diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crazy Chicken Kart 3 Crack How to Solve the Common Problems and Issues with the Game.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Crazy Chicken Kart 3 Crack How to Solve the Common Problems and Issues with the Game.md
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-Crazy Chicken Kart 3 Crack: How to Download and Play the Wacky Racing Game
- If you are looking for a fun and hilarious kart racing game, you might want to check out Crazy Chicken Kart 3. This game features crazy chicken and his friends as they race through different eras, from the past to the future, with explosive excitement around every corner. You can collect weapons and power-ups as you speed along and ruffle some feathers when you strategically blast your opponents off the road.
- However, there is a catch. Crazy Chicken Kart 3 is not a free game. You need to buy it from an online store or download it from a website that offers it. But what if you don't want to spend money or risk downloading viruses or malware? Is there a way to play Crazy Chicken Kart 3 for free?
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The answer is yes. You can use a crack to bypass the security and activation of the game and play it without any restrictions. A crack is a modified version of the game's executable file that allows you to run it without needing a license key or a disc. In this article, we will show you how to download and install the crack for Crazy Chicken Kart 3, how to play the game with the crack, and what features and gameplay you can expect from this wacky racing game.
- How to Download and Install the Crack for Crazy Chicken Kart 3
- Before you can use the crack for Crazy Chicken Kart 3, you need to have the game installed on your PC. You can either buy it from an online store like Youdagames.com or download it from a website that offers it for free. However, be careful when downloading games from unknown sources, as they may contain viruses or malware that can harm your computer.
- Once you have the game installed, you need to find a reliable source for the crack. One of the websites that offers a crack for Crazy Chicken Kart 3 is npmjs.com. This website provides a package called crazy_chicken_kart_3_crack_14_extra_quality_mtm that contains the modified executable file for the game. To download this package, you need to have Node.js installed on your PC. Node.js is a software that allows you to run JavaScript code outside of a web browser.
- To install Node.js, go to nodejs.org and download the latest version for your operating system. Follow the instructions on how to install it on your PC. Once you have Node.js installed, open a command prompt window and type npm i crazy_chicken_kart_3_crack_14_extra_quality_mtm. This will download and install the package on your PC.
- After installing the package, go to the folder where you installed Crazy Chicken Kart 3. Locate the original executable file of the game, which is usually named CCKart.exe or something similar. Rename this file to something else, like CCKart_old.exe or CCKart_backup.exe. This will prevent the game from running with the original file.
- Then, go to the folder where you installed Node.js. Locate the folder named node_modules and open it. Inside this folder, find another folder named crazy_chicken_kart_3_crack_14_extra_quality_mtm and open it. Inside this folder, find a file named CCKart.exe or something similar. This is the cracked executable file of the game.
- the folder where you installed Crazy Chicken Kart 3. Make sure that this file has the same name as the original executable file of the game, which is usually CCKart.exe or something similar. This will replace the original file with the cracked file.
- Now, you have successfully installed the crack for Crazy Chicken Kart 3. You can run the game by double-clicking on CCKart.exe or by creating a shortcut on your desktop or start menu.
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- How to Play Crazy Chicken Kart 3 with the Crack
- Playing Crazy Chicken Kart 3 with the crack is very easy and straightforward. You don't need any license key or disc to run it. Just launch CCKart.exe and enjoy.
- When you start the game, you will see a menu with several options: Single Player, Multiplayer, Options, Credits, Exit Game. You can choose any of these options depending on what mode of gameplay you want.
- If you choose Single Player, you will be able to play against computer-controlled opponents in various racing modes: Championship, Time Trial, Single Race, Training Mode. You can also choose different difficulty levels: Easy, Normal, Hard.
- If you choose Multiplayer, you will be able to play against another human player on the same PC using split-screen mode. You can also choose different racing modes: Championship, Time Trial, Single Race.
- If you choose Options, you will be able to customize various settings of the game: Graphics Quality, Sound Volume, Music Volume, Language.
- If you choose Credits, you will be able to see who made this game.
- If you choose Exit Game, you will be able to quit playing.
- Crazy Chicken Kart 3 Features and Gameplay
- Crazy Chicken Kart 3 is a fun and hilarious kart racing game that offers many features and gameplay elements that will keep you entertained for hours.
- The Characters and Karts You Can Choose From
- In Crazy Chicken Kart 3, you can choose from eight different characters: Crazy Chicken (the main protagonist), Snowman (a friendly snowman), Hank (a tough cowboy), Pumpkin (a spooky pumpkin), Skeleton (a scary skeleton), Alien (a green alien), Robot (a futuristic robot), Professor (a mad scientist).
- Each character has their own unique kart design that matches their personality and theme. For example, Crazy Chicken drives a red kart with chicken wings on its sides; Snowman drives a blue kart with snowflakes on its wheels; Hank drives a brown kart with horseshoes on its front; Pumpkin drives an orange kart with pumpkin seeds on its back; Skeleton drives a black kart with bones on its hood; Alien drives a green kart with UFOs on its roof; Robot drives a silver kart with gears on its spoiler; Professor drives a purple kart with test tubes on its bumper.
- You can also unlock more karts by completing certain achievements in Championship mode.
- The Racing Modes and Tracks You Can Explore
- In Crazy Chicken Kart 3, you can race through eight exciting eras: past, present and future. Each era has two tracks that are based on historical or fictional events or locations.
- The eras are:
-
-Stone Age: Race through caves filled with dinosaurs and mammoths
-Ancient Egypt: Race through pyramids filled with mummies and sphinxes
-Middle Ages: Race through castles filled with knights and dragons
-Wild West: Race through deserts filled with cowboys and Indians
-Modern Times: Race through cities filled with cars and skyscrapers
-Halloween: Race through graveyards filled with ghosts and zombies
-Space Age: Race through planets filled with aliens and spaceships
-Future World: Race through futuristic cities filled with robots and lasers
-
-You can race in four different modes:
-
-Championship: Compete against seven other racers in four tracks per era; win medals based on your position; unlock new karts by completing achievements
-Time Trial: Race against yourself or against a ghost racer in any track; beat your own time or set new records
-Single Race: Race against seven other racers in any track; choose your own difficulty level and number of laps
-Training Mode: Practice your skills in any track without any opponents or time limit
-
-The Weapons and Power-Ups You Can Use to Blast Your Opponents
- In Crazy Chicken Kart 3, you can collect various weapons and power-ups as you speed along the tracks. These items can help you gain an advantage over your rivals or hinder their progress.
- The weapons and power-ups are:
-
-Rocket: Launches a rocket that flies straight ahead and explodes on impact; can damage multiple racers if they are close together
-Mine: Drops a mine behind your kart that explodes when someone drives over it; can damage multiple racers if they are close together
-Bomb: Throws a bomb ahead of your kart that explodes after a few seconds or when someone drives over it; can damage multiple racers if they are close together
-Oil Slick: Spills oil behind your kart that makes the track slippery; can cause racers to lose control and spin out
-Turbo Boost: Gives your kart a temporary speed boost; can help you overtake your opponents or escape from attacks
-Magnet: Attracts nearby racers to your kart; can slow them down or make them crash into obstacles
-Shield: Protects your kart from any attacks for a short time; can also reflect rockets back to their sender
-Invisibility: Makes your kart invisible for a short time; can help you avoid attacks or surprise your opponents
-Giant Chicken Head: Transforms your kart into a giant chicken head for a short time; can crush other racers or obstacles in your way
-Time Freeze: Freezes time for everyone except you for a short time; can help you gain a huge lead or avoid attacks
-
-Crazy Chicken Kart 3 Tips and Tricks
- Crazy Chicken Kart 3 is not just about driving fast and shooting randomly. You need to use some strategy and skill to win the races. Here are some tips and tricks that can help you improve your performance:
- How to Master the Controls and Steering of Your Kart
- The controls of Crazy Chicken Kart 3 are simple but effective. You use the arrow keys to steer your kart left or right, accelerate or brake. You use the space bar to use the weapon or power-up you have collected. You use the enter key to pause the game or skip cutscenes.
- The steering of your kart is responsive but not too sensitive. You need to adjust your speed and direction according to the terrain and curves of each track. You also need to avoid crashing into walls or obstacles that can slow you down or damage your kart.
- You can also perform some tricks with your kart that can give you an edge over your opponents. For example, you can drift around corners by tapping the brake key while turning. This will make your kart slide sideways and maintain speed. You can also jump over gaps or obstacles by pressing the up arrow key while driving over a ramp. This will make your kart fly briefly in the air and avoid collisions.
- How to Use the Shortcuts and Secrets on Each Track
- Each track in Crazy Chicken Kart 3 has some shortcuts and secrets that can help you save time or gain an advantage over your rivals. These shortcuts and secrets are usually hidden or hard to find, so you need to pay attention to the environment and look for clues.
- Some examples of shortcuts and secrets are:
-
-A hidden tunnel in the Stone Age track that leads to a shortcut across the lava lake
-A secret passage in the Ancient Egypt track that leads to a hidden chamber inside the pyramid
-A breakable wall in the Middle Ages track that leads to a shortcut through the castle dungeon
-A hidden bridge in the Wild West track that leads to a shortcut across the canyon
-A secret elevator in the Modern Times track that leads to a shortcut through the skyscraper roof
-A hidden grave in the Halloween track that leads to a shortcut through the underworld
-A secret portal in the Space Age track that leads to a shortcut through another planet
-A hidden switch in the Future World track that activates a shortcut through a wormhole
-
-: Pirates: A game that features crazy chicken as he battles against pirates and sea monsters in various islands
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Crazy Chicken: Atlantis: A game that features crazy chicken as he searches for the lost city of Atlantis and faces various challenges and enemies
-Crazy Chicken: Tales: A game that features crazy chicken as he goes through different fairy tales and stories and interacts with various characters and objects
-
-Other Kart Racing Games
- If you like kart racing games in general, you might also enjoy some other games that offer similar or better features and gameplay. Some of them are:
-
-Mario Kart: The most popular and iconic kart racing game series; features characters from the Mario franchise and other Nintendo games; has various modes, tracks, items, and customization options
-Crash Team Racing: A kart racing game series that features characters from the Crash Bandicoot franchise; has various modes, tracks, items, and customization options
-Sonic & All-Stars Racing: A kart racing game series that features characters from the Sonic the Hedgehog franchise and other Sega games; has various modes, tracks, items, and customization options
-Team Sonic Racing: A kart racing game that features characters from the Sonic the Hedgehog franchise; has a unique team-based gameplay mechanic that allows players to cooperate and compete with each other
-Garfield Kart: A kart racing game that features characters from the Garfield comic strip and animated series; has various modes, tracks, items, and customization options
-
-Conclusion
- Crazy Chicken Kart 3 is a fun and hilarious kart racing game that features crazy chicken and his friends as they race through different eras, from the past to the future, with explosive excitement around every corner. You can collect weapons and power-ups as you speed along and ruffle some feathers when you strategically blast your opponents off the road.
- To play Crazy Chicken Kart 3 for free, you can use a crack to bypass the security and activation of the game. In this article, we showed you how to download and install the crack for Crazy Chicken Kart 3, how to play the game with the crack, and what features and gameplay you can expect from this wacky racing game.
- If you enjoyed this article, please share it with your friends who might also like Crazy Chicken Kart 3. If you have any questions or feedback, please leave a comment below. We would love to hear from you.
- Thank you for reading and happy racing!
- FAQs
- Here are some frequently asked questions about Crazy Chicken Kart 3 and its crack:
- Q: Is Crazy Chicken Kart 3 safe to download and play?
- A: Yes, Crazy Chicken Kart 3 is safe to download and play if you get it from a trusted source like Youdagames.com or Archive.org. However, be careful when downloading games from unknown sources, as they may contain viruses or malware that can harm your computer.
- Q: Is using a crack for Crazy Chicken Kart 3 legal?
- A: No, using a crack for Crazy Chicken Kart 3 is not legal. A crack is a modified version of the game's executable file that allows you to run it without needing a license key or a disc. This violates the terms of service and copyright of the game's developer and publisher. Using a crack for Crazy Chicken Kart 3 may also expose your computer to security risks or legal consequences.
- Q: Where can I find more information about Crazy Chicken Kart 3?
- A: You can find more information about Crazy Chicken Kart 3 on its official website at Youdagames.com or on its Wikipedia page at https://en.wikipedia.org/wiki/Crazy_Chicken_Kart_3. You can also watch gameplay videos or read reviews of Crazy Chicken Kart 3 on YouTube or other gaming websites.
- Q: What are some other games like Crazy Chicken Kart 3?
- A: Some other games like Crazy Chicken Kart 3 are Mario Kart, Crash Team Racing, Sonic & All-Stars Racing, Team Sonic Racing, Garfield Kart, etc. You can also try other games in the Crazy Chicken series like Crazy Chicken: The Original, Crazy Chicken: The Winged Pharaoh, Crazy Chicken: Pirates, Crazy Chicken: Atlantis, Crazy Chicken: Tales, etc.
- Q: How can I contact the developer or publisher of Crazy Chicken Kart 3?
- A: You can contact the developer or publisher of Crazy Chicken Kart 3 by visiting their website at https://www.phenomedia.com/ or by sending them an email at info@phenomedia.com.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Aptoide TV APK 3.2 5 and Enjoy Unlimited Apps on Your Android TV without Restrictions.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Aptoide TV APK 3.2 5 and Enjoy Unlimited Apps on Your Android TV without Restrictions.md
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-Once the app is installed, open it and log in with your Garena account. If you don't have one, you can create one for free by tapping on the Register button. You can also log in with your Facebook or Google account if you have linked them to your Garena account.
-For PC devices
-Step 1: Go to the official website of Garena Next
-Open your web browser and go to https://next.garena.com/ , the official website of Garena Next.
-Step 2: Click on Download for PC and run the installer
-Click on the Download for PC button and save the installer file on your computer. Run the installer file and follow the instructions to install Garena Next on your PC.
-Step 3: Open the app and log in with your Garena account
-Once the app is installed, open it and log in with your Garena account. If you don't have one, you can create one for free by clicking on the Register button. You can also log in with your Facebook or Google account if you have linked them to your Garena account.
- Congratulations! You have successfully downloaded Garena Next and you are ready to enjoy the latest updates of Free Fire and other games. Have fun!
- Conclusion
-Garena Next is a platform that lets you enjoy the latest updates of Free Fire and other games, as well as chat with your friends, access exclusive rewards and benefits, and join a community of gamers and friends. Downloading Garena Next is easy and simple, as you just need to follow a few steps depending on your device. If you are a fan of Garena games, you should definitely download Garena Next and experience a new level of gaming and socializing.
- Frequently Asked Questions
-
-What is Free Fire MAX? Free Fire MAX is a version of Free Fire that has improved graphics, sound effects, animations, and gameplay. It is compatible with Free Fire, so you can play with your friends who are using either version.
What are some of the games that I can play on Garena Next? Some of the games that you can play on Garena Next are League of Legends: Wild Rift, Call of Duty Mobile: Warzone Edition, FIFA Online 4 M by EA Sports™️ , and more. These games have been optimized for mobile devices, so you can enjoy a smooth and immersive gaming experience.
How can I get free diamonds, coins, vouchers, skins, characters, weapons, and more from Garena Next? You can get free diamonds, coins, vouchers, skins, characters, weapons, and more from Garena Next by participating in various events and activities that are exclusive to Garena Next users, such as quizzes, lucky draws, missions, and more. You can also earn points and badges that you can use to redeem more rewards and benefits.
How can I chat with my friends, join groups, send stickers, voice messages, and more on Garena Next? You can chat with your friends, join groups, send stickers, voice messages, and more on Garena Next by tapping on the Chat icon on the bottom menu. You can also create your own profile, customize your avatar, and show off your achievements. You can also discover new games, watch live streams, and follow your favorite streamers.
How can I update my games automatically on Garena Next? You can update your games automatically on Garena Next by tapping on the Games icon on the bottom menu. You can see all the games that you have installed on your device, or download new ones from the app. You can also see if there are any updates available for your games and tap on the Update button to install them.
197e85843dFRAG Download APK: A Guide to the Ultimate PvP Hero Game
-If you are looking for a fun and friendly PvP hero game that you can play on your Android device, you should check out FRAG. FRAG is a free-to-play game developed by Oh BiBi, a studio that specializes in creating mobile games with stunning graphics and engaging gameplay. In this game, you can choose from over 100 characters, each with their own unique weapons and abilities, and compete against players from all over the world in explosive 1v1 or 2v2 battles. You can also customize your characters with skins and upgrades, join or create a club with your friends, participate in events and contests, and share your content with other players. In this article, we will show you how to download and install FRAG APK on your Android device, how to play FRAG and enjoy its features, how to customize your gameplay and improve your skills, and how to join the FRAG community and become a superstar.
-frag download apk Download --->>> https://jinyurl.com/2uNObx
How to Download and Install FRAG APK on Your Android Device
-Downloading and installing FRAG APK on your Android device is very easy. Just follow these simple steps:
-
-Step 1: Go to the official website of FRAG or use a trusted APK source. You can find the official website of FRAG at [12](https://www.fragthegame.com/), where you can also learn more about the game and its features. Alternatively, you can use a trusted APK source like [11](https://apkcombo.com/frag/com.ohbibi.fps/) or [10](https://apkpure.com/frag-pro-shooter/com.ohbibi.fps) to download the APK file.
Step 2: Download the APK file and allow installation from unknown sources. Once you have found the APK file, tap on it to start downloading it. You may need to allow installation from unknown sources on your device settings if you haven't done so before. To do this, go to Settings > Security > Unknown Sources and enable it.
Step 3: Open the APK file and follow the instructions to install FRAG. After downloading the APK file, open it and follow the instructions on the screen to install FRAG on your device. It may take a few minutes for the installation to complete. Once the installation is done, you can launch FRAG and start playing.
- How to Play FRAG and Enjoy Its Features
-Playing FRAG is very simple and fun. You just need to follow these basic steps:
-
-Step 1: Choose your hero, create your team, and enter the arena. When you start the game, you will be able to choose one of the available heroes, each with their own role and power. You can also create your own team of five heroes, or join a random team with other players. Then, you can enter the arena and get ready for the battle.
Step 2: Control your character in FPS or TPS view and switch between them. You can control your character in either first-person shooter (FPS) or third-person shooter (TPS) view, depending on your preference. You can also switch between them by tapping on the camera icon on the screen. You can move your character with the joystick on the left, and aim and shoot with the buttons on the right. You can also use your special abilities by tapping on the icons on the bottom.
Step 3: Use your weapons and abilities to destroy the enemy bunker. The objective of the game is to destroy the enemy bunker before they destroy yours. You can do this by shooting at it with your weapons, or using your abilities to deal more damage. You can also destroy the enemy towers and drones to gain more points and resources. Be careful, though, as the enemy will try to stop you and do the same to your bunker.
- How to Customize Your Gameplay and Improve Your Skills
-If you want to make your gameplay more personalized and improve your skills, you can do the following things:
-frag pro shooter apk download
-
-Step 1: Build your own FRAG team from over 100 heroes with different roles and powers. You can unlock new heroes by playing the game, completing missions, or using gold and diamonds. You can also upgrade your heroes by using cards and coins. You can build your own FRAG team by choosing five heroes that complement each other's roles and powers. For example, you can have a tank, a healer, a sniper, a damage dealer, and a support.
Step 2: Personalize your characters with skins and upgrades. You can also customize your characters with skins and upgrades that change their appearance and performance. You can buy skins with gold or diamonds, or get them from chests or events. You can also upgrade your weapons and abilities with coins and cards.
Step 3: Choose from four game modes and adapt your strategy to the map. You can choose from four game modes in FRAG: Classic, Payload, Street Frag, and Challenge. Each game mode has its own rules and objectives, so you need to adapt your strategy accordingly. You can also play on different maps that have different layouts and features, such as bridges, tunnels, ramps, etc.
- How to Join the FRAG Community and Become a Superstar
-If you want to join the FRAG community and become a superstar, you can do the following things:
-
-Step 1: Follow FRAG on social media and join the Discord server. You can follow FRAG on social media platforms like [9](https://www.facebook.com/FRAGTheGame/), [8](https://twitter.com/FRAGProShooter), [7](https://www.instagram.com/fragthegame/), [6](https://www.youtube.com/channel/UCQj5ZXFo0rZ4xAPJbUFio7g), [5](https://www.tiktok.com/@fragthegame), [4](https://www.reddit.com/r/FRAGProShooter/), etc., where you can get the latest news, updates, tips, tricks, contests, giveaways and more. You can also join the FRAG Discord server at [3](https://discord.gg/FRAGProShooter), where you can chat with other players, get support, share feedback, and have fun.
Step 2: Participate in events, contests, and missions for rewards and fame. You can also participate in various events, contests, and missions that are regularly held in FRAG. These include seasonal events, weekly challenges, daily missions, tournaments, leaderboards, etc. By participating in these activities, you can earn rewards such as gold, diamonds, chests, cards, skins, and more. You can also gain fame and recognition by ranking up in the leaderboards, winning tournaments, or getting featured in the game or on social media.
Step 3: Create and share your content with other players and fans. You can also create and share your own content with other players and fans of FRAG. You can use the in-game video editor to record and edit your best moments, or use external tools to make your own videos, streams, blogs, podcasts, etc. You can then share your content on social media platforms like YouTube, Twitch, Facebook, Instagram, TikTok, Reddit, etc., or on the FRAG Discord server. You can also watch and support other content creators who make FRAG content.
- Conclusion
-FRAG is a fun and friendly PvP hero game that you can download and play for free on your Android device. It has stunning graphics, engaging gameplay, and a large community of players and fans. You can choose from over 100 characters with different roles and powers, customize them with skins and upgrades, compete against players from all over the world in four game modes and different maps, join or create a club with your friends, participate in events and contests for rewards and fame, and create and share your own content with other players. If you are looking for a game that will keep you entertained and challenged for hours, you should download FRAG APK today and join the FRAG family.
- FAQs
-
-Q1: Is FRAG safe to download and play?
-A1: Yes, FRAG is safe to download and play. It is developed by a reputable studio that follows the best practices of security and privacy. It does not contain any viruses or malware that could harm your device or data. However, you should always download FRAG APK from the official website or a trusted source to avoid any potential risks.
Q2: Can I play FRAG offline?
-A2: No, you cannot play FRAG offline. FRAG is an online game that requires an internet connection to play. You need to connect to the game servers to access the game features and modes, as well as to interact with other players. If you lose your internet connection while playing FRAG, you may experience lag or disconnection issues.
Q3: What are the best characters to use in FRAG?
-A3: There is no definitive answer to this question, as different characters have different strengths and weaknesses, and different players have different preferences and playstyles. However, some general tips to choose the best characters are:
-
-Pick characters that suit your role and strategy. For example, if you want to be a tank that can absorb damage and protect your team, you may want to use characters like Big Paku or Lucha Muerta. If you want to be a sniper that can deal high damage from a distance and snipe the enemy bunker, you may want to use characters like Lolly Pop or Andrometa.
Pick characters that complement each other's powers and abilities. For example, if you want to create a team that can heal and support each other, you may want to use characters like Cyber Cop or Dr. Frost. If you want to create a team that can deal massive damage and stun the enemy, you may want to use characters like R0N1N or Mekkalodon.
Pick characters that match your skill level and playstyle. For example, if you are a beginner or casual player, you may want to use characters that are easy to control and have simple abilities, like Jet or Dan. If you are an advanced or competitive player, you may want to use characters that are more challenging and have complex abilities, like VR-0N1CA or Volcano.
Q4: How can I get more gold and diamonds in FRAG?
-A4: Gold and diamonds are the main currencies in FRAG. You can use them to buy new characters, skins, upgrades, chests, etc. You can get more gold and diamonds by doing the following things:
-
-Play the game regularly and complete missions. You can earn gold and diamonds by playing the game and completing daily, weekly, and seasonal missions. You can also get bonus rewards by logging in every day and watching ads.
Open chests and collect cards. You can open chests that contain gold, diamonds, cards, and other items. You can get chests by winning battles, ranking up in the leaderboards, participating in events and contests, or buying them with gold or diamonds. You can also collect cards that can be converted into gold or diamonds.
Join or create a club and share gifts. You can join or create a club with your friends or other players and share gifts with them. You can send and receive gold and diamonds as gifts every day.
Buy gold and diamonds with real money. You can also buy gold and diamonds with real money if you want to support the game and get more resources faster. You can do this by tapping on the shop icon on the main menu and choosing the amount of gold or diamonds you want to buy.
Q5: How can I contact the developers of FRAG?
-A5: If you have any questions, feedback, suggestions, bug reports, or issues with FRAG, you can contact the developers of FRAG by doing the following things:
-
-Use the in-game support system. You can use the in-game support system to send a message to the developers of FRAG. You can do this by tapping on the settings icon on the main menu and choosing the support option. You can then write your message and attach screenshots if needed.
Email the developers of FRAG. You can also email the developers of FRAG directly at [2](mailto:support@ohbibi.com). You can write your message in English or French and attach screenshots if needed.
Post on the FRAG Discord server or social media platforms. You can also post your message on the FRAG Discord server or social media platforms like Facebook, Twitter, Instagram, etc., where the developers of FRAG may see it and respond to it. However, this is not a guaranteed way to contact them, so you may want to use the other methods first.
401be4b1e0
-
-
-
-
-
-
-
-
-
-
-
- )
-}
-
-function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
- OpenAI icon
-
- )
-}
-
-function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
- GitHub
-
- )
-}
-
-function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconUser({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconMore({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconStop({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconSun({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconClose({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconShare({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconExternalLink({
- className,
- ...props
-}: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-function IconChevronUpDown({
- className,
- ...props
-}: React.ComponentProps<'svg'>) {
- return (
-
-
- )
-}
-
-export {
- IconEdit,
- IconNextChat,
- IconOpenAI,
- IconGitHub,
- IconSeparator,
- IconArrowDown,
- IconArrowRight,
- IconUser,
- IconPlus,
- IconArrowElbow,
- IconSpinner,
- IconMessage,
- IconTrash,
- IconMore,
- IconRefresh,
- IconStop,
- IconSidebar,
- IconMoon,
- IconSun,
- IconCopy,
- IconCheck,
- IconDownload,
- IconClose,
- IconShare,
- IconUsers,
- IconExternalLink,
- IconChevronUpDown
-}
diff --git a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Code Reviews 2b60c26d2a2e4a348f8f14c77023c385.md b/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Code Reviews 2b60c26d2a2e4a348f8f14c77023c385.md
deleted file mode 100644
index f0e8232fc7b2bbcb901301c1be1200de781b1b76..0000000000000000000000000000000000000000
--- a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Code Reviews 2b60c26d2a2e4a348f8f14c77023c385.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# Code Reviews
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Codebase
-
-
-💡 This template documents how to review code. Helpful for new and remote employees to get and stay aligned.
-
-
-
-# Philosophy
-
-Why do you perform code reviews? What are your guiding principles for these reviews?
-
-You may want to mention other pages here. Like Engineering Guidelines. To link to another page inline, type `@` followed by the name of the page: [Engineering Guidelines](Engineering%20Guidelines%204208cbd4733d4f6f94982f3fb24f6379.md)
-
-# Preparing Code for Review
-
-Preparation sets your reviewers up for success.
-
-### Commit Messages
-
-Make sure your commit messages are descriptive.
-
-### Github PR Descriptions
-
-Your PR descriptions should be an extension of your commit messages. Write about both what the commit changes, and how you implemented the change.
-
-# Performing Code Reviews
-
-### How to Review
-
-- Make two passes over the PR if it's substantial.
- - On the first pass, come to an understanding of the code change at a high level.
- - On the second pass, pay more attention to semantic details.
-
-# Examples
-
-```jsx
-var commentCount = 0;
-```
-
-You might suggest that this be a `let` instead of `var`.
\ No newline at end of file
diff --git a/spaces/AIConsultant/MusicGen/Dockerfile b/spaces/AIConsultant/MusicGen/Dockerfile
deleted file mode 100644
index efc2431ec0fe674c22fe2fdb9d7045cdf6cd2748..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM nvidia/cuda:11.8.0-base-ubuntu22.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
- PYTHONUNBUFFERED=1 \
- PYTHONIOENCODING=UTF-8
-RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt apt update &&\
- apt install -y \
- wget \
- git \
- pkg-config \
- python3 \
- python3-pip \
- python-is-python3 \
- ffmpeg \
- libnvrtc11.2 \
- libtcmalloc-minimal4
-
-RUN useradd -m -u 1000 ac
-RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip wheel
-ENV TORCH_COMMAND="pip install torch==2.0.1+cu118 torchaudio --extra-index-url https://download.pytorch.org/whl/cu118"
-RUN --mount=type=cache,target=/root/.cache python -m $TORCH_COMMAND
-RUN ln -s /usr/lib/x86_64-linux-gnu/libnvrtc.so.11.2 /usr/lib/x86_64-linux-gnu/libnvrtc.so
-USER 1000
-RUN mkdir ~/.cache
-RUN --mount=type=cache,target=/home/ac/.cache --mount=source=.,target=/home/ac/audiocraft python -m pip install -r /home/ac/audiocraft/requirements.txt
-WORKDIR /home/ac/audiocraft
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py
deleted file mode 100644
index fc79d63d5430b972ac6ec1c4bfea9af80922da4d..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '0.2.1'
diff --git a/spaces/AIGText/GlyphControl/ldm/modules/diffusionmodules/util.py b/spaces/AIGText/GlyphControl/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index fb1a13aa167b5f1a34cc1e1fefe236cecfe5b5da..0000000000000000000000000000000000000000
--- a/spaces/AIGText/GlyphControl/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- if schedule == "linear":
- betas = (
- torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
- )
-
- elif schedule == "cosine":
- timesteps = (
- torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
- )
- alphas = timesteps / (1 + cosine_s) * np.pi / 2
- alphas = torch.cos(alphas).pow(2)
- alphas = alphas / alphas[0]
- betas = 1 - alphas[1:] / alphas[:-1]
- betas = np.clip(betas, a_min=0, a_max=0.999)
-
- elif schedule == "sqrt_linear":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
- elif schedule == "sqrt":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
- else:
- raise ValueError(f"schedule '{schedule}' unknown.")
- return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
- if ddim_discr_method == 'uniform':
- c = num_ddpm_timesteps // num_ddim_timesteps
- ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
- elif ddim_discr_method == 'quad':
- ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
- else:
- raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
- # assert ddim_timesteps.shape[0] == num_ddim_timesteps
- # add one to get the final alpha values right (the ones from first scale to data during sampling)
- steps_out = ddim_timesteps + 1
- if verbose:
- print(f'Selected timesteps for ddim sampler: {steps_out}')
- return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
- # select alphas for computing the variance schedule
- alphas = alphacums[ddim_timesteps]
- alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
- # according the the formula provided in https://arxiv.org/abs/2010.02502
- sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
- if verbose:
- print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
- print(f'For the chosen value of eta, which is {eta}, '
- f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
- return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
- """
- Create a beta schedule that discretizes the given alpha_t_bar function,
- which defines the cumulative product of (1-beta) over time from t = [0,1].
- :param num_diffusion_timesteps: the number of betas to produce.
- :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
- produces the cumulative product of (1-beta) up to that
- part of the diffusion process.
- :param max_beta: the maximum beta to use; use values lower than 1 to
- prevent singularities.
- """
- betas = []
- for i in range(num_diffusion_timesteps):
- t1 = i / num_diffusion_timesteps
- t2 = (i + 1) / num_diffusion_timesteps
- betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
- return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
- b, *_ = t.shape
- out = a.gather(-1, t)
- return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
- """
- Evaluate a function without caching intermediate activations, allowing for
- reduced memory at the expense of extra compute in the backward pass.
- :param func: the function to evaluate.
- :param inputs: the argument sequence to pass to `func`.
- :param params: a sequence of parameters `func` depends on but does not
- explicitly take as arguments.
- :param flag: if False, disable gradient checkpointing.
- """
- if flag:
- args = tuple(inputs) + tuple(params)
- return CheckpointFunction.apply(func, len(inputs), *args)
- else:
- return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
- @staticmethod
- def forward(ctx, run_function, length, *args):
- ctx.run_function = run_function
- ctx.input_tensors = list(args[:length])
- ctx.input_params = list(args[length:])
- ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
- "dtype": torch.get_autocast_gpu_dtype(),
- "cache_enabled": torch.is_autocast_cache_enabled()}
- with torch.no_grad():
- output_tensors = ctx.run_function(*ctx.input_tensors)
- return output_tensors
-
- @staticmethod
- def backward(ctx, *output_grads):
- ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
- with torch.enable_grad(), \
- torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
- # Fixes a bug where the first op in run_function modifies the
- # Tensor storage in place, which is not allowed for detach()'d
- # Tensors.
- shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
- output_tensors = ctx.run_function(*shallow_copies)
- input_grads = torch.autograd.grad(
- output_tensors,
- ctx.input_tensors + ctx.input_params,
- output_grads,
- allow_unused=True,
- )
- del ctx.input_tensors
- del ctx.input_params
- del output_tensors
- return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
- """
- Create sinusoidal timestep embeddings.
- :param timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param dim: the dimension of the output.
- :param max_period: controls the minimum frequency of the embeddings.
- :return: an [N x dim] Tensor of positional embeddings.
- """
- if not repeat_only:
- half = dim // 2
- freqs = torch.exp(
- -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
- ).to(device=timesteps.device)
- args = timesteps[:, None].float() * freqs[None]
- embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
- if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
- else:
- embedding = repeat(timesteps, 'b -> b d', d=dim)
- return embedding
-
-
-def zero_module(module):
- """
- Zero out the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().zero_()
- return module
-
-def identity_init_fc(module):
- """
- initial weights of a fc module as 1 and bias as 0.
- """
- nn.init.eye_(module.weight)
- nn.init.constant(module.bias, 0)
- # for p in module.parameters():
- # nn.init.ones_(p)
- return module
-
-def scale_module(module, scale):
- """
- Scale the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().mul_(scale)
- return module
-
-
-def mean_flat(tensor):
- """
- Take the mean over all non-batch dimensions.
- """
- return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
- """
- Make a standard normalization layer.
- :param channels: number of input channels.
- :return: an nn.Module for normalization.
- """
- return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
- def forward(self, x):
- return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
- def forward(self, x):
- return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D convolution module.
- """
- if dims == 1:
- return nn.Conv1d(*args, **kwargs)
- elif dims == 2:
- return nn.Conv2d(*args, **kwargs)
- elif dims == 3:
- return nn.Conv3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
- """
- Create a linear module.
- """
- return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D average pooling module.
- """
- if dims == 1:
- return nn.AvgPool1d(*args, **kwargs)
- elif dims == 2:
- return nn.AvgPool2d(*args, **kwargs)
- elif dims == 3:
- return nn.AvgPool3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
- def __init__(self, c_concat_config, c_crossattn_config):
- super().__init__()
- self.concat_conditioner = instantiate_from_config(c_concat_config)
- self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
- def forward(self, c_concat, c_crossattn):
- c_concat = self.concat_conditioner(c_concat)
- c_crossattn = self.crossattn_conditioner(c_crossattn)
- return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
- repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
- noise = lambda: torch.randn(shape, device=device)
- return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/AIWaves/Debate/src/agents/LLM/__init__.py b/spaces/AIWaves/Debate/src/agents/LLM/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIWaves/SOP_Generation-single/design_states.py b/spaces/AIWaves/SOP_Generation-single/design_states.py
deleted file mode 100644
index 6e2638ca8289e2ba4dfb8b2996cf9a5dcc4ba115..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/SOP_Generation-single/design_states.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import sys
-sys.path.append("../")
-import re
-from LLM.base_LLM import *
-from utils import extract
-from single_prompts import *
-
-
-llm = OpenAILLM()
-# design state
-
-def get_cot_result(target):
- chat_history = [{"role":"user","content":f"{target} "}]
- response = llm.get_response(chat_history,design_states_cot_system_prompt)
- print(response)
- return response
-
-def get_desgin_states(target,index):
- chat_history = [{"role":"user","content":f"{target} "}]
- design_state_system_prompt = get_design_state_system_prompt(index)
- response = llm.get_response(chat_history,system_prompt=design_state_system_prompt)
- print(response)
- # 使用正则表达式提取数据
- role = extract(response,"role")
- pattern = r'(.*?)<\/state>'
- states = re.findall(pattern, response, re.DOTALL)
- style = extract(response,"style")
- # 创建包含字典的列表
- result_list = []
- for state in states:
- state_name = extract(state,"state_name")
- rule = extract(state,"rule")
- task = extract(state,"task")
- judge = extract(state,"judge")
-
- # 创建字典并添加到结果列表
- state_dict = {
- "style":style,
- "role":role,
- "state_name": state_name,
- "task": task,
- "rule": rule,
- "judge" : judge
- }
- result_list.append(state_dict)
-
- # 打印结果
- print("design states")
- for item in result_list:
- print(item)
- return result_list
-
diff --git a/spaces/ASJMO/freegpt/client/css/style.css b/spaces/ASJMO/freegpt/client/css/style.css
deleted file mode 100644
index 918cf83eb9a36bf07c861e4476c60af65f5bf91d..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/client/css/style.css
+++ /dev/null
@@ -1,18 +0,0 @@
-@import "./global.css";
-@import "./hljs.css";
-@import "./main.css";
-@import "./sidebar.css";
-@import "./conversation.css";
-@import "./message.css";
-@import "./stop-generating.css";
-@import "./typing.css";
-@import "./checkbox.css";
-@import "./label.css";
-@import "./button.css";
-@import "./buttons.css";
-@import "./dropdown.css";
-@import "./field.css";
-@import "./select.css";
-@import "./options.css";
-@import "./settings.css";
-@import "./message-input.css";
diff --git a/spaces/AdamGoyer/is_it_fly/README.md b/spaces/AdamGoyer/is_it_fly/README.md
deleted file mode 100644
index 58c0cf0eb37cd467befb0593455aaeecdbfa4ec4..0000000000000000000000000000000000000000
--- a/spaces/AdamGoyer/is_it_fly/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-license: apache-2.0
-title: Is It Fly
-sdk: gradio
-emoji: 🌖
-colorFrom: indigo
-colorTo: pink
-app_file: app.py
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/AdithyaSNair/alzheimers_prediction_using_cnn/app.py b/spaces/AdithyaSNair/alzheimers_prediction_using_cnn/app.py
deleted file mode 100644
index 553b2bf43a15c34e22b5a8e772463d83131feefe..0000000000000000000000000000000000000000
--- a/spaces/AdithyaSNair/alzheimers_prediction_using_cnn/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-import os
-import keras
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-from keras.models import Sequential
-from PIL import Image
-from keras.layers import Conv2D, Flatten, Dense, Dropout, BatchNormalization, MaxPooling2D
-from sklearn.preprocessing import OneHotEncoder
-import pickle
-import tensorflow as tf
-import gradio as gr
-
-model_path = "model.h5"
-model = tf.keras.models.load_model(model_path)
-
-# Define the labels
-labels = ['Non Demented', 'Mild Dementia', 'Moderate Dementia', 'Very Mild Dementia']
-
-# Define the prediction function
-def predict_dementia(image):
- img = Image.fromarray(image.astype('uint8'))
- img = img.resize((128, 128))
- img = np.array(img)
- img = img.reshape(1, 128, 128, 3)
-
- prediction = model.predict(img)
- prediction_class = np.argmax(prediction)
- return labels[prediction_class]
-
-# Create the Gradio interface
-iface = gr.Interface(
- fn=predict_dementia,
- inputs="image",
- outputs="text",
- title="Deep Learning-Based Classification of Dementia Stages Using Brain Images",
- description="Dementia is a neurodegenerative disorder characterized by a decline in cognitive abilities. Early detection and classification of dementia stages are crucial for effective treatment and care. In this study, we propose a deep learning-based approach for classifying dementia stages using brain images. The objective is to develop a model that can accurately differentiate between different stages of dementia, including non-demented, mild dementia, moderate dementia, and very mild dementia.",
- article=''' To achieve this, we utilize a dataset consisting of brain images from individuals with varying dementia stages. The dataset is preprocessed to ensure uniformity and eliminate noise. A convolutional neural network (CNN) architecture is designed and trained on the preprocessed images. The model incorporates multiple convolutional layers, batch normalization, max pooling, and dropout layers to capture relevant features from the images. The training procedure involves optimizing the model using the Adamax optimizer and minimizing the categorical cross-entropy loss.
-The performance of the proposed model is evaluated using various metrics, including accuracy, validation accuracy, loss and validation loss. Additionally, a comparison is made with existing approaches for dementia classification to assess the effectiveness of the proposed method. The results demonstrate promising classification accuracy and highlight the potential of deep learning techniques in accurately diagnosing and classifying dementia stages based on brain images.
-The findings of this study contribute to the field of dementia research by providing a reliable and automated method for dementia classification. The developed model can assist medical professionals in early diagnosis and treatment planning, potentially improving patient outcomes and quality of life. Further research and refinement of the model could lead to more accurate and efficient diagnosis of dementia, enabling timely intervention and support for affected individuals
-''',
- examples=[["Non(1).jpg"],["Mild.jpg"],["Moderate.jpg"],["Very(1).jpg"]],
- allow_flagging=False
-)
-
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorcomponents/ColorComponents.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorcomponents/ColorComponents.d.ts
deleted file mode 100644
index 826d9628debf9f2424f136ccbd9c737c13113d39..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorcomponents/ColorComponents.d.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-import Sizer from '../../sizer/Sizer';
-import RoundRectangle from '../../roundrectangle/RoundRectangle';
-import Label from '../../label/Label';
-import CanvasInput from '../../canvasinput/CanvasInput';
-
-export default ColorComponents;
-
-declare namespace ColorComponents {
-
- interface IFormatLabelConfig {
- space?: {
- left?: number, right?: number, top?: number, bottom?: number,
- },
-
- background?: RoundRectangle.IConfig,
-
- text?: Phaser.GameObjects.TextStyle,
- expandTextWidth?: boolean,
- expandTextHeight?: boolean,
-
- align?: Label.AlignTypes,
- }
-
- interface IConfig extends Sizer.IConfig {
- background?: Phaser.GameObjects.GameObject,
-
- formatLabel?: Phaser.GameObjects.GameObject | IFormatLabelConfig;
-
- inputText0?: Phaser.GameObjects.GameObject,
- inputText1?: Phaser.GameObjects.GameObject,
- inputText2?: Phaser.GameObjects.GameObject,
- inputText?: CanvasInput.IConfig,
-
- proportion?: {
- formatLabel?: number,
-
- },
-
- valuechangeCallback: (newValue: number, oldValue: number, colorComponents: ColorComponents) => void,
-
- value?: number
- }
-}
-
-declare class ColorComponents extends Sizer {
- constructor(
- scene: Phaser.Scene,
- config?: ColorComponents.IConfig
- );
-
- setValue(value: number): this;
- value: number;
-
- setColor(color: number): this;
- color: number;
-
- setColorFormat(colorFormat: 'RGB' | 'HSV'): this;
- toggleColorFormat(): this;
- colorFormat: 'RGB' | 'HSV';
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalRowProportions.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalRowProportions.js
deleted file mode 100644
index ac6f0efbcb84cfafdbccfdf79a5ff66c6b761a47..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalRowProportions.js
+++ /dev/null
@@ -1,13 +0,0 @@
-var GetTotalRowProportions = function () {
- var result = 0,
- proportion;
- for (var i = 0; i < this.rowCount; i++) {
- proportion = this.rowProportions[i];
- if (proportion > 0) {
- result += proportion;
- }
- }
- return result;
-}
-
-export default GetTotalRowProportions;
\ No newline at end of file
diff --git a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/js/app.bf8a14e9.js b/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/js/app.bf8a14e9.js
deleted file mode 100644
index 3bb6b293c5f129724687a51cafcc426c412a67d8..0000000000000000000000000000000000000000
--- a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/js/app.bf8a14e9.js
+++ /dev/null
@@ -1,21 +0,0 @@
-/*!
-
-=========================================================
-* Vue Notus - v1.1.0 based on Tailwind Starter Kit by Creative Tim
-=========================================================
-
-* Product Page: https://www.creative-tim.com/product/vue-notus
-* Copyright 2021 Creative Tim (https://www.creative-tim.com)
-* Licensed under MIT (https://github.com/creativetimofficial/vue-notus/blob/main/LICENSE.md)
-
-* Tailwind Starter Kit Page: https://www.creative-tim.com/learning-lab/tailwind-starter-kit/presentation
-
-* Coded by Creative Tim
-
-=========================================================
-
-* The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-*/
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z-10 bg-transparent md:flex-row md:flex-nowrap md:justify-start flex items-center p-4"},N=(0,a.createElementVNode)("div",{class:"w-full mx-autp items-center flex justify-between md:flex-nowrap flex-wrap md:px-10 px-4"},[(0,a.createElementVNode)("a",{class:"text-white text-sm uppercase hidden lg:inline-block font-semibold",href:"javascript:void(0)"}," ChatGPT-Plugin ")],-1),V=[N];function C(e,t,l,o,r,n){return(0,a.openBlock)(),(0,a.createElementBlock)("nav",y,V)}var k={components:{}};const E=(0,p.Z)(k,[["render",C]]);var T=E;const S={class:"relative bg-emerald-600 pb-32 pt-12"},D={class:"px-4 md:px-10 mx-auto w-full"},G={class:"flex flex-wrap"},B={class:"w-full lg:w-6/12 xl:w-3/12 px-4"},U={class:"w-full lg:w-6/12 xl:w-3/12 px-4"},A={class:"w-full lg:w-6/12 xl:w-3/12 px-4"},P={class:"w-full lg:w-6/12 xl:w-3/12 px-4"};function z(e,t,l,o,r,n){const 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-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
- Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
- *key = Py_tss_NEEDS_INIT;
- return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
- PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
- return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
- PyThread_delete_key(*key);
- *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
- return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
- return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n) PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
- #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
-#else
- #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
- #define CYTHON_PEP393_ENABLED 1
- #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\
- 0 : _PyUnicode_Ready((PyObject *)(op)))
- #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
- #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
- #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u)
- #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u)
- #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u)
- #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
- #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch)
- #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
- #else
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u))
- #endif
-#else
- #define CYTHON_PEP393_ENABLED 0
- #define PyUnicode_1BYTE_KIND 1
- #define PyUnicode_2BYTE_KIND 2
- #define PyUnicode_4BYTE_KIND 4
- #define __Pyx_PyUnicode_READY(op) (0)
- #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
- #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
- #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
- #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
- #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
- #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
- #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
- #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
- #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
-#else
- #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
- #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
- PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
- #define PyUnicode_Contains(u, s) PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
- #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
- #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
-#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
-#else
- #define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
- #define PyObject_ASCII(o) PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyBaseString_Type PyUnicode_Type
- #define PyStringObject PyUnicodeObject
- #define PyString_Type PyUnicode_Type
- #define PyString_Check PyUnicode_Check
- #define PyString_CheckExact PyUnicode_CheckExact
-#ifndef PyObject_Unicode
- #define PyObject_Unicode PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
- #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
- #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
- #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#ifndef PySet_CheckExact
- #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
-#endif
-#if PY_VERSION_HEX >= 0x030900A4
- #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
- #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
-#else
- #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
- #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
-#endif
-#if CYTHON_ASSUME_SAFE_MACROS
- #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq)
-#else
- #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq)
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyIntObject PyLongObject
- #define PyInt_Type PyLong_Type
- #define PyInt_Check(op) PyLong_Check(op)
- #define PyInt_CheckExact(op) PyLong_CheckExact(op)
- #define PyInt_FromString PyLong_FromString
- #define PyInt_FromUnicode PyLong_FromUnicode
- #define PyInt_FromLong PyLong_FromLong
- #define PyInt_FromSize_t PyLong_FromSize_t
- #define PyInt_FromSsize_t PyLong_FromSsize_t
- #define PyInt_AsLong PyLong_AsLong
- #define PyInt_AS_LONG PyLong_AS_LONG
- #define PyInt_AsSsize_t PyLong_AsSsize_t
- #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
- #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
- #define PyNumber_Int PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyBoolObject PyLongObject
-#endif
-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
- #ifndef PyUnicode_InternFromString
- #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
- #endif
-#endif
-#if PY_VERSION_HEX < 0x030200A4
- typedef long Py_hash_t;
- #define __Pyx_PyInt_FromHash_t PyInt_FromLong
- #define __Pyx_PyInt_AsHash_t PyInt_AsLong
-#else
- #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
- #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
-#else
- #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
-#endif
-#if CYTHON_USE_ASYNC_SLOTS
- #if PY_VERSION_HEX >= 0x030500B1
- #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
- #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
- #else
- #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
- #endif
-#else
- #define __Pyx_PyType_AsAsync(obj) NULL
-#endif
-#ifndef __Pyx_PyAsyncMethodsStruct
- typedef struct {
- unaryfunc am_await;
- unaryfunc am_aiter;
- unaryfunc am_anext;
- } __Pyx_PyAsyncMethodsStruct;
-#endif
-
-#if defined(WIN32) || defined(MS_WINDOWS)
- #define _USE_MATH_DEFINES
-#endif
-#include
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
- float value;
- memset(&value, 0xFF, sizeof(value));
- return value;
-}
-#endif
-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
- { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
- { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
- #ifdef __cplusplus
- #define __PYX_EXTERN_C extern "C"
- #else
- #define __PYX_EXTERN_C extern
- #endif
-#endif
-
-#define __PYX_HAVE__monotonic_align__core
-#define __PYX_HAVE_API__monotonic_align__core
-/* Early includes */
-#include "pythread.h"
-#include
-#include
-#include
-#include "pystate.h"
-#ifdef _OPENMP
-#include
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
- const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\
- (sizeof(type) < sizeof(Py_ssize_t)) ||\
- (sizeof(type) > sizeof(Py_ssize_t) &&\
- likely(v < (type)PY_SSIZE_T_MAX ||\
- v == (type)PY_SSIZE_T_MAX) &&\
- (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
- v == (type)PY_SSIZE_T_MIN))) ||\
- (sizeof(type) == sizeof(Py_ssize_t) &&\
- (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
- v == (type)PY_SSIZE_T_MAX))) )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
- return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
- #include
- #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
- #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
- #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
- #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
- #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
- #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
- #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
- #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
- #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
- #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
- const Py_UNICODE *u_end = u;
- while (*u_end++) ;
- return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
- (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
- PyObject* sys;
- PyObject* default_encoding = NULL;
- PyObject* ascii_chars_u = NULL;
- PyObject* ascii_chars_b = NULL;
- const char* default_encoding_c;
- sys = PyImport_ImportModule("sys");
- if (!sys) goto bad;
- default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
- Py_DECREF(sys);
- if (!default_encoding) goto bad;
- default_encoding_c = PyBytes_AsString(default_encoding);
- if (!default_encoding_c) goto bad;
- if (strcmp(default_encoding_c, "ascii") == 0) {
- __Pyx_sys_getdefaultencoding_not_ascii = 0;
- } else {
- char ascii_chars[128];
- int c;
- for (c = 0; c < 128; c++) {
- ascii_chars[c] = c;
- }
- __Pyx_sys_getdefaultencoding_not_ascii = 1;
- ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
- if (!ascii_chars_u) goto bad;
- ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
- if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
- PyErr_Format(
- PyExc_ValueError,
- "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
- default_encoding_c);
- goto bad;
- }
- Py_DECREF(ascii_chars_u);
- Py_DECREF(ascii_chars_b);
- }
- Py_DECREF(default_encoding);
- return 0;
-bad:
- Py_XDECREF(default_encoding);
- Py_XDECREF(ascii_chars_u);
- Py_XDECREF(ascii_chars_b);
- return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
- PyObject* sys;
- PyObject* default_encoding = NULL;
- char* default_encoding_c;
- sys = PyImport_ImportModule("sys");
- if (!sys) goto bad;
- default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
- Py_DECREF(sys);
- if (!default_encoding) goto bad;
- default_encoding_c = PyBytes_AsString(default_encoding);
- if (!default_encoding_c) goto bad;
- __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
- if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
- strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
- Py_DECREF(default_encoding);
- return 0;
-bad:
- Py_XDECREF(default_encoding);
- return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
- #define likely(x) __builtin_expect(!!(x), 1)
- #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
- #define likely(x) (x)
- #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-
-static const char *__pyx_f[] = {
- "core.pyx",
- "stringsource",
-};
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
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- char *data;
- Py_ssize_t shape[8];
- Py_ssize_t strides[8];
- Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
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- #define CYTHON_ATOMICS 1
-#endif
-#define __pyx_atomic_int_type int
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- (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\
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- #warning "Using GNU atomics"
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- Py_ssize_t itemsize;
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- PyObject *obj;
- PyObject *_size;
- PyObject *_array_interface;
- PyThread_type_lock lock;
- __pyx_atomic_int acquisition_count[2];
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- Py_buffer view;
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- PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
- PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
- PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
- PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
- PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
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-struct __pyx_vtabstruct__memoryviewslice {
- struct __pyx_vtabstruct_memoryview __pyx_base;
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-static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
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-#define __Pyx_MEMVIEW_FULL 4
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-
-/* decode_c_string_utf16.proto */
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- int byteorder = 0;
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-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
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-/* decode_c_string.proto */
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- const char* encoding, const char* errors,
- PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
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-/* PyErrExceptionMatches.proto */
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-#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)
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-/* GetAttr3.proto */
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-
-/* PyDictVersioning.proto */
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-#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag)
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-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
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-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP);
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-/* GetModuleGlobalName.proto */
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-#define __Pyx_GetModuleGlobalName(var, name) {\
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-#define __Pyx_GetModuleGlobalNameUncached(var, name) {\
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-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
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-/* RaiseTooManyValuesToUnpack.proto */
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-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-/* GetTopmostException.proto */
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-#endif
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-/* SaveResetException.proto */
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-#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
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-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
- PyListObject* L = (PyListObject*) list;
- Py_ssize_t len = Py_SIZE(list);
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- return 0;
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- return PyList_Append(list, x);
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-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyIntBinop.proto */
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-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
- (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
-#endif
-
-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
- PyObject* none = _PyList_Extend((PyListObject*)L, v);
- if (unlikely(!none))
- return -1;
- Py_DECREF(none);
- return 0;
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- return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
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-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
- PyListObject* L = (PyListObject*) list;
- Py_ssize_t len = Py_SIZE(list);
- if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
- Py_INCREF(x);
- PyList_SET_ITEM(list, len, x);
- __Pyx_SET_SIZE(list, len + 1);
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- return PyList_Append(list, x);
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-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
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-/* None.proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
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-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
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-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
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-/* PyObject_GenericGetAttrNoDict.proto */
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-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
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-/* PyObject_GenericGetAttr.proto */
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-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
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-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable);
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-/* PyObjectGetAttrStrNoError.proto */
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-/* SetupReduce.proto */
-static int __Pyx_setup_reduce(PyObject* type_obj);
-
-/* CLineInTraceback.proto */
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-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-typedef struct {
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-struct __Pyx_CodeObjectCache {
- int count;
- int max_count;
- __Pyx_CodeObjectCacheEntry* entries;
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-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
- int py_line, const char *filename);
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- static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
- #define __Pyx_GetBuffer PyObject_GetBuffer
- #define __Pyx_ReleaseBuffer PyBuffer_Release
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-
-
-/* BufferStructDeclare.proto */
-typedef struct {
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-} __Pyx_Buf_DimInfo;
-typedef struct {
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-typedef struct {
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- char *data;
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-
-/* MemviewSliceIsContig.proto */
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-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
- __Pyx_memviewslice *slice2,
- int ndim, size_t itemsize);
-
-/* Capsule.proto */
-static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
- __Pyx_BufFmt_StackElem* stack,
- __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
- int *axes_specs,
- int c_or_f_flag,
- int buf_flags,
- int ndim,
- __Pyx_TypeInfo *dtype,
- __Pyx_BufFmt_StackElem stack[],
- __Pyx_memviewslice *memviewslice,
- PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
- const char *mode, int ndim,
- size_t sizeof_dtype, int contig_flag,
- int dtype_is_object);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-
-/* Module declarations from 'cython.view' */
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'monotonic_align.core' */
-static PyTypeObject *__pyx_array_type = 0;
-static PyTypeObject *__pyx_MemviewEnum_type = 0;
-static PyTypeObject *__pyx_memoryview_type = 0;
-static PyTypeObject *__pyx_memoryviewslice_type = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static void *__pyx_align_pointer(void *, size_t); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-#define __Pyx_MODULE_NAME "monotonic_align.core"
-extern int __pyx_module_is_main_monotonic_align__core;
-int __pyx_module_is_main_monotonic_align__core = 0;
-
-/* Implementation of 'monotonic_align.core' */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "stringsource";
-static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "";
-static const char __pyx_k_strided_and_indirect[] = "";
-static const char __pyx_k_contiguous_and_direct[] = "";
-static const char __pyx_k_MemoryView_of_r_object[] = "";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "";
-static const char __pyx_k_contiguous_and_indirect[] = "";
-static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
-static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-static PyObject *__pyx_n_s_ASCII;
-static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
-static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
-static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
-static PyObject *__pyx_n_s_Ellipsis;
-static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
-static PyObject *__pyx_n_s_IndexError;
-static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
-static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
-static PyObject *__pyx_n_s_MemoryError;
-static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-static PyObject *__pyx_kp_s_MemoryView_of_r_object;
-static PyObject *__pyx_n_b_O;
-static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
-static PyObject *__pyx_n_s_PickleError;
-static PyObject *__pyx_n_s_TypeError;
-static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
-static PyObject *__pyx_n_s_ValueError;
-static PyObject *__pyx_n_s_View_MemoryView;
-static PyObject *__pyx_n_s_allocate_buffer;
-static PyObject *__pyx_n_s_base;
-static PyObject *__pyx_n_s_c;
-static PyObject *__pyx_n_u_c;
-static PyObject *__pyx_n_s_class;
-static PyObject *__pyx_n_s_cline_in_traceback;
-static PyObject *__pyx_kp_s_contiguous_and_direct;
-static PyObject *__pyx_kp_s_contiguous_and_indirect;
-static PyObject *__pyx_n_s_dict;
-static PyObject *__pyx_n_s_dtype_is_object;
-static PyObject *__pyx_n_s_encode;
-static PyObject *__pyx_n_s_enumerate;
-static PyObject *__pyx_n_s_error;
-static PyObject *__pyx_n_s_flags;
-static PyObject *__pyx_n_s_format;
-static PyObject *__pyx_n_s_fortran;
-static PyObject *__pyx_n_u_fortran;
-static PyObject *__pyx_n_s_getstate;
-static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
-static PyObject *__pyx_n_s_id;
-static PyObject *__pyx_n_s_import;
-static PyObject *__pyx_n_s_itemsize;
-static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
-static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_memview;
-static PyObject *__pyx_n_s_mode;
-static PyObject *__pyx_n_s_name;
-static PyObject *__pyx_n_s_name_2;
-static PyObject *__pyx_n_s_ndim;
-static PyObject *__pyx_n_s_new;
-static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
-static PyObject *__pyx_n_s_obj;
-static PyObject *__pyx_n_s_pack;
-static PyObject *__pyx_n_s_paths;
-static PyObject *__pyx_n_s_pickle;
-static PyObject *__pyx_n_s_pyx_PickleError;
-static PyObject *__pyx_n_s_pyx_checksum;
-static PyObject *__pyx_n_s_pyx_getbuffer;
-static PyObject *__pyx_n_s_pyx_result;
-static PyObject *__pyx_n_s_pyx_state;
-static PyObject *__pyx_n_s_pyx_type;
-static PyObject *__pyx_n_s_pyx_unpickle_Enum;
-static PyObject *__pyx_n_s_pyx_vtable;
-static PyObject *__pyx_n_s_range;
-static PyObject *__pyx_n_s_reduce;
-static PyObject *__pyx_n_s_reduce_cython;
-static PyObject *__pyx_n_s_reduce_ex;
-static PyObject *__pyx_n_s_setstate;
-static PyObject *__pyx_n_s_setstate_cython;
-static PyObject *__pyx_n_s_shape;
-static PyObject *__pyx_n_s_size;
-static PyObject *__pyx_n_s_start;
-static PyObject *__pyx_n_s_step;
-static PyObject *__pyx_n_s_stop;
-static PyObject *__pyx_kp_s_strided_and_direct;
-static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
-static PyObject *__pyx_kp_s_strided_and_indirect;
-static PyObject *__pyx_kp_s_stringsource;
-static PyObject *__pyx_n_s_struct;
-static PyObject *__pyx_n_s_t_xs;
-static PyObject *__pyx_n_s_t_ys;
-static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
-static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
-static PyObject *__pyx_n_s_unpack;
-static PyObject *__pyx_n_s_update;
-static PyObject *__pyx_n_s_values;
-static PyObject *__pyx_pf_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_int_0;
-static PyObject *__pyx_int_1;
-static PyObject *__pyx_int_184977713;
-static PyObject *__pyx_int_neg_1;
-static float __pyx_k_;
-static PyObject *__pyx_tuple__2;
-static PyObject *__pyx_tuple__3;
-static PyObject *__pyx_tuple__4;
-static PyObject *__pyx_tuple__5;
-static PyObject *__pyx_tuple__6;
-static PyObject *__pyx_tuple__7;
-static PyObject *__pyx_tuple__8;
-static PyObject *__pyx_tuple__9;
-static PyObject *__pyx_slice__16;
-static PyObject *__pyx_tuple__10;
-static PyObject *__pyx_tuple__11;
-static PyObject *__pyx_tuple__12;
-static PyObject *__pyx_tuple__13;
-static PyObject *__pyx_tuple__14;
-static PyObject *__pyx_tuple__15;
-static PyObject *__pyx_tuple__17;
-static PyObject *__pyx_tuple__18;
-static PyObject *__pyx_tuple__19;
-static PyObject *__pyx_tuple__20;
-static PyObject *__pyx_tuple__21;
-static PyObject *__pyx_tuple__22;
-static PyObject *__pyx_tuple__23;
-static PyObject *__pyx_tuple__24;
-static PyObject *__pyx_tuple__25;
-static PyObject *__pyx_codeobj__26;
-/* Late includes */
-
-/* "monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<<
- * cdef int x
- * cdef int y
- */
-
-static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice __pyx_v_path, __Pyx_memviewslice __pyx_v_value, int __pyx_v_t_y, int __pyx_v_t_x, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args) {
- float __pyx_v_max_neg_val = __pyx_k_;
- int __pyx_v_x;
- int __pyx_v_y;
- float __pyx_v_v_prev;
- float __pyx_v_v_cur;
- int __pyx_v_index;
- int __pyx_t_1;
- int __pyx_t_2;
- int __pyx_t_3;
- long __pyx_t_4;
- int __pyx_t_5;
- long __pyx_t_6;
- long __pyx_t_7;
- int __pyx_t_8;
- Py_ssize_t __pyx_t_9;
- Py_ssize_t __pyx_t_10;
- float __pyx_t_11;
- float __pyx_t_12;
- float __pyx_t_13;
- int __pyx_t_14;
- Py_ssize_t __pyx_t_15;
- Py_ssize_t __pyx_t_16;
- if (__pyx_optional_args) {
- if (__pyx_optional_args->__pyx_n > 0) {
- __pyx_v_max_neg_val = __pyx_optional_args->max_neg_val;
- }
- }
-
- /* "monotonic_align/core.pyx":13
- * cdef float v_cur
- * cdef float tmp
- * cdef int index = t_x - 1 # <<<<<<<<<<<<<<
- *
- * for y in range(t_y):
- */
- __pyx_v_index = (__pyx_v_t_x - 1);
-
- /* "monotonic_align/core.pyx":15
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-/* "View.MemoryView":1140
- *
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<<
- * char *dst_data, Py_ssize_t *dst_strides,
- * Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
- CYTHON_UNUSED Py_ssize_t __pyx_v_i;
- CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
- Py_ssize_t __pyx_v_dst_extent;
- Py_ssize_t __pyx_v_src_stride;
- Py_ssize_t __pyx_v_dst_stride;
- int __pyx_t_1;
- int __pyx_t_2;
- int __pyx_t_3;
- Py_ssize_t __pyx_t_4;
- Py_ssize_t __pyx_t_5;
- Py_ssize_t __pyx_t_6;
-
- /* "View.MemoryView":1147
- *
- * cdef Py_ssize_t i
- * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<<
- * cdef Py_ssize_t dst_extent = dst_shape[0]
- * cdef Py_ssize_t src_stride = src_strides[0]
- */
- __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
- /* "View.MemoryView":1148
- * cdef Py_ssize_t i
- * cdef Py_ssize_t src_extent = src_shape[0]
- * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<<
- * cdef Py_ssize_t src_stride = src_strides[0]
- * cdef Py_ssize_t dst_stride = dst_strides[0]
- */
- __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
- /* "View.MemoryView":1149
- * cdef Py_ssize_t src_extent = src_shape[0]
- * cdef Py_ssize_t dst_extent = dst_shape[0]
- * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<<
- * cdef Py_ssize_t dst_stride = dst_strides[0]
- *
- */
- __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
- /* "View.MemoryView":1150
- * cdef Py_ssize_t dst_extent = dst_shape[0]
- * cdef Py_ssize_t src_stride = src_strides[0]
- * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<<
- *
- * if ndim == 1:
- */
- __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
- /* "View.MemoryView":1152
- * cdef Py_ssize_t dst_stride = dst_strides[0]
- *
- * if ndim == 1: # <<<<<<<<<<<<<<
- * if (src_stride > 0 and dst_stride > 0 and
- * src_stride == itemsize == dst_stride):
- */
- __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
- if (__pyx_t_1) {
-
- /* "View.MemoryView":1153
- *
- * if ndim == 1:
- * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<<
- * src_stride == itemsize == dst_stride):
- * memcpy(dst_data, src_data, itemsize * dst_extent)
- */
- __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);
- if (__pyx_t_2) {
- } else {
- __pyx_t_1 = __pyx_t_2;
- goto __pyx_L5_bool_binop_done;
- }
- __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
- if (__pyx_t_2) {
- } else {
- __pyx_t_1 = __pyx_t_2;
- goto __pyx_L5_bool_binop_done;
- }
-
- /* "View.MemoryView":1154
- * if ndim == 1:
- * if (src_stride > 0 and dst_stride > 0 and
- * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<<
- * memcpy(dst_data, src_data, itemsize * dst_extent)
- * else:
- */
- __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
- if (__pyx_t_2) {
- __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
- }
- __pyx_t_3 = (__pyx_t_2 != 0);
- __pyx_t_1 = __pyx_t_3;
- __pyx_L5_bool_binop_done:;
-
- /* "View.MemoryView":1153
- *
- * if ndim == 1:
- * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<<
- * src_stride == itemsize == dst_stride):
- * memcpy(dst_data, src_data, itemsize * dst_extent)
- */
- if (__pyx_t_1) {
-
- /* "View.MemoryView":1155
- * if (src_stride > 0 and dst_stride > 0 and
- * src_stride == itemsize == dst_stride):
- * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<<
- * else:
- * for i in range(dst_extent):
- */
- (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
- /* "View.MemoryView":1153
- *
- * if ndim == 1:
- * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<<
- * src_stride == itemsize == dst_stride):
- * memcpy(dst_data, src_data, itemsize * dst_extent)
- */
- goto __pyx_L4;
- }
-
- /* "View.MemoryView":1157
- * memcpy(dst_data, src_data, itemsize * dst_extent)
- * else:
- * for i in range(dst_extent): # <<<<<<<<<<<<<<
- * memcpy(dst_data, src_data, itemsize)
- * src_data += src_stride
- */
- /*else*/ {
- __pyx_t_4 = __pyx_v_dst_extent;
- __pyx_t_5 = __pyx_t_4;
- for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
- __pyx_v_i = __pyx_t_6;
-
- /* "View.MemoryView":1158
- * else:
- * for i in range(dst_extent):
- * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<<
- * src_data += src_stride
- * dst_data += dst_stride
- */
- (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
- /* "View.MemoryView":1159
- * for i in range(dst_extent):
- * memcpy(dst_data, src_data, itemsize)
- * src_data += src_stride # <<<<<<<<<<<<<<
- * dst_data += dst_stride
- * else:
- */
- __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
- /* "View.MemoryView":1160
- * memcpy(dst_data, src_data, itemsize)
- * src_data += src_stride
- * dst_data += dst_stride # <<<<<<<<<<<<<<
- * else:
- * for i in range(dst_extent):
- */
- __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
- }
- }
- __pyx_L4:;
-
- /* "View.MemoryView":1152
- * cdef Py_ssize_t dst_stride = dst_strides[0]
- *
- * if ndim == 1: # <<<<<<<<<<<<<<
- * if (src_stride > 0 and dst_stride > 0 and
- * src_stride == itemsize == dst_stride):
- */
- goto __pyx_L3;
- }
-
- /* "View.MemoryView":1162
- * dst_data += dst_stride
- * else:
- * for i in range(dst_extent): # <<<<<<<<<<<<<<
- * _copy_strided_to_strided(src_data, src_strides + 1,
- * dst_data, dst_strides + 1,
- */
- /*else*/ {
- __pyx_t_4 = __pyx_v_dst_extent;
- __pyx_t_5 = __pyx_t_4;
- for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
- __pyx_v_i = __pyx_t_6;
-
- /* "View.MemoryView":1163
- * else:
- * for i in range(dst_extent):
- * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<<
- * dst_data, dst_strides + 1,
- * src_shape + 1, dst_shape + 1,
- */
- _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
- /* "View.MemoryView":1167
- * src_shape + 1, dst_shape + 1,
- * ndim - 1, itemsize)
- * src_data += src_stride # <<<<<<<<<<<<<<
- * dst_data += dst_stride
- *
- */
- __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
- /* "View.MemoryView":1168
- * ndim - 1, itemsize)
- * src_data += src_stride
- * dst_data += dst_stride # <<<<<<<<<<<<<<
- *
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
- __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
- }
- }
- __pyx_L3:;
-
- /* "View.MemoryView":1140
- *
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<<
- * char *dst_data, Py_ssize_t *dst_strides,
- * Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
- /* function exit code */
-}
-
-/* "View.MemoryView":1170
- * dst_data += dst_stride
- *
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<<
- * __Pyx_memviewslice *dst,
- * int ndim, size_t itemsize) nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
- /* "View.MemoryView":1173
- * __Pyx_memviewslice *dst,
- * int ndim, size_t itemsize) nogil:
- * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<<
- * src.shape, dst.shape, ndim, itemsize)
- *
- */
- _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
- /* "View.MemoryView":1170
- * dst_data += dst_stride
- *
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<<
- * __Pyx_memviewslice *dst,
- * int ndim, size_t itemsize) nogil:
- */
-
- /* function exit code */
-}
-
-/* "View.MemoryView":1177
- *
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<<
- * "Return the size of the memory occupied by the slice in number of bytes"
- * cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
- Py_ssize_t __pyx_v_shape;
- Py_ssize_t __pyx_v_size;
- Py_ssize_t __pyx_r;
- Py_ssize_t __pyx_t_1;
- Py_ssize_t *__pyx_t_2;
- Py_ssize_t *__pyx_t_3;
- Py_ssize_t *__pyx_t_4;
-
- /* "View.MemoryView":1179
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
- * "Return the size of the memory occupied by the slice in number of bytes"
- * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<<
- *
- * for shape in src.shape[:ndim]:
- */
- __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
- __pyx_v_size = __pyx_t_1;
-
- /* "View.MemoryView":1181
- * cdef Py_ssize_t shape, size = src.memview.view.itemsize
- *
- * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<<
- * size *= shape
- *
- */
- __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
- for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
- __pyx_t_2 = __pyx_t_4;
- __pyx_v_shape = (__pyx_t_2[0]);
-
- /* "View.MemoryView":1182
- *
- * for shape in src.shape[:ndim]:
- * size *= shape # <<<<<<<<<<<<<<
- *
- * return size
- */
- __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
- }
-
- /* "View.MemoryView":1184
- * size *= shape
- *
- * return size # <<<<<<<<<<<<<<
- *
- * @cname('__pyx_fill_contig_strides_array')
- */
- __pyx_r = __pyx_v_size;
- goto __pyx_L0;
-
- /* "View.MemoryView":1177
- *
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<<
- * "Return the size of the memory occupied by the slice in number of bytes"
- * cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
- /* function exit code */
- __pyx_L0:;
- return __pyx_r;
-}
-
-/* "View.MemoryView":1187
- *
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<<
- * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- * int ndim, char order) nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
- int __pyx_v_idx;
- Py_ssize_t __pyx_r;
- int __pyx_t_1;
- int __pyx_t_2;
- int __pyx_t_3;
- int __pyx_t_4;
-
- /* "View.MemoryView":1196
- * cdef int idx
- *
- * if order == 'F': # <<<<<<<<<<<<<<
- * for idx in range(ndim):
- * strides[idx] = stride
- */
- __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
- if (__pyx_t_1) {
-
- /* "View.MemoryView":1197
- *
- * if order == 'F':
- * for idx in range(ndim): # <<<<<<<<<<<<<<
- * strides[idx] = stride
- * stride *= shape[idx]
- */
- __pyx_t_2 = __pyx_v_ndim;
- __pyx_t_3 = __pyx_t_2;
- for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
- __pyx_v_idx = __pyx_t_4;
-
- /* "View.MemoryView":1198
- * if order == 'F':
- * for idx in range(ndim):
- * strides[idx] = stride # <<<<<<<<<<<<<<
- * stride *= shape[idx]
- * else:
- */
- (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
- /* "View.MemoryView":1199
- * for idx in range(ndim):
- * strides[idx] = stride
- * stride *= shape[idx] # <<<<<<<<<<<<<<
- * else:
- * for idx in range(ndim - 1, -1, -1):
- */
- __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
- }
-
- /* "View.MemoryView":1196
- * cdef int idx
- *
- * if order == 'F': # <<<<<<<<<<<<<<
- * for idx in range(ndim):
- * strides[idx] = stride
- */
- goto __pyx_L3;
- }
-
- /* "View.MemoryView":1201
- * stride *= shape[idx]
- * else:
- * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<<
- * strides[idx] = stride
- * stride *= shape[idx]
- */
- /*else*/ {
- for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
- __pyx_v_idx = __pyx_t_2;
-
- /* "View.MemoryView":1202
- * else:
- * for idx in range(ndim - 1, -1, -1):
- * strides[idx] = stride # <<<<<<<<<<<<<<
- * stride *= shape[idx]
- *
- */
- (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
- /* "View.MemoryView":1203
- * for idx in range(ndim - 1, -1, -1):
- * strides[idx] = stride
- * stride *= shape[idx] # <<<<<<<<<<<<<<
- *
- * return stride
- */
- __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
- }
- }
- __pyx_L3:;
-
- /* "View.MemoryView":1205
- * stride *= shape[idx]
- *
- * return stride # <<<<<<<<<<<<<<
- *
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
- __pyx_r = __pyx_v_stride;
- goto __pyx_L0;
-
- /* "View.MemoryView":1187
- *
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<<
- * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- * int ndim, char order) nogil:
- */
-
- /* function exit code */
- __pyx_L0:;
- return __pyx_r;
-}
-
-/* "View.MemoryView":1208
- *
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<<
- * __Pyx_memviewslice *tmpslice,
- * char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
- int __pyx_v_i;
- void *__pyx_v_result;
- size_t __pyx_v_itemsize;
- size_t __pyx_v_size;
- void *__pyx_r;
- Py_ssize_t __pyx_t_1;
- int __pyx_t_2;
- int __pyx_t_3;
- struct __pyx_memoryview_obj *__pyx_t_4;
- int __pyx_t_5;
- int __pyx_t_6;
- int __pyx_lineno = 0;
- const char *__pyx_filename = NULL;
- int __pyx_clineno = 0;
-
- /* "View.MemoryView":1219
- * cdef void *result
- *
- * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<<
- * cdef size_t size = slice_get_size(src, ndim)
- *
- */
- __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
- __pyx_v_itemsize = __pyx_t_1;
-
- /* "View.MemoryView":1220
- *
- * cdef size_t itemsize = src.memview.view.itemsize
- * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<<
- *
- * result = malloc(size)
- */
- __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
-
- /* "View.MemoryView":1222
- * cdef size_t size = slice_get_size(src, ndim)
- *
- * result = malloc(size) # <<<<<<<<<<<<<<
- * if not result:
- * _err(MemoryError, NULL)
- */
- __pyx_v_result = malloc(__pyx_v_size);
-
- /* "View.MemoryView":1223
- *
- * result = malloc(size)
- * if not result: # <<<<<<<<<<<<<<
- * _err(MemoryError, NULL)
- *
- */
- __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1224
- * result = malloc(size)
- * if not result:
- * _err(MemoryError, NULL) # <<<<<<<<<<<<<<
- *
- *
- */
- __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1224, __pyx_L1_error)
-
- /* "View.MemoryView":1223
- *
- * result = malloc(size)
- * if not result: # <<<<<<<<<<<<<<
- * _err(MemoryError, NULL)
- *
- */
- }
-
- /* "View.MemoryView":1227
- *
- *
- * tmpslice.data = result # <<<<<<<<<<<<<<
- * tmpslice.memview = src.memview
- * for i in range(ndim):
- */
- __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
-
- /* "View.MemoryView":1228
- *
- * tmpslice.data = result
- * tmpslice.memview = src.memview # <<<<<<<<<<<<<<
- * for i in range(ndim):
- * tmpslice.shape[i] = src.shape[i]
- */
- __pyx_t_4 = __pyx_v_src->memview;
- __pyx_v_tmpslice->memview = __pyx_t_4;
-
- /* "View.MemoryView":1229
- * tmpslice.data = result
- * tmpslice.memview = src.memview
- * for i in range(ndim): # <<<<<<<<<<<<<<
- * tmpslice.shape[i] = src.shape[i]
- * tmpslice.suboffsets[i] = -1
- */
- __pyx_t_3 = __pyx_v_ndim;
- __pyx_t_5 = __pyx_t_3;
- for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
- __pyx_v_i = __pyx_t_6;
-
- /* "View.MemoryView":1230
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- __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1285
- *
- * if src_ndim < dst_ndim:
- * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<<
- * elif dst_ndim < src_ndim:
- * broadcast_leading(&dst, dst_ndim, src_ndim)
- */
- __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
-
- /* "View.MemoryView":1284
- * cdef __Pyx_memviewslice tmp
- *
- * if src_ndim < dst_ndim: # <<<<<<<<<<<<<<
- * broadcast_leading(&src, src_ndim, dst_ndim)
- * elif dst_ndim < src_ndim:
- */
- goto __pyx_L3;
- }
-
- /* "View.MemoryView":1286
- * if src_ndim < dst_ndim:
- * broadcast_leading(&src, src_ndim, dst_ndim)
- * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<<
- * broadcast_leading(&dst, dst_ndim, src_ndim)
- *
- */
- __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1287
- * broadcast_leading(&src, src_ndim, dst_ndim)
- * elif dst_ndim < src_ndim:
- * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<<
- *
- * cdef int ndim = max(src_ndim, dst_ndim)
- */
- __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
-
- /* "View.MemoryView":1286
- * if src_ndim < dst_ndim:
- * broadcast_leading(&src, src_ndim, dst_ndim)
- * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<<
- * broadcast_leading(&dst, dst_ndim, src_ndim)
- *
- */
- }
- __pyx_L3:;
-
- /* "View.MemoryView":1289
- * broadcast_leading(&dst, dst_ndim, src_ndim)
- *
- * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<<
- *
- * for i in range(ndim):
- */
- __pyx_t_3 = __pyx_v_dst_ndim;
- __pyx_t_4 = __pyx_v_src_ndim;
- if (((__pyx_t_3 > __pyx_t_4) != 0)) {
- __pyx_t_5 = __pyx_t_3;
- } else {
- __pyx_t_5 = __pyx_t_4;
- }
- __pyx_v_ndim = __pyx_t_5;
-
- /* "View.MemoryView":1291
- * cdef int ndim = max(src_ndim, dst_ndim)
- *
- * for i in range(ndim): # <<<<<<<<<<<<<<
- * if src.shape[i] != dst.shape[i]:
- * if src.shape[i] == 1:
- */
- __pyx_t_5 = __pyx_v_ndim;
- __pyx_t_3 = __pyx_t_5;
- for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
- __pyx_v_i = __pyx_t_4;
-
- /* "View.MemoryView":1292
- *
- * for i in range(ndim):
- * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<<
- * if src.shape[i] == 1:
- * broadcasting = True
- */
- __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1293
- * for i in range(ndim):
- * if src.shape[i] != dst.shape[i]:
- * if src.shape[i] == 1: # <<<<<<<<<<<<<<
- * broadcasting = True
- * src.strides[i] = 0
- */
- __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1294
- * if src.shape[i] != dst.shape[i]:
- * if src.shape[i] == 1:
- * broadcasting = True # <<<<<<<<<<<<<<
- * src.strides[i] = 0
- * else:
- */
- __pyx_v_broadcasting = 1;
-
- /* "View.MemoryView":1295
- * if src.shape[i] == 1:
- * broadcasting = True
- * src.strides[i] = 0 # <<<<<<<<<<<<<<
- * else:
- * _err_extents(i, dst.shape[i], src.shape[i])
- */
- (__pyx_v_src.strides[__pyx_v_i]) = 0;
-
- /* "View.MemoryView":1293
- * for i in range(ndim):
- * if src.shape[i] != dst.shape[i]:
- * if src.shape[i] == 1: # <<<<<<<<<<<<<<
- * broadcasting = True
- * src.strides[i] = 0
- */
- goto __pyx_L7;
- }
-
- /* "View.MemoryView":1297
- * src.strides[i] = 0
- * else:
- * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<<
- *
- * if src.suboffsets[i] >= 0:
- */
- /*else*/ {
- __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
- }
- __pyx_L7:;
-
- /* "View.MemoryView":1292
- *
- * for i in range(ndim):
- * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<<
- * if src.shape[i] == 1:
- * broadcasting = True
- */
- }
-
- /* "View.MemoryView":1299
- * _err_extents(i, dst.shape[i], src.shape[i])
- *
- * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<<
- * _err_dim(ValueError, "Dimension %d is not direct", i)
- *
- */
- __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1300
- *
- * if src.suboffsets[i] >= 0:
- * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<<
- *
- * if slices_overlap(&src, &dst, ndim, itemsize):
- */
- __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1300, __pyx_L1_error)
-
- /* "View.MemoryView":1299
- * _err_extents(i, dst.shape[i], src.shape[i])
- *
- * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<<
- * _err_dim(ValueError, "Dimension %d is not direct", i)
- *
- */
- }
- }
-
- /* "View.MemoryView":1302
- * _err_dim(ValueError, "Dimension %d is not direct", i)
- *
- * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<<
- *
- * if not slice_is_contig(src, order, ndim):
- */
- __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1304
- * if slices_overlap(&src, &dst, ndim, itemsize):
- *
- * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<<
- * order = get_best_order(&dst, ndim)
- *
- */
- __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1305
- *
- * if not slice_is_contig(src, order, ndim):
- * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<<
- *
- * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
- __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
- /* "View.MemoryView":1304
- * if slices_overlap(&src, &dst, ndim, itemsize):
- *
- * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<<
- * order = get_best_order(&dst, ndim)
- *
- */
- }
-
- /* "View.MemoryView":1307
- * order = get_best_order(&dst, ndim)
- *
- * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<<
- * src = tmp
- *
- */
- __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1307, __pyx_L1_error)
- __pyx_v_tmpdata = __pyx_t_7;
-
- /* "View.MemoryView":1308
- *
- * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- * src = tmp # <<<<<<<<<<<<<<
- *
- * if not broadcasting:
- */
- __pyx_v_src = __pyx_v_tmp;
-
- /* "View.MemoryView":1302
- * _err_dim(ValueError, "Dimension %d is not direct", i)
- *
- * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<<
- *
- * if not slice_is_contig(src, order, ndim):
- */
- }
-
- /* "View.MemoryView":1310
- * src = tmp
- *
- * if not broadcasting: # <<<<<<<<<<<<<<
- *
- *
- */
- __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1313
- *
- *
- * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<<
- * direct_copy = slice_is_contig(dst, 'C', ndim)
- * elif slice_is_contig(src, 'F', ndim):
- */
- __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1314
- *
- * if slice_is_contig(src, 'C', ndim):
- * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<<
- * elif slice_is_contig(src, 'F', ndim):
- * direct_copy = slice_is_contig(dst, 'F', ndim)
- */
- __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
- /* "View.MemoryView":1313
- *
- *
- * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<<
- * direct_copy = slice_is_contig(dst, 'C', ndim)
- * elif slice_is_contig(src, 'F', ndim):
- */
- goto __pyx_L12;
- }
-
- /* "View.MemoryView":1315
- * if slice_is_contig(src, 'C', ndim):
- * direct_copy = slice_is_contig(dst, 'C', ndim)
- * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<<
- * direct_copy = slice_is_contig(dst, 'F', ndim)
- *
- */
- __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1316
- * direct_copy = slice_is_contig(dst, 'C', ndim)
- * elif slice_is_contig(src, 'F', ndim):
- * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<<
- *
- * if direct_copy:
- */
- __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
- /* "View.MemoryView":1315
- * if slice_is_contig(src, 'C', ndim):
- * direct_copy = slice_is_contig(dst, 'C', ndim)
- * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<<
- * direct_copy = slice_is_contig(dst, 'F', ndim)
- *
- */
- }
- __pyx_L12:;
-
- /* "View.MemoryView":1318
- * direct_copy = slice_is_contig(dst, 'F', ndim)
- *
- * if direct_copy: # <<<<<<<<<<<<<<
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- */
- __pyx_t_2 = (__pyx_v_direct_copy != 0);
- if (__pyx_t_2) {
-
- /* "View.MemoryView":1320
- * if direct_copy:
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<<
- * memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- */
- __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
- /* "View.MemoryView":1321
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<<
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- * free(tmpdata)
- */
- (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
- /* "View.MemoryView":1322
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- * memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<<
- * free(tmpdata)
- * return 0
- */
- __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
- /* "View.MemoryView":1323
- * memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- * free(tmpdata) # <<<<<<<<<<<<<<
- * return 0
- *
- */
- free(__pyx_v_tmpdata);
-
- /* "View.MemoryView":1324
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- * free(tmpdata)
- * return 0 # <<<<<<<<<<<<<<
- *
- * if order == 'F' == get_best_order(&dst, ndim):
- */
- __pyx_r = 0;
- goto __pyx_L0;
-
- /* "View.MemoryView":1318
- * direct_copy = slice_is_contig(dst, 'F', ndim)
- *
- * if direct_copy: # <<<<<<<<<<<<<<
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- */
- }
-
- /* "View.MemoryView":1310
- * src = tmp
- *
- * if not broadcasting: # <<<<<<<<<<<<<<
- *
- *
- */
- }
-
- /* "View.MemoryView":1326
- * return 0
- *
- * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<<
- *
- *
- */
- __pyx_t_2 = (__pyx_v_order == 'F');
- if (__pyx_t_2) {
- __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
- }
- __pyx_t_8 = (__pyx_t_2 != 0);
- if (__pyx_t_8) {
-
- /* "View.MemoryView":1329
- *
- *
- * transpose_memslice(&src) # <<<<<<<<<<<<<<
- * transpose_memslice(&dst)
- *
- */
- __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1329, __pyx_L1_error)
-
- /* "View.MemoryView":1330
- *
- * transpose_memslice(&src)
- * transpose_memslice(&dst) # <<<<<<<<<<<<<<
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- */
- __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1330, __pyx_L1_error)
-
- /* "View.MemoryView":1326
- * return 0
- *
- * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<<
- *
- *
- */
- }
-
- /* "View.MemoryView":1332
- * transpose_memslice(&dst)
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<<
- * copy_strided_to_strided(&src, &dst, ndim, itemsize)
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- */
- __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
- /* "View.MemoryView":1333
- *
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<<
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- *
- */
- copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
- /* "View.MemoryView":1334
- * refcount_copying(&dst, dtype_is_object, ndim, False)
- * copy_strided_to_strided(&src, &dst, ndim, itemsize)
- * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<<
- *
- * free(tmpdata)
- */
- __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
- /* "View.MemoryView":1336
- * refcount_copying(&dst, dtype_is_object, ndim, True)
- *
- * free(tmpdata) # <<<<<<<<<<<<<<
- * return 0
- *
- */
- free(__pyx_v_tmpdata);
-
- /* "View.MemoryView":1337
- *
- * free(tmpdata)
- * return 0 # <<<<<<<<<<<<<<
- *
- * @cname('__pyx_memoryview_broadcast_leading')
- */
- __pyx_r = 0;
- goto __pyx_L0;
-
- /* "View.MemoryView":1268
- *
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<<
- * __Pyx_memviewslice dst,
- * int src_ndim, int dst_ndim,
- */
-
- /* function exit code */
- __pyx_L1_error:;
- {
- #ifdef WITH_THREAD
- PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
- #endif
- __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
- #ifdef WITH_THREAD
- __Pyx_PyGILState_Release(__pyx_gilstate_save);
- #endif
- }
- __pyx_r = -1;
- __pyx_L0:;
- return __pyx_r;
-}
-
-/* "View.MemoryView":1340
- *
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<<
- * int ndim,
- * int ndim_other) nogil:
- */
-
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
- int __pyx_v_i;
- int __pyx_v_offset;
- int __pyx_t_1;
- int __pyx_t_2;
- int __pyx_t_3;
-
- /* "View.MemoryView":1344
- * int ndim_other) nogil:
- * cdef int i
- * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<<
- *
- * for i in range(ndim - 1, -1, -1):
- */
- __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
-
- /* "View.MemoryView":1346
- * cdef int offset = ndim_other - ndim
- *
- * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<<
- * mslice.shape[i + offset] = mslice.shape[i]
- * mslice.strides[i + offset] = mslice.strides[i]
- */
- for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
- __pyx_v_i = __pyx_t_1;
-
- /* "View.MemoryView":1347
- *
- * for i in range(ndim - 1, -1, -1):
- * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<<
- * mslice.strides[i + offset] = mslice.strides[i]
- * mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- */
- (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);
-
- /* "View.MemoryView":1348
- * for i in range(ndim - 1, -1, -1):
- * mslice.shape[i + offset] = mslice.shape[i]
- * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<<
- * mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- *
- */
- (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);
-
- /* "View.MemoryView":1349
- * mslice.shape[i + offset] = mslice.shape[i]
- * mslice.strides[i + offset] = mslice.strides[i]
- * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<<
- *
- * for i in range(offset):
- */
- (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
- }
-
- /* "View.MemoryView":1351
- * mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- *
- * for i in range(offset): # <<<<<<<<<<<<<<
- * mslice.shape[i] = 1
- * mslice.strides[i] = mslice.strides[0]
- */
- __pyx_t_1 = __pyx_v_offset;
- __pyx_t_2 = __pyx_t_1;
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- PyVarObject_HEAD_INIT(0, 0)
- "monotonic_align.core.array", /*tp_name*/
- sizeof(struct __pyx_array_obj), /*tp_basicsize*/
- 0, /*tp_itemsize*/
- __pyx_tp_dealloc_array, /*tp_dealloc*/
- #if PY_VERSION_HEX < 0x030800b4
- 0, /*tp_print*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4
- 0, /*tp_vectorcall_offset*/
- #endif
- 0, /*tp_getattr*/
- 0, /*tp_setattr*/
- #if PY_MAJOR_VERSION < 3
- 0, /*tp_compare*/
- #endif
- #if PY_MAJOR_VERSION >= 3
- 0, /*tp_as_async*/
- #endif
- 0, /*tp_repr*/
- 0, /*tp_as_number*/
- &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
- &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
- 0, /*tp_hash*/
- 0, /*tp_call*/
- 0, /*tp_str*/
- __pyx_tp_getattro_array, /*tp_getattro*/
- 0, /*tp_setattro*/
- &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
- Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
- 0, /*tp_doc*/
- 0, /*tp_traverse*/
- 0, /*tp_clear*/
- 0, /*tp_richcompare*/
- 0, /*tp_weaklistoffset*/
- 0, /*tp_iter*/
- 0, /*tp_iternext*/
- __pyx_methods_array, /*tp_methods*/
- 0, /*tp_members*/
- __pyx_getsets_array, /*tp_getset*/
- 0, /*tp_base*/
- 0, /*tp_dict*/
- 0, /*tp_descr_get*/
- 0, /*tp_descr_set*/
- 0, /*tp_dictoffset*/
- 0, /*tp_init*/
- 0, /*tp_alloc*/
- __pyx_tp_new_array, /*tp_new*/
- 0, /*tp_free*/
- 0, /*tp_is_gc*/
- 0, /*tp_bases*/
- 0, /*tp_mro*/
- 0, /*tp_cache*/
- 0, /*tp_subclasses*/
- 0, /*tp_weaklist*/
- 0, /*tp_del*/
- 0, /*tp_version_tag*/
- #if PY_VERSION_HEX >= 0x030400a1
- 0, /*tp_finalize*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b1
- 0, /*tp_vectorcall*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
- 0, /*tp_print*/
- #endif
-};
-
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
- struct __pyx_MemviewEnum_obj *p;
- PyObject *o;
- if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
- o = (*t->tp_alloc)(t, 0);
- } else {
- o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
- }
- if (unlikely(!o)) return 0;
- p = ((struct __pyx_MemviewEnum_obj *)o);
- p->name = Py_None; Py_INCREF(Py_None);
- return o;
-}
-
-static void __pyx_tp_dealloc_Enum(PyObject *o) {
- struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
- #if CYTHON_USE_TP_FINALIZE
- if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
- if (PyObject_CallFinalizerFromDealloc(o)) return;
- }
- #endif
- PyObject_GC_UnTrack(o);
- Py_CLEAR(p->name);
- (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
- int e;
- struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
- if (p->name) {
- e = (*v)(p->name, a); if (e) return e;
- }
- return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
- PyObject* tmp;
- struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
- tmp = ((PyObject*)p->name);
- p->name = Py_None; Py_INCREF(Py_None);
- Py_XDECREF(tmp);
- return 0;
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
- {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},
- {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},
- {0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
- PyVarObject_HEAD_INIT(0, 0)
- "monotonic_align.core.Enum", /*tp_name*/
- sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
- 0, /*tp_itemsize*/
- __pyx_tp_dealloc_Enum, /*tp_dealloc*/
- #if PY_VERSION_HEX < 0x030800b4
- 0, /*tp_print*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4
- 0, /*tp_vectorcall_offset*/
- #endif
- 0, /*tp_getattr*/
- 0, /*tp_setattr*/
- #if PY_MAJOR_VERSION < 3
- 0, /*tp_compare*/
- #endif
- #if PY_MAJOR_VERSION >= 3
- 0, /*tp_as_async*/
- #endif
- __pyx_MemviewEnum___repr__, /*tp_repr*/
- 0, /*tp_as_number*/
- 0, /*tp_as_sequence*/
- 0, /*tp_as_mapping*/
- 0, /*tp_hash*/
- 0, /*tp_call*/
- 0, /*tp_str*/
- 0, /*tp_getattro*/
- 0, /*tp_setattro*/
- 0, /*tp_as_buffer*/
- Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- 0, /*tp_doc*/
- __pyx_tp_traverse_Enum, /*tp_traverse*/
- __pyx_tp_clear_Enum, /*tp_clear*/
- 0, /*tp_richcompare*/
- 0, /*tp_weaklistoffset*/
- 0, /*tp_iter*/
- 0, /*tp_iternext*/
- __pyx_methods_Enum, /*tp_methods*/
- 0, /*tp_members*/
- 0, /*tp_getset*/
- 0, /*tp_base*/
- 0, /*tp_dict*/
- 0, /*tp_descr_get*/
- 0, /*tp_descr_set*/
- 0, /*tp_dictoffset*/
- __pyx_MemviewEnum___init__, /*tp_init*/
- 0, /*tp_alloc*/
- __pyx_tp_new_Enum, /*tp_new*/
- 0, /*tp_free*/
- 0, /*tp_is_gc*/
- 0, /*tp_bases*/
- 0, /*tp_mro*/
- 0, /*tp_cache*/
- 0, /*tp_subclasses*/
- 0, /*tp_weaklist*/
- 0, /*tp_del*/
- 0, /*tp_version_tag*/
- #if PY_VERSION_HEX >= 0x030400a1
- 0, /*tp_finalize*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b1
- 0, /*tp_vectorcall*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
- 0, /*tp_print*/
- #endif
-};
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
- struct __pyx_memoryview_obj *p;
- PyObject *o;
- if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
- o = (*t->tp_alloc)(t, 0);
- } else {
- o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
- }
- if (unlikely(!o)) return 0;
- p = ((struct __pyx_memoryview_obj *)o);
- p->__pyx_vtab = __pyx_vtabptr_memoryview;
- p->obj = Py_None; Py_INCREF(Py_None);
- p->_size = Py_None; Py_INCREF(Py_None);
- p->_array_interface = Py_None; Py_INCREF(Py_None);
- p->view.obj = NULL;
- if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
- return o;
- bad:
- Py_DECREF(o); o = 0;
- return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
- struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
- #if CYTHON_USE_TP_FINALIZE
- if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
- if (PyObject_CallFinalizerFromDealloc(o)) return;
- }
- #endif
- PyObject_GC_UnTrack(o);
- {
- PyObject *etype, *eval, *etb;
- PyErr_Fetch(&etype, &eval, &etb);
- __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
- __pyx_memoryview___dealloc__(o);
- __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
- PyErr_Restore(etype, eval, etb);
- }
- Py_CLEAR(p->obj);
- Py_CLEAR(p->_size);
- Py_CLEAR(p->_array_interface);
- (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
- int e;
- struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
- if (p->obj) {
- e = (*v)(p->obj, a); if (e) return e;
- }
- if (p->_size) {
- e = (*v)(p->_size, a); if (e) return e;
- }
- if (p->_array_interface) {
- e = (*v)(p->_array_interface, a); if (e) return e;
- }
- if (p->view.obj) {
- e = (*v)(p->view.obj, a); if (e) return e;
- }
- return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
- PyObject* tmp;
- struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
- tmp = ((PyObject*)p->obj);
- p->obj = Py_None; Py_INCREF(Py_None);
- Py_XDECREF(tmp);
- tmp = ((PyObject*)p->_size);
- p->_size = Py_None; Py_INCREF(Py_None);
- Py_XDECREF(tmp);
- tmp = ((PyObject*)p->_array_interface);
- p->_array_interface = Py_None; Py_INCREF(Py_None);
- Py_XDECREF(tmp);
- Py_CLEAR(p->view.obj);
- return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
- PyObject *r;
- PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
- r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
- Py_DECREF(x);
- return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
- if (v) {
- return __pyx_memoryview___setitem__(o, i, v);
- }
- else {
- PyErr_Format(PyExc_NotImplementedError,
- "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
- return -1;
- }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
- {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
- {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
- {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
- {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
- {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
- {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
- {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
- {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
- {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
- {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
- {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
- {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
- {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
- {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
- {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
- {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
- {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
- __pyx_memoryview___len__, /*sq_length*/
- 0, /*sq_concat*/
- 0, /*sq_repeat*/
- __pyx_sq_item_memoryview, /*sq_item*/
- 0, /*sq_slice*/
- 0, /*sq_ass_item*/
- 0, /*sq_ass_slice*/
- 0, /*sq_contains*/
- 0, /*sq_inplace_concat*/
- 0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
- __pyx_memoryview___len__, /*mp_length*/
- __pyx_memoryview___getitem__, /*mp_subscript*/
- __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
- #if PY_MAJOR_VERSION < 3
- 0, /*bf_getreadbuffer*/
- #endif
- #if PY_MAJOR_VERSION < 3
- 0, /*bf_getwritebuffer*/
- #endif
- #if PY_MAJOR_VERSION < 3
- 0, /*bf_getsegcount*/
- #endif
- #if PY_MAJOR_VERSION < 3
- 0, /*bf_getcharbuffer*/
- #endif
- __pyx_memoryview_getbuffer, /*bf_getbuffer*/
- 0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
- PyVarObject_HEAD_INIT(0, 0)
- "monotonic_align.core.memoryview", /*tp_name*/
- sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
- 0, /*tp_itemsize*/
- __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
- #if PY_VERSION_HEX < 0x030800b4
- 0, /*tp_print*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4
- 0, /*tp_vectorcall_offset*/
- #endif
- 0, /*tp_getattr*/
- 0, /*tp_setattr*/
- #if PY_MAJOR_VERSION < 3
- 0, /*tp_compare*/
- #endif
- #if PY_MAJOR_VERSION >= 3
- 0, /*tp_as_async*/
- #endif
- __pyx_memoryview___repr__, /*tp_repr*/
- 0, /*tp_as_number*/
- &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
- &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
- 0, /*tp_hash*/
- 0, /*tp_call*/
- __pyx_memoryview___str__, /*tp_str*/
- 0, /*tp_getattro*/
- 0, /*tp_setattro*/
- &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
- Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
- 0, /*tp_doc*/
- __pyx_tp_traverse_memoryview, /*tp_traverse*/
- __pyx_tp_clear_memoryview, /*tp_clear*/
- 0, /*tp_richcompare*/
- 0, /*tp_weaklistoffset*/
- 0, /*tp_iter*/
- 0, /*tp_iternext*/
- __pyx_methods_memoryview, /*tp_methods*/
- 0, /*tp_members*/
- __pyx_getsets_memoryview, /*tp_getset*/
- 0, /*tp_base*/
- 0, /*tp_dict*/
- 0, /*tp_descr_get*/
- 0, /*tp_descr_set*/
- 0, /*tp_dictoffset*/
- 0, /*tp_init*/
- 0, /*tp_alloc*/
- __pyx_tp_new_memoryview, /*tp_new*/
- 0, /*tp_free*/
- 0, /*tp_is_gc*/
- 0, /*tp_bases*/
- 0, /*tp_mro*/
- 0, /*tp_cache*/
- 0, /*tp_subclasses*/
- 0, /*tp_weaklist*/
- 0, /*tp_del*/
- 0, /*tp_version_tag*/
- #if PY_VERSION_HEX >= 0x030400a1
- 0, /*tp_finalize*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b1
- 0, /*tp_vectorcall*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
- 0, /*tp_print*/
- #endif
-};
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
- struct __pyx_memoryviewslice_obj *p;
- PyObject *o = __pyx_tp_new_memoryview(t, a, k);
- if (unlikely(!o)) return 0;
- p = ((struct __pyx_memoryviewslice_obj *)o);
- p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
- p->from_object = Py_None; Py_INCREF(Py_None);
- p->from_slice.memview = NULL;
- return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
- struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
- #if CYTHON_USE_TP_FINALIZE
- if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
- if (PyObject_CallFinalizerFromDealloc(o)) return;
- }
- #endif
- PyObject_GC_UnTrack(o);
- {
- PyObject *etype, *eval, *etb;
- PyErr_Fetch(&etype, &eval, &etb);
- __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
- __pyx_memoryviewslice___dealloc__(o);
- __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
- PyErr_Restore(etype, eval, etb);
- }
- Py_CLEAR(p->from_object);
- PyObject_GC_Track(o);
- __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
- int e;
- struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
- e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
- if (p->from_object) {
- e = (*v)(p->from_object, a); if (e) return e;
- }
- return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
- PyObject* tmp;
- struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
- __pyx_tp_clear_memoryview(o);
- tmp = ((PyObject*)p->from_object);
- p->from_object = Py_None; Py_INCREF(Py_None);
- Py_XDECREF(tmp);
- __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
- return 0;
-}
-
-static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
- return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
- {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
- {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
- {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
- {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
- {0, 0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
- PyVarObject_HEAD_INIT(0, 0)
- "monotonic_align.core._memoryviewslice", /*tp_name*/
- sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
- 0, /*tp_itemsize*/
- __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
- #if PY_VERSION_HEX < 0x030800b4
- 0, /*tp_print*/
- #endif
- #if PY_VERSION_HEX >= 0x030800b4
- 0, /*tp_vectorcall_offset*/
- #endif
- 0, /*tp_getattr*/
- 0, /*tp_setattr*/
- #if PY_MAJOR_VERSION < 3
- 0, /*tp_compare*/
- #endif
- #if PY_MAJOR_VERSION >= 3
- 0, /*tp_as_async*/
- #endif
- #if CYTHON_COMPILING_IN_PYPY
- __pyx_memoryview___repr__, /*tp_repr*/
- #else
- 0, /*tp_repr*/
- #endif
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-static void __Pyx_RaiseDoubleKeywordsError(
- const char* func_name,
- PyObject* kw_name)
-{
- PyErr_Format(PyExc_TypeError,
- #if PY_MAJOR_VERSION >= 3
- "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
- #else
- "%s() got multiple values for keyword argument '%s'", func_name,
- PyString_AsString(kw_name));
- #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
- PyObject *kwds,
- PyObject **argnames[],
- PyObject *kwds2,
- PyObject *values[],
- Py_ssize_t num_pos_args,
- const char* function_name)
-{
- PyObject *key = 0, *value = 0;
- Py_ssize_t pos = 0;
- PyObject*** name;
- PyObject*** first_kw_arg = argnames + num_pos_args;
- while (PyDict_Next(kwds, &pos, &key, &value)) {
- name = first_kw_arg;
- while (*name && (**name != key)) name++;
- if (*name) {
- values[name-argnames] = value;
- continue;
- }
- name = first_kw_arg;
- #if PY_MAJOR_VERSION < 3
- if (likely(PyString_Check(key))) {
- while (*name) {
- if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
- && _PyString_Eq(**name, key)) {
- values[name-argnames] = value;
- break;
- }
- name++;
- }
- if (*name) continue;
- else {
- PyObject*** argname = argnames;
- while (argname != first_kw_arg) {
- if ((**argname == key) || (
- (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
- && _PyString_Eq(**argname, key))) {
- goto arg_passed_twice;
- }
- argname++;
- }
- }
- } else
- #endif
- if (likely(PyUnicode_Check(key))) {
- while (*name) {
- int cmp = (**name == key) ? 0 :
- #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
- (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
- #endif
- PyUnicode_Compare(**name, key);
- if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
- if (cmp == 0) {
- values[name-argnames] = value;
- break;
- }
- name++;
- }
- if (*name) continue;
- else {
- PyObject*** argname = argnames;
- while (argname != first_kw_arg) {
- int cmp = (**argname == key) ? 0 :
- #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
- (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
- #endif
- PyUnicode_Compare(**argname, key);
- if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
- if (cmp == 0) goto arg_passed_twice;
- argname++;
- }
- }
- } else
- goto invalid_keyword_type;
- if (kwds2) {
- if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
- } else {
- goto invalid_keyword;
- }
- }
- return 0;
-arg_passed_twice:
- __Pyx_RaiseDoubleKeywordsError(function_name, key);
- goto bad;
-invalid_keyword_type:
- PyErr_Format(PyExc_TypeError,
- "%.200s() keywords must be strings", function_name);
- goto bad;
-invalid_keyword:
- PyErr_Format(PyExc_TypeError,
- #if PY_MAJOR_VERSION < 3
- "%.200s() got an unexpected keyword argument '%.200s'",
- function_name, PyString_AsString(key));
- #else
- "%s() got an unexpected keyword argument '%U'",
- function_name, key);
- #endif
-bad:
- return -1;
-}
-
-/* None */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
- PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
- if (unlikely(!type)) {
- PyErr_SetString(PyExc_SystemError, "Missing type object");
- return 0;
- }
- else if (exact) {
- #if PY_MAJOR_VERSION == 2
- if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
- #endif
- }
- else {
- if (likely(__Pyx_TypeCheck(obj, type))) return 1;
- }
- PyErr_Format(PyExc_TypeError,
- "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
- name, type->tp_name, Py_TYPE(obj)->tp_name);
- return 0;
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
- PyObject *result;
- ternaryfunc call = func->ob_type->tp_call;
- if (unlikely(!call))
- return PyObject_Call(func, arg, kw);
- if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
- return NULL;
- result = (*call)(func, arg, kw);
- Py_LeaveRecursiveCall();
- if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
- PyErr_SetString(
- PyExc_SystemError,
- "NULL result without error in PyObject_Call");
- }
- return result;
-}
-#endif
-
-/* PyErrFetchRestore */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- tmp_type = tstate->curexc_type;
- tmp_value = tstate->curexc_value;
- tmp_tb = tstate->curexc_traceback;
- tstate->curexc_type = type;
- tstate->curexc_value = value;
- tstate->curexc_traceback = tb;
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
- *type = tstate->curexc_type;
- *value = tstate->curexc_value;
- *tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-}
-#endif
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
- CYTHON_UNUSED PyObject *cause) {
- __Pyx_PyThreadState_declare
- Py_XINCREF(type);
- if (!value || value == Py_None)
- value = NULL;
- else
- Py_INCREF(value);
- if (!tb || tb == Py_None)
- tb = NULL;
- else {
- Py_INCREF(tb);
- if (!PyTraceBack_Check(tb)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: arg 3 must be a traceback or None");
- goto raise_error;
- }
- }
- if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
- if (!value) {
- Py_INCREF(Py_None);
- value = Py_None;
- }
-#endif
- PyErr_NormalizeException(&type, &value, &tb);
- } else {
- if (value) {
- PyErr_SetString(PyExc_TypeError,
- "instance exception may not have a separate value");
- goto raise_error;
- }
- value = type;
- type = (PyObject*) Py_TYPE(type);
- Py_INCREF(type);
- if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: exception class must be a subclass of BaseException");
- goto raise_error;
- }
- }
- __Pyx_PyThreadState_assign
- __Pyx_ErrRestore(type, value, tb);
- return;
-raise_error:
- Py_XDECREF(value);
- Py_XDECREF(type);
- Py_XDECREF(tb);
- return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
- PyObject* owned_instance = NULL;
- if (tb == Py_None) {
- tb = 0;
- } else if (tb && !PyTraceBack_Check(tb)) {
- PyErr_SetString(PyExc_TypeError,
- "raise: arg 3 must be a traceback or None");
- goto bad;
- }
- if (value == Py_None)
- value = 0;
- if (PyExceptionInstance_Check(type)) {
- if (value) {
- PyErr_SetString(PyExc_TypeError,
- "instance exception may not have a separate value");
- goto bad;
- }
- value = type;
- type = (PyObject*) Py_TYPE(value);
- } else if (PyExceptionClass_Check(type)) {
- PyObject *instance_class = NULL;
- if (value && PyExceptionInstance_Check(value)) {
- instance_class = (PyObject*) Py_TYPE(value);
- if (instance_class != type) {
- int is_subclass = PyObject_IsSubclass(instance_class, type);
- if (!is_subclass) {
- instance_class = NULL;
- } else if (unlikely(is_subclass == -1)) {
- goto bad;
- } else {
- type = instance_class;
- }
- }
- }
- if (!instance_class) {
- PyObject *args;
- if (!value)
- args = PyTuple_New(0);
- else if (PyTuple_Check(value)) {
- Py_INCREF(value);
- args = value;
- } else
- args = PyTuple_Pack(1, value);
- if (!args)
- goto bad;
- owned_instance = PyObject_Call(type, args, NULL);
- Py_DECREF(args);
- if (!owned_instance)
- goto bad;
- value = owned_instance;
- if (!PyExceptionInstance_Check(value)) {
- PyErr_Format(PyExc_TypeError,
- "calling %R should have returned an instance of "
- "BaseException, not %R",
- type, Py_TYPE(value));
- goto bad;
- }
- }
- } else {
- PyErr_SetString(PyExc_TypeError,
- "raise: exception class must be a subclass of BaseException");
- goto bad;
- }
- if (cause) {
- PyObject *fixed_cause;
- if (cause == Py_None) {
- fixed_cause = NULL;
- } else if (PyExceptionClass_Check(cause)) {
- fixed_cause = PyObject_CallObject(cause, NULL);
- if (fixed_cause == NULL)
- goto bad;
- } else if (PyExceptionInstance_Check(cause)) {
- fixed_cause = cause;
- Py_INCREF(fixed_cause);
- } else {
- PyErr_SetString(PyExc_TypeError,
- "exception causes must derive from "
- "BaseException");
- goto bad;
- }
- PyException_SetCause(value, fixed_cause);
- }
- PyErr_SetObject(type, value);
- if (tb) {
-#if CYTHON_COMPILING_IN_PYPY
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
- Py_INCREF(tb);
- PyErr_Restore(tmp_type, tmp_value, tb);
- Py_XDECREF(tmp_tb);
-#else
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- PyObject* tmp_tb = tstate->curexc_traceback;
- if (tb != tmp_tb) {
- Py_INCREF(tb);
- tstate->curexc_traceback = tb;
- Py_XDECREF(tmp_tb);
- }
-#endif
- }
-bad:
- Py_XDECREF(owned_instance);
- return;
-}
-#endif
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
- PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
- PyCFunction meth = PyCFunction_GET_FUNCTION(func);
- PyObject *self = PyCFunction_GET_SELF(func);
- int flags = PyCFunction_GET_FLAGS(func);
- assert(PyCFunction_Check(func));
- assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
- assert(nargs >= 0);
- assert(nargs == 0 || args != NULL);
- /* _PyCFunction_FastCallDict() must not be called with an exception set,
- because it may clear it (directly or indirectly) and so the
- caller loses its exception */
- assert(!PyErr_Occurred());
- if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
- return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
- } else {
- return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
- }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
- PyObject *globals) {
- PyFrameObject *f;
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- PyObject **fastlocals;
- Py_ssize_t i;
- PyObject *result;
- assert(globals != NULL);
- /* XXX Perhaps we should create a specialized
- PyFrame_New() that doesn't take locals, but does
- take builtins without sanity checking them.
- */
- assert(tstate != NULL);
- f = PyFrame_New(tstate, co, globals, NULL);
- if (f == NULL) {
- return NULL;
- }
- fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
- for (i = 0; i < na; i++) {
- Py_INCREF(*args);
- fastlocals[i] = *args++;
- }
- result = PyEval_EvalFrameEx(f,0);
- ++tstate->recursion_depth;
- Py_DECREF(f);
- --tstate->recursion_depth;
- return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
- PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
- PyObject *globals = PyFunction_GET_GLOBALS(func);
- PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
- PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
- PyObject *kwdefs;
-#endif
- PyObject *kwtuple, **k;
- PyObject **d;
- Py_ssize_t nd;
- Py_ssize_t nk;
- PyObject *result;
- assert(kwargs == NULL || PyDict_Check(kwargs));
- nk = kwargs ? PyDict_Size(kwargs) : 0;
- if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
- return NULL;
- }
- if (
-#if PY_MAJOR_VERSION >= 3
- co->co_kwonlyargcount == 0 &&
-#endif
- likely(kwargs == NULL || nk == 0) &&
- co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
- if (argdefs == NULL && co->co_argcount == nargs) {
- result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
- goto done;
- }
- else if (nargs == 0 && argdefs != NULL
- && co->co_argcount == Py_SIZE(argdefs)) {
- /* function called with no arguments, but all parameters have
- a default value: use default values as arguments .*/
- args = &PyTuple_GET_ITEM(argdefs, 0);
- result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
- goto done;
- }
- }
- if (kwargs != NULL) {
- Py_ssize_t pos, i;
- kwtuple = PyTuple_New(2 * nk);
- if (kwtuple == NULL) {
- result = NULL;
- goto done;
- }
- k = &PyTuple_GET_ITEM(kwtuple, 0);
- pos = i = 0;
- while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
- Py_INCREF(k[i]);
- Py_INCREF(k[i+1]);
- i += 2;
- }
- nk = i / 2;
- }
- else {
- kwtuple = NULL;
- k = NULL;
- }
- closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
- kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
- if (argdefs != NULL) {
- d = &PyTuple_GET_ITEM(argdefs, 0);
- nd = Py_SIZE(argdefs);
- }
- else {
- d = NULL;
- nd = 0;
- }
-#if PY_MAJOR_VERSION >= 3
- result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
- args, (int)nargs,
- k, (int)nk,
- d, (int)nd, kwdefs, closure);
-#else
- result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
- args, (int)nargs,
- k, (int)nk,
- d, (int)nd, closure);
-#endif
- Py_XDECREF(kwtuple);
-done:
- Py_LeaveRecursiveCall();
- return result;
-}
-#endif
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
- PyObject *args, *result = NULL;
- #if CYTHON_FAST_PYCALL
- if (PyFunction_Check(function)) {
- PyObject *args[2] = {arg1, arg2};
- return __Pyx_PyFunction_FastCall(function, args, 2);
- }
- #endif
- #if CYTHON_FAST_PYCCALL
- if (__Pyx_PyFastCFunction_Check(function)) {
- PyObject *args[2] = {arg1, arg2};
- return __Pyx_PyCFunction_FastCall(function, args, 2);
- }
- #endif
- args = PyTuple_New(2);
- if (unlikely(!args)) goto done;
- Py_INCREF(arg1);
- PyTuple_SET_ITEM(args, 0, arg1);
- Py_INCREF(arg2);
- PyTuple_SET_ITEM(args, 1, arg2);
- Py_INCREF(function);
- result = __Pyx_PyObject_Call(function, args, NULL);
- Py_DECREF(args);
- Py_DECREF(function);
-done:
- return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
- PyObject *self, *result;
- PyCFunction cfunc;
- cfunc = PyCFunction_GET_FUNCTION(func);
- self = PyCFunction_GET_SELF(func);
- if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
- return NULL;
- result = cfunc(self, arg);
- Py_LeaveRecursiveCall();
- if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
- PyErr_SetString(
- PyExc_SystemError,
- "NULL result without error in PyObject_Call");
- }
- return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
- PyObject *result;
- PyObject *args = PyTuple_New(1);
- if (unlikely(!args)) return NULL;
- Py_INCREF(arg);
- PyTuple_SET_ITEM(args, 0, arg);
- result = __Pyx_PyObject_Call(func, args, NULL);
- Py_DECREF(args);
- return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
- if (PyFunction_Check(func)) {
- return __Pyx_PyFunction_FastCall(func, &arg, 1);
- }
-#endif
- if (likely(PyCFunction_Check(func))) {
- if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
- return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
- } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
- return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
- }
- }
- return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
- PyObject *result;
- PyObject *args = PyTuple_Pack(1, arg);
- if (unlikely(!args)) return NULL;
- result = __Pyx_PyObject_Call(func, args, NULL);
- Py_DECREF(args);
- return result;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
- return PyObject_RichCompareBool(s1, s2, equals);
-#else
- if (s1 == s2) {
- return (equals == Py_EQ);
- } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
- const char *ps1, *ps2;
- Py_ssize_t length = PyBytes_GET_SIZE(s1);
- if (length != PyBytes_GET_SIZE(s2))
- return (equals == Py_NE);
- ps1 = PyBytes_AS_STRING(s1);
- ps2 = PyBytes_AS_STRING(s2);
- if (ps1[0] != ps2[0]) {
- return (equals == Py_NE);
- } else if (length == 1) {
- return (equals == Py_EQ);
- } else {
- int result;
-#if CYTHON_USE_UNICODE_INTERNALS
- Py_hash_t hash1, hash2;
- hash1 = ((PyBytesObject*)s1)->ob_shash;
- hash2 = ((PyBytesObject*)s2)->ob_shash;
- if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
- return (equals == Py_NE);
- }
-#endif
- result = memcmp(ps1, ps2, (size_t)length);
- return (equals == Py_EQ) ? (result == 0) : (result != 0);
- }
- } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
- return (equals == Py_NE);
- } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
- return (equals == Py_NE);
- } else {
- int result;
- PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
- if (!py_result)
- return -1;
- result = __Pyx_PyObject_IsTrue(py_result);
- Py_DECREF(py_result);
- return result;
- }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
- return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
- PyObject* owned_ref = NULL;
-#endif
- int s1_is_unicode, s2_is_unicode;
- if (s1 == s2) {
- goto return_eq;
- }
- s1_is_unicode = PyUnicode_CheckExact(s1);
- s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
- if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
- owned_ref = PyUnicode_FromObject(s2);
- if (unlikely(!owned_ref))
- return -1;
- s2 = owned_ref;
- s2_is_unicode = 1;
- } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
- owned_ref = PyUnicode_FromObject(s1);
- if (unlikely(!owned_ref))
- return -1;
- s1 = owned_ref;
- s1_is_unicode = 1;
- } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
- return __Pyx_PyBytes_Equals(s1, s2, equals);
- }
-#endif
- if (s1_is_unicode & s2_is_unicode) {
- Py_ssize_t length;
- int kind;
- void *data1, *data2;
- if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
- return -1;
- length = __Pyx_PyUnicode_GET_LENGTH(s1);
- if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
- goto return_ne;
- }
-#if CYTHON_USE_UNICODE_INTERNALS
- {
- Py_hash_t hash1, hash2;
- #if CYTHON_PEP393_ENABLED
- hash1 = ((PyASCIIObject*)s1)->hash;
- hash2 = ((PyASCIIObject*)s2)->hash;
- #else
- hash1 = ((PyUnicodeObject*)s1)->hash;
- hash2 = ((PyUnicodeObject*)s2)->hash;
- #endif
- if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
- goto return_ne;
- }
- }
-#endif
- kind = __Pyx_PyUnicode_KIND(s1);
- if (kind != __Pyx_PyUnicode_KIND(s2)) {
- goto return_ne;
- }
- data1 = __Pyx_PyUnicode_DATA(s1);
- data2 = __Pyx_PyUnicode_DATA(s2);
- if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
- goto return_ne;
- } else if (length == 1) {
- goto return_eq;
- } else {
- int result = memcmp(data1, data2, (size_t)(length * kind));
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- return (equals == Py_EQ) ? (result == 0) : (result != 0);
- }
- } else if ((s1 == Py_None) & s2_is_unicode) {
- goto return_ne;
- } else if ((s2 == Py_None) & s1_is_unicode) {
- goto return_ne;
- } else {
- int result;
- PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- if (!py_result)
- return -1;
- result = __Pyx_PyObject_IsTrue(py_result);
- Py_DECREF(py_result);
- return result;
- }
-return_eq:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- return (equals == Py_EQ);
-return_ne:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(owned_ref);
- #endif
- return (equals == Py_NE);
-#endif
-}
-
-/* None */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
- Py_ssize_t q = a / b;
- Py_ssize_t r = a - q*b;
- q -= ((r != 0) & ((r ^ b) < 0));
- return q;
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
- if (likely(PyUnicode_Check(n)))
-#else
- if (likely(PyString_Check(n)))
-#endif
- return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
- return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
- PyObject *r;
- if (!j) return NULL;
- r = PyObject_GetItem(o, j);
- Py_DECREF(j);
- return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
- CYTHON_NCP_UNUSED int wraparound,
- CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- Py_ssize_t wrapped_i = i;
- if (wraparound & unlikely(i < 0)) {
- wrapped_i += PyList_GET_SIZE(o);
- }
- if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
- PyObject *r = PyList_GET_ITEM(o, wrapped_i);
- Py_INCREF(r);
- return r;
- }
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
- return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
- CYTHON_NCP_UNUSED int wraparound,
- CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- Py_ssize_t wrapped_i = i;
- if (wraparound & unlikely(i < 0)) {
- wrapped_i += PyTuple_GET_SIZE(o);
- }
- if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
- PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
- Py_INCREF(r);
- return r;
- }
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
- return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
- CYTHON_NCP_UNUSED int wraparound,
- CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
- if (is_list || PyList_CheckExact(o)) {
- Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
- if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
- PyObject *r = PyList_GET_ITEM(o, n);
- Py_INCREF(r);
- return r;
- }
- }
- else if (PyTuple_CheckExact(o)) {
- Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
- if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
- PyObject *r = PyTuple_GET_ITEM(o, n);
- Py_INCREF(r);
- return r;
- }
- } else {
- PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
- if (likely(m && m->sq_item)) {
- if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
- Py_ssize_t l = m->sq_length(o);
- if (likely(l >= 0)) {
- i += l;
- } else {
- if (!PyErr_ExceptionMatches(PyExc_OverflowError))
- return NULL;
- PyErr_Clear();
- }
- }
- return m->sq_item(o, i);
- }
- }
-#else
- if (is_list || PySequence_Check(o)) {
- return PySequence_GetItem(o, i);
- }
-#endif
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
- PyObject *runerr;
- Py_ssize_t key_value;
- PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
- if (unlikely(!(m && m->sq_item))) {
- PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
- return NULL;
- }
- key_value = __Pyx_PyIndex_AsSsize_t(index);
- if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
- return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
- }
- if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
- PyErr_Clear();
- PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
- }
- return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
- PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
- if (likely(m && m->mp_subscript)) {
- return m->mp_subscript(obj, key);
- }
- return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* decode_c_string */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
- const char* cstring, Py_ssize_t start, Py_ssize_t stop,
- const char* encoding, const char* errors,
- PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
- Py_ssize_t length;
- if (unlikely((start < 0) | (stop < 0))) {
- size_t slen = strlen(cstring);
- if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
- PyErr_SetString(PyExc_OverflowError,
- "c-string too long to convert to Python");
- return NULL;
- }
- length = (Py_ssize_t) slen;
- if (start < 0) {
- start += length;
- if (start < 0)
- start = 0;
- }
- if (stop < 0)
- stop += length;
- }
- if (unlikely(stop <= start))
- return __Pyx_NewRef(__pyx_empty_unicode);
- length = stop - start;
- cstring += start;
- if (decode_func) {
- return decode_func(cstring, length, errors);
- } else {
- return PyUnicode_Decode(cstring, length, encoding, errors);
- }
-}
-
-/* PyErrExceptionMatches */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
- Py_ssize_t i, n;
- n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
- for (i=0; icurexc_type;
- if (exc_type == err) return 1;
- if (unlikely(!exc_type)) return 0;
- if (unlikely(PyTuple_Check(err)))
- return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
- return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
- __Pyx_PyThreadState_declare
- __Pyx_PyThreadState_assign
- if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
- return NULL;
- __Pyx_PyErr_Clear();
- Py_INCREF(d);
- return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
- PyObject *r = __Pyx_GetAttr(o, n);
- return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
- PyObject *dict = Py_TYPE(obj)->tp_dict;
- return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
- PyObject **dictptr = NULL;
- Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
- if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
- dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
- dictptr = _PyObject_GetDictPtr(obj);
-#endif
- }
- return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
- PyObject *dict = Py_TYPE(obj)->tp_dict;
- if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
- return 0;
- return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
- PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
- result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- } else if (unlikely(PyErr_Occurred())) {
- return NULL;
- }
-#else
- result = PyDict_GetItem(__pyx_d, name);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- }
-#endif
-#else
- result = PyObject_GetItem(__pyx_d, name);
- __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
- if (likely(result)) {
- return __Pyx_NewRef(result);
- }
- PyErr_Clear();
-#endif
- return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
- PyErr_Format(PyExc_ValueError,
- "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
- PyErr_Format(PyExc_ValueError,
- "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
- index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
- PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
- if (unlikely(!type)) {
- PyErr_SetString(PyExc_SystemError, "Missing type object");
- return 0;
- }
- if (likely(__Pyx_TypeCheck(obj, type)))
- return 1;
- PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
- Py_TYPE(obj)->tp_name, type->tp_name);
- return 0;
-}
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
- _PyErr_StackItem *exc_info = tstate->exc_info;
- while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
- exc_info->previous_item != NULL)
- {
- exc_info = exc_info->previous_item;
- }
- return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
- #if CYTHON_USE_EXC_INFO_STACK
- _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
- *type = exc_info->exc_type;
- *value = exc_info->exc_value;
- *tb = exc_info->exc_traceback;
- #else
- *type = tstate->exc_type;
- *value = tstate->exc_value;
- *tb = tstate->exc_traceback;
- #endif
- Py_XINCREF(*type);
- Py_XINCREF(*value);
- Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- #if CYTHON_USE_EXC_INFO_STACK
- _PyErr_StackItem *exc_info = tstate->exc_info;
- tmp_type = exc_info->exc_type;
- tmp_value = exc_info->exc_value;
- tmp_tb = exc_info->exc_traceback;
- exc_info->exc_type = type;
- exc_info->exc_value = value;
- exc_info->exc_traceback = tb;
- #else
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = type;
- tstate->exc_value = value;
- tstate->exc_traceback = tb;
- #endif
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
- PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- local_type = tstate->curexc_type;
- local_value = tstate->curexc_value;
- local_tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-#else
- PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
- PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
- if (unlikely(tstate->curexc_type))
-#else
- if (unlikely(PyErr_Occurred()))
-#endif
- goto bad;
- #if PY_MAJOR_VERSION >= 3
- if (local_tb) {
- if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
- goto bad;
- }
- #endif
- Py_XINCREF(local_tb);
- Py_XINCREF(local_type);
- Py_XINCREF(local_value);
- *type = local_type;
- *value = local_value;
- *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
- #if CYTHON_USE_EXC_INFO_STACK
- {
- _PyErr_StackItem *exc_info = tstate->exc_info;
- tmp_type = exc_info->exc_type;
- tmp_value = exc_info->exc_value;
- tmp_tb = exc_info->exc_traceback;
- exc_info->exc_type = local_type;
- exc_info->exc_value = local_value;
- exc_info->exc_traceback = local_tb;
- }
- #else
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = local_type;
- tstate->exc_value = local_value;
- tstate->exc_traceback = local_tb;
- #endif
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-#else
- PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
- return 0;
-bad:
- *type = 0;
- *value = 0;
- *tb = 0;
- Py_XDECREF(local_type);
- Py_XDECREF(local_value);
- Py_XDECREF(local_tb);
- return -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- #if CYTHON_USE_EXC_INFO_STACK
- _PyErr_StackItem *exc_info = tstate->exc_info;
- tmp_type = exc_info->exc_type;
- tmp_value = exc_info->exc_value;
- tmp_tb = exc_info->exc_traceback;
- exc_info->exc_type = *type;
- exc_info->exc_value = *value;
- exc_info->exc_traceback = *tb;
- #else
- tmp_type = tstate->exc_type;
- tmp_value = tstate->exc_value;
- tmp_tb = tstate->exc_traceback;
- tstate->exc_type = *type;
- tstate->exc_value = *value;
- tstate->exc_traceback = *tb;
- #endif
- *type = tmp_type;
- *value = tmp_value;
- *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
- PyObject *tmp_type, *tmp_value, *tmp_tb;
- PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
- PyErr_SetExcInfo(*type, *value, *tb);
- *type = tmp_type;
- *value = tmp_value;
- *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
- PyObject *empty_list = 0;
- PyObject *module = 0;
- PyObject *global_dict = 0;
- PyObject *empty_dict = 0;
- PyObject *list;
- #if PY_MAJOR_VERSION < 3
- PyObject *py_import;
- py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
- if (!py_import)
- goto bad;
- #endif
- if (from_list)
- list = from_list;
- else {
- empty_list = PyList_New(0);
- if (!empty_list)
- goto bad;
- list = empty_list;
- }
- global_dict = PyModule_GetDict(__pyx_m);
- if (!global_dict)
- goto bad;
- empty_dict = PyDict_New();
- if (!empty_dict)
- goto bad;
- {
- #if PY_MAJOR_VERSION >= 3
- if (level == -1) {
- if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
- module = PyImport_ImportModuleLevelObject(
- name, global_dict, empty_dict, list, 1);
- if (!module) {
- if (!PyErr_ExceptionMatches(PyExc_ImportError))
- goto bad;
- PyErr_Clear();
- }
- }
- level = 0;
- }
- #endif
- if (!module) {
- #if PY_MAJOR_VERSION < 3
- PyObject *py_level = PyInt_FromLong(level);
- if (!py_level)
- goto bad;
- module = PyObject_CallFunctionObjArgs(py_import,
- name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
- Py_DECREF(py_level);
- #else
- module = PyImport_ImportModuleLevelObject(
- name, global_dict, empty_dict, list, level);
- #endif
- }
- }
-bad:
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(py_import);
- #endif
- Py_XDECREF(empty_list);
- Py_XDECREF(empty_dict);
- return module;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
- while (a) {
- a = a->tp_base;
- if (a == b)
- return 1;
- }
- return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
- PyObject *mro;
- if (a == b) return 1;
- mro = a->tp_mro;
- if (likely(mro)) {
- Py_ssize_t i, n;
- n = PyTuple_GET_SIZE(mro);
- for (i = 0; i < n; i++) {
- if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
- return 1;
- }
- return 0;
- }
- return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
- PyObject *exception, *value, *tb;
- int res;
- __Pyx_PyThreadState_declare
- __Pyx_PyThreadState_assign
- __Pyx_ErrFetch(&exception, &value, &tb);
- res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
- if (unlikely(res == -1)) {
- PyErr_WriteUnraisable(err);
- res = 0;
- }
- if (!res) {
- res = PyObject_IsSubclass(err, exc_type2);
- if (unlikely(res == -1)) {
- PyErr_WriteUnraisable(err);
- res = 0;
- }
- }
- __Pyx_ErrRestore(exception, value, tb);
- return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
- int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
- if (!res) {
- res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
- }
- return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
- Py_ssize_t i, n;
- assert(PyExceptionClass_Check(exc_type));
- n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
- for (i=0; i= 0 || (x^b) >= 0))
- return PyInt_FromLong(x);
- return PyLong_Type.tp_as_number->nb_add(op1, op2);
- }
- #endif
- #if CYTHON_USE_PYLONG_INTERNALS
- if (likely(PyLong_CheckExact(op1))) {
- const long b = intval;
- long a, x;
-#ifdef HAVE_LONG_LONG
- const PY_LONG_LONG llb = intval;
- PY_LONG_LONG lla, llx;
-#endif
- const digit* digits = ((PyLongObject*)op1)->ob_digit;
- const Py_ssize_t size = Py_SIZE(op1);
- if (likely(__Pyx_sst_abs(size) <= 1)) {
- a = likely(size) ? digits[0] : 0;
- if (size == -1) a = -a;
- } else {
- switch (size) {
- case -2:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
- lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- case 2:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
- lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- case -3:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
- lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- case 3:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
- lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- case -4:
- if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
- lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- case 4:
- if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
-#ifdef HAVE_LONG_LONG
- } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
- lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
- goto long_long;
-#endif
- }
- CYTHON_FALLTHROUGH;
- default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
- }
- }
- x = a + b;
- return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
- long_long:
- llx = lla + llb;
- return PyLong_FromLongLong(llx);
-#endif
-
-
- }
- #endif
- if (PyFloat_CheckExact(op1)) {
- const long b = intval;
- double a = PyFloat_AS_DOUBLE(op1);
- double result;
- PyFPE_START_PROTECT("add", return NULL)
- result = ((double)a) + (double)b;
- PyFPE_END_PROTECT(result)
- return PyFloat_FromDouble(result);
- }
- return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* None */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
- long q = a / b;
- long r = a - q*b;
- q -= ((r != 0) & ((r ^ b) < 0));
- return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
- PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
- if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
- PyErr_Format(PyExc_ImportError,
- #if PY_MAJOR_VERSION < 3
- "cannot import name %.230s", PyString_AS_STRING(name));
- #else
- "cannot import name %S", name);
- #endif
- }
- return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
- PyObject *r;
- if (unlikely(!__Pyx_PyBaseString_Check(n))) {
- PyErr_SetString(PyExc_TypeError,
- "hasattr(): attribute name must be string");
- return -1;
- }
- r = __Pyx_GetAttr(o, n);
- if (unlikely(!r)) {
- PyErr_Clear();
- return 0;
- } else {
- Py_DECREF(r);
- return 1;
- }
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
- PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
- "'%.50s' object has no attribute '%U'",
- tp->tp_name, attr_name);
-#else
- "'%.50s' object has no attribute '%.400s'",
- tp->tp_name, PyString_AS_STRING(attr_name));
-#endif
- return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
- PyObject *descr;
- PyTypeObject *tp = Py_TYPE(obj);
- if (unlikely(!PyString_Check(attr_name))) {
- return PyObject_GenericGetAttr(obj, attr_name);
- }
- assert(!tp->tp_dictoffset);
- descr = _PyType_Lookup(tp, attr_name);
- if (unlikely(!descr)) {
- return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
- }
- Py_INCREF(descr);
- #if PY_MAJOR_VERSION < 3
- if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
- #endif
- {
- descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
- if (unlikely(f)) {
- PyObject *res = f(descr, obj, (PyObject *)tp);
- Py_DECREF(descr);
- return res;
- }
- }
- return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
- if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
- return PyObject_GenericGetAttr(obj, attr_name);
- }
- return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
-#if PY_VERSION_HEX >= 0x02070000
- PyObject *ob = PyCapsule_New(vtable, 0, 0);
-#else
- PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
-#endif
- if (!ob)
- goto bad;
- if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
- goto bad;
- Py_DECREF(ob);
- return 0;
-bad:
- Py_XDECREF(ob);
- return -1;
-}
-
-/* PyObjectGetAttrStrNoError */
-static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
- __Pyx_PyThreadState_declare
- __Pyx_PyThreadState_assign
- if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
- __Pyx_PyErr_Clear();
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
- PyObject *result;
-#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
- PyTypeObject* tp = Py_TYPE(obj);
- if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
- return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
- }
-#endif
- result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
- if (unlikely(!result)) {
- __Pyx_PyObject_GetAttrStr_ClearAttributeError();
- }
- return result;
-}
-
-/* SetupReduce */
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
- int ret;
- PyObject *name_attr;
- name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
- if (likely(name_attr)) {
- ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
- } else {
- ret = -1;
- }
- if (unlikely(ret < 0)) {
- PyErr_Clear();
- ret = 0;
- }
- Py_XDECREF(name_attr);
- return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
- int ret = 0;
- PyObject *object_reduce = NULL;
- PyObject *object_reduce_ex = NULL;
- PyObject *reduce = NULL;
- PyObject *reduce_ex = NULL;
- PyObject *reduce_cython = NULL;
- PyObject *setstate = NULL;
- PyObject *setstate_cython = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
- if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#else
- if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#endif
-#if CYTHON_USE_PYTYPE_LOOKUP
- object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
- object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
- reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
- if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
- object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
- object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
- reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
- if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
- reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
- if (likely(reduce_cython)) {
- ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
- ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
- } else if (reduce == object_reduce || PyErr_Occurred()) {
- goto __PYX_BAD;
- }
- setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
- if (!setstate) PyErr_Clear();
- if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
- setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
- if (likely(setstate_cython)) {
- ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
- ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
- } else if (!setstate || PyErr_Occurred()) {
- goto __PYX_BAD;
- }
- }
- PyType_Modified((PyTypeObject*)type_obj);
- }
- }
- goto __PYX_GOOD;
-__PYX_BAD:
- if (!PyErr_Occurred())
- PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
- ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
- Py_XDECREF(object_reduce);
- Py_XDECREF(object_reduce_ex);
-#endif
- Py_XDECREF(reduce);
- Py_XDECREF(reduce_ex);
- Py_XDECREF(reduce_cython);
- Py_XDECREF(setstate);
- Py_XDECREF(setstate_cython);
- return ret;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
- PyObject *use_cline;
- PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
- PyObject **cython_runtime_dict;
-#endif
- if (unlikely(!__pyx_cython_runtime)) {
- return c_line;
- }
- __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
- cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
- if (likely(cython_runtime_dict)) {
- __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
- use_cline, *cython_runtime_dict,
- __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
- } else
-#endif
- {
- PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
- if (use_cline_obj) {
- use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
- Py_DECREF(use_cline_obj);
- } else {
- PyErr_Clear();
- use_cline = NULL;
- }
- }
- if (!use_cline) {
- c_line = 0;
- PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
- }
- else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
- c_line = 0;
- }
- __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
- return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
- int start = 0, mid = 0, end = count - 1;
- if (end >= 0 && code_line > entries[end].code_line) {
- return count;
- }
- while (start < end) {
- mid = start + (end - start) / 2;
- if (code_line < entries[mid].code_line) {
- end = mid;
- } else if (code_line > entries[mid].code_line) {
- start = mid + 1;
- } else {
- return mid;
- }
- }
- if (code_line <= entries[mid].code_line) {
- return mid;
- } else {
- return mid + 1;
- }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
- PyCodeObject* code_object;
- int pos;
- if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
- return NULL;
- }
- pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
- if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
- return NULL;
- }
- code_object = __pyx_code_cache.entries[pos].code_object;
- Py_INCREF(code_object);
- return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
- int pos, i;
- __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
- if (unlikely(!code_line)) {
- return;
- }
- if (unlikely(!entries)) {
- entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
- if (likely(entries)) {
- __pyx_code_cache.entries = entries;
- __pyx_code_cache.max_count = 64;
- __pyx_code_cache.count = 1;
- entries[0].code_line = code_line;
- entries[0].code_object = code_object;
- Py_INCREF(code_object);
- }
- return;
- }
- pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
- if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
- PyCodeObject* tmp = entries[pos].code_object;
- entries[pos].code_object = code_object;
- Py_DECREF(tmp);
- return;
- }
- if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
- int new_max = __pyx_code_cache.max_count + 64;
- entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
- __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
- if (unlikely(!entries)) {
- return;
- }
- __pyx_code_cache.entries = entries;
- __pyx_code_cache.max_count = new_max;
- }
- for (i=__pyx_code_cache.count; i>pos; i--) {
- entries[i] = entries[i-1];
- }
- entries[pos].code_line = code_line;
- entries[pos].code_object = code_object;
- __pyx_code_cache.count++;
- Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
- const char *funcname, int c_line,
- int py_line, const char *filename) {
- PyCodeObject *py_code = 0;
- PyObject *py_srcfile = 0;
- PyObject *py_funcname = 0;
- #if PY_MAJOR_VERSION < 3
- py_srcfile = PyString_FromString(filename);
- #else
- py_srcfile = PyUnicode_FromString(filename);
- #endif
- if (!py_srcfile) goto bad;
- if (c_line) {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #else
- py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #endif
- }
- else {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromString(funcname);
- #else
- py_funcname = PyUnicode_FromString(funcname);
- #endif
- }
- if (!py_funcname) goto bad;
- py_code = __Pyx_PyCode_New(
- 0,
- 0,
- 0,
- 0,
- 0,
- __pyx_empty_bytes, /*PyObject *code,*/
- __pyx_empty_tuple, /*PyObject *consts,*/
- __pyx_empty_tuple, /*PyObject *names,*/
- __pyx_empty_tuple, /*PyObject *varnames,*/
- __pyx_empty_tuple, /*PyObject *freevars,*/
- __pyx_empty_tuple, /*PyObject *cellvars,*/
- py_srcfile, /*PyObject *filename,*/
- py_funcname, /*PyObject *name,*/
- py_line,
- __pyx_empty_bytes /*PyObject *lnotab*/
- );
- Py_DECREF(py_srcfile);
- Py_DECREF(py_funcname);
- return py_code;
-bad:
- Py_XDECREF(py_srcfile);
- Py_XDECREF(py_funcname);
- return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
- int py_line, const char *filename) {
- PyCodeObject *py_code = 0;
- PyFrameObject *py_frame = 0;
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- if (c_line) {
- c_line = __Pyx_CLineForTraceback(tstate, c_line);
- }
- py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
- if (!py_code) {
- py_code = __Pyx_CreateCodeObjectForTraceback(
- funcname, c_line, py_line, filename);
- if (!py_code) goto bad;
- __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
- }
- py_frame = PyFrame_New(
- tstate, /*PyThreadState *tstate,*/
- py_code, /*PyCodeObject *code,*/
- __pyx_d, /*PyObject *globals,*/
- 0 /*PyObject *locals*/
- );
- if (!py_frame) goto bad;
- __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
- PyTraceBack_Here(py_frame);
-bad:
- Py_XDECREF(py_code);
- Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
- if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
- if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
- if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
- PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
- return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
- PyObject *obj = view->obj;
- if (!obj) return;
- if (PyObject_CheckBuffer(obj)) {
- PyBuffer_Release(view);
- return;
- }
- if ((0)) {}
- view->obj = NULL;
- Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
- int i, index, step, start;
- Py_ssize_t itemsize = mvs.memview->view.itemsize;
- if (order == 'F') {
- step = 1;
- start = 0;
- } else {
- step = -1;
- start = ndim - 1;
- }
- for (i = 0; i < ndim; i++) {
- index = start + step * i;
- if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
- return 0;
- itemsize *= mvs.shape[index];
- }
- return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
- void **out_start, void **out_end,
- int ndim, size_t itemsize)
-{
- char *start, *end;
- int i;
- start = end = slice->data;
- for (i = 0; i < ndim; i++) {
- Py_ssize_t stride = slice->strides[i];
- Py_ssize_t extent = slice->shape[i];
- if (extent == 0) {
- *out_start = *out_end = start;
- return;
- } else {
- if (stride > 0)
- end += stride * (extent - 1);
- else
- start += stride * (extent - 1);
- }
- }
- *out_start = start;
- *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
- __Pyx_memviewslice *slice2,
- int ndim, size_t itemsize)
-{
- void *start1, *end1, *start2, *end2;
- __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
- __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
- return (start1 < end2) && (start2 < end1);
-}
-
-/* Capsule */
-static CYTHON_INLINE PyObject *
-__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
-{
- PyObject *cobj;
-#if PY_VERSION_HEX >= 0x02070000
- cobj = PyCapsule_New(p, sig, NULL);
-#else
- cobj = PyCObject_FromVoidPtr(p, NULL);
-#endif
- return cobj;
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
- union {
- uint32_t u32;
- uint8_t u8[4];
- } S;
- S.u32 = 0x01020304;
- return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
- __Pyx_BufFmt_StackElem* stack,
- __Pyx_TypeInfo* type) {
- stack[0].field = &ctx->root;
- stack[0].parent_offset = 0;
- ctx->root.type = type;
- ctx->root.name = "buffer dtype";
- ctx->root.offset = 0;
- ctx->head = stack;
- ctx->head->field = &ctx->root;
- ctx->fmt_offset = 0;
- ctx->head->parent_offset = 0;
- ctx->new_packmode = '@';
- ctx->enc_packmode = '@';
- ctx->new_count = 1;
- ctx->enc_count = 0;
- ctx->enc_type = 0;
- ctx->is_complex = 0;
- ctx->is_valid_array = 0;
- ctx->struct_alignment = 0;
- while (type->typegroup == 'S') {
- ++ctx->head;
- ctx->head->field = type->fields;
- ctx->head->parent_offset = 0;
- type = type->fields->type;
- }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
- int count;
- const char* t = *ts;
- if (*t < '0' || *t > '9') {
- return -1;
- } else {
- count = *t++ - '0';
- while (*t >= '0' && *t <= '9') {
- count *= 10;
- count += *t++ - '0';
- }
- }
- *ts = t;
- return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
- int number = __Pyx_BufFmt_ParseNumber(ts);
- if (number == -1)
- PyErr_Format(PyExc_ValueError,\
- "Does not understand character buffer dtype format string ('%c')", **ts);
- return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
- PyErr_Format(PyExc_ValueError,
- "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
- switch (ch) {
- case '?': return "'bool'";
- case 'c': return "'char'";
- case 'b': return "'signed char'";
- case 'B': return "'unsigned char'";
- case 'h': return "'short'";
- case 'H': return "'unsigned short'";
- case 'i': return "'int'";
- case 'I': return "'unsigned int'";
- case 'l': return "'long'";
- case 'L': return "'unsigned long'";
- case 'q': return "'long long'";
- case 'Q': return "'unsigned long long'";
- case 'f': return (is_complex ? "'complex float'" : "'float'");
- case 'd': return (is_complex ? "'complex double'" : "'double'");
- case 'g': return (is_complex ? "'complex long double'" : "'long double'");
- case 'T': return "a struct";
- case 'O': return "Python object";
- case 'P': return "a pointer";
- case 's': case 'p': return "a string";
- case 0: return "end";
- default: return "unparseable format string";
- }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return 2;
- case 'i': case 'I': case 'l': case 'L': return 4;
- case 'q': case 'Q': return 8;
- case 'f': return (is_complex ? 8 : 4);
- case 'd': return (is_complex ? 16 : 8);
- case 'g': {
- PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
- return 0;
- }
- case 'O': case 'P': return sizeof(void*);
- default:
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return sizeof(short);
- case 'i': case 'I': return sizeof(int);
- case 'l': case 'L': return sizeof(long);
- #ifdef HAVE_LONG_LONG
- case 'q': case 'Q': return sizeof(PY_LONG_LONG);
- #endif
- case 'f': return sizeof(float) * (is_complex ? 2 : 1);
- case 'd': return sizeof(double) * (is_complex ? 2 : 1);
- case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
- case 'O': case 'P': return sizeof(void*);
- default: {
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
- }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
- case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
- case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
- case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
- case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
- case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
- case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
- case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
- default:
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
-}
-/* These are for computing the padding at the end of the struct to align
- on the first member of the struct. This will probably the same as above,
- but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
- switch (ch) {
- case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
- case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
- case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
- case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
- case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
- case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
- case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
- case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
- case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
- default:
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
- switch (ch) {
- case 'c':
- return 'H';
- case 'b': case 'h': case 'i':
- case 'l': case 'q': case 's': case 'p':
- return 'I';
- case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
- return 'U';
- case 'f': case 'd': case 'g':
- return (is_complex ? 'C' : 'R');
- case 'O':
- return 'O';
- case 'P':
- return 'P';
- default: {
- __Pyx_BufFmt_RaiseUnexpectedChar(ch);
- return 0;
- }
- }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
- if (ctx->head == NULL || ctx->head->field == &ctx->root) {
- const char* expected;
- const char* quote;
- if (ctx->head == NULL) {
- expected = "end";
- quote = "";
- } else {
- expected = ctx->head->field->type->name;
- quote = "'";
- }
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch, expected %s%s%s but got %s",
- quote, expected, quote,
- __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
- } else {
- __Pyx_StructField* field = ctx->head->field;
- __Pyx_StructField* parent = (ctx->head - 1)->field;
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
- field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
- parent->type->name, field->name);
- }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
- char group;
- size_t size, offset, arraysize = 1;
- if (ctx->enc_type == 0) return 0;
- if (ctx->head->field->type->arraysize[0]) {
- int i, ndim = 0;
- if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
- ctx->is_valid_array = ctx->head->field->type->ndim == 1;
- ndim = 1;
- if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
- PyErr_Format(PyExc_ValueError,
- "Expected a dimension of size %zu, got %zu",
- ctx->head->field->type->arraysize[0], ctx->enc_count);
- return -1;
- }
- }
- if (!ctx->is_valid_array) {
- PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
- ctx->head->field->type->ndim, ndim);
- return -1;
- }
- for (i = 0; i < ctx->head->field->type->ndim; i++) {
- arraysize *= ctx->head->field->type->arraysize[i];
- }
- ctx->is_valid_array = 0;
- ctx->enc_count = 1;
- }
- group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
- do {
- __Pyx_StructField* field = ctx->head->field;
- __Pyx_TypeInfo* type = field->type;
- if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
- size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
- } else {
- size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
- }
- if (ctx->enc_packmode == '@') {
- size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
- size_t align_mod_offset;
- if (align_at == 0) return -1;
- align_mod_offset = ctx->fmt_offset % align_at;
- if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
- if (ctx->struct_alignment == 0)
- ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
- ctx->is_complex);
- }
- if (type->size != size || type->typegroup != group) {
- if (type->typegroup == 'C' && type->fields != NULL) {
- size_t parent_offset = ctx->head->parent_offset + field->offset;
- ++ctx->head;
- ctx->head->field = type->fields;
- ctx->head->parent_offset = parent_offset;
- continue;
- }
- if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
- } else {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return -1;
- }
- }
- offset = ctx->head->parent_offset + field->offset;
- if (ctx->fmt_offset != offset) {
- PyErr_Format(PyExc_ValueError,
- "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
- (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
- return -1;
- }
- ctx->fmt_offset += size;
- if (arraysize)
- ctx->fmt_offset += (arraysize - 1) * size;
- --ctx->enc_count;
- while (1) {
- if (field == &ctx->root) {
- ctx->head = NULL;
- if (ctx->enc_count != 0) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return -1;
- }
- break;
- }
- ctx->head->field = ++field;
- if (field->type == NULL) {
- --ctx->head;
- field = ctx->head->field;
- continue;
- } else if (field->type->typegroup == 'S') {
- size_t parent_offset = ctx->head->parent_offset + field->offset;
- if (field->type->fields->type == NULL) continue;
- field = field->type->fields;
- ++ctx->head;
- ctx->head->field = field;
- ctx->head->parent_offset = parent_offset;
- break;
- } else {
- break;
- }
- }
- } while (ctx->enc_count);
- ctx->enc_type = 0;
- ctx->is_complex = 0;
- return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
- const char *ts = *tsp;
- int i = 0, number, ndim;
- ++ts;
- if (ctx->new_count != 1) {
- PyErr_SetString(PyExc_ValueError,
- "Cannot handle repeated arrays in format string");
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ndim = ctx->head->field->type->ndim;
- while (*ts && *ts != ')') {
- switch (*ts) {
- case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue;
- default: break;
- }
- number = __Pyx_BufFmt_ExpectNumber(&ts);
- if (number == -1) return NULL;
- if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
- return PyErr_Format(PyExc_ValueError,
- "Expected a dimension of size %zu, got %d",
- ctx->head->field->type->arraysize[i], number);
- if (*ts != ',' && *ts != ')')
- return PyErr_Format(PyExc_ValueError,
- "Expected a comma in format string, got '%c'", *ts);
- if (*ts == ',') ts++;
- i++;
- }
- if (i != ndim)
- return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
- ctx->head->field->type->ndim, i);
- if (!*ts) {
- PyErr_SetString(PyExc_ValueError,
- "Unexpected end of format string, expected ')'");
- return NULL;
- }
- ctx->is_valid_array = 1;
- ctx->new_count = 1;
- *tsp = ++ts;
- return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
- int got_Z = 0;
- while (1) {
- switch(*ts) {
- case 0:
- if (ctx->enc_type != 0 && ctx->head == NULL) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- if (ctx->head != NULL) {
- __Pyx_BufFmt_RaiseExpected(ctx);
- return NULL;
- }
- return ts;
- case ' ':
- case '\r':
- case '\n':
- ++ts;
- break;
- case '<':
- if (!__Pyx_Is_Little_Endian()) {
- PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
- return NULL;
- }
- ctx->new_packmode = '=';
- ++ts;
- break;
- case '>':
- case '!':
- if (__Pyx_Is_Little_Endian()) {
- PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
- return NULL;
- }
- ctx->new_packmode = '=';
- ++ts;
- break;
- case '=':
- case '@':
- case '^':
- ctx->new_packmode = *ts++;
- break;
- case 'T':
- {
- const char* ts_after_sub;
- size_t i, struct_count = ctx->new_count;
- size_t struct_alignment = ctx->struct_alignment;
- ctx->new_count = 1;
- ++ts;
- if (*ts != '{') {
- PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
- return NULL;
- }
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_type = 0;
- ctx->enc_count = 0;
- ctx->struct_alignment = 0;
- ++ts;
- ts_after_sub = ts;
- for (i = 0; i != struct_count; ++i) {
- ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
- if (!ts_after_sub) return NULL;
- }
- ts = ts_after_sub;
- if (struct_alignment) ctx->struct_alignment = struct_alignment;
- }
- break;
- case '}':
- {
- size_t alignment = ctx->struct_alignment;
- ++ts;
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_type = 0;
- if (alignment && ctx->fmt_offset % alignment) {
- ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
- }
- }
- return ts;
- case 'x':
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->fmt_offset += ctx->new_count;
- ctx->new_count = 1;
- ctx->enc_count = 0;
- ctx->enc_type = 0;
- ctx->enc_packmode = ctx->new_packmode;
- ++ts;
- break;
- case 'Z':
- got_Z = 1;
- ++ts;
- if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
- __Pyx_BufFmt_RaiseUnexpectedChar('Z');
- return NULL;
- }
- CYTHON_FALLTHROUGH;
- case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
- case 'l': case 'L': case 'q': case 'Q':
- case 'f': case 'd': case 'g':
- case 'O': case 'p':
- if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
- (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
- ctx->enc_count += ctx->new_count;
- ctx->new_count = 1;
- got_Z = 0;
- ++ts;
- break;
- }
- CYTHON_FALLTHROUGH;
- case 's':
- if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
- ctx->enc_count = ctx->new_count;
- ctx->enc_packmode = ctx->new_packmode;
- ctx->enc_type = *ts;
- ctx->is_complex = got_Z;
- ++ts;
- ctx->new_count = 1;
- got_Z = 0;
- break;
- case ':':
- ++ts;
- while(*ts != ':') ++ts;
- ++ts;
- break;
- case '(':
- if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
- break;
- default:
- {
- int number = __Pyx_BufFmt_ExpectNumber(&ts);
- if (number == -1) return NULL;
- ctx->new_count = (size_t)number;
- }
- }
- }
-}
-
-/* TypeInfoCompare */
- static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
- int i;
- if (!a || !b)
- return 0;
- if (a == b)
- return 1;
- if (a->size != b->size || a->typegroup != b->typegroup ||
- a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
- if (a->typegroup == 'H' || b->typegroup == 'H') {
- return a->size == b->size;
- } else {
- return 0;
- }
- }
- if (a->ndim) {
- for (i = 0; i < a->ndim; i++)
- if (a->arraysize[i] != b->arraysize[i])
- return 0;
- }
- if (a->typegroup == 'S') {
- if (a->flags != b->flags)
- return 0;
- if (a->fields || b->fields) {
- if (!(a->fields && b->fields))
- return 0;
- for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
- __Pyx_StructField *field_a = a->fields + i;
- __Pyx_StructField *field_b = b->fields + i;
- if (field_a->offset != field_b->offset ||
- !__pyx_typeinfo_cmp(field_a->type, field_b->type))
- return 0;
- }
- return !a->fields[i].type && !b->fields[i].type;
- }
- }
- return 1;
-}
-
-/* MemviewSliceValidateAndInit */
- static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
- if (buf->shape[dim] <= 1)
- return 1;
- if (buf->strides) {
- if (spec & __Pyx_MEMVIEW_CONTIG) {
- if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
- if (unlikely(buf->strides[dim] != sizeof(void *))) {
- PyErr_Format(PyExc_ValueError,
- "Buffer is not indirectly contiguous "
- "in dimension %d.", dim);
- goto fail;
- }
- } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
- PyErr_SetString(PyExc_ValueError,
- "Buffer and memoryview are not contiguous "
- "in the same dimension.");
- goto fail;
- }
- }
- if (spec & __Pyx_MEMVIEW_FOLLOW) {
- Py_ssize_t stride = buf->strides[dim];
- if (stride < 0)
- stride = -stride;
- if (unlikely(stride < buf->itemsize)) {
- PyErr_SetString(PyExc_ValueError,
- "Buffer and memoryview are not contiguous "
- "in the same dimension.");
- goto fail;
- }
- }
- } else {
- if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
- PyErr_Format(PyExc_ValueError,
- "C-contiguous buffer is not contiguous in "
- "dimension %d", dim);
- goto fail;
- } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
- PyErr_Format(PyExc_ValueError,
- "C-contiguous buffer is not indirect in "
- "dimension %d", dim);
- goto fail;
- } else if (unlikely(buf->suboffsets)) {
- PyErr_SetString(PyExc_ValueError,
- "Buffer exposes suboffsets but no strides");
- goto fail;
- }
- }
- return 1;
-fail:
- return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
-{
- if (spec & __Pyx_MEMVIEW_DIRECT) {
- if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
- PyErr_Format(PyExc_ValueError,
- "Buffer not compatible with direct access "
- "in dimension %d.", dim);
- goto fail;
- }
- }
- if (spec & __Pyx_MEMVIEW_PTR) {
- if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
- PyErr_Format(PyExc_ValueError,
- "Buffer is not indirectly accessible "
- "in dimension %d.", dim);
- goto fail;
- }
- }
- return 1;
-fail:
- return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
- int i;
- if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
- Py_ssize_t stride = 1;
- for (i = 0; i < ndim; i++) {
- if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) {
- PyErr_SetString(PyExc_ValueError,
- "Buffer not fortran contiguous.");
- goto fail;
- }
- stride = stride * buf->shape[i];
- }
- } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
- Py_ssize_t stride = 1;
- for (i = ndim - 1; i >- 1; i--) {
- if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) {
- PyErr_SetString(PyExc_ValueError,
- "Buffer not C contiguous.");
- goto fail;
- }
- stride = stride * buf->shape[i];
- }
- }
- return 1;
-fail:
- return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
- int *axes_specs,
- int c_or_f_flag,
- int buf_flags,
- int ndim,
- __Pyx_TypeInfo *dtype,
- __Pyx_BufFmt_StackElem stack[],
- __Pyx_memviewslice *memviewslice,
- PyObject *original_obj)
-{
- struct __pyx_memoryview_obj *memview, *new_memview;
- __Pyx_RefNannyDeclarations
- Py_buffer *buf;
- int i, spec = 0, retval = -1;
- __Pyx_BufFmt_Context ctx;
- int from_memoryview = __pyx_memoryview_check(original_obj);
- __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
- if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
- original_obj)->typeinfo)) {
- memview = (struct __pyx_memoryview_obj *) original_obj;
- new_memview = NULL;
- } else {
- memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
- original_obj, buf_flags, 0, dtype);
- new_memview = memview;
- if (unlikely(!memview))
- goto fail;
- }
- buf = &memview->view;
- if (unlikely(buf->ndim != ndim)) {
- PyErr_Format(PyExc_ValueError,
- "Buffer has wrong number of dimensions (expected %d, got %d)",
- ndim, buf->ndim);
- goto fail;
- }
- if (new_memview) {
- __Pyx_BufFmt_Init(&ctx, stack, dtype);
- if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
- }
- if (unlikely((unsigned) buf->itemsize != dtype->size)) {
- PyErr_Format(PyExc_ValueError,
- "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
- "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
- buf->itemsize,
- (buf->itemsize > 1) ? "s" : "",
- dtype->name,
- dtype->size,
- (dtype->size > 1) ? "s" : "");
- goto fail;
- }
- if (buf->len > 0) {
- for (i = 0; i < ndim; i++) {
- spec = axes_specs[i];
- if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
- goto fail;
- if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
- goto fail;
- }
- if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
- goto fail;
- }
- if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
- new_memview != NULL) == -1)) {
- goto fail;
- }
- retval = 0;
- goto no_fail;
-fail:
- Py_XDECREF(new_memview);
- retval = -1;
-no_fail:
- __Pyx_RefNannyFinishContext();
- return retval;
-}
-
-/* ObjectToMemviewSlice */
- static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
- __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
- __Pyx_BufFmt_StackElem stack[1];
- int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
- int retcode;
- if (obj == Py_None) {
- result.memview = (struct __pyx_memoryview_obj *) Py_None;
- return result;
- }
- retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
- (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
- &__Pyx_TypeInfo_int, stack,
- &result, obj);
- if (unlikely(retcode == -1))
- goto __pyx_fail;
- return result;
-__pyx_fail:
- result.memview = NULL;
- result.data = NULL;
- return result;
-}
-
-/* ObjectToMemviewSlice */
- static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
- __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
- __Pyx_BufFmt_StackElem stack[1];
- int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
- int retcode;
- if (obj == Py_None) {
- result.memview = (struct __pyx_memoryview_obj *) Py_None;
- return result;
- }
- retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
- (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
- &__Pyx_TypeInfo_float, stack,
- &result, obj);
- if (unlikely(retcode == -1))
- goto __pyx_fail;
- return result;
-__pyx_fail:
- result.memview = NULL;
- result.data = NULL;
- return result;
-}
-
-/* ObjectToMemviewSlice */
- static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
- __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
- __Pyx_BufFmt_StackElem stack[1];
- int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
- int retcode;
- if (obj == Py_None) {
- result.memview = (struct __pyx_memoryview_obj *) Py_None;
- return result;
- }
- retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
- (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
- &__Pyx_TypeInfo_int, stack,
- &result, obj);
- if (unlikely(retcode == -1))
- goto __pyx_fail;
- return result;
-__pyx_fail:
- result.memview = NULL;
- result.data = NULL;
- return result;
-}
-
-/* CIntToPy */
- static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
- const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
- const int is_unsigned = neg_one > const_zero;
- if (is_unsigned) {
- if (sizeof(int) < sizeof(long)) {
- return PyInt_FromLong((long) value);
- } else if (sizeof(int) <= sizeof(unsigned long)) {
- return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
- return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
- }
- } else {
- if (sizeof(int) <= sizeof(long)) {
- return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
- return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
- }
- }
- {
- int one = 1; int little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&value;
- return _PyLong_FromByteArray(bytes, sizeof(int),
- little, !is_unsigned);
- }
-}
-
-/* CIntFromPyVerify */
- #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
- __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
- __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
- {\
- func_type value = func_value;\
- if (sizeof(target_type) < sizeof(func_type)) {\
- if (unlikely(value != (func_type) (target_type) value)) {\
- func_type zero = 0;\
- if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
- return (target_type) -1;\
- if (is_unsigned && unlikely(value < zero))\
- goto raise_neg_overflow;\
- else\
- goto raise_overflow;\
- }\
- }\
- return (target_type) value;\
- }
-
-/* CIntToPy */
- static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
- const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
- const int is_unsigned = neg_one > const_zero;
- if (is_unsigned) {
- if (sizeof(long) < sizeof(long)) {
- return PyInt_FromLong((long) value);
- } else if (sizeof(long) <= sizeof(unsigned long)) {
- return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
- return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
- }
- } else {
- if (sizeof(long) <= sizeof(long)) {
- return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
- return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
- }
- }
- {
- int one = 1; int little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&value;
- return _PyLong_FromByteArray(bytes, sizeof(long),
- little, !is_unsigned);
- }
-}
-
-/* MemviewSliceCopyTemplate */
- static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
- const char *mode, int ndim,
- size_t sizeof_dtype, int contig_flag,
- int dtype_is_object)
-{
- __Pyx_RefNannyDeclarations
- int i;
- __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
- struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
- Py_buffer *buf = &from_memview->view;
- PyObject *shape_tuple = NULL;
- PyObject *temp_int = NULL;
- struct __pyx_array_obj *array_obj = NULL;
- struct __pyx_memoryview_obj *memview_obj = NULL;
- __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
- for (i = 0; i < ndim; i++) {
- if (unlikely(from_mvs->suboffsets[i] >= 0)) {
- PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
- "indirect dimensions (axis %d)", i);
- goto fail;
- }
- }
- shape_tuple = PyTuple_New(ndim);
- if (unlikely(!shape_tuple)) {
- goto fail;
- }
- __Pyx_GOTREF(shape_tuple);
- for(i = 0; i < ndim; i++) {
- temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
- if(unlikely(!temp_int)) {
- goto fail;
- } else {
- PyTuple_SET_ITEM(shape_tuple, i, temp_int);
- temp_int = NULL;
- }
- }
- array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
- if (unlikely(!array_obj)) {
- goto fail;
- }
- __Pyx_GOTREF(array_obj);
- memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
- (PyObject *) array_obj, contig_flag,
- dtype_is_object,
- from_mvs->memview->typeinfo);
- if (unlikely(!memview_obj))
- goto fail;
- if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
- goto fail;
- if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
- dtype_is_object) < 0))
- goto fail;
- goto no_fail;
-fail:
- __Pyx_XDECREF(new_mvs.memview);
- new_mvs.memview = NULL;
- new_mvs.data = NULL;
-no_fail:
- __Pyx_XDECREF(shape_tuple);
- __Pyx_XDECREF(temp_int);
- __Pyx_XDECREF(array_obj);
- __Pyx_RefNannyFinishContext();
- return new_mvs;
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
- const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(int) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (int) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (int) 0;
- case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
- case 2:
- if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
- return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
- return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
- return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (int) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(int) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (int) 0;
- case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0])
- case -2:
- if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
- return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
- return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(int) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- int val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (int) -1;
- }
- } else {
- int val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (int) -1;
- val = __Pyx_PyInt_As_int(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to int");
- return (int) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to int");
- return (int) -1;
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
- const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(long) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (long) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (long) 0;
- case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
- case 2:
- if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
- return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
- return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
- return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (long) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(long) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (long) 0;
- case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0])
- case -2:
- if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
- return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(long) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- long val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (long) -1;
- }
- } else {
- long val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (long) -1;
- val = __Pyx_PyInt_As_long(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to long");
- return (long) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to long");
- return (long) -1;
-}
-
-/* CIntFromPy */
- static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
- const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0;
- const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x))) {
- if (sizeof(char) < sizeof(long)) {
- __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
- } else {
- long val = PyInt_AS_LONG(x);
- if (is_unsigned && unlikely(val < 0)) {
- goto raise_neg_overflow;
- }
- return (char) val;
- }
- } else
-#endif
- if (likely(PyLong_Check(x))) {
- if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (char) 0;
- case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
- case 2:
- if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
- return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
- }
- }
- break;
- case 3:
- if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
- return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
- }
- }
- break;
- case 4:
- if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
- return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
- }
- }
- break;
- }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
- if (unlikely(Py_SIZE(x) < 0)) {
- goto raise_neg_overflow;
- }
-#else
- {
- int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
- if (unlikely(result < 0))
- return (char) -1;
- if (unlikely(result == 1))
- goto raise_neg_overflow;
- }
-#endif
- if (sizeof(char) <= sizeof(unsigned long)) {
- __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
- }
- } else {
-#if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)x)->ob_digit;
- switch (Py_SIZE(x)) {
- case 0: return (char) 0;
- case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
- case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0])
- case -2:
- if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
- return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- case 2:
- if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
- return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- case -3:
- if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
- return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- case 3:
- if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
- return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- case -4:
- if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
- return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- case 4:
- if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
- __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
- } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
- return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
- }
- }
- break;
- }
-#endif
- if (sizeof(char) <= sizeof(long)) {
- __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
- } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
- __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
- }
- }
- {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
- char val;
- PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
- if (likely(v) && !PyLong_Check(v)) {
- PyObject *tmp = v;
- v = PyNumber_Long(tmp);
- Py_DECREF(tmp);
- }
- #endif
- if (likely(v)) {
- int one = 1; int is_little = (int)*(unsigned char *)&one;
- unsigned char *bytes = (unsigned char *)&val;
- int ret = _PyLong_AsByteArray((PyLongObject *)v,
- bytes, sizeof(val),
- is_little, !is_unsigned);
- Py_DECREF(v);
- if (likely(!ret))
- return val;
- }
-#endif
- return (char) -1;
- }
- } else {
- char val;
- PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
- if (!tmp) return (char) -1;
- val = __Pyx_PyInt_As_char(tmp);
- Py_DECREF(tmp);
- return val;
- }
-raise_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "value too large to convert to char");
- return (char) -1;
-raise_neg_overflow:
- PyErr_SetString(PyExc_OverflowError,
- "can't convert negative value to char");
- return (char) -1;
-}
-
-/* CheckBinaryVersion */
- static int __Pyx_check_binary_version(void) {
- char ctversion[4], rtversion[4];
- PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
- PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
- if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
- char message[200];
- PyOS_snprintf(message, sizeof(message),
- "compiletime version %s of module '%.100s' "
- "does not match runtime version %s",
- ctversion, __Pyx_MODULE_NAME, rtversion);
- return PyErr_WarnEx(NULL, message, 1);
- }
- return 0;
-}
-
-/* InitStrings */
- static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
- while (t->p) {
- #if PY_MAJOR_VERSION < 3
- if (t->is_unicode) {
- *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
- } else if (t->intern) {
- *t->p = PyString_InternFromString(t->s);
- } else {
- *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
- }
- #else
- if (t->is_unicode | t->is_str) {
- if (t->intern) {
- *t->p = PyUnicode_InternFromString(t->s);
- } else if (t->encoding) {
- *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
- } else {
- *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
- }
- } else {
- *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
- }
- #endif
- if (!*t->p)
- return -1;
- if (PyObject_Hash(*t->p) == -1)
- return -1;
- ++t;
- }
- return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
- return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
- Py_ssize_t ignore;
- return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
- char* defenc_c;
- PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
- if (!defenc) return NULL;
- defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- {
- char* end = defenc_c + PyBytes_GET_SIZE(defenc);
- char* c;
- for (c = defenc_c; c < end; c++) {
- if ((unsigned char) (*c) >= 128) {
- PyUnicode_AsASCIIString(o);
- return NULL;
- }
- }
- }
-#endif
- *length = PyBytes_GET_SIZE(defenc);
- return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
- if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- if (likely(PyUnicode_IS_ASCII(o))) {
- *length = PyUnicode_GET_LENGTH(o);
- return PyUnicode_AsUTF8(o);
- } else {
- PyUnicode_AsASCIIString(o);
- return NULL;
- }
-#else
- return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
- if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
- __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
- PyUnicode_Check(o)) {
- return __Pyx_PyUnicode_AsStringAndSize(o, length);
- } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
- if (PyByteArray_Check(o)) {
- *length = PyByteArray_GET_SIZE(o);
- return PyByteArray_AS_STRING(o);
- } else
-#endif
- {
- char* result;
- int r = PyBytes_AsStringAndSize(o, &result, length);
- if (unlikely(r < 0)) {
- return NULL;
- } else {
- return result;
- }
- }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
- int is_true = x == Py_True;
- if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
- else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
- int retval;
- if (unlikely(!x)) return -1;
- retval = __Pyx_PyObject_IsTrue(x);
- Py_DECREF(x);
- return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
- if (PyLong_Check(result)) {
- if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
- "__int__ returned non-int (type %.200s). "
- "The ability to return an instance of a strict subclass of int "
- "is deprecated, and may be removed in a future version of Python.",
- Py_TYPE(result)->tp_name)) {
- Py_DECREF(result);
- return NULL;
- }
- return result;
- }
-#endif
- PyErr_Format(PyExc_TypeError,
- "__%.4s__ returned non-%.4s (type %.200s)",
- type_name, type_name, Py_TYPE(result)->tp_name);
- Py_DECREF(result);
- return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
- PyNumberMethods *m;
-#endif
- const char *name = NULL;
- PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
- if (likely(PyLong_Check(x)))
-#endif
- return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
- m = Py_TYPE(x)->tp_as_number;
- #if PY_MAJOR_VERSION < 3
- if (m && m->nb_int) {
- name = "int";
- res = m->nb_int(x);
- }
- else if (m && m->nb_long) {
- name = "long";
- res = m->nb_long(x);
- }
- #else
- if (likely(m && m->nb_int)) {
- name = "int";
- res = m->nb_int(x);
- }
- #endif
-#else
- if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
- res = PyNumber_Int(x);
- }
-#endif
- if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
- if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
- if (unlikely(!PyLong_CheckExact(res))) {
-#endif
- return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
- }
- }
- else if (!PyErr_Occurred()) {
- PyErr_SetString(PyExc_TypeError,
- "an integer is required");
- }
- return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
- Py_ssize_t ival;
- PyObject *x;
-#if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(b))) {
- if (sizeof(Py_ssize_t) >= sizeof(long))
- return PyInt_AS_LONG(b);
- else
- return PyInt_AsSsize_t(b);
- }
-#endif
- if (likely(PyLong_CheckExact(b))) {
- #if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)b)->ob_digit;
- const Py_ssize_t size = Py_SIZE(b);
- if (likely(__Pyx_sst_abs(size) <= 1)) {
- ival = likely(size) ? digits[0] : 0;
- if (size == -1) ival = -ival;
- return ival;
- } else {
- switch (size) {
- case 2:
- if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -2:
- if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case 3:
- if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -3:
- if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case 4:
- if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
- return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- case -4:
- if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
- return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
- }
- break;
- }
- }
- #endif
- return PyLong_AsSsize_t(b);
- }
- x = PyNumber_Index(b);
- if (!x) return -1;
- ival = PyInt_AsSsize_t(x);
- Py_DECREF(x);
- return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
- return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
- return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/custom_pipeline_overview.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/custom_pipeline_overview.md
deleted file mode 100644
index 0361e7b9edd5ad6ea1a071d9b32d9a032450cae3..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/custom_pipeline_overview.md
+++ /dev/null
@@ -1,56 +0,0 @@
-
-
-# 커스텀 파이프라인 불러오기
-
-[[open-in-colab]]
-
-커뮤니티 파이프라인은 논문에 명시된 원래의 구현체와 다른 형태로 구현된 모든 [`DiffusionPipeline`] 클래스를 의미합니다. (예를 들어, [`StableDiffusionControlNetPipeline`]는 ["Text-to-Image Generation with ControlNet Conditioning"](https://arxiv.org/abs/2302.05543) 해당) 이들은 추가 기능을 제공하거나 파이프라인의 원래 구현을 확장합니다.
-
-[Speech to Image](https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image) 또는 [Composable Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#composable-stable-diffusion) 과 같은 멋진 커뮤니티 파이프라인이 많이 있으며 [여기에서](https://github.com/huggingface/diffusers/tree/main/examples/community) 모든 공식 커뮤니티 파이프라인을 찾을 수 있습니다.
-
-허브에서 커뮤니티 파이프라인을 로드하려면, 커뮤니티 파이프라인의 리포지토리 ID와 (파이프라인 가중치 및 구성 요소를 로드하려는) 모델의 리포지토리 ID를 인자로 전달해야 합니다. 예를 들어, 아래 예시에서는 `hf-internal-testing/diffusers-dummy-pipeline`에서 더미 파이프라인을 불러오고, `google/ddpm-cifar10-32`에서 파이프라인의 가중치와 컴포넌트들을 로드합니다.
-
-
-
-🔒 허깅 페이스 허브에서 커뮤니티 파이프라인을 불러오는 것은 곧 해당 코드가 안전하다고 신뢰하는 것입니다. 코드를 자동으로 불러오고 실행하기 앞서 반드시 온라인으로 해당 코드의 신뢰성을 검사하세요!
-
-
-
-```py
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
-)
-```
-
-공식 커뮤니티 파이프라인을 불러오는 것은 비슷하지만, 공식 리포지토리 ID에서 가중치를 불러오는 것과 더불어 해당 파이프라인 내의 컴포넌트를 직접 지정하는 것 역시 가능합니다. 아래 예제를 보면 커뮤니티 [CLIP Guided Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#clip-guided-stable-diffusion) 파이프라인을 로드할 때, 해당 파이프라인에서 사용할 `clip_model` 컴포넌트와 `feature_extractor` 컴포넌트를 직접 설정하는 것을 확인할 수 있습니다.
-
-```py
-from diffusers import DiffusionPipeline
-from transformers import CLIPImageProcessor, CLIPModel
-
-clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
-
-feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
-clip_model = CLIPModel.from_pretrained(clip_model_id)
-
-pipeline = DiffusionPipeline.from_pretrained(
- "runwayml/stable-diffusion-v1-5",
- custom_pipeline="clip_guided_stable_diffusion",
- clip_model=clip_model,
- feature_extractor=feature_extractor,
-)
-```
-
-커뮤니티 파이프라인에 대한 자세한 내용은 [커뮤니티 파이프라인](https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/custom_pipeline_examples) 가이드를 살펴보세요. 커뮤니티 파이프라인 등록에 관심이 있는 경우 [커뮤니티 파이프라인에 기여하는 방법](https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/contribute_pipeline)에 대한 가이드를 확인하세요 !
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
deleted file mode 100644
index d410f23abbe57475e8b16dacded23b89ec33fb89..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://msra/hrnetv2_w32',
- backbone=dict(
- _delete_=True,
- type='HRNet',
- extra=dict(
- stage1=dict(
- num_modules=1,
- num_branches=1,
- block='BOTTLENECK',
- num_blocks=(4, ),
- num_channels=(64, )),
- stage2=dict(
- num_modules=1,
- num_branches=2,
- block='BASIC',
- num_blocks=(4, 4),
- num_channels=(32, 64)),
- stage3=dict(
- num_modules=4,
- num_branches=3,
- block='BASIC',
- num_blocks=(4, 4, 4),
- num_channels=(32, 64, 128)),
- stage4=dict(
- num_modules=3,
- num_branches=4,
- block='BASIC',
- num_blocks=(4, 4, 4, 4),
- num_channels=(32, 64, 128, 256)))),
- neck=dict(
- _delete_=True,
- type='HRFPN',
- in_channels=[32, 64, 128, 256],
- out_channels=256))
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/bbox_nms.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/bbox_nms.py
deleted file mode 100644
index 966d3a6ac86637a6be90edc3aab9b6863fb87764..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/post_processing/bbox_nms.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import torch
-from mmcv.ops.nms import batched_nms
-
-from mmdet.core.bbox.iou_calculators import bbox_overlaps
-
-
-def multiclass_nms(multi_bboxes,
- multi_scores,
- score_thr,
- nms_cfg,
- max_num=-1,
- score_factors=None,
- return_inds=False):
- """NMS for multi-class bboxes.
-
- Args:
- multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
- multi_scores (Tensor): shape (n, #class), where the last column
- contains scores of the background class, but this will be ignored.
- score_thr (float): bbox threshold, bboxes with scores lower than it
- will not be considered.
- nms_thr (float): NMS IoU threshold
- max_num (int, optional): if there are more than max_num bboxes after
- NMS, only top max_num will be kept. Default to -1.
- score_factors (Tensor, optional): The factors multiplied to scores
- before applying NMS. Default to None.
- return_inds (bool, optional): Whether return the indices of kept
- bboxes. Default to False.
-
- Returns:
- tuple: (bboxes, labels, indices (optional)), tensors of shape (k, 5),
- (k), and (k). Labels are 0-based.
- """
- num_classes = multi_scores.size(1) - 1
- # exclude background category
- if multi_bboxes.shape[1] > 4:
- bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4)
- else:
- bboxes = multi_bboxes[:, None].expand(
- multi_scores.size(0), num_classes, 4)
-
- scores = multi_scores[:, :-1]
-
- labels = torch.arange(num_classes, dtype=torch.long)
- labels = labels.view(1, -1).expand_as(scores)
-
- bboxes = bboxes.reshape(-1, 4)
- scores = scores.reshape(-1)
- labels = labels.reshape(-1)
-
- if not torch.onnx.is_in_onnx_export():
- # NonZero not supported in TensorRT
- # remove low scoring boxes
- valid_mask = scores > score_thr
- # multiply score_factor after threshold to preserve more bboxes, improve
- # mAP by 1% for YOLOv3
- if score_factors is not None:
- # expand the shape to match original shape of score
- score_factors = score_factors.view(-1, 1).expand(
- multi_scores.size(0), num_classes)
- score_factors = score_factors.reshape(-1)
- scores = scores * score_factors
-
- if not torch.onnx.is_in_onnx_export():
- # NonZero not supported in TensorRT
- inds = valid_mask.nonzero(as_tuple=False).squeeze(1)
- bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds]
- else:
- # TensorRT NMS plugin has invalid output filled with -1
- # add dummy data to make detection output correct.
- bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0)
- scores = torch.cat([scores, scores.new_zeros(1)], dim=0)
- labels = torch.cat([labels, labels.new_zeros(1)], dim=0)
-
- if bboxes.numel() == 0:
- if torch.onnx.is_in_onnx_export():
- raise RuntimeError('[ONNX Error] Can not record NMS '
- 'as it has not been executed this time')
- if return_inds:
- return bboxes, labels, inds
- else:
- return bboxes, labels
-
- dets, keep = batched_nms(bboxes, scores, labels, nms_cfg)
-
- if max_num > 0:
- dets = dets[:max_num]
- keep = keep[:max_num]
-
- if return_inds:
- return dets, labels[keep], keep
- else:
- return dets, labels[keep]
-
-
-def fast_nms(multi_bboxes,
- multi_scores,
- multi_coeffs,
- score_thr,
- iou_thr,
- top_k,
- max_num=-1):
- """Fast NMS in `YOLACT `_.
-
- Fast NMS allows already-removed detections to suppress other detections so
- that every instance can be decided to be kept or discarded in parallel,
- which is not possible in traditional NMS. This relaxation allows us to
- implement Fast NMS entirely in standard GPU-accelerated matrix operations.
-
- Args:
- multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
- multi_scores (Tensor): shape (n, #class+1), where the last column
- contains scores of the background class, but this will be ignored.
- multi_coeffs (Tensor): shape (n, #class*coeffs_dim).
- score_thr (float): bbox threshold, bboxes with scores lower than it
- will not be considered.
- iou_thr (float): IoU threshold to be considered as conflicted.
- top_k (int): if there are more than top_k bboxes before NMS,
- only top top_k will be kept.
- max_num (int): if there are more than max_num bboxes after NMS,
- only top max_num will be kept. If -1, keep all the bboxes.
- Default: -1.
-
- Returns:
- tuple: (bboxes, labels, coefficients), tensors of shape (k, 5), (k, 1),
- and (k, coeffs_dim). Labels are 0-based.
- """
-
- scores = multi_scores[:, :-1].t() # [#class, n]
- scores, idx = scores.sort(1, descending=True)
-
- idx = idx[:, :top_k].contiguous()
- scores = scores[:, :top_k] # [#class, topk]
- num_classes, num_dets = idx.size()
- boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4)
- coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1)
-
- iou = bbox_overlaps(boxes, boxes) # [#class, topk, topk]
- iou.triu_(diagonal=1)
- iou_max, _ = iou.max(dim=1)
-
- # Now just filter out the ones higher than the threshold
- keep = iou_max <= iou_thr
-
- # Second thresholding introduces 0.2 mAP gain at negligible time cost
- keep *= scores > score_thr
-
- # Assign each kept detection to its corresponding class
- classes = torch.arange(
- num_classes, device=boxes.device)[:, None].expand_as(keep)
- classes = classes[keep]
-
- boxes = boxes[keep]
- coeffs = coeffs[keep]
- scores = scores[keep]
-
- # Only keep the top max_num highest scores across all classes
- scores, idx = scores.sort(0, descending=True)
- if max_num > 0:
- idx = idx[:max_num]
- scores = scores[:max_num]
-
- classes = classes[idx]
- boxes = boxes[idx]
- coeffs = coeffs[idx]
-
- cls_dets = torch.cat([boxes, scores[:, None]], dim=1)
- return cls_dets, classes, coeffs
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/utils.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/utils.py
deleted file mode 100644
index 4756d7fcefd7cda1294c2662b4ca3e90c0a8e124..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/utils.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import functools
-
-import mmcv
-import torch.nn.functional as F
-
-
-def reduce_loss(loss, reduction):
- """Reduce loss as specified.
-
- Args:
- loss (Tensor): Elementwise loss tensor.
- reduction (str): Options are "none", "mean" and "sum".
-
- Return:
- Tensor: Reduced loss tensor.
- """
- reduction_enum = F._Reduction.get_enum(reduction)
- # none: 0, elementwise_mean:1, sum: 2
- if reduction_enum == 0:
- return loss
- elif reduction_enum == 1:
- return loss.mean()
- elif reduction_enum == 2:
- return loss.sum()
-
-
-@mmcv.jit(derivate=True, coderize=True)
-def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
- """Apply element-wise weight and reduce loss.
-
- Args:
- loss (Tensor): Element-wise loss.
- weight (Tensor): Element-wise weights.
- reduction (str): Same as built-in losses of PyTorch.
- avg_factor (float): Avarage factor when computing the mean of losses.
-
- Returns:
- Tensor: Processed loss values.
- """
- # if weight is specified, apply element-wise weight
- if weight is not None:
- loss = loss * weight
-
- # if avg_factor is not specified, just reduce the loss
- if avg_factor is None:
- loss = reduce_loss(loss, reduction)
- else:
- # if reduction is mean, then average the loss by avg_factor
- if reduction == 'mean':
- loss = loss.sum() / avg_factor
- # if reduction is 'none', then do nothing, otherwise raise an error
- elif reduction != 'none':
- raise ValueError('avg_factor can not be used with reduction="sum"')
- return loss
-
-
-def weighted_loss(loss_func):
- """Create a weighted version of a given loss function.
-
- To use this decorator, the loss function must have the signature like
- `loss_func(pred, target, **kwargs)`. The function only needs to compute
- element-wise loss without any reduction. This decorator will add weight
- and reduction arguments to the function. The decorated function will have
- the signature like `loss_func(pred, target, weight=None, reduction='mean',
- avg_factor=None, **kwargs)`.
-
- :Example:
-
- >>> import torch
- >>> @weighted_loss
- >>> def l1_loss(pred, target):
- >>> return (pred - target).abs()
-
- >>> pred = torch.Tensor([0, 2, 3])
- >>> target = torch.Tensor([1, 1, 1])
- >>> weight = torch.Tensor([1, 0, 1])
-
- >>> l1_loss(pred, target)
- tensor(1.3333)
- >>> l1_loss(pred, target, weight)
- tensor(1.)
- >>> l1_loss(pred, target, reduction='none')
- tensor([1., 1., 2.])
- >>> l1_loss(pred, target, weight, avg_factor=2)
- tensor(1.5000)
- """
-
- @functools.wraps(loss_func)
- def wrapper(pred,
- target,
- weight=None,
- reduction='mean',
- avg_factor=None,
- **kwargs):
- # get element-wise loss
- loss = loss_func(pred, target, **kwargs)
- loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
- return loss
-
- return wrapper
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 1420b97a4bd0dc0f5451623697666012a2de635c..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
- '../_base_/models/deeplabv3plus_r50-d8.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(align_corners=True),
- auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
diff --git a/spaces/Annotation-AI/fast-segment-everything-with-drawing-prompt/app.py b/spaces/Annotation-AI/fast-segment-everything-with-drawing-prompt/app.py
deleted file mode 100644
index 13e18fa4c55e670c3c359f7e784eecfb1a057c51..0000000000000000000000000000000000000000
--- a/spaces/Annotation-AI/fast-segment-everything-with-drawing-prompt/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-
-github_user = os.environ.get("GITHUB_USER")
-github_token = os.environ.get("GITHUB_TOKEN")
-
-repo_name = "annotation-ai/mlwiz-technical-demo"
-
-os.system(f"export GITHUB_USER={github_user}")
-os.system(f"export GITHUB_TOKEN={github_token}")
-os.system(f"git clone https://{github_user}:{github_token}@github.com/{repo_name}")
-
-cwd0 = os.getcwd()
-cwd1 = os.path.join(cwd0, "mlwiz-technical-demo/sam")
-os.chdir(cwd1)
-os.system("pip install -r requirements.txt")
-os.system("python app_everything_brush.py")
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/stare.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/stare.py
deleted file mode 100644
index cbd14e0920e7f6a73baff1432e5a32ccfdb0dfae..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/stare.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class STAREDataset(CustomDataset):
- """STARE dataset.
-
- In segmentation map annotation for STARE, 0 stands for background, which is
- included in 2 categories. ``reduce_zero_label`` is fixed to False. The
- ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
- '.ah.png'.
- """
-
- CLASSES = ('background', 'vessel')
-
- PALETTE = [[120, 120, 120], [6, 230, 230]]
-
- def __init__(self, **kwargs):
- super(STAREDataset, self).__init__(
- img_suffix='.png',
- seg_map_suffix='.ah.png',
- reduce_zero_label=False,
- **kwargs)
- assert osp.exists(self.img_dir)
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/docs/train.md b/spaces/Anonymous-sub/Rerender/ControlNet/docs/train.md
deleted file mode 100644
index fa773925e28c4100df4bf74f0536d432554db806..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/docs/train.md
+++ /dev/null
@@ -1,276 +0,0 @@
-# Train a ControlNet to Control SD
-
-You are here because you want to control SD in your own way, maybe you have an idea for your perfect research project, and you will annotate some data or have already annotated your own dataset automatically or manually. Herein, the control can be anything that can be converted to images, such as edges, keypoints, segments, etc.
-
-Before moving on to your own dataset, we highly recommend to first try the toy dataset, Fill50K, as a sanity check. This will help you get a "feeling" for the training. You will know how long it will take for the model to converge and whether your device will be able to complete the training in an acceptable amount of time. And what it "feels" like when the model converges.
-
-We hope that after you read this page, you will find that training a ControlNet is as easy as (or easier than) training a pix2pix.
-
-## Step 0 - Design your control
-
-Let us take a look at a very simple task to control SD to fill color in circles.
-
-
-
-This is simple: we want to control SD to fill a circle with colors, and the prompt contains some description of our target.
-
-Stable diffusion is trained on billions of images, and it already knows what is "cyan", what is "circle", what is "pink", and what is "background".
-
-But it does not know the meaning of that "Control Image (Source Image)". Our target is to let it know.
-
-## Step 1 - Get a dataset
-
-Just download the Fill50K dataset from [our huggingface page](https://huggingface.co/lllyasviel/ControlNet) (training/fill50k.zip, the file is only 200M!). Make sure that the data is decompressed as
-
- ControlNet/training/fill50k/prompt.json
- ControlNet/training/fill50k/source/X.png
- ControlNet/training/fill50k/target/X.png
-
-In the folder "fill50k/source", you will have 50k images of circle lines.
-
-
-
-In the folder "fill50k/target", you will have 50k images of filled circles.
-
-
-
-In the "fill50k/prompt.json", you will have their filenames and prompts. Each prompt is like "a balabala color circle in some other color background."
-
-
-
-## Step 2 - Load the dataset
-
-Then you need to write a simple script to read this dataset for pytorch. (In fact we have written it for you in "tutorial_dataset.py".)
-
-```python
-import json
-import cv2
-import numpy as np
-
-from torch.utils.data import Dataset
-
-
-class MyDataset(Dataset):
- def __init__(self):
- self.data = []
- with open('./training/fill50k/prompt.json', 'rt') as f:
- for line in f:
- self.data.append(json.loads(line))
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, idx):
- item = self.data[idx]
-
- source_filename = item['source']
- target_filename = item['target']
- prompt = item['prompt']
-
- source = cv2.imread('./training/fill50k/' + source_filename)
- target = cv2.imread('./training/fill50k/' + target_filename)
-
- # Do not forget that OpenCV read images in BGR order.
- source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB)
- target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
-
- # Normalize source images to [0, 1].
- source = source.astype(np.float32) / 255.0
-
- # Normalize target images to [-1, 1].
- target = (target.astype(np.float32) / 127.5) - 1.0
-
- return dict(jpg=target, txt=prompt, hint=source)
-
-```
-
-This will make your dataset into an array-like object in python. You can test this dataset simply by accessing the array, like this
-
-```python
-from tutorial_dataset import MyDataset
-
-dataset = MyDataset()
-print(len(dataset))
-
-item = dataset[1234]
-jpg = item['jpg']
-txt = item['txt']
-hint = item['hint']
-print(txt)
-print(jpg.shape)
-print(hint.shape)
-
-```
-
-The outputs of this simple test on my machine are
-
- 50000
- burly wood circle with orange background
- (512, 512, 3)
- (512, 512, 3)
-
-And this code is in "tutorial_dataset_test.py".
-
-In this way, the dataset is an array-like object with 50000 items. Each item is a dict with three entry "jpg", "txt", and "hint". The "jpg" is the target image, the "hint" is the control image, and the "txt" is the prompt.
-
-Do not ask us why we use these three names - this is related to the dark history of a library called LDM.
-
-## Step 3 - What SD model do you want to control?
-
-Then you need to decide which Stable Diffusion Model you want to control. In this example, we will just use standard SD1.5. You can download it from the [official page of Stability](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main). You want the file ["v1-5-pruned.ckpt"](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main).
-
-(Or ["v2-1_512-ema-pruned.ckpt"](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/tree/main) if you are using SD2.)
-
-Then you need to attach a control net to the SD model. The architecture is
-
-
-
-Note that all weights inside the ControlNet are also copied from SD so that no layer is trained from scratch, and you are still finetuning the entire model.
-
-We provide a simple script for you to achieve this easily. If your SD filename is "./models/v1-5-pruned.ckpt" and you want the script to save the processed model (SD+ControlNet) at location "./models/control_sd15_ini.ckpt", you can just run:
-
- python tool_add_control.py ./models/v1-5-pruned.ckpt ./models/control_sd15_ini.ckpt
-
-Or if you are using SD2:
-
- python tool_add_control_sd21.py ./models/v2-1_512-ema-pruned.ckpt ./models/control_sd21_ini.ckpt
-
-You may also use other filenames as long as the command is "python tool_add_control.py input_path output_path".
-
-This is the correct output from my machine:
-
-
-
-## Step 4 - Train!
-
-Happy! We finally come to the most exciting part: training!
-
-The training code in "tutorial_train.py" is actually surprisingly simple:
-
-```python
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-from tutorial_dataset import MyDataset
-from cldm.logger import ImageLogger
-from cldm.model import create_model, load_state_dict
-
-
-# Configs
-resume_path = './models/control_sd15_ini.ckpt'
-batch_size = 4
-logger_freq = 300
-learning_rate = 1e-5
-sd_locked = True
-only_mid_control = False
-
-
-# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
-model = create_model('./models/cldm_v15.yaml').cpu()
-model.load_state_dict(load_state_dict(resume_path, location='cpu'))
-model.learning_rate = learning_rate
-model.sd_locked = sd_locked
-model.only_mid_control = only_mid_control
-
-
-# Misc
-dataset = MyDataset()
-dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True)
-logger = ImageLogger(batch_frequency=logger_freq)
-trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger])
-
-
-# Train!
-trainer.fit(model, dataloader)
-
-```
-(or "tutorial_train_sd21.py" if you are using SD2)
-
-Thanks to our organized dataset pytorch object and the power of pytorch_lightning, the entire code is just super short.
-
-Now, you may take a look at [Pytorch Lightning Official DOC](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.trainer.trainer.Trainer.html#trainer) to find out how to enable many useful features like gradient accumulation, multiple GPU training, accelerated dataset loading, flexible checkpoint saving, etc. All these only need about one line of code. Great!
-
-Note that if you find OOM, perhaps you need to enable [Low VRAM mode](low_vram.md), and perhaps you also need to use smaller batch size and gradient accumulation. Or you may also want to use some “advanced” tricks like sliced attention or xformers. For example:
-
-```python
-# Configs
-batch_size = 1
-
-# Misc
-trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger], accumulate_grad_batches=4) # But this will be 4x slower
-```
-
-Note that training with 8 GB laptop GPU is challenging. We will need some GPU memory optimization at least as good as automatic1111’s UI. This may require expert modifications to the code.
-
-### Screenshots
-
-The training is fast. After 4000 steps (batch size 4, learning rate 1e-5, about 50 minutes on PCIE 40G), the results on my machine (in an output folder "image_log") is
-
-Control:
-
-
-
-Prompt:
-
-
-
-Prediction:
-
-
-
-Ground Truth:
-
-
-
-Note that the SD's capability is preserved. Even training on this super aligned dataset, it still draws some random textures and those snow decorations. (Besides, note that the ground truth looks a bit modified because it is converted from SD's latent image.)
-
-Larger batch size and longer training will further improve this. Adequate training will make the filling perfect.
-
-Of course, training SD to fill circles is meaningless, but this is a successful beginning of your story.
-
-Let us work together to control large models more and more.
-
-## Other options
-
-Beyond standard things, we also provide two important parameters "sd_locked" and "only_mid_control" that you need to know.
-
-### only_mid_control
-
-By default, only_mid_control is False. When it is True, you will train the below architecture.
-
-
-
-This can be helpful when your computation power is limited and want to speed up the training, or when you want to facilitate the "global" context learning. Note that sometimes you may pause training, set it to True, resume training, and pause again, and set it again, and resume again.
-
-If your computation device is good, perhaps you do not need this. But I also know some artists are willing to train a model on their laptop for a month - in that case, perhaps this option can be useful.
-
-### sd_locked
-
-By default, sd_locked is True. When it is False, you will train the below architecture.
-
-
-
-This will unlock some layers in SD and you will train them as a whole.
-
-This option is DANGEROUS! If your dataset is not good enough, this may downgrade the capability of your SD model.
-
-However, this option is also very useful when you are training on images with some specific style, or when you are training with special datasets (like medical dataset with X-ray images or geographic datasets with lots of Google Maps). You can understand this as simultaneously training the ControlNet and something like a DreamBooth.
-
-Also, if your dataset is large, you may want to end the training with a few thousands of steps with those layer unlocked. This usually improve the "problem-specific" solutions a little. You may try it yourself to feel the difference.
-
-Also, if you unlock some original layers, you may want a lower learning rate, like 2e-6.
-
-## More Consideration: Sudden Converge Phenomenon and Gradient Accumulation
-
-
-
-Because we use zero convolutions, the SD should always be able to predict meaningful images. (If it cannot, the training has already failed.)
-
-You will always find that at some iterations, the model "suddenly" be able to fit some training conditions. This means that you will get a basically usable model at about 3k to 7k steps (future training will improve it, but that model after the first "sudden converge" should be basically functional).
-
-Note that 3k to 7k steps is not very large, and you should consider larger batch size rather than more training steps. If you can observe the "sudden converge" at 3k step using batch size 4, then, rather than train it with 300k further steps, a better idea is to use 100× gradient accumulation to re-train that 3k steps with 100× batch size. Note that perhaps we should not do this *too* extremely (perhaps 100x accumulation is too extreme), but you should consider that, since "sudden converge" will *always* happen at that certain point, getting a better converge is more important.
-
-Because that "sudden converge" always happens, lets say "sudden converge" will happen at 3k step and our money can optimize 90k step, then we have two options: (1) train 3k steps, sudden converge, then train 87k steps. (2) 30x gradient accumulation, train 3k steps (90k real computation steps), then sudden converge.
-
-In my experiments, (2) is usually better than (1). However, in real cases, perhaps you may need to balance the steps before and after the "sudden converge" on your own to find a balance. The training after "sudden converge" is also important.
-
-But usually, if your logic batch size is already bigger than 256, then further extending the batch size is not very meaningful. In that case, perhaps a better idea is to train more steps. I tried some "common" logic batch size at 64 or 96 or 128 (by gradient accumulation), it seems that many complicated conditions can be solved very well already.
diff --git a/spaces/Ariharasudhan/YoloV5/models/experimental.py b/spaces/Ariharasudhan/YoloV5/models/experimental.py
deleted file mode 100644
index 02d35b9ebd11d3407d64ae436142aca6100c9084..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/models/experimental.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Experimental modules
-"""
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from utils.downloads import attempt_download
-
-
-class Sum(nn.Module):
- # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
- def __init__(self, n, weight=False): # n: number of inputs
- super().__init__()
- self.weight = weight # apply weights boolean
- self.iter = range(n - 1) # iter object
- if weight:
- self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights
-
- def forward(self, x):
- y = x[0] # no weight
- if self.weight:
- w = torch.sigmoid(self.w) * 2
- for i in self.iter:
- y = y + x[i + 1] * w[i]
- else:
- for i in self.iter:
- y = y + x[i + 1]
- return y
-
-
-class MixConv2d(nn.Module):
- # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
- def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy
- super().__init__()
- n = len(k) # number of convolutions
- if equal_ch: # equal c_ per group
- i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices
- c_ = [(i == g).sum() for g in range(n)] # intermediate channels
- else: # equal weight.numel() per group
- b = [c2] + [0] * n
- a = np.eye(n + 1, n, k=-1)
- a -= np.roll(a, 1, axis=1)
- a *= np.array(k) ** 2
- a[0] = 1
- c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
-
- self.m = nn.ModuleList([
- nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU()
-
- def forward(self, x):
- return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
- # Ensemble of models
- def __init__(self):
- super().__init__()
-
- def forward(self, x, augment=False, profile=False, visualize=False):
- y = [module(x, augment, profile, visualize)[0] for module in self]
- # y = torch.stack(y).max(0)[0] # max ensemble
- # y = torch.stack(y).mean(0) # mean ensemble
- y = torch.cat(y, 1) # nms ensemble
- return y, None # inference, train output
-
-
-def attempt_load(weights, device=None, inplace=True, fuse=True):
- # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
- from models.yolo import Detect, Model
-
- model = Ensemble()
- for w in weights if isinstance(weights, list) else [weights]:
- ckpt = torch.load(attempt_download(w), map_location='cpu') # load
- ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
-
- # Model compatibility updates
- if not hasattr(ckpt, 'stride'):
- ckpt.stride = torch.tensor([32.])
- if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
- ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
-
- model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
-
- # Module compatibility updates
- for m in model.modules():
- t = type(m)
- if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
- m.inplace = inplace # torch 1.7.0 compatibility
- if t is Detect and not isinstance(m.anchor_grid, list):
- delattr(m, 'anchor_grid')
- setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
- elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
- m.recompute_scale_factor = None # torch 1.11.0 compatibility
-
- # Return model
- if len(model) == 1:
- return model[-1]
-
- # Return detection ensemble
- print(f'Ensemble created with {weights}\n')
- for k in 'names', 'nc', 'yaml':
- setattr(model, k, getattr(model[0], k))
- model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
- assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
- return model
diff --git a/spaces/Asahi402/Real-CUGAN/app.py b/spaces/Asahi402/Real-CUGAN/app.py
deleted file mode 100644
index 2439c5cec6b61e8a517f957daf710cbb6b5c3cf6..0000000000000000000000000000000000000000
--- a/spaces/Asahi402/Real-CUGAN/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from upcunet_v3 import RealWaifuUpScaler
-import gradio as gr
-import time
-import logging
-import os
-from PIL import ImageOps
-import numpy as np
-import math
-
-
-def greet(input_img, input_model_name, input_tile_mode):
- # if input_img.size[0] * input_img.size[1] > 256 * 256:
- # y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
- # x = int(input_img.size[0]/input_img.size[1]*y)
- # input_img = ImageOps.fit(input_img, (x, y))
- input_img = np.array(input_img)
- if input_model_name not in model_cache:
- t1 = time.time()
- upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
- t2 = time.time()
- logger.info(f'load model time, {t2 - t1}')
- model_cache[input_model_name] = upscaler
- else:
- upscaler = model_cache[input_model_name]
- logger.info(f'load model from cache')
-
- start = time.time()
- result = upscaler(input_img, tile_mode=input_tile_mode)
- end = time.time()
- logger.info(f'input_model_name, {input_model_name}')
- logger.info(f'input_tile_mode, {input_tile_mode}')
- logger.info(f'input shape, {input_img.shape}')
- logger.info(f'output shape, {result.shape}')
- logger.info(f'speed time, {end - start}')
- return result
-
-
-if __name__ == '__main__':
- logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
- logger = logging.getLogger()
-
- ModelPath = "weights_v3/"
- model_cache = {}
-
- input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-denoise2x.pth", label='选择model')
- input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
- input_img = gr.inputs.Image(label='image', type='pil')
-
- inputs = [input_img, input_model_name, input_tile_mode]
- outputs = "image"
- iface = gr.Interface(fn=greet,
- inputs=inputs,
- outputs=outputs,
- allow_screenshot=False,
- allow_flagging='never',
- examples=[['test-img.jpg', "up2x-latest-denoise2x.pth", 2]],
- article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)` for more information.
-
-
- Options accepted:
-
- `style`
- The style to use, can be a string or a Style subclass (default:
- ``'default'``).
-
- `linenos`
- Set to ``True`` to have line numbers on the terminal output as well
- (default: ``False`` = no line numbers).
- """
- name = 'Terminal256'
- aliases = ['terminal256', 'console256', '256']
- filenames = []
-
- def __init__(self, **options):
- Formatter.__init__(self, **options)
-
- self.xterm_colors = []
- self.best_match = {}
- self.style_string = {}
-
- self.usebold = 'nobold' not in options
- self.useunderline = 'nounderline' not in options
- self.useitalic = 'noitalic' not in options
-
- self._build_color_table() # build an RGB-to-256 color conversion table
- self._setup_styles() # convert selected style's colors to term. colors
-
- self.linenos = options.get('linenos', False)
- self._lineno = 0
-
- def _build_color_table(self):
- # colors 0..15: 16 basic colors
-
- self.xterm_colors.append((0x00, 0x00, 0x00)) # 0
- self.xterm_colors.append((0xcd, 0x00, 0x00)) # 1
- self.xterm_colors.append((0x00, 0xcd, 0x00)) # 2
- self.xterm_colors.append((0xcd, 0xcd, 0x00)) # 3
- self.xterm_colors.append((0x00, 0x00, 0xee)) # 4
- self.xterm_colors.append((0xcd, 0x00, 0xcd)) # 5
- self.xterm_colors.append((0x00, 0xcd, 0xcd)) # 6
- self.xterm_colors.append((0xe5, 0xe5, 0xe5)) # 7
- self.xterm_colors.append((0x7f, 0x7f, 0x7f)) # 8
- self.xterm_colors.append((0xff, 0x00, 0x00)) # 9
- self.xterm_colors.append((0x00, 0xff, 0x00)) # 10
- self.xterm_colors.append((0xff, 0xff, 0x00)) # 11
- self.xterm_colors.append((0x5c, 0x5c, 0xff)) # 12
- self.xterm_colors.append((0xff, 0x00, 0xff)) # 13
- self.xterm_colors.append((0x00, 0xff, 0xff)) # 14
- self.xterm_colors.append((0xff, 0xff, 0xff)) # 15
-
- # colors 16..232: the 6x6x6 color cube
-
- valuerange = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff)
-
- for i in range(217):
- r = valuerange[(i // 36) % 6]
- g = valuerange[(i // 6) % 6]
- b = valuerange[i % 6]
- self.xterm_colors.append((r, g, b))
-
- # colors 233..253: grayscale
-
- for i in range(1, 22):
- v = 8 + i * 10
- self.xterm_colors.append((v, v, v))
-
- def _closest_color(self, r, g, b):
- distance = 257*257*3 # "infinity" (>distance from #000000 to #ffffff)
- match = 0
-
- for i in range(0, 254):
- values = self.xterm_colors[i]
-
- rd = r - values[0]
- gd = g - values[1]
- bd = b - values[2]
- d = rd*rd + gd*gd + bd*bd
-
- if d < distance:
- match = i
- distance = d
- return match
-
- def _color_index(self, color):
- index = self.best_match.get(color, None)
- if color in ansicolors:
- # strip the `ansi/#ansi` part and look up code
- index = color
- self.best_match[color] = index
- if index is None:
- try:
- rgb = int(str(color), 16)
- except ValueError:
- rgb = 0
-
- r = (rgb >> 16) & 0xff
- g = (rgb >> 8) & 0xff
- b = rgb & 0xff
- index = self._closest_color(r, g, b)
- self.best_match[color] = index
- return index
-
- def _setup_styles(self):
- for ttype, ndef in self.style:
- escape = EscapeSequence()
- # get foreground from ansicolor if set
- if ndef['ansicolor']:
- escape.fg = self._color_index(ndef['ansicolor'])
- elif ndef['color']:
- escape.fg = self._color_index(ndef['color'])
- if ndef['bgansicolor']:
- escape.bg = self._color_index(ndef['bgansicolor'])
- elif ndef['bgcolor']:
- escape.bg = self._color_index(ndef['bgcolor'])
- if self.usebold and ndef['bold']:
- escape.bold = True
- if self.useunderline and ndef['underline']:
- escape.underline = True
- if self.useitalic and ndef['italic']:
- escape.italic = True
- self.style_string[str(ttype)] = (escape.color_string(),
- escape.reset_string())
-
- def _write_lineno(self, outfile):
- self._lineno += 1
- outfile.write("%s%04d: " % (self._lineno != 1 and '\n' or '', self._lineno))
-
- def format(self, tokensource, outfile):
- return Formatter.format(self, tokensource, outfile)
-
- def format_unencoded(self, tokensource, outfile):
- if self.linenos:
- self._write_lineno(outfile)
-
- for ttype, value in tokensource:
- not_found = True
- while ttype and not_found:
- try:
- # outfile.write( "<" + str(ttype) + ">" )
- on, off = self.style_string[str(ttype)]
-
- # Like TerminalFormatter, add "reset colors" escape sequence
- # on newline.
- spl = value.split('\n')
- for line in spl[:-1]:
- if line:
- outfile.write(on + line + off)
- if self.linenos:
- self._write_lineno(outfile)
- else:
- outfile.write('\n')
-
- if spl[-1]:
- outfile.write(on + spl[-1] + off)
-
- not_found = False
- # outfile.write( '#' + str(ttype) + '#' )
-
- except KeyError:
- # ottype = ttype
- ttype = ttype.parent
- # outfile.write( '!' + str(ottype) + '->' + str(ttype) + '!' )
-
- if not_found:
- outfile.write(value)
-
- if self.linenos:
- outfile.write("\n")
-
-
-
-class TerminalTrueColorFormatter(Terminal256Formatter):
- r"""
- Format tokens with ANSI color sequences, for output in a true-color
- terminal or console. Like in `TerminalFormatter` color sequences
- are terminated at newlines, so that paging the output works correctly.
-
- .. versionadded:: 2.1
-
- Options accepted:
-
- `style`
- The style to use, can be a string or a Style subclass (default:
- ``'default'``).
- """
- name = 'TerminalTrueColor'
- aliases = ['terminal16m', 'console16m', '16m']
- filenames = []
-
- def _build_color_table(self):
- pass
-
- def _color_tuple(self, color):
- try:
- rgb = int(str(color), 16)
- except ValueError:
- return None
- r = (rgb >> 16) & 0xff
- g = (rgb >> 8) & 0xff
- b = rgb & 0xff
- return (r, g, b)
-
- def _setup_styles(self):
- for ttype, ndef in self.style:
- escape = EscapeSequence()
- if ndef['color']:
- escape.fg = self._color_tuple(ndef['color'])
- if ndef['bgcolor']:
- escape.bg = self._color_tuple(ndef['bgcolor'])
- if self.usebold and ndef['bold']:
- escape.bold = True
- if self.useunderline and ndef['underline']:
- escape.underline = True
- if self.useitalic and ndef['italic']:
- escape.italic = True
- self.style_string[str(ttype)] = (escape.true_color_string(),
- escape.reset_string())
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/common.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/common.py
deleted file mode 100644
index 1859fb79cc4e78850b69742fca56698041ce59f8..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/common.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# common.py
-from .core import *
-from .helpers import delimited_list, any_open_tag, any_close_tag
-from datetime import datetime
-
-
-# some other useful expressions - using lower-case class name since we are really using this as a namespace
-class pyparsing_common:
- """Here are some common low-level expressions that may be useful in
- jump-starting parser development:
-
- - numeric forms (:class:`integers`, :class:`reals`,
- :class:`scientific notation`)
- - common :class:`programming identifiers`
- - network addresses (:class:`MAC`,
- :class:`IPv4`, :class:`IPv6`)
- - ISO8601 :class:`dates` and
- :class:`datetime`
- - :class:`UUID`
- - :class:`comma-separated list`
- - :class:`url`
-
- Parse actions:
-
- - :class:`convertToInteger`
- - :class:`convertToFloat`
- - :class:`convertToDate`
- - :class:`convertToDatetime`
- - :class:`stripHTMLTags`
- - :class:`upcaseTokens`
- - :class:`downcaseTokens`
-
- Example::
-
- pyparsing_common.number.runTests('''
- # any int or real number, returned as the appropriate type
- 100
- -100
- +100
- 3.14159
- 6.02e23
- 1e-12
- ''')
-
- pyparsing_common.fnumber.runTests('''
- # any int or real number, returned as float
- 100
- -100
- +100
- 3.14159
- 6.02e23
- 1e-12
- ''')
-
- pyparsing_common.hex_integer.runTests('''
- # hex numbers
- 100
- FF
- ''')
-
- pyparsing_common.fraction.runTests('''
- # fractions
- 1/2
- -3/4
- ''')
-
- pyparsing_common.mixed_integer.runTests('''
- # mixed fractions
- 1
- 1/2
- -3/4
- 1-3/4
- ''')
-
- import uuid
- pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID))
- pyparsing_common.uuid.runTests('''
- # uuid
- 12345678-1234-5678-1234-567812345678
- ''')
-
- prints::
-
- # any int or real number, returned as the appropriate type
- 100
- [100]
-
- -100
- [-100]
-
- +100
- [100]
-
- 3.14159
- [3.14159]
-
- 6.02e23
- [6.02e+23]
-
- 1e-12
- [1e-12]
-
- # any int or real number, returned as float
- 100
- [100.0]
-
- -100
- [-100.0]
-
- +100
- [100.0]
-
- 3.14159
- [3.14159]
-
- 6.02e23
- [6.02e+23]
-
- 1e-12
- [1e-12]
-
- # hex numbers
- 100
- [256]
-
- FF
- [255]
-
- # fractions
- 1/2
- [0.5]
-
- -3/4
- [-0.75]
-
- # mixed fractions
- 1
- [1]
-
- 1/2
- [0.5]
-
- -3/4
- [-0.75]
-
- 1-3/4
- [1.75]
-
- # uuid
- 12345678-1234-5678-1234-567812345678
- [UUID('12345678-1234-5678-1234-567812345678')]
- """
-
- convert_to_integer = token_map(int)
- """
- Parse action for converting parsed integers to Python int
- """
-
- convert_to_float = token_map(float)
- """
- Parse action for converting parsed numbers to Python float
- """
-
- integer = Word(nums).set_name("integer").set_parse_action(convert_to_integer)
- """expression that parses an unsigned integer, returns an int"""
-
- hex_integer = (
- Word(hexnums).set_name("hex integer").set_parse_action(token_map(int, 16))
- )
- """expression that parses a hexadecimal integer, returns an int"""
-
- signed_integer = (
- Regex(r"[+-]?\d+")
- .set_name("signed integer")
- .set_parse_action(convert_to_integer)
- )
- """expression that parses an integer with optional leading sign, returns an int"""
-
- fraction = (
- signed_integer().set_parse_action(convert_to_float)
- + "/"
- + signed_integer().set_parse_action(convert_to_float)
- ).set_name("fraction")
- """fractional expression of an integer divided by an integer, returns a float"""
- fraction.add_parse_action(lambda tt: tt[0] / tt[-1])
-
- mixed_integer = (
- fraction | signed_integer + Opt(Opt("-").suppress() + fraction)
- ).set_name("fraction or mixed integer-fraction")
- """mixed integer of the form 'integer - fraction', with optional leading integer, returns float"""
- mixed_integer.add_parse_action(sum)
-
- real = (
- Regex(r"[+-]?(?:\d+\.\d*|\.\d+)")
- .set_name("real number")
- .set_parse_action(convert_to_float)
- )
- """expression that parses a floating point number and returns a float"""
-
- sci_real = (
- Regex(r"[+-]?(?:\d+(?:[eE][+-]?\d+)|(?:\d+\.\d*|\.\d+)(?:[eE][+-]?\d+)?)")
- .set_name("real number with scientific notation")
- .set_parse_action(convert_to_float)
- )
- """expression that parses a floating point number with optional
- scientific notation and returns a float"""
-
- # streamlining this expression makes the docs nicer-looking
- number = (sci_real | real | signed_integer).setName("number").streamline()
- """any numeric expression, returns the corresponding Python type"""
-
- fnumber = (
- Regex(r"[+-]?\d+\.?\d*([eE][+-]?\d+)?")
- .set_name("fnumber")
- .set_parse_action(convert_to_float)
- )
- """any int or real number, returned as float"""
-
- identifier = Word(identchars, identbodychars).set_name("identifier")
- """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')"""
-
- ipv4_address = Regex(
- r"(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}"
- ).set_name("IPv4 address")
- "IPv4 address (``0.0.0.0 - 255.255.255.255``)"
-
- _ipv6_part = Regex(r"[0-9a-fA-F]{1,4}").set_name("hex_integer")
- _full_ipv6_address = (_ipv6_part + (":" + _ipv6_part) * 7).set_name(
- "full IPv6 address"
- )
- _short_ipv6_address = (
- Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
- + "::"
- + Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
- ).set_name("short IPv6 address")
- _short_ipv6_address.add_condition(
- lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8
- )
- _mixed_ipv6_address = ("::ffff:" + ipv4_address).set_name("mixed IPv6 address")
- ipv6_address = Combine(
- (_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).set_name(
- "IPv6 address"
- )
- ).set_name("IPv6 address")
- "IPv6 address (long, short, or mixed form)"
-
- mac_address = Regex(
- r"[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}"
- ).set_name("MAC address")
- "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)"
-
- @staticmethod
- def convert_to_date(fmt: str = "%Y-%m-%d"):
- """
- Helper to create a parse action for converting parsed date string to Python datetime.date
-
- Params -
- - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``)
-
- Example::
-
- date_expr = pyparsing_common.iso8601_date.copy()
- date_expr.setParseAction(pyparsing_common.convertToDate())
- print(date_expr.parseString("1999-12-31"))
-
- prints::
-
- [datetime.date(1999, 12, 31)]
- """
-
- def cvt_fn(ss, ll, tt):
- try:
- return datetime.strptime(tt[0], fmt).date()
- except ValueError as ve:
- raise ParseException(ss, ll, str(ve))
-
- return cvt_fn
-
- @staticmethod
- def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"):
- """Helper to create a parse action for converting parsed
- datetime string to Python datetime.datetime
-
- Params -
- - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``)
-
- Example::
-
- dt_expr = pyparsing_common.iso8601_datetime.copy()
- dt_expr.setParseAction(pyparsing_common.convertToDatetime())
- print(dt_expr.parseString("1999-12-31T23:59:59.999"))
-
- prints::
-
- [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
- """
-
- def cvt_fn(s, l, t):
- try:
- return datetime.strptime(t[0], fmt)
- except ValueError as ve:
- raise ParseException(s, l, str(ve))
-
- return cvt_fn
-
- iso8601_date = Regex(
- r"(?P\d{4})(?:-(?P\d\d)(?:-(?P\d\d))?)?"
- ).set_name("ISO8601 date")
- "ISO8601 date (``yyyy-mm-dd``)"
-
- iso8601_datetime = Regex(
- r"(?P\d{4})-(?P\d\d)-(?P\d\d)[T ](?P\d\d):(?P\d\d)(:(?P\d\d(\.\d*)?)?)?(?PZ|[+-]\d\d:?\d\d)?"
- ).set_name("ISO8601 datetime")
- "ISO8601 datetime (``yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)``) - trailing seconds, milliseconds, and timezone optional; accepts separating ``'T'`` or ``' '``"
-
- uuid = Regex(r"[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}").set_name("UUID")
- "UUID (``xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx``)"
-
- _html_stripper = any_open_tag.suppress() | any_close_tag.suppress()
-
- @staticmethod
- def strip_html_tags(s: str, l: int, tokens: ParseResults):
- """Parse action to remove HTML tags from web page HTML source
-
- Example::
-
- # strip HTML links from normal text
- text = 'More info at the pyparsing wiki page '
- td, td_end = makeHTMLTags("TD")
- table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
- print(table_text.parseString(text).body)
-
- Prints::
-
- More info at the pyparsing wiki page
- """
- return pyparsing_common._html_stripper.transform_string(tokens[0])
-
- _commasepitem = (
- Combine(
- OneOrMore(
- ~Literal(",")
- + ~LineEnd()
- + Word(printables, exclude_chars=",")
- + Opt(White(" \t") + ~FollowedBy(LineEnd() | ","))
- )
- )
- .streamline()
- .set_name("commaItem")
- )
- comma_separated_list = delimited_list(
- Opt(quoted_string.copy() | _commasepitem, default="")
- ).set_name("comma separated list")
- """Predefined expression of 1 or more printable words or quoted strings, separated by commas."""
-
- upcase_tokens = staticmethod(token_map(lambda t: t.upper()))
- """Parse action to convert tokens to upper case."""
-
- downcase_tokens = staticmethod(token_map(lambda t: t.lower()))
- """Parse action to convert tokens to lower case."""
-
- # fmt: off
- url = Regex(
- # https://mathiasbynens.be/demo/url-regex
- # https://gist.github.com/dperini/729294
- r"^" +
- # protocol identifier (optional)
- # short syntax // still required
- r"(?:(?:(?Phttps?|ftp):)?\/\/)" +
- # user:pass BasicAuth (optional)
- r"(?:(?P\S+(?::\S*)?)@)?" +
- r"(?P" +
- # IP address exclusion
- # private & local networks
- r"(?!(?:10|127)(?:\.\d{1,3}){3})" +
- r"(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})" +
- r"(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})" +
- # IP address dotted notation octets
- # excludes loopback network 0.0.0.0
- # excludes reserved space >= 224.0.0.0
- # excludes network & broadcast addresses
- # (first & last IP address of each class)
- r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" +
- r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" +
- r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" +
- r"|" +
- # host & domain names, may end with dot
- # can be replaced by a shortest alternative
- # (?![-_])(?:[-\w\u00a1-\uffff]{0,63}[^-_]\.)+
- r"(?:" +
- r"(?:" +
- r"[a-z0-9\u00a1-\uffff]" +
- r"[a-z0-9\u00a1-\uffff_-]{0,62}" +
- r")?" +
- r"[a-z0-9\u00a1-\uffff]\." +
- r")+" +
- # TLD identifier name, may end with dot
- r"(?:[a-z\u00a1-\uffff]{2,}\.?)" +
- r")" +
- # port number (optional)
- r"(:(?P\d{2,5}))?" +
- # resource path (optional)
- r"(?P\/[^?# ]*)?" +
- # query string (optional)
- r"(\?(?P[^#]*))?" +
- # fragment (optional)
- r"(#(?P\S*))?" +
- r"$"
- ).set_name("url")
- # fmt: on
-
- # pre-PEP8 compatibility names
- convertToInteger = convert_to_integer
- convertToFloat = convert_to_float
- convertToDate = convert_to_date
- convertToDatetime = convert_to_datetime
- stripHTMLTags = strip_html_tags
- upcaseTokens = upcase_tokens
- downcaseTokens = downcase_tokens
-
-
-_builtin_exprs = [
- v for v in vars(pyparsing_common).values() if isinstance(v, ParserElement)
-]
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py
deleted file mode 100644
index af02141172bebe9a2a27a88c81673c2710b4d73f..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/backbone/build.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.layers import ShapeSpec
-from detectron2.utils.registry import Registry
-
-from .backbone import Backbone
-
-BACKBONE_REGISTRY = Registry("BACKBONE")
-BACKBONE_REGISTRY.__doc__ = """
-Registry for backbones, which extract feature maps from images
-
-The registered object must be a callable that accepts two arguments:
-
-1. A :class:`detectron2.config.CfgNode`
-2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification.
-
-Registered object must return instance of :class:`Backbone`.
-"""
-
-
-def build_backbone(cfg, input_shape=None):
- """
- Build a backbone from `cfg.MODEL.BACKBONE.NAME`.
-
- Returns:
- an instance of :class:`Backbone`
- """
- if input_shape is None:
- input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
-
- backbone_name = cfg.MODEL.BACKBONE.NAME
- backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
- assert isinstance(backbone, Backbone)
- return backbone
diff --git a/spaces/Axesys/Private-WebUI/README.md b/spaces/Axesys/Private-WebUI/README.md
deleted file mode 100644
index 028654eb8ec1d7c2a3f1b35bcf8d206dd3ec2d67..0000000000000000000000000000000000000000
--- a/spaces/Axesys/Private-WebUI/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Waifu AI
-emoji: 💻
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: Axesys/Waifu-AI-WebUI
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Coche Extremo Simulador De Conduccin Mod Apk Hack Descargar Para Pc.md b/spaces/Benson/text-generation/Examples/Coche Extremo Simulador De Conduccin Mod Apk Hack Descargar Para Pc.md
deleted file mode 100644
index 1dc3c28eeae3b7cc875f215ef86722b598b80c7f..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Coche Extremo Simulador De Conduccin Mod Apk Hack Descargar Para Pc.md
+++ /dev/null
@@ -1,44 +0,0 @@
-
-Simulador de conducción de coche extremo Mod APK Hack Descargar para PC
-¿Te encanta conducir coches rápidos y realizar acrobacias increíbles? ¿Quieres experimentar la emoción de conducir en un entorno realista de mundo abierto? Si es así, entonces usted debe probar Extreme Car Driving Simulator, uno de los mejores juegos de conducción de coches simulador para Android. Y si usted quiere hacer el juego aún más divertido y emocionante, usted debe descargar la versión hack apk mod para PC, que le da dinero ilimitado, coches, y otros beneficios. En este artículo, le diremos todo lo que necesita saber sobre Extreme Car Driving Simulator, por qué debe descargar el hack apk mod, y cómo instalarlo en su PC.
-¿Qué es Extreme Car Driving Simulator?
-Extreme Car Driving Simulator es un simulador de conducción de coches en 3D desarrollado por AxesInMotion Racing. Está disponible de forma gratuita en Google Play Store y tiene más de 100 millones de descargas. El juego te permite conducir varios tipos de coches, desde coches deportivos hasta SUV, en una gran ciudad de mundo abierto. Puede conducir libremente, seguir las reglas de tráfico, o romperlas y causar caos. También puede realizar acrobacias, derivas, saltos y accidentes con física realista y daños en el coche. El juego tiene diferentes modos, como el modo libre, el modo de punto de control, el modo de tráfico y el modo fantasma. También puede personalizar sus coches con diferentes colores, ruedas y vinilos.
-coche extremo simulador de conducción mod apk hack descargar para pc Download File >>> https://bltlly.com/2v6LEr
Características del simulador de conducción de automóviles extremos
-Extreme Car Driving Simulator tiene muchas características que lo convierten en uno de los mejores juegos de simulador de conducción de automóviles para Android. Aquí están algunos de ellos:
-Unidad con tráfico
-Puedes elegir conducir con o sin tráfico en el juego. Conducir con tráfico añade más realismo y desafío al juego, ya que tienes que evitar colisiones y seguir las reglas de tráfico. También puede tocar la bocina, encender las luces y usar indicadores para comunicarse con otros conductores.
-HUD real completo
-
-Simulación de ABS, TC y ESP
-El juego simula el sistema de frenos antibloqueo (ABS), el control de tracción (TC) y el programa de estabilidad electrónica (ESP) de los coches. También puede desactivarlos si desea tener más control sobre el comportamiento de su automóvil.
-Explora un entorno de mundo abierto detallado
-El juego tiene una gran ciudad de mundo abierto que se puede explorar libremente. La ciudad tiene diferentes áreas, como el centro, el aeropuerto, la zona industrial y el campo. La ciudad también tiene un clima dinámico y un ciclo día-noche que afectan las condiciones de conducción.
-Daños realistas en el coche
-El juego tiene daños realistas coche que muestra el impacto de sus accidentes y colisiones. Puede ver las partes del cuerpo de su automóvil abolladas, rayadas o cayéndose. También puede reparar su automóvil presionando un botón o visitando un garaje.
-Física precisa
-El juego tiene la física precisa que hacen la experiencia de conducción más realista y divertido. Puede sentir el peso, la velocidad y la inercia de su automóvil mientras conduce. También puedes realizar acrobacias, derrapes, saltos y volteretas con tu auto usando rampas,
Controla tu auto con diferentes opciones
-El juego te da diferentes opciones para controlar tu coche, como inclinación, botones o volante. También puede ajustar la sensibilidad y la retroalimentación de los controles para adaptarse a sus preferencias. También puede cambiar el modo de engranaje de automático a manual.
- ¿Por qué descargar Extreme Car Driving Simulator mod apk hack?
-Extreme Car Driving Simulator es un juego divertido y adictivo, pero también puede ser frustrante y consume mucho tiempo si desea desbloquear todos los coches y características. Es por eso que usted debe descargar la versión mod apk hack para PC, que le da muchas ventajas sobre el juego original. Aquí están algunos de ellos:
-
-Dinero y coches ilimitados
-
-No se requieren anuncios ni root
-El mod apk hack versión también elimina todos los anuncios molestos que interrumpen su juego. Usted puede jugar el juego sin ninguna distracción o interrupciones. Además, usted no necesita rootear su dispositivo para instalar la versión mod apk hack. Puedes simplemente descargarlo e instalarlo en tu PC usando un emulador de Android.
-Cómo descargar e instalar Extreme Car Driving Simulator mod apk hack para PC?
-Si desea descargar e instalar Extreme Car Driving Simulator mod apk hack para PC, debe seguir estos sencillos pasos:
-Paso 1: Descargar un emulador de Android
-Un emulador de Android es un software que le permite ejecutar aplicaciones y juegos de Android en su PC. Hay muchos emuladores de Android disponibles en línea, como BlueStacks, NoxPlayer, MEmu, etc. Puede elegir cualquiera de ellos y descargarlo desde su sitio web oficial. Luego, instálalo en tu PC siguiendo las instrucciones.
-Paso 2: Descargar el archivo apk mod de una fuente de confianza
-El siguiente paso es descargar el archivo apk mod de Extreme Car Driving Simulator de una fuente de confianza. Puede buscarlo en Google o utilizar el enlace que se proporciona a continuación. Asegúrese de descargar la última versión del archivo apk mod que es compatible con su emulador.
-Descargar Extreme Car Driving Simulator mod apk hack
-Paso 3: Instalar el archivo apk mod en el emulador
-Después de descargar el archivo apk mod, es necesario instalarlo en el emulador. Puede hacer esto arrastrando y soltando el archivo en la ventana del emulador o navegando y seleccionándolo desde la carpeta de su PC. El emulador instalará automáticamente el archivo mod apk en tu dispositivo virtual.
-Paso 4: Iniciar el juego y disfrutar de
-
-Conclusión
-Extreme Car Driving Simulator es uno de los mejores juegos de simulador de conducción de coches para Android que te permite conducir varios tipos de coches en un entorno realista de mundo abierto. También puede descargar la versión mod apk hack para PC que le da dinero ilimitado, coches, y no hay anuncios. Solo tienes que seguir los pasos mencionados anteriormente para descargarlo e instalarlo en tu PC usando un emulador de Android. Entonces, ¿qué estás esperando? Descargar Extreme Car Driving Simulator mod apk hack para PC hoy y divertirse conduciendo coches rápidos y realizar acrobacias increíbles.
- Q: ¿Es Extreme Car Driving Simulator mod apk hack seguro de usar? A: Sí, Extreme Car Driving Simulator mod apk hack es seguro de usar siempre y cuando se descarga de una fuente de confianza y utilizar un emulador de Android confiable. P: ¿Puedo jugar Extreme Car Driving Simulator en línea con otros jugadores? R: No, Extreme Car Driving Simulator es un juego sin conexión que no admite el modo multijugador en línea. P: ¿Cómo puedo actualizar Extreme Car Driving Simulator mod apk hack? A: Para actualizar Extreme Car Driving Simulator mod apk hack, es necesario descargar e instalar la última versión del archivo apk mod de la misma fuente que antes. P: ¿Cuáles son algunos otros juegos similares a Extreme Car Driving Simulator? R: Algunos otros juegos similares a Extreme Car Driving Simulator son Real Racing 3, Asphalt 9: Legends, CSR Racing 2, Need for Speed: No Limits, etc. P: ¿Cómo puedo contactar con el desarrollador de Extreme Car Driving Simulator? R: Puede ponerse en contacto con el desarrollador de Extreme Car Driving Simulator enviando un correo electrónico a support@axesinmotion.com o visitando Ya he escrito el artículo según sus instrucciones. No hay nada más que escribir. Espero que estén satisfechos con mi trabajo. Si tienen algún comentario o sugerencia, por favor háganmelo saber. Gracias por elegirme como tu escritor de contenido. 64aa2da5cf.
-
-:copyright: (c) 2017 by Kenneth Reitz.
-:license: Apache 2.0, see LICENSE for more details.
-"""
-
-import warnings
-
-from pip._vendor import urllib3
-
-from .exceptions import RequestsDependencyWarning
-
-charset_normalizer_version = None
-
-try:
- from pip._vendor.chardet import __version__ as chardet_version
-except ImportError:
- chardet_version = None
-
-
-def check_compatibility(urllib3_version, chardet_version, charset_normalizer_version):
- urllib3_version = urllib3_version.split(".")
- assert urllib3_version != ["dev"] # Verify urllib3 isn't installed from git.
-
- # Sometimes, urllib3 only reports its version as 16.1.
- if len(urllib3_version) == 2:
- urllib3_version.append("0")
-
- # Check urllib3 for compatibility.
- major, minor, patch = urllib3_version # noqa: F811
- major, minor, patch = int(major), int(minor), int(patch)
- # urllib3 >= 1.21.1, <= 1.26
- assert major == 1
- assert minor >= 21
- assert minor <= 26
-
- # Check charset_normalizer for compatibility.
- if chardet_version:
- major, minor, patch = chardet_version.split(".")[:3]
- major, minor, patch = int(major), int(minor), int(patch)
- # chardet_version >= 3.0.2, < 6.0.0
- assert (3, 0, 2) <= (major, minor, patch) < (6, 0, 0)
- elif charset_normalizer_version:
- major, minor, patch = charset_normalizer_version.split(".")[:3]
- major, minor, patch = int(major), int(minor), int(patch)
- # charset_normalizer >= 2.0.0 < 4.0.0
- assert (2, 0, 0) <= (major, minor, patch) < (4, 0, 0)
- else:
- raise Exception("You need either charset_normalizer or chardet installed")
-
-
-def _check_cryptography(cryptography_version):
- # cryptography < 1.3.4
- try:
- cryptography_version = list(map(int, cryptography_version.split(".")))
- except ValueError:
- return
-
- if cryptography_version < [1, 3, 4]:
- warning = "Old version of cryptography ({}) may cause slowdown.".format(
- cryptography_version
- )
- warnings.warn(warning, RequestsDependencyWarning)
-
-
-# Check imported dependencies for compatibility.
-try:
- check_compatibility(
- urllib3.__version__, chardet_version, charset_normalizer_version
- )
-except (AssertionError, ValueError):
- warnings.warn(
- "urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "
- "version!".format(
- urllib3.__version__, chardet_version, charset_normalizer_version
- ),
- RequestsDependencyWarning,
- )
-
-# Attempt to enable urllib3's fallback for SNI support
-# if the standard library doesn't support SNI or the
-# 'ssl' library isn't available.
-try:
- # Note: This logic prevents upgrading cryptography on Windows, if imported
- # as part of pip.
- from pip._internal.utils.compat import WINDOWS
- if not WINDOWS:
- raise ImportError("pip internals: don't import cryptography on Windows")
- try:
- import ssl
- except ImportError:
- ssl = None
-
- if not getattr(ssl, "HAS_SNI", False):
- from pip._vendor.urllib3.contrib import pyopenssl
-
- pyopenssl.inject_into_urllib3()
-
- # Check cryptography version
- from cryptography import __version__ as cryptography_version
-
- _check_cryptography(cryptography_version)
-except ImportError:
- pass
-
-# urllib3's DependencyWarnings should be silenced.
-from pip._vendor.urllib3.exceptions import DependencyWarning
-
-warnings.simplefilter("ignore", DependencyWarning)
-
-# Set default logging handler to avoid "No handler found" warnings.
-import logging
-from logging import NullHandler
-
-from . import packages, utils
-from .__version__ import (
- __author__,
- __author_email__,
- __build__,
- __cake__,
- __copyright__,
- __description__,
- __license__,
- __title__,
- __url__,
- __version__,
-)
-from .api import delete, get, head, options, patch, post, put, request
-from .exceptions import (
- ConnectionError,
- ConnectTimeout,
- FileModeWarning,
- HTTPError,
- JSONDecodeError,
- ReadTimeout,
- RequestException,
- Timeout,
- TooManyRedirects,
- URLRequired,
-)
-from .models import PreparedRequest, Request, Response
-from .sessions import Session, session
-from .status_codes import codes
-
-logging.getLogger(__name__).addHandler(NullHandler())
-
-# FileModeWarnings go off per the default.
-warnings.simplefilter("default", FileModeWarning, append=True)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/memory.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/memory.py
deleted file mode 100644
index d495a1681f460668c96f64454e31e7f2fca8137a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/memory.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-import logging
-from contextlib import contextmanager
-from functools import wraps
-import torch
-
-__all__ = ["retry_if_cuda_oom"]
-
-
-@contextmanager
-def _ignore_torch_cuda_oom():
- """
- A context which ignores CUDA OOM exception from pytorch.
- """
- try:
- yield
- except RuntimeError as e:
- # NOTE: the string may change?
- if "CUDA out of memory. " in str(e):
- pass
- else:
- raise
-
-
-def retry_if_cuda_oom(func):
- """
- Makes a function retry itself after encountering
- pytorch's CUDA OOM error.
- It will first retry after calling `torch.cuda.empty_cache()`.
-
- If that still fails, it will then retry by trying to convert inputs to CPUs.
- In this case, it expects the function to dispatch to CPU implementation.
- The return values may become CPU tensors as well and it's user's
- responsibility to convert it back to CUDA tensor if needed.
-
- Args:
- func: a stateless callable that takes tensor-like objects as arguments
-
- Returns:
- a callable which retries `func` if OOM is encountered.
-
- Examples:
-
- .. code-block:: python
-
- output = retry_if_cuda_oom(some_torch_function)(input1, input2)
- # output may be on CPU even if inputs are on GPU
-
- Note:
- 1. When converting inputs to CPU, it will only look at each argument and check
- if it has `.device` and `.to` for conversion. Nested structures of tensors
- are not supported.
-
- 2. Since the function might be called more than once, it has to be
- stateless.
- """
-
- def maybe_to_cpu(x):
- try:
- like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to")
- except AttributeError:
- like_gpu_tensor = False
- if like_gpu_tensor:
- return x.to(device="cpu")
- else:
- return x
-
- @wraps(func)
- def wrapped(*args, **kwargs):
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Clear cache and retry
- torch.cuda.empty_cache()
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Try on CPU. This slows down the code significantly, therefore print a notice.
- logger = logging.getLogger(__name__)
- logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func)))
- new_args = (maybe_to_cpu(x) for x in args)
- new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
- return func(*new_args, **new_kwargs)
-
- return wrapped
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_checkpoint.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_checkpoint.py
deleted file mode 100644
index 725b488fdaec5d2b3a5c6d11c11d2c362453a2a4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_checkpoint.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import unittest
-from collections import OrderedDict
-import torch
-from torch import nn
-
-from detectron2.checkpoint.c2_model_loading import align_and_update_state_dicts
-from detectron2.utils.logger import setup_logger
-
-
-class TestCheckpointer(unittest.TestCase):
- def setUp(self):
- setup_logger()
-
- def create_complex_model(self):
- m = nn.Module()
- m.block1 = nn.Module()
- m.block1.layer1 = nn.Linear(2, 3)
- m.layer2 = nn.Linear(3, 2)
- m.res = nn.Module()
- m.res.layer2 = nn.Linear(3, 2)
-
- state_dict = OrderedDict()
- state_dict["layer1.weight"] = torch.rand(3, 2)
- state_dict["layer1.bias"] = torch.rand(3)
- state_dict["layer2.weight"] = torch.rand(2, 3)
- state_dict["layer2.bias"] = torch.rand(2)
- state_dict["res.layer2.weight"] = torch.rand(2, 3)
- state_dict["res.layer2.bias"] = torch.rand(2)
- return m, state_dict
-
- def test_complex_model_loaded(self):
- for add_data_parallel in [False, True]:
- model, state_dict = self.create_complex_model()
- if add_data_parallel:
- model = nn.DataParallel(model)
- model_sd = model.state_dict()
-
- align_and_update_state_dicts(model_sd, state_dict)
- for loaded, stored in zip(model_sd.values(), state_dict.values()):
- # different tensor references
- self.assertFalse(id(loaded) == id(stored))
- # same content
- self.assertTrue(loaded.equal(stored))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/CVPR/LIVE/parallel.cpp b/spaces/CVPR/LIVE/parallel.cpp
deleted file mode 100644
index 365fc5bb305f9cacc780fb5276905e37d3b37e34..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/parallel.cpp
+++ /dev/null
@@ -1,273 +0,0 @@
-#include "parallel.h"
-#include
-#include
-#include
-#include
-#include
-
-// From https://github.com/mmp/pbrt-v3/blob/master/src/core/parallel.cpp
-
-static std::vector threads;
-static bool shutdownThreads = false;
-struct ParallelForLoop;
-static ParallelForLoop *workList = nullptr;
-static std::mutex workListMutex;
-
-struct ParallelForLoop {
- ParallelForLoop(std::function func1D, int64_t maxIndex, int chunkSize)
- : func1D(std::move(func1D)), maxIndex(maxIndex), chunkSize(chunkSize) {
- }
- ParallelForLoop(const std::function &f, const Vector2i count)
- : func2D(f), maxIndex(count[0] * count[1]), chunkSize(1) {
- nX = count[0];
- }
-
- std::function func1D;
- std::function func2D;
- const int64_t maxIndex;
- const int chunkSize;
- int64_t nextIndex = 0;
- int activeWorkers = 0;
- ParallelForLoop *next = nullptr;
- int nX = -1;
-
- bool Finished() const {
- return nextIndex >= maxIndex && activeWorkers == 0;
- }
-};
-
-void Barrier::Wait() {
- std::unique_lock lock(mutex);
- assert(count > 0);
- if (--count == 0) {
- // This is the last thread to reach the barrier; wake up all of the
- // other ones before exiting.
- cv.notify_all();
- } else {
- // Otherwise there are still threads that haven't reached it. Give
- // up the lock and wait to be notified.
- cv.wait(lock, [this] { return count == 0; });
- }
-}
-
-static std::condition_variable workListCondition;
-
-static void worker_thread_func(const int tIndex, std::shared_ptr barrier) {
- ThreadIndex = tIndex;
-
- // The main thread sets up a barrier so that it can be sure that all
- // workers have called ProfilerWorkerThreadInit() before it continues
- // (and actually starts the profiling system).
- barrier->Wait();
-
- // Release our reference to the Barrier so that it's freed once all of
- // the threads have cleared it.
- barrier.reset();
-
- std::unique_lock lock(workListMutex);
- while (!shutdownThreads) {
- if (!workList) {
- // Sleep until there are more tasks to run
- workListCondition.wait(lock);
- } else {
- // Get work from _workList_ and run loop iterations
- ParallelForLoop &loop = *workList;
-
- // Run a chunk of loop iterations for _loop_
-
- // Find the set of loop iterations to run next
- int64_t indexStart = loop.nextIndex;
- int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex);
-
- // Update _loop_ to reflect iterations this thread will run
- loop.nextIndex = indexEnd;
- if (loop.nextIndex == loop.maxIndex)
- workList = loop.next;
- loop.activeWorkers++;
-
- // Run loop indices in _[indexStart, indexEnd)_
- lock.unlock();
- for (int64_t index = indexStart; index < indexEnd; ++index) {
- if (loop.func1D) {
- loop.func1D(index);
- }
- // Handle other types of loops
- else {
- assert(loop.func2D != nullptr);
- loop.func2D(Vector2i{int(index % loop.nX),
- int(index / loop.nX)});
- }
- }
- lock.lock();
-
- // Update _loop_ to reflect completion of iterations
- loop.activeWorkers--;
- if (loop.Finished()) {
- workListCondition.notify_all();
- }
- }
- }
-}
-
-void parallel_for_host(const std::function &func,
- int64_t count,
- int chunkSize) {
- // Run iterations immediately if not using threads or if _count_ is small
- if (threads.empty() || count < chunkSize) {
- for (int64_t i = 0; i < count; ++i) {
- func(i);
- }
- return;
- }
-
- // Create and enqueue _ParallelForLoop_ for this loop
- ParallelForLoop loop(func, count, chunkSize);
- workListMutex.lock();
- loop.next = workList;
- workList = &loop;
- workListMutex.unlock();
-
- // Notify worker threads of work to be done
- std::unique_lock lock(workListMutex);
- workListCondition.notify_all();
-
- // Help out with parallel loop iterations in the current thread
- while (!loop.Finished()) {
- // Run a chunk of loop iterations for _loop_
-
- // Find the set of loop iterations to run next
- int64_t indexStart = loop.nextIndex;
- int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex);
-
- // Update _loop_ to reflect iterations this thread will run
- loop.nextIndex = indexEnd;
- if (loop.nextIndex == loop.maxIndex) {
- workList = loop.next;
- }
- loop.activeWorkers++;
-
- // Run loop indices in _[indexStart, indexEnd)_
- lock.unlock();
- for (int64_t index = indexStart; index < indexEnd; ++index) {
- if (loop.func1D) {
- loop.func1D(index);
- }
- // Handle other types of loops
- else {
- assert(loop.func2D != nullptr);
- loop.func2D(Vector2i{int(index % loop.nX),
- int(index / loop.nX)});
- }
- }
- lock.lock();
-
- // Update _loop_ to reflect completion of iterations
- loop.activeWorkers--;
- }
-}
-
-thread_local int ThreadIndex;
-
-void parallel_for_host(
- std::function func, const Vector2i count) {
- // Launch worker threads if needed
- if (threads.empty() || count.x * count.y <= 1) {
- for (int y = 0; y < count.y; ++y) {
- for (int x = 0; x < count.x; ++x) {
- func(Vector2i{x, y});
- }
- }
- return;
- }
-
- ParallelForLoop loop(std::move(func), count);
- {
- std::lock_guard lock(workListMutex);
- loop.next = workList;
- workList = &loop;
- }
-
- std::unique_lock lock(workListMutex);
- workListCondition.notify_all();
-
- // Help out with parallel loop iterations in the current thread
- while (!loop.Finished()) {
- // Run a chunk of loop iterations for _loop_
-
- // Find the set of loop iterations to run next
- int64_t indexStart = loop.nextIndex;
- int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex);
-
- // Update _loop_ to reflect iterations this thread will run
- loop.nextIndex = indexEnd;
- if (loop.nextIndex == loop.maxIndex) {
- workList = loop.next;
- }
- loop.activeWorkers++;
-
- // Run loop indices in _[indexStart, indexEnd)_
- lock.unlock();
- for (int64_t index = indexStart; index < indexEnd; ++index) {
- if (loop.func1D) {
- loop.func1D(index);
- }
- // Handle other types of loops
- else {
- assert(loop.func2D != nullptr);
- loop.func2D(Vector2i{int(index % loop.nX),
- int(index / loop.nX)});
- }
- }
- lock.lock();
-
- // Update _loop_ to reflect completion of iterations
- loop.activeWorkers--;
- }
-}
-
-int num_system_cores() {
- // return 1;
- int ret = std::thread::hardware_concurrency();
- if (ret == 0) {
- return 16;
- }
- return ret;
-}
-
-void parallel_init() {
- assert(threads.size() == 0);
- int nThreads = num_system_cores();
- ThreadIndex = 0;
-
- // Create a barrier so that we can be sure all worker threads get past
- // their call to ProfilerWorkerThreadInit() before we return from this
- // function. In turn, we can be sure that the profiling system isn't
- // started until after all worker threads have done that.
- std::shared_ptr barrier = std::make_shared(nThreads);
-
- // Launch one fewer worker thread than the total number we want doing
- // work, since the main thread helps out, too.
- for (int i = 0; i < nThreads - 1; ++i) {
- threads.push_back(std::thread(worker_thread_func, i + 1, barrier));
- }
-
- barrier->Wait();
-}
-
-void parallel_cleanup() {
- if (threads.empty()) {
- return;
- }
-
- {
- std::lock_guard lock(workListMutex);
- shutdownThreads = true;
- workListCondition.notify_all();
- }
-
- for (std::thread &thread : threads) {
- thread.join();
- }
- threads.erase(threads.begin(), threads.end());
- shutdownThreads = false;
-}
diff --git a/spaces/CVPR/regionclip-demo/detectron2/solver/build.py b/spaces/CVPR/regionclip-demo/detectron2/solver/build.py
deleted file mode 100644
index c01c0e0c0f88089dbd45eb06b0bdbe4f37343fbe..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/solver/build.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import logging
-from enum import Enum
-from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
-import torch
-from fvcore.common.param_scheduler import CosineParamScheduler, MultiStepParamScheduler
-
-from detectron2.config import CfgNode
-
-from .lr_scheduler import LRMultiplier, WarmupParamScheduler
-
-_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]]
-_GradientClipper = Callable[[_GradientClipperInput], None]
-
-
-class GradientClipType(Enum):
- VALUE = "value"
- NORM = "norm"
-
-
-def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:
- """
- Creates gradient clipping closure to clip by value or by norm,
- according to the provided config.
- """
- cfg = copy.deepcopy(cfg)
-
- def clip_grad_norm(p: _GradientClipperInput):
- torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE)
-
- def clip_grad_value(p: _GradientClipperInput):
- torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE)
-
- _GRADIENT_CLIP_TYPE_TO_CLIPPER = {
- GradientClipType.VALUE: clip_grad_value,
- GradientClipType.NORM: clip_grad_norm,
- }
- return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)]
-
-
-def _generate_optimizer_class_with_gradient_clipping(
- optimizer: Type[torch.optim.Optimizer],
- *,
- per_param_clipper: Optional[_GradientClipper] = None,
- global_clipper: Optional[_GradientClipper] = None,
-) -> Type[torch.optim.Optimizer]:
- """
- Dynamically creates a new type that inherits the type of a given instance
- and overrides the `step` method to add gradient clipping
- """
- assert (
- per_param_clipper is None or global_clipper is None
- ), "Not allowed to use both per-parameter clipping and global clipping"
-
- def optimizer_wgc_step(self, closure=None):
- if per_param_clipper is not None:
- for group in self.param_groups:
- for p in group["params"]:
- per_param_clipper(p)
- else:
- # global clipper for future use with detr
- # (https://github.com/facebookresearch/detr/pull/287)
- all_params = itertools.chain(*[g["params"] for g in self.param_groups])
- global_clipper(all_params)
- super(type(self), self).step(closure)
-
- OptimizerWithGradientClip = type(
- optimizer.__name__ + "WithGradientClip",
- (optimizer,),
- {"step": optimizer_wgc_step},
- )
- return OptimizerWithGradientClip
-
-
-def maybe_add_gradient_clipping(
- cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]
-) -> Type[torch.optim.Optimizer]:
- """
- If gradient clipping is enabled through config options, wraps the existing
- optimizer type to become a new dynamically created class OptimizerWithGradientClip
- that inherits the given optimizer and overrides the `step` method to
- include gradient clipping.
-
- Args:
- cfg: CfgNode, configuration options
- optimizer: type. A subclass of torch.optim.Optimizer
-
- Return:
- type: either the input `optimizer` (if gradient clipping is disabled), or
- a subclass of it with gradient clipping included in the `step` method.
- """
- if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
- return optimizer
- if isinstance(optimizer, torch.optim.Optimizer):
- optimizer_type = type(optimizer)
- else:
- assert issubclass(optimizer, torch.optim.Optimizer), optimizer
- optimizer_type = optimizer
-
- grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)
- OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(
- optimizer_type, per_param_clipper=grad_clipper
- )
- if isinstance(optimizer, torch.optim.Optimizer):
- optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended
- return optimizer
- else:
- return OptimizerWithGradientClip
-
-
-def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
- """
- Build an optimizer from config.
- """
- params = get_default_optimizer_params(
- model,
- base_lr=cfg.SOLVER.BASE_LR,
- weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
- bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
- weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
- )
- return maybe_add_gradient_clipping(cfg, torch.optim.SGD)(
- params,
- lr=cfg.SOLVER.BASE_LR,
- momentum=cfg.SOLVER.MOMENTUM,
- nesterov=cfg.SOLVER.NESTEROV,
- weight_decay=cfg.SOLVER.WEIGHT_DECAY,
- )
-
-
-def get_default_optimizer_params(
- model: torch.nn.Module,
- base_lr: Optional[float] = None,
- weight_decay: Optional[float] = None,
- weight_decay_norm: Optional[float] = None,
- bias_lr_factor: Optional[float] = 1.0,
- weight_decay_bias: Optional[float] = None,
- overrides: Optional[Dict[str, Dict[str, float]]] = None,
-):
- """
- Get default param list for optimizer, with support for a few types of
- overrides. If no overrides needed, this is equivalent to `model.parameters()`.
-
- Args:
- base_lr: lr for every group by default. Can be omitted to use the one in optimizer.
- weight_decay: weight decay for every group by default. Can be omitted to use the one
- in optimizer.
- weight_decay_norm: override weight decay for params in normalization layers
- bias_lr_factor: multiplier of lr for bias parameters.
- weight_decay_bias: override weight decay for bias parameters
- overrides: if not `None`, provides values for optimizer hyperparameters
- (LR, weight decay) for module parameters with a given name; e.g.
- ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and
- weight decay values for all module parameters named `embedding`.
-
- For common detection models, ``weight_decay_norm`` is the only option
- needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings
- from Detectron1 that are not found useful.
-
- Example:
- ::
- torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0),
- lr=0.01, weight_decay=1e-4, momentum=0.9)
- """
- if overrides is None:
- overrides = {}
- defaults = {}
- if base_lr is not None:
- defaults["lr"] = base_lr
- if weight_decay is not None:
- defaults["weight_decay"] = weight_decay
- bias_overrides = {}
- if bias_lr_factor is not None and bias_lr_factor != 1.0:
- # NOTE: unlike Detectron v1, we now by default make bias hyperparameters
- # exactly the same as regular weights.
- if base_lr is None:
- raise ValueError("bias_lr_factor requires base_lr")
- bias_overrides["lr"] = base_lr * bias_lr_factor
- if weight_decay_bias is not None:
- bias_overrides["weight_decay"] = weight_decay_bias
- if len(bias_overrides):
- if "bias" in overrides:
- raise ValueError("Conflicting overrides for 'bias'")
- overrides["bias"] = bias_overrides
-
- norm_module_types = (
- torch.nn.BatchNorm1d,
- torch.nn.BatchNorm2d,
- torch.nn.BatchNorm3d,
- torch.nn.SyncBatchNorm,
- # NaiveSyncBatchNorm inherits from BatchNorm2d
- torch.nn.GroupNorm,
- torch.nn.InstanceNorm1d,
- torch.nn.InstanceNorm2d,
- torch.nn.InstanceNorm3d,
- torch.nn.LayerNorm,
- torch.nn.LocalResponseNorm,
- )
- params: List[Dict[str, Any]] = []
- memo: Set[torch.nn.parameter.Parameter] = set()
- for module in model.modules():
- for module_param_name, value in module.named_parameters(recurse=False):
- if not value.requires_grad:
- continue
- # Avoid duplicating parameters
- if value in memo:
- continue
- memo.add(value)
-
- hyperparams = copy.copy(defaults)
- if isinstance(module, norm_module_types) and weight_decay_norm is not None:
- hyperparams["weight_decay"] = weight_decay_norm
- hyperparams.update(overrides.get(module_param_name, {}))
- params.append({"params": [value], **hyperparams})
- return params
-
-
-def build_lr_scheduler(
- cfg: CfgNode, optimizer: torch.optim.Optimizer
-) -> torch.optim.lr_scheduler._LRScheduler:
- """
- Build a LR scheduler from config.
- """
- name = cfg.SOLVER.LR_SCHEDULER_NAME
-
- if name == "WarmupMultiStepLR":
- steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER]
- if len(steps) != len(cfg.SOLVER.STEPS):
- logger = logging.getLogger(__name__)
- logger.warning(
- "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. "
- "These values will be ignored."
- )
- sched = MultiStepParamScheduler(
- values=[cfg.SOLVER.GAMMA ** k for k in range(len(steps) + 1)],
- milestones=steps,
- num_updates=cfg.SOLVER.MAX_ITER,
- )
- elif name == "WarmupCosineLR":
- sched = CosineParamScheduler(1, 0)
- else:
- raise ValueError("Unknown LR scheduler: {}".format(name))
-
- sched = WarmupParamScheduler(
- sched,
- cfg.SOLVER.WARMUP_FACTOR,
- min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0),
- cfg.SOLVER.WARMUP_METHOD,
- )
- return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER)
diff --git a/spaces/CarlDennis/HYTTS/text/cleaners.py b/spaces/CarlDennis/HYTTS/text/cleaners.py
deleted file mode 100644
index 01aaf27805b92438582142e99a3498ddedb4877e..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/cleaners.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import re
-from text.japanese import japanese_to_romaji_with_accent
-from text.mandarin import chinese_to_romaji
-from text.english import english_to_ipa2
-from text.german import german_to_ipa
-from text.croatia_to_ipa import croatian_to_ipa
-
-def cjehd_cleaners(text):
- chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
- japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
- croatian_texts = re.findall(r'\[CR\].*?\[CR\]', text)
- english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
- german_texts = re.findall(r'\[DE\].*?\[DE\]', text)
- for chinese_text in chinese_texts:
- cleaned_text = chinese_to_romaji(chinese_text[4:-4])
- text = text.replace(chinese_text, cleaned_text+' ', 1)
- for japanese_text in japanese_texts:
- cleaned_text = japanese_to_romaji_with_accent(
- japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')
- text = text.replace(japanese_text, cleaned_text+' ', 1)
- for english_text in english_texts:
- cleaned_text = english_to_ipa2(english_text[4:-4])
- text = text.replace(english_text, cleaned_text+' ', 1)
- for croatian_text in croatian_texts:
- cleaned_text = croatian_to_ipa(croatian_text[4:-4])
- cleaned_text = cleaned_text.replace('ḱ','k')
- text = text.replace(croatian_text, cleaned_text + ' ', 1)
- for german_text in german_texts:
- german_text = german_text.replace('...','').replace('--','').replace('-','')
- cleaned_text = german_to_ipa(german_text[4:-4])
- text = text.replace(german_text, cleaned_text + ' ', 1)
- text = text[:-1]
- if re.match(r'[^\.,!\?\-…~]', text[-1]):
- text += '.'
- return text
diff --git a/spaces/Chitranshu/Dashboard-Zomato/README.md b/spaces/Chitranshu/Dashboard-Zomato/README.md
deleted file mode 100644
index 3c8855c3636881fd6e8702acd5252001d5201a11..0000000000000000000000000000000000000000
--- a/spaces/Chitranshu/Dashboard-Zomato/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Zomato-Dashboard
-emoji: 📊
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: false
-
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/trainer_pt_utils.py b/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/trainer_pt_utils.py
deleted file mode 100644
index 5b8f92cf3be3ecdf2ad10c3e1c6693be792d2fe5..0000000000000000000000000000000000000000
--- a/spaces/Cletrason/Cletrason-toad-in-the-mario-movie/trainer_pt_utils.py
+++ /dev/null
@@ -1,1106 +0,0 @@
-# coding=utf-8
-# Copyright 2020-present the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Torch utilities for the Trainer class.
-"""
-
-import datetime
-import json
-import math
-import os
-import sys
-import warnings
-from collections.abc import Mapping
-from contextlib import contextmanager
-from dataclasses import dataclass
-from logging import StreamHandler
-from typing import Any, Dict, Iterator, List, Optional, Union
-
-import numpy as np
-import torch
-import torch.distributed as dist
-from torch import nn
-from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
-from torch.utils.data.distributed import DistributedSampler
-
-from .tokenization_utils_base import BatchEncoding
-from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
-
-
-if is_training_run_on_sagemaker():
- logging.add_handler(StreamHandler(sys.stdout))
-
-if is_torch_tpu_available(check_device=False):
- import torch_xla.core.xla_model as xm
-
-# this is used to suppress an undesired warning emitted by pytorch versions 1.4.2-1.7.0
-try:
- from torch.optim.lr_scheduler import SAVE_STATE_WARNING
-except ImportError:
- SAVE_STATE_WARNING = ""
-
-logger = logging.get_logger(__name__)
-
-
-def atleast_1d(tensor_or_array: Union[torch.Tensor, np.ndarray]):
- if isinstance(tensor_or_array, torch.Tensor):
- if hasattr(torch, "atleast_1d"):
- tensor_or_array = torch.atleast_1d(tensor_or_array)
- elif tensor_or_array.ndim < 1:
- tensor_or_array = tensor_or_array[None]
- else:
- tensor_or_array = np.atleast_1d(tensor_or_array)
- return tensor_or_array
-
-
-def torch_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
- """Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
- tensor1 = atleast_1d(tensor1)
- tensor2 = atleast_1d(tensor2)
-
- if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
- return torch.cat((tensor1, tensor2), dim=0)
-
- # Let's figure out the new shape
- new_shape = (tensor1.shape[0] + tensor2.shape[0], max(tensor1.shape[1], tensor2.shape[1])) + tensor1.shape[2:]
-
- # Now let's fill the result tensor
- result = tensor1.new_full(new_shape, padding_index)
- result[: tensor1.shape[0], : tensor1.shape[1]] = tensor1
- result[tensor1.shape[0] :, : tensor2.shape[1]] = tensor2
- return result
-
-
-def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
- """Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary."""
- array1 = atleast_1d(array1)
- array2 = atleast_1d(array2)
-
- if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
- return np.concatenate((array1, array2), axis=0)
-
- # Let's figure out the new shape
- new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:]
-
- # Now let's fill the result tensor
- result = np.full_like(array1, padding_index, shape=new_shape)
- result[: array1.shape[0], : array1.shape[1]] = array1
- result[array1.shape[0] :, : array2.shape[1]] = array2
- return result
-
-
-def nested_concat(tensors, new_tensors, padding_index=-100):
- """
- Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
- nested list/tuples/dict of tensors.
- """
- assert type(tensors) == type(
- new_tensors
- ), f"Expected `tensors` and `new_tensors` to have the same type but found {type(tensors)} and {type(new_tensors)}."
- if isinstance(tensors, (list, tuple)):
- return type(tensors)(nested_concat(t, n, padding_index=padding_index) for t, n in zip(tensors, new_tensors))
- elif isinstance(tensors, torch.Tensor):
- return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
- elif isinstance(tensors, Mapping):
- return type(tensors)(
- {k: nested_concat(t, new_tensors[k], padding_index=padding_index) for k, t in tensors.items()}
- )
- elif isinstance(tensors, np.ndarray):
- return numpy_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
- else:
- raise TypeError(f"Unsupported type for concatenation: got {type(tensors)}")
-
-
-def find_batch_size(tensors):
- """
- Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
- """
- if isinstance(tensors, (list, tuple)):
- for t in tensors:
- result = find_batch_size(t)
- if result is not None:
- return result
- elif isinstance(tensors, Mapping):
- for key, value in tensors.items():
- result = find_batch_size(value)
- if result is not None:
- return result
- elif isinstance(tensors, torch.Tensor):
- return tensors.shape[0] if len(tensors.shape) >= 1 else None
- elif isinstance(tensors, np.ndarray):
- return tensors.shape[0] if len(tensors.shape) >= 1 else None
-
-
-def nested_numpify(tensors):
- "Numpify `tensors` (even if it's a nested list/tuple/dict of tensors)."
- if isinstance(tensors, (list, tuple)):
- return type(tensors)(nested_numpify(t) for t in tensors)
- if isinstance(tensors, Mapping):
- return type(tensors)({k: nested_numpify(t) for k, t in tensors.items()})
-
- t = tensors.cpu()
- if t.dtype == torch.bfloat16:
- # As of Numpy 1.21.4, NumPy does not support bfloat16 (see
- # https://github.com/numpy/numpy/blob/a47ecdea856986cd60eabbd53265c2ca5916ad5d/doc/source/user/basics.types.rst ).
- # Until Numpy adds bfloat16, we must convert float32.
- t = t.to(torch.float32)
- return t.numpy()
-
-
-def nested_detach(tensors):
- "Detach `tensors` (even if it's a nested list/tuple/dict of tensors)."
- if isinstance(tensors, (list, tuple)):
- return type(tensors)(nested_detach(t) for t in tensors)
- elif isinstance(tensors, Mapping):
- return type(tensors)({k: nested_detach(t) for k, t in tensors.items()})
- return tensors.detach()
-
-
-def nested_xla_mesh_reduce(tensors, name):
- if is_torch_tpu_available():
- import torch_xla.core.xla_model as xm
-
- if isinstance(tensors, (list, tuple)):
- return type(tensors)(nested_xla_mesh_reduce(t, f"{name}_{i}") for i, t in enumerate(tensors))
- if isinstance(tensors, Mapping):
- return type(tensors)(
- {k: nested_xla_mesh_reduce(t, f"{name}_{i}") for i, (k, t) in enumerate(tensors.items())}
- )
-
- tensors = atleast_1d(tensors)
- return xm.mesh_reduce(name, tensors, torch.cat)
- else:
- raise ImportError("Torch xla must be installed to use `nested_xla_mesh_reduce`")
-
-
-def distributed_concat(tensor: Any, num_total_examples: Optional[int] = None) -> Any:
- try:
- if isinstance(tensor, (tuple, list)):
- return type(tensor)(distributed_concat(t, num_total_examples) for t in tensor)
- if isinstance(tensor, Mapping):
- return type(tensor)({k: distributed_concat(t, num_total_examples) for k, t in tensor.items()})
- tensor = atleast_1d(tensor).contiguous()
- output_tensors = [tensor.clone() for _ in range(dist.get_world_size())]
- dist.all_gather(output_tensors, tensor)
- concat = torch.cat(output_tensors, dim=0)
-
- # truncate the dummy elements added by SequentialDistributedSampler
- if num_total_examples is not None:
- concat = concat[:num_total_examples]
- return concat
- except AssertionError:
- raise AssertionError("Not currently using distributed training")
-
-
-def distributed_broadcast_scalars(
- scalars: List[Union[int, float]],
- num_total_examples: Optional[int] = None,
- device: Optional[torch.device] = torch.device("cuda"),
-) -> torch.Tensor:
- try:
- tensorized_scalar = torch.tensor(scalars).to(device)
- output_tensors = [tensorized_scalar.clone() for _ in range(dist.get_world_size())]
- dist.all_gather(output_tensors, tensorized_scalar)
- concat = torch.cat(output_tensors, dim=0)
-
- # truncate the dummy elements added by SequentialDistributedSampler
- if num_total_examples is not None:
- concat = concat[:num_total_examples]
- return concat
- except AssertionError:
- raise AssertionError("Not currently using distributed training")
-
-
-def reissue_pt_warnings(caught_warnings):
- # Reissue warnings that are not the SAVE_STATE_WARNING
- if len(caught_warnings) > 1:
- for w in caught_warnings:
- if w.category != UserWarning or w.message != SAVE_STATE_WARNING:
- warnings.warn(w.message, w.category)
-
-
-@contextmanager
-def torch_distributed_zero_first(local_rank: int):
- """
- Decorator to make all processes in distributed training wait for each local_master to do something.
-
- Args:
- local_rank (`int`): The rank of the local process.
- """
- if local_rank not in [-1, 0]:
- dist.barrier()
- yield
- if local_rank == 0:
- dist.barrier()
-
-
-class DistributedSamplerWithLoop(DistributedSampler):
- """
- Like a torch.utils.data.distributed.DistributedSampler` but loops at the end back to the beginning of the shuffled
- samples to make each process have a round multiple of batch_size samples.
-
- Args:
- dataset (`torch.utils.data.Dataset`):
- Dataset used for sampling.
- batch_size (`int`):
- The batch size used with this sampler
- kwargs:
- All other keyword arguments passed to `DistributedSampler`.
- """
-
- def __init__(self, dataset, batch_size, **kwargs):
- super().__init__(dataset, **kwargs)
- self.batch_size = batch_size
-
- def __iter__(self):
- indices = list(super().__iter__())
- remainder = 0 if len(indices) % self.batch_size == 0 else self.batch_size - len(indices) % self.batch_size
- # DistributedSampler already added samples from the beginning to make the number of samples a round multiple
- # of the world size, so we skip those.
- start_remainder = 1 if self.rank < len(self.dataset) % self.num_replicas else 0
- indices += indices[start_remainder : start_remainder + remainder]
- return iter(indices)
-
-
-class SequentialDistributedSampler(Sampler):
- """
- Distributed Sampler that subsamples indices sequentially, making it easier to collate all results at the end.
-
- Even though we only use this sampler for eval and predict (no training), which means that the model params won't
- have to be synced (i.e. will not hang for synchronization even if varied number of forward passes), we still add
- extra samples to the sampler to make it evenly divisible (like in `DistributedSampler`) to make it easy to `gather`
- or `reduce` resulting tensors at the end of the loop.
- """
-
- def __init__(self, dataset, num_replicas=None, rank=None, batch_size=None):
- warnings.warn(
- "SequentialDistributedSampler is deprecated and will be removed in v5 of Transformers.",
- FutureWarning,
- )
- if num_replicas is None:
- if not dist.is_available():
- raise RuntimeError("Requires distributed package to be available")
- num_replicas = dist.get_world_size()
- if rank is None:
- if not dist.is_available():
- raise RuntimeError("Requires distributed package to be available")
- rank = dist.get_rank()
- self.dataset = dataset
- self.num_replicas = num_replicas
- self.rank = rank
- num_samples = len(self.dataset)
- # Add extra samples to make num_samples a multiple of batch_size if passed
- if batch_size is not None:
- self.num_samples = int(math.ceil(num_samples / (batch_size * num_replicas))) * batch_size
- else:
- self.num_samples = int(math.ceil(num_samples / num_replicas))
- self.total_size = self.num_samples * self.num_replicas
- self.batch_size = batch_size
-
- def __iter__(self):
- indices = list(range(len(self.dataset)))
-
- # add extra samples to make it evenly divisible
- indices += indices[: (self.total_size - len(indices))]
- assert (
- len(indices) == self.total_size
- ), f"Indices length {len(indices)} and total size {self.total_size} mismatched"
-
- # subsample
- indices = indices[self.rank * self.num_samples : (self.rank + 1) * self.num_samples]
- assert (
- len(indices) == self.num_samples
- ), f"Indices length {len(indices)} and sample number {self.num_samples} mismatched"
-
- return iter(indices)
-
- def __len__(self):
- return self.num_samples
-
-
-def get_tpu_sampler(dataset: torch.utils.data.Dataset, batch_size: int):
- if xm.xrt_world_size() <= 1:
- return RandomSampler(dataset)
- return DistributedSampler(dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
-
-
-def nested_new_like(arrays, num_samples, padding_index=-100):
- """Create the same nested structure as `arrays` with a first dimension always at `num_samples`."""
- if isinstance(arrays, (list, tuple)):
- return type(arrays)(nested_new_like(x, num_samples) for x in arrays)
- return np.full_like(arrays, padding_index, shape=(num_samples, *arrays.shape[1:]))
-
-
-def expand_like(arrays, new_seq_length, padding_index=-100):
- """Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding."""
- result = np.full_like(arrays, padding_index, shape=(arrays.shape[0], new_seq_length) + arrays.shape[2:])
- result[:, : arrays.shape[1]] = arrays
- return result
-
-
-def nested_truncate(tensors, limit):
- "Truncate `tensors` at `limit` (even if it's a nested list/tuple/dict of tensors)."
- if isinstance(tensors, (list, tuple)):
- return type(tensors)(nested_truncate(t, limit) for t in tensors)
- if isinstance(tensors, Mapping):
- return type(tensors)({k: nested_truncate(t, limit) for k, t in tensors.items()})
-
- return tensors[:limit]
-
-
-class DistributedTensorGatherer:
- """
- A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks.
-
- If our dataset has 16 samples with a batch size of 2 on 3 processes and we gather then transfer on CPU at every
- step, our sampler will generate the following indices:
-
- `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1]`
-
- to get something of size a multiple of 3 (so that each process gets the same dataset length). Then process 0, 1 and
- 2 will be responsible of making predictions for the following samples:
-
- - P0: `[0, 1, 2, 3, 4, 5]`
- - P1: `[6, 7, 8, 9, 10, 11]`
- - P2: `[12, 13, 14, 15, 0, 1]`
-
- The first batch treated on each process will be
-
- - P0: `[0, 1]`
- - P1: `[6, 7]`
- - P2: `[12, 13]`
-
- So if we gather at the end of the first batch, we will get a tensor (nested list/tuple of tensor) corresponding to
- the following indices:
-
- `[0, 1, 6, 7, 12, 13]`
-
- If we directly concatenate our results without taking any precautions, the user will then get the predictions for
- the indices in this order at the end of the prediction loop:
-
- `[0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1]`
-
- For some reason, that's not going to roll their boat. This class is there to solve that problem.
-
- Args:
- world_size (`int`):
- The number of processes used in the distributed training.
- num_samples (`int`):
- The number of samples in our dataset.
- make_multiple_of (`int`, *optional*):
- If passed, the class assumes the datasets passed to each process are made to be a multiple of this argument
- (by adding samples).
- padding_index (`int`, *optional*, defaults to -100):
- The padding index to use if the arrays don't all have the same sequence length.
- """
-
- def __init__(self, world_size, num_samples, make_multiple_of=None, padding_index=-100):
- warnings.warn(
- "DistributedTensorGatherer is deprecated and will be removed in v5 of Transformers.",
- FutureWarning,
- )
- self.world_size = world_size
- self.num_samples = num_samples
- total_size = world_size if make_multiple_of is None else world_size * make_multiple_of
- self.total_samples = int(np.ceil(num_samples / total_size)) * total_size
- self.process_length = self.total_samples // world_size
- self._storage = None
- self._offsets = None
- self.padding_index = padding_index
-
- def add_arrays(self, arrays):
- """
- Add `arrays` to the internal storage, Will initialize the storage to the full size at the first arrays passed
- so that if we're bound to get an OOM, it happens at the beginning.
- """
- if arrays is None:
- return
- if self._storage is None:
- self._storage = nested_new_like(arrays, self.total_samples, padding_index=self.padding_index)
- self._offsets = list(range(0, self.total_samples, self.process_length))
-
- slice_len, self._storage = self._nested_set_tensors(self._storage, arrays)
- for i in range(self.world_size):
- self._offsets[i] += slice_len
-
- def _nested_set_tensors(self, storage, arrays):
- if isinstance(arrays, (list, tuple)):
- result = [self._nested_set_tensors(x, y) for x, y in zip(storage, arrays)]
- return result[0][0], type(arrays)(r[1] for r in result)
- assert (
- arrays.shape[0] % self.world_size == 0
- ), f"Arrays passed should all have a first dimension multiple of {self.world_size}, found {arrays.shape[0]}."
-
- slice_len = arrays.shape[0] // self.world_size
- for i in range(self.world_size):
- if len(arrays.shape) == 1:
- storage[self._offsets[i] : self._offsets[i] + slice_len] = arrays[i * slice_len : (i + 1) * slice_len]
- else:
- # Expand the array on the fly if needed.
- if len(storage.shape) > 1 and storage.shape[1] < arrays.shape[1]:
- storage = expand_like(storage, arrays.shape[1], padding_index=self.padding_index)
- storage[self._offsets[i] : self._offsets[i] + slice_len, : arrays.shape[1]] = arrays[
- i * slice_len : (i + 1) * slice_len
- ]
- return slice_len, storage
-
- def finalize(self):
- """
- Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
- to get each process a dataset of the same length).
- """
- if self._storage is None:
- return
- if self._offsets[0] != self.process_length:
- logger.warning("Not all data has been set. Are you sure you passed all values?")
- return nested_truncate(self._storage, self.num_samples)
-
-
-@dataclass
-class LabelSmoother:
- """
- Adds label-smoothing on a pre-computed output from a Transformers model.
-
- Args:
- epsilon (`float`, *optional*, defaults to 0.1):
- The label smoothing factor.
- ignore_index (`int`, *optional*, defaults to -100):
- The index in the labels to ignore when computing the loss.
- """
-
- epsilon: float = 0.1
- ignore_index: int = -100
-
- def __call__(self, model_output, labels, shift_labels=False):
- logits = model_output["logits"] if isinstance(model_output, dict) else model_output[0]
- if shift_labels:
- logits = logits[..., :-1, :].contiguous()
- labels = labels[..., 1:].contiguous()
-
- log_probs = -nn.functional.log_softmax(logits, dim=-1)
- if labels.dim() == log_probs.dim() - 1:
- labels = labels.unsqueeze(-1)
-
- padding_mask = labels.eq(self.ignore_index)
- # In case the ignore_index is -100, the gather will fail, so we replace labels by 0. The padding_mask
- # will ignore them in any case.
- labels = torch.clamp(labels, min=0)
- nll_loss = log_probs.gather(dim=-1, index=labels)
- # works for fp16 input tensor too, by internally upcasting it to fp32
- smoothed_loss = log_probs.sum(dim=-1, keepdim=True, dtype=torch.float32)
-
- nll_loss.masked_fill_(padding_mask, 0.0)
- smoothed_loss.masked_fill_(padding_mask, 0.0)
-
- # Take the mean over the label dimensions, then divide by the number of active elements (i.e. not-padded):
- num_active_elements = padding_mask.numel() - padding_mask.long().sum()
- nll_loss = nll_loss.sum() / num_active_elements
- smoothed_loss = smoothed_loss.sum() / (num_active_elements * log_probs.shape[-1])
- return (1 - self.epsilon) * nll_loss + self.epsilon * smoothed_loss
-
-
-def get_length_grouped_indices(lengths, batch_size, mega_batch_mult=None, generator=None):
- """
- Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar
- lengths. To do this, the indices are:
-
- - randomly permuted
- - grouped in mega-batches of size `mega_batch_mult * batch_size`
- - sorted by length in each mega-batch
-
- The result is the concatenation of all mega-batches, with the batch of `batch_size` containing the element of
- maximum length placed first, so that an OOM happens sooner rather than later.
- """
- # Default for mega_batch_mult: 50 or the number to get 4 megabatches, whichever is smaller.
- if mega_batch_mult is None:
- mega_batch_mult = min(len(lengths) // (batch_size * 4), 50)
- # Just in case, for tiny datasets
- if mega_batch_mult == 0:
- mega_batch_mult = 1
-
- # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
- indices = torch.randperm(len(lengths), generator=generator)
- megabatch_size = mega_batch_mult * batch_size
- megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
- megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
-
- # The rest is to get the biggest batch first.
- # Since each megabatch is sorted by descending length, the longest element is the first
- megabatch_maximums = [lengths[megabatch[0]] for megabatch in megabatches]
- max_idx = torch.argmax(torch.tensor(megabatch_maximums)).item()
- # Switch to put the longest element in first position
- megabatches[0][0], megabatches[max_idx][0] = megabatches[max_idx][0], megabatches[0][0]
-
- return [i for megabatch in megabatches for i in megabatch]
-
-
-class LengthGroupedSampler(Sampler):
- r"""
- Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
- keeping a bit of randomness.
- """
-
- def __init__(
- self,
- batch_size: int,
- dataset: Optional[Dataset] = None,
- lengths: Optional[List[int]] = None,
- model_input_name: Optional[str] = None,
- generator=None,
- ):
- if dataset is None and lengths is None:
- raise ValueError("One of dataset and lengths must be provided.")
-
- self.batch_size = batch_size
- if lengths is None:
- model_input_name = model_input_name if model_input_name is not None else "input_ids"
- if (
- not (isinstance(dataset[0], dict) or isinstance(dataset[0], BatchEncoding))
- or model_input_name not in dataset[0]
- ):
- raise ValueError(
- "Can only automatically infer lengths for datasets whose items are dictionaries with an "
- f"'{model_input_name}' key."
- )
- lengths = [len(feature[model_input_name]) for feature in dataset]
- elif isinstance(lengths, torch.Tensor):
- logger.info(
- "If lengths is a torch.Tensor, LengthGroupedSampler will be slow. Converting lengths to List[int]..."
- )
- lengths = lengths.tolist()
-
- self.lengths = lengths
- self.generator = generator
-
- def __len__(self):
- return len(self.lengths)
-
- def __iter__(self):
- indices = get_length_grouped_indices(self.lengths, self.batch_size, generator=self.generator)
- return iter(indices)
-
-
-class DistributedLengthGroupedSampler(DistributedSampler):
- r"""
- Distributed Sampler that samples indices in a way that groups together features of the dataset of roughly the same
- length while keeping a bit of randomness.
- """
-
- # Copied and adapted from PyTorch DistributedSampler.
- def __init__(
- self,
- batch_size: int,
- dataset: Optional[Dataset] = None,
- num_replicas: Optional[int] = None,
- rank: Optional[int] = None,
- seed: int = 0,
- drop_last: bool = False,
- lengths: Optional[List[int]] = None,
- model_input_name: Optional[str] = None,
- ):
- if dataset is None and lengths is None:
- raise ValueError("One of dataset and lengths must be provided.")
- if num_replicas is None:
- if not dist.is_available():
- raise RuntimeError("Requires distributed package to be available")
- num_replicas = dist.get_world_size()
- if rank is None:
- if not dist.is_available():
- raise RuntimeError("Requires distributed package to be available")
- rank = dist.get_rank()
-
- self.batch_size = batch_size
- self.num_replicas = num_replicas
- self.rank = rank
- self.epoch = 0
- self.drop_last = drop_last
-
- if lengths is None:
- model_input_name = model_input_name if model_input_name is not None else "input_ids"
- if (
- not (isinstance(dataset[0], dict) or isinstance(dataset[0], BatchEncoding))
- or model_input_name not in dataset[0]
- ):
- raise ValueError(
- "Can only automatically infer lengths for datasets whose items are dictionaries with an "
- f"'{model_input_name}' key."
- )
- lengths = [len(feature[model_input_name]) for feature in dataset]
- elif isinstance(lengths, torch.Tensor):
- logger.info(
- "If lengths is a torch.Tensor, DistributedLengthGroupedSampler will be slow. Converting lengths to"
- " List[int]..."
- )
- lengths = lengths.tolist()
-
- self.lengths = lengths
-
- # If the dataset length is evenly divisible by # of replicas, then there
- # is no need to drop any data, since the dataset will be split equally.
- if self.drop_last and len(self.lengths) % self.num_replicas != 0:
- # Split to nearest available length that is evenly divisible.
- # This is to ensure each rank receives the same amount of data when
- # using this Sampler.
- self.num_samples = math.ceil((len(self.lengths) - self.num_replicas) / self.num_replicas)
- else:
- self.num_samples = math.ceil(len(self.lengths) / self.num_replicas)
- self.total_size = self.num_samples * self.num_replicas
- self.seed = seed
-
- def __iter__(self) -> Iterator:
- # Deterministically shuffle based on epoch and seed
- g = torch.Generator()
- g.manual_seed(self.seed + self.epoch)
- indices = get_length_grouped_indices(self.lengths, self.batch_size, generator=g)
-
- if not self.drop_last:
- # add extra samples to make it evenly divisible
- indices += indices[: (self.total_size - len(indices))]
- else:
- # remove tail of data to make it evenly divisible.
- indices = indices[: self.total_size]
- assert len(indices) == self.total_size
-
- # subsample
- indices = indices[self.rank : self.total_size : self.num_replicas]
- assert len(indices) == self.num_samples
-
- return iter(indices)
-
-
-class ShardSampler(Sampler):
- """
- Sampler that shards batches between several processes. Dispatches indices batch by batch: on 2 processes with batch
- size 4, the first two batches are `[0, 1, 2, 3, 4, 5, 6, 7]` and `[8, 9, 10, 11, 12, 13, 14, 15]`, which shard into
- `[0, 1, 2, 3]` and `[8, 9, 10, 11]` for GPU-0 and `[4, 5, 6, 7]` and `[12, 13, 14, 15]` for GPU-1.
-
- The sampler thus yields `[0, 1, 2, 3, 8, 9, 10, 11]` on GPU-0 and `[4, 5, 6, 7, 12, 13, 14, 15]` on GPU-1.
- """
-
- def __init__(
- self,
- dataset: Dataset,
- batch_size: int = 1,
- drop_last: bool = False,
- num_processes: int = 1,
- process_index: int = 0,
- ):
- self.dataset = dataset
- self.batch_size = batch_size
- self.drop_last = drop_last
- self.num_processes = num_processes
- self.process_index = process_index
-
- self.total_batch_size = total_batch_size = batch_size * num_processes
-
- num_batches = len(dataset) // total_batch_size if drop_last else math.ceil(len(dataset) / total_batch_size)
- self.total_num_samples = num_batches * total_batch_size
-
- def __iter__(self):
- indices = list(range(len(self.dataset)))
-
- # Add extra samples to make it evenly divisible. While loop is there in the edge case we have a tiny dataset
- # and it needs to be done several times.
- while len(indices) < self.total_num_samples:
- indices += indices[: (self.total_num_samples - len(indices))]
-
- result = []
- for batch_start in range(self.batch_size * self.process_index, self.total_num_samples, self.total_batch_size):
- result += indices[batch_start : batch_start + self.batch_size]
-
- return iter(result)
-
- def __len__(self):
- # Each shard only sees a fraction of total_num_samples.
- return self.total_num_samples // self.num_processes
-
-
-class IterableDatasetShard(IterableDataset):
- """
- Wraps a PyTorch `IterableDataset` to generate samples for one of the processes only. Instances of this class will
- always yield a number of samples that is a round multiple of the actual batch size (which is `batch_size x
- num_processes`). Depending on the value of the `drop_last` attribute, it will either stop the iteration at the
- first batch that would be too small or loop with indices from the beginning.
-
- On two processes with an iterable dataset yielding of `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]` with a batch size of
- 2:
-
- - the shard on process 0 will yield `[0, 1, 4, 5, 8, 9]` so will see batches `[0, 1]`, `[4, 5]`, `[8, 9]`
- - the shard on process 1 will yield `[2, 3, 6, 7, 10, 11]` so will see batches `[2, 3]`, `[6, 7]`, `[10, 11]`
-
-
-
- If your IterableDataset implements some randomization that needs to be applied the same way on all processes
- (for instance, a shuffling), you should use a `torch.Generator` in a `generator` attribute of the `dataset` to
- generate your random numbers and call the [`~trainer_pt_utils.IterableDatasetShard.set_epoch`] method of this
- object. It will set the seed of this `generator` to `seed + epoch` on all processes before starting the
- iteration. Alternatively, you can also implement a `set_epoch()` method in your iterable dataset to deal with
- this.
-
-
-
- Args:
- dataset (`torch.utils.data.IterableDataset`):
- The batch sampler to split in several shards.
- batch_size (`int`, *optional*, defaults to 1):
- The size of the batches per shard.
- drop_last (`bool`, *optional*, defaults to `False`):
- Whether or not to drop the last incomplete batch or complete the last batches by using the samples from the
- beginning.
- num_processes (`int`, *optional*, defaults to 1):
- The number of processes running concurrently.
- process_index (`int`, *optional*, defaults to 0):
- The index of the current process.
- seed (`int`, *optional*, defaults to 0):
- A random seed that will be used for the random number generation in
- [`~trainer_pt_utils.IterableDatasetShard.set_epoch`].
- """
-
- def __init__(
- self,
- dataset: IterableDataset,
- batch_size: int = 1,
- drop_last: bool = False,
- num_processes: int = 1,
- process_index: int = 0,
- seed: int = 0,
- ):
- self.dataset = dataset
- self.batch_size = batch_size
- self.drop_last = drop_last
- self.num_processes = num_processes
- self.process_index = process_index
- self.seed = seed
- self.epoch = 0
- self.num_examples = 0
-
- def set_epoch(self, epoch):
- self.epoch = epoch
- if hasattr(self.dataset, "set_epoch"):
- self.dataset.set_epoch(epoch)
-
- def __iter__(self):
- self.num_examples = 0
- if (
- not hasattr(self.dataset, "set_epoch")
- and hasattr(self.dataset, "generator")
- and isinstance(self.dataset.generator, torch.Generator)
- ):
- self.dataset.generator.manual_seed(self.seed + self.epoch)
- real_batch_size = self.batch_size * self.num_processes
- process_slice = range(self.process_index * self.batch_size, (self.process_index + 1) * self.batch_size)
-
- first_batch = None
- current_batch = []
- for element in self.dataset:
- self.num_examples += 1
- current_batch.append(element)
- # Wait to have a full batch before yielding elements.
- if len(current_batch) == real_batch_size:
- for i in process_slice:
- yield current_batch[i]
- if first_batch is None:
- first_batch = current_batch.copy()
- current_batch = []
-
- # Finished if drop_last is True, otherwise complete the last batch with elements from the beginning.
- if not self.drop_last and len(current_batch) > 0:
- if first_batch is None:
- first_batch = current_batch.copy()
- while len(current_batch) < real_batch_size:
- current_batch += first_batch
- for i in process_slice:
- yield current_batch[i]
-
- def __len__(self):
- # Will raise an error if the underlying dataset is not sized.
- if self.drop_last:
- return (len(self.dataset) // (self.batch_size * self.num_processes)) * self.batch_size
- else:
- return math.ceil(len(self.dataset) / (self.batch_size * self.num_processes)) * self.batch_size
-
-
-# In order to keep `trainer.py` compact and easy to understand, place any secondary PT Trainer
-# helper methods here
-
-
-def _get_learning_rate(self):
- if self.deepspeed:
- # with deepspeed's fp16 and dynamic loss scale enabled the optimizer/scheduler steps may
- # not run for the first few dozen steps while loss scale is too large, and thus during
- # that time `get_last_lr` will fail if called during that warm up stage, so work around it:
- try:
- last_lr = self.lr_scheduler.get_last_lr()[0]
- except AssertionError as e:
- if "need to call step" in str(e):
- logger.warning("tried to get lr value before scheduler/optimizer started stepping, returning lr=0")
- last_lr = 0
- else:
- raise
- else:
- last_lr = self.lr_scheduler.get_last_lr()[0]
- if torch.is_tensor(last_lr):
- last_lr = last_lr.item()
- return last_lr
-
-
-def _secs2timedelta(secs):
- """
- convert seconds to hh:mm:ss.msec, msecs rounded to 2 decimals
- """
-
- msec = int(abs(secs - int(secs)) * 100)
- return f"{datetime.timedelta(seconds=int(secs))}.{msec:02d}"
-
-
-def metrics_format(self, metrics: Dict[str, float]) -> Dict[str, float]:
- """
- Reformat Trainer metrics values to a human-readable format
-
- Args:
- metrics (`Dict[str, float]`):
- The metrics returned from train/evaluate/predict
-
- Returns:
- metrics (`Dict[str, float]`): The reformatted metrics
- """
-
- metrics_copy = metrics.copy()
- for k, v in metrics_copy.items():
- if "_mem_" in k:
- metrics_copy[k] = f"{ v >> 20 }MB"
- elif "_runtime" in k:
- metrics_copy[k] = _secs2timedelta(v)
- elif k == "total_flos":
- metrics_copy[k] = f"{ int(v) >> 30 }GF"
- elif type(metrics_copy[k]) == float:
- metrics_copy[k] = round(v, 4)
-
- return metrics_copy
-
-
-def log_metrics(self, split, metrics):
- """
- Log metrics in a specially formatted way
-
- Under distributed environment this is done only for a process with rank 0.
-
- Args:
- split (`str`):
- Mode/split name: one of `train`, `eval`, `test`
- metrics (`Dict[str, float]`):
- The metrics returned from train/evaluate/predictmetrics: metrics dict
-
- Notes on memory reports:
-
- In order to get memory usage report you need to install `psutil`. You can do that with `pip install psutil`.
-
- Now when this method is run, you will see a report that will include: :
-
- ```
- init_mem_cpu_alloc_delta = 1301MB
- init_mem_cpu_peaked_delta = 154MB
- init_mem_gpu_alloc_delta = 230MB
- init_mem_gpu_peaked_delta = 0MB
- train_mem_cpu_alloc_delta = 1345MB
- train_mem_cpu_peaked_delta = 0MB
- train_mem_gpu_alloc_delta = 693MB
- train_mem_gpu_peaked_delta = 7MB
- ```
-
- **Understanding the reports:**
-
- - the first segment, e.g., `train__`, tells you which stage the metrics are for. Reports starting with `init_`
- will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
- `__init__` will be reported along with the `eval_` metrics.
- - the third segment, is either `cpu` or `gpu`, tells you whether it's the general RAM or the gpu0 memory
- metric.
- - `*_alloc_delta` - is the difference in the used/allocated memory counter between the end and the start of the
- stage - it can be negative if a function released more memory than it allocated.
- - `*_peaked_delta` - is any extra memory that was consumed and then freed - relative to the current allocated
- memory counter - it is never negative. When you look at the metrics of any stage you add up `alloc_delta` +
- `peaked_delta` and you know how much memory was needed to complete that stage.
-
- The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
- main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
- use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
- memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
- future these reports will evolve to measure those too.
-
- The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
- memory shared with other processes. It is important to note that it does not include swapped out memory, so the
- reports could be imprecise.
-
- The CPU peak memory is measured using a sampling thread. Due to python's GIL it may miss some of the peak memory if
- that thread didn't get a chance to run when the highest memory was used. Therefore this report can be less than
- reality. Using `tracemalloc` would have reported the exact peak memory, but it doesn't report memory allocations
- outside of python. So if some C++ CUDA extension allocated its own memory it won't be reported. And therefore it
- was dropped in favor of the memory sampling approach, which reads the current process memory usage.
-
- The GPU allocated and peak memory reporting is done with `torch.cuda.memory_allocated()` and
- `torch.cuda.max_memory_allocated()`. This metric reports only "deltas" for pytorch-specific allocations, as
- `torch.cuda` memory management system doesn't track any memory allocated outside of pytorch. For example, the very
- first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.
-
- Note that this tracker doesn't account for memory allocations outside of [`Trainer`]'s `__init__`, `train`,
- `evaluate` and `predict` calls.
-
- Because `evaluation` calls may happen during `train`, we can't handle nested invocations because
- `torch.cuda.max_memory_allocated` is a single counter, so if it gets reset by a nested eval call, `train`'s tracker
- will report incorrect info. If this [pytorch issue](https://github.com/pytorch/pytorch/issues/16266) gets resolved
- it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
- `train`, `evaluate` and `predict` methods. Which means that if `eval` is called during `train`, it's the latter
- that will account for its memory usage and that of the former.
-
- This also means that if any other tool that is used along the [`Trainer`] calls
- `torch.cuda.reset_peak_memory_stats`, the gpu peak memory stats could be invalid. And the [`Trainer`] will disrupt
- the normal behavior of any such tools that rely on calling `torch.cuda.reset_peak_memory_stats` themselves.
-
- For best performance you may want to consider turning the memory profiling off for production runs.
- """
- if not self.is_world_process_zero():
- return
-
- print(f"***** {split} metrics *****")
- metrics_formatted = self.metrics_format(metrics)
- k_width = max(len(str(x)) for x in metrics_formatted.keys())
- v_width = max(len(str(x)) for x in metrics_formatted.values())
- for key in sorted(metrics_formatted.keys()):
- print(f" {key: <{k_width}} = {metrics_formatted[key]:>{v_width}}")
-
-
-def save_metrics(self, split, metrics, combined=True):
- """
- Save metrics into a json file for that split, e.g. `train_results.json`.
-
- Under distributed environment this is done only for a process with rank 0.
-
- Args:
- split (`str`):
- Mode/split name: one of `train`, `eval`, `test`, `all`
- metrics (`Dict[str, float]`):
- The metrics returned from train/evaluate/predict
- combined (`bool`, *optional*, defaults to `True`):
- Creates combined metrics by updating `all_results.json` with metrics of this call
-
- To understand the metrics please read the docstring of [`~Trainer.log_metrics`]. The only difference is that raw
- unformatted numbers are saved in the current method.
-
- """
- if not self.is_world_process_zero():
- return
-
- path = os.path.join(self.args.output_dir, f"{split}_results.json")
- with open(path, "w") as f:
- json.dump(metrics, f, indent=4, sort_keys=True)
-
- if combined:
- path = os.path.join(self.args.output_dir, "all_results.json")
- if os.path.exists(path):
- with open(path, "r") as f:
- all_metrics = json.load(f)
- else:
- all_metrics = {}
-
- all_metrics.update(metrics)
- with open(path, "w") as f:
- json.dump(all_metrics, f, indent=4, sort_keys=True)
-
-
-def save_state(self):
- """
- Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model
-
- Under distributed environment this is done only for a process with rank 0.
- """
- if not self.is_world_process_zero():
- return
-
- path = os.path.join(self.args.output_dir, "trainer_state.json")
- self.state.save_to_json(path)
-
-
-def get_parameter_names(model, forbidden_layer_types):
- """
- Returns the names of the model parameters that are not inside a forbidden layer.
- """
- result = []
- for name, child in model.named_children():
- result += [
- f"{name}.{n}"
- for n in get_parameter_names(child, forbidden_layer_types)
- if not isinstance(child, tuple(forbidden_layer_types))
- ]
- # Add model specific parameters (defined with nn.Parameter) since they are not in any child.
- result += list(model._parameters.keys())
- return result
-
-
-def get_module_class_from_name(module, name):
- """
- Gets a class from a module by its name.
-
- Args:
- module (`torch.nn.Module`): The module to get the class from.
- name (`str`): The name of the class.
- """
- modules_children = list(module.children())
- if module.__class__.__name__ == name:
- return module.__class__
- elif len(modules_children) == 0:
- return
- else:
- for child_module in modules_children:
- module_class = get_module_class_from_name(child_module, name)
- if module_class is not None:
- return module_class
-
-
-if is_sagemaker_mp_enabled():
- import smdistributed.modelparallel.torch as smp
-
- @smp.step()
- def smp_forward_backward(model, inputs, gradient_accumulation_steps=1):
- outputs = model(**inputs)
- loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
- loss /= gradient_accumulation_steps
- model.backward(loss)
- return loss
-
- @smp.step()
- def smp_forward_only(model, inputs):
- return model(**inputs)
-
- def smp_gather(tensor):
- if isinstance(tensor, (list, tuple)):
- return type(tensor)(smp_gather(t) for t in tensor)
- elif isinstance(tensor, dict):
- return type(tensor)({k: smp_gather(v) for k, v in tensor.items()})
- elif not isinstance(tensor, torch.Tensor):
- raise TypeError(
- f"Can't gather the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors."
- )
- all_tensors = smp.allgather(tensor, smp.CommGroup.DP_GROUP)
- all_tensors = [atleast_1d(t) for t in all_tensors]
- return torch.cat([t.cpu() for t in all_tensors], dim=0)
-
- def smp_nested_concat(tensor):
- if isinstance(tensor, (list, tuple)):
- return type(tensor)(smp_nested_concat(t) for t in tensor)
- elif isinstance(tensor, dict):
- return type(tensor)({k: smp_nested_concat(v) for k, v in tensor.items()})
- # It doesn't seem possible to check here if `tensor` is a StepOutput because StepOutput lives in `smp.step`
- # which is also the name of the decorator so Python is confused.
- return tensor.concat().detach().cpu()
\ No newline at end of file
diff --git a/spaces/CognitiveLabs/GPT-4-Vision-Chat/Dockerfile b/spaces/CognitiveLabs/GPT-4-Vision-Chat/Dockerfile
deleted file mode 100644
index e2691aec424fa2f26eef00a7e323b40fb9eebc0a..0000000000000000000000000000000000000000
--- a/spaces/CognitiveLabs/GPT-4-Vision-Chat/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-FROM python:3.9
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-WORKDIR $HOME/app
-COPY --chown=user . $HOME/app
-RUN chown -R user:user $HOME/app
-RUN chmod -R 755 $HOME/app
-COPY ./requirements.txt ~/app/requirements.txt
-RUN pip install -r requirements.txt
-COPY . .
-CMD ["chainlit", "run", "app.py", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/CohereForAI/pokemon-cards-explorer/README.md b/spaces/CohereForAI/pokemon-cards-explorer/README.md
deleted file mode 100644
index b9660646e36d960aff080f8d635a63c272073fa3..0000000000000000000000000000000000000000
--- a/spaces/CohereForAI/pokemon-cards-explorer/README.md
+++ /dev/null
@@ -1,126 +0,0 @@
----
-title: Pokemon Cards Explorer
-emoji: 🔍
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: ./src/app.py
-pinned: false
----
-
-
-
-# [Pokemon Card Explorer](https://pokemoncards.streamlit.app/)
-
-A simple semantic vector search engine over all **13000+ trading cards** ever to be released by Niantic, using a very straightforward stack including **Pinecone** (for Vector Database), **OpenAI** (for embeddings), **Cohere** (for Re-ranking) and **Streamlit** (for deployment).
-
-Data Augmentation via web-scrapping was done to improve the search accuracy. Web-scraping was done using **requests** and **BS4**.
-
-
-
-
-
-
-
-# Motivation 🤔
-
-Why? 'cause WHY NOT!
-
-Any pokemon fan would agree 😌
-
-
-
-# Implimentation 🛠️
-
-The entire implementation can be divided into the following parts:
-
-- Data Preparation Step
-- Data Injestion Step
-- Query Step
-
-## Data Preparation Step
-
-The original [Pokemon Cards dataset](https://huggingface.co/datasets/TheFusion21/PokemonCards) is available on HuggingFace (uploaded by TheFusion21 💙) which has a 13.1K rows, containing the following information:
-
-```json
-{
- "id": ... ,
- "image_url" : ... ,
- "caption" : ... ,
- "name" : ... ,
- "hp" : ... ,
- "set_name" : ...
-}
-```
-
-The ideal candidate to be converted to embeddings would be the `name + caption` which is what I did in `version 1`, but noticed that it sometimes made some errors -- it wasn't able to identify pokemon accurately based on description and needed longer descriptions for better accuracy.
-
-The data doesn't contain what the pokemon look like, which is what the expected average case user will end up querying. So the conclusion was that the data needed to be augmented.
-
-I used [PokemonDB](https://pokemondb.net/) pages of individual pokemon, extracted data and images of the pokemon and created a supplementary dataset. All of this was done using **BS4** and **requests**.
-
-Further information on "how" the pokemon looked like was extracted using BLIP to caption images of pokemon extracted through the PokemonDB.
-
-The entire pipeline can be visualized through the diagram below.
-
-
-
-
-The final supplemented data, a.k.a Pokemon Cards++, had the following fields:
-
-```json
-{
- "id": ... ,
- "card_image_url" : ... ,
- "caption" : ... ,
- "name" : ... ,
- "hp" : ... ,
- "set_name" : ...,
- "poke_image_url" : ... ,
- "poke_image_caption" : ... ,
- "pokedex_entries" : ... ,
- "pokedb_intro_text" : ...
-}
-```
-
-And the final text used for generating the embeddings was `name + caption + poke_image_caption + pokedb_intro_text + pokedex_entries` which allowed for a more holistic embedding to be generated for each pokemon.
-
-## Data Injestion Step
-
-Once the embeddings for all the data have been created, you need to put it in a vector database storage for quick semantic similarity search (using HNSWor other approx algo). Something I used for this step was Pinecone, which made it really easy to do.
-
-Essentially, this can be summarized by the diagram below.
-
-
-
-
-## Query Step
-
-
-
-In the query step, the user provided question is simply passed into the **same** embedding model that was used in the injestion and sent to the vectorDB for semantic search against the Card Embeddings, which then gives out the K nearest matches for the query embeddings. Now, the k-nearest matches are sent to a re-ranker model which rankes each of the matches against a query on the match relevancy and provides us our final output, ranked Pokemon Cards!
-
-
-
-
-## That's all Folks!
-
-
-
-# FAQ
-
-## How much does it cost to run and maintain this site?
-Glad you asked! It costs me nothing to keep the Pinecone Vector DB running (but it might shutdown in a few days if not queried) and for CO:here's reranking API which is free. OpenAI charges me per token but the value is quite affordable. It cost me about $2 to get embeddings for the entire dataset. So this entire project just costs me $2 and about 3 days of time.
-
-## The site is down with a error, why is it not running?
-Probably because Pinecone deleted the index, which means that I would have to re-upload the embeddings on Pinecone again. Pinecone deletes indices that haven't been used in a week under the free version.
-
-## You're so awesome, how can I be like you?
-You can't. Sorry.
-
-# Acknowledgements
-
-Thank you to **Pokemon** for making my childhood special! 💙
-
-
diff --git a/spaces/CompVis/text2img-latent-diffusion/README.md b/spaces/CompVis/text2img-latent-diffusion/README.md
deleted file mode 100644
index 0ddcc3059e02c815726f4907d098972309592b51..0000000000000000000000000000000000000000
--- a/spaces/CompVis/text2img-latent-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LDM Text-to-image
-emoji: 🧨
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DAMO-NLP-SG/CLEX-Chat/clex_layer.py b/spaces/DAMO-NLP-SG/CLEX-Chat/clex_layer.py
deleted file mode 100644
index 29953337bbf11ff53d926d16c4fa29a6679268b4..0000000000000000000000000000000000000000
--- a/spaces/DAMO-NLP-SG/CLEX-Chat/clex_layer.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import torch
-import torch.nn as nn
-from torchdiffeq import odeint
-
-
-
-import math
-
-class ODELinear(nn.Module):
- def __init__(
- self,
- dim: int,
- factor,
- **kwargs
- ):
- super().__init__()
- self.ode_up_proj = nn.Parameter(torch.empty(dim//2, factor*dim).to(torch.float32))
- self.ode_down_proj = nn.Parameter(torch.empty(factor*dim, dim//2).to(torch.float32))
- self.dim = dim
- self.act = torch.nn.SiLU()
- self.reset_parameters()
-
- def reset_parameters(self):
- nn.init.kaiming_uniform_(self.ode_up_proj, a=math.sqrt(5))
- nn.init.zeros_(self.ode_down_proj)
-
- def get_time_embedding(self, t, base=10000, device='cuda', dtype=torch.float32):
- if t < 1:
- alpha = 1
- else:
- alpha = 2*t-1
- ntk_base = base * alpha ** (self.dim / (self.dim-2))
- ntk_inv_freq = 1.0 / (ntk_base ** (torch.arange(0, self.dim, 2, dtype=torch.float32).to(device) / self.dim))
- index = torch.arange(0, self.dim, 2, dtype=torch.float32).to(device)
- delta_ntk_freq = -2*index/(self.dim-2) * 1 / (base ** (index/self.dim) * (alpha ** (index/(self.dim-2) + 1)))
- return delta_ntk_freq.to(device, dtype=dtype), ntk_inv_freq.to(device, dtype=dtype)
-
- def forward(self, t, x: torch.Tensor):
- delta_time, time = self.get_time_embedding(t, device=x.device, dtype=x.dtype)
- x = x + torch.log(time)
- time_embed = delta_time / time
- delta_inv_freq = self.act(x @ self.ode_up_proj.float()) @ self.ode_down_proj.float() + time_embed
- return delta_inv_freq
-
-
-
-class LlamaCLEXScalingRotaryEmbedding(nn.Module):
-
- def __init__(self, dim, max_position_embeddings=2048, rope_scaling=None, base=10000, device=None) -> None:
- super().__init__()
-
- self.max_t = rope_scaling["max_factor"]
- self.dim = dim
- self.max_position_embeddings = max_position_embeddings
- self.base = base
- inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
- self.register_buffer("inv_freq", inv_freq)
-
- self.proj_func = ODELinear(dim, rope_scaling["param_factor"])
- self.rope_cached = None
- self.max_t_cached = 0
- self.freq_cached = None
- self.time_dt = 0.01
- self.ode_args = {
- "method": "rk4",
- "options": {"step_size": self.time_dt},
- }
-
- def sample_random_times(self, max_t, device):
- return torch.randint(2, max_t, (1,), dtype = torch.long, device=device)
-
- def get_random_position_ids(self, n=2048, max=8192):
- positions = torch.randperm(max)[:n].sort().values
- # positions = positions.to(device=device)
- return positions
-
-
- def get_continuous_freq(self, time_grid, ex_positions, device):
- solution = odeint(
- self.proj_func, torch.log(self.inv_freq.to(device, dtype=torch.float32)), time_grid, **self.ode_args
- )
- if time_grid.size(0) == 2:
- training
- scale_inv_freq = torch.exp(solution[1])
- # print(time_grid[1].tolist(), torch.sum(scale_inv_freq).tolist(), torch.sum(self.proj_func.ode_down_proj).tolist())
- freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq)
- else:
- scale_inv_freq = torch.exp(solution)
- # freqs = torch.einsum('i, kl -> kil', ex_positions, scale_inv_freq)
- return scale_inv_freq
- embed = torch.cat((freqs,freqs), dim=-1)
- return embed
-
-
-
- def forward(self, device, dtype, seq_len, do_train=False):
- device = self.proj_func.ode_up_proj.device
- scale_factor = seq_len // self.max_position_embeddings
- if do_train:
- t_val = self.sample_random_times(self.max_t+1, device)[0]
- import math
- sampled_position_ids = self.get_random_position_ids(n=seq_len-2, max=seq_len*t_val-2).float()
- ex_positions = torch.cat([
- torch.tensor([0]),
- (sampled_position_ids + 1) / scale_factor,
- torch.tensor([seq_len*t_val//scale_factor-1])]
- ).to(device, dtype=torch.float32)
- else:
- t_val = scale_factor if seq_len%self.max_position_embeddings == 0.0 else scale_factor + 1
- t_val = t_val if t_val <= self.max_t else self.max_t
- ex_positions = torch.arange(0, self.max_position_embeddings * t_val, dtype=torch.float32).to(device)
-
-
-
- if t_val == 1.0:
- scale_inv_freq = self.inv_freq.to(device)
- freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq)
- embed = torch.cat((freqs,freqs), dim=-1)
- cos, sin = embed.cos()[None, None, :, :], embed.sin()[None, None, :, :]
- elif do_train:
- time_grid = torch.tensor([1.0, t_val]).float().to(device)
- embed = self.get_continuous_freq(time_grid, ex_positions, device)
- cos, sin = embed.cos()[None, None, :, :], embed.sin()[None, None, :, :]
- else:
- if t_val > self.max_t_cached:
- if self.freq_cached is None:
- time_grid = torch.arange(1.0, self.max_t, dtype=torch.float32).to(device)
- self.freq_cached = self.get_continuous_freq(time_grid, ex_positions, device)
- scale_inv_freq = self.freq_cached[int(t_val-1.0)]
- freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq)
- embed = torch.cat((freqs,freqs), dim=-1)
- self.rope_cached = torch.cat((embed.cos()[None, None, None, :, :], embed.sin()[None, None, None, :, :]), dim=0)
- self.max_t_cached = t_val
- cos, sin = self.rope_cached
-
- return torch.cat(
- (cos[None, :, :, :seq_len, ...].to(dtype=dtype),
- sin[None, :, :, :seq_len, ...].to(dtype=dtype)),
- dim=0
- )
-
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImagePalette.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImagePalette.py
deleted file mode 100644
index f0c094708634ecdac25eab95d054f7a63f14eecf..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImagePalette.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# image palette object
-#
-# History:
-# 1996-03-11 fl Rewritten.
-# 1997-01-03 fl Up and running.
-# 1997-08-23 fl Added load hack
-# 2001-04-16 fl Fixed randint shadow bug in random()
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1996-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import array
-
-from . import GimpGradientFile, GimpPaletteFile, ImageColor, PaletteFile
-
-
-class ImagePalette:
- """
- Color palette for palette mapped images
-
- :param mode: The mode to use for the palette. See:
- :ref:`concept-modes`. Defaults to "RGB"
- :param palette: An optional palette. If given, it must be a bytearray,
- an array or a list of ints between 0-255. The list must consist of
- all channels for one color followed by the next color (e.g. RGBRGBRGB).
- Defaults to an empty palette.
- """
-
- def __init__(self, mode="RGB", palette=None):
- self.mode = mode
- self.rawmode = None # if set, palette contains raw data
- self.palette = palette or bytearray()
- self.dirty = None
-
- @property
- def palette(self):
- return self._palette
-
- @palette.setter
- def palette(self, palette):
- self._colors = None
- self._palette = palette
-
- @property
- def colors(self):
- if self._colors is None:
- mode_len = len(self.mode)
- self._colors = {}
- for i in range(0, len(self.palette), mode_len):
- color = tuple(self.palette[i : i + mode_len])
- if color in self._colors:
- continue
- self._colors[color] = i // mode_len
- return self._colors
-
- @colors.setter
- def colors(self, colors):
- self._colors = colors
-
- def copy(self):
- new = ImagePalette()
-
- new.mode = self.mode
- new.rawmode = self.rawmode
- if self.palette is not None:
- new.palette = self.palette[:]
- new.dirty = self.dirty
-
- return new
-
- def getdata(self):
- """
- Get palette contents in format suitable for the low-level
- ``im.putpalette`` primitive.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- return self.rawmode, self.palette
- return self.mode, self.tobytes()
-
- def tobytes(self):
- """Convert palette to bytes.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(self.palette, bytes):
- return self.palette
- arr = array.array("B", self.palette)
- return arr.tobytes()
-
- # Declare tostring as an alias for tobytes
- tostring = tobytes
-
- def getcolor(self, color, image=None):
- """Given an rgb tuple, allocate palette entry.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(color, tuple):
- if self.mode == "RGB":
- if len(color) == 4:
- if color[3] != 255:
- msg = "cannot add non-opaque RGBA color to RGB palette"
- raise ValueError(msg)
- color = color[:3]
- elif self.mode == "RGBA":
- if len(color) == 3:
- color += (255,)
- try:
- return self.colors[color]
- except KeyError as e:
- # allocate new color slot
- if not isinstance(self.palette, bytearray):
- self._palette = bytearray(self.palette)
- index = len(self.palette) // 3
- special_colors = ()
- if image:
- special_colors = (
- image.info.get("background"),
- image.info.get("transparency"),
- )
- while index in special_colors:
- index += 1
- if index >= 256:
- if image:
- # Search for an unused index
- for i, count in reversed(list(enumerate(image.histogram()))):
- if count == 0 and i not in special_colors:
- index = i
- break
- if index >= 256:
- msg = "cannot allocate more than 256 colors"
- raise ValueError(msg) from e
- self.colors[color] = index
- if index * 3 < len(self.palette):
- self._palette = (
- self.palette[: index * 3]
- + bytes(color)
- + self.palette[index * 3 + 3 :]
- )
- else:
- self._palette += bytes(color)
- self.dirty = 1
- return index
- else:
- msg = f"unknown color specifier: {repr(color)}"
- raise ValueError(msg)
-
- def save(self, fp):
- """Save palette to text file.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(fp, str):
- fp = open(fp, "w")
- fp.write("# Palette\n")
- fp.write(f"# Mode: {self.mode}\n")
- for i in range(256):
- fp.write(f"{i}")
- for j in range(i * len(self.mode), (i + 1) * len(self.mode)):
- try:
- fp.write(f" {self.palette[j]}")
- except IndexError:
- fp.write(" 0")
- fp.write("\n")
- fp.close()
-
-
-# --------------------------------------------------------------------
-# Internal
-
-
-def raw(rawmode, data):
- palette = ImagePalette()
- palette.rawmode = rawmode
- palette.palette = data
- palette.dirty = 1
- return palette
-
-
-# --------------------------------------------------------------------
-# Factories
-
-
-def make_linear_lut(black, white):
- lut = []
- if black == 0:
- for i in range(256):
- lut.append(white * i // 255)
- else:
- raise NotImplementedError # FIXME
- return lut
-
-
-def make_gamma_lut(exp):
- lut = []
- for i in range(256):
- lut.append(int(((i / 255.0) ** exp) * 255.0 + 0.5))
- return lut
-
-
-def negative(mode="RGB"):
- palette = list(range(256 * len(mode)))
- palette.reverse()
- return ImagePalette(mode, [i // len(mode) for i in palette])
-
-
-def random(mode="RGB"):
- from random import randint
-
- palette = []
- for i in range(256 * len(mode)):
- palette.append(randint(0, 255))
- return ImagePalette(mode, palette)
-
-
-def sepia(white="#fff0c0"):
- bands = [make_linear_lut(0, band) for band in ImageColor.getrgb(white)]
- return ImagePalette("RGB", [bands[i % 3][i // 3] for i in range(256 * 3)])
-
-
-def wedge(mode="RGB"):
- palette = list(range(256 * len(mode)))
- return ImagePalette(mode, [i // len(mode) for i in palette])
-
-
-def load(filename):
- # FIXME: supports GIMP gradients only
-
- with open(filename, "rb") as fp:
- for paletteHandler in [
- GimpPaletteFile.GimpPaletteFile,
- GimpGradientFile.GimpGradientFile,
- PaletteFile.PaletteFile,
- ]:
- try:
- fp.seek(0)
- lut = paletteHandler(fp).getpalette()
- if lut:
- break
- except (SyntaxError, ValueError):
- # import traceback
- # traceback.print_exc()
- pass
- else:
- msg = "cannot load palette"
- raise OSError(msg)
-
- return lut # data, rawmode
diff --git a/spaces/DaleChen/AutoGPT/autogpt/commands/image_gen.py b/spaces/DaleChen/AutoGPT/autogpt/commands/image_gen.py
deleted file mode 100644
index 0809fcdd3e38b52a2ce09ca1444f2574813d40f9..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/commands/image_gen.py
+++ /dev/null
@@ -1,163 +0,0 @@
-""" Image Generation Module for AutoGPT."""
-import io
-import os.path
-import uuid
-from base64 import b64decode
-
-import openai
-import requests
-from PIL import Image
-
-from autogpt.config import Config
-from autogpt.workspace import path_in_workspace
-
-CFG = Config()
-
-
-def generate_image(prompt: str, size: int = 256) -> str:
- """Generate an image from a prompt.
-
- Args:
- prompt (str): The prompt to use
- size (int, optional): The size of the image. Defaults to 256. (Not supported by HuggingFace)
-
- Returns:
- str: The filename of the image
- """
- filename = f"{str(uuid.uuid4())}.jpg"
-
- # DALL-E
- if CFG.image_provider == "dalle":
- return generate_image_with_dalle(prompt, filename, size)
- # HuggingFace
- elif CFG.image_provider == "huggingface":
- return generate_image_with_hf(prompt, filename)
- # SD WebUI
- elif CFG.image_provider == "sdwebui":
- return generate_image_with_sd_webui(prompt, filename, size)
- return "No Image Provider Set"
-
-
-def generate_image_with_hf(prompt: str, filename: str) -> str:
- """Generate an image with HuggingFace's API.
-
- Args:
- prompt (str): The prompt to use
- filename (str): The filename to save the image to
-
- Returns:
- str: The filename of the image
- """
- API_URL = (
- f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}"
- )
- if CFG.huggingface_api_token is None:
- raise ValueError(
- "You need to set your Hugging Face API token in the config file."
- )
- headers = {
- "Authorization": f"Bearer {CFG.huggingface_api_token}",
- "X-Use-Cache": "false",
- }
-
- response = requests.post(
- API_URL,
- headers=headers,
- json={
- "inputs": prompt,
- },
- )
-
- image = Image.open(io.BytesIO(response.content))
- print(f"Image Generated for prompt:{prompt}")
-
- image.save(path_in_workspace(filename))
-
- return f"Saved to disk:{filename}"
-
-
-def generate_image_with_dalle(prompt: str, filename: str) -> str:
- """Generate an image with DALL-E.
-
- Args:
- prompt (str): The prompt to use
- filename (str): The filename to save the image to
-
- Returns:
- str: The filename of the image
- """
- openai.api_key = CFG.openai_api_key
-
- # Check for supported image sizes
- if size not in [256, 512, 1024]:
- closest = min([256, 512, 1024], key=lambda x: abs(x - size))
- print(
- f"DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. Setting to {closest}, was {size}."
- )
- size = closest
-
- response = openai.Image.create(
- prompt=prompt,
- n=1,
- size=f"{size}x{size}",
- response_format="b64_json",
- )
-
- print(f"Image Generated for prompt:{prompt}")
-
- image_data = b64decode(response["data"][0]["b64_json"])
-
- with open(path_in_workspace(filename), mode="wb") as png:
- png.write(image_data)
-
- return f"Saved to disk:{filename}"
-
-
-def generate_image_with_sd_webui(
- prompt: str,
- filename: str,
- size: int = 512,
- negative_prompt: str = "",
- extra: dict = {},
-) -> str:
- """Generate an image with Stable Diffusion webui.
- Args:
- prompt (str): The prompt to use
- filename (str): The filename to save the image to
- size (int, optional): The size of the image. Defaults to 256.
- negative_prompt (str, optional): The negative prompt to use. Defaults to "".
- extra (dict, optional): Extra parameters to pass to the API. Defaults to {}.
- Returns:
- str: The filename of the image
- """
- # Create a session and set the basic auth if needed
- s = requests.Session()
- if CFG.sd_webui_auth:
- username, password = CFG.sd_webui_auth.split(":")
- s.auth = (username, password or "")
-
- # Generate the images
- response = requests.post(
- f"{CFG.sd_webui_url}/sdapi/v1/txt2img",
- json={
- "prompt": prompt,
- "negative_prompt": negative_prompt,
- "sampler_index": "DDIM",
- "steps": 20,
- "cfg_scale": 7.0,
- "width": size,
- "height": size,
- "n_iter": 1,
- **extra,
- },
- )
-
- print(f"Image Generated for prompt:{prompt}")
-
- # Save the image to disk
- response = response.json()
- b64 = b64decode(response["images"][0].split(",", 1)[0])
- image = Image.open(io.BytesIO(b64))
- image.save(path_in_workspace(filename))
-
- return f"Saved to disk:{filename}"
diff --git a/spaces/DemoLou/moe-tts/app.py b/spaces/DemoLou/moe-tts/app.py
deleted file mode 100644
index ebd358833641060d09c2bc6a51fc1bd00a1200ef..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/app.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import argparse
-import json
-import os
-import re
-import tempfile
-from pathlib import Path
-
-import librosa
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import commons
-import utils
-import gradio as gr
-import gradio.utils as gr_utils
-import gradio.processing_utils as gr_processing_utils
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from mel_processing import spectrogram_torch
-
-limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces
-
-audio_postprocess_ori = gr.Audio.postprocess
-
-
-def audio_postprocess(self, y):
- data = audio_postprocess_ori(self, y)
- if data is None:
- return None
- return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-
-
-gr.Audio.postprocess = audio_postprocess
-
-
-def get_text(text, hps, is_symbol):
- text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = LongTensor(text_norm)
- return text_norm
-
-
-def create_tts_fn(model, hps, speaker_ids):
- def tts_fn(text, speaker, speed, is_symbol):
- print("generating audio...")
- if limitation:
- text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
- max_len = 150
- if is_symbol:
- max_len *= 3
- if text_len > max_len:
- return "Error: Text is too long", None
-
- speaker_id = speaker_ids[speaker]
- stn_tst = get_text(text, hps, is_symbol)
- with no_grad():
- x_tst = stn_tst.unsqueeze(0).to(device)
- x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
- sid = LongTensor([speaker_id]).to(device)
- audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
- length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
- del stn_tst, x_tst, x_tst_lengths, sid
- return "Success", (hps.data.sampling_rate, audio)
-
- return tts_fn
-
-
-def create_vc_fn(model, hps, speaker_ids):
- def vc_fn(original_speaker, target_speaker, input_audio):
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if limitation and duration > 30:
- return "Error: Audio is too long", None
- original_speaker_id = speaker_ids[original_speaker]
- target_speaker_id = speaker_ids[target_speaker]
-
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != hps.data.sampling_rate:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
- with no_grad():
- y = torch.FloatTensor(audio)
- y = y.unsqueeze(0)
- spec = spectrogram_torch(y, hps.data.filter_length,
- hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
- center=False).to(device)
- spec_lengths = LongTensor([spec.size(-1)]).to(device)
- sid_src = LongTensor([original_speaker_id]).to(device)
- sid_tgt = LongTensor([target_speaker_id]).to(device)
- audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
- 0, 0].data.cpu().float().numpy()
- del y, spec, spec_lengths, sid_src, sid_tgt
- return "Success", (hps.data.sampling_rate, audio)
-
- return vc_fn
-
-
-def create_soft_vc_fn(model, hps, speaker_ids):
- def soft_vc_fn(target_speaker, input_audio1, input_audio2):
- input_audio = input_audio1
- if input_audio is None:
- input_audio = input_audio2
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- if limitation and duration > 30:
- return "Error: Audio is too long", None
- target_speaker_id = speaker_ids[target_speaker]
-
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- with torch.inference_mode():
- units = hubert.units(torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0).to(device))
- with no_grad():
- unit_lengths = LongTensor([units.size(1)]).to(device)
- sid = LongTensor([target_speaker_id]).to(device)
- audio = model.infer(units, unit_lengths, sid=sid, noise_scale=.667,
- noise_scale_w=0.8)[0][0, 0].data.cpu().float().numpy()
- del units, unit_lengths, sid
- return "Success", (hps.data.sampling_rate, audio)
-
- return soft_vc_fn
-
-
-def create_to_symbol_fn(hps):
- def to_symbol_fn(is_symbol_input, input_text, temp_text):
- return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \
- else (temp_text, temp_text)
-
- return to_symbol_fn
-
-
-download_audio_js = """
-() =>{{
- let root = document.querySelector("body > gradio-app");
- if (root.shadowRoot != null)
- root = root.shadowRoot;
- let audio = root.querySelector("#{audio_id}").querySelector("audio");
- if (audio == undefined)
- return;
- audio = audio.src;
- let oA = document.createElement("a");
- oA.download = Math.floor(Math.random()*100000000)+'.wav';
- oA.href = audio;
- document.body.appendChild(oA);
- oA.click();
- oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--device', type=str, default='cpu')
- parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
- args = parser.parse_args()
-
- device = torch.device(args.device)
- models_tts = []
- models_vc = []
- models_soft_vc = []
- with open("saved_model/info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for i, info in models_info.items():
- name = info["title"]
- author = info["author"]
- lang = info["lang"]
- example = info["example"]
- config_path = f"saved_model/{i}/config.json"
- model_path = f"saved_model/{i}/model.pth"
- cover = info["cover"]
- cover_path = f"saved_model/{i}/{cover}" if cover else None
- hps = utils.get_hparams_from_file(config_path)
- model = SynthesizerTrn(
- len(hps.symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model)
- utils.load_checkpoint(model_path, model, None)
- model.eval().to(device)
- speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"]
- speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"]
-
- t = info["type"]
- if t == "vits":
- models_tts.append((name, author, cover_path, speakers, lang, example,
- hps.symbols, create_tts_fn(model, hps, speaker_ids),
- create_to_symbol_fn(hps)))
- models_vc.append((name, author, cover_path, speakers, create_vc_fn(model, hps, speaker_ids)))
- elif t == "soft-vits-vc":
- models_soft_vc.append((name, author, cover_path, speakers, create_soft_vc_fn(model, hps, speaker_ids)))
-
- hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).to(device)
-
- app = gr.Blocks()
-
- with app:
- gr.Markdown("# Moe TTS And Voice Conversion Using VITS Model\n\n"
- "\n\n"
- "[Open In Colab]"
- "(https://colab.research.google.com/drive/14Pb8lpmwZL-JI5Ub6jpG4sz2-8KS0kbS?usp=sharing)"
- " without queue and length limitation.\n\n"
- "Feel free to [open discussion](https://huggingface.co/spaces/skytnt/moe-tts/discussions/new) "
- "if you want to add your model to this app.")
- with gr.Tabs():
- with gr.TabItem("TTS"):
- with gr.Tabs():
- for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn,
- to_symbol_fn) in enumerate(models_tts):
- with gr.TabItem(f"model{i}"):
- with gr.Column():
- cover_markdown = f"\n\n" if cover_path else ""
- gr.Markdown(f"## {name}\n\n"
- f"{cover_markdown}"
- f"model author: {author}\n\n"
- f"language: {lang}")
- tts_input1 = gr.TextArea(label="Text (150 words limitation)", value=example,
- elem_id=f"tts-input{i}")
- tts_input2 = gr.Dropdown(label="Speaker", choices=speakers,
- type="index", value=speakers[0])
- tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1)
- with gr.Accordion(label="Advanced Options", open=False):
- temp_text_var = gr.Variable()
- symbol_input = gr.Checkbox(value=False, label="Symbol input")
- symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1],
- samples=[[x] for x in symbols],
- elem_id=f"symbol-list{i}")
- symbol_list_json = gr.Json(value=symbols, visible=False)
- tts_submit = gr.Button("Generate", variant="primary")
- tts_output1 = gr.Textbox(label="Output Message")
- tts_output2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio{i}")
- download = gr.Button("Download Audio")
- download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}"))
-
- tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input],
- [tts_output1, tts_output2])
- symbol_input.change(to_symbol_fn,
- [symbol_input, tts_input1, temp_text_var],
- [tts_input1, temp_text_var])
- symbol_list.click(None, [symbol_list, symbol_list_json], [],
- _js=f"""
- (i,symbols) => {{
- let root = document.querySelector("body > gradio-app");
- if (root.shadowRoot != null)
- root = root.shadowRoot;
- let text_input = root.querySelector("#tts-input{i}").querySelector("textarea");
- let startPos = text_input.selectionStart;
- let endPos = text_input.selectionEnd;
- let oldTxt = text_input.value;
- let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
- text_input.value = result;
- let x = window.scrollX, y = window.scrollY;
- text_input.focus();
- text_input.selectionStart = startPos + symbols[i].length;
- text_input.selectionEnd = startPos + symbols[i].length;
- text_input.blur();
- window.scrollTo(x, y);
- return [];
- }}""")
-
- with gr.TabItem("Voice Conversion"):
- with gr.Tabs():
- for i, (name, author, cover_path, speakers, vc_fn) in enumerate(models_vc):
- with gr.TabItem(f"model{i}"):
- cover_markdown = f"\n\n" if cover_path else ""
- gr.Markdown(f"## {name}\n\n"
- f"{cover_markdown}"
- f"model author: {author}")
- vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index",
- value=speakers[0])
- vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index",
- value=speakers[min(len(speakers) - 1, 1)])
- vc_input3 = gr.Audio(label="Input Audio (30s limitation)")
- vc_submit = gr.Button("Convert", variant="primary")
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio", elem_id=f"vc-audio{i}")
- download = gr.Button("Download Audio")
- download.click(None, [], [], _js=download_audio_js.format(audio_id=f"vc-audio{i}"))
- vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2])
- with gr.TabItem("Soft Voice Conversion"):
- with gr.Tabs():
- for i, (name, author, cover_path, speakers, soft_vc_fn) in enumerate(models_soft_vc):
- with gr.TabItem(f"model{i}"):
- cover_markdown = f"\n\n" if cover_path else ""
- gr.Markdown(f"## {name}\n\n"
- f"{cover_markdown}"
- f"model author: {author}")
- vc_input1 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index",
- value=speakers[0])
- source_tabs = gr.Tabs()
- with source_tabs:
- with gr.TabItem("microphone"):
- vc_input2 = gr.Audio(label="Input Audio (30s limitation)", source="microphone")
- with gr.TabItem("upload"):
- vc_input3 = gr.Audio(label="Input Audio (30s limitation)", source="upload")
- vc_submit = gr.Button("Convert", variant="primary")
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio", elem_id=f"svc-audio{i}")
- download = gr.Button("Download Audio")
- download.click(None, [], [], _js=download_audio_js.format(audio_id=f"svc-audio{i}"))
- # clear inputs
- source_tabs.set_event_trigger("change", None, [], [vc_input2, vc_input3],
- js="()=>[null,null]")
- vc_submit.click(soft_vc_fn, [vc_input1, vc_input2, vc_input3],
- [vc_output1, vc_output2])
- gr.Markdown(
- "unofficial demo for \n\n"
- "- [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)\n"
- "- [https://github.com/Francis-Komizu/VITS](https://github.com/Francis-Komizu/VITS)\n"
- "- [https://github.com/luoyily/MoeTTS](https://github.com/luoyily/MoeTTS)\n"
- "- [https://github.com/Francis-Komizu/Sovits](https://github.com/Francis-Komizu/Sovits)"
- )
- app.queue(concurrency_count=3).launch(show_api=True, share=True)
- def greet(name):
- return "hahaha " + name + "!"
-
- demo = gr.Interface(fn=greet, inputs="text", outputs="text")
- demo.launch()
diff --git a/spaces/DonDoesStuff/streamusic/style.css b/spaces/DonDoesStuff/streamusic/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/DonDoesStuff/streamusic/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/DrishtiSharma/Text-to-Image-search-using-CLIP/app.py b/spaces/DrishtiSharma/Text-to-Image-search-using-CLIP/app.py
deleted file mode 100644
index e00459f5da1cca9f8fa31bb8170e4700e92cc202..0000000000000000000000000000000000000000
--- a/spaces/DrishtiSharma/Text-to-Image-search-using-CLIP/app.py
+++ /dev/null
@@ -1,65 +0,0 @@
-#Acknowledgments:
-#This project is inspired by:
-#1. https://github.com/haltakov/natural-language-image-search by Vladimir Haltakov
-#2. OpenAI's CLIP
-
-
-
-#Import all the necessary libraries
-import torch
-import requests
-import numpy as np
-import pandas as pd
-import gradio as gr
-from io import BytesIO
-from PIL import Image as PILIMAGE
-from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
-
-#Selecting device based on availability of GPUs
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-#Defining model, processor and tokenizer
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-
-
-#Loading the data
-photos = pd.read_csv("./photos_debug.tsv000", sep='\t', header=0)
-photo_features = np.load("./features_debug.npy")
-photo_ids = pd.read_csv("./photo_ids_debug.csv")
-photo_ids = list(photo_ids['photo_id'])
-
-def find_best_matches(text):
-
- #Inference
- with torch.no_grad():
- # Encode and normalize the description using CLIP
- inputs = tokenizer([text], padding=True, return_tensors="pt")
- inputs = processor(text=[text], images=None, return_tensors="pt", padding=True)
- text_encoded = model.get_text_features(**inputs).detach().numpy()
-
-
- # Finding Cosine similarity
- similarities = list((text_encoded @ photo_features.T).squeeze(0))
-
- #Block of code for displaying top 3 best matches (images)
- matched_images = []
- for i in range(3):
- idx = sorted(zip(similarities, range(photo_features.shape[0])), key=lambda x: x[0], reverse=True)[i][1]
- photo_id = photo_ids[idx]
- photo_data = photos[photos["photo_id"] == photo_id].iloc[0]
- response = requests.get(photo_data["photo_image_url"] + "?w=640")
- img = PILIMAGE.open(BytesIO(response.content))
- matched_images.append(img)
- return matched_images
-
-
-#Gradio app
-iface = gr.Interface(fn=find_best_matches, inputs=[gr.inputs.Textbox(lines=1, label="Text query", placeholder="Introduce the search text...",)],
- examples=[["Dog sticking its tongue out"],["Traffic light on the right"],["Honey bee eating honey"],["Leaves of Bryophyllum fallen on the ground"], ["Cute Kangaroo"], ["Athlete holding a bike in his hands"], ["Happy puppy"], ["Sad puppy"], ["Leopard hiding in the bushes"]],
- theme = "grass",
- outputs=gr.outputs.Carousel([gr.outputs.Image(type="pil")]),
- enable_queue=True,
- title= "Text to Image search using CLIP",
- description="This application displays TOP THREE images from Unsplash dataset that best match the natural language search query provided by the user.").launch()
\ No newline at end of file
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/detr4seg_r50_psg.py b/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/detr4seg_r50_psg.py
deleted file mode 100644
index 07324d4942419d7879ce771a19cc8215a45fd5d2..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/detr4seg_r50_psg.py
+++ /dev/null
@@ -1,152 +0,0 @@
-_base_ = ['./detr4seg_r50.py', '../datasets/psg.py', '../custom_runtime.py']
-
-custom_imports = dict(imports=[
- 'openpsg.models.frameworks.detr4seg',
- 'openpsg.models.relation_heads.detr4seg_head', 'openpsg.datasets',
- 'openpsg.datasets.pipelines.loading',
- 'openpsg.datasets.pipelines.rel_randomcrop',
- 'openpsg.models.relation_heads.approaches.matcher',
- 'openpsg.models.losses.seg_losses'
-],
- allow_failed_imports=False)
-
-object_classes = [
- 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
- 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
- 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
- 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
- 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
- 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
- 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
- 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
- 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
- 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
- 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
- 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
- 'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard',
- 'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit',
- 'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform',
- 'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea',
- 'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone',
- 'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other',
- 'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
- 'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged',
- 'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged',
- 'food-other-merged', 'building-other-merged', 'rock-merged',
- 'wall-other-merged', 'rug-merged'
-]
-
-model = dict(bbox_head=dict(
- num_classes=len(object_classes),
- object_classes=object_classes,
-))
-
-img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- to_rgb=True)
-# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
-# from the default setting in mmdet.
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadPanopticSceneGraphAnnotations',
- with_bbox=True,
- with_mask=True,
- with_seg=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(
- type='AutoAugment',
- policies=[
- [
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- keep_ratio=True)
- ],
- [
- dict(type='Resize',
- img_scale=[(400, 1333), (500, 1333), (600, 1333)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=False), # no empty relations
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- override=True,
- keep_ratio=True)
- ]
- ]),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=1),
- dict(type='RelsFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
-]
-# test_pipeline, NOTE the Pad's size_divisor is different from the default
-# setting (size_divisor=32). While there is little effect on the performance
-# whether we use the default setting or use size_divisor=1.
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=1),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-data = dict(samples_per_gpu=1,
- workers_per_gpu=1,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='AdamW',
- lr=0.00001,
- weight_decay=0.0001,
- paramwise_cfg=dict(
- custom_keys={
- 'backbone': dict(lr_mult=0.1, decay_mult=1.0),
- 'bbox_attention': dict(lr_mult=10.0, decay_mult=1.0),
- 'mask_head': dict(lr_mult=10.0, decay_mult=1.0)
- }))
-optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
-
-# learning policy
-lr_config = dict(policy='step', step=8)
-runner = dict(type='EpochBasedRunner', max_epochs=10)
-
-evaluation = dict(interval=1, metric='PQ')
-checkpoint_config = dict(interval=1, max_keep_ckpts=10)
-
-project_name = 'detr4seg'
-expt_name = 'test_detr4seg_r50_psg'
-work_dir = f'./work_dirs/{expt_name}'
-
-log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook'),
- dict(type='TensorboardLoggerHook'),
- dict(
- type='WandbLoggerHook',
- init_kwargs=dict(
- project=project_name,
- name=expt_name,
- # config=work_dir + "/cfg.yaml"
- ))
- ],
-)
-
-load_from = 'detr_pan_r50.pth'
diff --git a/spaces/EPFL-VILAB/MultiMAE/multimae/multimae.py b/spaces/EPFL-VILAB/MultiMAE/multimae/multimae.py
deleted file mode 100644
index ee11110f3abc667639c70d273921cb9548d8e1c2..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/multimae/multimae.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) EPFL VILAB.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/facebookresearch/deit
-# https://github.com/facebookresearch/dino
-# https://github.com/facebookresearch/moco-v3
-# https://github.com/microsoft/unilm/tree/master/beit
-# https://github.com/BUPT-PRIV/MAE-priv
-# https://github.com/facebookresearch/mae
-# --------------------------------------------------------
-
-import itertools
-import math
-from collections import OrderedDict
-from functools import partial
-from typing import Dict, List, Optional, Union
-
-import torch
-from einops import rearrange, repeat
-from torch import nn
-from torch.distributions.dirichlet import Dirichlet
-
-from utils.registry import register_model
-
-from .multimae_utils import Block, trunc_normal_
-
-__all__ = [
- 'pretrain_multimae_base',
- 'pretrain_multimae_large',
- 'multivit_base',
- 'multivit_large',
-]
-
-
-class MultiMAE(nn.Module):
- """MultiMAE: Multi-task Multi-modal Masked Autoencoder
- This module performs masking in its forward pass.
- The MultiViT module defined below inherits from this module and performs a regular forward pass,
- and should be used instead for downstream tasks
-
-
- :param input_adapters: Dictionary of task -> input adapters
- :param output_adapters: Optional dictionary of task -> output adapters
-
- :param num_global_tokens: Number of additional global tokens to add (like cls tokens), default is 1
- :param dim_tokens: Dimension of encoder tokens
- :param depth: Depth of encoder
- :param num_heads: Number of attention heads
- :param mlp_ratio: MLP hidden dim ratio
- :param qkv_bias: Set to False to disable bias
- :param drop_rate: Dropout after MLPs and Attention
- :param attn_drop_rate: Attention matrix drop rate
- :param drop_path_rate: DropPath drop rate
- :param norm_layer: Type of normalization layer
- """
- def __init__(self,
- input_adapters: Dict[str, nn.Module],
- output_adapters: Optional[Dict[str, nn.Module]],
- num_global_tokens: int = 1,
- dim_tokens: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = True,
- drop_rate: float = 0.0,
- attn_drop_rate: float = 0.0,
- drop_path_rate: float = 0.0,
- norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6)):
- super().__init__()
-
- # Initialize input and output adapters
- for adapter in input_adapters.values():
- adapter.init(dim_tokens=dim_tokens)
- self.input_adapters = nn.ModuleDict(input_adapters)
- if output_adapters is not None:
- for adapter in output_adapters.values():
- adapter.init(dim_tokens_enc=dim_tokens)
- self.output_adapters = nn.ModuleDict(output_adapters)
- else:
- self.output_adapters = None
-
- # Additional learnable tokens that can be used by encoder to process/store global information
- self.num_global_tokens = num_global_tokens
- self.global_tokens = nn.Parameter(torch.zeros(1, num_global_tokens, dim_tokens))
- trunc_normal_(self.global_tokens, std=0.02)
-
- # Transformer encoder
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
- self.encoder = nn.Sequential(*[
- Block(dim=dim_tokens, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
- for i in range(depth)
- ])
-
- self.apply(self._init_weights)
- for name, m in self.named_modules():
- if isinstance(m, nn.Linear):
- if 'qkv' in name:
- # treat the weights of Q, K, V separately
- val = math.sqrt(6. / float(m.weight.shape[0] // 3 + m.weight.shape[1]))
- nn.init.uniform_(m.weight, -val, val)
- elif 'kv' in name:
- # treat the weights of K, V separately
- val = math.sqrt(6. / float(m.weight.shape[0] // 2 + m.weight.shape[1]))
- nn.init.uniform_(m.weight, -val, val)
-
- if isinstance(m, nn.Conv2d):
- if '.proj' in name:
- # From MAE, initialize projection like nn.Linear (instead of nn.Conv2d)
- w = m.weight.data
- nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- nn.init.xavier_uniform_(m.weight)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- def get_num_layers(self):
- return len(self.encoder)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- no_wd_set = {'global_tokens'}
-
- for task, adapter in self.input_adapters.items():
- if hasattr(adapter, 'no_weight_decay'):
- to_skip = adapter.no_weight_decay()
- to_skip = set([f'input_adapters.{task}.{name}' for name in to_skip])
- no_wd_set = no_wd_set | to_skip
-
- for task, adapter in self.output_adapters.items():
- if hasattr(adapter, 'no_weight_decay'):
- to_skip = adapter.no_weight_decay()
- to_skip = set([f'output_adapters.{task}.{name}' for name in to_skip])
- no_wd_set = no_wd_set | to_skip
-
- return no_wd_set
-
- def sample_alphas(self, B: int, n_tasks: int, alphas: float = 1.0, eps: float = 1e-5):
- """
- Sample alphas for Dirichlet sampling such that tasks are first uniformly chosen and then Dirichlet sampling
- is performed over the chosen ones.
-
- :param B: Batch size
- :param n_tasks: Number of input tasks
- :param alphas: Float or list to multiply task choices {0,1} by
- :param eps: Small constant since Dirichlet alphas need to be positive
- """
- valid_task_choices = torch.Tensor([list(i) for i in itertools.product([0, 1], repeat=n_tasks)][1:])
- rand_per_sample_choice = torch.randint(0, len(valid_task_choices), (B,))
- alphas_tensor = torch.index_select(valid_task_choices, 0, rand_per_sample_choice)
- alphas_tensor = alphas_tensor * torch.tensor(alphas) + eps
- return alphas_tensor
-
- def generate_random_masks(self,
- input_tokens: Dict[str, torch.Tensor],
- num_encoded_tokens: int,
- alphas: Union[float, List[float]] = 1.0,
- sample_tasks_uniformly: bool = False) :
- """
- Sample a total of num_encoded_tokens from different tasks using Dirichlet sampling.
-
- :param input_tokens: Dictionary of tensors to sample num_encoded_tokens from
- :param num_encoded_tokens: Number of tokens to select
- :param alphas: Dirichlet distribution parameter alpha. Lower alpha = harder,
- less uniform sampling. Can be float or list of floats.
- :param sample_tasks_uniformly: Set to True to first sample 1-n_tasks uniformly at random
- for each sample in the batch. Dirichlet sampling is then done over selected subsets.
- """
- B = list(input_tokens.values())[0].shape[0]
- device = list(input_tokens.values())[0].device
-
- alphas = [alphas] * len(input_tokens) if isinstance(alphas, float) else alphas
- if sample_tasks_uniformly:
- alphas = self.sample_alphas(B, len(input_tokens), alphas=alphas)
- task_sampling_dist = Dirichlet(alphas).sample().to(device)
- else:
- task_sampling_dist = Dirichlet(torch.Tensor(alphas)).sample((B,)).to(device)
-
- samples_per_task = (task_sampling_dist * num_encoded_tokens).round().long()
-
- task_masks = []
- num_tokens_per_task = [task_tokens.shape[1] for task_tokens in input_tokens.values()]
- for i, num_tokens in enumerate(num_tokens_per_task):
- # Use noise to shuffle arange
- noise = torch.rand(B, num_tokens, device=device) # noise in [0, 1]
- ids_arange_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
- mask = torch.arange(num_tokens, device=device).unsqueeze(0).expand(B, -1)
- mask = torch.gather(mask, dim=1, index=ids_arange_shuffle)
- # 0 is keep (unmasked), 1 is remove (masked)
- mask = torch.where(mask < samples_per_task[:, i].unsqueeze(1), 0, 1)
- task_masks.append(mask)
-
- mask_all = torch.cat(task_masks, dim=1)
- ids_shuffle = torch.argsort(mask_all + torch.rand_like(mask_all.float()), dim=1)
- ids_restore = torch.argsort(ids_shuffle, dim=1)
- ids_keep = ids_shuffle[:, :num_encoded_tokens]
-
- # Update binary mask to adjust for task rounding
- mask_all = torch.ones_like(mask_all)
- mask_all[:, :num_encoded_tokens] = 0
- # Unshuffle to get the binary mask
- mask_all = torch.gather(mask_all, dim=1, index=ids_restore)
- # Split to get task masks
- task_masks = torch.split(mask_all, num_tokens_per_task, dim=1)
- # Convert to dict
- task_masks = {domain: mask for domain, mask in zip(input_tokens.keys(), task_masks)}
-
- return task_masks, ids_keep, ids_restore
-
- @staticmethod
- def make_mask(N_H, N_W, xy_idxs, full_tasks=[], indicate_visible=True, flatten=True, device='cuda'):
- """
- Creates masks for each task, given lists of un-masked x,y coordinates.
- """
- xy_idxs = {
- k: torch.LongTensor(v)
- for k, v in xy_idxs.items()
- }
-
- task_masks = {
- k: torch.ones(N_H, N_W).to(device)
- for k in xy_idxs.keys()
- }
-
- for k in xy_idxs.keys():
- if len(xy_idxs[k]) > 0:
- task_masks[k][xy_idxs[k][:, 1], xy_idxs[k][:, 0]] = 0
-
- for task in full_tasks:
- task_masks[task][:] = 0
-
- if not indicate_visible:
- task_masks = {k: 1 - v for k, v in task_masks.items()}
-
- if flatten:
- task_masks = {k: v.flatten().unsqueeze(0) for k, v in task_masks.items()}
-
- return task_masks
-
- def generate_input_info(self, input_task_tokens, image_size):
- input_info = OrderedDict()
- i = 0
- input_info['tasks'] = {}
- for domain, tensor in input_task_tokens.items():
- num_tokens = tensor.shape[1]
- d = {
- 'num_tokens': num_tokens,
- 'has_2d_posemb': True, # TODO: Modify when adding non-2D tasks
- 'start_idx': i,
- 'end_idx': i + num_tokens,
- }
- i += num_tokens
- input_info['tasks'][domain] = d
-
- input_info['image_size'] = image_size
- input_info['num_task_tokens'] = i
- input_info['num_global_tokens'] = self.num_global_tokens
-
- return input_info
-
- def forward(self,
- x: Union[Dict[str, torch.Tensor], torch.Tensor],
- mask_inputs: bool = True,
- task_masks: Dict[str, torch.Tensor] = None,
- num_encoded_tokens: int = 128,
- alphas: Union[float, List[float]] = 1.0,
- sample_tasks_uniformly: bool = False,
- fp32_output_adapters: List[str] = []):
- """
- Forward pass through input adapters, transformer encoder and output adapters.
- If specified, will randomly drop input tokens.
-
- :param x: Input tensor or dictionary of tensors
- :param mask_inputs: Set to True to enable random masking of input patches
- :param task_masks: Optional dictionary of task->mask pairs.
- :param num_encoded_tokens: Number of tokens to randomly select for encoder.
- Only used if mask_inputs is True.
- :param alphas: Dirichlet distribution parameter alpha for task sampling.
- Higher alpha = harder, less uniform sampling. Can be float or list of floats.
- :param sample_tasks_uniformly: Set to True if tasks should be uniformly presampled,
- before Dirichlet sampling decides share of masked tokens between them.
- :param fp32_output_adapters: List of task identifiers to force output adapters to
- run with mixed precision turned off for stability reasons.
- """
-
- ## Processing input modalities
- # If input x is a Tensor, assume it's RGB
- x = {'rgb': x} if isinstance(x, torch.Tensor) else x
-
- # Need image size for tokens->image reconstruction
- # We assume that at least one of rgb or semseg is given as input before masking
- if 'rgb' in x:
- B, C, H, W = x['rgb'].shape
- elif 'semseg' in x:
- B, H, W = x['semseg'].shape
- H *= self.input_adapters['semseg'].stride_level
- W *= self.input_adapters['semseg'].stride_level
- else:
- B, C, H, W = list(x.values())[0].shape # TODO: Deal with case where not all have same shape
-
- # Encode selected inputs to tokens
- input_task_tokens = {
- domain: self.input_adapters[domain](tensor)
- for domain, tensor in x.items()
- if domain in self.input_adapters
- }
-
- input_info = self.generate_input_info(input_task_tokens=input_task_tokens, image_size=(H, W))
-
- # Select random subset of tokens from the chosen input tasks and concatenate them
- if mask_inputs:
- num_encoded_tokens = num_encoded_tokens if num_encoded_tokens is not None else self.num_encoded_tokens
- else:
- num_encoded_tokens = sum([tensor.shape[1] for tensor in input_task_tokens.values()])
-
- ## Generating masks
- if task_masks is None:
- task_masks, ids_keep, ids_restore = self.generate_random_masks(
- input_task_tokens,
- num_encoded_tokens,
- alphas=alphas,
- sample_tasks_uniformly=sample_tasks_uniformly
- )
- else:
- mask_all = torch.cat([task_masks[task] for task in input_task_tokens.keys()], dim=1)
- ids_shuffle = torch.argsort(mask_all, dim=1)
- ids_restore = torch.argsort(ids_shuffle, dim=1)
- ids_keep = ids_shuffle[:, :(mask_all == 0).sum()]
-
- input_tokens = torch.cat([task_tokens for task_tokens in input_task_tokens.values()], dim=1)
-
- # Apply mask
- input_tokens = torch.gather(input_tokens, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, input_tokens.shape[2]))
-
- # Add global tokens to input tokens
- global_tokens = repeat(self.global_tokens, '() n d -> b n d', b=B)
- input_tokens = torch.cat([input_tokens, global_tokens], dim=1)
-
- ## Transformer forward pass
- encoder_tokens = self.encoder(input_tokens)
-
- ## Output decoders
- if self.output_adapters is None:
- return encoder_tokens, task_masks
-
- # Decode tokens for each task using task-specific output adapters
- preds = {
- domain: self.output_adapters[domain](
- encoder_tokens=encoder_tokens,
- input_info=input_info,
- ids_keep=ids_keep,
- ids_restore=ids_restore,
- )
- for domain in self.output_adapters
- if domain not in fp32_output_adapters
- }
- # Force running selected output adapters in fp32 mode
- with torch.cuda.amp.autocast(enabled=False):
- for domain in fp32_output_adapters:
- if domain not in self.output_adapters:
- continue
- preds[domain] = self.output_adapters[domain](
- encoder_tokens=encoder_tokens.float(),
- input_info=input_info,
- ids_keep=ids_keep,
- ids_restore=ids_restore,
- )
-
- return preds, task_masks
-
-
-@register_model
-def pretrain_multimae_base(
- input_adapters: Dict[str, nn.Module],
- output_adapters: Optional[Dict[str, nn.Module]],
- **kwargs):
- model = MultiMAE(
- input_adapters=input_adapters,
- output_adapters=output_adapters,
- dim_tokens=768,
- depth=12,
- num_heads=12,
- mlp_ratio=4,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- **kwargs
- )
- return model
-
-@register_model
-def pretrain_multimae_large(
- input_adapters: Dict[str, nn.Module],
- output_adapters: Optional[Dict[str, nn.Module]],
- **kwargs):
- model = MultiMAE(
- input_adapters=input_adapters,
- output_adapters=output_adapters,
- dim_tokens=1024,
- depth=24,
- num_heads=16,
- mlp_ratio=4,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- **kwargs
- )
- return model
-
-
-class MultiViT(MultiMAE):
- """MultiViT: Multi-modal Vision Transformer
- This is MultiMAE without masking and with a simplified / faster forward pass
-
-
- :param input_adapters: Dictionary of task -> input adapters
- :param output_adapters: Optional dictionary of task -> output adapters
-
- :param num_global_tokens: Number of additional global tokens to add (like cls tokens), default is 1
- :param dim_tokens: Dimension of encoder tokens
- :param depth: Depth of encoder
- :param num_heads: Number of attention heads
- :param mlp_ratio: MLP hidden dim ratio
- :param qkv_bias: Set to False to disable bias
- :param drop_rate: Dropout after MLPs and Attention
- :param attn_drop_rate: Attention matrix drop rate
- :param drop_path_rate: DropPath drop rate
- :param norm_layer: Type of normalization layer
- """
-
- def process_input(self, x):
-
- # If input x is a Tensor, assume it's RGB
- x = {'rgb': x} if isinstance(x, torch.Tensor) else x
- # Need image size for tokens->image reconstruction
- if 'rgb' in x:
- B, _, H, W = x['rgb'].shape
- elif 'semseg' in x:
- B, H, W = x['semseg'].shape
- H *= self.input_adapters['semseg'].stride_level
- W *= self.input_adapters['semseg'].stride_level
- else:
- B, _, H, W = list(x.values())[0].shape # TODO: Deal with case where not all have same shape
-
- # Encode selected inputs to tokens
- input_task_tokens = {
- domain: self.input_adapters[domain](tensor)
- for domain, tensor in x.items()
- if domain in self.input_adapters
- }
-
- input_info = self.generate_input_info(input_task_tokens=input_task_tokens, image_size=(H, W))
- input_tokens = torch.cat([task_tokens for task_tokens in input_task_tokens.values()], dim=1)
-
- # Add global tokens to input tokens
- global_tokens = repeat(self.global_tokens, '() n d -> b n d', b=B)
- input_tokens = torch.cat([input_tokens, global_tokens], dim=1)
-
- return input_tokens, input_info
-
- def forward(self, x: Union[Dict[str, torch.Tensor], torch.Tensor], return_all_layers=False, **kwargs):
- """
- Forward pass through input adapters, transformer encoder and output adapters.
-
- :param x: Input tensor or dictionary of tensors
- :param return_all_layers: Set to True to return all transformer layers
- """
-
- input_tokens, input_info = self.process_input(x)
-
- # Pass tokens through Transformer
- if not return_all_layers:
- encoder_tokens = self.encoder(input_tokens)
- else:
- # Optionally access every intermediate layer
- encoder_tokens = []
- tokens = input_tokens
- for block in self.encoder:
- tokens = block(tokens)
- encoder_tokens.append(tokens)
-
- if self.output_adapters is None:
- return encoder_tokens
-
- # Decode tokens for each task using task-specific output adapters
- preds = {
- domain: self.output_adapters[domain](
- encoder_tokens=encoder_tokens,
- input_info=input_info,
- )
- for domain in self.output_adapters
- }
-
- return preds
-
-
-@register_model
-def multivit_base(
- input_adapters: Dict[str, nn.Module],
- output_adapters: Optional[Dict[str, nn.Module]],
- **kwargs):
- model = MultiViT(
- input_adapters=input_adapters,
- output_adapters=output_adapters,
- dim_tokens=768,
- depth=12,
- num_heads=12,
- mlp_ratio=4,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- **kwargs
- )
- return model
-
-@register_model
-def multivit_large(
- input_adapters: Dict[str, nn.Module],
- output_adapters: Optional[Dict[str, nn.Module]],
- **kwargs):
- model = MultiViT(
- input_adapters=input_adapters,
- output_adapters=output_adapters,
- dim_tokens=1024,
- depth=24,
- num_heads=16,
- mlp_ratio=4,
- qkv_bias=True,
- norm_layer=partial(nn.LayerNorm, eps=1e-6),
- **kwargs
- )
- return model
diff --git a/spaces/EnigmaOfTheWorld/GenZBot/app.py b/spaces/EnigmaOfTheWorld/GenZBot/app.py
deleted file mode 100644
index f8fb0d1f949540152b152e3f4c3549b6e106094d..0000000000000000000000000000000000000000
--- a/spaces/EnigmaOfTheWorld/GenZBot/app.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import csv
-import warnings
-import io
-import pathlib
-from typing import Union
-import os
-import random
-from PIL import Image
-# import whisper
-import openai
-import gradio as gr
-from transformers import pipeline
-from stability_sdk import client
-import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
-from pytube import YouTube
-from pytube import Search
-from serpapi import GoogleSearch
-import grpc
-from langchain.embeddings.openai import OpenAIEmbeddings
-from PyPDF2 import PdfReader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-from langchain.agents import create_pandas_dataframe_agent
-import pandas as pd
-import docx
-from pandasai import PandasAI
-from pandasai.llm.openai import OpenAI as pai_openai
-
-
-
-openai.api_key = os.environ['OPENAI_API_KEY']
-stability_api = client.StabilityInference(
- key=os.environ['STABILITY_KEY'], #os.environ("STABILITY_KEY"), # key=os.environ['STABILITY_KEY'], # API Key reference.
- verbose=True, # Print debug messages.
- engine="stable-diffusion-v1-5", # Set the engine to use for generation.
- # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0
- # stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-inpainting-v1-0 stable-inpainting-512-v2-0
-)
-
-
-whisper_from_pipeline = pipeline("automatic-speech-recognition",model="openai/whisper-medium")
-EMBEDIDNGS = None
-DATAFRAME_FILE = None
-DATAFRAME = None
-DOCSEARCH = None
-RANDOM_USER = ''.join(chr(random.randint(65,90)) for i in range(8))+f'{random.randint(1,10000000000)}'
-print(f'{RANDOM_USER} chat started')
-
-############# FUNCTION DEPENDING ON IPYTHON FUNCTIONS FROM OPENAI RESPONSE
-def gen_draw(user_query:str)->tuple:
- ###USES STABLE DIFFUSION
- answers = stability_api.generate(
- prompt = user_query,
- seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
- # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
- # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
- steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
- cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
- # Setting this value higher increases the strength in which it tries to match your prompt.
- # Defaults to 7.0 if not specified.
- width=512, # Generation width, defaults to 512 if not included.
- height=512, # Generation height, defaults to 512 if not included.
- samples=1, # Number of images to generate, defaults to 1 if not included.
- sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
- # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
- # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
- )
- try:
- for resp in answers:
- for artifact in resp.artifacts:
- if artifact.finish_reason == generation.FILTER:
- warnings.warn(
- "Your request activated the API's safety filters and could not be processed."
- "Please modify the prompt and try again.")
- if artifact.type == generation.ARTIFACT_IMAGE:
- img = Image.open(io.BytesIO(artifact.binary))
- image_file = f'/tmp/{artifact.seed}.png'
- img.save(image_file)
- return (image_file,)
- except grpc._channel._MultiThreadedRendezvous as e:
- print(f'Exception : {e.__class__}')
- print(e)
- return "Invalid prompt"
-
-
-def vid_tube(user_query:str) -> tuple:
-
- video_id = Search(user_query).results[0].video_id
- return f'VIDEO '
- # first_video = py_tube_list_of_videos.results[0]
- # yt_flag = False
- # for vid in py_tube_list_of_videos.results:
- # print(vid.vid_info.keys())
- # if vid.vid_info.get('streamingData'):
- # print(vid.vid_info.keys(),'-')
- # yt_flag = True
- # file_path = vid.streams.get_highest_resolution().download('/tmp/')
- # break
-
- return (file_path,) if yt_flag else "The system cannot fulfill your request currently please try later"
-
-
-def search_internet(user_query:str,*,key_number:int) -> str:
- if key_number >= 9:
- raise gr.Error("Out of Google API Keys")
- try:
- params = {
- "q": user_query,
- "location": "Bengaluru, Karnataka, India",
- "hl": "hi",
- "gl": "in",
- "google_domain": "google.co.in",
- # "api_key": ""
- "api_key": os.environ[f'GOOGLE_API{key_number}'] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API']
- }
- search = GoogleSearch(params)
- results = search.get_dict()
- print(results)
- organic_results = results["organic_results"]
- print(f"Key {key_number} used")
-
-
- snippets = ""
- counter = 1
- for item in organic_results:
- snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['link'] + '\n'
- counter += 1
-
- # snippets
-
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{user_query}\n\nAnswer-''',
- temperature=0.49,
- max_tokens=256,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0)
-
-
- result = response.choices[0].text
-
- except Exception as e:
- print(f'search google: ')
- print(f'GOOGLE_API{key_number} OUT OF LIMIT!')
- print(f'Exception: {e.__class__}, {e}')
- return search_internet(user_query,key_number = key_number+1)
- return result
-
-def search_document_uploaded(user_query:str) -> str:
- print('Searching uploaded document......')
- # docsearch = FAISS.load_local(folder_path = f'/tmp/{RANDOM_USER}embeddings',embeddings=EMBEDIDNGS)
- chain = load_qa_chain(OpenAI(), chain_type="stuff")
- docs = DOCSEARCH.similarity_search(user_query)
- return chain.run(input_documents=docs, question=user_query)
-
-
-def ask_dataframes(user_query):
- return DATAFRAME_FILE.run(DATAFRAME, prompt = user_query)
-
-############# GET OPENAI RESPONSE
-def get_open_ai_reponse(user_query:str)->Union[tuple,str]:
- print(EMBEDIDNGS)
- if (EMBEDIDNGS is not None) and (DOCSEARCH is not None):
- print('Searching document')
- return search_document_uploaded(user_query)
-
- if DATAFRAME_FILE is not None:
- print('Dataframe')
- return ask_dataframes(user_query)
-
-
- open_ai_response = openai.Completion.create(
- model="text-davinci-003",
- prompt=f'''Your name is GenZBot and knowledge cutoff date is 2021-09, and you are not aware of any events after that time. if the
- Answer to following questions is not from your knowledge base or in case of queries like date, time, weather
- updates / stock updates / current affairs / news or people which requires you to have internet connection then print i don't have access to internet to answer your question,
- if question is related to image or painting or drawing or diagram generation then print ipython type output function gen_draw("detailed prompt of image to be generated")
- if the question is related to playing a song or video or music of a singer then print ipython type output function vid_tube("relevent search query")
- if the question is related to operating home appliances then print ipython type output function home_app(" action(ON/Off),appliance(TV,Geaser,Fridge,Lights,fans,AC)") .
- if question is realted to sending mail or sms then print ipython type output function messenger_app(" message of us ,messenger(email,sms)")
- \nQuestion-{user_query}
- \nAnswer -''',
- temperature=0.49,
- max_tokens=256,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
- result_from_open_ai = open_ai_response.choices[0].text
- if 'gen_draw' in result_from_open_ai:
- result = gen_draw(user_query) ## will write drawn image to file
-
- elif 'vid_tube' in result_from_open_ai:
- try:
- result = vid_tube(user_query) ## play youtube video
- except KeyError as e:
- print(e)
- result = "The system is spacing an issue please try again later"
-
- elif ("don't" in result_from_open_ai) or ("internet" in result_from_open_ai):
- result = search_internet(user_query,key_number = 1)
- else:
- result = result_from_open_ai
- return result
-
-
-############### DIFFERENT OUTPUT FUNCTIONS
-def user_input(chat_history:list,user_query:str)->list:
- result = get_open_ai_reponse(user_query)
- print(f'user_input: {chat_history + [(user_query,result)]}')
- return chat_history + [(user_query,result)]
-
-def transcribe(chat_history:list,user_audio_query:str)->list:
- print(user_audio_query.__class__)
- # text_from_speech = p(user_audio_query)["text"]
- try:
- user_query_from_audio = whisper_from_pipeline(user_audio_query)["text"]
- except Exception as e:
- print('EXCEPTION AS E')
- result = f'We are having a problem : {e}'
- else:
- result = get_open_ai_reponse(user_query_from_audio)
-
- # user_query_from_audio if user_query_from_audio else result
- print(result)
- print(f'transcribe: {chat_history + [(user_query_from_audio,result)]}')
- return chat_history + [(user_query_from_audio,result)]
-
-
-def pdf(file_name):
- print(f'Processing {file_name} pdf file')
- reader = PdfReader(file_name)
- raw_text = ''
- for i, page in enumerate(reader.pages):
- text = page.extract_text()
- if text:
- raw_text += text
- text_splitter = CharacterTextSplitter(
- separator = "\n",
- chunk_size = 1000,
- chunk_overlap = 200,
- length_function = len,
- )
- texts = text_splitter.split_text(raw_text)
- return texts
-
-def docx_file(file_name):
- print(f'Processing .docx file: {file_name}')
- doc = docx.Document(file_name)
-
- # iterate over paragraphs and print their text
- raw_text = ''
- for para in doc.paragraphs:
- raw_text += para.text
- text_splitter = CharacterTextSplitter(
- separator = "\n",
- chunk_size = 1000,
- chunk_overlap = 200,
- length_function = len,
- )
- texts = text_splitter.split_text(raw_text)
- return texts
-
-def text_file(file_name):
- print('Processing text file')
- with open(file_name) as file:
- raw_text = ''
- for line in file:
- raw_text += line
- text_splitter = CharacterTextSplitter(
- separator = "\n",
- chunk_size = 1000,
- chunk_overlap = 200,
- length_function = len,
- )
- texts = text_splitter.split_text(raw_text)
- return texts
-
-
-
-
-
-def build_embeddings(file_name,file_ext):
-
-
- functions_by_file_type = { 'pdf': pdf,
- 'docx': docx_file,
- 'txt': text_file
-
- }
-
- texts = functions_by_file_type.get(file_ext.replace('.','').strip())(file_name)
- print(texts)
-
- global EMBEDIDNGS
- EMBEDIDNGS = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
- global DOCSEARCH
- DOCSEARCH = FAISS.from_texts(texts, EMBEDIDNGS)
- # if not os.path.exists(f'/tmp/{RANDOM_USER}embeddings'):
- # os.mkdir(f'/tmp/{RANDOM_USER}embeddings')
- # docsearch.save_local(f'/tmp/{RANDOM_USER}embeddings')
- # print(f'Embeddings created to /tmp/{RANDOM_USER}embeddings')
-
-
-def ask_questions_abt_dataframes(file,file_ext):
- print(file_ext)
- global EMBEDIDNGS
- EMBEDIDNGS = None
-
- reader_function = { '.csv': pd.read_csv, '.xlsx': pd.read_excel }.get(file_ext)
- print(reader_function.__name__)
- global DATAFRAME_FILE
- global DATAFRAME
- DATAFRAME = reader_function(file.name)
- llm = pai_openai(api_token=os.environ['OPENAI_API_KEY'])
- DATAFRAME_FILE = PandasAI(llm)
-
-
-
-
-def upload_file(chatbot_history,file_uploaded):
- file_ext = os.path.splitext(file_uploaded.name)[-1]
- if file_ext not in ['.csv','.docx','.xlsx','.pdf','.txt']:
- return chatbot_history + [(None, 'Invalid file format. We currently only csv, docx, pdf, txt, xlsx file extensions.')]
-
- print(file_uploaded.__class__)
-
- if file_ext not in ['.csv','.xlsx']:
- build_embeddings(file_uploaded.name,file_ext)
- else:
- try:
- ask_questions_abt_dataframes(file_uploaded,file_ext)
- except Exception as e:
- print(f'Dataframes {e}')
- return chatbot_history + [(None, f'Kindly attempt again at a subsequent time.')]
-
-
- return chatbot_history + [(None, f'You have uploaded {os.path.split(file_uploaded.name)[-1]} successfully. You can start asking questions about the document.If you want to stop asking questions about the uploaded document click on "clear chat history".')]
-
-
-def clear_chat_history(history:list)->list:
- history.clear()
- global EMBEDIDNGS
- EMBEDIDNGS = None
-
- global DATAFRAME_FILE
- DATAFRAME_FILE = None
-
- global DOCSEARCH
- DOCSEARCH = None
-
- # storing_folder = pathlib.Path('/tmp/')
- # for file in storing_folder.iterdir():
- # if file.is_file():
- # print(f'{file} to be deleted')
- # file.unlink()
- # print(f'{file} deleted')
-
- # global EMBEDIDNGS
- # EMBEDIDNGS = None
-
- # global DATAFRAME_FILE
- # DATAFRAME_FILE = None
- return history
-
-
-
-
-
-#################### DRIVER SCRIPT #####################
-with gr.Blocks(theme='freddyaboulton/test-blue') as demo:
- gr.Markdown(gr.__version__)
- gr.Markdown("""GenZBot """)
- gr.Markdown("""GenZBot is a virtual assistant that employs advanced artificial intelligence (AI) technologies to enhance its capabilities. Utilizing cutting-edge AI techniques such as Whisper, chatgpt, internet, Dall-E and OpenAI and Langchain, GenZBot can provide users with a wide range of useful features. By leveraging AI, GenZBot can understand and respond to users' requests in a natural and intuitive manner, allowing for a more seamless and personalized experience. Its ability to generate paintings, drawings, and abstract art, play music and videos, and you can Upload your documents and ask questions about the document, is made possible by sophisticated AI algorithms that can produce complex and nuanced results. Overall, GenZBot's extensive use of AI technology enables it to serve as a powerful and versatile digital assistant that can adapt to the needs of its users.""")
- chatbot = gr.Chatbot()
-
- with gr.Row():
- with gr.Column():
- user_text_query = gr.Text(label="Your Query",placeholder="Your Query")
- with gr.Column(scale=0.15, min_width=0):#
- user_audio_microphone_query = gr.Audio(label="Record",source="microphone",type="filepath")
- user_audio_microphone_submit_button = gr.Button("Get me result")
- with gr.Column(scale=0.15, min_width=0):
- upload_button = gr.UploadButton("📁", info="Upload text files and start talking to them")
- gr.Markdown("Upload document by clicking on the directory icon.")
- clear_button = gr.Button("Clear chat history")
-
-
- user_text_query.submit(fn=user_input,inputs=[chatbot,user_text_query],outputs=[chatbot])
- user_audio_microphone_submit_button.click(fn=transcribe,inputs=[chatbot,user_audio_microphone_query],outputs=[chatbot])
- clear_button.click(fn=clear_chat_history,inputs=[chatbot],outputs=[chatbot])
- upload_button.upload(upload_file,inputs=[chatbot,upload_button],outputs=[chatbot])
-
-
-
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_models/satrn.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_models/satrn.py
deleted file mode 100644
index f7a6de8637c77a18a930e032bfb752434b173ba4..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_models/satrn.py
+++ /dev/null
@@ -1,11 +0,0 @@
-label_convertor = dict(
- type='AttnConvertor', dict_type='DICT36', with_unknown=True, lower=True)
-
-model = dict(
- type='SATRN',
- backbone=dict(type='ShallowCNN'),
- encoder=dict(type='SatrnEncoder'),
- decoder=dict(type='TFDecoder'),
- loss=dict(type='TFLoss'),
- label_convertor=label_convertor,
- max_seq_len=40)
diff --git a/spaces/Fox1997/vits-uma-genshin-honkai/text/cleaners.py b/spaces/Fox1997/vits-uma-genshin-honkai/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/Fox1997/vits-uma-genshin-honkai/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text!='':
- text+=' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil','pau']:
- text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
- else:
- continue
- n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
- a2_next=-1
- else:
- a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i str:
- """
- Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
- This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
- """
- outputs = [[]]
- for token in tokens:
- if token >= self.timestamp_begin:
- timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
- outputs.append(timestamp)
- outputs.append([])
- else:
- outputs[-1].append(token)
- outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
- return "".join(outputs)
-
- @property
- @lru_cache()
- def eot(self) -> int:
- return self.tokenizer.eos_token_id
-
- @property
- @lru_cache()
- def sot(self) -> int:
- return self._get_single_token_id("<|startoftranscript|>")
-
- @property
- @lru_cache()
- def sot_lm(self) -> int:
- return self._get_single_token_id("<|startoflm|>")
-
- @property
- @lru_cache()
- def sot_prev(self) -> int:
- return self._get_single_token_id("<|startofprev|>")
-
- @property
- @lru_cache()
- def no_speech(self) -> int:
- return self._get_single_token_id("<|nospeech|>")
-
- @property
- @lru_cache()
- def no_timestamps(self) -> int:
- return self._get_single_token_id("<|notimestamps|>")
-
- @property
- @lru_cache()
- def timestamp_begin(self) -> int:
- return self.tokenizer.all_special_ids[-1] + 1
-
- @property
- @lru_cache()
- def language_token(self) -> int:
- """Returns the token id corresponding to the value of the `language` field"""
- if self.language is None:
- raise ValueError(f"This tokenizer does not have language token configured")
-
- additional_tokens = dict(
- zip(
- self.tokenizer.additional_special_tokens,
- self.tokenizer.additional_special_tokens_ids,
- )
- )
- candidate = f"<|{self.language}|>"
- if candidate in additional_tokens:
- return additional_tokens[candidate]
-
- raise KeyError(f"Language {self.language} not found in tokenizer.")
-
- @property
- @lru_cache()
- def all_language_tokens(self) -> Tuple[int]:
- result = []
- for token, token_id in zip(
- self.tokenizer.additional_special_tokens,
- self.tokenizer.additional_special_tokens_ids,
- ):
- if token.strip("<|>") in LANGUAGES:
- result.append(token_id)
- return tuple(result)
-
- @property
- @lru_cache()
- def all_language_codes(self) -> Tuple[str]:
- return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
-
- @property
- @lru_cache()
- def sot_sequence_including_notimestamps(self) -> Tuple[int]:
- return tuple(list(self.sot_sequence) + [self.no_timestamps])
-
- @property
- @lru_cache()
- def non_speech_tokens(self) -> Tuple[int]:
- """
- Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
- annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
-
- - ♪♪♪
- - ( SPEAKING FOREIGN LANGUAGE )
- - [DAVID] Hey there,
-
- keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
- """
- symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
- symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
-
- # symbols that may be a single token or multiple tokens depending on the tokenizer.
- # In case they're multiple tokens, suppress the first token, which is safe because:
- # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
- # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
- miscellaneous = set("♩♪♫♬♭♮♯")
- assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
-
- # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
- result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
- for symbol in symbols + list(miscellaneous):
- for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
- if len(tokens) == 1 or symbol in miscellaneous:
- result.add(tokens[0])
-
- return tuple(sorted(result))
-
- def _get_single_token_id(self, text) -> int:
- tokens = self.tokenizer.encode(text)
- assert len(tokens) == 1, f"{text} is not encoded as a single token"
- return tokens[0]
-
-
-@lru_cache(maxsize=None)
-def build_tokenizer(name: str = "gpt2"):
- os.environ["TOKENIZERS_PARALLELISM"] = "false"
- path = os.path.join(os.path.dirname(__file__), "assets", name)
- tokenizer = GPT2TokenizerFast.from_pretrained(path)
-
- specials = [
- "<|startoftranscript|>",
- *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
- "<|translate|>",
- "<|transcribe|>",
- "<|startoflm|>",
- "<|startofprev|>",
- "<|nospeech|>",
- "<|notimestamps|>",
- ]
-
- tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
- return tokenizer
-
-
-@lru_cache(maxsize=None)
-def get_tokenizer(
- multilingual: bool,
- *,
- task: Optional[str] = None, # Literal["transcribe", "translate", None]
- language: Optional[str] = None,
-) -> Tokenizer:
- if language is not None:
- language = language.lower()
- if language not in LANGUAGES:
- if language in TO_LANGUAGE_CODE:
- language = TO_LANGUAGE_CODE[language]
- else:
- raise ValueError(f"Unsupported language: {language}")
-
- if multilingual:
- tokenizer_name = "multilingual"
- task = task or "transcribe"
- language = language or "en"
- else:
- tokenizer_name = "gpt2"
- task = None
- language = None
-
- tokenizer = build_tokenizer(name=tokenizer_name)
- all_special_ids: List[int] = tokenizer.all_special_ids
- sot: int = all_special_ids[1]
- translate: int = all_special_ids[-6]
- transcribe: int = all_special_ids[-5]
-
- langs = tuple(LANGUAGES.keys())
- sot_sequence = [sot]
- if language is not None:
- sot_sequence.append(sot + 1 + langs.index(language))
- if task is not None:
- sot_sequence.append(transcribe if task == "transcribe" else translate)
-
- return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/spaces/Frorozcol/dreambooth-training/README.md b/spaces/Frorozcol/dreambooth-training/README.md
deleted file mode 100644
index a47e04f01dd63d785fd417f1284a27192aa3e570..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/dreambooth-training/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Dreambooth Training
-emoji: ☁️
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.11
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: mackaber/dreambooth-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GT4SD/advanced_manufacturing/model_cards/description.md b/spaces/GT4SD/advanced_manufacturing/model_cards/description.md
deleted file mode 100644
index 4e50bb680d40d61ada66d90b9fb8670d5da3b685..0000000000000000000000000000000000000000
--- a/spaces/GT4SD/advanced_manufacturing/model_cards/description.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-// Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-// Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-// Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include
-#include
-#if PLATFORM_WINDOWS
-#include
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES] =
-{
- 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
- 0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
- 0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
- 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
- 0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
- 0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
- 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
- 0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
- 0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
- 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
- 0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
- 0xf9,0xfa
-};
-
-// Low-level helper functions.
-template inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast
-
-*AdvancedManufacturing* is a sequence-based molecular generator tuned to generate catalysts for the Suzuki cross-coupling. The model relies on a Variational Autoencoder with a binding-energy predictor trained on the latent space. The framework uses Gaussian Processes for generating targeted molecules. The model was trained on 7054 Catalysts provided by
-[Meyer et al.](https://doi.org/10.1039/C8SC01949E).
-
-For **examples** and **documentation** of the model parameters, please see below.
-Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.
-
diff --git a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp b/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich