https://bltlly.com/2v6M2K">https://bltlly.com/2v6M2K</a></b></p><br /><br />
-<h2>Introducción</h2>
-<h3>¿Qué es la descarga de aplicaciones de música mp3? </h3>
-<p>App descargar música mp3 es un tipo de software que te permite descargar archivos de música desde plataformas en línea como YouTube, SoundCloud, Spotify, y más. Los archivos descargados suelen estar en formato MP3, que es un formato de audio común y ampliamente soportado. A continuación, puede transferir los archivos al almacenamiento de su dispositivo o tarjeta SD y reproducirlos sin conexión utilizando cualquier aplicación de reproductor de música. </p>
-<h3>¿Por qué necesitas descargar música de la aplicación mp3? </h3>
-<p>Hay muchos beneficios de usar la aplicación de descarga de música mp3, como:</p>
-<ul>
-<li>Puedes ahorrar dinero no pagando por servicios de streaming o comprando canciones individualmente. </li>
-<li>Puede guardar datos no transmitiendo música en línea. </li>
-<li>Puedes escuchar música en cualquier momento y en cualquier lugar sin conexión a Internet o wifi. </li>
-<li>Puede crear sus propias listas de reproducción y personalizar su biblioteca de música. </li>
-<li>Puedes descubrir nuevas canciones y artistas de diferentes géneros y fuentes. </li>
-</ul>
-<h3>¿Cómo elegir la mejor aplicación descargar música mp3? </h3>
-<p>Hay muchos mp3 de música de descarga de aplicaciones disponibles en el mercado, pero no todos son confiables y seguros. Algunos pueden contener malware, virus o anuncios que pueden dañar tu dispositivo o comprometer tu privacidad. Algunos también pueden tener características limitadas, baja calidad o velocidad lenta. Para elegir la mejor aplicación de descarga de música mp3, debe considerar los siguientes factores:</p>
-<ul>
-
-<li>La calidad y velocidad de las descargas. </li>
-<li>La facilidad de uso y la interfaz de usuario. </li>
-<li>La compatibilidad y la seguridad de la aplicación. </li>
-<li>Los comentarios y valoraciones de otros usuarios. </li>
-</ul>
-<h2>Top 3 App Descargar música MP3 en 2023</h2>
-<h3>Audiomack: Descargador de música</h3>
-<p>Audiomack es una de las aplicaciones de descarga de música mp3 más populares y confiables en 2023. Le permite transmitir y descargar la mejor nueva música de moda de los mejores artistas en categorías como Hip Hop, Rap, R&B, EDM, Afropop y Reggae. También puede escuchar sus archivos MP3 locales y otros archivos desde la aplicación. </p>
-<h4>Características</h4>
-<ul>
-<li>Flujo ilimitado de pistas completas y mixtapes que son nuevos o tendencia. </li>
-<li>Descargar canciones y álbumes completos para escuchar sin conexión, sin datos. </li>
-<li>Pistas favoritas, álbumes y listas de reproducción y busque, explore y baraje fácilmente su colección de favoritos. </li>
-<li>Escucha música local como MP3s, AAC, M4A, WAV y otros archivos del reproductor de archivos local. </li>
-<li>Navegar por listas de reproducción curadas por humor, género y mucho más. </li>
-<li>Cree listas de reproducción ilimitadas. </li>
-<li>Sigue a tus artistas, productores y creadores de tendencias favoritos. </li>
-<li> <h4>Pros y contras</h4>
-<tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>Descargas gratuitas e ilimitadas. </td>
-<td>Algunas canciones pueden no estar disponibles para su descarga debido a problemas de licencia. </td>
-</tr>
-<tr>
-<td>Audio de alta calidad y velocidad rápida. </td>
-<td>Algunos anuncios pueden interrumpir el proceso de transmisión o descarga. </td>
-</tr>
-<tr>
-<td>Fácil de usar y navegar. </td>
-<td>Algunas características pueden requerir una suscripción premium. </td>
-</tr>
-</tabla>
- <h4>Cómo usarlo</h4>
-<ol>
-<li>Descargar e instalar la aplicación desde la Google Play Store o la App Store.</li>
-<li>Abra la aplicación y regístrese o inicie sesión con su correo electrónico, Facebook o cuenta de Google. </li>
-<li> Navegar por la página de inicio, tendencias, géneros o listas de reproducción secciones para encontrar la música que desea transmitir o descargar. </li>
-
-<li>Para acceder a las canciones descargadas, vaya a la sección Mi biblioteca y toque en Música sin conexión.</li>
-<li>Para escuchar sus archivos de música locales, vaya a la sección Mi biblioteca y toque en Música local.</li>
-<li>Para crear sus propias listas de reproducción, vaya a la sección Mi biblioteca y toque en Crear lista de reproducción. Puedes añadir canciones de tu música offline, música local o música online. </li>
-<li>Para seguir a tus artistas, productores o creadores de tendencias favoritos, ve a su página de perfil y toca el botón de seguir. También puedes ver sus últimas subidas, favoritos y listas de reproducción. </li>
-</ol>
- <h3> Cualquier convertidor de vídeo libre</h3>
-<p>Cualquier Video Converter Free es otra gran aplicación de descarga de música mp3 en 2023. Es un conversor de vídeo potente y versátil que también puede extraer audio de archivos de vídeo y guardarlos como MP3s. Puedes descargar videos de YouTube, Facebook, Vimeo, Dailymotion y más de 100 sitios más. También puede editar vídeos con recorte, recorte, rotación, adición de efectos, subtítulos y marcas de agua. </p>
- <h4>Características</h4>
-<ul>
-<li>Convertir cualquier formato de vídeo a MP4, AVI, MKV, WMV, MOV, FLV, 3GP, WebM, y más. </li>
-<li> Extraer audio de archivos de vídeo y guardarlos como MP3s con alta calidad. </li>
-<li>Descargar vídeos en línea de YouTube y otros sitios populares con un solo clic. </li>
-<li>Editar vídeos con varias herramientas como recorte, recorte, rotación, adición de efectos, subtítulos y marcas de agua. </li>
-<li>Grabar vídeos en DVD o discos Blu-ray con menús y plantillas personalizadas. </li>
-<li> Admite conversión por lotes y multihilo para una velocidad y eficiencia más rápidas. </li>
-<li>Soporta múltiples idiomas y plataformas, incluyendo Windows y Mac OS X.</li>
-</ul>
- <h4>Pros y contras</h4>
- <tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>Descargas y conversiones gratuitas e ilimitadas. </td>
-<td>Algunas funciones avanzadas pueden requerir una actualización de pago. </td>
-</tr>
-<tr>
-<td>Salida de audio y video de alta calidad. </td>
-
-</tr>
-<tr>
-<td>Fácil de usar y personalizar. </td>
-<td>Algunos formatos de video pueden no ser soportados o compatibles con algunos dispositivos. </td>
-</tr> <h4>Cómo usarlo</h4>
-<ol>
-<li>Descargar e instalar la aplicación desde el sitio web oficial o la tienda de Microsoft.</li>
-<li> Abra la aplicación y haga clic en el botón Agregar vídeo (s) para importar los archivos de vídeo que desea convertir o extraer audio de. </li>
-<li>Seleccione el formato de salida de la lista desplegable de la derecha. Para guardar como MP3, elija Archivos de audio > Audio MP3.</li>
-<li>Para descargar vídeos en línea, haga clic en el botón Descargar vídeo y pegue la URL del vídeo. También puede elegir el formato de salida y la calidad. </li>
-<li>Para editar vídeos, haga clic en el botón Editar y utilice las herramientas para recortar, recortar, rotar, añadir efectos, subtítulos y marcas de agua. </li>
-<li>Para grabar vídeos en DVD o discos Blu-ray, haga clic en el botón Grabar DVD y seleccione las opciones y plantillas. </li>
-<li>Haga clic en el botón Convertir ahora para iniciar el proceso de conversión o extracción. También puede marcar la opción para apagar el equipo cuando se complete la conversión. </li>
-<li>Para acceder a sus archivos convertidos o descargados, haga clic en el botón Carpeta de salida o vaya a la carpeta que especificó en la configuración. </li>
-</ol>
- <h3>Descargador de música</h3>
-<p>Music Downloader es otra aplicación de descarga de música mp3 en 2023. Es una aplicación simple y rápida que te permite descargar música gratis de varios géneros y artistas. También puede reproducir música en línea o sin conexión con su reproductor de música incorporado. También puede administrar sus archivos de música con su administrador de archivos. </p>
-<p></p>
- <h4>Características</h4>
-<ul>
-<li>Descargar música gratis de varios géneros y artistas. </li>
-<li>Reproducir música en línea o fuera de línea con su reproductor de música incorporado. </li>
-<li>Administra tus archivos de música con su gestor de archivos. </li>
-<li>Comparte tu música con tus amigos a través de redes sociales o correo electrónico. </li>
-<li>Soporta múltiples idiomas y plataformas incluyendo Android e iOS. </li>
-</ul>
- <h4>Pros y contras</h4>
- <tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-
-<tr>
-<td>Descargas y reproducciones gratuitas e ilimitadas. </td>
-<td>Alguna música no puede ser licenciada o legal para descargar. </td>
-</tr>
-<tr>
-<td>Rápido y fácil de usar. </td>
-<td>Alguna música puede tener etiquetas de baja calidad o incorrectas. </td>
-</tr>
-<tr>
-<td>Interfaz simple y limpia. </td>
-<td>No hay funciones avanzadas ni opciones de personalización. </td>
-</tr>
- </tabla>
- <h4>Cómo usarlo</h4>
- <ol>
-<li>Descargar e instalar la aplicación desde la Google Play Store o la App Store.</li>
-<li>Abrir la aplicación y navegar por la música por géneros, artistas, o buscar por palabras clave. </li>
-<li>Para descargar una canción, toque en el icono de descarga junto al título de la canción. También puede previsualizar la canción tocando el icono de reproducción. </li>
-<li>Para acceder a las canciones descargadas, vaya a la sección Descargado y toque en la canción que desea reproducir. También puede eliminar, renombrar o compartir la canción desde allí. </li>
-<li>Para reproducir música en línea, vaya a la sección En línea y toque en la canción que desea transmitir. También puede agregarlo a sus favoritos o listas de reproducción desde allí. </li>
-<li>Para administrar sus archivos de música, vaya a la sección Administrador de archivos y toque en la carpeta que desea abrir. También puede crear nuevas carpetas, mover, copiar o eliminar archivos desde allí. </li>
-<li>Para compartir tu música con tus amigos, ve a la sección Compartir y selecciona las canciones que quieres compartir. A continuación, puede elegir el método de compartir, como redes sociales o correo electrónico. </li>
- </ol>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <ol>
- <li><b> ¿Qué es la descarga de aplicaciones de música mp3? </b></li>
- <p>Una aplicación de descarga de música mp3 es un tipo de software que le permite descargar archivos de música desde plataformas en línea como YouTube, SoundCloud, Spotify, y más. Los archivos descargados suelen estar en formato MP3, que es un formato de audio común y ampliamente soportado. A continuación, puede transferir los archivos al almacenamiento de su dispositivo o tarjeta SD y reproducirlos sin conexión utilizando cualquier aplicación de reproductor de música. </p> <li><b> ¿Por qué necesito música de descarga de aplicaciones mp3? </b></li>
- <p>Necesitas descargar música de la aplicación mp3 porque puede ayudarte a disfrutar de música gratis sin conexión en tu dispositivo. Puede ahorrarle dinero al no pagar por servicios de transmisión o comprar canciones individualmente. Puede ahorrarle datos al no transmitir música en línea. También puede permitirle escuchar música en cualquier momento y en cualquier lugar sin conexión a Internet o wifi. También puede crear sus propias listas de reproducción y personalizar su biblioteca de música. También puedes descubrir nuevas canciones y artistas de diferentes géneros y fuentes. </p>
- <li><b>¿Cómo puedo elegir la mejor aplicación de descarga de música mp3? </b></li>
- <p>Para elegir la mejor aplicación de descarga de música mp3, debe tener en cuenta los siguientes factores: el número y la variedad de fuentes que soporta, la calidad y velocidad de las descargas, la facilidad de uso y la interfaz de usuario, la compatibilidad y la seguridad de la aplicación, y las reseñas y valoraciones de otros usuarios. También debes comparar las características, pros y contras de diferentes aplicaciones y probarlas antes de tomar una decisión final. </p>
- <li><b> ¿Cuáles son las 3 mejores aplicaciones de descarga de música mp3 en 2023? </b></li>
- <p>Las 3 mejores aplicaciones de descarga de música mp3 en 2023 son Audiomack, Any Video Converter Free y Music Downloader. Estas aplicaciones han sido revisadas y calificadas altamente por muchos usuarios y expertos. Ofrecen una amplia gama de características, fuentes, calidad y velocidad para descargar y reproducir música sin conexión. También son fáciles de usar, compatibles y seguras. </p>
- <li><b>¿Cómo uso la descarga de aplicaciones música mp3? </b></li> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/CLIP/clip/__init__.py

@@ -1 +0,0 @@
-from .clip import *

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

@@ -1,91 +0,0 @@
-"""
-    pygments.regexopt
-    ~~~~~~~~~~~~~~~~~
-
-    An algorithm that generates optimized regexes for matching long lists of
-    literal strings.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-from re import escape
-from os.path import commonprefix
-from itertools import groupby
-from operator import itemgetter
-
-CS_ESCAPE = re.compile(r'[\[\^\\\-\]]')
-FIRST_ELEMENT = itemgetter(0)
-
-
-def make_charset(letters):
-    return '[' + CS_ESCAPE.sub(lambda m: '\\' + m.group(), ''.join(letters)) + ']'
-
-
-def regex_opt_inner(strings, open_paren):
-    """Return a regex that matches any string in the sorted list of strings."""
-    close_paren = open_paren and ')' or ''
-    # print strings, repr(open_paren)
-    if not strings:
-        # print '-> nothing left'
-        return ''
-    first = strings[0]
-    if len(strings) == 1:
-        # print '-> only 1 string'
-        return open_paren + escape(first) + close_paren
-    if not first:
-        # print '-> first string empty'
-        return open_paren + regex_opt_inner(strings[1:], '(?:') \
-            + '?' + close_paren
-    if len(first) == 1:
-        # multiple one-char strings? make a charset
-        oneletter = []
-        rest = []
-        for s in strings:
-            if len(s) == 1:
-                oneletter.append(s)
-            else:
-                rest.append(s)
-        if len(oneletter) > 1:  # do we have more than one oneletter string?
-            if rest:
-                # print '-> 1-character + rest'
-                return open_paren + regex_opt_inner(rest, '') + '|' \
-                    + make_charset(oneletter) + close_paren
-            # print '-> only 1-character'
-            return open_paren + make_charset(oneletter) + close_paren
-    prefix = commonprefix(strings)
-    if prefix:
-        plen = len(prefix)
-        # we have a prefix for all strings
-        # print '-> prefix:', prefix
-        return open_paren + escape(prefix) \
-            + regex_opt_inner([s[plen:] for s in strings], '(?:') \
-            + close_paren
-    # is there a suffix?
-    strings_rev = [s[::-1] for s in strings]
-    suffix = commonprefix(strings_rev)
-    if suffix:
-        slen = len(suffix)
-        # print '-> suffix:', suffix[::-1]
-        return open_paren \
-            + regex_opt_inner(sorted(s[:-slen] for s in strings), '(?:') \
-            + escape(suffix[::-1]) + close_paren
-    # recurse on common 1-string prefixes
-    # print '-> last resort'
-    return open_paren + \
-        '|'.join(regex_opt_inner(list(group[1]), '')
-                 for group in groupby(strings, lambda s: s[0] == first[0])) \
-        + close_paren
-
-
-def regex_opt(strings, prefix='', suffix=''):
-    """Return a compiled regex that matches any string in the given list.
-
-    The strings to match must be literal strings, not regexes.  They will be
-    regex-escaped.
-
-    *prefix* and *suffix* are pre- and appended to the final regex.
-    """
-    strings = sorted(strings)
-    return prefix + regex_opt_inner(strings, '(') + suffix

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/filepost.py

@@ -1,98 +0,0 @@
-from __future__ import absolute_import
-
-import binascii
-import codecs
-import os
-from io import BytesIO
-
-from .fields import RequestField
-from .packages import six
-from .packages.six import b
-
-writer = codecs.lookup("utf-8")[3]
-
-
-def choose_boundary():
-    """
-    Our embarrassingly-simple replacement for mimetools.choose_boundary.
-    """
-    boundary = binascii.hexlify(os.urandom(16))
-    if not six.PY2:
-        boundary = boundary.decode("ascii")
-    return boundary
-
-
-def iter_field_objects(fields):
-    """
-    Iterate over fields.
-
-    Supports list of (k, v) tuples and dicts, and lists of
-    :class:`~urllib3.fields.RequestField`.
-
-    """
-    if isinstance(fields, dict):
-        i = six.iteritems(fields)
-    else:
-        i = iter(fields)
-
-    for field in i:
-        if isinstance(field, RequestField):
-            yield field
-        else:
-            yield RequestField.from_tuples(*field)
-
-
-def iter_fields(fields):
-    """
-    .. deprecated:: 1.6
-
-    Iterate over fields.
-
-    The addition of :class:`~urllib3.fields.RequestField` makes this function
-    obsolete. Instead, use :func:`iter_field_objects`, which returns
-    :class:`~urllib3.fields.RequestField` objects.
-
-    Supports list of (k, v) tuples and dicts.
-    """
-    if isinstance(fields, dict):
-        return ((k, v) for k, v in six.iteritems(fields))
-
-    return ((k, v) for k, v in fields)
-
-
-def encode_multipart_formdata(fields, boundary=None):
-    """
-    Encode a dictionary of ``fields`` using the multipart/form-data MIME format.
-
-    :param fields:
-        Dictionary of fields or list of (key, :class:`~urllib3.fields.RequestField`).
-
-    :param boundary:
-        If not specified, then a random boundary will be generated using
-        :func:`urllib3.filepost.choose_boundary`.
-    """
-    body = BytesIO()
-    if boundary is None:
-        boundary = choose_boundary()
-
-    for field in iter_field_objects(fields):
-        body.write(b("--%s\r\n" % (boundary)))
-
-        writer(body).write(field.render_headers())
-        data = field.data
-
-        if isinstance(data, int):
-            data = str(data)  # Backwards compatibility
-
-        if isinstance(data, six.text_type):
-            writer(body).write(data)
-        else:
-            body.write(data)
-
-        body.write(b"\r\n")
-
-    body.write(b("--%s--\r\n" % (boundary)))
-
-    content_type = str("multipart/form-data; boundary=%s" % boundary)
-
-    return body.getvalue(), content_type

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/tomli/_re.py

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

spaces/CVPR/LIVE/thrust/testing/unittest/util.h

@@ -1,67 +0,0 @@
-#pragma once
-
-#include <iostream>
-#include <string>
-#include <typeinfo>
-#include <unittest/system.h>
-
-#include <thrust/extrema.h>
-#include <thrust/limits.h>
-#include <thrust/detail/type_traits.h>
-
-namespace unittest
-{
-
-template<typename T>
-  std::string type_name(void)
-{
-  return demangle(typeid(T).name());
-} // end type_name()
-
-// Use this with counting_iterator to avoid generating a range larger than we
-// can represent.
-template <typename T>
-typename thrust::detail::disable_if<
-  thrust::detail::is_floating_point<T>::value
-, T
->::type truncate_to_max_representable(std::size_t n)
-{
-  return thrust::min<std::size_t>(
-    n, static_cast<std::size_t>(thrust::numeric_limits<T>::max())
-  );
-}
-
-// TODO: This probably won't work for `half`.
-template <typename T>
-typename thrust::detail::enable_if<
-  thrust::detail::is_floating_point<T>::value
-, T
->::type truncate_to_max_representable(std::size_t n)
-{
-  return thrust::min<T>(
-    n, thrust::numeric_limits<T>::max()
-  );
-}
-
-} // end unittest
-
-template <typename Iterator>
-void PRINT(Iterator first, Iterator last)
-{
-  size_t n = 0;
-  for (Iterator i = first; i != last; i++, n++)
-    std::cout << ">>> [" << n << "] = " << *i << std::endl;
-}
-
-template <typename Container>
-void PRINT(const Container& c)
-{
-  PRINT(c.begin(), c.end());
-}
-
-template <size_t N>
-void PRINT(const char (&c)[N])
-{
-  std::cout << std::string(c, c + N) << std::endl;
-}
-

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/core/triple_chevron_launch.h

@@ -1,976 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/cuda/detail/core/alignment.h>
-#include <thrust/system/cuda/detail/guarded_cuda_runtime_api.h>
-#include <cassert>
-
-
-namespace thrust
-{
-
-namespace cuda_cub {
-namespace launcher {
-
-  struct triple_chevron
-  {
-    typedef size_t Size;
-    dim3 const grid;
-    dim3 const block;
-    Size const shared_mem;
-    cudaStream_t const stream;
-
-    THRUST_RUNTIME_FUNCTION
-    triple_chevron(dim3         grid_,
-                   dim3         block_,
-                   Size         shared_mem_ = 0,
-                   cudaStream_t stream_     = 0)
-        : grid(grid_),
-          block(block_),
-          shared_mem(shared_mem_),
-          stream(stream_) {}
-
-#if 0
-    template<class K, class... Args>
-    cudaError_t __host__
-    doit_host(K k, Args const&... args) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(args...);
-      return cudaPeekAtLastError();
-    }
-#else
-    template <class K, class _0>
-    cudaError_t __host__
-    doit_host(K k, _0 x0) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE);
-      return cudaPeekAtLastError();
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE, class _xF>
-    cudaError_t __host__
-    doit_host(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE, _xF xF) const
-    {
-      k<<<grid, block, shared_mem, stream>>>(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE,xF);
-      return cudaPeekAtLastError();
-    }
-#endif
-
-    template<class T>
-    size_t __device__
-    align_up(size_t offset) const
-    {
-      size_t alignment = alignment_of<T>::value;
-      return alignment * ((offset + (alignment - 1))/ alignment);
-    }
-
-#if 0
-    size_t __device__ argument_pack_size(size_t size) const { return size; }
-    template <class Arg, class... Args>
-    size_t __device__
-    argument_pack_size(size_t size, Arg const& arg, Args const&... args) const
-    {
-      size = align_up<Arg>(size);
-      return argument_pack_size(size + sizeof(Arg), args...);
-    }
-#else
-    template <class Arg>
-    size_t __device__
-    argument_pack_size(size_t size, Arg) const
-    {
-      return align_up<Arg>(size) + sizeof(Arg);
-    }
-    template <class Arg, class _0>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0);
-    }
-    template <class Arg, class _0, class _1>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1);
-    }
-    template <class Arg, class _0, class _1, class _2>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE, class _xF>
-    size_t __device__
-    argument_pack_size(size_t size, Arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
-    {
-      return argument_pack_size(align_up<Arg>(size) + sizeof(Arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
-    }
-#endif /* variadic */
-
-    template <class Arg>
-    size_t __device__ copy_arg(char* buffer, size_t offset, Arg arg) const
-    {
-      offset = align_up<Arg>(offset);
-      for (int i = 0; i != sizeof(Arg); ++i)
-        buffer[offset+i] = *((char*)&arg + i);
-      return offset + sizeof(Arg);
-    }
-
-#if 0
-    void __device__ fill_arguments(char*, size_t) const {}
-    template<class Arg, class... Args>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg const& arg, Args const& ... args) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), args...);
-    }
-#else
-    template<class Arg>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg) const
-    {
-      copy_arg(buffer, offset, arg);
-    }
-    template<class Arg, class _0>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0);
-    }
-    template <class Arg, class _0, class _1>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1);
-    }
-    template <class Arg, class _0, class _1, class _2>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-    }
-    template <class Arg, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE, class _xF>
-    void __device__
-    fill_arguments(char* buffer, size_t offset, Arg arg, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
-    {
-      fill_arguments(buffer, copy_arg(buffer, offset, arg), x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
-    }
-#endif /* variadic */
-
-#if 0
-    template<class K, class... Args>
-    cudaError_t __device__
-    doit_device(K k, Args const&... args) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,args...);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, args...);
-      status = launch_device(k, param_buffer);
-#endif
-      return status;
-    }
-#else
-    template<class K, class _0>
-    cudaError_t __device__
-    doit_device(K k, _0 x0) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-      THRUST_UNUSED_VAR(xB);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-      THRUST_UNUSED_VAR(xB);
-      THRUST_UNUSED_VAR(xC);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-      THRUST_UNUSED_VAR(xB);
-      THRUST_UNUSED_VAR(xC);
-      THRUST_UNUSED_VAR(xD);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-      THRUST_UNUSED_VAR(xB);
-      THRUST_UNUSED_VAR(xC);
-      THRUST_UNUSED_VAR(xD);
-      THRUST_UNUSED_VAR(xE);
-#endif
-      return status;
-    }
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE, class _xF>
-    cudaError_t __device__
-    doit_device(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC,_xD xD, _xE xE, _xF xF) const
-    {
-      cudaError_t status = cudaErrorNotSupported;
-#if __THRUST_HAS_CUDART__
-      const size_t size = argument_pack_size(0,x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE,xF);
-      void *param_buffer = cudaGetParameterBuffer(64,size);
-      fill_arguments((char*)param_buffer, 0, x0,x1,x2,x3,x4,x5,x6,x7,x8,x9,xA,xB,xC,xD,xE,xF);
-      status = launch_device(k, param_buffer);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(x0);
-      THRUST_UNUSED_VAR(x1);
-      THRUST_UNUSED_VAR(x2);
-      THRUST_UNUSED_VAR(x3);
-      THRUST_UNUSED_VAR(x4);
-      THRUST_UNUSED_VAR(x5);
-      THRUST_UNUSED_VAR(x6);
-      THRUST_UNUSED_VAR(x7);
-      THRUST_UNUSED_VAR(x8);
-      THRUST_UNUSED_VAR(x9);
-      THRUST_UNUSED_VAR(xA);
-      THRUST_UNUSED_VAR(xB);
-      THRUST_UNUSED_VAR(xC);
-      THRUST_UNUSED_VAR(xD);
-      THRUST_UNUSED_VAR(xE);
-      THRUST_UNUSED_VAR(xF);
-#endif
-      return status;
-    }
-#endif /* variadic */
-
-    template <class K>
-    cudaError_t __device__
-    launch_device(K k, void* buffer) const
-    {
-#if __THRUST_HAS_CUDART__
-      return cudaLaunchDevice((void*)k,
-                              buffer,
-                              dim3(grid),
-                              dim3(block),
-                              shared_mem,
-                              stream);
-#else
-      THRUST_UNUSED_VAR(k);
-      THRUST_UNUSED_VAR(buffer);
-      return cudaErrorNotSupported;
-#endif
-    }
-
-
-#if defined(__NVCOMPILER_CUDA__)
-#  define THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(...) \
-      (__builtin_is_device_code() ?              \
-          doit_device(__VA_ARGS__) : doit_host(__VA_ARGS__))
-#elif defined(__CUDA_ARCH__)
-#  define THRUST_TRIPLE_LAUNCHER_HOSTDEVICE doit_device
-#else
-#  define THRUST_TRIPLE_LAUNCHER_HOSTDEVICE doit_host
-#endif
-
-#if 0
-    __thrust_exec_check_disable__
-    template <class K, class... Args>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, Args const&... args) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, args...);
-    }
-#else
-    __thrust_exec_check_disable__
-    template <class K, class _0>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE);
-    }
-    __thrust_exec_check_disable__
-    template <class K, class _0, class _1, class _2, class _3, class _4, class _5, class _6, class _7, class _8, class _9, class _xA, class _xB, class _xC, class _xD, class _xE, class _xF>
-    cudaError_t THRUST_FUNCTION
-    doit(K k, _0 x0, _1 x1, _2 x2, _3 x3, _4 x4, _5 x5, _6 x6, _7 x7, _8 x8, _9 x9, _xA xA, _xB xB, _xC xC, _xD xD, _xE xE, _xF xF) const
-    {
-      return THRUST_TRIPLE_LAUNCHER_HOSTDEVICE(k, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, xA, xB, xC, xD, xE, xF);
-    }
-#endif
-#undef THRUST_TRIPLE_LAUNCHER_HOSTDEVICE
-  }; // struct triple_chevron
-
-}    // namespace launcher
-}    // namespace cuda_
-
-} // end namespace thrust

spaces/ChallengeHub/Chinese-LangChain/assets/custom.css

@@ -1,190 +0,0 @@
-:root {
-    --chatbot-color-light: rgba(255, 255, 255, 0.08);
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-
-
-/* usage_display */
-#usage_display {
-    height: 1em;
-}
-#usage_display p{
-    padding: 0 1em;
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* Thank @Keldos-Li for fixing it */
-/* Light mode (default) */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-    color: #000000 !important;
-}
-[data-testid = "bot"] {
-    background-color: rgba(255, 255, 255, 0.08) !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-
-/* Dark mode */
-.dark #chuanhu_chatbot {
-    background-color: var(--chatbot-color-dark) !important;
-    color: rgba(255, 255, 255, 0.08) !important;
-}
-.dark [data-testid = "bot"] {
-    background-color: #2C2C2C !important;
-}
-.dark [data-testid = "user"] {
-    background-color: #26B561 !important;
-}
-
-#chuanhu_chatbot {
-    height: 100%;
-    min-height: 400px;
-}
-
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* Table */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* Inline code */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* Code block */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* Hightlight */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */

spaces/ChandraMohanNayal/AutoGPT/tests/integration/memory_tests.py

@@ -1,49 +0,0 @@
-import random
-import string
-import sys
-import unittest
-from pathlib import Path
-
-from autogpt.config import Config
-from autogpt.memory.local import LocalCache
-
-
-class TestLocalCache(unittest.TestCase):
-    def random_string(self, length):
-        return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
-    def setUp(self):
-        cfg = cfg = Config()
-        self.cache = LocalCache(cfg)
-        self.cache.clear()
-
-        # Add example texts to the cache
-        self.example_texts = [
-            "The quick brown fox jumps over the lazy dog",
-            "I love machine learning and natural language processing",
-            "The cake is a lie, but the pie is always true",
-            "ChatGPT is an advanced AI model for conversation",
-        ]
-
-        for text in self.example_texts:
-            self.cache.add(text)
-
-        # Add some random strings to test noise
-        for _ in range(5):
-            self.cache.add(self.random_string(10))
-
-    def test_get_relevant(self):
-        query = "I'm interested in artificial intelligence and NLP"
-        k = 3
-        relevant_texts = self.cache.get_relevant(query, k)
-
-        print(f"Top {k} relevant texts for the query '{query}':")
-        for i, text in enumerate(relevant_texts, start=1):
-            print(f"{i}. {text}")
-
-        self.assertEqual(len(relevant_texts), k)
-        self.assertIn(self.example_texts[1], relevant_texts)
-
-
-if __name__ == "__main__":
-    unittest.main()

spaces/Cletrason/Cletrason-toad-in-the-mario-movie/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Cletrason/toad-in-the-mario-movie").launch()

spaces/CodeDoes/FrostAura-gpt-neox-20b-fiction-novel-generation/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/FrostAura/gpt-neox-20b-fiction-novel-generation").launch()

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/diffusionmodules/upscaling.py

@@ -1,81 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
-from ldm.util import default
-
-
-class AbstractLowScaleModel(nn.Module):
-    # for concatenating a downsampled image to the latent representation
-    def __init__(self, noise_schedule_config=None):
-        super(AbstractLowScaleModel, self).__init__()
-        if noise_schedule_config is not None:
-            self.register_schedule(**noise_schedule_config)
-
-    def register_schedule(self, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                   cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def forward(self, x):
-        return x, None
-
-    def decode(self, x):
-        return x
-
-
-class SimpleImageConcat(AbstractLowScaleModel):
-    # no noise level conditioning
-    def __init__(self):
-        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
-        self.max_noise_level = 0
-
-    def forward(self, x):
-        # fix to constant noise level
-        return x, torch.zeros(x.shape[0], device=x.device).long()
-
-
-class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
-    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
-        super().__init__(noise_schedule_config=noise_schedule_config)
-        self.max_noise_level = max_noise_level
-
-    def forward(self, x, noise_level=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        z = self.q_sample(x, noise_level)
-        return z, noise_level
-
-
-

spaces/CyberPeace-Institute/Cybersecurity-Knowledge-Graph-Extraction/app.py

@@ -1,103 +0,0 @@
-import streamlit as st
-from transformers import AutoModelForTokenClassification
-from annotated_text import annotated_text
-import numpy as np
-import os, joblib
-
-from utils import get_idxs_from_text
-
-model = AutoModelForTokenClassification.from_pretrained("CyberPeace-Institute/Cybersecurity-Knowledge-Graph", trust_remote_code=True)
-
-role_classifiers = {}
-folder_path = '/arg_role_models'
-for filename in os.listdir(os.getcwd() + folder_path):
-    if filename.endswith('.joblib'):
-        file_path = os.getcwd() + os.path.join(folder_path, filename)
-        clf = joblib.load(file_path)
-        arg = filename.split(".")[0]
-        role_classifiers[arg] = clf
-
-def annotate(name):
-    tokens = [item["token"] for item in output]
-    tokens = [token.replace(" ", "") for token in tokens]
-    text = model.tokenizer.decode([item["id"] for item in output])
-    idxs = get_idxs_from_text(text, tokens)
-    labels = [item[name] for item in output]
-
-    annotated_text_list = []
-    last_label = ""
-    cumulative_tokens = "" 
-    last_id = 0
-    for idx, label in zip(idxs, labels):
-        to_label = label
-        label_short = to_label.split("-")[1] if "-" in to_label else to_label
-        if last_label == label_short:
-            cumulative_tokens += text[last_id : idx["end_idx"]]
-            last_id = idx["end_idx"]
-        else:
-            if last_label != "":
-                if last_label == "O":
-                    annotated_text_list.append(cumulative_tokens)
-                else:
-                    annotated_text_list.append((cumulative_tokens, last_label))
-            last_label = label_short
-            cumulative_tokens = idx["word"]
-            last_id = idx["end_idx"]
-    if last_label == "O":
-        annotated_text_list.append(cumulative_tokens)
-    else:  
-        annotated_text_list.append((cumulative_tokens, last_label))
-    annotated_text(annotated_text_list)
-
-def get_arg_roles(output):
-    args = [(idx, item["argument"], item["token"]) for idx, item in enumerate(output) if item["argument"]!= "O"]
-        
-    entities = []
-    current_entity = None
-    for position, label, token in args:
-        if label.startswith('B-'):
-            if current_entity is not None:
-                entities.append(current_entity)
-            current_entity = {'label': label[2:], 'text': token.replace(" ", ""), 'start': position, 'end': position}
-        elif label.startswith('I-'):
-            if current_entity is not None:
-                current_entity['text'] += ' ' + token.replace(" ", "")
-                current_entity['end'] = position
-    for entity in entities:
-        context = model.tokenizer.decode([item["id"] for item in output[max(0, entity["start"] - 15) : min(len(output), entity["end"] + 15)]])
-        entity["context"] = context
-    
-    for entity in entities:
-        if len(model.arg_2_role[entity["label"]]) > 1:
-            sent_embed = model.embed_model.encode(entity["context"])
-            arg_embed = model.embed_model.encode(entity["text"])
-            embed = np.concatenate((sent_embed, arg_embed))
-            arg_clf = role_classifiers[entity["label"]]
-            role_id = arg_clf.predict(embed.reshape(1, -1))
-            role = model.arg_2_role[entity["label"]][role_id[0]]
-            entity["role"] = role
-        else:
-            entity["role"] = model.arg_2_role[entity["label"]][0]
-    
-    for item in output:
-        item["role"] = "O"
-    for entity in entities:
-        for i in range(entity["start"], entity["end"] + 1):
-            output[i]["role"] = entity["role"]
-    return output
-
-st.title("Create Knowledge Graphs from Cyber Incidents")
-
-text_input = st.text_area("Enter your text here", height=100)
-
-if text_input or st.button('Apply'):
-    output = model(text_input)
-    st.subheader("Event Nuggets")
-    annotate("nugget")
-    st.subheader("Event Arguments")
-    annotate("argument")
-    st.subheader("Realis of Event Nuggets")
-    annotate("realis")
-    output = get_arg_roles(output)
-    st.subheader("Role of the Event Arguments")
-    annotate("role")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/B_A_S_E_.py

@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_B_A_S_E_(BaseTTXConverter):
-    pass

spaces/DQChoi/image_sticker/README.md

@@ -1,12 +0,0 @@
----
-title: Image Sticker
-emoji: 💻
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Dagfinn1962/prodia2/README.md

@@ -1,14 +0,0 @@
----
-title: Prodia
-emoji: 🔥
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: pikto/prodia
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Datasculptor/StyleGAN-NADA/model/sg2_model.py

@@ -1,817 +0,0 @@
-import math
-import random
-import functools
-import operator
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-
-from op import conv2d_gradfix
-
-if torch.cuda.is_available():
-    from op.fused_act import FusedLeakyReLU, fused_leaky_relu
-    from op.upfirdn2d import upfirdn2d
-else:
-    from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
-    from op.upfirdn2d_cpu import upfirdn2d
-
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer("kernel", kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer("kernel", kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer("kernel", kernel)
-
-        self.pad = pad
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, pad=self.pad)
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = conv2d_gradfix.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
-            f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = fused_leaky_relu(out, self.bias * self.lr_mul)
-
-        else:
-            out = F.linear(
-                input, self.weight * self.scale, bias=self.bias * self.lr_mul
-            )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
-        )
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        demodulate=True,
-        upsample=False,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        fused=True,
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-        self.fused = fused
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
-            f"upsample={self.upsample}, downsample={self.downsample})"
-        )
-
-    def forward(self, input, style, is_s_code=False):
-        batch, in_channel, height, width = input.shape
-
-        if not self.fused:
-            weight = self.scale * self.weight.squeeze(0)
-
-            if is_s_code:
-                style = style[self.modulation]
-            else:
-                style = self.modulation(style)
-
-            if self.demodulate:
-                w = weight.unsqueeze(0) * style.view(batch, 1, in_channel, 1, 1)
-                dcoefs = (w.square().sum((2, 3, 4)) + 1e-8).rsqrt()
-
-            input = input * style.reshape(batch, in_channel, 1, 1)
-
-            if self.upsample:
-                weight = weight.transpose(0, 1)
-                out = conv2d_gradfix.conv_transpose2d(
-                    input, weight, padding=0, stride=2
-                )
-                out = self.blur(out)
-
-            elif self.downsample:
-                input = self.blur(input)
-                out = conv2d_gradfix.conv2d(input, weight, padding=0, stride=2)
-
-            else:
-                out = conv2d_gradfix.conv2d(input, weight, padding=self.padding)
-
-            if self.demodulate:
-                out = out * dcoefs.view(batch, -1, 1, 1)
-
-            return out
-
-        if is_s_code:
-            style = style[self.modulation]
-        else:
-            style = self.modulation(style)
-
-        style = style.view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = conv2d_gradfix.conv_transpose2d(
-                input, weight, padding=0, stride=2, groups=batch
-            )
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = conv2d_gradfix.conv2d(
-                input, weight, padding=0, stride=2, groups=batch
-            )
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = conv2d_gradfix.conv2d(
-                input, weight, padding=self.padding, groups=batch
-            )
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise is None:
-            batch, _, height, width = image.shape
-            noise = image.new_empty(batch, 1, height, width).normal_()
-
-        return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input, is_s_code=False):
-        if not is_s_code:
-            batch = input.shape[0]
-        else: 
-            batch = next(iter(input.values())).shape[0]
-
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        upsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        demodulate=True,
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-        )
-
-        self.noise = NoiseInjection()
-        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        # self.activate = ScaledLeakyReLU(0.2)
-        self.activate = FusedLeakyReLU(out_channel)
-
-    def forward(self, input, style, noise=None, is_s_code=False):
-        out = self.conv(input, style, is_s_code=is_s_code)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None, is_s_code=False):
-        out = self.conv(input, style, is_s_code=is_s_code)
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out
-
-
-class Generator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-    ):
-        super().__init__()
-
-        self.size = size
-
-        self.style_dim = style_dim
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
-        )
-        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
-        self.log_size = int(math.log(size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channel = self.channels[4]
-
-        for layer_idx in range(self.num_layers):
-            res = (layer_idx + 5) // 2
-            shape = [1, 1, 2 ** res, 2 ** res]
-            self.noises.register_buffer(f"noise_{layer_idx}", torch.randn(*shape))
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-
-        self.modulation_layers = [self.conv1.conv.modulation, self.to_rgb1.conv.modulation] + \
-                                 [layer.conv.modulation for layer in self.convs]            + \
-                                 [layer.conv.modulation for layer in self.to_rgbs]
-
-    def make_noise(self):
-        device = self.input.input.device
-
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def get_s_code(self, styles, input_is_latent):
-
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        s_codes = [{# const block
-                   self.modulation_layers[0]:  self.modulation_layers[0](style[:, 0]),   #s0
-                   self.modulation_layers[1]:  self.modulation_layers[1](style[:, 1]),   #s1
-                   # conv layers
-                   self.modulation_layers[2]:  self.modulation_layers[2](style[:, 1]),   #s2
-                   self.modulation_layers[3]:  self.modulation_layers[3](style[:, 2]),   #s3
-                   self.modulation_layers[4]:  self.modulation_layers[4](style[:, 3]),   #s5
-                   self.modulation_layers[5]:  self.modulation_layers[5](style[:, 4]),   #s6
-                   self.modulation_layers[6]:  self.modulation_layers[6](style[:, 5]),   #s8
-                   self.modulation_layers[7]:  self.modulation_layers[7](style[:, 6]),   #s9
-                   self.modulation_layers[8]:  self.modulation_layers[8](style[:, 7]),   #s11
-                   self.modulation_layers[9]:  self.modulation_layers[9](style[:, 8]),   #s12
-                   self.modulation_layers[10]: self.modulation_layers[10](style[:, 9]), #s14
-                   self.modulation_layers[11]: self.modulation_layers[11](style[:, 10]), #s15
-                   self.modulation_layers[12]: self.modulation_layers[12](style[:, 11]), #s17
-                   self.modulation_layers[13]: self.modulation_layers[13](style[:, 12]), #s18
-                   self.modulation_layers[14]: self.modulation_layers[14](style[:, 13]), #s20
-                   self.modulation_layers[15]: self.modulation_layers[15](style[:, 14]), #s21
-                   self.modulation_layers[16]: self.modulation_layers[16](style[:, 15]), #s23
-                   self.modulation_layers[17]: self.modulation_layers[17](style[:, 16]), #s24
-                   # toRGB layers
-                   self.modulation_layers[18]: self.modulation_layers[18](style[:, 3]),  #s4
-                   self.modulation_layers[19]: self.modulation_layers[19](style[:, 5]),  #s7
-                   self.modulation_layers[20]: self.modulation_layers[20](style[:, 7]),  #s10
-                   self.modulation_layers[21]: self.modulation_layers[21](style[:, 9]),  #s13
-                   self.modulation_layers[22]: self.modulation_layers[22](style[:, 11]), #s16
-                   self.modulation_layers[23]: self.modulation_layers[23](style[:, 13]), #s19
-                   self.modulation_layers[24]: self.modulation_layers[24](style[:, 15]), #s22
-                   self.modulation_layers[25]: self.modulation_layers[25](style[:, 17]), #s25
-        } for style in styles]
-            
-        return s_codes    
-
-
-    def forward(
-        self,
-        styles,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        input_is_s_code=False,
-        noise=None,
-        randomize_noise=True,
-    ):
-        if not input_is_s_code:
-            return self.forward_with_w(styles, return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise, randomize_noise)
-        
-        return self.forward_with_s(styles, return_latents, noise, randomize_noise)
-
-    def forward_with_w(
-            self,
-            styles,
-            return_latents=False,
-            inject_index=None,
-            truncation=1,
-            truncation_latent=None,
-            input_is_latent=False,
-            noise=None,
-            randomize_noise=True,
-        ):
-            if not input_is_latent:
-                styles = [self.style(s) for s in styles]
-
-            if noise is None:
-                if randomize_noise:
-                    noise = [None] * self.num_layers
-                else:
-                    noise = [
-                        getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
-                    ]
-
-            if truncation < 1:
-                style_t = []
-
-                for style in styles:
-                    style_t.append(
-                        truncation_latent + truncation * (style - truncation_latent)
-                    )
-
-                styles = style_t
-
-            if len(styles) < 2:
-                inject_index = self.n_latent
-
-                if styles[0].ndim < 3:
-                    latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-                else:
-                    latent = styles[0]
-
-            else:
-                if inject_index is None:
-                    inject_index = random.randint(1, self.n_latent - 1)
-
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-                latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-                latent = torch.cat([latent, latent2], 1)
-
-            out = self.input(latent)
-            out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-            skip = self.to_rgb1(out, latent[:, 1])
-
-            i = 1
-            for conv1, conv2, noise1, noise2, to_rgb in zip(
-                self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-            ):
-                out = conv1(out, latent[:, i], noise=noise1)
-                out = conv2(out, latent[:, i + 1], noise=noise2)
-                skip = to_rgb(out, latent[:, i + 2], skip)
-
-                i += 2
-
-            image = skip
-
-            if return_latents:
-                return image, latent
-
-            else:
-                return image, None
-
-    def forward_with_s(
-            self,
-            styles,
-            return_latents=False,
-            noise=None,
-            randomize_noise=True,
-        ):
-
-            if noise is None:
-                if randomize_noise:
-                    noise = [None] * self.num_layers
-                else:
-                    noise = [
-                        getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
-                    ]
-
-            out = self.input(styles, is_s_code=True)
-            out = self.conv1(out, styles, is_s_code=True, noise=noise[0])
-
-            skip = self.to_rgb1(out, styles, is_s_code=True)
-
-            i = 1
-            for conv1, conv2, noise1, noise2, to_rgb in zip(
-                self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-            ):
-                out = conv1(out, styles, is_s_code=True, noise=noise1)
-                out = conv2(out, styles, is_s_code=True, noise=noise2)
-                skip = to_rgb(out, styles, skip, is_s_code=True)
-
-                i += 2
-
-            image = skip
-
-            if return_latents:
-                return image, styles
-
-            else:
-                return image, None
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            layers.append(FusedLeakyReLU(out_channel, bias=bias))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-
-        return out
-

spaces/Detomo/ai-avatar-frontend/Dockerfile

@@ -1,20 +0,0 @@
-# Sử dụng image Node.js phiên bản mới nhất
-FROM node:latest
-
-# Thiết lập thư mục làm việc trong container
-WORKDIR /app
-
-# Sao chép package.json và yarn.lock vào container
-COPY package.json yarn.lock ./
-
-# Cài đặt các gói phụ thuộc bằng Yarn
-RUN yarn install
-
-# Sao chép toàn bộ mã nguồn và các tệp khác vào container
-COPY . .
-
-# Expose port (Bạn cần xác định cổng mà ứng dụng của bạn đang chạy, ví dụ: 3000)
-EXPOSE 3000
-
-# Chạy ứng dụng khi khởi động container
-CMD ["yarn", "start"]

spaces/DonDoesStuff/sd_xl_base_0.9/README.md

@@ -1,19 +0,0 @@
----
-title: DreamlikeArt-PhotoReal 2.0
-emoji: 🧘🏻‍♀️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-duplicated_from: phenomenon1981/DreamlikeArt-PhotoReal-2.0
----
----
-title: DreamlikeArt-PhotoReal 2.0
-emoji: 🧘🏻‍♀️
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py

spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/GetCode.py

@@ -1,232 +0,0 @@
-
-
-
-import os
-import pickle
-import numpy as np
-from dnnlib import tflib  
-import tensorflow as tf 
-
-import argparse
-
-def LoadModel(dataset_name):
-    # Initialize TensorFlow.
-    tflib.init_tf()
-    model_path='./model/'
-    model_name=dataset_name+'.pkl'
-    
-    tmp=os.path.join(model_path,model_name)
-    with open(tmp, 'rb') as f:
-        _, _, Gs = pickle.load(f)
-    return Gs
-
-def lerp(a,b,t):
-     return a + (b - a) * t
-
-#stylegan-ada
-def SelectName(layer_name,suffix):
-    if suffix==None:
-        tmp1='add:0' in layer_name 
-        tmp2='shape=(?,' in layer_name
-        tmp4='G_synthesis_1' in layer_name
-        tmp= tmp1 and tmp2 and tmp4  
-    else:
-        tmp1=('/Conv0_up'+suffix) in layer_name 
-        tmp2=('/Conv1'+suffix) in layer_name 
-        tmp3=('4x4/Conv'+suffix) in layer_name 
-        tmp4='G_synthesis_1' in layer_name
-        tmp5=('/ToRGB'+suffix) in layer_name
-        tmp= (tmp1 or tmp2 or tmp3 or tmp5) and tmp4 
-    return tmp
-
-
-def GetSNames(suffix):
-    #get style tensor name 
-    with tf.Session() as sess:
-        op = sess.graph.get_operations()
-    layers=[m.values() for m in op]
-    
-    
-    select_layers=[]
-    for layer in layers:
-        layer_name=str(layer)
-        if SelectName(layer_name,suffix):
-            select_layers.append(layer[0])
-    return select_layers
-
-def SelectName2(layer_name):
-    tmp1='mod_bias' in layer_name 
-    tmp2='mod_weight' in layer_name
-    tmp3='ToRGB' in layer_name 
-    
-    tmp= (tmp1 or tmp2) and (not tmp3) 
-    return tmp
-
-def GetKName(Gs):
-    
-    layers=[var for name, var in Gs.components.synthesis.vars.items()]
-    
-    select_layers=[]
-    for layer in layers:
-        layer_name=str(layer)
-        if SelectName2(layer_name):
-            select_layers.append(layer)
-    return select_layers
-
-def GetCode(Gs,random_state,num_img,num_once,dataset_name):
-    rnd = np.random.RandomState(random_state)  #5
-    
-    truncation_psi=0.7
-    truncation_cutoff=8
-    
-    dlatent_avg=Gs.get_var('dlatent_avg')
-    
-    dlatents=np.zeros((num_img,512),dtype='float32')
-    for i in range(int(num_img/num_once)):
-        src_latents =  rnd.randn(num_once, Gs.input_shape[1])
-        src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
-        
-        # Apply truncation trick.
-        if truncation_psi is not None and truncation_cutoff is not None:
-                layer_idx = np.arange(src_dlatents.shape[1])[np.newaxis, :, np.newaxis]
-                ones = np.ones(layer_idx.shape, dtype=np.float32)
-                coefs = np.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones)
-                src_dlatents_np=lerp(dlatent_avg, src_dlatents, coefs)
-                src_dlatents=src_dlatents_np[:,0,:].astype('float32')
-                dlatents[(i*num_once):((i+1)*num_once),:]=src_dlatents
-    print('get all z and w')
-    
-    tmp='./npy/'+dataset_name+'/W'
-    np.save(tmp,dlatents)
-
-    
-def GetImg(Gs,num_img,num_once,dataset_name,save_name='images'):
-    print('Generate Image')
-    tmp='./npy/'+dataset_name+'/W.npy'
-    dlatents=np.load(tmp) 
-    fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
-    
-    all_images=[]
-    for i in range(int(num_img/num_once)):
-        print(i)
-        images=[]
-        for k in range(num_once):
-            tmp=dlatents[i*num_once+k]
-            tmp=tmp[None,None,:]
-            tmp=np.tile(tmp,(1,Gs.components.synthesis.input_shape[1],1))
-            image2= Gs.components.synthesis.run(tmp, randomize_noise=False, output_transform=fmt)
-            images.append(image2)
-            
-        images=np.concatenate(images)
-        
-        all_images.append(images)
-        
-    all_images=np.concatenate(all_images)
-    
-    tmp='./npy/'+dataset_name+'/'+save_name
-    np.save(tmp,all_images)
-
-def GetS(dataset_name,num_img):
-    print('Generate S')
-    tmp='./npy/'+dataset_name+'/W.npy'
-    dlatents=np.load(tmp)[:num_img]
-    
-    with tf.Session() as sess:
-        init = tf.global_variables_initializer()
-        sess.run(init)
-        
-        Gs=LoadModel(dataset_name)
-        Gs.print_layers()  #for ada
-        select_layers1=GetSNames(suffix=None)  #None,'/mul_1:0','/mod_weight/read:0','/MatMul:0'
-        dlatents=dlatents[:,None,:]
-        dlatents=np.tile(dlatents,(1,Gs.components.synthesis.input_shape[1],1))
-        
-        all_s = sess.run(
-            select_layers1,
-            feed_dict={'G_synthesis_1/dlatents_in:0': dlatents})
-    
-    layer_names=[layer.name for layer in select_layers1]
-    save_tmp=[layer_names,all_s]
-    return save_tmp
-
-    
-
-
-def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False):
-    """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
-    Can be used as an output transformation for Network.run().
-    """
-    if nchw_to_nhwc:
-        images = np.transpose(images, [0, 2, 3, 1])
-    
-    scale = 255 / (drange[1] - drange[0])
-    images = images * scale + (0.5 - drange[0] * scale)
-    
-    np.clip(images, 0, 255, out=images)
-    images=images.astype('uint8')
-    return images
-
-
-def GetCodeMS(dlatents):
-        m=[]
-        std=[]
-        for i in range(len(dlatents)):
-            tmp= dlatents[i] 
-            tmp_mean=tmp.mean(axis=0)
-            tmp_std=tmp.std(axis=0)
-            m.append(tmp_mean)
-            std.append(tmp_std)
-        return m,std
-
-
-
-#%%
-if __name__ == "__main__":
-    
-    
-    parser = argparse.ArgumentParser(description='Process some integers.')
-    
-    parser.add_argument('--dataset_name',type=str,default='ffhq',
-                    help='name of dataset, for example, ffhq')
-    parser.add_argument('--code_type',choices=['w','s','s_mean_std'],default='w')
-    
-    args = parser.parse_args()
-    random_state=5
-    num_img=100_000 
-    num_once=1_000
-    dataset_name=args.dataset_name
-    
-    if not os.path.isfile('./model/'+dataset_name+'.pkl'):
-        url='https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/'
-        name='stylegan2-'+dataset_name+'-config-f.pkl'
-        os.system('wget ' +url+name + '  -P  ./model/')
-        os.system('mv ./model/'+name+' ./model/'+dataset_name+'.pkl')
-    
-    if not os.path.isdir('./npy/'+dataset_name):
-        os.system('mkdir ./npy/'+dataset_name)
-    
-    if args.code_type=='w':
-        Gs=LoadModel(dataset_name=dataset_name)
-        GetCode(Gs,random_state,num_img,num_once,dataset_name)
-#        GetImg(Gs,num_img=num_img,num_once=num_once,dataset_name=dataset_name,save_name='images_100K') #no need 
-    elif args.code_type=='s':
-        save_name='S'
-        save_tmp=GetS(dataset_name,num_img=2_000)
-        tmp='./npy/'+dataset_name+'/'+save_name
-        with open(tmp, "wb") as fp:
-            pickle.dump(save_tmp, fp)
-        
-    elif args.code_type=='s_mean_std':
-        save_tmp=GetS(dataset_name,num_img=num_img)
-        dlatents=save_tmp[1]
-        m,std=GetCodeMS(dlatents)
-        save_tmp=[m,std]
-        save_name='S_mean_std'
-        tmp='./npy/'+dataset_name+'/'+save_name
-        with open(tmp, "wb") as fp:
-            pickle.dump(save_tmp, fp)
-    
-    
-    
-    
-