<. Reviews of Chess Prince
- Chess Prince has received many positive reviews from users who enjoy playing chess on their mobile devices. Here are some of the reviews from Google Play and App Store:
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-Username
-Rating
-Review
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-John Smith
-5 stars
-This is the best chess app I have ever played. The graphics are amazing and the game is very smooth. I love the different themes and board views. The game assistant and hints are very helpful. The puzzles are challenging and fun. I highly recommend this app to anyone who loves chess.
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-Mary Jones
-4 stars
-I really like this app. It is easy to use and has many features. The levels of difficulty are suitable for beginners and experts alike. The puzzles are a great way to improve your skills. The only thing I would suggest is to add more themes and board views.
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-David Lee
-3 stars
-The app is good, but it has some flaws. The game sometimes freezes or crashes. The ads are annoying and pop up too often. The game assistant and hints are sometimes wrong or misleading. The puzzles are too hard or too easy. The app needs some improvement.
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- How to Download Chess Prince on Android Devices
- If you have an Android device, you can download Chess Prince from Google Play Store. Here are the steps to follow:
- Step 1: Go to Google Play Store
- Open the Google Play Store app on your Android device. You can find it on your home screen or in your app drawer.
- Step 2: Search for Chess Prince
- In the search bar at the top of the screen, type "Chess Prince" and tap the search icon. You will see a list of results related to your search.
- Step 3: Install the App
- Find the app that has the name "Chess Prince" and the icon of a chess piece with a crown. Tap on it to open its page. You will see some information about the app, such as its description, screenshots, ratings, and reviews. Tap on the green "Install" button to start downloading and installing the app on your device.
- Step 4: Enjoy the Game
- Once the installation is complete, you can open the app by tapping on the "Open" button or by finding it on your home screen or in your app drawer. You can now enjoy playing chess with Chess Prince.
- How to Download Chess Prince on iOS Devices
- If you have an iOS device, you can download Chess Prince from App Store. Here are the steps to follow:
- Step 1: Go to App Store
- Open the App Store app on your iOS device. You can find it on your home screen.
- Step 2: Search for Chess Prince
- In the search bar at the bottom of the screen, type "Chess Prince" and tap the search icon. You will see a list of results related to your search.
- Step 3: Install the App
- Find the app that has the name "Chess Prince" and the icon of a chess piece with a crown. Tap on it to open its page. You will see some information about the app, such as its description, screenshots, ratings, and reviews. Tap on the blue "Get" button to start downloading and installing the app on your device.
- Step 4: Enjoy the Game
- Once the installation is complete, you can open the app by tapping on it or by finding it on your home screen. You can now enjoy playing chess with Chess Prince.
- Tips and Tricks to Play Chess Prince Better
- If you want to play chess better with Chess Prince, here are some tips and tricks that you can use:
- Learn the Basic Rules and Moves of Chess
- If you are new to chess or need a refresher, you can learn the basic rules and moves of chess by using the tutorial mode in Chess Prince. The tutorial mode will teach you how each piece moves, how to capture other pieces, how to check and checkmate your opponent, how to castle, how to promote pawns, how to en passant, and how to draw or stalemate a game.
- Choose the Right Level of Difficulty
- You can choose from ten levels of difficulty in Chess Prince, from beginner to master. You can choose the level that matches your skill level and challenge yourself. The higher the level, the smarter and faster the computer will play. You can also change the level anytime during the game by tapping on the menu icon and selecting "Change level".
Use the Game Assistant and Hints
- If you are stuck or unsure about what move to make, you can use the game assistant and hints to help you. The game assistant will show you the possible moves and their consequences, such as whether they will result in a check, a capture, or a threat. The hints will show you the best move according to the computer analysis. You can access the game assistant and hints by tapping on the menu icon and selecting "Game assistant" or "Hint". However, keep in mind that using the game assistant and hints will reduce your score and prevent you from earning stars.
- Practice with Chess Puzzles and Challenges
- You can improve your chess skills and knowledge by practicing with chess puzzles and challenges. Chess puzzles are problems that require you to find the best move or sequence of moves to achieve a certain goal, such as checkmate, stalemate, or material advantage. Chess challenges are scenarios that test your ability to handle different situations, such as defending against an attack, exploiting a weakness, or creating a trap. You can access chess puzzles and challenges by tapping on the menu icon and selecting "Puzzles" or "Challenges". There are hundreds of puzzles and challenges available, ranging from easy to hard.
- Try Different Themes and Board Views
- You can change the appearance of your game by trying different themes and board views. Themes are color schemes that affect the look of the board, pieces, and background. Board views are perspectives that affect the angle and orientation of the board. You can choose from seven themes: wood, marble, metal, blue, green, red, and purple. You can choose from two board views: vertical (2D) and horizontal (3D). You can change the theme and board view by tapping on the menu icon and selecting "Theme" or "Board view". You can also swipe left or right on the screen to switch between board views.
- Conclusion
- Chess Prince is a chess app that offers various features and modes to suit your preferences and skill level. You can play chess against the computer or another player, with ten levels of difficulty to choose from. You can also solve chess puzzles, use the game assistant and hints, earn stars for completing levels without undoing moves, and customize your game with seven different themes and two board views. Chess Prince is a great app for chess lovers who want to have fun and challenge themselves.
- FAQs
- Here are some frequently asked questions about Chess Prince:
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-Q: How much does Chess Prince cost? A: Chess Prince is free to download and play. However, it contains ads that may interrupt your game. You can remove the ads by purchasing the premium version for $1.99.
-Q: Is Chess Prince compatible with my device? A: Chess Prince is compatible with Android devices running Android 4.4 or higher, and iOS devices running iOS 9.0 or higher.
-Q: How can I contact the developer of Chess Prince? A: You can contact the developer of Chess Prince by emailing them at geniouscodestudio@gmail.com or visiting their website at https://geniouscodestudio.com/.
-Q: How can I rate and review Chess Prince? A: You can rate and review Chess Prince by going to Google Play Store or App Store, finding the app page, and tapping on the stars or writing a comment.
-Q: How can I share Chess Prince with my friends? A: You can share Chess Prince with your friends by tapping on the menu icon and selecting "Share". You can then choose from various options to send a link to your friends via email, text message, social media, or other apps.
- 197e85843dHow to Download and Play Free Fire on Your Mobile Device
-If you are looking for a thrilling and exciting survival shooter game on your mobile device, you should definitely try out Free Fire. Free Fire is one of the most popular battle royale games on the mobile platform, with over 1 billion downloads on Google Play Store. In this article, we will show you how to download and play Free Fire on your mobile device, as well as some tips and tricks to help you become a pro in the game.
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What is Free Fire?
-A popular survival shooter game
-Free Fire is a world-famous survival shooter game available on mobile. Each 10-minute game places you on a remote island where you are pit against 49 other players, all seeking survival. Players freely choose their starting point with their parachute, and aim to stay in the safe zone for as long as possible. Drive vehicles to explore the vast map, hide in the wild, or become invisible by proning under grass or rifts. Ambush, snipe, survive, there is only one goal: to survive and answer the call of duty.
- Features of Free Fire
-Different game modes
-Free Fire offers a variety of exciting game modes for different tastes and preferences. You can play the classic survival mode, where you have to be the last one standing among 50 players. You can also play the clash squad mode, where you have to manage your economy, purchase weapons, and defeat the enemy squad in fast-paced 4v4 matches. You can also play special events and modes that are available for a limited time, such as spider-verse mode, where you can swing around like Spider-Man.
- Various characters and skills
-Free Fire features a character system of 50+ characters. Each character has a unique skill which can be active or passive. Active skills can be triggered manually by an in-game button and passive skills are triggered automatically. You can choose your favorite character based on their appearance, backstory, and skill. Some of the popular characters are Alok, K, Chrono, Kelly, Moco, Jota, and more.
- Realistic and smooth graphics
-Free Fire boasts realistic and smooth graphics that promise the optimum survival experience you will find on mobile. You can enjoy stunning visuals of the maps, weapons, vehicles, characters, and effects. You can also customize your graphics settings according to your device performance and preference. You can also use BlueStacks to play Free Fire on your PC with enhanced graphics and controls.
- How to Download Free Fire APK?
-Steps to download from Google Play Store
-The easiest way to download Free Fire APK is from Google Play Store. All you need to do is follow these simple steps:
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-Open Google Play Store on your mobile device.
-Search for "Free Fire" or "Garena Free Fire" in the search bar.
-Select the app with the icon of a burning skull and tap on install.
-Wait for the app to download and install on your device.
-Open the app and enjoy playing Free Fire.
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-Note: You may need to grant some permissions to the app, such as access to your storage, microphone, and location. You may also need to update the app regularly to get the latest features and bug fixes.
- Steps to download from APKPure.com
-If you cannot access Google Play Store or want to download Free Fire APK from another source, you can use APKPure.com. APKPure.com is a trusted website that provides safe and fast downloads of various apps and games. Here are the steps to download Free Fire APK from APKPure.com:
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-Open your browser and go to https://apkpure.com/garena-free-fire-rampage/com.dts.freefireth .
-Tap on the green "Download APK" button and wait for the file to download.
-Locate the downloaded file in your device's file manager and tap on it to install.
-If you see a warning message that says "Install blocked", go to your device's settings and enable "Unknown sources" or "Allow from this source".
-Once the installation is complete, open the app and enjoy playing Free Fire.
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-Note: You may need to update the app manually by downloading the latest version from APKPure.com whenever there is a new update available.
- Steps to download from BlueStacks
-If you want to play Free Fire on your PC, you can use BlueStacks. BlueStacks is a powerful Android emulator that allows you to run Android apps and games on your PC with enhanced graphics and controls. Here are the steps to download Free Fire APK from BlueStacks:
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-Download and install BlueStacks on your PC from https://www.bluestacks.com/ .
-Launch BlueStacks and sign in with your Google account.
-Go to the "Game Center" tab and search for "Free Fire" or "Garena Free Fire" in the search bar.
-Select the app with the icon of a burning skull and click on "Install".
-Wait for the app to download and install on BlueStacks.
-Open the app and enjoy playing Free Fire on your PC.
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-Note: You can customize your keyboard and mouse settings by clicking on the gear icon on the top right corner of BlueStacks. You can also use BlueStacks' features such as multi-instance, macro recorder, game controls, and more to enhance your gaming experience.
- How to Play Free Fire on Your Mobile Device?
-Tips and tricks for beginners
-If you are new to Free Fire, you may find it challenging to survive and win in the game. Don't worry, we have some tips and tricks for beginners that will help you improve your skills and have fun in the game.
- Never loot in the open
-Looting is an essential part of Free Fire, as you need to find weapons, ammo, armor, healing items, and other useful items. However, looting in the open is very risky, as you expose yourself to enemy fire. Always try to loot inside buildings or behind cover, and avoid looting dead bodies unless you are sure it is safe. Also, don't spend too much time looting, as you may miss out on important opportunities or get caught by the shrinking zone.
- Use the thumb layout
-The thumb layout is one of the most common and comfortable control layouts for mobile gaming. It allows you to use both thumbs to move, aim, shoot, jump, crouch, prone, reload, switch weapons, and use items. You can adjust the size and position of each button according to your preference in the settings menu. Using the thumb layout will help you perform better in combat and react faster to different situations.
- Enable the default aim precision
-The default aim precision is a feature that helps you aim more accurately at enemies. It automatically adjusts your crosshair according to the distance and movement of your target. You can enable this feature in the settings menu under "Controls". Enabling the default aim precision will help you land more shots and eliminate enemies faster.
- Adjust your sensitivity settings
-The sensitivity settings affect how fast or slow your camera moves when you swipe or tilt your screen. You can adjust your sensitivity settings according to your device performance and preference in the settings menu under "Sensitivity". You can also use different sensitivity settings for different scopes, such as red dot, 2x, 4x, and 8x. Adjusting your sensitivity settings will help you aim more smoothly and precisely at different ranges.
- Never unnecessarily jump-shoot
-Jump-shooting is a technique that involves jumping and shooting at the same time. It can be useful in some situations, such as when you need to dodge enemy fire or surprise your opponent. However, jump-shooting also has some disadvantages, such as reducing your accuracy, exposing your head, and making you predictable. Therefore, you should never unnecessarily jump-shoot, especially when you are in close range or have a clear shot. Instead, you should use other techniques, such as crouching, proning, strafing, or peeking to gain an advantage in combat.
- Tips and tricks for advanced players
-If you are already familiar with Free Fire and want to take your game to the next level, you may need some tips and tricks for advanced players. These tips and tricks will help you improve your strategy, tactics, and skills in the game.
- Take fights in the shrink zone
-The shrink zone is the area outside the safe zone that gradually shrinks and damages players who are inside it. Most players tend to avoid the shrink zone and move towards the safe zone as soon as possible. However, this also makes them vulnerable to enemies who are waiting for them in the safe zone. Therefore, a smart strategy is to take fights in the shrink zone, as you can catch your enemies off guard and eliminate them easily. You can also loot their items and heal yourself with medkits or mushrooms. However, you should also be careful of the shrink zone damage and time your movements accordingly.
- Use the claw control
-The claw control is an advanced control layout that involves using four fingers to play Free Fire. It allows you to use your index fingers to aim, shoot, jump, crouch, prone, reload, switch weapons, and use items, while using your thumbs to move and look around. You can enable this layout in the settings menu under "Controls". Using the claw control will give you more flexibility and speed in combat and allow you to perform multiple actions at once.
- Use the jump-shoot at the right time
-As we mentioned earlier, jump-shooting can be useful in some situations, but not in others. Therefore, you should know when to use it and when not to use it. The right time to use jump-shooting is when you are in mid or long range, have a scope on your weapon, have cover nearby, and have enough health. The wrong time to use jump-shooting is when you are in close range, have no scope on your weapon, have no cover nearby, and have low health. Using the jump-shoot at the right time will help you surprise your enemies and deal more damage to them.
- Use vehicles wisely
-Vehicles are a great way to travel across the map quickly and safely. However, they also have some drawbacks, such as making noise, consuming fuel, and attracting attention. Therefore, you should use vehicles wisely and not rely on them too much. You should only use vehicles when you need to escape from danger or reach a certain location fast. You should also avoid driving vehicles in the late game stages or near buildings or compounds where enemies may be hiding. You should also park your vehicles behind cover or away from sight when you get off them.
- Communicate with your squad
-Communication is key to winning in Free Fire, especially when you are playing with your squad. You should always communicate with your squad members using voice chat or quick chat messages. You should share information such as enemy locations, loot, weapons, skills, and strategies. You should also coordinate your actions, such as landing, moving, fighting, and reviving. You should also support your squad members, such as giving them items, covering them, or saving them. Communicating with your squad will help you work as a team and increase your chances of winning.
- Conclusion
-Free Fire is a fun and addictive survival shooter game that you can download and play on your mobile device. You can enjoy different game modes, characters, skills, and graphics in the game. You can also use various tips and tricks to improve your performance and skills in the game. We hope this article has helped you learn how to download and play Free Fire on your mobile device. Now, what are you waiting for? Download Free Fire APK today and join the ultimate survival adventure!
- FAQs
-Q: Is Free Fire free to play?
-A: Yes, Free Fire is free to play. However, you can also purchase in-game items such as diamonds, skins, characters, and more with real money.
- Q: Is Free Fire safe to download?
-A: Yes, Free Fire is safe to download from Google Play Store or APKPure.com. However, you should always be careful of downloading from unknown or untrusted sources, as they may contain viruses or malware.
- Q: Can I play Free Fire offline?
-A: No, you cannot play Free Fire offline. You need an internet connection to play the game.
- Q: Can I play Free Fire with friends?
-A: Yes, you can play Free Fire with friends. You can invite your friends to join your squad or join their squad in the game. You can also chat with them using voice chat or quick chat messages.
- Q: Can I play Free Fire on PC?
-A: Yes, you can play Free Fire on PC using BlueStacks. BlueStacks is an Android emulator that allows you to run Android apps and games on your PC with enhanced graphics and controls.
197e85843dHow to Download One Piece World Seeker
-If you are a fan of One Piece, the popular manga and anime series by Eiichiro Oda, you might be interested in playing One Piece World Seeker, an action-adventure video game based on the franchise. In this article, we will tell you everything you need to know about this game, including what it is, why you should play it, where you can download it, and what are the different editions available. So, let's get started!
- What is One Piece World Seeker?
-One Piece World Seeker is a video game developed by Ganbarion and published by Bandai Namco Entertainment. It is the first video game in the franchise to feature an open world environment, where you can explore a vast and seamless island as Monkey D. Luffy, the protagonist of One Piece. The game was released on March 15, 2019 for PC, PlayStation 4, and Xbox One.
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Why should you play One Piece World Seeker?
-There are many reasons why you should play One Piece World Seeker, but here are some of the main ones:
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-The Freedom of the Pirate King : You can experience the powers of Luffy's Gum-Gum fruit, which allows him to stretch his limbs and swing into action. You can also use his powerful Haki abilities to sense enemies and unleash devastating attacks. You can explore different areas of the island, such as cities, farms, beaches, mines, and prisons, and interact with various objects and characters.An Original Story with Original Characters : The game features an original story that takes place on Prison Island, a mysterious island that is under the control of the Navy. The Straw Hat Pirates arrive on the island and get involved in a dramatic story full of twists and turns. The game also includes original characters designed by Eiichiro Oda himself, such as Jeanne, a young woman who leads a rebel group against the Navy; Isaac, a former Marine scientist who rules over Prison Island; and Karakuri Island Automata (KIA), mechanical soldiers created by Isaac.Fierce Battles Between Popular Characters : The game lets you fight against some of the most iconic enemies from the One Piece series, such as Crocodile, Rob Lucci, Akainu, Kizaru, and more. You can also encounter some of the allies and friends of Luffy, such as Sabo, Law, Hancock, and more. You can enjoy the dynamic and thrilling combat system that combines melee, ranged, and stealth attacks.
- Where can you download One Piece World Seeker?
-One Piece World Seeker is available for download on PC, PlayStation 4, and Xbox One. Here are the steps on how to download the game for each platform:
- Download One Piece World Seeker for PC
-If you want to play One Piece World Seeker on your PC, you need to download it from Steam, the online gaming platform. Here are the steps to do so:
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-Create a Steam account if you don't have one already. You can do this by visiting https://store.steampowered.com/join/ and following the instructions.
-Download and install the Steam client on your PC. You can do this by visiting https://store.steampowered.com/about/ and clicking on the "Install Steam" button.
-Launch the Steam client and log in with your Steam account.
-Search for One Piece World Seeker in the Steam store. You can do this by typing the name of the game in the search bar or browsing through the categories.
-Select One Piece World Seeker from the search results and click on the "Add to Cart" button.
-Proceed to checkout and pay for the game using your preferred payment method.
-Wait for the game to download and install on your PC. You can check the progress of the download in the "Library" section of the Steam client.
-Once the game is installed, you can launch it from your Steam library and enjoy playing it.
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- System Requirements for PC
-Before you download One Piece World Seeker for PC, you need to make sure that your PC meets the minimum or recommended system requirements for the game. Here is a table of the system requirements for PC:
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- | Minimum | Recommended | | --- | --- | | OS: Windows 7 64-bit SP1 | OS: Windows 10 64-bit | | Processor: Intel Core i5-2300 or AMD A10-7850K | Processor: Intel Core i7-3770 or AMD FX-8350 | | Memory: 4 GB RAM | Memory: 8 GB RAM | | Graphics: GeForce GTX 660 or Radeon HD 7950 | Graphics: GeForce GTX 1060 or Radeon RX 580 | | DirectX: Version 11 | DirectX: Version 11 | | Storage: 25 GB available space | Storage: 25 GB available space | | Sound Card: DirectX compatible soundcard or onboard chipset | Sound Card: DirectX compatible soundcard or onboard chipset | Download One Piece World Seeker for PlayStation 4
-If you want to play One Piece World Seeker on your PlayStation 4, you need to download it from PlayStation Store, the online gaming platform. Here are the steps to do so:
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-Create a PlayStation Network account if you don't have one already. You can do this by visiting https://www.playstation.com/en-us/network/onlineid/ and following the instructions.
-Download and install the PlayStation Store app on your PlayStation 4. You can do this by selecting "PlayStation Store" from the home screen of your console.
-Launch the PlayStation Store app and log in with your PlayStation Network account.
-Search for One Piece World Seeker in the PlayStation Store. You can do this by typing the name of the game in the search bar or browsing through the categories.
-Select One Piece World Seeker from the search results and click on the "Add to Cart" button.
-Proceed to checkout and pay for the game using your preferred payment method.
-Wait for the game to download and install on your PlayStation 4. You can check the progress of the download in the "Notifications" section of your console.
-Once the game is installed, you can launch it from your home screen and enjoy playing it.
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- System Requirements for PlayStation 4
-Before you download One Piece World Seeker for PlayStation 4, you need to make sure that your PlayStation 4 meets the minimum or recommended system requirements for the game. Here is a table of the system requirements for PlayStation 4:
- | Minimum | Recommended | | --- | --- | | OS: PlayStation 4 | OS: PlayStation 4 Pro | | Processor: AMD Jaguar 8-core | Processor: AMD Jaguar 8-core | | Memory: 8 GB GDDR5 | Memory: 8 GB GDDR5 | | Graphics: AMD Radeon GCN 1.84 TFLOPS | Graphics: AMD Radeon GCN 4.2 TFLOPS | | Storage: 25 GB available space | Storage: 25 GB available space | Download One Piece World Seeker for Xbox One
-If you want to play One Piece World Seeker on your Xbox One, you need to download it from Microsoft Store, the online gaming platform. Here are the steps to do so:
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-Create a Microsoft account if you don't have one already. You can do this by visiting https://account.microsoft.com/account and following the instructions.
-Download and install the Microsoft Store app on your Xbox One. You can do this by selecting "Microsoft Store" from the home screen of your console.
-Launch the Microsoft Store app and log in with your Microsoft account.
-Search for One Piece World Seeker in the Microsoft Store. You can do this by typing the name of the game in the search bar or browsing through the categories.
-Select One Piece World Seeker from the search results and click on the "Buy" button.
-Proceed to checkout and pay for the game using your preferred payment method.
-Wait for the game to download and install on your Xbox One. You can check the progress of the download in the "My games & apps" section of your console.
-Once the game is installed, you can launch it from your home screen and enjoy playing it.
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- System Requirements for Xbox One
-Before you download One Piece World Seeker for Xbox One, you need to make sure that your Xbox One meets the minimum or recommended system requirements for the game. Here is a table of the system requirements for Xbox One:
- | Minimum | Recommended | | --- | --- | | OS: Xbox One | OS: Xbox One X | | Processor: AMD Jaguar 8-core | Processor: AMD Jaguar 8-core | | Memory: 8 GB DDR3 | Memory: 12 GB GDDR5 | | Graphics: AMD Radeon GCN 1.31 TFLOPS | Graphics: AMD Radeon GCN 6 TFLOPS | | Storage: 25 GB available space | Storage: 25 GB available space | What are the different editions of One Piece World Seeker?
-One Piece World Seeker has three different editions that you can choose from, depending on your budget and preferences. They are the standard edition, the deluxe edition, and the pirate king edition. Here is a comparison of what each edition offers:
- Standard Edition
-The standard edition of One Piece World Seeker is the basic version of the game that includes only the main game itself. It costs $59.99 USD. If you pre-ordered the standard edition, you also received some bonus items, such as a swimsuit outfit for Luffy, a military outfit for Luffy, and a quest called "Strange Island Rocks".
- Deluxe Edition
-The deluxe edition of One Piece World Seeker is an upgraded version of the game that includes not only the main game, but also an episode pass that gives you access to three additional episodes that expand the story and gameplay of the game. The episode pass also includes some extra items, such as a raid suit for Luffy, a kung fu outfit for Luffy, and a white suit outfit for Luffy. The deluxe edition costs $89.99 USD.
- Pirate King Edition
-The pirate king edition of One Piece World Seeker is the ultimate version of the game that includes everything from the deluxe edition, plus some exclusive physical items that are perfect for collectors and fans of One Piece. The pirate king edition includes a figurine of Luffy in his Gear Fourth form, a replica of Luffy's straw hat, a CD with selected tracks from the game's soundtrack, and a season pass that gives you access to all future DLCs for the game. The pirate king edition costs $129.99 USD.
- Conclusion
-One Piece World Seeker is an amazing video game that lets you experience the world of One Piece like never before. You can explore a vast and beautiful island as Luffy and use his amazing abilities to fight against his enemies and allies. You can also enjoy an original story with original characters that are designed by the creator of One Piece himself, Eiichiro Oda. You can download the game for PC, PlayStation 4, or Xbox One from various online platforms, and choose from different editions that offer different content and bonuses. If you are looking for a fun and immersive game that will make you feel like a pirate king, you should definitely try One Piece World Seeker. You won't regret it!
- FAQs
-Here are some of the frequently asked questions about One Piece World Seeker:
-
-Q: How long is the game? A: The game's main story takes about 15 to 20 hours to complete, depending on your playstyle and difficulty level. The game also has many side quests and activities that can extend the gameplay time to over 40 hours.
-Q: Can you play as other characters besides Luffy? A: No, you can only play as Luffy in the game. However, you can interact with other characters from the One Piece series, and some of them will join you as support characters in combat.
-Q: Can you customize Luffy's appearance and skills? A: Yes, you can change Luffy's outfits and accessories in the game, as well as upgrade his skills and abilities using skill points that you earn by completing missions and defeating enemies.
-Q: Is the game multiplayer or co-op? A: No, the game is single-player only. There is no online or local multiplayer or co-op mode in the game.
-Q: Is the game canon to the One Piece series? A: The game is not canon to the One Piece series, but it is an original story that is supervised by Eiichiro Oda himself. The game takes place in an alternate timeline after the Whole Cake Island arc of the manga and anime.
- 197e85843dTank Hero: A Fast-Paced 3D Tank Action Game for Android
-If you are looking for a fun and addictive tank game for your Android device, you should check out Tank Hero. This is a game that will test your skills and reflexes as you battle against cunning enemies in various arenas. In this article, we will tell you what Tank Hero is, how to download it from Uptodown, and how to play it like a pro.
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What is Tank Hero?
-Tank Hero is a 3D tank action game developed by Clapfoot Inc. It was released in 2011 and has been played by over 4 million users around the world. It is available for iOS and Android devices, but in this article, we will focus on the Android version.
- The gameplay and features of Tank Hero
-The gameplay of Tank Hero is simple but challenging. You control a green tank with a virtual joystick or swipe gestures, and you tap on the screen to fire at your enemies. You have to destroy all the enemy tanks before they destroy you, while avoiding their bullets and missiles. You can also use obstacles and walls as cover, or bounce your shots off them to hit your targets.
-Tank Hero has three game modes: Campaign, Survival, and Time Trial. In Campaign mode, you have to complete 80 levels across two worlds, each with different enemies and environments. In Survival mode, you have to survive as long as possible against endless waves of enemies. In Time Trial mode, you have to clear each level as fast as possible.
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-Tank Hero also has five weapons to choose from: cannons, heat seekers, grenades, howitzers, and lasers. Each weapon has its own advantages and disadvantages, and you can switch between them during the game. You can also unlock new tanks with different attributes and abilities.
- The graphics and sound effects of Tank Hero
-Tank Hero has impressive graphics for a mobile game. It uses OpenGL 3D graphics with real-time shadows and dynamic lighting, creating a realistic and immersive experience. The tanks and the environments are well-designed and detailed, and the explosions and smoke effects are satisfying. The game also runs smoothly on most devices, without any lag or glitches.
-The sound effects of Tank Hero are also great. They add to the excitement and tension of the game, with realistic sounds of gunfire, explosions, engines, and metal clashing. The game also has a catchy background music that matches the mood of each level.
- How to download Tank Hero from Uptodown?
-If you want to download Tank Hero for your Android device, you can do so from Uptodown. Uptodown is an alternative app store that offers thousands of apps and games for free. You can find apps that are not available on Google Play Store, or download older versions of apps that are compatible with your device.
- The benefits of using Uptodown as an alternative app store
-There are many benefits of using Uptodown as an alternative app store for your Android device. Some of them are:
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- The steps to download and install Tank Hero from Uptodown
-To download and install Tank Hero from Uptodown, you need to follow these steps:
-
-Go to the Uptodown website or app and search for Tank Hero. You can also use this link: Tank Hero 1.5.8 for Android - Download .
-Click on the green Download button and wait for the APK file to be downloaded to your device.
-Before installing the APK file, you need to enable the Unknown Sources option on your device. This will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Once you have enabled the Unknown Sources option, locate the APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.
-After the installation is complete, you can launch Tank Hero from your app drawer or home screen and enjoy the game.
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- How to play Tank Hero like a pro?
-Now that you have downloaded and installed Tank Hero from Uptodown, you might be wondering how to play it like a pro. Here are some tips and tricks that will help you master the game and have more fun.
- The tips and tricks to master the game
-Here are some tips and tricks that will help you master Tank Hero:
-
-Learn how to use each weapon effectively. Each weapon has its own strengths and weaknesses, and you need to know when and how to use them. For example, cannons are good for direct shots, heat seekers are good for tracking enemies, grenades are good for area damage, howitzers are good for long-range shots, and lasers are good for piercing through multiple enemies.
-Use the environment to your advantage. You can use obstacles and walls as cover, or bounce your shots off them to hit your targets. You can also destroy some objects in the environment, such as barrels and crates, to cause damage or create distractions.
-Be aware of your surroundings. You need to keep an eye on your enemies, their positions, their movements, their weapons, and their bullets. You also need to watch out for power-ups that can boost your health, speed, or firepower. You can also find hidden items that can unlock new weapons and tanks.
-Be strategic and flexible. You need to plan your moves ahead, but also be ready to adapt to changing situations. You need to choose the best weapon and strategy for each level and mode, but also be prepared to switch them if necessary. You also need to balance offense and defense, knowing when to attack and when to retreat.
-
- The best weapons and strategies to use in different modes and levels
-Here are some of the best weapons and strategies to use in different modes and levels of Tank Hero:
-
-Mode/Level Weapon Strategy Campaign mode - World 1 Cannon This is the default weapon that you start with. It is good for direct shots and has a fast reload time. Use it to take out enemies quickly and accurately. Campaign mode - World 2 Grenade This is a weapon that you unlock in World 2. It is good for area damage and has a high impact force. Use it to blast enemies in groups or behind cover. Survival mode Laser This is a weapon that you unlock in World 2. It is good for piercing through multiple enemies and has a long range. Use it to clear waves of enemies efficiently and safely. Time Trial mode Heat Seeker This is a weapon that you unlock in World 1. It is good for tracking enemies and has a high speed. Use it to hit moving targets easily and quickly. Boss levels Howitzer This is a weapon that you unlock in World 1. It is good for long-range shots and has a high damage. Use it to deal massive damage to the boss and avoid its attacks.
- Conclusion
-Tank Hero is a fast-paced 3D tank action game that will keep you entertained and challenged for hours. You can download it from Uptodown, an alternative app store that offers many benefits and features. You can also use our tips and tricks to master the game and have more fun. So what are you waiting for? Download Tank Hero today and become the ultimate tank hero!
- FAQs
-What are the minimum requirements to play Tank Hero on Android?
-To play Tank Hero on Android, you need to have a device that runs on Android 4.1 or higher, and has at least 50 MB of free storage space.
- How many levels are there in Tank Hero?
-There are 80 levels in Tank Hero, divided into two worlds: Desert and Ice. Each world has 40 levels, with different enemies and environments.
- How can I unlock new weapons and tanks in Tank Hero?
-You can unlock new weapons and tanks in Tank Hero by completing levels, finding hidden items, or buying them with coins. Coins are earned by playing the game, or by watching ads or completing offers.
- Is Tank Hero a multiplayer game?
-No, Tank Hero is not a multiplayer game. It is a single-player game that you can play offline or online.
- Is Tank Hero safe to download from Uptodown?
-Yes, Tank Hero is safe to download from Uptodown. Uptodown is a reputable and secure app store that scans all the apps and games for viruses and malware. You can download Tank Hero from Uptodown without any risk or worry.
401be4b1e0Devil May Cry: Peak of Combat APK Free Download
-If you are a fan of the action-adventure hack-and-slash video game series Devil May Cry, you might be interested in playing its official mobile spin-off, Devil May Cry: Peak of Combat. This game lets you enjoy the exhilarating and stylish combat system of the DMC franchise on your Android device. However, this game is not available on the Google Play Store, so you need to download and install its APK file from a third-party source. In this article, we will tell you everything you need to know about Devil May Cry: Peak of Combat APK, including its features, how to download and install it, and its pros and cons.
- What is Devil May Cry: Peak of Combat?
-Devil May Cry: Peak of Combat is a free action mobile video game developed by NebulaJoy, also known as Yunchang Game, with the participation of the official team of Capcom Devil May Cry. It is an authorized mobile game for the DMC franchise and features classic characters with their signature play styles. The game follows the story of Dante, Nero, Vergil, and Lady, who are demon hunters that fight against various forces of evil. The game has a fast-paced 3D adventure that showcases the flexible and fun combo system of the DMC series. The game also has impressive graphics and voice-overs that bring the characters to life.
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Features of Devil May Cry: Peak of Combat
-Flexible combat system
-One of the main attractions of Devil May Cry: Peak of Combat is its flexible combat system that allows you to juggle enemies in mid-air with your attacks and rack up combo points for higher style rankings. You can use different weapons, skills, and items to create your own fighting style and unleash devastating moves on your foes. You can also switch between characters during battles to take advantage of their unique abilities and weapons.
- Classic characters and voice-overs
-Another feature of Devil May Cry: Peak of Combat is its classic characters and voice-overs that make you feel like you are playing the original DMC series. You can play as Dante, Nero, Vergil, and Lady, who are all voiced by their original actors from the DMC franchise. Each character has their own personality, backstory, and dialogue that add depth and humor to the game. You can also customize their appearance and outfits to suit your preferences.
- Stunning graphics and sound effects
-Last but not least, Devil May Cry: Peak of Combat has stunning graphics and sound effects that enhance the gaming experience. The game has high-quality 3D models and animations that capture the details and expressions of the characters. The game also has realistic lighting and shadow effects that create a dynamic and immersive environment. The game also has epic soundtracks and sound effects that match the mood and intensity of the battles.
- How to download and install Devil May Cry: Peak of Combat APK?
-Requirements for Devil May Cry: Peak of Combat APK
-Before you download and install Devil May Cry: Peak of Combat APK, you need to make sure that your device meets the following requirements:
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-Your device must have Android 9.0 or higher.
-Your device must have at least 3 GB of RAM.
-Your device must have at least 5 GB of free storage space.
-You must enable unknown sources on your device settings.
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- Steps to download and install Devil May Cry: Peak of Combat APK
-After you have checked the requirements, you can follow these steps to download and install Devil May Cry: Peak of Combat APK:
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-Go to a trusted website that provides the APK file for Devil May Cry: Peak of Combat, such as [APKPure] or [APKCombo].
-Download the APK file and the OBB file to your device.
-Locate the downloaded files on your device and extract the OBB file to the Android/OBB folder.
-Tap on the APK file and follow the instructions to install the game.
-Launch the game and enjoy!
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- Pros and cons of Devil May Cry: Peak of Combat APK
-Pros
-Some of the pros of Devil May Cry: Peak of Combat APK are:
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-It is free to download and play.
-It has a faithful adaptation of the DMC franchise with its original characters, voice-overs, and combat system.
-It has amazing graphics and sound effects that create a thrilling and immersive gaming experience.
-It has various game modes and challenges that keep you entertained and challenged.
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- Cons
-Some of the cons of Devil May Cry: Peak of Combat APK are:
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-It is not available on the Google Play Store, so you need to download it from a third-party source, which may pose some security risks.
-It requires a lot of storage space and RAM to run smoothly.
-It may have some bugs and glitches that affect the gameplay.
-It may have some compatibility issues with some devices or regions.
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- Conclusion
-Devil May Cry: Peak of Combat is a great mobile game for fans of the DMC franchise and action games in general. It has a flexible and fun combat system, classic characters and voice-overs, stunning graphics and sound effects, and various game modes and challenges. However, it also has some drawbacks, such as not being available on the Google Play Store, requiring a lot of storage space and RAM, and having some bugs and glitches. If you want to try this game, you need to download and install its APK file from a trusted website and follow the steps we have provided. We hope this article has helped you learn more about Devil May Cry: Peak of Combat APK and how to download and install it. Have fun playing!
- FAQs
-Here are some frequently asked questions about Devil May Cry: Peak of Combat APK:
- Is Devil May Cry: Peak of Combat APK safe to download?
-Devil May Cry: Peak of Combat APK is safe to download as long as you get it from a reliable website that does not contain any malware or viruses. However, you should always be careful when downloading any APK file from a third-party source, as there may be some risks involved. You should also scan the downloaded files with an antivirus program before installing them.
- Is Devil May Cry: Peak of Combat APK legal?
-Devil May Cry: Peak of Combat APK is legal as long as you do not use it for any illegal or unethical purposes. It is an authorized mobile game for the DMC franchise and does not violate any copyrights or trademarks. However, you should respect the rights and policies of the developers and the publishers of the game and not distribute or modify the APK file without their permission.
- Can I play Devil May Cry: Peak of Combat APK offline?
-No, you cannot play Devil May Cry: Peak of Combat APK offline. You need to have a stable internet connection to access the game and its features. The game also requires you to log in with your account and verify your identity before playing. If you lose your internet connection during the game, you may experience some errors or interruptions.
- Can I play Devil May Cry: Peak of Combat APK with friends?
-Yes, you can play Devil May Cry: Peak of Combat APK with friends. The game has a multiplayer mode that allows you to team up with other players and fight against enemies or other teams. You can also chat with your friends and other players in the game and share your achievements and tips. The game also has a ranking system that shows your progress and performance compared to other players.
- How can I update Devil May Cry: Peak of Combat APK?
-To update Devil May Cry: Peak of Combat APK, you need to download the latest version of the APK file and the OBB file from the same website that you got the previous version. You also need to uninstall the old version of the game before installing the new one. You can also check for updates in the game settings and follow the instructions to download and install them.
401be4b1e0FM WhatsApp Messenger APK: A Complete Guide
-Are you looking for a way to enhance your WhatsApp experience on your Android device? Do you want to enjoy more features and functions that are not available in the official WhatsApp app? If yes, then you should try FM WhatsApp Messenger APK, one of the best WhatsApp mods for Android users. In this article, we will tell you everything you need to know about FM WhatsApp Messenger APK, including what it is, what it offers, how to download and install it, and how to use it. Let's get started!
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What is FM WhatsApp Messenger APK?
-FM WhatsApp Messenger APK is a modified version of the original WhatsApp app, developed by Fouad Mokdad. It is also known as Fouad WhatsApp or FMWA. It allows you to customize and change various aspects of your WhatsApp app, such as themes, fonts, emojis, privacy settings, and more. It also provides you with many additional features that are not present in the official WhatsApp app, such as anti-delete messages, hide view status, send large files, increase image quality, and more.
- Features of FM WhatsApp Messenger APK
-Here are some of the main features of FM WhatsApp Messenger APK that make it stand out from other WhatsApp mods:
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-Anti-ban: You don't have to worry about getting banned from using WhatsApp for using a modded version. FM WhatsApp Messenger APK has an anti-ban feature that protects your account from being detected and blocked by WhatsApp.Customization: You can customize and change different parts of your WhatsApp app, such as themes, fonts, emojis, icons, wallpapers, and more. You can also create your own themes and share them with other users.Privacy: You can control your privacy settings and hide your online status, last seen, blue ticks, double ticks, typing status, and more. You can also freeze your last seen status and view deleted statuses and messages.Media sharing: You can send more than 90 images at once and video files up to 700 MB. You can also increase the quality while sending images and videos. You can also send any type of file format, such as PDF, ZIP, APK, etc.And many more: There are many more features that you can enjoy with FM WhatsApp Messenger APK, such as call blocker, pin chats, message scheduler, auto-reply, security lock, DND mode, etc.
- Benefits of using FM WhatsApp Messenger APK
-Here are some of the benefits of using FM WhatsApp Messenger APK instead of the official WhatsApp app:
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-More fun and enjoyment: You can have more fun and enjoyment with your friends and family by using the various features and functions of FM WhatsApp Messenger APK. You can also express yourself better with the customized themes and emojis.More security and privacy: You can protect your chats and data from prying eyes by using the privacy and security features of FM WhatsApp Messenger APK. You can also prevent others from deleting messages or viewing your status.More convenience and flexibility: You can use two WhatsApp accounts on the same device by using FM WhatsApp Messenger APK. You can also backup and restore your data easily with the built-in backup feature.
- How to download and install FM WhatsApp Messenger APK on Android?
-If you are interested in trying out FM WhatsApp Messenger APK on your Android device, you need to follow these steps:
- Requirements for FM WhatsApp Messenger APK
-Before you download and install FM WhatsApp Messenger APK on your Android device, you need to make sure that you have the following requirements:
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-An Android device running on OS 5.0 or later: FM WhatsApp Messenger APK is compatible with Android devices that have OS 5.0 or higher. You can check your device's OS version by going to Settings > About phone > Software information.A stable internet connection: You need to have a reliable internet connection to download and install FM WhatsApp Messenger APK on your device. You can use Wi-Fi or mobile data, but make sure that you have enough data allowance.Enough storage space: You need to have enough free space on your device's internal or external storage to store the FM WhatsApp Messenger APK file and its data. The APK file size is about 50.2 MB, and the data size may vary depending on your usage.Permission to install apps from unknown sources: Since FM WhatsApp Messenger APK is not available on the Google Play store, you need to enable the option to install apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown sources and toggling it on.
- Steps to download and install FM WhatsApp Messenger APK
-Once you have met the requirements for FM WhatsApp Messenger APK, you can follow these steps to download and install it on your Android device:
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-Download the FM WhatsApp Messenger APK file: You can download the latest version of FM WhatsApp Messenger APK from this link. Alternatively, you can also scan the QR code below with your device's camera to download the APK file directly.Locate and open the FM WhatsApp Messenger APK file: After downloading the APK file, you need to locate it on your device's file manager and tap on it to open it. You may get a warning message saying that the file may harm your device, but you can ignore it and tap on OK.Install the FM WhatsApp Messenger APK file: Next, you need to follow the on-screen instructions to install the FM WhatsApp Messenger APK file on your device. It may take a few minutes for the installation process to complete.Launch the FM WhatsApp Messenger app: Finally, you can launch the FM WhatsApp Messenger app from your app drawer or home screen. You will see a welcome screen asking you to agree to the terms and conditions of the app. Tap on Agree and Continue to proceed.
- How to use FM WhatsApp Messenger APK?
-Now that you have successfully downloaded and installed FM WhatsApp Messenger APK on your Android device, you can start using it and enjoy its features and functions. Here are some tips on how to use FM WhatsApp Messenger APK:
- How to customize FM WhatsApp Messenger APK
-If you want to customize and change different aspects of your FM WhatsApp Messenger app, such as themes, fonts, emojis, icons, wallpapers, and more, you can do so by following these steps:
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-Tap on the three-dot icon at the top right corner of the app: This will open a menu with various options.Tap on Fouad Mods: This will open a submenu with different categories of mods that you can apply to your app.Select the category that you want to customize: For example, if you want to change the theme of your app, tap on Universal > Style > Theme Store. Here, you can browse through hundreds of themes that are available for free. You can also create your own theme by tapping on Create Theme.Apply the mod that you want: Once you have selected the mod that you want to apply, tap on Apply or Download (depending on the mod). The app will ask you to restart it for the changes to take effect. Tap on OK and wait for the app to restart.
- How to backup and restore FM WhatsApp Messenger APK data
-If you want to backup and restore your FM WhatsApp Messenger app data, such as chats, media, contacts, etc., you can do so by following these steps:
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-Tap on the three-dot icon at the top right corner of the app: This will open a menu with various options.Tap on Settings: This will open a submenu with different settings that you can adjust for your app.Tap on Chats: This will open a submenu with different options related to your chats.Tap on Chat backup: This will open a submenu with different options for backing up and restoring your data. You can choose to backup your data to Google Drive, your device's internal storage, or an external SD card. You can also choose the frequency of the backup, such as daily, weekly, monthly, or manually.Tap on Backup: This will start the backup process and save your data to the selected location. You can also tap on Restore to restore your data from a previous backup. You will need to verify your phone number and grant permission to access your data.
- FAQs about FM WhatsApp Messenger APK
-Here are some of the frequently asked questions about FM WhatsApp Messenger APK that you may have:
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-Is FM WhatsApp Messenger APK safe to use?
-FM WhatsApp Messenger APK is generally safe to use, as it has an anti-ban feature that prevents your account from being blocked by WhatsApp. However, it is not an official app, so there may be some risks involved, such as malware, data theft, or legal issues. Therefore, you should use it at your own discretion and responsibility.
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-Can I use FM WhatsApp Messenger APK with the official WhatsApp app?
-No, you cannot use FM WhatsApp Messenger APK with the official WhatsApp app on the same device. You need to uninstall the official WhatsApp app before installing FM WhatsApp Messenger APK. However, you can use FM WhatsApp Messenger APK with another WhatsApp mod, such as GBWhatsApp or YoWhatsApp, on the same device.
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-Will I lose my chats and data if I switch from the official WhatsApp app to FM WhatsApp Messenger APK?
-No, you will not lose your chats and data if you switch from the official WhatsApp app to FM WhatsApp Messenger APK. You can backup your data from the official WhatsApp app and restore it on FM WhatsApp Messenger APK. You can also use the same phone number and verification code for both apps.
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-How can I update FM WhatsApp Messenger APK to the latest version?
-You can update FM WhatsApp Messenger APK to the latest version by downloading the updated APK file from this link or scanning the QR code below. You can also check for updates within the app by tapping on the three-dot icon > Fouad Mods > Updates. You will need to install the updated APK file over the existing one without uninstalling it.
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-How can I contact the developer of FM WhatsApp Messenger APK?
-You can contact the developer of FM WhatsApp Messenger APK by visiting his website or following him on Twitter. You can also join his Telegram group or channel for more information and support.
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- Conclusion
-FM WhatsApp Messenger APK is a great alternative to the official WhatsApp app for Android users who want to enjoy more features and functions that are not available in the original app. It allows you to customize and change various aspects of your WhatsApp app, such as themes, fonts, emojis, privacy settings, and more. It also provides you with many additional features that are not present in the official WhatsApp app, such as anti-delete messages, hide view status, send large files, increase image quality, and more. You can download and install FM WhatsApp Messenger APK on your Android device by following the steps mentioned above. You can also use it with another WhatsApp mod on the same device. However, you should be aware of the potential risks involved in using a modded app, such as malware, data theft, or legal issues. Therefore, you should use it at your own discretion and responsibility.
- I hope this article has helped you understand what FM WhatsApp Messenger APK is and how to use it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
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-Word searches are one of the most popular and enjoyable types of puzzles that can be played by people of all ages and backgrounds. They are not only fun, but also educational, as they can help improve your vocabulary, spelling, concentration, memory, and brain health. In this article, we will explore what word searches are, why they are so popular, how to download them for free or for a fee, and how to solve them effectively and quickly.
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What are word searches and why are they popular?
-The basics of word search puzzles
-A word search puzzle is a grid of letters that contains a number of hidden words that can be found by looking horizontally, vertically, diagonally, or backwards. The words are usually related to a certain theme or category, such as animals, sports, or food. The words can be of different lengths and difficulties, depending on the level of the puzzle. The goal is to find all the words in the grid and mark them off the list.
- The benefits of playing word search games
-Playing word search games can have many benefits for your mind and body. Some of the benefits are:
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-They support language fluency by exposing you to new words and their spellings.
-They can be used as a learning strategy to memorize or review important terms in any subject or discipline.
-They improve your concentration and focus by requiring you to scan the grid carefully and ignore distractions.
-They boost your mood and motivation by giving you a sense of achievement and satisfaction when you find a word.
-They prevent and delay the onset of neurological disorders such as dementia and Alzheimer's disease by keeping your brain active and stimulated.
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- How to download word searches for free or for a fee?
-Printable word searches from online sources
-If you prefer to play word searches on paper, you can download printable word searches from various online sources. Some of these sources are:
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-[TheWordSearch.com](^1^): This website offers hundreds of free printable word searches on different topics and levels. You can also create your own custom word search puzzles with your own words and themes.
-[Reader's Digest](^3^): This website provides 26 free printable word search puzzles on various themes, such as animals, holidays, movies, and more. You can also find tips on how to solve word searches effectively.
-[Puzzles.ca](https://www.puzzles.ca/wordsearch/): This website features over 1000 free printable word search puzzles on various categories, such as geography, history, music, sports, and more. You can also find crossword puzzles, sudoku puzzles, and other types of puzzles on this website.
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- Word search apps and games for mobile devices
-If you like to play word searches on your smartphone or tablet, you can download word search apps and games from various app stores. Some of these apps and games are:
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-[Word Search](^2^): This app by Razzle Puzzles offers thousands of fun puzzles with different modes and difficulties. You can also look up the definitions of the words you find in the dictionary.
-[Word Search World Traveler](https://play.google.com/store/apps/details?id=com.wordsearchworldtraveler): This app by Hoyt Games lets you travel around the world by solving word search puzzles with beautiful images and sounds. You can also collect souvenirs and learn facts about different countries.
-[Word Search Quest](https://play.google.com/store/apps /details?id=com.blackout.wordsearch): This app by Blackout Lab offers hundreds of challenging puzzles with different themes and sizes. You can also play online with other players and compete for the best score.
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- Word search software and programs for computers
-If you want to play word searches on your computer, you can download word search software and programs from various online sources. Some of these sources are:
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-[Super Word Search Maker](https://www.superwordsearchmaker.com/): This software by Steve Bushman allows you to create your own word search puzzles with your own words and clues. You can also print, save, and share your puzzles with others.
-[Word Search Creator](https://www.wordsearchcreator.org/): This program by Word Search Creator is a free and easy-to-use tool that lets you make word search puzzles with different shapes, colors, and fonts. You can also export your puzzles as PDF or image files.
-[Word Search Puzzle Maker](https://www.wordsearchpuzzlemaker.com/): This software by Word Search Puzzle Maker is a professional and powerful tool that enables you to create word search puzzles with various features, such as hidden messages, answer keys, word lists, and more. You can also customize your puzzles with different layouts, styles, and graphics.
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- How to solve word search puzzles effectively and quickly?
-Some general tips and strategies for word search solvers
-Solving word search puzzles can be a fun and rewarding activity, but it can also be frustrating and time-consuming if you don't know how to do it properly. Here are some general tips and strategies that can help you solve word search puzzles effectively and quickly:
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-Scan the grid for the first letter of the word you are looking for, then look around it for the second letter. Repeat this process until you find the whole word.
-Use a highlighter, a pencil, or your finger to mark the words you find in the grid. This will help you keep track of your progress and avoid missing any words.
-Look for common prefixes, suffixes, and word endings, such as -ing, -ed, -s, -tion, etc. These can help you narrow down your search and spot the words faster.
-Look for words that share letters or cross each other. This can help you save time and space in the grid.
-Work systematically from left to right, top to bottom, or in any other order that suits you. This will help you cover the whole grid and avoid skipping any areas.
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- Some specific tips and tricks for different types of word searches
-Depending on the type of word search puzzle you are playing, there may be some specific tips and tricks that can help you solve it more easily. Here are some examples of different types of word searches and how to solve them:
-
-Type of word search Tips and tricks Diagonal word search In this type of word search, the words can only be found diagonally in the grid. To solve it, you can tilt your head or rotate the paper to make the diagonal lines look horizontal or vertical. Backwards word search In this type of word search, the words can be found backwards in the grid. To solve it, you can read the grid from right to left or from bottom to top. You can also use a mirror or reverse the image on your device to see the words normally. Hidden message word search In this type of word search, there is a hidden message or a secret phrase that can be revealed by using the leftover letters in the grid after finding all the words. To solve it, you can write down or circle the unused letters in order, then read them from left to right or from top to bottom. Crossword-style word search In this type of word search, there are clues or definitions for each word instead of a list. To solve it, you can use your general knowledge or look up the answers online. You can also use crossword-solving tools or apps to help you find the words. Themed word search In this type of word search, all the words are related to a certain theme or category, such as animals, sports, or food. To solve it, you can use your prior knowledge or interest in the topic to help you guess the words. You can also use online resources or dictionaries to learn more about the theme.
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- Word searches are a fun and educational activity that can be enjoyed by everyone. They can help you improve your language skills, cognitive abilities, and mental health. You can download word searches for free or for a fee from various online sources, such as websites, apps, and software. You can also create your own word search puzzles with your own words and themes. To solve word search puzzles effectively and quickly, you can use some general tips and strategies, as well as some specific tips and tricks for different types of word searches. We hope this article has helped you learn more about word searches and how to download and solve them.
- FAQs
-Here are some frequently asked questions about word searches:
-
-How many words are in a typical word search puzzle?
-The number of words in a word search puzzle can vary depending on the size of the grid, the length of the words, and the difficulty level of the puzzle. A typical word search puzzle can have anywhere from 10 to 50 words.
-What is the difference between a word search and a word find?
-A word search and a word find are two names for the same type of puzzle. They both involve finding hidden words in a grid of letters. However, some people may use the term word find to refer to a simpler version of a word search that does not have a list of words to find, but only a theme or a category.
-What is the origin of word search puzzles?
-The origin of word search puzzles is not clear, but some sources suggest that they were invented by Norman E. Gibat, a teacher and publisher from Oklahoma, who created the first known word search puzzle in 1968 and published it in his magazine called Selenby Digest. He called it "Word Square" and it had 34 words related to fun and games.
-What are some other names for word search puzzles?
-Word search puzzles have many other names in different languages and regions. Some of these names are:
-
-Word sleuth or word seek in Canada and Australia
-Sopa de letras or soup of letters in Spanish
-Mots cachés or hidden words in French
-Wortsuche or word search in German
-Cari kata or find words in Indonesian
-
-What are some variations of word search puzzles?
-There are many variations of word search puzzles that can make them more challenging or interesting. Some of these variations are:
-
-Word fit or criss-cross: In this variation, the words can be placed in any direction, including overlapping or intersecting each other.
-Word jumble or scramble: In this variation, the words are not given in their correct order, but jumbled or scrambled. The solver has to unscramble the words before finding them in the grid.
-Word ladder or chain: In this variation, the words are connected by changing one letter at a time. The solver has to find the words in the grid and follow the chain from the first word to the last word.
-Word maze or path: In this variation, the words are arranged in a maze-like pattern. The solver has to find the words in the grid and trace the path from the start to the end.
-Word pyramid or triangle: In this variation, the words are arranged in a pyramid-like or triangle-like shape. The solver has to find the words in the grid and fill in the blanks in the shape.
- 401be4b1e0Car Parking Multiplayer APK Download New Version: Everything You Need to Know
-If you are looking for a realistic and fun car parking game, you should try Car Parking Multiplayer. It is one of the most popular and downloaded games in the simulation genre, with over 50 million installs on Google Play Store. In this game, you can experience the thrill of driving and parking various cars in different scenarios, as well as interact with other players online. In this article, we will tell you everything you need to know about Car Parking Multiplayer, including its features, how to download and install it, and some tips and tricks for playing it.
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Features of Car Parking Multiplayer
-Car Parking Multiplayer is not just a simple parking game. It offers a lot of features that make it stand out from other games in the same category. Here are some of them:
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-Multiplayer open world mode with real players and cars : You can join or create a room with up to 100 players, and explore a huge open world map with real gas stations, car services, traffic lights, police stations, etc. You can also chat with other players using voice or text messages, make friends, join clans, or compete in races.Free walking and driving with realistic physics and graphics : You can walk around the map freely, enter any building or vehicle, or even ride a bike or a horse. You can also drive any car you want, from sedans to trucks, with realistic physics and damage effects. The game also has stunning graphics and sounds that make you feel like you are in a real city.Customization and tuning of your own car : You can customize your car's appearance by changing its color, wheels, stickers, license plate, etc. You can also tune its performance by adjusting its engine, suspension, brakes, gearbox, etc. You can also add accessories like spoilers, neon lights, nitro boosters, etc.Thousands of parking and racing challenges : You can test your parking skills by completing various levels with different difficulty and objectives. You can also join or create racing events with other players, and race on different tracks with different rules and rewards.Different game modes and maps : You can choose from different game modes, such as free roam, parking, racing, police chase, zombie apocalypse, etc. You can also switch between different maps, such as city, airport, desert, snow, etc.Voice chat and online friends : You can communicate with other players using voice chat, and make online friends by sending and accepting friend requests. You can also see your friends' online status, join their rooms, or invite them to yours.
- How to Download and Install Car Parking Multiplayer APK
-If you want to download and install Car Parking Multiplayer APK on your Android device, you can follow these simple steps:
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-Go to the official website or Google Play Store : You can either visit the official website of Car Parking Multiplayer, or search for the game on Google Play Store. Both sources are safe and reliable.Choose the latest version of the game and download the APK file : You can check the latest version of the game on the website or the store, and click on the download button to get the APK file. The file size is about 300 MB, so make sure you have enough space on your device.Enable unknown sources on your device settings : Before you can install the APK file, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources, and toggle it on.Locate the downloaded file and tap on it to install : After you have downloaded the APK file, you can find it in your downloads folder or notification bar. Tap on it to start the installation process. You may need to grant some permissions for the app to run properly.Launch the game and enjoy : Once the installation is done, you can launch the game from your app drawer or home screen. You can now create your account, customize your car, and join the multiplayer world of Car Parking Multiplayer.
- Tips and Tricks for Playing Car Parking Multiplayer
-Car Parking Multiplayer is a fun and challenging game that requires skill and strategy. Here are some tips and tricks that can help you improve your gameplay and enjoy the game more:
-
-Use the camera angles to park your car accurately : The game offers different camera angles that you can switch between by tapping on the camera icon. You can use them to see your car from different perspectives, and park it more precisely. You can also zoom in or out by pinching the screen.Follow the traffic rules and avoid collisions : The game simulates real traffic conditions, so you need to follow the traffic rules and signs, such as speed limits, stop signs, traffic lights, etc. You also need to avoid colliding with other cars or objects, as this will damage your car and reduce your score.Upgrade your car's performance and appearance : You can use the money and reputation you earn from parking and racing to upgrade your car's performance and appearance. You can buy new parts or accessories from the shop, or visit the garage to tune your car. You can also sell or buy cars from other players in the market.Join a clan or create your own : You can join a clan or create your own by paying a fee. Clans are groups of players who share a common name, logo, chat room, etc. You can cooperate with your clan members, challenge other clans, or participate in clan wars for rewards and rankings.Challenge other players and earn money and reputation : You can challenge other players to parking or racing duels by tapping on their cars or names. You can also accept challenges from other players who want to compete with you. If you win a challenge, you will earn money and reputation points that will increase your level and rank.
- Conclusion
-Car Parking Multiplayer is a realistic and fun car parking game that offers a lot of features and options for players who love cars and driving. You can download and install Car Parking Multiplayer APK on your Android device easily by following our guide above. You can also use our tips and tricks to improve your gameplay and enjoy the game more. So what are you waiting for? Download Car Parking Multiplayer APK now and join the multiplayer world of car parking!
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- FAQs
-Here are some frequently asked questions about Car Parking Multiplayer:
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-Is Car Parking Multiplayer free to play? Yes, Car Parking Multiplayer is free to download and play, but it contains in-app purchases and ads. You can buy coins, gems, cars, or premium features with real money, or watch ads to get some rewards. You can also disable ads by paying a one-time fee.
-What are the minimum requirements for Car Parking Multiplayer? You need an Android device with version 4.1 or higher, and at least 300 MB of free space. The game also requires an internet connection to play online. The game may not run smoothly on low-end devices or slow networks.
-How can I play Car Parking Multiplayer on PC? You can use an Android emulator like BlueStacks or NoxPlayer to run the game on your PC. You need to download and install the emulator on your PC, and then download and install the game APK on the emulator. You can then use your mouse and keyboard to control the game.
-How can I contact the developers of Car Parking Multiplayer? You can send them an email at olzhas@olzhas.com or follow them on Facebook, Instagram, or YouTube. You can also visit their website or join their Discord server for more information and updates. You can also rate and review the game on Google Play Store or leave feedback in the game settings.
-What are some similar games to Car Parking Multiplayer? Some similar games are Real Car Parking 2, Dr. Parking 4, Parking Mania 2, and Manual Gearbox Car Parking. These games also offer realistic and challenging parking and driving scenarios, as well as multiplayer and customization features.
- 401be4b1e0Human Evolution Clicker Game Mod APK: A Fun and Educational Simulation
-Have you ever wondered how life evolved on Earth? How did simple organisms become complex and diverse creatures? How did humans emerge from the animal kingdom? If you are curious about these questions, then you will love Human Evolution Clicker Game, a fun and educational simulation game that lets you explore the history of life on our planet.
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Human Evolution Clicker Game is a game that combines idle clicker and evolution mechanics. You can create different creatures by combining two of the same species, and watch them evolve over time. You can also tap on the screen to speed up the evolution process, and discover new forms of life. You can start from the simplest bacteria, and evolve into fish, reptiles, mammals, birds, humans, and even aliens!
-But what if you want to enjoy the game without any limitations or interruptions? What if you want to have unlimited money and gems, no ads, faster evolution, and all stages and creatures unlocked? Well, there is a way to do that, and it is by downloading Human Evolution Clicker Game Mod APK. In this article, we will tell you what Human Evolution Clicker Game Mod APK is, why you should download it, and how to download and install it on your Android device.
- What is Human Evolution Clicker Game?
-Human Evolution Clicker Game is a game developed by Banana4apps, a studio that specializes in casual and simulation games. The game was released in 2018, and has since gained over 10 million downloads on Google Play Store. The game has a rating of 4.5 out of 5 stars, based on more than 500 thousand reviews.
-The game is suitable for all ages, as it is not only entertaining, but also educational. You can learn about the different stages of evolution, such as the ocean stage, the land stage, the dinosaur stage, the mammal stage, the primate stage, the human stage, and the future stage. You can also learn about the different creatures that existed in each stage, such as trilobites, sharks, dinosaurs, mammoths, monkeys, cavemen, cyborgs, and more.
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-The game has simple graphics and sound effects, but they are colorful and charming. The game also has a humorous tone, as you can create funny and absurd combinations of creatures. For example, you can combine a fish and a bird to create a flying fish, or a human and a cow to create a minotaur. The game does not take itself too seriously, and encourages you to experiment and have fun.
- How to play Human Evolution Clicker Game
-The gameplay of Human Evolution Clicker Game is very easy and intuitive. You just need to tap on the screen to create new creatures. You can drag two creatures of the same species to combine them into a new one. You can also tap on the creatures to make them evolve faster. You can use money and gems to buy upgrades and boosters that will help you progress faster.
-The game has different stages that represent different eras of evolution. You can unlock new stages by reaching certain milestones in each stage. For example, to unlock the land stage, you need to create an amphibian creature in the ocean stage. To unlock the dinosaur stage, you need to create a reptile creature in the land stage. And so on.
-The game also has different planets that represent different scenarios of evolution. You can unlock new planets by reaching certain levels in each stage. For example, to unlock Mars, you need to reach level 10 in the future stage. To unlock Pluto, you need to reach level 20 in the future stage. And so on.
-The game has no end goal or final boss. You can play as long as you want, and create as many creatures as you want. You can also reset your progress and start over with a new planet or stage. The game is designed to be relaxing and enjoyable, not stressful or competitive.
- Why download Human Evolution Clicker Game Mod APK?
-Human Evolution Clicker Game is a free game, but it has some limitations and drawbacks that might affect your gaming experience. For example, the game has ads that pop up every now and then, which can be annoying and distracting. The game also has in-app purchases that require you to spend real money to buy more money and gems, which are the main currencies of the game. The game also has some stages and creatures that are locked, and you need to reach certain levels or pay money to unlock them.
-If you want to avoid these problems, and enjoy the game to the fullest, then you should download Human Evolution Clicker Game Mod APK. This is a modified version of the game that gives you several benefits and advantages, such as:
- Unlimited money and gems
-With Human Evolution Clicker Game Mod APK, you will have unlimited money and gems in your account. You can use them to buy any upgrade or booster you want, without worrying about running out of resources. You can also use them to unlock any stage or creature you want, without having to wait or grind for levels. You can enjoy the game at your own pace, and create any creature you can imagine.
- No ads and faster evolution
-With Human Evolution Clicker Game Mod APK, you will not see any ads in the game. You can play the game without any interruption or distraction. You can also enjoy a faster evolution process, as you can tap on the screen as much as you want, without any cooldown or limit. You can evolve your creatures in seconds, and see the results of your actions instantly.
- All stages and creatures unlocked
-With Human Evolution Clicker Game Mod APK, you will have access to all the stages and creatures in the game. You can explore the different eras of evolution, from the ocean stage to the future stage. You can also create any creature you want, from bacteria to aliens. You can mix and match different species, and see what happens. You can have fun with the game's humor and creativity.
- How to download and install Human Evolution Clicker Game Mod APK?
-If you are interested in downloading Human Evolution Clicker Game Mod APK, then you need to follow these simple steps:
- Step 1: Enable unknown sources
-Before you can install Human Evolution Clicker Game Mod APK on your Android device, you need to enable unknown sources in your settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device's settings, then security, then unknown sources. Turn on the switch or check the box that says "allow installation of apps from unknown sources".
- Step 2: Download the APK file
-Next, you need to download the APK file of Human Evolution Clicker Game Mod APK from a reliable source. You can use this link to download the file directly to your device. Alternatively, you can use your computer to download the file, then transfer it to your device via USB cable or Bluetooth.
- Step 3: Install the APK file
-Once you have downloaded the APK file, you need to locate it on your device's storage. You can use a file manager app to find it easily. Then, tap on the file to start the installation process. Follow the instructions on the screen to complete the installation.
- Step 4: Enjoy the game
-After installing Human Evolution Clicker Game Mod APK on your device, you can launch it from your app drawer or home screen. You can start playing the game right away, with all the benefits and features of the modded version. Have fun creating and evolving different creatures!
- Conclusion
-Human Evolution Clicker Game is a fun and educational simulation game that lets you explore the history of life on Earth. You can create different creatures by combining two of the same species, and watch them evolve over time. You can also tap on the screen to speed up the evolution process, and discover new forms of life.
-If you want to enjoy the game without any limitations or interruptions, then you should download Human Evolution Clicker Game Mod APK. This is a modified version of the game that gives you unlimited money and gems, no ads, faster evolution, and all stages and creatures unlocked. You can download and install Human Evolution Clicker Game Mod APK by following these simple steps:
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-Enable unknown sources in your device's settings.
-Download the APK file from this link [^ [^] or transfer it from your computer.
-Install the APK file by tapping on it and following the instructions.
-Launch the game from your app drawer or home screen and enjoy!
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-Human Evolution Clicker Game Mod APK is a great way to have fun and learn about the history of life on Earth. You can create and evolve different creatures, from bacteria to aliens, and see how they change over time. You can also enjoy unlimited money and gems, no ads, faster evolution, and all stages and creatures unlocked. Download Human Evolution Clicker Game Mod APK today and start your evolutionary adventure!
- FAQs
-Here are some frequently asked questions about Human Evolution Clicker Game Mod APK:
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-Is Human Evolution Clicker Game Mod APK safe to download and install?
-Yes, Human Evolution Clicker Game Mod APK is safe to download and install, as long as you use a reliable source like this link [^]. The modded version does not contain any viruses or malware that could harm your device or compromise your privacy. However, you should always be careful when downloading and installing apps from unknown sources, and scan them with an antivirus app before opening them.
-Does Human Evolution Clicker Game Mod APK require root access?
-No, Human Evolution Clicker Game Mod APK does not require root access to work. You can install and play the game on any Android device, without having to root it or modify its system settings. However, if you have a rooted device, you can still use the modded version without any problems.
-Will Human Evolution Clicker Game Mod APK affect my original game progress?
-No, Human Evolution Clicker Game Mod APK will not affect your original game progress. The modded version will create a separate folder in your device's storage, where it will store its own data and files. You can play both the original and the modded version of the game on the same device, without any conflicts or issues. However, you should not use the same account or Google Play Games login for both versions, as this might cause some errors or glitches.
-Can I update Human Evolution Clicker Game Mod APK?
-No, you cannot update Human Evolution Clicker Game Mod APK through the Google Play Store or any other official source. The modded version is not compatible with the latest updates of the original game, and might stop working or crash if you try to update it. If you want to use the latest version of the modded game, you need to download and install it again from this link [^] or another reliable source.
-Can I play Human Evolution Clicker Game Mod APK online or offline?
-You can play Human Evolution Clicker Game Mod APK both online and offline. The game does not require an internet connection to work, and you can play it anywhere and anytime you want. However, some features of the game might require an internet connection, such as saving your progress to the cloud, accessing social media features, or watching optional video ads to earn extra rewards.
- 401be4b1e0Download Slither Io Mod Apk Tidak Bisa Mati: A Fun and Addictive Snake Game
-Do you remember the classic game of Snake that you used to play on your old Nokia phone? Do you want to relive that nostalgia with a modern twist? Do you want to play a game that is simple, fun, and addictive? If you answered yes to any of these questions, then you should try Slither Io , a game that has taken the internet by storm. And if you want to make the game even more exciting, you should download Slither Io Mod Apk Tidak Bisa Mati , a modified version of the game that gives you unlimited coins, skins, and immortality. In this article, we will tell you everything you need to know about Slither Io and Slither Io Mod Apk, including how to download, install, play, and win the game.
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What Is Slither Io?
-Slither Io is a casual arcade game that is inspired by the very popular game Snakes, seen on the Nokia feature phones. It is one of the best spin-offs we have seen recently[^3]. It is also a massively multiplayer online (MMO) game, which means anyone on the internet can play with you at the same time. And it is a competitive game where you are trying to beat the other snakes and grow longer.
- A Modern Recreation of Snake
-Slither Io is a game that recreates the classic Snake game with modern graphics and features. You play as a snake in the game, with the objective of growing as long as you can in length. To grow, you need to eat the orbs scattered all around you. The orbs vary in size, from tiny dots to large glowing orbs. The larger the orb, the more it counts in growing your length.
- A Massively Multiplayer Online Game
-Slither Io is a game that you can play through your web browser, or through apps on Android and iOS. You don't even need to register to start playing, making it one of those excellent no-signup websites for daily use[^8]. Type a nickname and you're ready to begin. You can play against other people online, who are also playing as snakes. You can see their nicknames and skins, and also their scores on the leaderboard. You can also chat with them using emojis.
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- A Competitive Game with Simple Rules
-Slither Io is a game that is easy to play but hard to master. The rules are simple: if your head touches another snake, you will explode and then it's game over. But if others run into you, then they will explode, and you can eat their remains. In Slither Io, you have a chance to win even if you're tiny. You can swerve in front of a much larger player to defeat them, no matter how big you are[^4]. The bigger your snake grows, the more difficult gameplay becomes. You need to avoid hitting the walls or other snakes, and you need to be careful of your own tail. The game is very competitive, as you are trying to survive and grow longer than the others. You can also see your rank on the leaderboard, which shows the top 10 players in the game. You can also see your own score and length on the bottom right corner of the screen.
- What Is Slither Io Mod Apk?
-Slither Io Mod Apk is a modified version of the original Slither Io game, which gives you some extra features and advantages. It is a game that you can download and install on your Android device, and play offline or online. It is a game that lets you customize your snake with unlimited coins and skins, and also gives you immortality and anti-death features.
- A Modified Version of the Original Game
-Slither Io Mod Apk is a game that is created by third-party developers, who have tweaked the original game code to add some new features and functions. It is not an official version of the game, and it is not endorsed by the original developers. Therefore, it may have some bugs or glitches, and it may not be compatible with all devices or updates. However, it is a game that many players enjoy, as it gives them more options and fun.
- A Game with Unlimited Coins and Skins
-Slither Io Mod Apk is a game that gives you unlimited coins, which you can use to buy different skins for your snake. Skins are the colors and patterns that you can choose for your snake, to make it look more unique and attractive. There are many skins available in the game, such as rainbow, flag, candy, animal, emoji, and more. You can also create your own custom skin by mixing different colors and patterns. With Slither Io Mod Apk, you can unlock all the skins for free, and change them anytime you want.
- A Game with Immortality and Anti-Death Features
-Slither Io Mod Apk is a game that gives you immortality, which means you can never die in the game. You can touch other snakes or walls without exploding, and you can also go through yourself without any harm. This makes the game much easier and less frustrating, as you don't have to worry about losing your progress or score. You can also use the anti-death feature, which lets you revive yourself after dying. You can choose to respawn at your previous location, or at a random location on the map. This way, you can continue playing without starting over.
- How to Download and Install Slither Io Mod Apk?
-Slither Io Mod Apk is a game that you can download and install on your Android device easily. Here are the steps to follow:
- Download the Apk File from a Trusted Source
-The first step is to download the apk file of Slither Io Mod Apk from a trusted source. You can search for it on Google or any other search engine, or you can use this link to download it directly. Make sure you download the latest version of the game, which is 1.6 as of June 2023.
- Enable Unknown Sources on Your Device
-The next step is to enable unknown sources on your device, which allows you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources. Turn on the toggle switch to allow unknown sources. You may see a warning message that says installing from unknown sources may harm your device or data. Ignore this message and tap OK.
- Install the Apk File and Launch the Game
-The final step is to install the apk file and launch the game. To do this, go to your file manager app, then locate the downloaded apk file of Slither Io Mod Apk. Tap on it to start the installation process. You may see a pop-up window that asks for your permission to install the app. Tap on install and wait for a few seconds until the installation is complete. Then tap on open to launch the game.
- How to Play Slither Io Mod Apk?
-Slither Io Mod Apk is a game that is very easy to play, but also very challenging and addictive. Here are some tips on how to play it:
- Choose Your Nickname and Skin
-The first thing you need to do when you launch the game is to choose your nickname and skin. You can type any name you want in the nickname box, or leave it blank if you prefer. Then you can choose your skin from the list of available skins, or create your own custom skin by mixing different colors and patterns. You can also change your skin anytime during the game by tapping on the change skin button on the bottom left corner of the screen.
- Control Your Snake with One or Two Fingers
-The next thing you need to do is to control your snake with one or two fingers. You can use one finger to swipe on the screen to move your snake in any direction. You can also use two fingers to tap on the left or right side of the screen to turn your snake left or right. The more you move, the more you lose some length, so be careful not to waste too much energy. You can also use the boost button on the bottom right corner of the screen to make your snake move faster, but this will also make you lose more length.
- Eat Orbs and Kill Other Snakes to Grow Bigger
-The main objective of the game is to eat orbs and kill other snakes to grow bigger. You can eat the orbs that are scattered all over the map, or the ones that are left behind by other snakes when they die. The more orbs you eat, the longer and thicker you become. You can also kill other snakes by making them run into your body. When they explode, you can eat their remains and grow even bigger. However, be careful not to run into other snakes yourself, or you will die too. Unless you are playing with Slither Io Mod Apk, which gives you immortality and anti-death features.
- How to Win Slither Io Mod Apk?
-Slither Io Mod Apk is a game that is very competitive and challenging, even with the extra features and advantages. Here are some strategies on how to win it:
- Use Boost to Dash and Dine
-One of the best ways to win Slither Io Mod Apk is to use boost to dash and dine. Boosting is when you make your snake move faster by pressing the boost button on the bottom right corner of the screen. This will make you lose some length, but it will also give you an edge over other snakes. You can use boost to dash in front of other snakes and make them run into you, or to dine on their remains after they die. You can also use boost to escape from dangerous situations, such as being surrounded by bigger snakes or being chased by faster ones.
- Coil Around Orbs or Smaller Snakes
-Another way to win Slither Io Mod Apk is to coil around orbs or smaller snakes. Coiling is when you make your snake form a circle around something or someone, trapping them inside. This will prevent them from escaping or moving freely, and it will also give you a chance to eat them or their orbs. You can coil around large orbs that are worth more points, or smaller snakes that are easier to catch. However, be careful not to coil around bigger snakes that can break free from your coil, or other snakes that can coil around you.
- Escape from Coils or Traps
-The last way to win Slither Io Mod Apk is to escape from coils or traps. Coils and traps are when other snakes try to encircle you or block your way, making you unable to move or escape. This will put you in a dangerous position, as you may run out of space or run into other snakes. To escape from coils or traps, you need to be quick and smart. You can use boost to dash out of a coil before it closes, or find a gap in a trap before it blocks you. You can also use the anti-death feature of Slither Io Mod Apk, which lets you revive yourself after dying.
- Conclusion
-Slither Io Mod Apk Tidak Bisa Mati is a fun and addictive snake game that lets you play with unlimited coins, skins, and immortality. It is a game that is easy to play but hard to master, as you need to avoid other snakes and grow longer than them. It is a game that is suitable for all ages and preferences, as you can customize your snake and chat with other players online. It is a game that you can download and install on your Android device easily, and play offline or online anytime you want.
- FAQs
-Here are some frequently asked questions about Slither Io Mod Apk:
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-Q: Is Slither Io Mod Apk safe? A: Slither Io Mod Apk is generally safe, as long as you download it from a trusted source and enable unknown sources on your device. However, it may have some bugs or glitches, and it may not be compatible with all devices or updates. Q: Is Slither Io Mod Apk legal? A: Slither Io Mod Apk is not legal, as it violates the terms and conditions of the original game. It is also not fair to other players who play the game without any modifications. Therefore, we do not recommend using Slither Io Mod Apk, and we are not responsible for any consequences that may arise from using it. Q: Is Slither Io Mod Apk online or offline? A: Slither Io Mod Apk can be played both online and offline. You can play online with other players who are also using the mod apk, or you can play offline with bots. However, playing online may expose you to some risks, such as being banned by the original game or getting viruses from untrusted sources. Q: How to update Slither Io Mod Apk? A: To update Slither Io Mod Apk, you need to download the latest version of the apk file from a trusted source and install it on your device. You may need to uninstall the previous version of the game before installing the new one. You may also need to enable unknown sources on your device again if you have disabled it. Q: How to uninstall Slither Io Mod Apk? A: To uninstall Slither Io Mod Apk, you need to go to your device settings, then apps, then find Slither Io Mod Apk and tap on it. Then tap on uninstall and confirm your choice. You may also need to delete the apk file from your file manager app if you still have it. Q: How to get Slither Io Mod Apk for iOS? A: Unfortunately, there is no Slither Io Mod Apk for iOS devices, as iOS does not support apk files. You can only play the original Slither Io game on iOS devices, which you can download from the App Store.
401be4b1e0How to Download and Play Last Island of Survival on Your Laptop
-Last Island of Survival is a multiplayer zombie survival game that challenges you to scavenge resources, craft weapons, build shelter, and fight against other players and the undead in a post-apocalyptic island. The game is available for Android and iOS devices, but what if you want to play it on your laptop?
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Playing Last Island of Survival on your laptop has many advantages, such as a bigger screen, improved visibility, better controls, longer battery life, and no interruptions. In this article, we will show you three methods to download and play Last Island of Survival on your laptop using different tools and platforms. Let's get started!
- Method 1: Using BlueStacks Android emulator
-BlueStacks is a popular Android emulator that lets you run Android apps and games on your PC or Mac. It has many features that enhance your gaming experience, such as Eco Mode, Macros, Script, Multi-Instance, Multi-Instance Sync, Real-Time Translation, and more. Here are the steps to use BlueStacks to play Last Island of Survival on your laptop:
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-Download and install BlueStacks from here .
-Launch BlueStacks and sign in to Google Play Store with your Google account.
-Search for Last Island of Survival in the search bar.
-Click on the game icon and install it.
-Once the installation is complete, click on the game icon on the home screen to start playing.
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-You can customize the game controls using your mouse and keyboard, or use the default ones provided by BlueStacks. You can also sync your game progress across devices with a single sign-in to your Google account.
- Method 2: Using Google Play Games on PC
-Google Play Games is a PC application that lets you browse, download, and play select mobile games on Google's gaming platform for PC. You can enjoy bigger, bolder versions of your favorite Android games on your laptop with improved performance and graphics. Here are the steps to use Google Play Games to play Last Island of Survival on your laptop:
-
-Download and install Google Play Games from here .
-Launch Google Play Games and sign in with your Google account.
-Browse the available games and look for Last Island of Survival.
-Click on the game icon and install it.
-Once the installation is complete, click on the game icon to start playing.
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-You can use your mouse and keyboard to play the game with more agility and accuracy. You can also sync your game progress and library across devices with a single sign-in to your Google account.
- Method 3: Using Your Phone app
-Your Phone is a Windows app that lets you link your PC and your Android phone and access your phone's apps on your PC. You can use it to play Last Island of Survival on your laptop without downloading any additional software. Here are the steps to use Your Phone app to play Last Island of Survival on your laptop:
-
-Download and install Your Phone app from here .
-Launch Your Phone app and follow the instructions to link your PC and your Android phone.
-On your phone, open Settings > Apps & notifications > Advanced > Default apps > Opening links > Your Phone Companion > Open supported links > In this app.
-On your PC, open Your Phone app and click on Apps.
-Select Last Island of Survival from the list of apps on your phone.
-The game will open in a separate window on your PC. You can use your mouse or touchpad to play the game.
-Conclusion
-Last Island of Survival is a thrilling and addictive zombie survival game that you can enjoy on your laptop with any of the three methods we have shown you. Whether you use BlueStacks, Google Play Games, or Your Phone app, you can experience the game with better graphics, controls, and performance than on your mobile device. You can also sync your game progress and achievements across devices with your Google account.
-Now that you know how to download and play Last Island of Survival on your laptop, you can explore the island, craft weapons, build shelter, and fight for survival with other players and zombies. Be careful, though, as the island is full of dangers and surprises. You never know what you might encounter next.
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-We hope you found this article helpful and informative. If you have any questions or feedback, please let us know in the comments below. Happy gaming!
- FAQs
-Is Last Island of Survival free to play?
-Yes, Last Island of Survival is free to play on Android and iOS devices. However, it may contain in-app purchases and ads that require real money.
- Can I play Last Island of Survival offline?
-No, Last Island of Survival requires an internet connection to play. You need to connect to the game server and interact with other players online.
- How can I save my game progress in Last Island of Survival?
-You can save your game progress in Last Island of Survival by signing in to your Google account on your device. This way, you can sync your game data across devices and platforms.
- How can I change the language in Last Island of Survival?
-You can change the language in Last Island of Survival by going to Settings > Language and selecting your preferred language from the list.
- How can I report a bug or a problem in Last Island of Survival?
-You can report a bug or a problem in Last Island of Survival by going to Settings > Feedback and filling out the form with your issue and contact information.
401be4b1e0How to Download RespeM Pa Pou Fake by King Street and Izolan
-If you are a fan of Haitian music, you may have heard of the song RespeM Pa Pou Fake by King Street and Izolan . This song is a hit among Haitians and non-Haitians alike, thanks to its catchy beat, witty lyrics, and powerful message. In this article, we will tell you what RespeM Pa Pou Fake is, why you should download it, and how to do it legally and safely.
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What is RespeM Pa Pou Fake?
-RespeM Pa Pou Fake is a Haitian Creole phrase that means \"respect is not for fake\". It is also the title of a song by King Street, a Haitian rap group, featuring Izolan, a Haitian rapper and singer. The song was released in March 2023 as a single from King Street's album of the same name.
-A popular Haitian song
-RespeM Pa Pou Fake is one of the most popular Haitian songs of 2023. It has over 8 million views on YouTube, over 175 thousand downloads on Dyaspora Mizik, and over 120 thousand streams on Boomplay. It has also been featured on various playlists and radio stations, such as Best Haitian Music 2023, DJAKOUT #1 Habitude, and KADO de Ti Lunet Power of Love.
-A collaboration between King Street and Izolan
-RespeM Pa Pou Fake is a collaboration between King Street and Izolan, two of the most prominent figures in the Haitian music scene. King Street is a rap group composed of four members: Pedro Force, Maestro Jolicoeur, Bourik the Latalay, and Esther Surpris. They are known for their songs that mix rap, kompa, reggae, and R&B. Izolan is a rapper and singer who rose to fame as a member of Barikad Crew, a rap group that was influential in the Haitian hip-hop movement. He is also a solo artist who has released several albums and singles.
-A message of respect and authenticity
-RespeM Pa Pou Fake is not just a catchy song, but also a message of respect and authenticity. The song challenges the listeners to be true to themselves and not to pretend to be someone they are not. It also urges them to respect others who are genuine and honest, and not to fall for fake people who lie and deceive. The song reflects the values and struggles of many Haitians who face poverty, corruption, violence, and discrimination in their country.
-Why download RespeM Pa Pou Fake?
-There are many reasons why you should download RespeM Pa Pou Fake. Here are some of them:
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-To enjoy the catchy beat and lyrics
-RespeM Pa Pou Fake has a catchy beat that will make you want to dance and sing along. The song combines rap, kompa, reggae, and R&B elements to create a unique sound that appeals to different tastes. The lyrics are witty, clever, and humorous, using wordplay, metaphors, and references to convey the message of the song. Some of the memorable lines are: - RespeM pa pou fake, respeM pou moun ki gen lavi (Respect is not for fake, respect for people who have life) - Mwen pa bezwen moun ki pa renmen mwen, mwen bezwen moun ki renmen mwen pou sa mwen ye (I don't need people who don't love me, I need people who love me for who I am) - Mwen pa janm chanje, se moun yo ki chanje, se fake yo ki ap monte (I never change, it's the people who change, it's the fake ones who are rising)
-To support Haitian music and artists
-By downloading RespeM Pa Pou Fake, you are also supporting Haitian music and artists. Haiti has a rich and diverse musical heritage that reflects its history, culture, and identity. Haitian music is influenced by African, European, Caribbean, and American styles, such as vodou, kompa, rara, rap kreyol, zouk, reggae, and jazz. Haitian music is also a form of expression and resistance against oppression and injustice. By downloading RespeM Pa Pou Fake, you are showing appreciation and solidarity for Haitian music and artists who work hard to create quality content and represent their country.
-To learn more about Haitian culture and history
-Another reason to download RespeM Pa Pou Fake is to learn more about Haitian culture and history. Haiti is a country with a rich and complex history that has shaped its culture and society. Haiti was the first independent black republic in the world, after a successful slave revolt against French colonial rule in 1804. Haiti has also faced many challenges and difficulties, such as political instability, natural disasters, poverty, and foreign intervention. Haiti has a vibrant and diverse culture that includes art, literature, religion, cuisine, sports, and music. By downloading RespeM Pa Pou Fake, you are exposing yourself to a glimpse of Haitian culture and history that you may not find elsewhere.
-How to download RespeM Pa Pou Fake legally and safely?
-Now that you know what RespeM Pa Pou Fake is and why you should download it, you may be wondering how to do it legally and safely. There are many ways to download RespeM Pa Pou Fake online, but not all of them are legal or safe. Some websites or programs may contain viruses, malware, or spyware that can harm your device or compromise your privacy. Some websites or programs may also violate the intellectual property rights of the artists or the producers of the song. To avoid these risks and respect the work of the creators, here are some ways to download RespeM Pa Pou Fake legally and safely:
-Buy the song from iTunes or Google Play
-One of the easiest and safest ways to download RespeM Pa Pou Fake is to buy it from iTunes or Google Play. These are official platforms that offer high-quality downloads of songs for a reasonable price. You can also access other features such as lyrics, album art, ratings, reviews, and playlists. By buying the song from iTunes or Google Play, you are also supporting the artists financially and legally.
-Stream the song from YouTube or Spotify
-Another way to download RespeM Pa Pou Fake is to stream it from YouTube or Spotify. These are popular platforms that offer free access to millions of songs online. You can also enjoy other benefits such as videos, subtitles, recommendations, social media integration, and offline mode. However, streaming the song from YouTube or Spotify does not mean that you own the song or that you can download it to your device. You need an internet connection to access the song and you may encounter ads or interruptions. If you want to download the song to your device, you need to use a third-party website or program that can convert YouTube videos or Spotify songs to MP3 files.
-Use a reliable and secure website or program to convert YouTube videos to MP3 files
-If you want to download RespeM Pa Pou Fake from YouTube as an MP3 file, you need to use a reliable and secure website or program that can convert YouTube videos to MP3 files. There are many websites and programs that offer this service, but not all of them are trustworthy or safe. Some of them may contain viruses, malware, spyware, pop-ups, or other unwanted elements that can harm your device or compromise your privacy. Some of them may also have low-quality conversions, slow speeds, limited options, or hidden fees. To avoid these problems, here are some of the best websites and programs that can convert YouTube videos to MP3 files legally and safely:
-Dirpy
-Dirpy is a website that allows you to download YouTube videos as MP3 files for free. It has a simple and user-friendly interface that lets you paste the URL of the YouTube video you want to download, choose the audio quality and format, edit the ID3 tags, and download the file. Dirpy also has a bookmarklet that you can add to your browser for easy access. Dirpy respects the intellectual property rights of the content owners and only allows downloads of videos that are in the public domain or have a Creative Commons license.
-Motionbox's YouTube to MP3
-Motionbox's YouTube to MP3 is a website that allows you to download YouTube videos as MP3 files for free. It has a sleek and modern interface that lets you paste the URL of the YouTube video you want to download, choose the audio quality and format, and download the file. Motionbox's YouTube to MP3 also has a Chrome extension that you can install for easy access. Motionbox's YouTube to MP3 is fast, secure, and reliable, and does not require any registration or installation.
-Free MP3 Hunter
-Free MP3 Hunter is a website that allows you to download YouTube videos as MP3 files for free. It has a simple and minimalist interface that lets you paste the URL of the YouTube video you want to download, choose the audio quality and format, and download the file. Free MP3 Hunter also has an app that you can download for Android devices. Free MP3 Hunter is safe, efficient, and convenient, and does not have any ads or pop-ups.
-Conclusion
-RespeM Pa Pou Fake by King Street and Izolan is a popular Haitian song that has a catchy beat, witty lyrics, and a powerful message of respect and authenticity. It is also a way to enjoy, support, and learn more about Haitian music and culture. To download RespeM Pa Pou Fake legally and safely, you can buy it from iTunes or Google Play, stream it from YouTube or Spotify, or use a reliable and secure website or program to convert YouTube videos to MP3 files. We hope this article has helped you find the best way to download RespeM Pa Pou Fake by King Street and Izolan.
-FAQs
-Here are some frequently asked questions about RespeM Pa Pou Fake by King Street and Izolan:
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-What does RespeM Pa Pou Fake mean? RespeM Pa Pou Fake is a Haitian Creole phrase that means \"respect is not for fake\". It is also the title of a song by King Street and Izolan.
-Who are King Street and Izolan? King Street is a Haitian rap group composed of four members: Pedro Force, Maestro Jolicoeur, Bourik the Latalay, and Esther Surpris. Izolan is a Haitian rapper and singer who was formerly a member of Barikad Crew.
-When was RespeM Pa Pou Fake released? RespeM Pa Pou Fake was released in March 2023 as a single from King Street's album of the same name.
-How popular is RespeM Pa Pou Fake? RespeM Pa Pou Fake is one of the most popular Haitian songs of 2023. It has over 8 million views on YouTube[^1 ^], over 175 thousand downloads on Dyaspora Mizik, and over 120 thousand streams on Boomplay. It has also been featured on various playlists and radio stations, such as Best Haitian Music 2023, DJAKOUT #1 Habitude, and KADO de Ti Lunet Power of Love.
-How can I download RespeM Pa Pou Fake legally and safely? You can download RespeM Pa Pou Fake legally and safely by buying it from iTunes or Google Play, streaming it from YouTube or Spotify, or using a reliable and secure website or program to convert YouTube videos to MP3 files, such as Dirpy, Motionbox's YouTube to MP3, or Free MP3 Hunter.
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-X Explorer Mod APK is a file system management utility for Android 2.1+ developed by the Slovakian software house Lonely Cat Games. The app is only available for free on Google Play without even the support of advertising banners. However, it is possible to donate to the developer.
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The modded version of X-Plore File Manager, which is also known as X Explorer Mod APK, unlocks some premium features that are otherwise available only to donors. These features include dual-pane mode, root access, cloud storage integration, media player and viewer, archive and encryption support, FTP and SSH client, SQLite database explorer, hex editor and text editor, and more.
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-X Explorer Mod APK can connect to FTP or SSH servers and transfer files between your device and the server. You can also browse or edit files on the server. The app supports FTPS (SSL/TLS) and SFTP (SSH File Transfer Protocol) for secure connections.
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-Note: You may need to uninstall the original X-Plore File Manager app before installing the modded version, as they may conflict with each other.
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-X Explorer Mod APK is a file manager app that offers a lot of features and functions for managing your files on your Android device. It allows you to access and modify system files, connect to cloud storage services, play or view media files, create or extract archives, encrypt or decrypt files, connect to FTP or SSH servers, view or edit SQLite databases, hex files, text files, and more. It also has a dual-pane mode that lets you view two folders or files side by side. However, the app is not available on Google Play Store, so you need to download it from a third-party source. It may also not be compatible with some devices or Android versions, or may require root access for some features. The app may also be too complex or overwhelming for some users who prefer a simpler or more minimalist interface. Therefore, you should weigh the pros and cons of using this app before installing it on your device.
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-X Explorer Mod APK is a modified version of X-Plore File Manager that unlocks some premium features that are otherwise available only to donors. These features include dual-pane mode, root access, cloud storage integration, media player and viewer, archive and encryption support, FTP and SSH client, SQLite database explorer, hex editor and text editor, and more.
- Is X Explorer Mod APK safe to use?
-X Explorer Mod APK is generally safe to use if you download it from a trusted source. However, you should always scan the APK file with an antivirus program before installing it on your device. You should also be careful when accessing or modifying system files or folders with this app, as you might damage your device or lose your data.
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-If you want to become the ultimate star in Zooba, you need to know some tips and tricks that will help you win more matches and have more fun. Here are some tips and tricks for playing Zooba:
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Use the environment to your advantage
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Collect and upgrade weapons
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Stay alert and avoid the fire zone
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Conclusion
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-Q: Is Zooba free to play?
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-Q: How many players can play in one match?
-A: Zooba supports up to 20 players in one match, either solo or in teams of 2 or 4.
-Q: How long does one match last?
-A: One match lasts for about 3-5 minutes, depending on how fast the fire zone shrinks and how many players are left.
-Q: What are the minimum requirements for playing Zooba on Android?
-A: Zooba requires Android 6.0 or higher and at least 2 GB of RAM to run smoothly.
-Q: How can I contact the developers of Zooba?
-A: You can contact the developers of Zooba by sending an email to support@wildlifestudios.com or by visiting their website at [https://wildlifestudios.com/].
- 197e85843d`_.
-
- Args:
- img (ndarray): Image to be adjusted lighting. BGR order.
- eigval (ndarray): the eigenvalue of the convariance matrix of pixel
- values, respectively.
- eigvec (ndarray): the eigenvector of the convariance matrix of pixel
- values, respectively.
- alphastd (float): The standard deviation for distribution of alpha.
- Defaults to 0.1
- to_rgb (bool): Whether to convert img to rgb.
-
- Returns:
- ndarray: The adjusted image.
- """
- assert isinstance(eigval, np.ndarray) and isinstance(eigvec, np.ndarray), \
- f'eigval and eigvec should both be of type np.ndarray, got ' \
- f'{type(eigval)} and {type(eigvec)} instead.'
-
- assert eigval.ndim == 1 and eigvec.ndim == 2
- assert eigvec.shape == (3, eigval.shape[0])
- n_eigval = eigval.shape[0]
- assert isinstance(alphastd, float), 'alphastd should be of type float, ' \
- f'got {type(alphastd)} instead.'
-
- img = img.copy().astype(np.float32)
- if to_rgb:
- cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
-
- alpha = np.random.normal(0, alphastd, n_eigval)
- alter = eigvec \
- * np.broadcast_to(alpha.reshape(1, n_eigval), (3, n_eigval)) \
- * np.broadcast_to(eigval.reshape(1, n_eigval), (3, n_eigval))
- alter = np.broadcast_to(alter.sum(axis=1).reshape(1, 1, 3), img.shape)
- img_adjusted = img + alter
- return img_adjusted
-
-
-def lut_transform(img, lut_table):
- """Transform array by look-up table.
-
- The function lut_transform fills the output array with values from the
- look-up table. Indices of the entries are taken from the input array.
-
- Args:
- img (ndarray): Image to be transformed.
- lut_table (ndarray): look-up table of 256 elements; in case of
- multi-channel input array, the table should either have a single
- channel (in this case the same table is used for all channels) or
- the same number of channels as in the input array.
-
- Returns:
- ndarray: The transformed image.
- """
- assert isinstance(img, np.ndarray)
- assert 0 <= np.min(img) and np.max(img) <= 255
- assert isinstance(lut_table, np.ndarray)
- assert lut_table.shape == (256, )
-
- return cv2.LUT(np.array(img, dtype=np.uint8), lut_table)
-
-
-def clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
- """Use CLAHE method to process the image.
-
- See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
- Graphics Gems, 1994:474-485.` for more information.
-
- Args:
- img (ndarray): Image to be processed.
- clip_limit (float): Threshold for contrast limiting. Default: 40.0.
- tile_grid_size (tuple[int]): Size of grid for histogram equalization.
- Input image will be divided into equally sized rectangular tiles.
- It defines the number of tiles in row and column. Default: (8, 8).
-
- Returns:
- ndarray: The processed image.
- """
- assert isinstance(img, np.ndarray)
- assert img.ndim == 2
- assert isinstance(clip_limit, (float, int))
- assert is_tuple_of(tile_grid_size, int)
- assert len(tile_grid_size) == 2
-
- clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
- return clahe.apply(np.array(img, dtype=np.uint8))
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/parrots_jit.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/parrots_jit.py
deleted file mode 100644
index 61873f6dbb9b10ed972c90aa8faa321e3cb3249e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/parrots_jit.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-
-from .parrots_wrapper import TORCH_VERSION
-
-parrots_jit_option = os.getenv('PARROTS_JIT_OPTION')
-
-if TORCH_VERSION == 'parrots' and parrots_jit_option == 'ON':
- from parrots.jit import pat as jit
-else:
-
- def jit(func=None,
- check_input=None,
- full_shape=True,
- derivate=False,
- coderize=False,
- optimize=False):
-
- def wrapper(func):
-
- def wrapper_inner(*args, **kargs):
- return func(*args, **kargs)
-
- return wrapper_inner
-
- if func is None:
- return wrapper
- else:
- return func
-
-
-if TORCH_VERSION == 'parrots':
- from parrots.utils.tester import skip_no_elena
-else:
-
- def skip_no_elena(func):
-
- def wrapper(*args, **kargs):
- return func(*args, **kargs)
-
- return wrapper
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/common.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/common.py
deleted file mode 100644
index aa69a6a6546030aee818b195a0fbb399d5b776f6..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/common.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import contextlib
-import copy
-import itertools
-import logging
-import numpy as np
-import pickle
-import random
-from typing import Callable, Union
-import torch
-import torch.utils.data as data
-from torch.utils.data.sampler import Sampler
-
-from annotator.oneformer.detectron2.utils.serialize import PicklableWrapper
-
-__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"]
-
-logger = logging.getLogger(__name__)
-
-
-def _shard_iterator_dataloader_worker(iterable):
- # Shard the iterable if we're currently inside pytorch dataloader worker.
- worker_info = data.get_worker_info()
- if worker_info is None or worker_info.num_workers == 1:
- # do nothing
- yield from iterable
- else:
- yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers)
-
-
-class _MapIterableDataset(data.IterableDataset):
- """
- Map a function over elements in an IterableDataset.
-
- Similar to pytorch's MapIterDataPipe, but support filtering when map_func
- returns None.
-
- This class is not public-facing. Will be called by `MapDataset`.
- """
-
- def __init__(self, dataset, map_func):
- self._dataset = dataset
- self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work
-
- def __len__(self):
- return len(self._dataset)
-
- def __iter__(self):
- for x in map(self._map_func, self._dataset):
- if x is not None:
- yield x
-
-
-class MapDataset(data.Dataset):
- """
- Map a function over the elements in a dataset.
- """
-
- def __init__(self, dataset, map_func):
- """
- Args:
- dataset: a dataset where map function is applied. Can be either
- map-style or iterable dataset. When given an iterable dataset,
- the returned object will also be an iterable dataset.
- map_func: a callable which maps the element in dataset. map_func can
- return None to skip the data (e.g. in case of errors).
- How None is handled depends on the style of `dataset`.
- If `dataset` is map-style, it randomly tries other elements.
- If `dataset` is iterable, it skips the data and tries the next.
- """
- self._dataset = dataset
- self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work
-
- self._rng = random.Random(42)
- self._fallback_candidates = set(range(len(dataset)))
-
- def __new__(cls, dataset, map_func):
- is_iterable = isinstance(dataset, data.IterableDataset)
- if is_iterable:
- return _MapIterableDataset(dataset, map_func)
- else:
- return super().__new__(cls)
-
- def __getnewargs__(self):
- return self._dataset, self._map_func
-
- def __len__(self):
- return len(self._dataset)
-
- def __getitem__(self, idx):
- retry_count = 0
- cur_idx = int(idx)
-
- while True:
- data = self._map_func(self._dataset[cur_idx])
- if data is not None:
- self._fallback_candidates.add(cur_idx)
- return data
-
- # _map_func fails for this idx, use a random new index from the pool
- retry_count += 1
- self._fallback_candidates.discard(cur_idx)
- cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
-
- if retry_count >= 3:
- logger = logging.getLogger(__name__)
- logger.warning(
- "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
- idx, retry_count
- )
- )
-
-
-class _TorchSerializedList(object):
- """
- A list-like object whose items are serialized and stored in a torch tensor. When
- launching a process that uses TorchSerializedList with "fork" start method,
- the subprocess can read the same buffer without triggering copy-on-access. When
- launching a process that uses TorchSerializedList with "spawn/forkserver" start
- method, the list will be pickled by a special ForkingPickler registered by PyTorch
- that moves data to shared memory. In both cases, this allows parent and child
- processes to share RAM for the list data, hence avoids the issue in
- https://github.com/pytorch/pytorch/issues/13246.
-
- See also https://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/
- on how it works.
- """
-
- def __init__(self, lst: list):
- self._lst = lst
-
- def _serialize(data):
- buffer = pickle.dumps(data, protocol=-1)
- return np.frombuffer(buffer, dtype=np.uint8)
-
- logger.info(
- "Serializing {} elements to byte tensors and concatenating them all ...".format(
- len(self._lst)
- )
- )
- self._lst = [_serialize(x) for x in self._lst]
- self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
- self._addr = torch.from_numpy(np.cumsum(self._addr))
- self._lst = torch.from_numpy(np.concatenate(self._lst))
- logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024**2))
-
- def __len__(self):
- return len(self._addr)
-
- def __getitem__(self, idx):
- start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
- end_addr = self._addr[idx].item()
- bytes = memoryview(self._lst[start_addr:end_addr].numpy())
-
- # @lint-ignore PYTHONPICKLEISBAD
- return pickle.loads(bytes)
-
-
-_DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = _TorchSerializedList
-
-
-@contextlib.contextmanager
-def set_default_dataset_from_list_serialize_method(new):
- """
- Context manager for using custom serialize function when creating DatasetFromList
- """
-
- global _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
- orig = _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
- _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = new
- yield
- _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = orig
-
-
-class DatasetFromList(data.Dataset):
- """
- Wrap a list to a torch Dataset. It produces elements of the list as data.
- """
-
- def __init__(
- self,
- lst: list,
- copy: bool = True,
- serialize: Union[bool, Callable] = True,
- ):
- """
- Args:
- lst (list): a list which contains elements to produce.
- copy (bool): whether to deepcopy the element when producing it,
- so that the result can be modified in place without affecting the
- source in the list.
- serialize (bool or callable): whether to serialize the stroage to other
- backend. If `True`, the default serialize method will be used, if given
- a callable, the callable will be used as serialize method.
- """
- self._lst = lst
- self._copy = copy
- if not isinstance(serialize, (bool, Callable)):
- raise TypeError(f"Unsupported type for argument `serailzie`: {serialize}")
- self._serialize = serialize is not False
-
- if self._serialize:
- serialize_method = (
- serialize
- if isinstance(serialize, Callable)
- else _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
- )
- logger.info(f"Serializing the dataset using: {serialize_method}")
- self._lst = serialize_method(self._lst)
-
- def __len__(self):
- return len(self._lst)
-
- def __getitem__(self, idx):
- if self._copy and not self._serialize:
- return copy.deepcopy(self._lst[idx])
- else:
- return self._lst[idx]
-
-
-class ToIterableDataset(data.IterableDataset):
- """
- Convert an old indices-based (also called map-style) dataset
- to an iterable-style dataset.
- """
-
- def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True):
- """
- Args:
- dataset: an old-style dataset with ``__getitem__``
- sampler: a cheap iterable that produces indices to be applied on ``dataset``.
- shard_sampler: whether to shard the sampler based on the current pytorch data loader
- worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple
- workers, it is responsible for sharding its data based on worker id so that workers
- don't produce identical data.
-
- Most samplers (like our TrainingSampler) do not shard based on dataloader worker id
- and this argument should be set to True. But certain samplers may be already
- sharded, in that case this argument should be set to False.
- """
- assert not isinstance(dataset, data.IterableDataset), dataset
- assert isinstance(sampler, Sampler), sampler
- self.dataset = dataset
- self.sampler = sampler
- self.shard_sampler = shard_sampler
-
- def __iter__(self):
- if not self.shard_sampler:
- sampler = self.sampler
- else:
- # With map-style dataset, `DataLoader(dataset, sampler)` runs the
- # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))`
- # will run sampler in every of the N worker. So we should only keep 1/N of the ids on
- # each worker. The assumption is that sampler is cheap to iterate so it's fine to
- # discard ids in workers.
- sampler = _shard_iterator_dataloader_worker(self.sampler)
- for idx in sampler:
- yield self.dataset[idx]
-
- def __len__(self):
- return len(self.sampler)
-
-
-class AspectRatioGroupedDataset(data.IterableDataset):
- """
- Batch data that have similar aspect ratio together.
- In this implementation, images whose aspect ratio < (or >) 1 will
- be batched together.
- This improves training speed because the images then need less padding
- to form a batch.
-
- It assumes the underlying dataset produces dicts with "width" and "height" keys.
- It will then produce a list of original dicts with length = batch_size,
- all with similar aspect ratios.
- """
-
- def __init__(self, dataset, batch_size):
- """
- Args:
- dataset: an iterable. Each element must be a dict with keys
- "width" and "height", which will be used to batch data.
- batch_size (int):
- """
- self.dataset = dataset
- self.batch_size = batch_size
- self._buckets = [[] for _ in range(2)]
- # Hard-coded two aspect ratio groups: w > h and w < h.
- # Can add support for more aspect ratio groups, but doesn't seem useful
-
- def __iter__(self):
- for d in self.dataset:
- w, h = d["width"], d["height"]
- bucket_id = 0 if w > h else 1
- bucket = self._buckets[bucket_id]
- bucket.append(d)
- if len(bucket) == self.batch_size:
- data = bucket[:]
- # Clear bucket first, because code after yield is not
- # guaranteed to execute
- del bucket[:]
- yield data
diff --git a/spaces/coyotte508/test-req/README.md b/spaces/coyotte508/test-req/README.md
deleted file mode 100644
index a517f7c4a5dcc2885605eb12040f4cec8cea4e8b..0000000000000000000000000000000000000000
--- a/spaces/coyotte508/test-req/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Test Req
-emoji: 📊
-colorFrom: gray
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/arch_util.py b/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/arch_util.py
deleted file mode 100644
index bad45ab34e901c47fb539152fca714a3795b0de2..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/CodeFormer/basicsr/archs/arch_util.py
+++ /dev/null
@@ -1,318 +0,0 @@
-import collections.abc
-import math
-import torch
-import torchvision
-import warnings
-from distutils.version import LooseVersion
-from itertools import repeat
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from basicsr.ops.dcn import ModulatedDeformConvPack, modulated_deform_conv
-from basicsr.utils import get_root_logger
-
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
- """Initialize network weights.
-
- Args:
- module_list (list[nn.Module] | nn.Module): Modules to be initialized.
- scale (float): Scale initialized weights, especially for residual
- blocks. Default: 1.
- bias_fill (float): The value to fill bias. Default: 0
- kwargs (dict): Other arguments for initialization function.
- """
- if not isinstance(module_list, list):
- module_list = [module_list]
- for module in module_list:
- for m in module.modules():
- if isinstance(m, nn.Conv2d):
- init.kaiming_normal_(m.weight, **kwargs)
- m.weight.data *= scale
- if m.bias is not None:
- m.bias.data.fill_(bias_fill)
- elif isinstance(m, nn.Linear):
- init.kaiming_normal_(m.weight, **kwargs)
- m.weight.data *= scale
- if m.bias is not None:
- m.bias.data.fill_(bias_fill)
- elif isinstance(m, _BatchNorm):
- init.constant_(m.weight, 1)
- if m.bias is not None:
- m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
- """Make layers by stacking the same blocks.
-
- Args:
- basic_block (nn.module): nn.module class for basic block.
- num_basic_block (int): number of blocks.
-
- Returns:
- nn.Sequential: Stacked blocks in nn.Sequential.
- """
- layers = []
- for _ in range(num_basic_block):
- layers.append(basic_block(**kwarg))
- return nn.Sequential(*layers)
-
-
-class ResidualBlockNoBN(nn.Module):
- """Residual block without BN.
-
- It has a style of:
- ---Conv-ReLU-Conv-+-
- |________________|
-
- Args:
- num_feat (int): Channel number of intermediate features.
- Default: 64.
- res_scale (float): Residual scale. Default: 1.
- pytorch_init (bool): If set to True, use pytorch default init,
- otherwise, use default_init_weights. Default: False.
- """
-
- def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
- super(ResidualBlockNoBN, self).__init__()
- self.res_scale = res_scale
- self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
- self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
- self.relu = nn.ReLU(inplace=True)
-
- if not pytorch_init:
- default_init_weights([self.conv1, self.conv2], 0.1)
-
- def forward(self, x):
- identity = x
- out = self.conv2(self.relu(self.conv1(x)))
- return identity + out * self.res_scale
-
-
-class Upsample(nn.Sequential):
- """Upsample module.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
- """
-
- def __init__(self, scale, num_feat):
- m = []
- if (scale & (scale - 1)) == 0: # scale = 2^n
- for _ in range(int(math.log(scale, 2))):
- m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(2))
- elif scale == 3:
- m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(3))
- else:
- raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
- super(Upsample, self).__init__(*m)
-
-
-def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
- """Warp an image or feature map with optical flow.
-
- Args:
- x (Tensor): Tensor with size (n, c, h, w).
- flow (Tensor): Tensor with size (n, h, w, 2), normal value.
- interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
- padding_mode (str): 'zeros' or 'border' or 'reflection'.
- Default: 'zeros'.
- align_corners (bool): Before pytorch 1.3, the default value is
- align_corners=True. After pytorch 1.3, the default value is
- align_corners=False. Here, we use the True as default.
-
- Returns:
- Tensor: Warped image or feature map.
- """
- assert x.size()[-2:] == flow.size()[1:3]
- _, _, h, w = x.size()
- # create mesh grid
- grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
- grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2
- grid.requires_grad = False
-
- vgrid = grid + flow
- # scale grid to [-1,1]
- vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
- vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
- vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
- output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
-
- # TODO, what if align_corners=False
- return output
-
-
-def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
- """Resize a flow according to ratio or shape.
-
- Args:
- flow (Tensor): Precomputed flow. shape [N, 2, H, W].
- size_type (str): 'ratio' or 'shape'.
- sizes (list[int | float]): the ratio for resizing or the final output
- shape.
- 1) The order of ratio should be [ratio_h, ratio_w]. For
- downsampling, the ratio should be smaller than 1.0 (i.e., ratio
- < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
- ratio > 1.0).
- 2) The order of output_size should be [out_h, out_w].
- interp_mode (str): The mode of interpolation for resizing.
- Default: 'bilinear'.
- align_corners (bool): Whether align corners. Default: False.
-
- Returns:
- Tensor: Resized flow.
- """
- _, _, flow_h, flow_w = flow.size()
- if size_type == 'ratio':
- output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
- elif size_type == 'shape':
- output_h, output_w = sizes[0], sizes[1]
- else:
- raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
-
- input_flow = flow.clone()
- ratio_h = output_h / flow_h
- ratio_w = output_w / flow_w
- input_flow[:, 0, :, :] *= ratio_w
- input_flow[:, 1, :, :] *= ratio_h
- resized_flow = F.interpolate(
- input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
- return resized_flow
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x, scale):
- """ Pixel unshuffle.
-
- Args:
- x (Tensor): Input feature with shape (b, c, hh, hw).
- scale (int): Downsample ratio.
-
- Returns:
- Tensor: the pixel unshuffled feature.
- """
- b, c, hh, hw = x.size()
- out_channel = c * (scale**2)
- assert hh % scale == 0 and hw % scale == 0
- h = hh // scale
- w = hw // scale
- x_view = x.view(b, c, h, scale, w, scale)
- return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-class DCNv2Pack(ModulatedDeformConvPack):
- """Modulated deformable conv for deformable alignment.
-
- Different from the official DCNv2Pack, which generates offsets and masks
- from the preceding features, this DCNv2Pack takes another different
- features to generate offsets and masks.
-
- Ref:
- Delving Deep into Deformable Alignment in Video Super-Resolution.
- """
-
- def forward(self, x, feat):
- out = self.conv_offset(feat)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
-
- offset_absmean = torch.mean(torch.abs(offset))
- if offset_absmean > 50:
- logger = get_root_logger()
- logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.')
-
- if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'):
- return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
- self.dilation, mask)
- else:
- return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding,
- self.dilation, self.groups, self.deformable_groups)
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
- # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
- # Cut & paste from PyTorch official master until it's in a few official releases - RW
- # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
- def norm_cdf(x):
- # Computes standard normal cumulative distribution function
- return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
- if (mean < a - 2 * std) or (mean > b + 2 * std):
- warnings.warn(
- 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
- 'The distribution of values may be incorrect.',
- stacklevel=2)
-
- with torch.no_grad():
- # Values are generated by using a truncated uniform distribution and
- # then using the inverse CDF for the normal distribution.
- # Get upper and lower cdf values
- low = norm_cdf((a - mean) / std)
- up = norm_cdf((b - mean) / std)
-
- # Uniformly fill tensor with values from [low, up], then translate to
- # [2l-1, 2u-1].
- tensor.uniform_(2 * low - 1, 2 * up - 1)
-
- # Use inverse cdf transform for normal distribution to get truncated
- # standard normal
- tensor.erfinv_()
-
- # Transform to proper mean, std
- tensor.mul_(std * math.sqrt(2.))
- tensor.add_(mean)
-
- # Clamp to ensure it's in the proper range
- tensor.clamp_(min=a, max=b)
- return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
- r"""Fills the input Tensor with values drawn from a truncated
- normal distribution.
-
- From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
-
- The values are effectively drawn from the
- normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
- with values outside :math:`[a, b]` redrawn until they are within
- the bounds. The method used for generating the random values works
- best when :math:`a \leq \text{mean} \leq b`.
-
- Args:
- tensor: an n-dimensional `torch.Tensor`
- mean: the mean of the normal distribution
- std: the standard deviation of the normal distribution
- a: the minimum cutoff value
- b: the maximum cutoff value
-
- Examples:
- >>> w = torch.empty(3, 5)
- >>> nn.init.trunc_normal_(w)
- """
- return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-# From PyTorch
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
\ No newline at end of file
diff --git a/spaces/cscan/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/datasets.py b/spaces/cscan/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/datasets.py
deleted file mode 100644
index e672b136f56fd6b05038e24377908361a54fe519..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/datasets.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import cv2
-import numpy as np
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True):
- # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
- shape = img.shape[:2] # current shape [height, width]
- if isinstance(new_shape, int):
- new_shape = (new_shape, new_shape)
-
- # Scale ratio (new / old)
- r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
- if not scaleup: # only scale down, do not scale up (for better test mAP)
- r = min(r, 1.0)
-
- # Compute padding
- ratio = r, r # width, height ratios
- new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
- dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
- if auto: # minimum rectangle
- dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
- elif scale_fill: # stretch
- dw, dh = 0.0, 0.0
- new_unpad = (new_shape[1], new_shape[0])
- ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
-
- dw /= 2 # divide padding into 2 sides
- dh /= 2
-
- if shape[::-1] != new_unpad: # resize
- img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
- top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
- left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
- img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
- return img, ratio, (dw, dh)
diff --git a/spaces/csuhan/opendet2/opendet2/engine/defaults.py b/spaces/csuhan/opendet2/opendet2/engine/defaults.py
deleted file mode 100644
index b7ae381add473c83b8096ee531cae8cb9a404cff..0000000000000000000000000000000000000000
--- a/spaces/csuhan/opendet2/opendet2/engine/defaults.py
+++ /dev/null
@@ -1,441 +0,0 @@
-import logging
-import os
-import weakref
-from collections import OrderedDict
-from typing import Dict
-
-import torch
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import CfgNode
-from detectron2.data import MetadataCatalog
-from detectron2.engine import (AMPTrainer, SimpleTrainer,
- TrainerBase, create_ddp_model, hooks, create_ddp_model, default_writers)
-from detectron2.evaluation import (DatasetEvaluator, DatasetEvaluators,
- inference_on_dataset, print_csv_format,
- verify_results)
-from detectron2.modeling import GeneralizedRCNNWithTTA, build_model
-from detectron2.solver import build_lr_scheduler
-from detectron2.utils import comm
-from detectron2.utils.logger import setup_logger
-from fvcore.nn.precise_bn import get_bn_modules
-
-from ..data import build_detection_test_loader, build_detection_train_loader
-from ..evaluation import PascalVOCDetectionEvaluator
-from ..solver import build_optimizer
-
-
-class OpenDetTrainer(TrainerBase):
- """
- A trainer with default training logic. It does the following:
-
- 1. Create a :class:`SimpleTrainer` using model, optimizer, dataloader
- defined by the given config. Create a LR scheduler defined by the config.
- 2. Load the last checkpoint or `cfg.MODEL.WEIGHTS`, if exists, when
- `resume_or_load` is called.
- 3. Register a few common hooks defined by the config.
-
- It is created to simplify the **standard model training workflow** and reduce code boilerplate
- for users who only need the standard training workflow, with standard features.
- It means this class makes *many assumptions* about your training logic that
- may easily become invalid in a new research. In fact, any assumptions beyond those made in the
- :class:`SimpleTrainer` are too much for research.
-
- The code of this class has been annotated about restrictive assumptions it makes.
- When they do not work for you, you're encouraged to:
-
- 1. Overwrite methods of this class, OR:
- 2. Use :class:`SimpleTrainer`, which only does minimal SGD training and
- nothing else. You can then add your own hooks if needed. OR:
- 3. Write your own training loop similar to `tools/plain_train_net.py`.
-
- See the :doc:`/tutorials/training` tutorials for more details.
-
- Note that the behavior of this class, like other functions/classes in
- this file, is not stable, since it is meant to represent the "common default behavior".
- It is only guaranteed to work well with the standard models and training workflow in detectron2.
- To obtain more stable behavior, write your own training logic with other public APIs.
-
- Examples:
- ::
- trainer = DefaultTrainer(cfg)
- trainer.resume_or_load() # load last checkpoint or MODEL.WEIGHTS
- trainer.train()
-
- Attributes:
- scheduler:
- checkpointer (DetectionCheckpointer):
- cfg (CfgNode):
- """
-
- def __init__(self, cfg):
- """
- Args:
- cfg (CfgNode):
- """
- super().__init__()
- logger = logging.getLogger("detectron2")
- # setup_logger is not called for d2
- if not logger.isEnabledFor(logging.INFO):
- setup_logger()
- cfg = OpenDetTrainer.auto_scale_workers(cfg, comm.get_world_size())
-
- # Assume these objects must be constructed in this order.
- model = self.build_model(cfg)
- optimizer = self.build_optimizer(cfg, model)
- data_loader = self.build_train_loader(cfg)
-
- model = create_ddp_model(
- model, broadcast_buffers=False, find_unused_parameters=True)
- # model = create_ddp_model(model, broadcast_buffers=False)
- self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
- model, data_loader, optimizer
- )
-
- self.scheduler = self.build_lr_scheduler(cfg, optimizer)
- self.checkpointer = DetectionCheckpointer(
- # Assume you want to save checkpoints together with logs/statistics
- model,
- cfg.OUTPUT_DIR,
- trainer=weakref.proxy(self),
- )
- self.start_iter = 0
- self.max_iter = cfg.SOLVER.MAX_ITER
- self.cfg = cfg
-
- self.register_hooks(self.build_hooks())
-
- def resume_or_load(self, resume=True):
- """
- If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by
- a `last_checkpoint` file), resume from the file. Resuming means loading all
- available states (eg. optimizer and scheduler) and update iteration counter
- from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.
-
- Otherwise, this is considered as an independent training. The method will load model
- weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start
- from iteration 0.
-
- Args:
- resume (bool): whether to do resume or not
- """
- self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)
- if resume and self.checkpointer.has_checkpoint():
- # The checkpoint stores the training iteration that just finished, thus we start
- # at the next iteration
- self.start_iter = self.iter + 1
-
- def build_hooks(self):
- """
- Build a list of default hooks, including timing, evaluation,
- checkpointing, lr scheduling, precise BN, writing events.
-
- Returns:
- list[HookBase]:
- """
- cfg = self.cfg.clone()
- cfg.defrost()
- cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
-
- ret = [
- hooks.IterationTimer(),
- hooks.LRScheduler(),
- hooks.PreciseBN(
- # Run at the same freq as (but before) evaluation.
- cfg.TEST.EVAL_PERIOD,
- self.model,
- # Build a new data loader to not affect training
- self.build_train_loader(cfg),
- cfg.TEST.PRECISE_BN.NUM_ITER,
- )
- if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
- else None,
- ]
-
- # Do PreciseBN before checkpointer, because it updates the model and need to
- # be saved by checkpointer.
- # This is not always the best: if checkpointing has a different frequency,
- # some checkpoints may have more precise statistics than others.
- if comm.is_main_process():
- ret.append(hooks.PeriodicCheckpointer(
- self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))
-
- def test_and_save_results():
- self._last_eval_results = self.test(self.cfg, self.model)
- return self._last_eval_results
-
- # Do evaluation after checkpointer, because then if it fails,
- # we can use the saved checkpoint to debug.
- ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
-
- if comm.is_main_process():
- # Here the default print/log frequency of each writer is used.
- # run writers in the end, so that evaluation metrics are written
- ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
- return ret
-
- def build_writers(self):
- """
- Build a list of writers to be used using :func:`default_writers()`.
- If you'd like a different list of writers, you can overwrite it in
- your trainer.
-
- Returns:
- list[EventWriter]: a list of :class:`EventWriter` objects.
- """
- return default_writers(self.cfg.OUTPUT_DIR, self.max_iter)
-
- def train(self):
- """
- Run training.
-
- Returns:
- OrderedDict of results, if evaluation is enabled. Otherwise None.
- """
- super().train(self.start_iter, self.max_iter)
- if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process():
- assert hasattr(
- self, "_last_eval_results"
- ), "No evaluation results obtained during training!"
- verify_results(self.cfg, self._last_eval_results)
- return self._last_eval_results
-
- def run_step(self):
- self._trainer.iter = self.iter
- self._trainer.run_step()
-
- @classmethod
- def build_model(cls, cfg):
- """
- Returns:
- torch.nn.Module:
-
- It now calls :func:`detectron2.modeling.build_model`.
- Overwrite it if you'd like a different model.
- """
- model = build_model(cfg)
- logger = logging.getLogger(__name__)
- logger.info("Model:\n{}".format(model))
- return model
-
- @classmethod
- def build_optimizer(cls, cfg, model):
- """
- Returns:
- torch.optim.Optimizer:
-
- It now calls :func:`detectron2.solver.build_optimizer`.
- Overwrite it if you'd like a different optimizer.
- """
- return build_optimizer(cfg, model)
-
- @classmethod
- def build_lr_scheduler(cls, cfg, optimizer):
- """
- It now calls :func:`detectron2.solver.build_lr_scheduler`.
- Overwrite it if you'd like a different scheduler.
- """
- return build_lr_scheduler(cfg, optimizer)
-
- @classmethod
- def build_train_loader(cls, cfg):
- """
- Returns:
- iterable
-
- It now calls :func:`detectron2.data.build_detection_train_loader`.
- Overwrite it if you'd like a different data loader.
- """
- return build_detection_train_loader(cfg)
-
- @classmethod
- def build_test_loader(cls, cfg, dataset_name):
- """
- Returns:
- iterable
-
- It now calls :func:`detectron2.data.build_detection_test_loader`.
- Overwrite it if you'd like a different data loader.
- """
- return build_detection_test_loader(cfg, dataset_name)
-
- @classmethod
- def build_evaluator(cls, cfg, dataset_name, output_folder=None):
- if output_folder is None:
- output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
- evaluator_list = []
- evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
-
- if evaluator_type == "pascal_voc":
- return PascalVOCDetectionEvaluator(dataset_name, cfg)
-
- if len(evaluator_list) == 0:
- raise NotImplementedError(
- "no Evaluator for the dataset {} with the type {}".format(
- dataset_name, evaluator_type
- )
- )
- elif len(evaluator_list) == 1:
- return evaluator_list[0]
- return DatasetEvaluators(evaluator_list)
-
- @classmethod
- def test_with_TTA(cls, cfg, model):
- logger = logging.getLogger("detectron2.trainer")
- # In the end of training, run an evaluation with TTA
- # Only support some R-CNN models.
- logger.info("Running inference with test-time augmentation ...")
- model = GeneralizedRCNNWithTTA(cfg, model)
- evaluators = [
- cls.build_evaluator(
- cfg, name, output_folder=os.path.join(
- cfg.OUTPUT_DIR, "inference_TTA")
- )
- for name in cfg.DATASETS.TEST
- ]
- res = cls.test(cfg, model, evaluators)
- res = OrderedDict({k + "_TTA": v for k, v in res.items()})
- return res
-
- @classmethod
- def test(cls, cfg, model, evaluators=None):
- """
- Args:
- cfg (CfgNode):
- model (nn.Module):
- evaluators (list[DatasetEvaluator] or None): if None, will call
- :meth:`build_evaluator`. Otherwise, must have the same length as
- ``cfg.DATASETS.TEST``.
-
- Returns:
- dict: a dict of result metrics
- """
- logger = logging.getLogger(__name__)
- if isinstance(evaluators, DatasetEvaluator):
- evaluators = [evaluators]
- if evaluators is not None:
- assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
- len(cfg.DATASETS.TEST), len(evaluators)
- )
-
- results = OrderedDict()
- for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
- data_loader = cls.build_test_loader(cfg, dataset_name)
- # When evaluators are passed in as arguments,
- # implicitly assume that evaluators can be created before data_loader.
- if evaluators is not None:
- evaluator = evaluators[idx]
- else:
- try:
- evaluator = cls.build_evaluator(cfg, dataset_name)
- except NotImplementedError:
- logger.warn(
- "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
- "or implement its `build_evaluator` method."
- )
- results[dataset_name] = {}
- continue
- results_i = inference_on_dataset(model, data_loader, evaluator)
- results[dataset_name] = results_i
- if comm.is_main_process():
- assert isinstance(
- results_i, dict
- ), "Evaluator must return a dict on the main process. Got {} instead.".format(
- results_i
- )
- logger.info(
- "Evaluation results for {} in csv format:".format(dataset_name))
- print_csv_format(results_i)
-
- if len(results) == 1:
- results = list(results.values())[0]
- return results
-
- @staticmethod
- def auto_scale_workers(cfg, num_workers: int):
- """
- When the config is defined for certain number of workers (according to
- ``cfg.SOLVER.REFERENCE_WORLD_SIZE``) that's different from the number of
- workers currently in use, returns a new cfg where the total batch size
- is scaled so that the per-GPU batch size stays the same as the
- original ``IMS_PER_BATCH // REFERENCE_WORLD_SIZE``.
-
- Other config options are also scaled accordingly:
- * training steps and warmup steps are scaled inverse proportionally.
- * learning rate are scaled proportionally, following :paper:`ImageNet in 1h`.
-
- For example, with the original config like the following:
-
- .. code-block:: yaml
-
- IMS_PER_BATCH: 16
- BASE_LR: 0.1
- REFERENCE_WORLD_SIZE: 8
- MAX_ITER: 5000
- STEPS: (4000,)
- CHECKPOINT_PERIOD: 1000
-
- When this config is used on 16 GPUs instead of the reference number 8,
- calling this method will return a new config with:
-
- .. code-block:: yaml
-
- IMS_PER_BATCH: 32
- BASE_LR: 0.2
- REFERENCE_WORLD_SIZE: 16
- MAX_ITER: 2500
- STEPS: (2000,)
- CHECKPOINT_PERIOD: 500
-
- Note that both the original config and this new config can be trained on 16 GPUs.
- It's up to user whether to enable this feature (by setting ``REFERENCE_WORLD_SIZE``).
-
- Returns:
- CfgNode: a new config. Same as original if ``cfg.SOLVER.REFERENCE_WORLD_SIZE==0``.
- """
- old_world_size = cfg.SOLVER.REFERENCE_WORLD_SIZE
- if old_world_size == 0 or old_world_size == num_workers:
- return cfg
- cfg = cfg.clone()
- frozen = cfg.is_frozen()
- cfg.defrost()
-
- assert (
- cfg.SOLVER.IMS_PER_BATCH % old_world_size == 0
- ), "Invalid REFERENCE_WORLD_SIZE in config!"
- scale = num_workers / old_world_size
- bs = cfg.SOLVER.IMS_PER_BATCH = int(
- round(cfg.SOLVER.IMS_PER_BATCH * scale))
- lr = cfg.SOLVER.BASE_LR = cfg.SOLVER.BASE_LR * scale
- max_iter = cfg.SOLVER.MAX_ITER = int(
- round(cfg.SOLVER.MAX_ITER / scale))
- warmup_iter = cfg.SOLVER.WARMUP_ITERS = int(
- round(cfg.SOLVER.WARMUP_ITERS / scale))
- cfg.SOLVER.STEPS = tuple(int(round(s / scale))
- for s in cfg.SOLVER.STEPS)
- cfg.TEST.EVAL_PERIOD = int(round(cfg.TEST.EVAL_PERIOD / scale))
- cfg.SOLVER.CHECKPOINT_PERIOD = int(
- round(cfg.SOLVER.CHECKPOINT_PERIOD / scale))
- cfg.SOLVER.REFERENCE_WORLD_SIZE = num_workers # maintain invariant
- logger = logging.getLogger(__name__)
- logger.info(
- f"Auto-scaling the config to batch_size={bs}, learning_rate={lr}, "
- f"max_iter={max_iter}, warmup={warmup_iter}."
- )
-
- if frozen:
- cfg.freeze()
- return cfg
-
-
-# Access basic attributes from the underlying trainer
-for _attr in ["model", "data_loader", "optimizer"]:
- setattr(
- OpenDetTrainer,
- _attr,
- property(
- # getter
- lambda self, x=_attr: getattr(self._trainer, x),
- # setter
- lambda self, value, x=_attr: setattr(self._trainer, x, value),
- ),
- )
diff --git a/spaces/cupkake14/bean_vit_classifier/app.py b/spaces/cupkake14/bean_vit_classifier/app.py
deleted file mode 100644
index 021beb8126aec0492b19046f3b6915023e1315c8..0000000000000000000000000000000000000000
--- a/spaces/cupkake14/bean_vit_classifier/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gradio as gr
-import datasets
-import transformers
-import torch
-
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-
-dataset = datasets.load_dataset('beans')
-
-extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
-model = AutoModelForImageClassification.from_pretrained("saved_model_files")
-
-labels = dataset['train'].features['labels'].names
-
-def classify(im):
- features = extractor(im, return_tensors='pt')
- with torch.no_grad():
- logits = model(**features).logits
- probability = torch.nn.functional.softmax(logits, dim=-1)
- probs = probability[0].detach().numpy()
- confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
- return confidences
-
-
-gr.Interface(fn = classify,
- inputs = "image",
- outputs = "label",
- examples = "examples",
- title='Leaf classification on beans dataset',
- description='Fine-tuning a ViT for bean plant health classification'
- ).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/python/dqn/dqn.py b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/python/dqn/dqn.py
deleted file mode 100644
index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/python/dqn/dqn.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
-
-import gym
-import numpy as np
-import torch as th
-from torch.nn import functional as F
-
-from stable_baselines3.common import logger
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.preprocessing import maybe_transpose
-from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
-from stable_baselines3.dqn.policies import DQNPolicy
-
-
-class DQN(OffPolicyAlgorithm):
- """
- Deep Q-Network (DQN)
-
- Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
- Default hyperparameters are taken from the nature paper,
- except for the optimizer and learning rate that were taken from Stable Baselines defaults.
-
- :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
- :param env: The environment to learn from (if registered in Gym, can be str)
- :param learning_rate: The learning rate, it can be a function
- of the current progress remaining (from 1 to 0)
- :param buffer_size: size of the replay buffer
- :param learning_starts: how many steps of the model to collect transitions for before learning starts
- :param batch_size: Minibatch size for each gradient update
- :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
- :param gamma: the discount factor
- :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
- like ``(5, "step")`` or ``(2, "episode")``.
- :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
- Set to ``-1`` means to do as many gradient steps as steps done in the environment
- during the rollout.
- :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
- at a cost of more complexity.
- See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
- :param target_update_interval: update the target network every ``target_update_interval``
- environment steps.
- :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
- :param exploration_initial_eps: initial value of random action probability
- :param exploration_final_eps: final value of random action probability
- :param max_grad_norm: The maximum value for the gradient clipping
- :param tensorboard_log: the log location for tensorboard (if None, no logging)
- :param create_eval_env: Whether to create a second environment that will be
- used for evaluating the agent periodically. (Only available when passing string for the environment)
- :param policy_kwargs: additional arguments to be passed to the policy on creation
- :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
- :param seed: Seed for the pseudo random generators
- :param device: Device (cpu, cuda, ...) on which the code should be run.
- Setting it to auto, the code will be run on the GPU if possible.
- :param _init_setup_model: Whether or not to build the network at the creation of the instance
- """
-
- def __init__(
- self,
- policy: Union[str, Type[DQNPolicy]],
- env: Union[GymEnv, str],
- learning_rate: Union[float, Schedule] = 1e-4,
- buffer_size: int = 1000000,
- learning_starts: int = 50000,
- batch_size: Optional[int] = 32,
- tau: float = 1.0,
- gamma: float = 0.99,
- train_freq: Union[int, Tuple[int, str]] = 4,
- gradient_steps: int = 1,
- optimize_memory_usage: bool = False,
- target_update_interval: int = 10000,
- exploration_fraction: float = 0.1,
- exploration_initial_eps: float = 1.0,
- exploration_final_eps: float = 0.05,
- max_grad_norm: float = 10,
- tensorboard_log: Optional[str] = None,
- create_eval_env: bool = False,
- policy_kwargs: Optional[Dict[str, Any]] = None,
- verbose: int = 0,
- seed: Optional[int] = None,
- device: Union[th.device, str] = "auto",
- _init_setup_model: bool = True,
- ):
-
- super(DQN, self).__init__(
- policy,
- env,
- DQNPolicy,
- learning_rate,
- buffer_size,
- learning_starts,
- batch_size,
- tau,
- gamma,
- train_freq,
- gradient_steps,
- action_noise=None, # No action noise
- policy_kwargs=policy_kwargs,
- tensorboard_log=tensorboard_log,
- verbose=verbose,
- device=device,
- create_eval_env=create_eval_env,
- seed=seed,
- sde_support=False,
- optimize_memory_usage=optimize_memory_usage,
- supported_action_spaces=(gym.spaces.Discrete,),
- )
-
- self.exploration_initial_eps = exploration_initial_eps
- self.exploration_final_eps = exploration_final_eps
- self.exploration_fraction = exploration_fraction
- self.target_update_interval = target_update_interval
- self.max_grad_norm = max_grad_norm
- # "epsilon" for the epsilon-greedy exploration
- self.exploration_rate = 0.0
- # Linear schedule will be defined in `_setup_model()`
- self.exploration_schedule = None
- self.q_net, self.q_net_target = None, None
-
- if _init_setup_model:
- self._setup_model()
-
- def _setup_model(self) -> None:
- super(DQN, self)._setup_model()
- self._create_aliases()
- self.exploration_schedule = get_linear_fn(
- self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
- )
-
- def _create_aliases(self) -> None:
- self.q_net = self.policy.q_net
- self.q_net_target = self.policy.q_net_target
-
- def _on_step(self) -> None:
- """
- Update the exploration rate and target network if needed.
- This method is called in ``collect_rollouts()`` after each step in the environment.
- """
- if self.num_timesteps % self.target_update_interval == 0:
- polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
-
- self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
- logger.record("rollout/exploration rate", self.exploration_rate)
-
- def train(self, gradient_steps: int, batch_size: int = 100) -> None:
- # Update learning rate according to schedule
- self._update_learning_rate(self.policy.optimizer)
-
- losses = []
- for _ in range(gradient_steps):
- # Sample replay buffer
- replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
-
- with th.no_grad():
- # Compute the next Q-values using the target network
- next_q_values = self.q_net_target(replay_data.next_observations)
- # Follow greedy policy: use the one with the highest value
- next_q_values, _ = next_q_values.max(dim=1)
- # Avoid potential broadcast issue
- next_q_values = next_q_values.reshape(-1, 1)
- # 1-step TD target
- target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
-
- # Get current Q-values estimates
- current_q_values = self.q_net(replay_data.observations)
-
- # Retrieve the q-values for the actions from the replay buffer
- current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
-
- # Compute Huber loss (less sensitive to outliers)
- loss = F.smooth_l1_loss(current_q_values, target_q_values)
- losses.append(loss.item())
-
- # Optimize the policy
- self.policy.optimizer.zero_grad()
- loss.backward()
- # Clip gradient norm
- th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
- self.policy.optimizer.step()
-
- # Increase update counter
- self._n_updates += gradient_steps
-
- logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
- logger.record("train/loss", np.mean(losses))
-
- def predict(
- self,
- observation: np.ndarray,
- state: Optional[np.ndarray] = None,
- mask: Optional[np.ndarray] = None,
- deterministic: bool = False,
- ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
- """
- Overrides the base_class predict function to include epsilon-greedy exploration.
-
- :param observation: the input observation
- :param state: The last states (can be None, used in recurrent policies)
- :param mask: The last masks (can be None, used in recurrent policies)
- :param deterministic: Whether or not to return deterministic actions.
- :return: the model's action and the next state
- (used in recurrent policies)
- """
- if not deterministic and np.random.rand() < self.exploration_rate:
- if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
- n_batch = observation.shape[0]
- action = np.array([self.action_space.sample() for _ in range(n_batch)])
- else:
- action = np.array(self.action_space.sample())
- else:
- action, state = self.policy.predict(observation, state, mask, deterministic)
- return action, state
-
- def learn(
- self,
- total_timesteps: int,
- callback: MaybeCallback = None,
- log_interval: int = 4,
- eval_env: Optional[GymEnv] = None,
- eval_freq: int = -1,
- n_eval_episodes: int = 5,
- tb_log_name: str = "DQN",
- eval_log_path: Optional[str] = None,
- reset_num_timesteps: bool = True,
- ) -> OffPolicyAlgorithm:
-
- return super(DQN, self).learn(
- total_timesteps=total_timesteps,
- callback=callback,
- log_interval=log_interval,
- eval_env=eval_env,
- eval_freq=eval_freq,
- n_eval_episodes=n_eval_episodes,
- tb_log_name=tb_log_name,
- eval_log_path=eval_log_path,
- reset_num_timesteps=reset_num_timesteps,
- )
-
- def _excluded_save_params(self) -> List[str]:
- return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
-
- def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
- state_dicts = ["policy", "policy.optimizer"]
-
- return state_dicts, []
diff --git a/spaces/danterivers/music-generation-samples/audiocraft/modules/codebooks_patterns.py b/spaces/danterivers/music-generation-samples/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q']) # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]] # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
- """Base implementation of a pattern over a sequence with multiple codebooks.
-
- The codebook pattern consists in a layout, defining for each sequence step
- the list of coordinates of each codebook timestep in the resulting interleaved sequence.
- The first item of the pattern is always an empty list in order to properly insert a special token
- to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
- and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
- The pattern provides convenient methods to build and revert interleaved sequences from it:
- ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
- to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
- K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
- for the output sequence. The unfilled positions are replaced with a special token and the built sequence
- is returned along with a mask indicating valid tokens.
- ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
- of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
- to fill and specify invalid positions if needed.
- See the dedicated methods for more details.
- """
- # Pattern layout, for each sequence step, we have a list of coordinates
- # corresponding to the original codebook timestep and position.
- # The first list is always an empty list in order to properly insert
- # a special token to start with.
- layout: PatternLayout
- timesteps: int
- n_q: int
-
- def __post_init__(self):
- assert len(self.layout) > 0
- assert self.layout[0] == []
- self._validate_layout()
- self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
- self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
- logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
- def _validate_layout(self):
- """Runs checks on the layout to ensure a valid pattern is defined.
- A pattern is considered invalid if:
- - Multiple timesteps for a same codebook are defined in the same sequence step
- - The timesteps for a given codebook are not in ascending order as we advance in the sequence
- (this would mean that we have future timesteps before past timesteps).
- """
- q_timesteps = {q: 0 for q in range(self.n_q)}
- for s, seq_coords in enumerate(self.layout):
- if len(seq_coords) > 0:
- qs = set()
- for coord in seq_coords:
- qs.add(coord.q)
- last_q_timestep = q_timesteps[coord.q]
- assert coord.t >= last_q_timestep, \
- f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
- q_timesteps[coord.q] = coord.t
- # each sequence step contains at max 1 coordinate per codebook
- assert len(qs) == len(seq_coords), \
- f"Multiple entries for a same codebook are found at step {s}"
-
- @property
- def num_sequence_steps(self):
- return len(self.layout) - 1
-
- @property
- def max_delay(self):
- max_t_in_seq_coords = 0
- for seq_coords in self.layout[1:]:
- for coords in seq_coords:
- max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
- return max_t_in_seq_coords - self.timesteps
-
- @property
- def valid_layout(self):
- valid_step = len(self.layout) - self.max_delay
- return self.layout[:valid_step]
-
- def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
- """Get codebook coordinates in the layout that corresponds to the specified timestep t
- and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
- and the actual codebook coordinates.
- """
- assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
- if q is not None:
- assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
- coords = []
- for s, seq_codes in enumerate(self.layout):
- for code in seq_codes:
- if code.t == t and (q is None or code.q == q):
- coords.append((s, code))
- return coords
-
- def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
- return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
- def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
- steps_with_timesteps = self.get_steps_with_timestep(t, q)
- return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
- def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
- device: tp.Union[torch.device, str] = 'cpu'):
- """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
- Args:
- timesteps (int): Maximum number of timesteps steps to consider.
- keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
- device (Union[torch.device, str]): Device for created tensors.
- Returns:
- indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
- """
- assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
- assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
- # use the proper layout based on whether we limit ourselves to valid steps only or not,
- # note that using the valid_layout will result in a truncated sequence up to the valid steps
- ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
- # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
- indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
- mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
- # fill indexes with last sequence step value that will correspond to our special token
- # the last value is n_q * timesteps as we have flattened z and append special token as the last token
- # which will correspond to the index: n_q * timesteps
- indexes[:] = n_q * timesteps
- # iterate over the pattern and fill scattered indexes and mask
- for s, sequence_coords in enumerate(ref_layout):
- for coords in sequence_coords:
- if coords.t < timesteps:
- indexes[coords.q, s] = coords.t + coords.q * timesteps
- mask[coords.q, s] = 1
- indexes = torch.from_numpy(indexes).to(device)
- mask = torch.from_numpy(mask).to(device)
- return indexes, mask
-
- def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
- """Build sequence corresponding to the pattern from the input tensor z.
- The sequence is built using up to sequence_steps if specified, and non-pattern
- coordinates are filled with the special token.
-
- Args:
- z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
- special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
- keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
- Steps that are beyond valid steps will be replaced by the special_token in that case.
- Returns:
- values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
- corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
- indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
- """
- B, K, T = z.shape
- indexes, mask = self._build_pattern_sequence_scatter_indexes(
- T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
- )
- z = z.view(B, -1)
- # we append the special token as the last index of our flattened z tensor
- z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
- values = z[:, indexes.view(-1)]
- values = values.view(B, K, indexes.shape[-1])
- return values, indexes, mask
-
- def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
- keep_only_valid_steps: bool = False,
- is_model_output: bool = False,
- device: tp.Union[torch.device, str] = 'cpu'):
- """Builds scatter indexes required to retrieve the original multi-codebook sequence
- from interleaving pattern.
-
- Args:
- sequence_steps (int): Sequence steps.
- n_q (int): Number of codebooks.
- keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
- Steps that are beyond valid steps will be replaced by the special_token in that case.
- is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
- device (Union[torch.device, str]): Device for created tensors.
- Returns:
- torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
- """
- ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
- # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
- timesteps = self.timesteps
- assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
- assert sequence_steps <= len(ref_layout), \
- f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
- # ensure we take the appropriate indexes to keep the model output from the first special token as well
- if is_model_output:
- ref_layout = ref_layout[1:]
-
- # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
- indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
- mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
- # fill indexes with last sequence step value that will correspond to our special token
- indexes[:] = n_q * sequence_steps
- for s, sequence_codes in enumerate(ref_layout):
- if s < sequence_steps:
- for code in sequence_codes:
- if code.t < timesteps:
- indexes[code.q, code.t] = s + code.q * sequence_steps
- mask[code.q, code.t] = 1
- indexes = torch.from_numpy(indexes).to(device)
- mask = torch.from_numpy(mask).to(device)
- return indexes, mask
-
- def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
- """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
- The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
- are filled with the special token.
-
- Args:
- s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
- special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
- Returns:
- values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
- corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
- indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
- mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
- """
- B, K, S = s.shape
- indexes, mask = self._build_reverted_sequence_scatter_indexes(
- S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
- )
- s = s.view(B, -1)
- # we append the special token as the last index of our flattened z tensor
- s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
- values = s[:, indexes.view(-1)]
- values = values.view(B, K, indexes.shape[-1])
- return values, indexes, mask
-
- def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
- """Revert model logits obtained on a sequence built from the pattern
- back to a tensor matching the original sequence.
-
- This method is similar to ``revert_pattern_sequence`` with the following specificities:
- 1. It is designed to work with the extra cardinality dimension
- 2. We return the logits for the first sequence item that matches the special_token and
- which matching target in the original sequence is the first item of the sequence,
- while we skip the last logits as there is no matching target
- """
- B, card, K, S = logits.shape
- indexes, mask = self._build_reverted_sequence_scatter_indexes(
- S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
- )
- logits = logits.reshape(B, card, -1)
- # we append the special token as the last index of our flattened z tensor
- logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1) # [B, card, K x S]
- values = logits[:, :, indexes.view(-1)]
- values = values.view(B, card, K, indexes.shape[-1])
- return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
- """Abstraction around providing pattern for interleaving codebooks.
-
- The CodebooksPatternProvider abstraction allows to implement various strategies to
- define interleaving pattern of sequences composed of multiple codebooks. For a given
- number of codebooks `n_q`, the pattern provider can generate a specified pattern
- corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
- can be used to construct a new sequence from the original codes respecting the specified
- pattern. The pattern is defined as a list of list of code coordinates, code coordinate
- being a tuple with the original timestep and codebook to build the new sequence.
- Note that all patterns must start with an empty list that is then used to insert a first
- sequence step of special tokens in the newly generated sequence.
-
- Args:
- n_q (int): number of codebooks.
- cached (bool): if True, patterns for a given length are cached. In general
- that should be true for efficiency reason to avoid synchronization points.
- """
- def __init__(self, n_q: int, cached: bool = True):
- assert n_q > 0
- self.n_q = n_q
- self.get_pattern = lru_cache(100)(self.get_pattern) # type: ignore
-
- @abstractmethod
- def get_pattern(self, timesteps: int) -> Pattern:
- """Builds pattern with specific interleaving between codebooks.
-
- Args:
- timesteps (int): Total numer of timesteps.
- """
- raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
- """Provider for delayed pattern across delayed codebooks.
- Codebooks are delayed in the sequence and sequence steps will contain codebooks
- from different timesteps.
-
- Example:
- Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- The resulting sequence obtained from the returned pattern is:
- [[S, 1, 2, 3, 4],
- [S, S, 1, 2, 3],
- [S, S, S, 1, 2]]
- (with S being a special token)
-
- Args:
- n_q (int): Number of codebooks.
- delays (Optional[List[int]]): Delay for each of the codebooks.
- If delays not defined, each codebook is delayed by 1 compared to the previous one.
- flatten_first (int): Flatten the first N timesteps.
- empty_initial (int): Prepend with N empty list of coordinates.
- """
- def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
- flatten_first: int = 0, empty_initial: int = 0):
- super().__init__(n_q)
- if delays is None:
- delays = list(range(n_q))
- self.delays = delays
- self.flatten_first = flatten_first
- self.empty_initial = empty_initial
- assert len(self.delays) == self.n_q
- assert sorted(self.delays) == self.delays
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- max_delay = max(self.delays)
- if self.empty_initial:
- out += [[] for _ in range(self.empty_initial)]
- if self.flatten_first:
- for t in range(min(timesteps, self.flatten_first)):
- for q in range(self.n_q):
- out.append([LayoutCoord(t, q)])
- for t in range(self.flatten_first, timesteps + max_delay):
- v = []
- for q, delay in enumerate(self.delays):
- t_for_q = t - delay
- if t_for_q >= self.flatten_first:
- v.append(LayoutCoord(t_for_q, q))
- out.append(v)
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
- """Provider for parallel pattern across codebooks.
- This pattern provider is a special case of the delayed pattern with actually no delay,
- hence delays=repeat(0, n_q).
-
- Args:
- n_q (int): Number of codebooks.
- """
- def __init__(self, n_q: int):
- super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
- """Provider for unrolling codebooks pattern.
- This pattern provider enables to represent the codebook flattened completely or only to some extend
- while also specifying a given delay between the flattened codebooks representation, allowing to
- unroll the codebooks in the sequence.
-
- Example:
- 1. Flattening of the codebooks.
- By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
- taking n_q = 3 and timesteps = 4:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
- [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
- 2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
- for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
- taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
- [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
- 3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
- allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
- same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
- and delays = [0, 3, 3]:
- [[1, 2, 3, 4],
- [1, 2, 3, 4],
- [1, 2, 3, 4]]
- will result into:
- [[S, S, S, 1, S, 2, S, 3, S, 4],
- [S, S, S, 1, S, 2, S, 3, S, 4],
- [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
- Args:
- n_q (int): Number of codebooks.
- flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
- the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
- have n_q extra steps for each timestep.
- delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
- no delay is added and therefore will default to [0] * ``n_q``.
- Note that two codebooks that will be flattened to the same inner step
- should have the same delay, otherwise the pattern is considered as invalid.
- """
- FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
- def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
- delays: tp.Optional[tp.List[int]] = None):
- super().__init__(n_q)
- if flattening is None:
- flattening = list(range(n_q))
- if delays is None:
- delays = [0] * n_q
- assert len(flattening) == n_q
- assert len(delays) == n_q
- assert sorted(flattening) == flattening
- assert sorted(delays) == delays
- self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
- self.max_delay = max(delays)
-
- def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
- """Build a flattened codebooks representation as a dictionary of inner step
- and the actual codebook indices corresponding to the flattened codebook. For convenience, we
- also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
- """
- flattened_codebooks: dict = {}
- for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
- if inner_step not in flattened_codebooks:
- flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
- else:
- flat_codebook = flattened_codebooks[inner_step]
- assert flat_codebook.delay == delay, (
- "Delay and flattening between codebooks is inconsistent: ",
- "two codebooks flattened to the same position should have the same delay."
- )
- flat_codebook.codebooks.append(q)
- flattened_codebooks[inner_step] = flat_codebook
- return flattened_codebooks
-
- @property
- def _num_inner_steps(self):
- """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
- """
- return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
- def num_virtual_steps(self, timesteps: int) -> int:
- return timesteps * self._num_inner_steps + 1
-
- def get_pattern(self, timesteps: int) -> Pattern:
- """Builds pattern for delay across codebooks.
-
- Args:
- timesteps (int): Total numer of timesteps.
- """
- # the PatternLayout is built as a tuple of sequence position and list of coordinates
- # so that it can be reordered properly given the required delay between codebooks of given timesteps
- indexed_out: list = [(-1, [])]
- max_timesteps = timesteps + self.max_delay
- for t in range(max_timesteps):
- # for each timestep, we unroll the flattened codebooks,
- # emitting the sequence step with the corresponding delay
- for step in range(self._num_inner_steps):
- if step in self._flattened_codebooks:
- # we have codebooks at this virtual step to emit
- step_codebooks = self._flattened_codebooks[step]
- t_for_q = t + step_codebooks.delay
- coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
- if t_for_q < max_timesteps and t < max_timesteps:
- indexed_out.append((t_for_q, coords))
- else:
- # there is no codebook in this virtual step so we emit an empty list
- indexed_out.append((t, []))
- out = [coords for _, coords in sorted(indexed_out)]
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
- """Almost VALL-E style pattern. We futher allow some delays for the
- codebooks other than the first one.
-
- Args:
- n_q (int): Number of codebooks.
- delays (Optional[List[int]]): Delay for each of the codebooks.
- If delays not defined, each codebook is delayed by 1 compared to the previous one.
- """
- def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
- super().__init__(n_q)
- if delays is None:
- delays = [0] * (n_q - 1)
- self.delays = delays
- assert len(self.delays) == self.n_q - 1
- assert sorted(self.delays) == self.delays
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- for t in range(timesteps):
- out.append([LayoutCoord(t, 0)])
- max_delay = max(self.delays)
- for t in range(timesteps + max_delay):
- v = []
- for q, delay in enumerate(self.delays):
- t_for_q = t - delay
- if t_for_q >= 0:
- v.append(LayoutCoord(t_for_q, q + 1))
- out.append(v)
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
- """Almost MusicLM style pattern. This is equivalent to full flattening
- but in a different order.
-
- Args:
- n_q (int): Number of codebooks.
- group_by (int): Number of codebooks to group together.
- """
- def __init__(self, n_q: int, group_by: int = 2):
- super().__init__(n_q)
- self.group_by = group_by
-
- def get_pattern(self, timesteps: int) -> Pattern:
- out: PatternLayout = [[]]
- for offset in range(0, self.n_q, self.group_by):
- for t in range(timesteps):
- for q in range(offset, offset + self.group_by):
- out.append([LayoutCoord(t, q)])
- return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/__init__.py
deleted file mode 100644
index 9e7511148df475d077a1c0af4d8edf71a17a6c7b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiofiles/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Utilities for asyncio-friendly file handling."""
-from .threadpool import (
- open,
- stdin,
- stdout,
- stderr,
- stdin_bytes,
- stdout_bytes,
- stderr_bytes,
-)
-from . import tempfile
-
-__all__ = [
- "open",
- "tempfile",
- "stdin",
- "stdout",
- "stderr",
- "stdin_bytes",
- "stdout_bytes",
- "stderr_bytes",
-]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/reference.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/reference.py
deleted file mode 100644
index c960a59f9bcdb3fc13605e044866fc3d263de959..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/reference.py
+++ /dev/null
@@ -1,1080 +0,0 @@
-import base64
-import collections
-import io
-import itertools
-import logging
-import math
-import os
-from functools import lru_cache
-from typing import TYPE_CHECKING
-
-import fsspec.core
-
-try:
- import ujson as json
-except ImportError:
- if not TYPE_CHECKING:
- import json
-
-from ..asyn import AsyncFileSystem
-from ..callbacks import _DEFAULT_CALLBACK
-from ..core import filesystem, open, split_protocol
-from ..utils import isfilelike, merge_offset_ranges, other_paths
-
-logger = logging.getLogger("fsspec.reference")
-
-
-class ReferenceNotReachable(RuntimeError):
- def __init__(self, reference, target, *args):
- super().__init__(*args)
- self.reference = reference
- self.target = target
-
- def __str__(self):
- return f'Reference "{self.reference}" failed to fetch target {self.target}'
-
-
-def _first(d):
- return list(d.values())[0]
-
-
-def _prot_in_references(path, references):
- ref = references.get(path)
- if isinstance(ref, (list, tuple)):
- return split_protocol(ref[0])[0] if ref[0] else ref[0]
-
-
-def _protocol_groups(paths, references):
- if isinstance(paths, str):
- return {_prot_in_references(paths, references): [paths]}
- out = {}
- for path in paths:
- protocol = _prot_in_references(path, references)
- out.setdefault(protocol, []).append(path)
- return out
-
-
-class RefsValuesView(collections.abc.ValuesView):
- def __iter__(self):
- for val in self._mapping.zmetadata.values():
- yield json.dumps(val).encode()
- yield from self._mapping._items.values()
- for field in self._mapping.listdir():
- chunk_sizes = self._mapping._get_chunk_sizes(field)
- if len(chunk_sizes) == 0:
- yield self._mapping[field + "/0"]
- continue
- yield from self._mapping._generate_all_records(field)
-
-
-class RefsItemsView(collections.abc.ItemsView):
- def __iter__(self):
- return zip(self._mapping.keys(), self._mapping.values())
-
-
-def ravel_multi_index(idx, sizes):
- val = 0
- mult = 1
- for i, s in zip(idx[::-1], sizes[::-1]):
- val += i * mult
- mult *= s
- return val
-
-
-class LazyReferenceMapper(collections.abc.MutableMapping):
- """Interface to read parquet store as if it were a standard kerchunk
- references dict."""
-
- # import is class level to prevent numpy dep requirement for fsspec
- @property
- def np(self):
- import numpy as np
-
- return np
-
- @property
- def pd(self):
- import pandas as pd
-
- return pd
-
- def __init__(
- self, root, fs=None, out_root=None, cache_size=128, categorical_threshold=10
- ):
- """
- Parameters
- ----------
- root : str
- Root of parquet store
- fs : fsspec.AbstractFileSystem
- fsspec filesystem object, default is local filesystem.
- cache_size : int
- Maximum size of LRU cache, where cache_size*record_size denotes
- the total number of references that can be loaded in memory at once.
- """
- self.root = root
- self.chunk_sizes = {}
- self._items = {}
- self.dirs = None
- self.fs = fsspec.filesystem("file") if fs is None else fs
- with self.fs.open("/".join([self.root, ".zmetadata"]), "rb") as f:
- self._items[".zmetadata"] = f.read()
- met = json.loads(self._items[".zmetadata"])
- self.record_size = met["record_size"]
- self.zmetadata = met["metadata"]
- self.url = self.root + "/{field}/refs.{record}.parq"
- self.out_root = out_root or self.root
- self.cat_thresh = categorical_threshold
-
- # Define function to open and decompress refs
- @lru_cache(maxsize=cache_size)
- def open_refs(field, record):
- """cached parquet file loader"""
- path = self.url.format(field=field, record=record)
- with self.fs.open(path) as f:
- # TODO: since all we do is iterate, is arrow without pandas
- # better here?
- df = self.pd.read_parquet(f, engine="fastparquet")
- refs = {c: df[c].values for c in df.columns}
- return refs
-
- self.open_refs = open_refs
-
- @staticmethod
- def create(record_size, root, fs, **kwargs):
- met = {"metadata": {}, "record_size": record_size}
- fs.pipe("/".join([root, ".zmetadata"]), json.dumps(met).encode())
- return LazyReferenceMapper(root, fs, **kwargs)
-
- def listdir(self, basename=True):
- """List top-level directories"""
- if self.dirs is None:
- dirs = [p.split("/", 1)[0] for p in self.zmetadata]
- self.dirs = set(sorted(p for p in dirs if p and not p.startswith(".")))
- listing = self.dirs
- if basename:
- listing = [os.path.basename(path) for path in listing]
- return listing
-
- def ls(self, path="", detail=True):
- """Shortcut file listings"""
- if not path:
- dirnames = self.listdir()
- others = set(
- [".zmetadata"]
- + [name for name in self.zmetadata if "/" not in name]
- + [name for name in self._items if "/" not in name]
- )
- if detail is False:
- others.update(dirnames)
- return sorted(others)
- dirinfo = [
- {"name": name, "type": "directory", "size": 0} for name in dirnames
- ]
- fileinfo = [
- {
- "name": name,
- "type": "file",
- "size": len(
- json.dumps(self.zmetadata[name])
- if name in self.zmetadata
- else self._items[name]
- ),
- }
- for name in others
- ]
- return sorted(dirinfo + fileinfo, key=lambda s: s["name"])
- parts = path.split("/", 1)
- if len(parts) > 1:
- raise FileNotFoundError("Cannot list within directories right now")
- field = parts[0]
- others = set(
- [name for name in self.zmetadata if name.startswith(f"{path}/")]
- + [name for name in self._items if name.startswith(f"{path}/")]
- )
- fileinfo = [
- {
- "name": name,
- "type": "file",
- "size": len(
- json.dumps(self.zmetadata[name])
- if name in self.zmetadata
- else self._items[name]
- ),
- }
- for name in others
- ]
- keys = self._keys_in_field(field)
-
- if detail is False:
- return list(others) + list(keys)
- recs = self._generate_all_records(field)
- recinfo = [
- {"name": name, "type": "file", "size": rec[-1]}
- for name, rec in zip(keys, recs)
- if rec[0] # filters out path==None, deleted/missing
- ]
- return fileinfo + recinfo
-
- def _load_one_key(self, key):
- """Get the reference for one key
-
- Returns bytes, one-element list or three-element list.
- """
- if key in self._items:
- return self._items[key]
- elif key in self.zmetadata:
- return json.dumps(self.zmetadata[key]).encode()
- elif "/" not in key or self._is_meta(key):
- raise KeyError(key)
- field, sub_key = key.split("/")
- record, _, _ = self._key_to_record(key)
- maybe = self._items.get((field, key), {}).get(sub_key, False)
- if maybe is None:
- # explicitly deleted
- raise KeyError
- elif maybe:
- return maybe
-
- # Chunk keys can be loaded from row group and cached in LRU cache
- try:
- record, ri, chunk_size = self._key_to_record(key)
- if chunk_size == 0:
- return b""
- refs = self.open_refs(field, record)
- except (ValueError, TypeError, FileNotFoundError):
- raise KeyError(key)
- columns = ["path", "offset", "size", "raw"]
- selection = [refs[c][ri] if c in refs else None for c in columns]
- raw = selection[-1]
- if raw is not None:
- return raw
- if selection[0] is None:
- raise KeyError("This reference has been deleted")
- if selection[1:3] == [0, 0]:
- # URL only
- return selection[:1]
- # URL, offset, size
- return selection[:3]
-
- @lru_cache(4096)
- def _key_to_record(self, key):
- """Details needed to construct a reference for one key"""
- field, chunk = key.split("/")
- chunk_sizes = self._get_chunk_sizes(field)
- if len(chunk_sizes) == 0:
- return 0, 0, 0
- chunk_idx = [int(c) for c in chunk.split(".")]
- chunk_number = ravel_multi_index(chunk_idx, chunk_sizes)
- record = chunk_number // self.record_size
- ri = chunk_number % self.record_size
- return record, ri, len(chunk_sizes)
-
- def _get_chunk_sizes(self, field):
- """The number of chunks along each axis for a given field"""
- if field not in self.chunk_sizes:
- zarray = self.zmetadata[f"{field}/.zarray"]
- size_ratio = [
- math.ceil(s / c) for s, c in zip(zarray["shape"], zarray["chunks"])
- ]
- self.chunk_sizes[field] = size_ratio
- return self.chunk_sizes[field]
-
- def _generate_record(self, field, record):
- """The references for a given parquet file of a given field"""
- refs = self.open_refs(field, record)
- it = iter(zip(refs.values()))
- if len(refs) == 3:
- # All urls
- return (list(t) for t in it)
- elif len(refs) == 1:
- # All raws
- return refs["raw"]
- else:
- # Mix of urls and raws
- return (list(t[:3]) if not t[3] else t[3] for t in it)
-
- def _generate_all_records(self, field):
- """Load all the references within a field by iterating over the parquet files"""
- nrec = 1
- for ch in self._get_chunk_sizes(field):
- nrec *= ch
- nrec = math.ceil(nrec / self.record_size)
- for record in range(nrec):
- yield from self._generate_record(field, record)
-
- def values(self):
- return RefsValuesView(self)
-
- def items(self):
- return RefsItemsView(self)
-
- def __hash__(self):
- return id(self)
-
- @lru_cache(20)
- def __getitem__(self, key):
- return self._load_one_key(key)
-
- def __setitem__(self, key, value):
- if "/" in key and not self._is_meta(key):
- field, chunk = key.split("/")
- record, i, _ = self._key_to_record(key)
- subdict = self._items.setdefault((field, record), {})
- subdict[i] = value
- if len(subdict) == self.record_size:
- self.write(field, record)
- else:
- # metadata or top-level
- self._items[key] = value
- self.zmetadata[key] = json.loads(
- value.decode() if isinstance(value, bytes) else value
- )
-
- @staticmethod
- def _is_meta(key):
- return key.startswith(".z") or "/.z" in key
-
- def __delitem__(self, key):
- if key in self._items:
- del self._items[key]
- elif key in self.zmetadata:
- del self.zmetadata[key]
- else:
- if "/" in key and not self._is_meta(key):
- field, chunk = key.split("/")
- record, _, _ = self._key_to_record(key)
- subdict = self._items.setdefault((field, record), {})
- subdict[chunk] = None
- if len(subdict) == self.record_size:
- self.write(field, record)
- else:
- # metadata or top-level
- self._items[key] = None
-
- def write(self, field, record, base_url=None, storage_options=None):
- # extra requirements if writing
- import kerchunk.df
- import numpy as np
- import pandas as pd
-
- # TODO: if the dict is incomplete, also load records and merge in
- partition = self._items[(field, record)]
- fn = f"{base_url or self.out_root}/{field}/refs.{record}.parq"
-
- ####
- paths = np.full(self.record_size, np.nan, dtype="O")
- offsets = np.zeros(self.record_size, dtype="int64")
- sizes = np.zeros(self.record_size, dtype="int64")
- raws = np.full(self.record_size, np.nan, dtype="O")
- nraw = 0
- npath = 0
- for j, data in partition.items():
- if isinstance(data, list):
- npath += 1
- paths[j] = data[0]
- if len(data) > 1:
- offsets[j] = data[1]
- sizes[j] = data[2]
- else:
- nraw += 1
- raws[j] = kerchunk.df._proc_raw(data)
- # TODO: only save needed columns
- df = pd.DataFrame(
- dict(
- path=paths,
- offset=offsets,
- size=sizes,
- raw=raws,
- ),
- copy=False,
- )
- if df.path.count() / (df.path.nunique() or 1) > self.cat_thresh:
- df["path"] = df["path"].astype("category")
- object_encoding = dict(raw="bytes", path="utf8")
- has_nulls = ["path", "raw"]
-
- self.fs.mkdirs(f"{base_url or self.out_root}/{field}", exist_ok=True)
- df.to_parquet(
- fn,
- engine="fastparquet",
- storage_options=storage_options
- or getattr(self.fs, "storage_options", None),
- compression="zstd",
- index=False,
- stats=False,
- object_encoding=object_encoding,
- has_nulls=has_nulls,
- # **kwargs,
- )
- partition.clear()
- self._items.pop((field, record))
-
- def flush(self, base_url=None, storage_options=None):
- """Output any modified or deleted keys
-
- Parameters
- ----------
- base_url: str
- Location of the output
- """
- # write what we have so far and clear sub chunks
- for thing in list(self._items):
- if isinstance(thing, tuple):
- field, record = thing
- self.write(
- field,
- record,
- base_url=base_url,
- storage_options=storage_options,
- )
-
- # gather .zmetadata from self._items and write that too
- for k in list(self._items):
- if k != ".zmetadata" and ".z" in k:
- self.zmetadata[k] = json.loads(self._items.pop(k))
- met = {"metadata": self.zmetadata, "record_size": self.record_size}
- self._items[".zmetadata"] = json.dumps(met).encode()
- self.fs.pipe(
- "/".join([base_url or self.out_root, ".zmetadata"]),
- self._items[".zmetadata"],
- )
-
- # TODO: only clear those that we wrote to?
- self.open_refs.cache_clear()
-
- def __len__(self):
- # Caveat: This counts expected references, not actual
- count = 0
- for field in self.listdir():
- if field.startswith("."):
- count += 1
- else:
- chunk_sizes = self._get_chunk_sizes(field)
- nchunks = self.np.product(chunk_sizes)
- count += nchunks
- count += len(self.zmetadata) # all metadata keys
- count += len(self._items) # the metadata file itself
- return count
-
- def __iter__(self):
- # Caveat: Note that this generates all expected keys, but does not
- # account for reference keys that are missing.
- metas = set(self.zmetadata)
- metas.update(self._items)
- for bit in metas:
- if isinstance(bit, str):
- yield bit
- for field in self.listdir():
- yield from self._keys_in_field(field)
-
- def __contains__(self, item):
- try:
- self._load_one_key(item)
- return True
- except KeyError:
- return False
-
- def _keys_in_field(self, field):
- """List key names in given field
-
- Produces strings like "field/x.y" appropriate from the chunking of the array
- """
- chunk_sizes = self._get_chunk_sizes(field)
- if len(chunk_sizes) == 0:
- yield field + "/0"
- return
- inds = itertools.product(*(range(i) for i in chunk_sizes))
- for ind in inds:
- yield field + "/" + ".".join([str(c) for c in ind])
-
-
-class ReferenceFileSystem(AsyncFileSystem):
- """View byte ranges of some other file as a file system
- Initial version: single file system target, which must support
- async, and must allow start and end args in _cat_file. Later versions
- may allow multiple arbitrary URLs for the targets.
- This FileSystem is read-only. It is designed to be used with async
- targets (for now). This FileSystem only allows whole-file access, no
- ``open``. We do not get original file details from the target FS.
- Configuration is by passing a dict of references at init, or a URL to
- a JSON file containing the same; this dict
- can also contain concrete data for some set of paths.
- Reference dict format:
- {path0: bytes_data, path1: (target_url, offset, size)}
- https://github.com/fsspec/kerchunk/blob/main/README.md
- """
-
- protocol = "reference"
-
- def __init__(
- self,
- fo,
- target=None,
- ref_storage_args=None,
- target_protocol=None,
- target_options=None,
- remote_protocol=None,
- remote_options=None,
- fs=None,
- template_overrides=None,
- simple_templates=True,
- max_gap=64_000,
- max_block=256_000_000,
- cache_size=128,
- **kwargs,
- ):
- """
- Parameters
- ----------
- fo : dict or str
- The set of references to use for this instance, with a structure as above.
- If str referencing a JSON file, will use fsspec.open, in conjunction
- with target_options and target_protocol to open and parse JSON at this
- location. If a directory, then assume references are a set of parquet
- files to be loaded lazily.
- target : str
- For any references having target_url as None, this is the default file
- target to use
- ref_storage_args : dict
- If references is a str, use these kwargs for loading the JSON file.
- Deprecated: use target_options instead.
- target_protocol : str
- Used for loading the reference file, if it is a path. If None, protocol
- will be derived from the given path
- target_options : dict
- Extra FS options for loading the reference file ``fo``, if given as a path
- remote_protocol : str
- The protocol of the filesystem on which the references will be evaluated
- (unless fs is provided). If not given, will be derived from the first
- URL that has a protocol in the templates or in the references, in that
- order.
- remote_options : dict
- kwargs to go with remote_protocol
- fs : AbstractFileSystem | dict(str, (AbstractFileSystem | dict))
- Directly provide a file system(s):
- - a single filesystem instance
- - a dict of protocol:filesystem, where each value is either a filesystem
- instance, or a dict of kwargs that can be used to create in
- instance for the given protocol
-
- If this is given, remote_options and remote_protocol are ignored.
- template_overrides : dict
- Swap out any templates in the references file with these - useful for
- testing.
- simple_templates: bool
- Whether templates can be processed with simple replace (True) or if
- jinja is needed (False, much slower). All reference sets produced by
- ``kerchunk`` are simple in this sense, but the spec allows for complex.
- max_gap, max_block: int
- For merging multiple concurrent requests to the same remote file.
- Neighboring byte ranges will only be merged when their
- inter-range gap is <= ``max_gap``. Default is 64KB. Set to 0
- to only merge when it requires no extra bytes. Pass a negative
- number to disable merging, appropriate for local target files.
- Neighboring byte ranges will only be merged when the size of
- the aggregated range is <= ``max_block``. Default is 256MB.
- cache_size : int
- Maximum size of LRU cache, where cache_size*record_size denotes
- the total number of references that can be loaded in memory at once.
- Only used for lazily loaded references.
- kwargs : passed to parent class
- """
- super().__init__(**kwargs)
- self.target = target
- self.template_overrides = template_overrides
- self.simple_templates = simple_templates
- self.templates = {}
- self.fss = {}
- self._dircache = {}
- self.max_gap = max_gap
- self.max_block = max_block
- if isinstance(fo, str):
- dic = dict(
- **(ref_storage_args or target_options or {}), protocol=target_protocol
- )
- ref_fs, fo2 = fsspec.core.url_to_fs(fo, **dic)
- if ref_fs.isfile(fo):
- # text JSON
- with fsspec.open(fo, "rb", **dic) as f:
- logger.info("Read reference from URL %s", fo)
- text = json.load(f)
- self._process_references(text, template_overrides)
- else:
- # Lazy parquet refs
- logger.info("Open lazy reference dict from URL %s", fo)
- self.references = LazyReferenceMapper(
- fo2,
- fs=ref_fs,
- cache_size=cache_size,
- )
- else:
- # dictionaries
- self._process_references(fo, template_overrides)
- if isinstance(fs, dict):
- self.fss = {
- k: (
- fsspec.filesystem(k.split(":", 1)[0], **opts)
- if isinstance(opts, dict)
- else opts
- )
- for k, opts in fs.items()
- }
- if None not in self.fss:
- self.fss[None] = filesystem("file")
- return
- if fs is not None:
- # single remote FS
- remote_protocol = (
- fs.protocol[0] if isinstance(fs.protocol, tuple) else fs.protocol
- )
- self.fss[remote_protocol] = fs
-
- if remote_protocol is None:
- # get single protocol from any templates
- for ref in self.templates.values():
- if callable(ref):
- ref = ref()
- protocol, _ = fsspec.core.split_protocol(ref)
- if protocol and protocol not in self.fss:
- fs = filesystem(protocol, **(remote_options or {}))
- self.fss[protocol] = fs
- if remote_protocol is None:
- # get single protocol from references
- for ref in self.references.values():
- if callable(ref):
- ref = ref()
- if isinstance(ref, list) and ref[0]:
- protocol, _ = fsspec.core.split_protocol(ref[0])
- if protocol not in self.fss:
- fs = filesystem(protocol, **(remote_options or {}))
- self.fss[protocol] = fs
- # only use first remote URL
- break
-
- if remote_protocol and remote_protocol not in self.fss:
- fs = filesystem(remote_protocol, **(remote_options or {}))
- self.fss[remote_protocol] = fs
-
- self.fss[None] = fs or filesystem("file") # default one
-
- def _cat_common(self, path, start=None, end=None):
- path = self._strip_protocol(path)
- logger.debug(f"cat: {path}")
- try:
- part = self.references[path]
- except KeyError:
- raise FileNotFoundError(path)
- if isinstance(part, str):
- part = part.encode()
- if isinstance(part, bytes):
- logger.debug(f"Reference: {path}, type bytes")
- if part.startswith(b"base64:"):
- part = base64.b64decode(part[7:])
- return part, None, None
-
- if len(part) == 1:
- logger.debug(f"Reference: {path}, whole file => {part}")
- url = part[0]
- start1, end1 = start, end
- else:
- url, start0, size = part
- logger.debug(f"Reference: {path} => {url}, offset {start0}, size {size}")
- end0 = start0 + size
-
- if start is not None:
- if start >= 0:
- start1 = start0 + start
- else:
- start1 = end0 + start
- else:
- start1 = start0
- if end is not None:
- if end >= 0:
- end1 = start0 + end
- else:
- end1 = end0 + end
- else:
- end1 = end0
- if url is None:
- url = self.target
- return url, start1, end1
-
- async def _cat_file(self, path, start=None, end=None, **kwargs):
- part_or_url, start0, end0 = self._cat_common(path, start=start, end=end)
- if isinstance(part_or_url, bytes):
- return part_or_url[start:end]
- protocol, _ = split_protocol(part_or_url)
- try:
- await self.fss[protocol]._cat_file(part_or_url, start=start, end=end)
- except Exception as e:
- raise ReferenceNotReachable(path, part_or_url) from e
-
- def cat_file(self, path, start=None, end=None, **kwargs):
- part_or_url, start0, end0 = self._cat_common(path, start=start, end=end)
- if isinstance(part_or_url, bytes):
- return part_or_url[start:end]
- protocol, _ = split_protocol(part_or_url)
- try:
- return self.fss[protocol].cat_file(part_or_url, start=start0, end=end0)
- except Exception as e:
- raise ReferenceNotReachable(path, part_or_url) from e
-
- def pipe_file(self, path, value, **_):
- """Temporarily add binary data or reference as a file"""
- self.references[path] = value
-
- async def _get_file(self, rpath, lpath, **kwargs):
- if self.isdir(rpath):
- return os.makedirs(lpath, exist_ok=True)
- data = await self._cat_file(rpath)
- with open(lpath, "wb") as f:
- f.write(data)
-
- def get_file(self, rpath, lpath, callback=_DEFAULT_CALLBACK, **kwargs):
- if self.isdir(rpath):
- return os.makedirs(lpath, exist_ok=True)
- data = self.cat_file(rpath, **kwargs)
- callback.set_size(len(data))
- if isfilelike(lpath):
- lpath.write(data)
- else:
- with open(lpath, "wb") as f:
- f.write(data)
- callback.absolute_update(len(data))
-
- def get(self, rpath, lpath, recursive=False, **kwargs):
- if recursive:
- # trigger directory build
- self.ls("")
- rpath = self.expand_path(rpath, recursive=recursive)
- fs = fsspec.filesystem("file", auto_mkdir=True)
- targets = other_paths(rpath, lpath)
- if recursive:
- data = self.cat([r for r in rpath if not self.isdir(r)])
- else:
- data = self.cat(rpath)
- for remote, local in zip(rpath, targets):
- if remote in data:
- fs.pipe_file(local, data[remote])
-
- def cat(self, path, recursive=False, on_error="raise", **kwargs):
- if isinstance(path, str) and recursive:
- raise NotImplementedError
- if isinstance(path, list) and (recursive or any("*" in p for p in path)):
- raise NotImplementedError
- proto_dict = _protocol_groups(path, self.references)
- out = {}
- for proto, paths in proto_dict.items():
- fs = self.fss[proto]
- urls, starts, ends = [], [], []
- for p in paths:
- # find references or label not-found. Early exit if any not
- # found and on_error is "raise"
- try:
- u, s, e = self._cat_common(p)
- urls.append(u)
- starts.append(s)
- ends.append(e)
- except FileNotFoundError as err:
- if on_error == "raise":
- raise
- if on_error != "omit":
- out[p] = err
-
- # process references into form for merging
- urls2 = []
- starts2 = []
- ends2 = []
- paths2 = []
- whole_files = set()
- for u, s, e, p in zip(urls, starts, ends, paths):
- if isinstance(u, bytes):
- # data
- out[p] = u
- elif s is None:
- # whole file - limits are None, None, but no further
- # entries take for this file
- whole_files.add(u)
- urls2.append(u)
- starts2.append(s)
- ends2.append(e)
- paths2.append(p)
- for u, s, e, p in zip(urls, starts, ends, paths):
- # second run to account for files that are to be loaded whole
- if s is not None and u not in whole_files:
- urls2.append(u)
- starts2.append(s)
- ends2.append(e)
- paths2.append(p)
-
- # merge and fetch consolidated ranges
- new_paths, new_starts, new_ends = merge_offset_ranges(
- list(urls2),
- list(starts2),
- list(ends2),
- sort=True,
- max_gap=self.max_gap,
- max_block=self.max_block,
- )
- bytes_out = fs.cat_ranges(new_paths, new_starts, new_ends)
-
- # unbundle from merged bytes - simple approach
- for u, s, e, p in zip(urls, starts, ends, paths):
- if p in out:
- continue # was bytes, already handled
- for np, ns, ne, b in zip(new_paths, new_starts, new_ends, bytes_out):
- if np == u and (ns is None or ne is None):
- if isinstance(b, Exception):
- out[p] = b
- else:
- out[p] = b[s:e]
- elif np == u and s >= ns and e <= ne:
- if isinstance(b, Exception):
- out[p] = b
- else:
- out[p] = b[s - ns : (e - ne) or None]
-
- for k, v in out.copy().items():
- # these were valid references, but fetch failed, so transform exc
- if isinstance(v, Exception) and k in self.references:
- ex = out[k]
- new_ex = ReferenceNotReachable(k, self.references[k])
- new_ex.__cause__ = ex
- if on_error == "raise":
- raise new_ex
- elif on_error != "omit":
- out[k] = new_ex
-
- if len(out) == 1 and isinstance(path, str) and "*" not in path:
- return _first(out)
- return out
-
- def _process_references(self, references, template_overrides=None):
- vers = references.get("version", None)
- if vers is None:
- self._process_references0(references)
- elif vers == 1:
- self._process_references1(references, template_overrides=template_overrides)
- else:
- raise ValueError(f"Unknown reference spec version: {vers}")
- # TODO: we make dircache by iterating over all entries, but for Spec >= 1,
- # can replace with programmatic. Is it even needed for mapper interface?
-
- def _process_references0(self, references):
- """Make reference dict for Spec Version 0"""
- self.references = references
-
- def _process_references1(self, references, template_overrides=None):
- if not self.simple_templates or self.templates:
- import jinja2
- self.references = {}
- self._process_templates(references.get("templates", {}))
-
- @lru_cache(1000)
- def _render_jinja(u):
- return jinja2.Template(u).render(**self.templates)
-
- for k, v in references.get("refs", {}).items():
- if isinstance(v, str):
- if v.startswith("base64:"):
- self.references[k] = base64.b64decode(v[7:])
- self.references[k] = v
- elif self.templates:
- u = v[0]
- if "{{" in u:
- if self.simple_templates:
- u = (
- u.replace("{{", "{")
- .replace("}}", "}")
- .format(**self.templates)
- )
- else:
- u = _render_jinja(u)
- self.references[k] = [u] if len(v) == 1 else [u, v[1], v[2]]
- else:
- self.references[k] = v
- self.references.update(self._process_gen(references.get("gen", [])))
-
- def _process_templates(self, tmp):
- self.templates = {}
- if self.template_overrides is not None:
- tmp.update(self.template_overrides)
- for k, v in tmp.items():
- if "{{" in v:
- import jinja2
-
- self.templates[k] = lambda temp=v, **kwargs: jinja2.Template(
- temp
- ).render(**kwargs)
- else:
- self.templates[k] = v
-
- def _process_gen(self, gens):
- out = {}
- for gen in gens:
- dimension = {
- k: v
- if isinstance(v, list)
- else range(v.get("start", 0), v["stop"], v.get("step", 1))
- for k, v in gen["dimensions"].items()
- }
- products = (
- dict(zip(dimension.keys(), values))
- for values in itertools.product(*dimension.values())
- )
- for pr in products:
- import jinja2
-
- key = jinja2.Template(gen["key"]).render(**pr, **self.templates)
- url = jinja2.Template(gen["url"]).render(**pr, **self.templates)
- if ("offset" in gen) and ("length" in gen):
- offset = int(
- jinja2.Template(gen["offset"]).render(**pr, **self.templates)
- )
- length = int(
- jinja2.Template(gen["length"]).render(**pr, **self.templates)
- )
- out[key] = [url, offset, length]
- elif ("offset" in gen) ^ ("length" in gen):
- raise ValueError(
- "Both 'offset' and 'length' are required for a "
- "reference generator entry if either is provided."
- )
- else:
- out[key] = [url]
- return out
-
- def _dircache_from_items(self):
- self.dircache = {"": []}
- it = self.references.items()
- for path, part in it:
- if isinstance(part, (bytes, str)):
- size = len(part)
- elif len(part) == 1:
- size = None
- else:
- _, start, size = part
- par = path.rsplit("/", 1)[0] if "/" in path else ""
- par0 = par
- while par0 and par0 not in self.dircache:
- # build parent directories
- self.dircache[par0] = []
- self.dircache.setdefault(
- par0.rsplit("/", 1)[0] if "/" in par0 else "", []
- ).append({"name": par0, "type": "directory", "size": 0})
- par0 = self._parent(par0)
-
- self.dircache[par].append({"name": path, "type": "file", "size": size})
-
- def _open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs):
- data = self.cat_file(path) # load whole chunk into memory
- return io.BytesIO(data)
-
- def ls(self, path, detail=True, **kwargs):
- path = self._strip_protocol(path)
- if isinstance(self.references, LazyReferenceMapper):
- try:
- return self.references.ls(path, detail)
- except KeyError:
- pass
- raise FileNotFoundError(f"'{path}' is not a known key")
- if not self.dircache:
- self._dircache_from_items()
- out = self._ls_from_cache(path)
- if out is None:
- raise FileNotFoundError(path)
- if detail:
- return out
- return [o["name"] for o in out]
-
- def exists(self, path, **kwargs): # overwrite auto-sync version
- return self.isdir(path) or self.isfile(path)
-
- def isdir(self, path): # overwrite auto-sync version
- if self.dircache:
- return path in self.dircache
- elif isinstance(self.references, LazyReferenceMapper):
- return path in self.references.listdir("")
- else:
- # this may be faster than building dircache for single calls, but
- # by looping will be slow for many calls; could cache it?
- return any(_.startswith(f"{path}/") for _ in self.references)
-
- def isfile(self, path): # overwrite auto-sync version
- return path in self.references
-
- async def _ls(self, path, detail=True, **kwargs): # calls fast sync code
- return self.ls(path, detail, **kwargs)
-
- def find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
- if withdirs:
- return super().find(
- path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
- )
- if path:
- path = self._strip_protocol(path)
- r = sorted(k for k in self.references if k.startswith(path))
- else:
- r = sorted(self.references)
- if detail:
- if not self.dircache:
- self._dircache_from_items()
- return {k: self._ls_from_cache(k)[0] for k in r}
- else:
- return r
-
- def info(self, path, **kwargs):
- out = self.references.get(path)
- if out is not None:
- if isinstance(out, (str, bytes)):
- # decode base64 here
- return {"name": path, "type": "file", "size": len(out)}
- elif len(out) > 1:
- return {"name": path, "type": "file", "size": out[2]}
- else:
- out0 = [{"name": path, "type": "file", "size": None}]
- else:
- out = self.ls(path, True)
- out0 = [o for o in out if o["name"] == path]
- if not out0:
- return {"name": path, "type": "directory", "size": 0}
- if out0[0]["size"] is None:
- # if this is a whole remote file, update size using remote FS
- prot, _ = split_protocol(self.references[path][0])
- out0[0]["size"] = self.fss[prot].size(self.references[path][0])
- return out0[0]
-
- async def _info(self, path, **kwargs): # calls fast sync code
- return self.info(path)
-
- async def _rm_file(self, path, **kwargs):
- self.references.pop(
- path, None
- ) # ignores FileNotFound, just as well for directories
- self.dircache.clear() # this is a bit heavy handed
-
- async def _pipe_file(self, path, data):
- # can be str or bytes
- self.references[path] = data
- self.dircache.clear() # this is a bit heavy handed
-
- async def _put_file(self, lpath, rpath):
- # puts binary
- with open(lpath, "rb") as f:
- self.references[rpath] = f.read()
- self.dircache.clear() # this is a bit heavy handed
-
- def save_json(self, url, **storage_options):
- """Write modified references into new location"""
- out = {}
- for k, v in self.references.items():
- if isinstance(v, bytes):
- try:
- out[k] = v.decode("ascii")
- except UnicodeDecodeError:
- out[k] = (b"base64:" + base64.b64encode(v)).decode()
- else:
- out[k] = v
- with fsspec.open(url, "wb", **storage_options) as f:
- f.write(json.dumps({"version": 1, "refs": out}).encode())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/dataframe.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/dataframe.py
deleted file mode 100644
index 8c8c78a3d9c653d4fb5c5c50a0de8eb2ffbccab6..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/dataframe.py
+++ /dev/null
@@ -1,304 +0,0 @@
-"""gr.Dataframe() component"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, Callable, Literal
-
-import numpy as np
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import JSONSerializable
-
-from gradio import utils
-from gradio.components.base import IOComponent, _Keywords
-from gradio.events import (
- Changeable,
- EventListenerMethod,
- Inputable,
- Selectable,
-)
-
-if TYPE_CHECKING:
- from typing import TypedDict
-
- class DataframeData(TypedDict):
- headers: list[str]
- data: list[list[str | int | bool]]
-
-
-set_documentation_group("component")
-
-
-@document()
-class Dataframe(Changeable, Inputable, Selectable, IOComponent, JSONSerializable):
- """
- Accepts or displays 2D input through a spreadsheet-like component for dataframes.
- Preprocessing: passes the uploaded spreadsheet data as a {pandas.DataFrame}, {numpy.array}, {List[List]}, or {List} depending on `type`
- Postprocessing: expects a {pandas.DataFrame}, {numpy.array}, {List[List]}, {List}, a {Dict} with keys `data` (and optionally `headers`), or {str} path to a csv, which is rendered in the spreadsheet.
- Examples-format: a {str} filepath to a csv with data, a pandas dataframe, or a list of lists (excluding headers) where each sublist is a row of data.
- Demos: filter_records, matrix_transpose, tax_calculator
- """
-
- markdown_parser = None
-
- def __init__(
- self,
- value: list[list[Any]] | Callable | None = None,
- *,
- headers: list[str] | None = None,
- row_count: int | tuple[int, str] = (1, "dynamic"),
- col_count: int | tuple[int, str] | None = None,
- datatype: str | list[str] = "str",
- type: Literal["pandas", "numpy", "array"] = "pandas",
- max_rows: int | None = 20,
- max_cols: int | None = None,
- overflow_row_behaviour: Literal["paginate", "show_ends"] = "paginate",
- label: str | None = None,
- every: float | None = None,
- show_label: bool | None = None,
- scale: int | None = None,
- min_width: int = 160,
- interactive: bool | None = None,
- visible: bool = True,
- elem_id: str | None = None,
- elem_classes: list[str] | str | None = None,
- wrap: bool = False,
- **kwargs,
- ):
- """
- Parameters:
- value: Default value as a 2-dimensional list of values. If callable, the function will be called whenever the app loads to set the initial value of the component.
- headers: List of str header names. If None, no headers are shown.
- row_count: Limit number of rows for input and decide whether user can create new rows. The first element of the tuple is an `int`, the row count; the second should be 'fixed' or 'dynamic', the new row behaviour. If an `int` is passed the rows default to 'dynamic'
- col_count: Limit number of columns for input and decide whether user can create new columns. The first element of the tuple is an `int`, the number of columns; the second should be 'fixed' or 'dynamic', the new column behaviour. If an `int` is passed the columns default to 'dynamic'
- datatype: Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
- type: Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
- label: component name in interface.
- max_rows: Maximum number of rows to display at once. Set to None for infinite.
- max_cols: Maximum number of columns to display at once. Set to None for infinite.
- overflow_row_behaviour: If set to "paginate", will create pages for overflow rows. If set to "show_ends", will show initial and final rows and truncate middle rows.
- label: component name in interface.
- every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
- show_label: if True, will display label.
- scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
- min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
- interactive: if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
- visible: If False, component will be hidden.
- elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
- elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
- wrap: if True text in table cells will wrap when appropriate, if False the table will scroll horizontally. Defaults to False.
- """
-
- self.wrap = wrap
- self.row_count = self.__process_counts(row_count)
- self.col_count = self.__process_counts(
- col_count, len(headers) if headers else 3
- )
-
- self.__validate_headers(headers, self.col_count[0])
-
- self.headers = (
- headers if headers is not None else list(range(1, self.col_count[0] + 1))
- )
- self.datatype = (
- datatype if isinstance(datatype, list) else [datatype] * self.col_count[0]
- )
- valid_types = ["pandas", "numpy", "array"]
- if type not in valid_types:
- raise ValueError(
- f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}"
- )
- self.type = type
- values = {
- "str": "",
- "number": 0,
- "bool": False,
- "date": "01/01/1970",
- "markdown": "",
- "html": "",
- }
- column_dtypes = (
- [datatype] * self.col_count[0] if isinstance(datatype, str) else datatype
- )
- self.empty_input = [
- [values[c] for c in column_dtypes] for _ in range(self.row_count[0])
- ]
-
- self.max_rows = max_rows
- self.max_cols = max_cols
- self.overflow_row_behaviour = overflow_row_behaviour
- self.select: EventListenerMethod
- """
- Event listener for when the user selects cell within Dataframe.
- Uses event data gradio.SelectData to carry `value` referring to value of selected cell, and `index` tuple to refer to index row and column.
- See EventData documentation on how to use this event data.
- """
- IOComponent.__init__(
- self,
- label=label,
- every=every,
- show_label=show_label,
- scale=scale,
- min_width=min_width,
- interactive=interactive,
- visible=visible,
- elem_id=elem_id,
- elem_classes=elem_classes,
- value=value,
- **kwargs,
- )
-
- def get_config(self):
- return {
- "headers": self.headers,
- "datatype": self.datatype,
- "row_count": self.row_count,
- "col_count": self.col_count,
- "value": self.value,
- "max_rows": self.max_rows,
- "max_cols": self.max_cols,
- "overflow_row_behaviour": self.overflow_row_behaviour,
- "wrap": self.wrap,
- **IOComponent.get_config(self),
- }
-
- @staticmethod
- def update(
- value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
- max_rows: int | None = None,
- max_cols: str | None = None,
- label: str | None = None,
- show_label: bool | None = None,
- scale: int | None = None,
- min_width: int | None = None,
- interactive: bool | None = None,
- visible: bool | None = None,
- ):
- return {
- "max_rows": max_rows,
- "max_cols": max_cols,
- "label": label,
- "show_label": show_label,
- "scale": scale,
- "min_width": min_width,
- "interactive": interactive,
- "visible": visible,
- "value": value,
- "__type__": "update",
- }
-
- def preprocess(self, x: DataframeData):
- """
- Parameters:
- x: 2D array of str, numeric, or bool data
- Returns:
- Dataframe in requested format
- """
- if self.type == "pandas":
- if x.get("headers") is not None:
- return pd.DataFrame(x["data"], columns=x.get("headers"))
- else:
- return pd.DataFrame(x["data"])
- if self.type == "numpy":
- return np.array(x["data"])
- elif self.type == "array":
- return x["data"]
- else:
- raise ValueError(
- "Unknown type: "
- + str(self.type)
- + ". Please choose from: 'pandas', 'numpy', 'array'."
- )
-
- def postprocess(
- self, y: str | pd.DataFrame | np.ndarray | list[list[str | float]] | dict
- ) -> dict:
- """
- Parameters:
- y: dataframe in given format
- Returns:
- JSON object with key 'headers' for list of header names, 'data' for 2D array of string or numeric data
- """
- if y is None:
- return self.postprocess(self.empty_input)
- if isinstance(y, dict):
- return y
- if isinstance(y, str):
- dataframe = pd.read_csv(y)
- return {
- "headers": list(dataframe.columns),
- "data": Dataframe.__process_markdown(
- dataframe.to_dict(orient="split")["data"], self.datatype
- ),
- }
- if isinstance(y, pd.DataFrame):
- return {
- "headers": list(y.columns), # type: ignore
- "data": Dataframe.__process_markdown(
- y.to_dict(orient="split")["data"], self.datatype # type: ignore
- ),
- }
- if isinstance(y, (np.ndarray, list)):
- if len(y) == 0:
- return self.postprocess([[]])
- if isinstance(y, np.ndarray):
- y = y.tolist()
- assert isinstance(y, list), "output cannot be converted to list"
-
- _headers = self.headers
-
- if len(self.headers) < len(y[0]):
- _headers = [
- *self.headers,
- *list(range(len(self.headers) + 1, len(y[0]) + 1)),
- ]
- elif len(self.headers) > len(y[0]):
- _headers = self.headers[: len(y[0])]
-
- return {
- "headers": _headers,
- "data": Dataframe.__process_markdown(y, self.datatype),
- }
- raise ValueError("Cannot process value as a Dataframe")
-
- @staticmethod
- def __process_counts(count, default=3) -> tuple[int, str]:
- if count is None:
- return (default, "dynamic")
- if type(count) == int or type(count) == float:
- return (int(count), "dynamic")
- else:
- return count
-
- @staticmethod
- def __validate_headers(headers: list[str] | None, col_count: int):
- if headers is not None and len(headers) != col_count:
- raise ValueError(
- f"The length of the headers list must be equal to the col_count int.\n"
- f"The column count is set to {col_count} but `headers` has {len(headers)} items. "
- f"Check the values passed to `col_count` and `headers`."
- )
-
- @classmethod
- def __process_markdown(cls, data: list[list[Any]], datatype: list[str]):
- if "markdown" not in datatype:
- return data
-
- if cls.markdown_parser is None:
- cls.markdown_parser = utils.get_markdown_parser()
-
- for i in range(len(data)):
- for j in range(len(data[i])):
- if datatype[j] == "markdown":
- data[i][j] = cls.markdown_parser.render(data[i][j])
-
- return data
-
- def as_example(self, input_data: pd.DataFrame | np.ndarray | str | None):
- if input_data is None:
- return ""
- elif isinstance(input_data, pd.DataFrame):
- return input_data.head(n=5).to_dict(orient="split")["data"] # type: ignore
- elif isinstance(input_data, np.ndarray):
- return input_data.tolist()
- return input_data
diff --git a/spaces/deelerb/3dselfie/PIFu/apps/eval.py b/spaces/deelerb/3dselfie/PIFu/apps/eval.py
deleted file mode 100644
index a0ee3fa66c75a144da5c155b927f63170b7e923c..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/apps/eval.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import tqdm
-import glob
-import torchvision.transforms as transforms
-from PIL import Image
-from lib.model import *
-from lib.train_util import *
-from lib.sample_util import *
-from lib.mesh_util import *
-# from lib.options import BaseOptions
-from torch.utils.data import DataLoader
-import torch
-import numpy as np
-import json
-import time
-import sys
-import os
-
-sys.path.insert(0, os.path.abspath(
- os.path.join(os.path.dirname(__file__), '..')))
-ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-# # get options
-# opt = BaseOptions().parse()
-
-class Evaluator:
- def __init__(self, opt, projection_mode='orthogonal'):
- self.opt = opt
- self.load_size = self.opt.loadSize
- self.to_tensor = transforms.Compose([
- transforms.Resize(self.load_size),
- transforms.ToTensor(),
- transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
- ])
- # set cuda
- cuda = torch.device(
- 'cuda:%d' % opt.gpu_id) if torch.cuda.is_available() else torch.device('cpu')
-
- # create net
- netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
- print('Using Network: ', netG.name)
-
- if opt.load_netG_checkpoint_path:
- netG.load_state_dict(torch.load(
- opt.load_netG_checkpoint_path, map_location=cuda))
-
- if opt.load_netC_checkpoint_path is not None:
- print('loading for net C ...', opt.load_netC_checkpoint_path)
- netC = ResBlkPIFuNet(opt).to(device=cuda)
- netC.load_state_dict(torch.load(
- opt.load_netC_checkpoint_path, map_location=cuda))
- else:
- netC = None
-
- os.makedirs(opt.results_path, exist_ok=True)
- os.makedirs('%s/%s' % (opt.results_path, opt.name), exist_ok=True)
-
- opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
- with open(opt_log, 'w') as outfile:
- outfile.write(json.dumps(vars(opt), indent=2))
-
- self.cuda = cuda
- self.netG = netG
- self.netC = netC
-
- def load_image(self, image_path, mask_path):
- # Name
- img_name = os.path.splitext(os.path.basename(image_path))[0]
- # Calib
- B_MIN = np.array([-1, -1, -1])
- B_MAX = np.array([1, 1, 1])
- projection_matrix = np.identity(4)
- projection_matrix[1, 1] = -1
- calib = torch.Tensor(projection_matrix).float()
- # Mask
- mask = Image.open(mask_path).convert('L')
- mask = transforms.Resize(self.load_size)(mask)
- mask = transforms.ToTensor()(mask).float()
- # image
- image = Image.open(image_path).convert('RGB')
- image = self.to_tensor(image)
- image = mask.expand_as(image) * image
- return {
- 'name': img_name,
- 'img': image.unsqueeze(0),
- 'calib': calib.unsqueeze(0),
- 'mask': mask.unsqueeze(0),
- 'b_min': B_MIN,
- 'b_max': B_MAX,
- }
-
- def load_image_from_memory(self, image_path, mask_path, img_name):
- # Calib
- B_MIN = np.array([-1, -1, -1])
- B_MAX = np.array([1, 1, 1])
- projection_matrix = np.identity(4)
- projection_matrix[1, 1] = -1
- calib = torch.Tensor(projection_matrix).float()
- # Mask
- mask = Image.fromarray(mask_path).convert('L')
- mask = transforms.Resize(self.load_size)(mask)
- mask = transforms.ToTensor()(mask).float()
- # image
- image = Image.fromarray(image_path).convert('RGB')
- image = self.to_tensor(image)
- image = mask.expand_as(image) * image
- return {
- 'name': img_name,
- 'img': image.unsqueeze(0),
- 'calib': calib.unsqueeze(0),
- 'mask': mask.unsqueeze(0),
- 'b_min': B_MIN,
- 'b_max': B_MAX,
- }
-
- def eval(self, data, use_octree=False):
- '''
- Evaluate a data point
- :param data: a dict containing at least ['name'], ['image'], ['calib'], ['b_min'] and ['b_max'] tensors.
- :return:
- '''
- opt = self.opt
- with torch.no_grad():
- self.netG.eval()
- if self.netC:
- self.netC.eval()
- save_path = '%s/%s/result_%s.obj' % (
- opt.results_path, opt.name, data['name'])
- if self.netC:
- gen_mesh_color(opt, self.netG, self.netC, self.cuda,
- data, save_path, use_octree=use_octree)
- else:
- gen_mesh(opt, self.netG, self.cuda, data,
- save_path, use_octree=use_octree)
-
-
-if __name__ == '__main__':
- evaluator = Evaluator(opt)
-
- test_images = glob.glob(os.path.join(opt.test_folder_path, '*'))
- test_images = [f for f in test_images if (
- 'png' in f or 'jpg' in f) and (not 'mask' in f)]
- test_masks = [f[:-4]+'_mask.png' for f in test_images]
-
- print("num; ", len(test_masks))
-
- for image_path, mask_path in tqdm.tqdm(zip(test_images, test_masks)):
- try:
- print(image_path, mask_path)
- data = evaluator.load_image(image_path, mask_path)
- evaluator.eval(data, True)
- except Exception as e:
- print("error:", e.args)
diff --git a/spaces/deeplearning/audioldm-text-to-audio-generation/audioldm/clap/open_clip/tokenizer.py b/spaces/deeplearning/audioldm-text-to-audio-generation/audioldm/clap/open_clip/tokenizer.py
deleted file mode 100644
index ee4d28450ec5dd12a79daf38cf3088e9e73c2cd5..0000000000000000000000000000000000000000
--- a/spaces/deeplearning/audioldm-text-to-audio-generation/audioldm/clap/open_clip/tokenizer.py
+++ /dev/null
@@ -1,197 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-
-@lru_cache()
-def default_bpe():
- return os.path.join(
- os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
- )
-
-
-@lru_cache()
-def bytes_to_unicode():
- """
- Returns list of utf-8 byte and a corresponding list of unicode strings.
- The reversible bpe codes work on unicode strings.
- This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
- When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a signficant percentage of your normal, say, 32K bpe vocab.
- To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
- And avoids mapping to whitespace/control characters the bpe code barfs on.
- """
- bs = (
- list(range(ord("!"), ord("~") + 1))
- + list(range(ord("¡"), ord("¬") + 1))
- + list(range(ord("®"), ord("ÿ") + 1))
- )
- cs = bs[:]
- n = 0
- for b in range(2**8):
- if b not in bs:
- bs.append(b)
- cs.append(2**8 + n)
- n += 1
- cs = [chr(n) for n in cs]
- return dict(zip(bs, cs))
-
-
-def get_pairs(word):
- """Return set of symbol pairs in a word.
- Word is represented as tuple of symbols (symbols being variable-length strings).
- """
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-def basic_clean(text):
- text = ftfy.fix_text(text)
- text = html.unescape(html.unescape(text))
- return text.strip()
-
-
-def whitespace_clean(text):
- text = re.sub(r"\s+", " ", text)
- text = text.strip()
- return text
-
-
-class SimpleTokenizer(object):
- def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
- self.byte_encoder = bytes_to_unicode()
- self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
- merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
- merges = merges[1 : 49152 - 256 - 2 + 1]
- merges = [tuple(merge.split()) for merge in merges]
- vocab = list(bytes_to_unicode().values())
- vocab = vocab + [v + "" for v in vocab]
- for merge in merges:
- vocab.append("".join(merge))
- if not special_tokens:
- special_tokens = ["", ""]
- else:
- special_tokens = ["", ""] + special_tokens
- vocab.extend(special_tokens)
- self.encoder = dict(zip(vocab, range(len(vocab))))
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.bpe_ranks = dict(zip(merges, range(len(merges))))
- self.cache = {t: t for t in special_tokens}
- special = "|".join(special_tokens)
- self.pat = re.compile(
- special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
- re.IGNORECASE,
- )
-
- self.vocab_size = len(self.encoder)
- self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
- def bpe(self, token):
- if token in self.cache:
- return self.cache[token]
- word = tuple(token[:-1]) + (token[-1] + "",)
- pairs = get_pairs(word)
-
- if not pairs:
- return token + ""
-
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except:
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = " ".join(word)
- self.cache[token] = word
- return word
-
- def encode(self, text):
- bpe_tokens = []
- text = whitespace_clean(basic_clean(text)).lower()
- for token in re.findall(self.pat, text):
- token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
- bpe_tokens.extend(
- self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
- )
- return bpe_tokens
-
- def decode(self, tokens):
- text = "".join([self.decoder[token] for token in tokens])
- text = (
- bytearray([self.byte_decoder[c] for c in text])
- .decode("utf-8", errors="replace")
- .replace("", " ")
- )
- return text
-
-
-_tokenizer = SimpleTokenizer()
-
-
-def tokenize(
- texts: Union[str, List[str]], context_length: int = 77
-) -> torch.LongTensor:
- """
- Returns the tokenized representation of given input string(s)
-
- Parameters
- ----------
- texts : Union[str, List[str]]
- An input string or a list of input strings to tokenize
- context_length : int
- The context length to use; all CLIP models use 77 as the context length
-
- Returns
- -------
- A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
- """
- if isinstance(texts, str):
- texts = [texts]
-
- sot_token = _tokenizer.encoder[""]
- eot_token = _tokenizer.encoder[""]
- all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
- result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
- for i, tokens in enumerate(all_tokens):
- if len(tokens) > context_length:
- tokens = tokens[:context_length] # Truncate
- result[i, : len(tokens)] = torch.tensor(tokens)
-
- return result
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/bfm.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/bfm.py
deleted file mode 100644
index 74ea0be57ef63a1732940e9088184a1da0b4e0d0..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/bfm.py
+++ /dev/null
@@ -1,331 +0,0 @@
-"""This script defines the parametric 3d face model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from scipy.io import loadmat
-from sad_talker.src.face3d.util.load_mats import transferBFM09
-import os
-
-def perspective_projection(focal, center):
- # return p.T (N, 3) @ (3, 3)
- return np.array([
- focal, 0, center,
- 0, focal, center,
- 0, 0, 1
- ]).reshape([3, 3]).astype(np.float32).transpose()
-
-class SH:
- def __init__(self):
- self.a = [np.pi, 2 * np.pi / np.sqrt(3.), 2 * np.pi / np.sqrt(8.)]
- self.c = [1/np.sqrt(4 * np.pi), np.sqrt(3.) / np.sqrt(4 * np.pi), 3 * np.sqrt(5.) / np.sqrt(12 * np.pi)]
-
-
-
-class ParametricFaceModel:
- def __init__(self,
- bfm_folder='./BFM',
- recenter=True,
- camera_distance=10.,
- init_lit=np.array([
- 0.8, 0, 0, 0, 0, 0, 0, 0, 0
- ]),
- focal=1015.,
- center=112.,
- is_train=True,
- default_name='BFM_model_front.mat'):
-
- if not os.path.isfile(os.path.join(bfm_folder, default_name)):
- transferBFM09(bfm_folder)
-
- model = loadmat(os.path.join(bfm_folder, default_name))
- # mean face shape. [3*N,1]
- self.mean_shape = model['meanshape'].astype(np.float32)
- # identity basis. [3*N,80]
- self.id_base = model['idBase'].astype(np.float32)
- # expression basis. [3*N,64]
- self.exp_base = model['exBase'].astype(np.float32)
- # mean face texture. [3*N,1] (0-255)
- self.mean_tex = model['meantex'].astype(np.float32)
- # texture basis. [3*N,80]
- self.tex_base = model['texBase'].astype(np.float32)
- # face indices for each vertex that lies in. starts from 0. [N,8]
- self.point_buf = model['point_buf'].astype(np.int64) - 1
- # vertex indices for each face. starts from 0. [F,3]
- self.face_buf = model['tri'].astype(np.int64) - 1
- # vertex indices for 68 landmarks. starts from 0. [68,1]
- self.keypoints = np.squeeze(model['keypoints']).astype(np.int64) - 1
-
- if is_train:
- # vertex indices for small face region to compute photometric error. starts from 0.
- self.front_mask = np.squeeze(model['frontmask2_idx']).astype(np.int64) - 1
- # vertex indices for each face from small face region. starts from 0. [f,3]
- self.front_face_buf = model['tri_mask2'].astype(np.int64) - 1
- # vertex indices for pre-defined skin region to compute reflectance loss
- self.skin_mask = np.squeeze(model['skinmask'])
-
- if recenter:
- mean_shape = self.mean_shape.reshape([-1, 3])
- mean_shape = mean_shape - np.mean(mean_shape, axis=0, keepdims=True)
- self.mean_shape = mean_shape.reshape([-1, 1])
-
- self.persc_proj = perspective_projection(focal, center)
- self.device = 'cpu'
- self.camera_distance = camera_distance
- self.SH = SH()
- self.init_lit = init_lit.reshape([1, 1, -1]).astype(np.float32)
-
-
- def to(self, device):
- self.device = device
- for key, value in self.__dict__.items():
- if type(value).__module__ == np.__name__:
- setattr(self, key, torch.tensor(value).to(device))
-
-
- def compute_shape(self, id_coeff, exp_coeff):
- """
- Return:
- face_shape -- torch.tensor, size (B, N, 3)
-
- Parameters:
- id_coeff -- torch.tensor, size (B, 80), identity coeffs
- exp_coeff -- torch.tensor, size (B, 64), expression coeffs
- """
- batch_size = id_coeff.shape[0]
- id_part = torch.einsum('ij,aj->ai', self.id_base, id_coeff)
- exp_part = torch.einsum('ij,aj->ai', self.exp_base, exp_coeff)
- face_shape = id_part + exp_part + self.mean_shape.reshape([1, -1])
- return face_shape.reshape([batch_size, -1, 3])
-
-
- def compute_texture(self, tex_coeff, normalize=True):
- """
- Return:
- face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
-
- Parameters:
- tex_coeff -- torch.tensor, size (B, 80)
- """
- batch_size = tex_coeff.shape[0]
- face_texture = torch.einsum('ij,aj->ai', self.tex_base, tex_coeff) + self.mean_tex
- if normalize:
- face_texture = face_texture / 255.
- return face_texture.reshape([batch_size, -1, 3])
-
-
- def compute_norm(self, face_shape):
- """
- Return:
- vertex_norm -- torch.tensor, size (B, N, 3)
-
- Parameters:
- face_shape -- torch.tensor, size (B, N, 3)
- """
-
- v1 = face_shape[:, self.face_buf[:, 0]]
- v2 = face_shape[:, self.face_buf[:, 1]]
- v3 = face_shape[:, self.face_buf[:, 2]]
- e1 = v1 - v2
- e2 = v2 - v3
- face_norm = torch.cross(e1, e2, dim=-1)
- face_norm = F.normalize(face_norm, dim=-1, p=2)
- face_norm = torch.cat([face_norm, torch.zeros(face_norm.shape[0], 1, 3).to(self.device)], dim=1)
-
- vertex_norm = torch.sum(face_norm[:, self.point_buf], dim=2)
- vertex_norm = F.normalize(vertex_norm, dim=-1, p=2)
- return vertex_norm
-
-
- def compute_color(self, face_texture, face_norm, gamma):
- """
- Return:
- face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
-
- Parameters:
- face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
- face_norm -- torch.tensor, size (B, N, 3), rotated face normal
- gamma -- torch.tensor, size (B, 27), SH coeffs
- """
- batch_size = gamma.shape[0]
- v_num = face_texture.shape[1]
- a, c = self.SH.a, self.SH.c
- gamma = gamma.reshape([batch_size, 3, 9])
- gamma = gamma + self.init_lit
- gamma = gamma.permute(0, 2, 1)
- Y = torch.cat([
- a[0] * c[0] * torch.ones_like(face_norm[..., :1]).to(self.device),
- -a[1] * c[1] * face_norm[..., 1:2],
- a[1] * c[1] * face_norm[..., 2:],
- -a[1] * c[1] * face_norm[..., :1],
- a[2] * c[2] * face_norm[..., :1] * face_norm[..., 1:2],
- -a[2] * c[2] * face_norm[..., 1:2] * face_norm[..., 2:],
- 0.5 * a[2] * c[2] / np.sqrt(3.) * (3 * face_norm[..., 2:] ** 2 - 1),
- -a[2] * c[2] * face_norm[..., :1] * face_norm[..., 2:],
- 0.5 * a[2] * c[2] * (face_norm[..., :1] ** 2 - face_norm[..., 1:2] ** 2)
- ], dim=-1)
- r = Y @ gamma[..., :1]
- g = Y @ gamma[..., 1:2]
- b = Y @ gamma[..., 2:]
- face_color = torch.cat([r, g, b], dim=-1) * face_texture
- return face_color
-
-
- def compute_rotation(self, angles):
- """
- Return:
- rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
-
- Parameters:
- angles -- torch.tensor, size (B, 3), radian
- """
-
- batch_size = angles.shape[0]
- ones = torch.ones([batch_size, 1]).to(self.device)
- zeros = torch.zeros([batch_size, 1]).to(self.device)
- x, y, z = angles[:, :1], angles[:, 1:2], angles[:, 2:],
-
- rot_x = torch.cat([
- ones, zeros, zeros,
- zeros, torch.cos(x), -torch.sin(x),
- zeros, torch.sin(x), torch.cos(x)
- ], dim=1).reshape([batch_size, 3, 3])
-
- rot_y = torch.cat([
- torch.cos(y), zeros, torch.sin(y),
- zeros, ones, zeros,
- -torch.sin(y), zeros, torch.cos(y)
- ], dim=1).reshape([batch_size, 3, 3])
-
- rot_z = torch.cat([
- torch.cos(z), -torch.sin(z), zeros,
- torch.sin(z), torch.cos(z), zeros,
- zeros, zeros, ones
- ], dim=1).reshape([batch_size, 3, 3])
-
- rot = rot_z @ rot_y @ rot_x
- return rot.permute(0, 2, 1)
-
-
- def to_camera(self, face_shape):
- face_shape[..., -1] = self.camera_distance - face_shape[..., -1]
- return face_shape
-
- def to_image(self, face_shape):
- """
- Return:
- face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
-
- Parameters:
- face_shape -- torch.tensor, size (B, N, 3)
- """
- # to image_plane
- face_proj = face_shape @ self.persc_proj
- face_proj = face_proj[..., :2] / face_proj[..., 2:]
-
- return face_proj
-
-
- def transform(self, face_shape, rot, trans):
- """
- Return:
- face_shape -- torch.tensor, size (B, N, 3) pts @ rot + trans
-
- Parameters:
- face_shape -- torch.tensor, size (B, N, 3)
- rot -- torch.tensor, size (B, 3, 3)
- trans -- torch.tensor, size (B, 3)
- """
- return face_shape @ rot + trans.unsqueeze(1)
-
-
- def get_landmarks(self, face_proj):
- """
- Return:
- face_lms -- torch.tensor, size (B, 68, 2)
-
- Parameters:
- face_proj -- torch.tensor, size (B, N, 2)
- """
- return face_proj[:, self.keypoints]
-
- def split_coeff(self, coeffs):
- """
- Return:
- coeffs_dict -- a dict of torch.tensors
-
- Parameters:
- coeffs -- torch.tensor, size (B, 256)
- """
- id_coeffs = coeffs[:, :80]
- exp_coeffs = coeffs[:, 80: 144]
- tex_coeffs = coeffs[:, 144: 224]
- angles = coeffs[:, 224: 227]
- gammas = coeffs[:, 227: 254]
- translations = coeffs[:, 254:]
- return {
- 'id': id_coeffs,
- 'exp': exp_coeffs,
- 'tex': tex_coeffs,
- 'angle': angles,
- 'gamma': gammas,
- 'trans': translations
- }
- def compute_for_render(self, coeffs):
- """
- Return:
- face_vertex -- torch.tensor, size (B, N, 3), in camera coordinate
- face_color -- torch.tensor, size (B, N, 3), in RGB order
- landmark -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
- Parameters:
- coeffs -- torch.tensor, size (B, 257)
- """
- coef_dict = self.split_coeff(coeffs)
- face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
- rotation = self.compute_rotation(coef_dict['angle'])
-
-
- face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
- face_vertex = self.to_camera(face_shape_transformed)
-
- face_proj = self.to_image(face_vertex)
- landmark = self.get_landmarks(face_proj)
-
- face_texture = self.compute_texture(coef_dict['tex'])
- face_norm = self.compute_norm(face_shape)
- face_norm_roted = face_norm @ rotation
- face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
- return face_vertex, face_texture, face_color, landmark
-
- def compute_for_render_woRotation(self, coeffs):
- """
- Return:
- face_vertex -- torch.tensor, size (B, N, 3), in camera coordinate
- face_color -- torch.tensor, size (B, N, 3), in RGB order
- landmark -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
- Parameters:
- coeffs -- torch.tensor, size (B, 257)
- """
- coef_dict = self.split_coeff(coeffs)
- face_shape = self.compute_shape(coef_dict['id'], coef_dict['exp'])
- #rotation = self.compute_rotation(coef_dict['angle'])
-
-
- #face_shape_transformed = self.transform(face_shape, rotation, coef_dict['trans'])
- face_vertex = self.to_camera(face_shape)
-
- face_proj = self.to_image(face_vertex)
- landmark = self.get_landmarks(face_proj)
-
- face_texture = self.compute_texture(coef_dict['tex'])
- face_norm = self.compute_norm(face_shape)
- face_norm_roted = face_norm # @ rotation
- face_color = self.compute_color(face_texture, face_norm_roted, coef_dict['gamma'])
-
- return face_vertex, face_texture, face_color, landmark
-
-
-if __name__ == '__main__':
- transferBFM09()
\ No newline at end of file
diff --git "a/spaces/diacanFperku/AutoGPT/Adobe Universal Patcher (Latest CC 2014) Is Here\302\240!!!.md" "b/spaces/diacanFperku/AutoGPT/Adobe Universal Patcher (Latest CC 2014) Is Here\302\240!!!.md"
deleted file mode 100644
index 43155274cc1905f6cf881ab87bea53e133872fef..0000000000000000000000000000000000000000
--- "a/spaces/diacanFperku/AutoGPT/Adobe Universal Patcher (Latest CC 2014) Is Here\302\240!!!.md"
+++ /dev/null
@@ -1,98 +0,0 @@
-
-Adobe Universal Patcher (Latest CC 2014) is Here !!!
-Do you want to use the latest Adobe products without paying any fees or subscriptions? Do you want to activate any Adobe CC 2014 product with just a few clicks? If yes, then you need to download Adobe Universal Patcher, an amazing tool created by PainteR that can crack almost all the products of Adobe CC 2014. In this article, we will show you how to download, install, and use Adobe Universal Patcher for CC 2014 and enjoy its full features.
-What is Adobe Universal Patcher?
-Adobe Universal Patcher is a small but powerful tool that can patch any Adobe CC 2014 product with a single click. It works by replacing the original amtlib.dll file with a modified one that can bypass the activation process and unlock all the features of the product. Adobe Universal Patcher can patch almost all the products of Adobe CC 2014, such as Photoshop, Illustrator, InDesign, Premiere Pro, After Effects, Dreamweaver, Muse, Flash, and more. It is compatible with Windows and Mac OS X platforms.
-Adobe Universal Patcher (Latest CC 2014) is Here !!! DOWNLOAD »»» https://gohhs.com/2uFVFp
What are the advantages of using Adobe Universal Patcher?
-By using Adobe Universal Patcher for CC 2014, you can enjoy many advantages that will enhance your experience with Adobe products. Some of the advantages are:
-
-You can use any Adobe CC 2014 product for free without paying any fees or subscriptions.
-You can use any Adobe CC 2014 product for unlimited time without any expiration date or trial period.
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-
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-
-Click on this link to download the Adobe Universal Patcher CC 2014.rar file. This file contains the Adobe Universal Patcher for Windows and Mac OS X.
-Extract the rar file using a software like WinRAR or 7-Zip.
-You will get two folders: one for Windows and one for Mac OS X. Choose the folder that matches your operating system.
-You have successfully downloaded Adobe Universal Patcher for CC 2014.
-
-How to install and use Adobe Universal Patcher?
-If you want to install and use Adobe Universal Patcher for CC 2014, you need to follow these steps (taking Photoshop CC 2014 as an example):
-
-Download the setup of Photoshop CC 2014 from this link. This file contains the Photoshop CC 2014 software for Windows and Mac OS X.
-Run the setup file and follow the instructions to install Photoshop CC 2014 on your computer.
-Choose Try in the first screen and click Sign In in the next window. If you have already logged in your computer, click Not Your Adobe ID.
-It will show an error, then click Sign In later and accept the license terms and install.
-When the setup finishes, click Launch Now. In the next window, click Sign In Later again.
-You will get a trial notification. Just click Start Trial there and wait till Photoshop CC 2014 loads completely then close it.
-Now run the Adobe Universal Patcher for CC 2014 that you downloaded earlier. Click Patch drop-down list and select Photoshop CC (64 bit) if you have a 64-bit system or Photoshop CC if you have a 32-bit system.
-Now click Patch button and browse to the installation path of Photoshop CC 2014 - ex: C:\Program Files\Adobe\Adobe Photoshop CC 2014. Select amtlib.dll file and click Open.
-Wait till patching finishes and close the patcher.
-You have successfully installed and used Adobe Universal Patcher for CC 2014. You can now launch Photoshop CC 2014 and enjoy its full features.
-
-Conclusion
-Adobe Universal Patcher is a wonderful tool that can crack any Adobe CC 2014 product with ease. It allows you to use the latest Adobe products without any limitations or costs. You can download, install, and use Adobe Universal Patcher for CC 2014 from this article and enjoy its full features. However, we recommend that you buy the original software from Adobe if you can afford it, as it will support the developers and ensure future updates and improvements.
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-If you want to update your Adobe CC 2014 products with Adobe Universal Patcher, you need to follow these steps:
-
-Download the Adobe Offline Update installer 7.1 from this link. This tool will help you to install the latest updates for your Adobe CC 2014 products without killing the patch.
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-
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-
-Download the XFORCE keygen for Windows or Mac OS X from this link.
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-
-Conclusion
-Adobe Universal Patcher is a wonderful tool that can crack any Adobe CC 2014 product with ease. It allows you to use the latest Adobe products without any limitations or costs. You can download, install, and use Adobe Universal Patcher for CC 2014 from this article and enjoy its full features. However, we recommend that you buy the original software from Adobe if you can afford it, as it will support the developers and ensure future updates and improvements.
-
-How to fix common issues with Adobe Universal Patcher?
-Although Adobe Universal Patcher is a reliable and effective tool, sometimes you may encounter some issues or errors while using it. Here are some common issues and their solutions:
-
-If you get an error message saying "The application was unable to start correctly (0xc000007b). Click OK to close the application.", you need to install the Microsoft Visual C++ Redistributable Packages for Visual Studio 2013 from this link.
-If you get an error message saying "The program can't start because api-ms-win-crt-runtime-l1-1-0.dll is missing from your computer. Try reinstalling the program to fix this problem.", you need to install the Windows Update KB2999226 from this link.
-If you get an error message saying "The program can't start because MSVCR120.dll is missing from your computer. Try reinstalling the program to fix this problem.", you need to install the Microsoft Visual C++ Redistributable Packages for Visual Studio 2013 from this link.
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-If you get an error message saying "This patch is not applicable for you. Please check for updates from Help>Updates menu in your product.", you need to make sure that you have selected the correct product and version from the patch drop-down list.
-If you get an error message saying "This patch does not support your version of product. Please update your product or use another patch.", you need to make sure that you have updated your product to the latest version or use another patch that supports your version.
-If you get an error message saying "This patch does not support your platform. Please use another patch.", you need to make sure that you have downloaded and run the correct patch for your operating system (Windows or Mac OS X).
-
-How to contact Adobe Universal Patcher support?
-If you have any questions, feedback, or suggestions regarding Adobe Universal Patcher, you can contact its developer PainteR through his email address: painter@adobe.com. You can also visit his website: https://painter.ru/ for more information and updates about his work.
-How to download Adobe CC 2014 products?
-If you don't have the setup files of Adobe CC 2014 products, you can download them from this link. This link contains the direct download links for all Adobe CC 2014 products for Windows and Mac OS X. You can choose the product that you want to download and install from the list. You will need an Adobe account to access these links, but you don't need to pay anything or subscribe to any plan.
-How to backup and restore Adobe CC 2014 products?
-If you want to backup and restore your Adobe CC 2014 products, you can use the Adobe Creative Cloud Desktop app. This app allows you to manage, update, and sync your Adobe CC 2014 products across multiple devices. You can also use it to backup and restore your preferences, settings, presets, libraries, and more. To backup and restore your Adobe CC 2014 products, you need to follow these steps:
-
-Launch the Adobe Creative Cloud Desktop app and sign in with your Adobe account.
-Click on the gear icon at the top right corner and select Preferences.
-Click on Creative Cloud and then select Syncing.
-Check the box next to Sync Settings and choose what you want to sync (Preferences, Presets, Libraries, etc.).
-Click on Done and wait for the sync process to complete.
-You have successfully backed up your Adobe CC 2014 products.
-To restore your Adobe CC 2014 products, you need to repeat the same steps on another device or after reinstalling your products.
-You have successfully restored your Adobe CC 2014 products.
-
-Conclusion
-In this article, we have shown you how to download, install, and use Adobe Universal Patcher for CC 2014, a powerful tool that can crack any Adobe CC 2014 product with a single click. We have also explained how to update, backup, and restore your Adobe CC 2014 products with Adobe Universal Patcher. By using this tool, you can enjoy the latest Adobe products without any limitations or costs. However, we recommend that you buy the original software from Adobe if you can afford it, as it will support the developers and ensure future updates and improvements.
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diff --git a/spaces/diacanFperku/AutoGPT/Eltima USB Network Gate 8.1.2013 Activator 6 MB !!LINK!!.md b/spaces/diacanFperku/AutoGPT/Eltima USB Network Gate 8.1.2013 Activator 6 MB !!LINK!!.md
deleted file mode 100644
index 27c30a7a9fcc8caca9d3e74e1d74235aec694caa..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Eltima USB Network Gate 8.1.2013 Activator 6 MB !!LINK!!.md
+++ /dev/null
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-When you install Windows 10, your computer installs new drivers for built-in devices, Windows also installs new drivers and patches for your USB devices. These are referred to as "layers" in the USB protocol. Standard systems will install USB drivers that support all the necessary layers for your USB devices. Windows comes with a driver called the Universal Serial Bus Driver (USB) that provides basic USB support for many USB devices. If you have a legacy USB device, you may need to install a more specific driver. For USB Network Gate (10 shared USB devices) you will need a driver that includes support for the following layers:
-Connecting the USB Network Gate to Windows 10
Before you plug the USB Network Gate (10 shared USB devices) into your computer, you must prepare your computer for successful sharing. Follow these steps to configure Windows for sharing:
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Eltima USB Network Gate for Mac comes with a free mobile app (version for iOS and Android) that lets you keep a virtual dongle on your mobile device and browse, share and synchronize the USB resource over the Internet.
-The device supports 12KB/sec upload and 50KB/sec download, supporting both USB 1.1 and USB 2.0 devices. It operates on both WiFi and Ethernet connections. To verify the network connection, use the F5 button that appears on the Mac interface. You can also select the network connection by pressing the FN button.
-If your network connection uses the Dynamic Host Configuration Protocol (DHCP), a DHCP server is required for Smart Mode to work. Using a Dynamic IP address allows for network connection changes without the need to manually enter the IP address. For an Ethernet connection, the IP address is usually given by the DHCP server.
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Manualpolyboard4castellano3 !LINK!.md b/spaces/falterWliame/Face_Mask_Detection/Manualpolyboard4castellano3 !LINK!.md
deleted file mode 100644
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--- a/spaces/falterWliame/Face_Mask_Detection/Manualpolyboard4castellano3 !LINK!.md
+++ /dev/null
@@ -1,198 +0,0 @@
-
-Manualpolyboard4castellano3: Everything You Need to Know About Your Poly CCX Business Media Phone
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-Are you looking for a user manual that can help you set up, use, and troubleshoot your Poly CCX 400, 500, 600, or 700 business media phone? If so, you are in luck. Manualpolyboard4castellano3 is a PDF document that contains all the information you need to get the most out of your phone. Whether you want to make audio calls, transfer calls, record calls, park and retrieve calls, or use advanced features like Broadsoft UC-One and Remote Office, Manualpolyboard4castellano3 has got you covered. In this article, we will give you a brief overview of Manualpolyboard4castellano3 and how to download it for free.
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-What is Manualpolyboard4castellano3?
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-Manualpolyboard4castellano3 is a user manual that provides detailed instructions on how to use your Poly CCX business media phone with OpenSIP. OpenSIP is a standard protocol that allows you to connect your phone to any compatible SIP server or service provider. With OpenSIP, you can enjoy a variety of features and functions on your phone.
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Manualpolyboard4castellano3 covers the following topics:
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-Before You Begin: This section introduces you to the audience, purpose, and required skills of the manual, as well as the related Poly and partner resources that you can access online.
-Getting Started: This section gives you an overview of the CCX phone hardware, LED indicators, navigation, screens, onscreen keyboard, and login and lock features.
-Audio Calls: This section explains how to use the handset, headset, or speakerphone to place, answer, hold, resume, transfer, mute, park, retrieve, record, and manage audio calls.
-Calling Contacts from Directories: This section shows you how to call a contact from a directory or a directory search.
-Placing Intercom Calls: This section teaches you how to place and answer intercom calls.
-Call Precedence and Preemption: This section describes how to handle calls with different priority levels.
-Managing Calls Remotely with Broadworks Server: This section demonstrates how to use Broadsoft UC-One Broadworks Anywhere and Remote Office features to manage your calls from anywhere.
-Ignoring or Rejecting Incoming Calls: This section tells you how to ignore or silence an incoming call, reject incoming calls, automatically reject calls from a contact, reject anonymous calls, and enable Do Not Disturb mode.
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-Why You Need Manualpolyboard4castellano3?
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-Manualpolyboard4castellano3 is a must-have document for anyone who owns or uses a Poly CCX business media phone. Here are some of the benefits of using Manualpolyboard4castellano3:
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-You can learn how to set up your phone quickly and easily.
-You can discover all the features and functions of your phone and how to use them effectively.
-You can troubleshoot any issues or problems that you may encounter with your phone.
-You can access additional resources and support from Poly and its partners.
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-How to Download Manualpolyboard4castellano3?
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-You can download Manualpolyboard4castellano3 for free from the Xiaomi Community website. Xiaomi Community is a platform where Xiaomi users can share their experiences, tips, and feedback about Xiaomi products. To download Manualpolyboard4castellano3, follow these steps:
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-Go to https://new.c.mi.com/ng/post/76797/Manualpolyboard4castellano3
-Click on the Download ⚙ link at the top of the post.
-Save the PDF file to your computer or device.
-Open the PDF file with any PDF reader application.
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-Conclusion
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-Manualpolyboard4castellano3 is a comprehensive guide for Poly CCX business media phone users who want to make the most of their phones. It provides detailed instructions on how to set up, operate, and troubleshoot your phone with OpenSIP. It also covers advanced features like Broadsoft UC-One and Remote Office. You can download Manualpolyboard4castellano3 for free from the Xiaomi Community website. If you have any questions or feedback about Manualpolyboard4castellano3 or your Poly CCX phone, feel free to contact Poly support or visit their website at https://www.poly.com/us/en/support.
-How to Update Your Phone Software
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-One of the advantages of using a Poly CCX business media phone is that you can always keep your phone software up to date with the latest features and security patches. Manualpolyboard4castellano3 explains how to update your phone software manually or automatically. You can also check the current software version and release date of your phone.
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-To update your phone software manually, follow these steps:
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-On your phone, go to Settings > Status > Platform > Phone Information.
-Check the Software Version and Release Date fields.
-If there is a newer version available, go to Settings > Basic > Update Configuration.
-Select Check for Updates.
-If an update is available, select Install Update.
-Wait for the update to complete and your phone to restart.
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-To update your phone software automatically, follow these steps:
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-On your phone, go to Settings > Basic > Update Configuration.
-Select Automatic Updates.
-Select On or Off to enable or disable automatic updates.
-Select Check Interval to set how often your phone checks for updates.
-Select Install Time to set when your phone installs updates.
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-How to Customize Your Phone Settings
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-Another benefit of using a Poly CCX business media phone is that you can customize your phone settings according to your preferences and needs. Manualpolyboard4castellano3 shows you how to change various settings on your phone, such as language, time and date, ringtone, volume, brightness, wallpaper, and more.
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-To change your phone language, follow these steps:
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-On your phone, go to Settings > Basic > Preferences.
-Select Language.
-Select the language you want to use on your phone.
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-To change your time and date settings, follow these steps:
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-On your phone, go to Settings > Basic > Preferences.
-Select Time & Date.
-Select Time Zone and choose your time zone from the list.
-Select Time Format and choose 12-hour or 24-hour format.
-Select Date Format and choose the date format you want to use.
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-To change your ringtone settings, follow these steps:
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-On your phone, go to Settings > Basic > Ring Type.
-Select Default Ringtone and choose a ringtone from the list.
-Select Contact Ringtone and choose a ringtone for a specific contact from the list.
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-To change your volume settings, follow these steps:
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-On your phone, press the Volume keys on the right side of the phone.
-Adjust the volume level for the handset, headset, speakerphone, or ringer as needed.
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-To change your brightness settings, follow these steps:
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-On your phone, go to Settings > Basic > Brightness Level.
-Adjust the brightness level of the screen as needed.
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-To change your wallpaper settings, follow these steps:
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-On your phone, go to Settings > Basic > Wallpaper.
-Select Default Wallpaper and choose a wallpaper from the list.
-Select Custom Wallpaper and choose a wallpaper from a USB flash drive or an online source.
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-How to Use Your Phone with Other Devices
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-One of the features of your Poly CCX business media phone is that you can use it with other devices, such as a USB flash drive, a Bluetooth headset, or a computer. Manualpolyboard4castellano3 explains how to connect your phone to these devices and how to use them with your phone.
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-To use a USB flash drive with your phone, follow these steps:
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-Insert the USB flash drive into the USB port on the back of your phone.
-On your phone, go to Settings > Basic > USB Configuration.
-Select USB Mode and choose Mass Storage Device.
-Select File Manager and browse the files on the USB flash drive.
-You can view, copy, delete, or rename the files on the USB flash drive.
-You can also use the USB flash drive to import or export contacts, update your phone software, or change your wallpaper.
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-To use a Bluetooth headset with your phone, follow these steps:
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-On your phone, go to Settings > Basic > Bluetooth Settings.
-Select Bluetooth and turn it on.
-Select Add Bluetooth Device and put your headset in pairing mode.
-Select your headset from the list of available devices and enter the PIN code if required.
-Your phone will pair with your headset and show a Bluetooth icon on the status bar.
-You can use your headset to make and receive calls on your phone.
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-To use your computer with your phone, follow these steps:
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-Connect your computer to your phone using a USB cable or a Wi-Fi network.
-On your phone, go to Settings > Basic > USB Configuration.
-Select USB Mode and choose Ethernet Emulation Mode (EEM).
-Your phone will act as a network adapter for your computer and share its internet connection.
-You can also use Polycom BToE Connector software on your computer to control your phone from your computer.
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-How to Troubleshoot Your Phone
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-Sometimes, you may encounter some issues or problems with your Poly CCX business media phone. Manualpolyboard4castellano3 provides some troubleshooting tips and solutions for common issues that you may face. Here are some examples:
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-If you cannot hear any sound from your phone, check the following:
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- Make sure that the handset, headset, or speakerphone is not muted.
- Make sure that the volume level is not too low or too high.
- Make sure that the handset or headset is properly connected to the phone.
- Make sure that there is no interference from other devices or sources of noise.
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-If you cannot make or receive calls on your phone, check the following:
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- Make sure that your phone is registered to a SIP server or service provider.
- Make sure that you have entered the correct dialing prefix or access code for outgoing calls.
- Make sure that you have not enabled Do Not Disturb mode or call forwarding on your phone.
- Make sure that there is no network outage or congestion that may affect your call quality or availability.
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-If you cannot update your phone software on your phone, check the following:
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- Make sure that you have downloaded the correct software version for your phone model and region.
- Make sure that you have enough free space on your phone memory or USB flash drive to store the software file.
- Make sure that you have a stable internet connection and power supply during the update process.
- Make sure that you have followed the update instructions correctly and do not interrupt the update process.
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-If you need more help or support with your Poly CCX business media phone, you can contact Poly support or visit their website at https://www.poly.com/us/en/support. You can also access online resources and forums from Poly and its partners on their websites.
-Conclusion
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-Manualpolyboard4castellano3 is a comprehensive user manual that covers everything you need to know about your Poly CCX business media phone with OpenSIP. It provides detailed instructions on how to set up, operate, and troubleshoot your phone. It also covers advanced features like Broadsoft UC-One and Remote Office. You can download Manualpolyboard4castellano3 for free from the Xiaomi Community website. Manualpolyboard4castellano3 is a must-have document for anyone who owns or uses a Poly CCX business media phone. It will help you get the most out of your phone and enjoy its features and functions. If you have any questions or feedback about Manualpolyboard4castellano3 or your Poly CCX phone, feel free to contact Poly support or visit their website at https://www.poly.com/us/en/support.
3cee63e6c2Unlock APK App: What Is It and How to Use It
-Have you ever downloaded an APK file from the internet and wanted to install it on your Android device, but found out that it was locked or incompatible? Or have you ever forgotten your screen lock password or Google account password and needed to bypass the FRP lock on your Android device? If you have faced any of these situations, then you may need an unlock APK app. In this article, we will explain what an unlock APK app is, what are its benefits and risks, and how to use it on different devices.
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Introduction
-An APK file is a package file that contains the installation files for an Android app. You can download APK files from various sources, such as official app stores, third-party websites, or your own device. However, not all APK files are easy to install or use. Some of them may be locked by the developer or the device manufacturer, preventing you from installing or running them. Some of them may also be incompatible with your device model, Android version, or region settings, causing errors or malfunctions.
-Unlocking an APK file means removing the restrictions or limitations that prevent you from installing or using it. By unlocking an APK file, you can enjoy some benefits, such as:
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-Accessing more apps that are not available in your region or device
-Installing apps that are not supported by your device or Android version
-Modifying apps to customize their features or appearance
-Bypassing screen lock or FRP lock on your Android device
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-However, unlocking an APK file also comes with some risks, such as:
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-Voiding your device warranty or violating the terms of service
-Exposing your device to malware or viruses
-Causing system instability or performance issues
-Losing your data or settings
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-Therefore, before you decide to unlock an APK file, you should weigh the pros and cons carefully and take some precautions, such as backing up your data, scanning the file for malware, and checking the source and reputation of the file.
- How to Unlock APK Files on Android Devices
-If you want to unlock an APK file on your Android device, there are two main methods that you can use: using a device unlock app or using a file manager app. Let's see how each method works and what are some examples of apps that you can use.
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- Method 1: Using a Device Unlock App
-A device unlock app is a software that can help you access your locked Android device without a password. Some of them can remove the screen lock on your phone without erasing your data. Others can unlock different types of locks, like PIN
, pattern, password, fingerprint, or face recognition. Some of them can also bypass the FRP lock, which is a security feature that prevents you from using your device after a factory reset without verifying your Google account.
-To use a device unlock app, you need to download and install it on your Android device or on another device that can connect to your locked device via USB cable. Then, you need to follow the instructions on the app to unlock your device. Depending on the app and the type of lock, you may need to enter some information, such as your device model, IMEI number, or Google account details.
-Some examples of device unlock apps are:
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-App Name
-Description
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-Dr.Fone - Screen Unlock (Android)
-A professional tool that can remove any type of screen lock on Android devices without data loss
-- Supports PIN, pattern, password, fingerprint, and face lock
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-FRP Bypass APK
-A free app that can bypass the FRP lock on Android devices by using an OTG cable and a flash drive
-- Compatible with most Android devices
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-iMyFone LockWiper (Android)
-A powerful tool that can remove any type of lock on Android devices, including screen lock and FRP lock
-- Supports over 6000 Android devices
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- Method 2: Using a File Manager App
-A file manager app is a software that can help you access and manage the files and folders on your Android device. Some of them can also access and modify the APK files that are installed or stored on your device. By using a file manager app, you can unlock an APK file by changing its permissions, renaming it, deleting it, or moving it to another location.
-To use a file manager app, you need to download and install it on your Android device. Then, you need to locate the APK file that you want to unlock and perform the desired action on it. For example, you can change the permissions of the APK file by tapping on it and selecting Properties. Then, you can check or uncheck the boxes for Read, Write, or Execute permissions. You can also rename the APK file by tapping on it and selecting Rename. Then, you can change the extension from .apk to .zip or .rar. You can also delete or move the APK file by tapping on it and selecting Delete or Move.
-Some examples of file manager apps are:
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-App Name
-Description
-Features
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-ES File Explorer
-A popular and versatile app that can manage all types of files and folders on Android devices
-- Supports local and network storage
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-ASTRO File Manager
-A simple and user-friendly app that can organize and manage files and folders on Android devices
-- Supports local and cloud storage
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-X-plore File Manager
-A dual-pane app that can explore and manage files and folders on Android devices
-- Supports local and network storage
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- How to Unlock APK Files on PC or Mac
- If you want to unlock an APK file on your PC or Mac , there are two main methods that you can use: using an Android emulator or using an APK editor. Let's see how each method works and what are some examples of software that you can use.
- Method 1: Using an Android Emulator
-An Android emulator is a software that can simulate an Android device on your PC or Mac. You can use an Android emulator to run and unlock APK files on your computer, just like you would do on your phone or tablet. You can also install and use device unlock apps or file manager apps on the emulator to unlock the APK files.
-To use an Android emulator, you need to download and install it on your PC or Mac. Then, you need to launch the emulator and set up your Google account and device settings. Then, you need to drag and drop the APK file that you want to unlock onto the emulator screen. The emulator will automatically install and run the APK file. You can then use the emulator controls to interact with the app and unlock it.
-Some examples of Android emulators are:
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-
-Software Name
-Description
-Features
-
-
-BlueStacks
-A popular and powerful emulator that can run Android apps and games on PC or Mac
-- Supports multiple instances and synchronization
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-NoxPlayer
-A fast and stable emulator that can run Android apps and games on PC or Mac
-- Supports multiple instances and synchronization
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-LDPlayer
-A lightweight and optimized emulator that can run Android apps and games on PC
-- Supports multiple instances and synchronization
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- Method 2: Using an APK Editor
-An APK editor is a software that can open and edit APK files on your PC or Mac. You can use an APK editor to unlock an APK file by changing its code, resources, or metadata. You can also modify the app's features, appearance, or behavior by editing the APK file.
-To use an APK editor, you need to download and install it on your PC or Mac. Then, you need to open the APK file that you want to unlock with the editor. The editor will show you the contents of the APK file, such as the manifest, classes, resources, etc. You can then edit the APK file as you wish. For example, you can change the app's name, icon, package name, version, permissions, etc. by editing the manifest file. You can also change the app's code by editing the classes.dex file. You can also change the app's graphics, sounds, fonts, etc. by editing the resources files.
-Some examples of APK editors are:
-
-
-Software Name
-Description
-Features
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-
-APK Editor Pro
-A powerful and professional tool that can edit APK files on PC or Mac
-- Supports full editing mode and simple editing mode
-
-
-APK Easy Tool A simple and user-friendly tool that can edit APK files on PC - Supports decompiling and recompiling APK files Apktool A command-line tool that can edit APK files on PC or Mac - Supports decompiling and recompiling APK files
Conclusion
- In this article, we have explained what an unlock APK app is, what are its benefits and risks, and how to use it on different devices . We have also shown you two methods for unlocking APK files on Android devices and two methods for unlocking APK files on PC or Mac. You can choose the method that suits your needs and preferences, but remember to be careful and responsible when unlocking APK files, as it may involve some risks and consequences.
-Unlocking APK files can be a useful and fun way to access more apps, customize your device, or bypass some locks. However, it can also be a risky and complicated process that requires some technical skills and knowledge. Therefore, we recommend you to do some research and backup before you attempt to unlock any APK file. We also advise you to only download and install APK files from trusted and reputable sources, and scan them for malware before opening them.
-We hope this article has helped you understand what an unlock APK app is and how to use it on different devices. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you!
- FAQs
-Here are some frequently asked questions about unlock APK app:
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-What is the difference between unlocking and rooting an Android device? Is unlocking an APK file legal? How can I tell if an APK file is locked or unlocked? Can I unlock any APK file? What are some alternatives to unlocking an APK file? 197e85843dSummertime Saga Download: How to Play the Hottest Dating Sim Game
- If you are looking for a fun and spicy game to spice up your life, you might want to check out Summertime Saga. This is a graphical adventure game for adults that has become one of the most popular dating sim games on the internet. In this game, you will play as a young man who is trying to cope with the death of his father, while also dealing with school, work, romance, and mysteries. You will have the chance to interact with over 70 characters, explore over 30 locations, and enjoy over 20 mini-games. Whether you are looking for comedy, drama, romance, or erotica, Summertime Saga has something for everyone.
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-Regular updates and new content : The game is still in development and receives frequent updates from the developers. Each update adds new stories, characters, locations, events, and features to the game. You can always expect something new and exciting in Summertime Saga.Multiple locations and characters to interact with : The game has a rich and diverse world that you can explore at your own pace. You can visit different places such as your home, school, beach, park, mall, hospital, gym, library, and more. You can also meet and interact with over 70 characters, each with their own personality, backstory, appearance, and voice. You can befriend them, date them, or even seduce them.Marvelous graphics and sound effects : The game has a beautiful art style that resembles anime and manga. The characters are well-designed and expressive, while the backgrounds are detailed and colorful. The game also has high-quality sound effects and music that enhance the atmosphere and mood of the game.Engaging mini-games and puzzles : The game is not just about talking and flirting. It also has many mini-games and puzzles that you can play to earn money, increase your stats, or progress the story. You can play guitar, solve Sudoku, rap battle, fish, box, shoot hoops, hack computers, and more.Customizable protagonist and stats : The game allows you to customize your protagonist's name, appearance, clothes, hairstyle, and accessories. You can also improve your stats such as strength, intelligence, charisma, dexterity, luck, fighting skills, guitar skills, etc. Your stats will affect your performance in mini-games, school grades, relationships with characters, and story outcomes.
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- If you are interested in playing Summertime Saga, you can download it for free from the official website or from other platforms. Here are the steps to download and install the game for different devices:
- PC and Mac
- You can download the game for PC and Mac from the official website or from Steam. The game is compatible with Windows 7 or higher, Mac OS X 10.9 or higher, and Linux. The file size is about 1 GB. To install the game, follow these steps:
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-Download the zip file from the website or Steam.
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- You can download the game for Android from the official website or from Google Play Store. The game is compatible with Android 5.0 or higher and requires at least 2 GB of RAM and 2 GB of free storage. The file size is about 800 MB. To install the game, follow these steps:
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-Download the apk file from the website or Google Play Store.
-Enable unknown sources in your device settings if you downloaded the apk file from the website.
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-Open the game and grant permissions if asked.
-Enjoy the game!
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- Tips and Tricks for Playing Summertime Saga
- Summertime Saga is a fun and addictive game, but it can also be challenging and confusing at times. Here are some tips and tricks that can help you get the most out of your gaming experience:
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-Don't sleep until you have to : Sleeping in the game will advance the time and trigger events, but it will also waste your opportunities to do other things. Try to sleep only when you have no energy left, when you have completed all your tasks for the day, or when you want to progress the story.Listen to the characters and follow the hints : The characters in the game will often give you clues about what to do next, where to go, or what to buy. Pay attention to their dialogues and look for keywords that indicate their interests, preferences, or needs. You can also check your phone for messages, tasks, and stats.Make money and increase your stats : Money and stats are essential for unlocking new content, buying items, dating characters, and completing quests. You can make money by doing jobs, selling items, playing mini-games, or gambling. You can increase your stats by studying, working out, playing guitar, reading books, or taking courses.Check the wiki and guides for walkthroughs : If you are stuck or lost in the game, you can always consult the wiki or guides for walkthroughs, tips, cheats, secrets, and more. You can also join the Discord server or Reddit community to chat with other players and get help.
- Alternatives to Summertime Saga
- If you love Summertime Saga and want to try out other games that are similar or different, here are some alternatives that you might enjoy:
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- Doki Doki Literature Club
- This is a psychological horror game disguised as a cute dating sim. You will join a literature club and meet four lovely girls who have different personalities and preferences. You will write poems, share them with your club members, and try to impress your crush. However, things will take a dark and twisted turn as you discover that there is more than meets the eye in this game. You can download it for free from Steam or itch.io.
- HuniePop
- This is a match-three puzzle game with dating sim elements. You will meet eight beautiful women who have different tastes and styles. You will talk to them, date them, and give them gifts to increase their affection towards you. You will also play match-three puzzles to impress them and unlock more intimate scenes. You can buy it from Steam or GOG.com.
- Love Esquire
- This is a RPG dating sim where you play as a squire in a medieval fantasy world. You will train hard, fight enemies, and save the world. You will also romance five lovely ladies who have different personalities and backgrounds. You will have to balance your time, money, and relationships as you make choices that affect the story and the endings. You can buy it from Steam or itch.io.
- Monster Prom
- This is a multiplayer dating sim where you date sexy monsters in high school. You will have three weeks to find a date for the prom and impress them with your charm, wit, and style. You will interact with six main love interests and many other characters, each with their own quirks and preferences. You will also encounter random events, mini-games, secrets, and endings. You can play solo or with up to four players online or locally. You can buy it from Steam or GOG.com.
- Conclusion
- Summertime Saga is a game that will keep you entertained for hours with its captivating story, hilarious dialogue, stunning graphics, and varied gameplay. It is a game that you can play at your own pace and style, as you explore the world, meet the characters, and shape your own adventure. If you are looking for a game that will make you laugh, cry, blush, and thrill, Summertime Saga is the game for you.
- So what are you waiting for? Download Summertime Saga today and start your saga!
- FAQs
-
-Is Summertime Saga free? : Yes, Summertime Saga is free to download and play. However, you can support the developers by donating on their Patreon page or buying their merchandise.Is Summertime Saga safe? : Yes, Summertime Saga is safe to download and play. However, it is not suitable for minors or people who are easily offended by adult content.How long is Summertime Saga? : Summertime Saga is still in development and does not have a definitive ending yet. However, it already has over 60 hours of gameplay content and over 20 storylines to complete.How do I update Summertime Saga? : You can update Summertime Saga by downloading the latest version from the official website or Steam. You can also enable automatic updates in your device settings.How do I save my progress in Summertime Saga? : You can save your progress in Summertime Saga by using the save slots in the game menu. You can also use the cloud save feature to sync your progress across different devices. 197e85843dDrift Racing: A Guide for Beginners
- Have you ever watched a movie or a video where a car slides sideways around a corner, leaving behind a trail of smoke and tire marks? If so, you have witnessed drift racing, one of the most exciting and challenging motorsports in the world. Drift racing is a driving technique where the driver intentionally oversteers, loses traction, and maintains control of the car through the entire corner. It requires skill, precision, and courage to master.
-drift legends DOWNLOAD ===> https://urllie.com/2uNGt4
In this article, we will explain what drift racing is, how it originated, and what are its benefits. We will also show you how to drift a car, what kind of car you need, and how to improve your drifting skills. Whether you want to drift for fun, for competition, or for self-expression, this guide will help you get started on your drifting journey.
- What is Drift Racing?
- The History of Drift Racing
- Drift racing is often associated with Japan, where it became popular in the 1970s. However, the origins of drifting go back to Europe in the 1950s, where drivers like Tazio Nuvolari and Stirling Moss used the four-wheel drift technique to corner faster in Formula One races. Later, in Japan, Kunimitsu Takahashi developed his own style of drifting in touring car races, earning him several championships and a large fan base. His drifting skills inspired Keiichi Tsuchiya, who became known as the "Drift King" and practiced his drifting on mountain roads. In 1987, Tsuchiya starred in a video called Pluspy, which showcased his drifting abilities and influenced many other drivers. In 1988, he helped organize one of the first events specifically for drifting, called the Ikaten. [^5]
- Since then, drifting has grown into a global phenomenon, with competitions held all over the world. Some of the most famous drifting events include D1 Grand Prix in Japan, Formula Drift in the USA, Drift Masters European Championship in Europe, and Red Bull Car Park Drift in the Middle East and Africa. Drifting has also been featured in movies, video games, manga, and anime, such as The Fast and the Furious: Tokyo Drift and Initial D.
-drift legends real car racing
- The Basics of Drift Racing
- Drifting is not about speed or lap times; it is about style and showmanship. Drifting is judged by several criteria, such as speed, angle, line, and smoke. Speed refers to how fast the car goes through the corner; angle refers to how much the car is sideways; line refers to how close the car follows the ideal path; and smoke refers to how much tire smoke the car produces. The more speed, angle, line, and smoke the driver can achieve while maintaining control of the car, the higher the score.
- To drift a car, the driver needs to make the rear wheels lose traction while keeping the front wheels pointed in the opposite direction of the turn. This is called oversteer and countersteer. There are different ways to initiate oversteer, such as using the throttle, the handbrake, or the clutch. Once oversteer is initiated, the driver needs to balance the throttle and steering inputs to maintain the drift through the corner.
- The Benefits of Drift Racing
- Drifting has several benefits for drivers who want to have fun and improve their driving skills. Some of these benefits are:
-
-Cardiovascular exercise: Drifting Drifting is a great way to get your heart rate up and burn calories. According to a study by the University of Tokyo, drifting can increase your heart rate by up to 40% and burn up to 400 calories per hour. Drifting also releases adrenaline and endorphins, which can boost your mood and energy levels.
-Car control: Drifting teaches you how to handle your car in different situations and conditions. You learn how to control oversteer, understeer, weight transfer, and traction. You also develop your reflexes, coordination, and spatial awareness. These skills can help you become a better and safer driver on the road.
-Creativity and expression: Drifting allows you to express your personality and style through your car and driving. You can customize your car with different parts, colors, stickers, and accessories. You can also choose your own drifting technique, line, and angle. Drifting is a form of art that lets you create your own masterpiece on the asphalt.
-
- How to Drift a Car?
- Choosing a Car and a Practice Spot
- The first step to drifting is choosing a suitable car and a safe place to practice. Ideally, you want a car that has:
-
-Rear-wheel drive: This means that the power from the engine goes to the rear wheels, which makes it easier to break traction and initiate oversteer.
-Manual transmission: This gives you more control over the engine speed and torque, which are essential for drifting. You can also use the clutch to kick the rear wheels into a slide.
-Limited-slip differential: This is a device that distributes the power evenly between the rear wheels, which prevents one wheel from spinning faster than the other. This helps you maintain stability and balance during a drift.
-Lightweight chassis: This reduces the inertia and resistance of the car, which makes it more responsive and agile.
-
- Some examples of popular drift cars are the Toyota AE86, Nissan 240SX, Mazda RX-7, BMW E36, and Ford Mustang. However, you can drift any car as long as you have the skill and confidence.
- Once you have a car, you need to find a place where you can practice drifting without endangering yourself or others. The best place to practice is a closed circuit or a track that has wide open spaces, smooth surfaces, and no obstacles or traffic. You can also look for empty parking lots, industrial areas, or rural roads that are legal and safe for drifting. Make sure you have permission from the owner or authority before you start drifting.
- Performing a Power Over to Drift
- One of the easiest ways to drift is by using the power over technique. This involves using the throttle to induce oversteer at the exit of a corner. Here are the steps to perform a power over drift:
-
-Approach the corner at a moderate speed and turn in smoothly.
-As you reach the apex of the corner, apply more throttle to accelerate out of the corner.
-The rear wheels will start to spin faster than the front wheels, causing them to lose grip and slide outwards.
-Countersteer by turning the steering wheel in the opposite direction of the slide. For example, if the rear end slides to the left, turn the wheel to the right.
-Modulate the throttle to control the angle and speed of the drift. If you apply too much throttle, you will spin out; if you apply too little throttle, you will straighten out.
-Maintain the drift until you exit the corner or reach your desired point.
-
- Using the Handbrake to Slide an Automatic
- If you have an automatic car, you can use the handbrake or emergency brake to initiate oversteer at the entry of a corner. The handbrake locks up the rear wheels, which makes them skid and slide sideways. Here are the steps to perform a handbrake drift:
-
-Approach the corner at a moderate speed and turn in slightly.
-Pull up the handbrake quickly and firmly to lock up the rear wheels and make them slide outwards.
-Countersteer by turning the steering wheel in the opposite direction of the slide.
-Release the handbrake and apply some throttle to keep the drift going.
-Modulate the throttle and steering to control the angle and speed of the drift.
-Maintain the drift until you exit the corner or reach your desired point.
-
- Using the Clutch Kick to Drift a Manual
- If you have a manual car, you can use the clutch kick technique to initiate oversteer at any point of a corner. The clutch kick involves pressing and releasing the clutch pedal quickly to create a sudden burst of power to the rear wheels, which makes them spin and slide. Here are the steps to perform a clutch kick drift:
-
-Approach the corner at a moderate speed and turn in slightly.
-Shift to a lower gear and rev up the engine to a high RPM.
-Press and release the clutch pedal quickly to send a surge of power to the rear wheels.
-The rear wheels will start to spin faster than the front wheels, causing them to lose grip and slide outwards.
-Countersteer by turning the steering wheel in the opposite direction of the slide.
-Modulate the throttle and clutch to control the angle and speed of the drift. If you press the clutch too long, you will lose power; if you release the clutch too soon, you will regain traction.
-Maintain the drift until you exit the corner or reach your desired point.
-
- How to Improve Your Drifting Skills?
- Upgrading Your Car and Suspension
- As you progress in your drifting skills, you may want to upgrade your car and suspension to enhance your performance and style. Some of the common upgrades for drift cars are:
-
-Tires: You want tires that have good grip, durability, and smoke production. You can also use different tires for the front and rear wheels, depending on your preference. For example, some drivers use grippy tires for the front wheels and slippery tires for the rear wheels, which makes it easier to initiate oversteer.
-Brakes: You want brakes that have good stopping power, modulation, and cooling. You can also use different brakes for the front and rear wheels, depending on your preference. For example, some drivers use larger brakes for the front wheels and smaller brakes for the rear wheels, which makes it easier to lock up the rear wheels with the handbrake.
-Suspension: You want suspension that has good stiffness, damping, and adjustability. You can also use different suspension settings for the front and rear wheels, depending on your preference. For example, some drivers use stiffer suspension for the front wheels and softer suspension for the rear wheels, which makes it easier to transfer weight and balance during a drift.
-
- Practicing Different Drifting Techniques
- Besides the techniques we have mentioned above, there are many other drifting techniques that you can learn and practice to improve your drifting skills. Some of these techniques are:
-
-Feint drift: This involves swerving or flicking the car in one direction before entering a corner in the opposite direction, which creates momentum and oversteer.
-Braking drift: This involves applying the brakes before entering a corner, which shifts the weight to the front wheels and makes the rear wheels lose traction.
-Shift lock drift: This involves downshifting to a lower gear without using the clutch, which makes the rear wheels lock up and skid.
-Dirt drop drift: This involves dropping one or both rear wheels off the road onto the dirt or grass, which reduces the grip and increases the angle of the drift.
-Jump drift: This involves jumping over a bump or a crest, which lifts the car off the ground and makes it slide sideways.
-
- You can practice these techniques on different types of corners, such as hairpins, chicanes, S-bends, and sweepers. You can also practice on different types of surfaces, such as asphalt, concrete, gravel, and snow. The more you practice, the more you will learn how to adapt to different situations and conditions.
- Finding Your Drifting Style and Purpose
- Drifting is not only a sport, but also a culture and a lifestyle. Drifting has many subcultures and communities that share their passion and knowledge of drifting. Some of these subcultures are:
-
-Touge: This is a Japanese term that refers to drifting on mountain roads, where drivers challenge each other in downhill or uphill races.
-Gymkhana: This is a type of motorsport that involves drifting around obstacles, such as cones, barrels, and tires, in a timed course.
-Driftkhana: This is a combination of drifting and gymkhana, where drivers perform stunts and tricks while drifting around obstacles.
-Freestyle: This is a type of drifting that involves expressing yourself through your car and driving, without following any rules or regulations.
-
- You can find your own drifting style and purpose by exploring different aspects of drifting and joining different drifting communities. You can also participate in drifting events, competitions, and shows, where you can showcase your skills and meet other drifters. Drifting is a fun and rewarding hobby that can enrich your life and make you happy.
- Conclusion
- Drift racing is an amazing motorsport that involves sliding a car sideways around corners. It originated in Europe and Japan in the 1950s and 1970s, and has since become a global phenomenon. Drifting has many benefits for drivers who want to have fun and improve their driving skills. It also allows drivers to express their creativity and style through their car and driving. To drift a car, you need a rear-wheel drive car with manual transmission and limited-slip differential. You also need to learn how to initiate oversteer, countersteer, and modulate the throttle. You can improve your drifting skills by upgrading your car and suspension, practicing different drifting techniques, and finding your drifting style and purpose. Drifting is a great way to enjoy driving and have an awesome time.
- FAQs
- Here are some frequently asked questions about drift racing:
-
-Q: Is drift racing illegal? A: Drift racing is illegal on public roads, as it can endanger yourself and others. However, drift racing is legal on private tracks or circuits that are authorized for drifting. You can also join drifting clubs or organizations that organize legal drifting events.
-Q: Is drift racing expensive? A: Drift racing can be expensive depending on how much you invest in your car and equipment. Drifting can also wear out your tires, brakes, clutch, engine, and suspension faster than normal driving. However, you can start drifting with a low-budget car and upgrade it gradually as you progress. You can also save money by buying used parts or doing your own maintenance.
-Q: Is drift racing dangerous? A: Drift racing can be dangerous if you are not careful or experienced enough. Drifting can cause accidents, injuries, or damages if you lose control of your car or hit something or someone. Therefore, you should always wear proper safety gear, such as helmet, gloves, seat belt, and fire extinguisher. You should also follow the rules and regulations of the track or event you are participating in. You should never drift under the influence of alcohol or drugs.
-Q: Is drift racing fun? A: Drift racing is fun for many reasons. It is fun to slide a car sideways around corners and feel the adrenaline rush. It is fun to challenge yourself and improve your driving skills. It is fun to customize your car and show your personality and style. It is fun to meet other drifters and share your passion and knowledge of drifting. Drifting is fun for anyone who loves driving and having a good time.
-Q: How can I learn more about drift racing? A: You can learn more about drift racing by reading books, magazines, blogs, or websites about drifting. You can also watch videos, movies, or shows about drifting. You can also join online forums, groups, or communities where you can ask questions, get tips, or share your experiences with other drifters. You can also attend drifting events, competitions, or shows where you can watch, learn, or participate in drifting.
-
- I hope you enjoyed this article and learned something new about drift racing. If you have any comments, feedback, or suggestions, please let me know. I would love to hear from you. Thank you for reading and happy drifting!
401be4b1e0 $config_json
-{
- "train_micro_batch_size_per_gpu":1,
- "steps_per_print":100,
- "gradient_clipping":1,
- "zero_optimization":{
- "stage": $ZERO_STAGE,
- "offload_optimizer":{
- "device":"cpu",
- "pin_memory":true
- },
- "offload_param":{
- "device":"cpu",
- "pin_memory":true
- },
- "overlap_comm":true,
- "contiguous_gradients":true,
- "sub_group_size":1000000000,
- "stage3_max_live_parameters":1000000000,
- "stage3_max_reuse_distance":1000000000,
- "stage3_gather_fp16_weights_on_model_save":true
- },
- "optimizer":{
- "type":"Adam",
- "params":{
- "lr": 1e-5,
- "weight_decay":0.01
- }
- },
- "scheduler":{
- "type":"WarmupLR",
- "params":{
- "warmup_min_lr":5e-6,
- "warmup_max_lr":1e-5
- }
- },
- "zero_allow_untested_optimizer":false,
- "fp16":{
- "enabled":true,
- "loss_scale":0,
- "loss_scale_window":1000,
- "hysteresis":2,
- "min_loss_scale":1
- },
- "activation_checkpointing":{
- "partition_activations":false,
- "contiguous_memory_optimization":false
- },
- "wall_clock_breakdown":false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-TRAINER_ARGS="
- --max_epochs 2 \
- --gpus 1 \
- --num_nodes 1 \
- --strategy deepspeed_stage_3 \
- --precision 16 \
- --default_root_dir $ROOT_DIR \
- --dirpath $ROOT_DIR/ckpt \
- --save_top_k 3 \
- --monitor train_loss \
- --mode min \
- --save_last \
-"
-DATA_DIR=/cognitive_comp/gaoxinyu/data/yuyuan
-DATA_ARGS="
- --data_dir $DATA_DIR \
- --train_batchsize $MICRO_BATCH_SIZE \
- --valid_batchsize $MICRO_BATCH_SIZE \
- --train_data train.txt \
- --valid_data valid.txt \
- --test_data test.txt
-"
-
-MODEL_ARGS="
- --pretrained_model_path /cognitive_comp/gaoxinyu/hf_model/wenzhong \
- --output_save_path $ROOT_DIR/predict.json \
- --learning_rate 1e-4 \
- --weight_decay 0.1 \
- --warmup 0.01 \
-"
-
-SCRIPTS_PATH=/cognitive_comp/gaoxinyu/FS/fengshen/finetune_wenzhong.py
-
-export CMD=" \
- $SCRIPTS_PATH \
- $TRAINER_ARGS \
- $MODEL_ARGS \
- $DATA_ARGS \
- "
-
-echo $CMD
-
-SINGULARITY_PATH=/cognitive_comp/gaoxinyu/docker/pytorch21_06_py3_docker_image_v2.sif
-
-# to debug - add echo (it exits and prints what it would have launched)
-#run_cmd="$PY_LAUNCHER $CMD"
-
-clear; srun --jobid $SLURM_JOBID singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c 'python $CMD'
-# bash -c 'python $CMD'
\ No newline at end of file
diff --git a/spaces/felicco/andite-pastel-mix/app.py b/spaces/felicco/andite-pastel-mix/app.py
deleted file mode 100644
index f90587fb1b4538cf5a92df4655fed8e4e2cf9579..0000000000000000000000000000000000000000
--- a/spaces/felicco/andite-pastel-mix/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/andite/pastel-mix").launch()
\ No newline at end of file
diff --git a/spaces/firsk/ai_otto/text/chinese.py b/spaces/firsk/ai_otto/text/chinese.py
deleted file mode 100644
index 51acb3ec401d7647278a25537576a0fb1775d827..0000000000000000000000000000000000000000
--- a/spaces/firsk/ai_otto/text/chinese.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {
- line.split("\t")[0]: line.strip().split("\t")[1]
- for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
-}
-
-import jieba.posseg as psg
-
-
-rep_map = {
- "：": ",",
- "；": ",",
- "，": ",",
- "。": ".",
- "！": "!",
- "？": "?",
- "\n": ".",
- "·": ",",
- "、": ",",
- "...": "…",
- "$": ".",
- "“": "'",
- "”": "'",
- "‘": "'",
- "’": "'",
- "（": "'",
- "）": "'",
- "(": "'",
- ")": "'",
- "《": "'",
- "》": "'",
- "【": "'",
- "】": "'",
- "[": "'",
- "]": "'",
- "—": "-",
- "～": "-",
- "~": "-",
- "「": "'",
- "」": "'",
-}
-
-tone_modifier = ToneSandhi()
-
-
-def replace_punctuation(text):
- text = text.replace("嗯", "恩").replace("呣", "母")
- pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
-
- replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
- replaced_text = re.sub(
- r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
- )
-
- return replaced_text
-
-
-def g2p(text):
- pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
- sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
- phones, tones, word2ph = _g2p(sentences)
- assert sum(word2ph) == len(phones)
- assert len(word2ph) == len(text) # Sometimes it will crash,you can add a try-catch.
- phones = ["_"] + phones + ["_"]
- tones = [0] + tones + [0]
- word2ph = [1] + word2ph + [1]
- return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
- initials = []
- finals = []
- orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
- orig_finals = lazy_pinyin(
- word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
- )
- for c, v in zip(orig_initials, orig_finals):
- initials.append(c)
- finals.append(v)
- return initials, finals
-
-
-def _g2p(segments):
- phones_list = []
- tones_list = []
- word2ph = []
- for seg in segments:
- # Replace all English words in the sentence
- seg = re.sub("[a-zA-Z]+", "", seg)
- seg_cut = psg.lcut(seg)
- initials = []
- finals = []
- seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
- for word, pos in seg_cut:
- if pos == "eng":
- continue
- sub_initials, sub_finals = _get_initials_finals(word)
- sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
- initials.append(sub_initials)
- finals.append(sub_finals)
-
- # assert len(sub_initials) == len(sub_finals) == len(word)
- initials = sum(initials, [])
- finals = sum(finals, [])
- #
- for c, v in zip(initials, finals):
- raw_pinyin = c + v
- # NOTE: post process for pypinyin outputs
- # we discriminate i, ii and iii
- if c == v:
- assert c in punctuation
- phone = [c]
- tone = "0"
- word2ph.append(1)
- else:
- v_without_tone = v[:-1]
- tone = v[-1]
-
- pinyin = c + v_without_tone
- assert tone in "12345"
-
- if c:
- # 多音节
- v_rep_map = {
- "uei": "ui",
- "iou": "iu",
- "uen": "un",
- }
- if v_without_tone in v_rep_map.keys():
- pinyin = c + v_rep_map[v_without_tone]
- else:
- # 单音节
- pinyin_rep_map = {
- "ing": "ying",
- "i": "yi",
- "in": "yin",
- "u": "wu",
- }
- if pinyin in pinyin_rep_map.keys():
- pinyin = pinyin_rep_map[pinyin]
- else:
- single_rep_map = {
- "v": "yu",
- "e": "e",
- "i": "y",
- "u": "w",
- }
- if pinyin[0] in single_rep_map.keys():
- pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
-
- assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
- phone = pinyin_to_symbol_map[pinyin].split(" ")
- word2ph.append(len(phone))
-
- phones_list += phone
- tones_list += [int(tone)] * len(phone)
- return phones_list, tones_list, word2ph
-
-
-def text_normalize(text):
- numbers = re.findall(r"\d+(?:\.?\d+)?", text)
- for number in numbers:
- text = text.replace(number, cn2an.an2cn(number), 1)
- text = replace_punctuation(text)
- return text
-
-
-def get_bert_feature(text, word2ph):
- from text import chinese_bert
-
- return chinese_bert.get_bert_feature(text, word2ph)
-
-
-if __name__ == "__main__":
- from text.chinese_bert import get_bert_feature
-
- text = "啊！但是《原神》是由,米哈\游自主， [研发]的一款全.新开放世界.冒险游戏"
- text = text_normalize(text)
- print(text)
- phones, tones, word2ph = g2p(text)
- bert = get_bert_feature(text, word2ph)
-
- print(phones, tones, word2ph, bert.shape)
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text)) # 输出: 这是一个示例文本你好这是一个测试
diff --git a/spaces/fiyen/YangyangChatGPT/modules/chat_func.py b/spaces/fiyen/YangyangChatGPT/modules/chat_func.py
deleted file mode 100644
index a4875fb03a27c5e29ef5e439ab7e3da3fc827155..0000000000000000000000000000000000000000
--- a/spaces/fiyen/YangyangChatGPT/modules/chat_func.py
+++ /dev/null
@@ -1,513 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import os
-import requests
-import urllib3
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from llama_index.indices.query.vector_store import GPTVectorStoreIndexQuery
-from llama_index.indices.query.schema import QueryBundle
-from langchain.llms import OpenAIChat
-
-from modules.presets import *
-from modules.llama_func import *
-from modules.utils import *
-import modules.shared as shared
-
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
-
-if TYPE_CHECKING:
- from typing import TypedDict
-
- class DataframeData(TypedDict):
- headers: List[str]
- data: List[List[str | int | bool]]
-
-
-initial_prompt = "You are a helpful assistant."
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-def get_response(
- openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model
-):
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}",
- }
-
- history = [construct_system(system_prompt), *history]
-
- payload = {
- "model": selected_model,
- "messages": history, # [{"role": "user", "content": f"{inputs}"}],
- "temperature": temperature, # 1.0,
- "top_p": top_p, # 1.0,
- "n": 1,
- "stream": stream,
- "presence_penalty": 0,
- "frequency_penalty": 0,
- }
- if stream:
- timeout = timeout_streaming
- else:
- timeout = timeout_all
-
- proxies = get_proxies()
-
- # 如果有自定义的api-url，使用自定义url发送请求，否则使用默认设置发送请求
- if shared.state.api_url != API_URL:
- logging.info(f"使用自定义API URL: {shared.state.api_url}")
-
- response = requests.post(
- shared.state.api_url,
- headers=headers,
- json=payload,
- stream=True,
- timeout=timeout,
- proxies=proxies,
- )
-
- return response
-
-
-def stream_predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=None,
- display_append=""
-):
- def get_return_value():
- return chatbot, history, status_text, all_token_counts
-
- logging.info("实时回答模式")
- partial_words = ""
- counter = 0
- status_text = "开始实时传输回答……"
- history.append(construct_user(inputs))
- history.append(construct_assistant(""))
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- user_token_count = 0
- if fake_input is not None:
- input_token_count = count_token(construct_user(fake_input))
- else:
- input_token_count = count_token(construct_user(inputs))
- if len(all_token_counts) == 0:
- system_prompt_token_count = count_token(construct_system(system_prompt))
- user_token_count = (
- input_token_count + system_prompt_token_count
- )
- else:
- user_token_count = input_token_count
- all_token_counts.append(user_token_count)
- logging.info(f"输入token计数: {user_token_count}")
- yield get_return_value()
- try:
- response = get_response(
- openai_api_key,
- system_prompt,
- history,
- temperature,
- top_p,
- True,
- selected_model,
- )
- except requests.exceptions.ConnectTimeout:
- status_text = (
- standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
- )
- yield get_return_value()
- return
- except requests.exceptions.ReadTimeout:
- status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
- yield get_return_value()
- return
-
- yield get_return_value()
- error_json_str = ""
-
- if fake_input is not None:
- history[-2] = construct_user(fake_input)
- for chunk in response.iter_lines():
- if counter == 0:
- counter += 1
- continue
- counter += 1
- # check whether each line is non-empty
- if chunk:
- chunk = chunk.decode()
- chunklength = len(chunk)
- try:
- chunk = json.loads(chunk[6:])
- except json.JSONDecodeError:
- logging.info(chunk)
- error_json_str += chunk
- status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
- yield get_return_value()
- continue
- # decode each line as response data is in bytes
- if chunklength > 6 and "delta" in chunk["choices"][0]:
- finish_reason = chunk["choices"][0]["finish_reason"]
- status_text = construct_token_message(
- sum(all_token_counts), stream=True
- )
- if finish_reason == "stop":
- yield get_return_value()
- break
- try:
- partial_words = (
- partial_words + chunk["choices"][0]["delta"]["content"]
- )
- except KeyError:
- status_text = (
- standard_error_msg
- + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: "
- + str(sum(all_token_counts))
- )
- yield get_return_value()
- break
- history[-1] = construct_assistant(partial_words)
- chatbot[-1] = (chatbot[-1][0], partial_words+display_append)
- all_token_counts[-1] += 1
- yield get_return_value()
-
-
-def predict_all(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=None,
- display_append=""
-):
- logging.info("一次性回答模式")
- history.append(construct_user(inputs))
- history.append(construct_assistant(""))
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- if fake_input is not None:
- all_token_counts.append(count_token(construct_user(fake_input)))
- else:
- all_token_counts.append(count_token(construct_user(inputs)))
- try:
- response = get_response(
- openai_api_key,
- system_prompt,
- history,
- temperature,
- top_p,
- False,
- selected_model,
- )
- except requests.exceptions.ConnectTimeout:
- status_text = (
- standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
- )
- return chatbot, history, status_text, all_token_counts
- except requests.exceptions.ProxyError:
- status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt
- return chatbot, history, status_text, all_token_counts
- except requests.exceptions.SSLError:
- status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt
- return chatbot, history, status_text, all_token_counts
- response = json.loads(response.text)
- if fake_input is not None:
- history[-2] = construct_user(fake_input)
- try:
- content = response["choices"][0]["message"]["content"]
- history[-1] = construct_assistant(content)
- chatbot[-1] = (chatbot[-1][0], content+display_append)
- total_token_count = response["usage"]["total_tokens"]
- if fake_input is not None:
- all_token_counts[-1] += count_token(construct_assistant(content))
- else:
- all_token_counts[-1] = total_token_count - sum(all_token_counts)
- status_text = construct_token_message(total_token_count)
- return chatbot, history, status_text, all_token_counts
- except KeyError:
- status_text = standard_error_msg + str(response)
- return chatbot, history, status_text, all_token_counts
-
-def is_repeated_string(s):
- n = len(s)
- for i in range(1, n // 2 + 1):
- if n % i == 0:
- sub = s[:i]
- if sub * (n // i) == s:
- return True
- return False
-
-def predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- stream=False,
- selected_model=MODELS[0],
- use_websearch=False,
- files = None,
- reply_language="中文",
- should_check_token_count=True,
-): # repetition_penalty, top_k
- logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
- if is_repeated_string(inputs):
- print("================== 有人来浪费了 ======================")
- yield chatbot+[(inputs, "🖕️🖕️🖕️🖕️🖕️看不起你")], history, "🖕️🖕️🖕️🖕️🖕️🖕️", all_token_counts
- return
- if should_check_token_count:
- yield chatbot+[(inputs, "")], history, "开始生成回答……", all_token_counts
- if reply_language == "跟随问题语言（不稳定）":
- reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
- old_inputs = None
- display_reference = []
- limited_context = False
- if files:
- limited_context = True
- old_inputs = inputs
- msg = "加载索引中……（这可能需要几分钟）"
- logging.info(msg)
- yield chatbot+[(inputs, "")], history, msg, all_token_counts
- index = construct_index(openai_api_key, file_src=files)
- msg = "索引构建完成，获取回答中……"
- logging.info(msg)
- yield chatbot+[(inputs, "")], history, msg, all_token_counts
- llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name=selected_model))
- prompt_helper = PromptHelper(max_input_size = 4096, num_output = 5, max_chunk_overlap = 20, chunk_size_limit=600)
- service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
- query_object = GPTVectorStoreIndexQuery(index.index_struct, service_context=service_context, similarity_top_k=5, vector_store=index._vector_store, docstore=index._docstore)
- query_bundle = QueryBundle(inputs)
- nodes = query_object.retrieve(query_bundle)
- reference_results = [n.node.text for n in nodes]
- reference_results = add_source_numbers(reference_results, use_source=False)
- display_reference = add_details(reference_results)
- display_reference = "\n\n" + "".join(display_reference)
- inputs = (
- replace_today(PROMPT_TEMPLATE)
- .replace("{query_str}", inputs)
- .replace("{context_str}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language )
- )
- elif use_websearch:
- limited_context = True
- search_results = ddg(inputs, max_results=5)
- old_inputs = inputs
- reference_results = []
- for idx, result in enumerate(search_results):
- logging.info(f"搜索结果{idx + 1}：{result}")
- domain_name = urllib3.util.parse_url(result["href"]).host
- reference_results.append([result["body"], result["href"]])
- display_reference.append(f"{idx+1}. [{domain_name}]({result['href']})\n")
- reference_results = add_source_numbers(reference_results)
- display_reference = "\n\n" + "".join(display_reference)
- inputs = (
- replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
- .replace("{query}", inputs)
- .replace("{web_results}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language )
- )
- else:
- display_reference = ""
-
- if len(openai_api_key) != 51:
- status_text = standard_error_msg + no_apikey_msg
- logging.info(status_text)
- chatbot.append((inputs, ""))
- if len(history) == 0:
- history.append(construct_user(inputs))
- history.append("")
- all_token_counts.append(0)
- else:
- history[-2] = construct_user(inputs)
- yield chatbot+[(inputs, "")], history, status_text, all_token_counts
- return
- elif len(inputs.strip()) == 0:
- status_text = standard_error_msg + no_input_msg
- logging.info(status_text)
- yield chatbot+[(inputs, "")], history, status_text, all_token_counts
- return
-
- if stream:
- logging.info("使用流式传输")
- iter = stream_predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=old_inputs,
- display_append=display_reference
- )
- for chatbot, history, status_text, all_token_counts in iter:
- if shared.state.interrupted:
- shared.state.recover()
- return
- yield chatbot, history, status_text, all_token_counts
- else:
- logging.info("不使用流式传输")
- chatbot, history, status_text, all_token_counts = predict_all(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- selected_model,
- fake_input=old_inputs,
- display_append=display_reference
- )
- yield chatbot, history, status_text, all_token_counts
-
- logging.info(f"传输完毕。当前token计数为{all_token_counts}")
- if len(history) > 1 and history[-1]["content"] != inputs:
- logging.info(
- "回答为："
- + colorama.Fore.BLUE
- + f"{history[-1]['content']}"
- + colorama.Style.RESET_ALL
- )
-
- if limited_context:
- history = history[-4:]
- all_token_counts = all_token_counts[-2:]
- yield chatbot, history, status_text, all_token_counts
-
- if stream:
- max_token = MODEL_SOFT_TOKEN_LIMIT[selected_model]["streaming"]
- else:
- max_token = MODEL_SOFT_TOKEN_LIMIT[selected_model]["all"]
-
- if sum(all_token_counts) > max_token and should_check_token_count:
- status_text = f"精简token中{all_token_counts}/{max_token}"
- logging.info(status_text)
- yield chatbot, history, status_text, all_token_counts
- iter = reduce_token_size(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- all_token_counts,
- top_p,
- temperature,
- max_token//2,
- selected_model=selected_model,
- )
- for chatbot, history, status_text, all_token_counts in iter:
- status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
- yield chatbot, history, status_text, all_token_counts
-
-
-def retry(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- token_count,
- top_p,
- temperature,
- stream=False,
- selected_model=MODELS[0],
- reply_language="中文",
-):
- logging.info("重试中……")
- if len(history) == 0:
- yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
- return
- history.pop()
- inputs = history.pop()["content"]
- token_count.pop()
- iter = predict(
- openai_api_key,
- system_prompt,
- history,
- inputs,
- chatbot,
- token_count,
- top_p,
- temperature,
- stream=stream,
- selected_model=selected_model,
- reply_language=reply_language,
- )
- logging.info("重试中……")
- for x in iter:
- yield x
- logging.info("重试完毕")
-
-
-def reduce_token_size(
- openai_api_key,
- system_prompt,
- history,
- chatbot,
- token_count,
- top_p,
- temperature,
- max_token_count,
- selected_model=MODELS[0],
- reply_language="中文",
-):
- logging.info("开始减少token数量……")
- iter = predict(
- openai_api_key,
- system_prompt,
- history,
- summarize_prompt,
- chatbot,
- token_count,
- top_p,
- temperature,
- selected_model=selected_model,
- should_check_token_count=False,
- reply_language=reply_language,
- )
- logging.info(f"chatbot: {chatbot}")
- flag = False
- for chatbot, history, status_text, previous_token_count in iter:
- num_chat = find_n(previous_token_count, max_token_count)
- logging.info(f"previous_token_count: {previous_token_count}, keeping {num_chat} chats")
- if flag:
- chatbot = chatbot[:-1]
- flag = True
- history = history[-2*num_chat:] if num_chat > 0 else []
- token_count = previous_token_count[-num_chat:] if num_chat > 0 else []
- msg = f"保留了最近{num_chat}轮对话"
- yield chatbot, history, msg + "，" + construct_token_message(
- sum(token_count) if len(token_count) > 0 else 0,
- ), token_count
- logging.info(msg)
- logging.info("减少token数量完毕")
diff --git a/spaces/flowers-team/SocialAISchool/textworld_utils/__init__.py b/spaces/flowers-team/SocialAISchool/textworld_utils/__init__.py
deleted file mode 100644
index 90f60fdd89ad8575faafe45188bd1d968852fc67..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/textworld_utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .utils import *
\ No newline at end of file
diff --git a/spaces/fmind/resume/files/linkedin.md b/spaces/fmind/resume/files/linkedin.md
deleted file mode 100644
index 78e4c96380544d5581a640d53f0a3946a6684f4a..0000000000000000000000000000000000000000
--- a/spaces/fmind/resume/files/linkedin.md
+++ /dev/null
@@ -1,1168 +0,0 @@
-# Profile
-
-## Overview
-
-- First name: Médéric
-- Last name: HURIER
-- Pseudo: Fmind
-- Followers: 4K
-- Location: Luxembourg, Luxembourg
-- Education: University of Luxembourg
-- Current position: Decathlon Technology
-- Public URL: www.linkedin.com/in/fmind-dev
-- Industry: Technology, Information and Internet
-- Address: 28 Avenue François Clément, 5612 Mondorf-les-Bains, Luxembourg
-- Headline: Freelancer | AI/ML/MLOps Engineer | Data Scientist | MLOps Community Organizer | OpenClassrooms Mentor | Hacker | PhD
-
-## Websites
-
-- Company website: https://www.fmind.dev
-- Twitter: https://twitter.com/fmind_dev
-- Portfolio: https://github.com/fmind/
-- Blog: https://fmind.medium.com/
-
-## About
-
-Note: I'm not available to work on new missions until the 1st of
-September 2024. Thank you for your understanding.
-
-When I worked as a teacher, I told my students that Artificial
-Intelligence and Machine Learning are the most effective levers to make
-a difference. Every day, new AI and ML solutions are released to empower
-companies and individuals alike. The question is: Is your business ready
-to provide the best AI/ML products for your customers?
-
-I'm a professional Machine Learning Engineer, Data Scientist, and MLOps
-ready to assist you in this quest. I've completed a Ph.D. in Machine
-Learning and several high-end AI/ML certifications to help you build
-leading data-driven services. My past experiences include working with
-companies like Google, BNP Paribas, ArcelorMittal, the European
-Commission, and Decathlon to frame their needs, create state-of-the-art
-models and deliver AI/ML artifacts at scale.
-
-I now work as a freelancer in Luxembourg, and I can carry out missions
-remotely in other European countries. You can get in touch with me on
-LinkedIn or at contact@fmind.dev. I'll be happy to collaborate with you
-or discuss your favored AI/ML topics in the MLOps Community.
-
-# Experience
-
-## Lead MLOps Engineer
-
-- Company: Decathlon Technology
-- Period: Sep 2022 - Present
-- Location: Luxembourg (Hybrid)
-- Mission: Continue the design and implementation of Decathlon's MLOps platform with Databricks and AWS.
-- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · ChatGPT · Agile Methodology · MLflow · Docker · Jira · Git · DataBricks · Apache Airflow · AWS SageMaker · Terraform · Apache Spark · Kubernetes
-
-## Mentor for aspiring Data Scientist and AI/ML Engineer
-
-- Company: OpenClassrooms
-- Period: Mar 2023 - Present
-- Location: France (Remote)
-- Mission: Tutoring adult students to become data scientists specializing in machine learning.
-- Courses:
- - https://openclassrooms.com/fr/paths/793-data-scientist
- - https://openclassrooms.com/fr/paths/794-machine-learning-engineer
- - https://openclassrooms.com/fr/paths/795-ai-engineer
-- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Deep Learning · Data Science · Statistics · ChatGPT · Big Data · Jupyter · Pandas · Git · Natural Language Processing (NLP) · Scikit-Learn
-
-## Senior Data Scientist & Project Manager
-
-- Company: Cronos Europa
-- Customer: European Commission
-- Period: Dec 2021 - Sep 2022
-- Location: Luxembourg (Hybrid)
-- Mission: Enhance the ARACHNE risk scoring tool (fraud detection).
-- Main tasks and responsibilities:
- - Develop a new version of Arachne using data mining techniques
- - Manage the development of the Arachne PoC/Project (SCRUM)
- - Assist data scientists in their projects (Virtual Assistant, NLP,
- …)
-- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Big Data · Agile Methodology · Project Management · Functional Programming · Jupyter · Pandas · Docker · Jira · Git · PostgreSQL · AWS SageMaker · Flask · UML · API REST · Terraform · Transformers · Natural Language Processing (NLP) · Data Engineering · Microsoft Azure Machine Learning · Neo4j
-
-## Project Manager & Machine Learning Engineer
-
-- Company: SFEIR Luxembourg
-- Period: Dec 2020 - Dec 2021
-- Location: Luxembourg (Remote)
-- Mission: Design and implement the next ML/MLOps platform on AWS and GCP.
-- Main tasks and responsibilities:
- - Design the functional & technical architecture of the platform
- - Manage the MLOps@Decathlon initiative (tasks, plannings)
- - Select the vendor solutions based on a user need analysis
- - Communicate the progress and success to stack-holders
- - Assist data scientists in their project (audience, forecast)
-- Technical stack:
- - Data Science: Python, TensorFlow, Spark, sklearn, Jupyter, Airflow
- - Management: Google Workspace, Jira, UML, Terraform, Jenkins
- - Environments: AWS (SageMaker), GCP (Vertex AI), DataBricks
-- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Big Data · Agile Methodology · Project Management · Functional Programming · Google Cloud Platform (GCP) · Tensorflow · MLflow · Jupyter · Pandas · Docker · Keras · Jira · Git · DataBricks · Apache Airflow · AWS SageMaker · Flask · UML · Terraform · Data Engineering · Vertex AI (GCP) · Apache Spark · Scikit-Learn · Kubernetes
-
-## Data Scientist
-
-- Company: SFEIR
-- Period: Oct 2020 - Nov 2020
-- Location: Luxembourg (Remote)
-- Mission: Improve the visibility and assets of SFEIR's Data Team.
-- Main tasks and responsibilities:
- - Design and create technical interviews for recruiting data scientists.
- - Become a Professional Machine Learning Engineer on Google Cloud.
- - Propose a strategy to improve the online visibility of SFEIR data team.
- - Share knowledge about data trends with non-technical staff members.
- - Create a group to write tutorials and kata on AI/ML for SFEIR developers.
-- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Deep Learning · Data Science · Agile Methodology · Functional Programming · Google Cloud Platform (GCP) · Tensorflow · Jupyter · Pandas · Keras · Git · MongoDB · Vertex AI (GCP) · Apache Spark · Scikit-Learn
-
-## Data Scientist
-
-- Company: SFEIR
-- Customer: ArcelorMittal
-- Period: Jan 2020 - Sep 2020
-- Location: Luxembourg (Remote)
-- Mission: Train and optimize machine learning models to recommend steel prices.
-- Main tasks and responsibilities:
- - Create and fine-tune machine-learning models (tree-based)
- - Evaluate the performance of the model on real datasets
- - Communicate the results to business stack-holders
-- Technical stack:
- - Data Science: Python, XGBoost, sklearn, Jupyter, SQL
- - Analytics: Matplotlib, Seaborn, Tableau, Plotly, Dash
- - Environment: MS-SQL, Azure Cloud, Jira, Papermill
-- Skills: Artificial Intelligence (AI) · Machine Learning · MLOps · Python · Data Science · Agile Methodology · Functional Programming · Jupyter · Pandas · Jira · Git · Natural Language Processing (NLP) · Scikit-Learn
-
-## Research And Development Specialist
-
-- Company: University of Luxembourg
-- Period: Sep 2019 - Jan 2020
-- Location: Luxembourg
-- Mission: Management and development of Natural Language Understanding (NLU) projects for BGL BNP Paribas.
-- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Data Science · Big Data · Functional Programming · Tensorflow · Jupyter · Pandas · Docker · Git · PostgreSQL · Ansible · Flask · UML · JSON · API REST · Transformers · Natural Language Processing (NLP) · Apache Spark · Scikit-Learn
-
-## Doctoral researcher
-
-- Company: University of Luxembourg
-- Period: Sep 2015 - Jan 2020
-- Location: Luxembourg
-- Missions:
- - Research activities focused on Android security and artificial intelligence.
- - Teaching big data, machine learning and Android programming to students.
- - Collaboration with Google, San Francisco on finding malicious Android artifacts.
-- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Deep Learning · Data Science · Statistics · Big Data · Cybersecurity · Functional Programming · Jupyter · Pandas · Docker · Git · NoSQL · MongoDB · PostgreSQL · ElasticSearch · Ansible · Flask · JSON · Android · API REST · Natural Language Processing (NLP) · Data Engineering · Apache Spark · Scikit-Learn
-
-## Mentor for aspiring Data Scientist
-
-- Company: OpenClassrooms
-- Period: Aug 2018 - Dec 2019
-- Location: France
-- Mission: Tutoring adult students to become data scientists specializing in machine learning.
-- Skills: Artificial Intelligence (AI) · Machine Learning · Python · Data Science · Jupyter · Pandas · Git · Flask · JSON · API REST · Scikit-Learn
-
-## Security engineer specialized in log management and analysis
-
-- Company: Clearstream
-- Period: Apr 2014 - Aug 2015
-- Location: Luxembourg
-- Mission: Selection and deployment of a SIEM solution, participating in security incident response.
-- Skills: Python · Big Data · ISO 27001 · Cybersecurity · Jupyter · Pandas · Git · ElasticSearch · Data Engineering
-
-## Web developer and administrator
-
-- Company: Freaxmind
-- Period: Aug 2011 - Aug 2013
-- Location: France
-- Mission: Various contracts ranging from web development to software maintenance and debugging.
-- Skills: Python · Object Oriented Programming (POO) · Git · Ansible · Flask
-
-## Web Developer
-
-- Company: Toul'embal
-- Period: Jun 2012 - Aug 2012
-- Location: Toul, France
-- Mission: Extension of a Prestashop e-commerce website and creation a portfolio website with WordPress.
-- Skills: Object Oriented Programming (POO)
-
-## Web Programmer
-
-- Company: Empreinte Studio
-- Period: Oct 2010 - Aug 2011
-- Location: Épernay, France
-- Mission: Creation of modern website in PHP and MySQL with professional writers and graphic designers.
-- Skills: Object Oriented Programming (POO) · Git
-
-## Software Developer
-
-- Company: GEOVARIANCES
-- Period: Sep 2009 - Sep 2010
-- Location: Avon, France
-- Mission: Development of a geostatistic application in C++ and Qt with experienced software engineers.
-- Skills: Object Oriented Programming (POO) · Git · UML
-
-## Web Developer
-
-- Company: CV Champagne Nicolas Feuillatte
-- Period: Apr 2009 - Aug 2009
-- Location: Épernay, France
-- Mission: Integration of customer and share management modules to J.D. Edwards with PHP and Oracle.
-- Skills: Object Oriented Programming (POO)
-
-# Education
-
-## Doctor of Philosophy (PhD) in computer security and artificial intelligence
-
-- School: University of Luxembourg
-- Location: Luxembourg
-- Grade: Very Good
-- Period: 2015 - 2019
-- Activities and Societies: Teach Big Data and Android to students.
-- Thesis title: Creating better ground truth to further understand Android malware
-
-## Master's degree in computer and information systems security
-
-- School: UFR Mathématiques, Informatique, Mécanique et Automatique
-- Location: Metz (France)
-- Period: 2013 - 2014
-
-## Bachelor and master years in computer science applied to business informatics
-
-- School: UFR Mathématiques et Informatique de l’Université de Lorraine
-- Location: Nancy (France)
-- Period: 2011 - 2013
-
-## Professional bachelor's degree in computer security and databases
-
-- School: IUT Sénart-Fontainebleau
-- Location: Fontainebleau (France)
-- Period: 2009 - 2010
-
-## Professional bachelor’s degree in web development and integration
-
-- School: IUT Nancy-Charlemagne
-- Location: Nancy (France)
-- Period: 2008 - 2009
-
-## Technical degree in network and software development
-
-- School: Lycée François 1er
-- Location: Vitry-le-François (France)
-- Period: 2006 - 2008
-
-## Baccalauréat général degree in science, specialized in biology
-
-- School: Lycée Marc Chagall
-- Location: Reims (France)
-- Period: 2003 - 2006
-
-# Volunteer Experiences
-
-## MLOps Community Organizer (Luxembourg)
-
-- Community: MLOps Community
-- Role: Organizer
-- Location: Luxembourg
-- Period: Nov 2022 - present
-- Field: Science and Technology
-- Mission: Organize regular meetups and events for the MLOps Community.
-- Partners: AWS and the University of Luxembourg.
-- Link: https://www.meetup.com/luxembourg-mlops-community/
-
-# Licenses & Certifications
-
-## Machine Learning Associate
-
-- Issuer: Databricks
-- Issued: Nov 2022
-- Credential ID: 61461287
-
-## Databricks Lakehouse Fundamentals
-
-- Issuer: Databricks
-- Issued: Oct 2022
-- Credential ID: 61029028
-
-## Architecting with Google Kubernetes Engine Specialization
-
-- Issuer: Google
-- Issued: Sep 2022
-- Credential ID: WLU4DBPSQ4B5
-
-## Architecting with Google Kubernetes Engine: Foundations
-
-- Issuer: Google
-- Issued: Sep 2022
-- Credential ID: DFWAC6BXLNGL
-
-## Architecting with Google Kubernetes Engine: Production
-
-- Issuer: Google
-- Issued: Sep 2022
-- Credential ID: K5SZHUST5HP2
-
-## Architecting with Google Kubernetes Engine: Workloads
-
-- Issuer: Google
-- Issued: Sep 2022
-- Credential ID: ULJQAXGDVKYK
-
-## Google Cloud Fundamentals: Core Infrastructure
-
-- Issuer: Google
-- Issued: Sep 2022
-- Credential ID: 4CE8WQ6AWKFF
-
-## Iterative Tools for Data Scientists and Analysts
-
-- Issuer: Iterative
-- Issued: Aug 2022
-- Credential ID: 62fcb79418f51945ea
-
-## Azure Data Scientist Associate
-
-- Issuer: Microsoft
-- Issued: Jul 2022
-- Credential ID: 992564946
-
-## Azure Machine Learning for Data Scientists
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: MZKV7LSTQ9HX
-
-## Build and Operate Machine Learning Solutions with Azure Microsoft
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: 7FBX68MH272C
-
-## Create Machine Learning Models in Microsoft Azure
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: SHALM9PM3MPX
-
-## Microsoft Azure Data Scientist Associate - DP-100 Test Prep Specialization
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: L5P3TYLAYLLT
-
-## Perform data science with Azure Databricks
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: RQ7PLFYZVLXX
-
-## Prepare for DP-100: Data Science on Microsoft Azure Exam
-
-- Issuer: Microsoft
-- Issued: Jun 2022
-- Credential ID: K5KW27AVMYS2
-
-## Neo4j Graph Data Science Certified
-
-- Issuer: Neo4j
-- Issued: Apr 2022
-- Credential ID: 17351346
-
-## Microsoft Certified: Azure AI Fundamentals
-
-- Issuer: Microsoft
-- Issued: Jan 2022
-- Credential ID: 1098-0884
-
-## Artificial Intelligence on Microsoft Azure
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: Z8FSWXBSAGLD
-
-## Computer Vision in Microsoft Azure
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: KDDPYLKM2DA5
-
-## Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: 96944QKZH9BU
-
-## Microsoft Azure Machine Learning
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: 32ES25845Q55
-
-## Natural Language Processing in Microsoft Azure
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: XVN23N8CKRGY
-
-## Preparing for AI-900: Microsoft Azure AI Fundamentals exam
-
-- Issuer: Microsoft
-- Issued: Dec 2021
-- Credential ID: YC83C22L8TBL
-
-## Build a Website on Google Cloud
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Build and Secure Networks in Google Cloud
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Create ML Models with BigQuery ML
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Create and Manage Cloud Resources
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Deploy to Kubernetes in Google Cloud
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Implement DevOps in Google Cloud
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Insights from Data with BigQuery
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Integrate with Machine Learning APIs
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Perform Foundational Infrastructure Tasks in Google Cloud
-
-- Issuer: Google
-- Issued: Aug 2021
-
-## Apache Spark Associate Developer
-
-- Issuer: Databricks
-- Issued: Jun 2021
-- Credential ID: fff03919-bbc9-304e-99ad-6f2ed47455ed
-
-## Scalable Machine Learning with Apache Spark
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: 0f4adf96-0412-32f2-8232-fa50c51c9b47
-
-## Apache Spark Programming with Databricks
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: 518a1d63-8894-3ab5-aaa5-50a9f169436c
-
-## Data Science Professional
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: f05164e1-5a78-37f8-9c69-3e996fdbb21f
-
-## Delta Lake Fundamentals Accreditation
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: 0d042e3f-50d3-3821-b064-f3c12ca6c17f
-
-## Deploying a Machine Learning Project with MLflow Projects
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: 2afa0c7f-48f4-35af-b366-f7c77d2cd20a
-
-## Tracking Experiments with MLflow
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: 0cbf87b7-e096-3792-a3b7-62d86aa6380d
-
-## Unified Data Analytics Accreditation
-
-- Issuer: Databricks
-- Issued: May 2021
-- Credential ID: afba5402-b5e4-3f9e-95f2-51d6bbb5fa64
-
-## ML Pipelines on Google Cloud
-
-- Issuer: Google
-- Issued: Mar 2021
-- Credential ID: FN5PYWX5PRCP
-
-## Introduction to Trading, Machine Learning & GCP
-
-- Issuer: Google
-- Issued: Nov 2020
-- Credential ID: YV9H5PF4YPLZ
-
-## MLOps (Machine Learning Operations) Fundamentals
-
-- Issuer: Google
-- Issued: Nov 2020
-- Credential ID: 4BDA24UL7K9Z
-
-## Machine Learning for Trading Specialization
-
-- Issuer: Google
-- Issued: Nov 2020
-- Credential ID: YSNPABSMV6JL
-
-## Reinforcement Learning for Trading Strategies
-
-- Issuer: Google
-- Issued: Nov 2020
-- Credential ID: VHKJLFPLLDLU
-
-## Using Machine Learning in Trading and Finance
-
-- Issuer: Google
-- Issued: Nov 2020
-- Credential ID: X5YYLBMPY4BU
-
-## DeepLearning.AI TensorFlow Developer Specialization
-
-- Issuer: DeepLearning.AI
-- Issued: Oct 2020
-- Credential ID: LQ4GHWJ6URBS
-
-## Perform Foundational Data, ML, and AI Tasks in Google Cloud
-
-- Issuer: Google
-- Issued: Oct 2020
-
-## Professional Machine Learning Engineer
-
-- Issuer: Google
-- Issued: Oct 2020
-- Credential ID: 24896478
-
-## Sequences, Time Series and Prediction
-
-- Issuer: Google
-- Issued: Oct 2020
-- Credential ID: WHBV68C4WJT5
-
-## Convolutional Neural Networks in TensorFlow
-
-- Issuer: Google
-- Issued: Sep 2020
-- Credential ID: 78HJEJZ3T2BB
-
-## Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
-
-- Issuer: Google
-- Issued: Sep 2020
-- Credential ID: SW885ZMDHTYM
-
-## Natural Language Processing in TensorFlow
-
-- Issuer: Google
-- Issued: Sep 2020
-- Credential ID: JZ9TBHXJFLWM
-
-## Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: V492QQ4JJKEB
-
-## End-to-End Machine Learning with TensorFlow on GCP
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: QLDMNADDBSRR
-
-## Image Understanding with TensorFlow on GCP
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: HY4HSSY8JSPN
-
-## Production Machine Learning Systems
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: THZZNW22LHKT
-
-## Recommendation Systems with TensorFlow on GCP
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: 2D4LT28697TC
-
-## Sequence Models for Time Series and Natural Language Processing
-
-- Issuer: Google
-- Issued: Jul 2020
-- Credential ID: 6XUV7YJFM3ZA
-
-## Building Batch Data Pipelines on GCP
-
-- Issuer: Google
-- Issued: May 2020
-- Credential ID: 5QYSK9E5EAFN
-
-## Building Resilient Streaming Analytics Systems on GCP
-
-- Issuer: Google
-- Issued: May 2020
-- Credential ID: FYQW7D4F6PD4
-
-## Data Engineering with Google Cloud Specialization
-
-- Issuer: Google
-- Issued: May 2020
-- Credential ID: EPZ3WQFC423E
-
-## Modernizing Data Lakes and Data Warehouses with GCP
-
-- Issuer: Google
-- Issued: May 2020
-- Credential ID: 393P3HLZWY8H
-
-## Smart Analytics, Machine Learning, and AI on GCP
-
-- Issuer: Google
-- Issued: May 2020
-- Credential ID: AK77VUVN4ARJ
-
-## Google Cloud Platform Big Data and Machine Learning Fundamentals
-
-- Issuer: Google
-- Issued: Apr 2020
-- Credential ID: 2Q35NYHYMW5E
-
-## Devenez Mentor Evaluateur
-
-- Issuer: OpenClassrooms
-- Issued: Feb 2019
-- Credential ID: 8151214336
-
-## Advanced AI: Deep Reinforcement Learning in Python
-
-- Issuer: Udemy
-- Issued: Aug 2018
-- Credential ID: UC-5FM0CC9S
-
-## Artificial Intelligence: Reinforcement Learning in Python
-
-- Issuer: Udemy
-- Issued: Jul 2018
-- Credential ID: UC-XALJEH7G
-
-## Concevez un site avec Flask
-
-- Issuer: OpenClassrooms
-- Issued: Jul 2018
-- Credential ID: 5343531703
-
-## Les étapes de la vie du Mentor
-
-- Issuer: OpenClassrooms
-- Issued: Jul 2018
-- Credential ID: 8431716200
-
-## Devenez Mentor chez OpenClassrooms
-
-- Issuer: OpenClassrooms
-- Issued: May 2018
-- Credential ID: 6193593386
-
-## Complete Guide to ElasticSearch
-
-- Issuer: Udemy
-- Issued: Mar 2018
-- Credential ID: UC-H5AJQVA3
-
-## Introduction to Hadoop
-
-- Issuer: The Linux Foundation
-- Issued: Oct 2017
-- Credential ID: ad676a8fe7994edea33516b80b540971
-
-## Artificial Intelligence Nanodegree
-
-- Issuer: Udacity
-- Issued: Sep 2017
-- Credential ID: PV7A7EAA
-
-## High Performance Computing
-
-- Issuer: University of Luxembourg
-- Issued: Feb 2017
-
-## Machine Learning
-
-- Issuer: Standford University
-- Issued: Sep 2015
-- Grade: 97%
-
-## TOEIC
-
-- Skills: Listening, Reading
-- Issued: Jan 2014
-- Score: 975/990
-
-# Publications
-
-## Is AI/ML Monitoring just Data Engineering? 🤔
-
-- Publisher: MLOps Community
-- Publication date: July 24, 2023
-- Link: https://mlops.community/is-ai-ml-monitoring-just-data-engineering-%f0%9f%a4%94/
-
-While the future of machine learning and MLOps is being debated,
-practitioners still need to attend to their machine learning models
-in production. This is no easy task, as ML engineers must constantly
-assess the quality of the data that enters and exits their
-pipelines, and ensure that their models generate the correct
-predictions. To assist ML engineers with this challenge, several
-AI/ML monitoring solutions have been developed.
-In this article, I will discuss the nature of AI/ML monitoring and
-how it relates to data engineering. First, I will present the
-similarities between AI/ML monitoring and data engineering. Second,
-I will enumerate additional features that AI/ML monitoring solutions
-can provide. Third, I will briefly touch on the topic of AI/ML
-observability and its relation to AI/ML monitoring. Finally, I will
-provide my conclusion about the field of AI/ML monitoring and how it
-should be considered to ensure the success of your AI/ML project.
-
-## A great MLOps project should start with a good Python Package 🐍
-
-- Publisher: MLOps Community
-- Publication date: June 28, 2023
-- Link: https://mlops.community/a-great-mlops-project-should-start-with-a-good-python-package-%f0%9f%90%8d/
-
-In this article, I present the implementation of a Python package on
-GitHub designed to support MLOps initiatives. The goal of this
-package is to make the coding workflow of data scientists and ML
-engineers as flexible, robust, and productive as possible. First, I
-start by motivating the use of Python packages. Then, I provide some
-tools and tips you can include in your MLOps project. Finally, I
-explain the follow-up steps required to take this package to the
-next level and make it work in your environment.
-
-## Fixing the MLOps Survey on LLMs with ChatGPT API: Lessons Learned
-
-- Publisher: MLOps Community
-- Publication date: May 11, 2023
-- Link: https://mlops.community/fixing-the-mlops-survey-on-llms-with-chatgpt-api-lessons-learned/
-
-Large Language Model (LLM) is such an existing topic. Since the
-release of ChatGPT, we saw a surge of innovation ranging from
-education mentorship to finance advisory. Each week is a new
-opportunity for addressing new kinds of problems, increasing human
-productivity, or improving existing solutions. Yet, we may wonder if
-this is just a new hype cycle or if organizations are truly adopting
-LLMs at scale …
-On March 2023, the MLOps Community issued a survey about LLMs in
-production to picture the state of adoption. The survey is full of
-interesting insights, but there is a catch: 80% of the questions are
-open-ended, which means respondents answered the survey freely from
-a few keywords to full sentences. I volunteered to clean up the
-answers with the help of ChatGPT and let the community get a grasp
-of the survey experiences.
-In this article, I present the steps and lessons learned from my
-journey to shed some light on the MLOps survey on LLMs. I’m first
-going to present the goal and questions of the survey. Then, I will
-explain how I used ChatGPT to review the data and standardize the
-content. Finally, I’m going to evaluate the performance of ChatGPT
-compared to a manual review.
-
-## Kubeflow: The Machine Learning Toolkit for Kubernetes
-
-- Publisher: MLOps Community in Luxembourg
-- Publication date: April 26, 2023
-- Link: https://docs.google.com/presentation/d/1WtN5mNAHsz7PiWE5kKZUQfQ9_zO_kfVoSBNTHQM-sMk/edit#slide=id.p1
-
-## MLflow: An open source platform for the machine learning lifecycle
-
-- Publisher: MLOps Community in Luxembourg
-- Publication date: April 26, 2023
-- Link: https://docs.google.com/presentation/d/1fjoQ2DnTzhyaMI9dC6E2_RIKylW8hAGS5iQ6TapZfZo/edit#slide=id.g1e1b3ce30cd_0_0
-
-## We need POSIX for MLOps
-
-- Publisher: MLOps Community
-- Publication date: April 21, 2023
-- Link: https://mlops.community/we-need-posix-for-mlops/
-
-If you work on MLOps, you must navigate an ever-growing landscape of
-tools and solutions. This is both an intense source of stimulation
-and fatigue for MLOps practitioners.
-Vendors and users face the same problem: How can we combine all
-these tools without the combinatorial complexity of creating custom
-integrations?
-In this article, I propose a solution analogous to POSIX to address
-this challenge. First, I motivate the creation of common protocols
-and schemas for combining MLOps tools. Second, I present a
-high-level architecture to support implementation. Third, I conclude
-with the benefits and limitations of standardizing MLOps.
-
-## How to install Kubeflow Pipelines v2 on Apple Silicon
-
-- Publisher: Medium
-- Publication date: September 24, 2022
-- Link: https://fmind.medium.com/how-to-install-kubeflow-on-apple-silicon-3565db8773f3
-
-Kubeflow Pipelines (KFP) is a powerful platform for building machine
-learning pipelines at scale with Kubernetes. The platform is well
-supported on major cloud platforms such as GCP (Vertex AI Pipelines)
-or AWS (Kubeflow on AWS). However, installing KFP on Apple Silicon
-(macOS 12.5.1 with Apple M1 Pro) proved to be more challenging than
-I imagined. Thus, I wanted to share my experience and tips to
-install KFP as easily as possible on your shiny Mac.
-In this article, I present 4 steps to install Kubeflow on Apple
-Silicon, using Rancher Desktop for setting up Docker/Kubernetes. In
-the end, I list the problems I encountered during the installation
-of Kubeflow Pipelines.
-
-## The Programming Trade-Off: Purpose, Productivity, Performance
-
-- Publisher: Medium
-- Publication: August 15, 2019
-- Link: https://fmind.medium.com/3p-principle-purpose-productivity-performance-630bed7623fc
-
-As programmers, we are continuously looking for languages that are
-performant, productive, and general purpose. Is there any
-programming language that currently satisfies these properties? Can
-we ever create one?
-In this article, I present a fundamental trade-off that affects the
-design of programming languages and the success of software
-projects.
-
-## Creating better ground truth to further understand Android malware: A large scale mining approach based on antivirus labels and malicious artifacts
-
-- Publisher: University of Luxembourg
-- Publication date: July 1, 2019
-- Link: https://orbilu.uni.lu/handle/10993/39903
-
-Mobile applications are essential for interacting with technology
-and other people. With more than 2 billion devices deployed all over
-the world, Android offers a thriving ecosystem by making accessible
-the work of thousands of developers on digital marketplaces such as
-Google Play. Nevertheless, the success of Android also exposes
-millions of users to malware authors who seek to siphon private
-information and hijack mobile devices for their benefits.
-To fight against the proliferation of Android malware, the security
-community embraced machine learning, a branch of artificial
-intelligence that powers a new generation of detection systems.
-Machine learning algorithms, however, require a substantial number
-of qualified samples to learn the classification rules enforced by
-security experts. Unfortunately, malware ground truths are
-notoriously hard to construct due to the inherent complexity of
-Android applications and the global lack of public information about
-malware. In a context where both information and human resources are
-limited, the security community is in demand for new approaches to
-aid practitioners to accurately define Android malware, automate
-classification decisions, and improve the comprehension of Android
-malware.
-This dissertation proposes three solutions to assist with the
-creation of malware ground truths.
-
-## Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
-
-- Publisher: MSR 2017
-- Publication date: May 21, 2017
-- Link: https://orbilu.uni.lu/handle/10993/31441
-
-Android malware is now pervasive and evolving rapidly. Thousands of
-malware samples are discovered every day with new models of attacks.
-The growth of these threats has come hand in hand with the
-proliferation of collective repositories sharing the latest
-specimens. Having access to a large number of samples opens new
-research directions aiming at efficiently vetting apps. However,
-automatically inferring a reference ground-truth from those
-repositories is not straightforward and can inadvertently lead to
-unforeseen misconceptions. On the one hand, samples are often
-mislabeled as different parties use distinct naming schemes for the
-same sample. On the other hand, samples are frequently misclassified
-due to conceptual errors made during labeling processes.
-In this paper, we analyze the associations between all labels given
-by different vendors and we propose a system called EUPHONY to
-systematically unify common samples into family groups. The key
-novelty of our approach is that no prior knowledge of malware
-families is needed. We evaluate our approach using reference
-datasets and more than 0.4 million additional samples outside of
-these datasets. Results show that EUPHONY provides competitive
-performance against the state-of-the-art.
-
-## On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware
-
-- Publisher: DIMVA 2016
-- Publication date: July 7, 2016
-- Link: https://orbilu.uni.lu/handle/10993/27845
-
-There is generally a lack of consensus in Antivirus (AV) engines'
-decisions on a given sample. This challenges the building of
-authoritative ground-truth datasets. Instead, researchers and
-practitioners may rely on unvalidated approaches to build their
-ground truth, e.g., by considering decisions from a selected set of
-Antivirus vendors or by setting up a threshold number of positive
-detections before classifying a sample. Both approaches are biased
-as they implicitly either decide on ranking AV products, or they
-consider that all AV decisions have equal weights. In this paper, we
-extensively investigate the lack of agreement among AV engines.
-To that end, we propose a set of metrics that quantitatively
-describe the different dimensions of this lack of consensus. We show
-how our metrics can bring important insights by using the detection
-results of 66 AV products on 2 million Android apps as a case study.
-Our analysis focuses not only on AV binary decision but also on the
-notoriously hard problem of labels that AVs associate with
-suspicious files, and allows to highlight biases hidden in the
-collection of a malware ground truth---a foundation stone of any
-machine learning-based malware detection approach.
-
-# Projects
-
-## MLOps Python Package
-
-- Date: Jun 2023 - Jun 2023
-- Description: Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
-- Link: https://github.com/fmind/mlops-python-package
-
-## Fixing the MLOps Survey with ChatGPT
-
-- Date: May 2023 - May 2023
-- Description: Fixing the MLOps Survey on LLMs with ChatGPT API.
-- Link: https://fmind.medium.com/fixing-the-mlops-survey-on-llms-with-chatgpt-api-lessons-learned-62d90e721331
-
-## Kubeflow Demo
-
-- Date: Apr 2023 - Apr 2023
-- Description: Kubeflow demo for the MLOps Community Meetup in Luxembourg.
-- Link: https://github.com/fmind/kubeflow-demo
-
-## MLflow Demo
-
-- Date: Apr 2023 - Apr 2023
-- Description: MLflow demo for the MLOps Community Meetup in Luxembourg.
-- Link: https://github.com/fmind/mlflow-demo
-
-## onet
-
-- Date: Aug 2020 - Sep 2020
-- Description: Train and predict procedures of DNN for binary image classification
-- Link: https://github.com/fmind/onet
-
-## fincrawl
-
-- Date: Nov 2019 - Dec 2019
-- Description: Crawl documents, metadata, and files from financial institutions
-- Link: https://github.com/fmind/fincrawl
-
-## invest
-
-- Date: Aug 2019 - Sep 2019
-- Description: Stock market analysis focused on dividends
-- Link: https://github.com/fmind/invest
-
-## parsoc
-
-- Date: Jul 2019 - Sep 2019
-- Description: Convert docx files to json
-- Link: https://github.com/fmind/parsoc
-
-## Bigdata Tutorials
-
-- Date: Sep 2015 - Jul 2019
-- Description: Tutorials for the Big Data course @ uni.lu
-- Link: https://github.com/fmind/bigdata-tutorials
-
-## STASE: A set of statistical metrics to better understand and qualify malware datasets
-
-- Date: Apr 2016 - Jul 2019
-- Description: A handful of statistical metrics to better understand and qualify malware datasets
-- Link: https://github.com/fmind/STASE
-
-## apkworkers
-
-- Date: Sep 2015 - Jul 2019
-- Description: A celery application to distribute Android malware analysis
-- Link: https://github.com/fmind/apkworkers
-
-## servalx
-
-- Date: Sep 2015 - Jul 2019
-- Description: A set of tools and modules to process Android malware with Androzoo
-- Link: https://github.com/fmind/servalx
-
-## Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
-
-- Date: Mar 2017 - Mar 2019
-- Description: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
-- Link: https://github.com/fmind/euphony
-
-## Automatic Speech Recognition with Tensorflow
-
-- Date: Sep 2017 - Sep 2017
-- Description: An automatic speech-recognition system based on Tensorflow
-- Link: https://github.com/fmind/AIND-VUI-Capstone
-
-## Dog Recognition with Tensorflow
-
-- Date: Aug 2017 - Aug 2017
-- Description: A machine-learning model train to recognize dogs, even from human faces
-- Link: https://github.com/fmind/dog-project
-
-## genius
-
-- Date: Jun 2017 - Jul 2017
-- Description: An implementation of LISP Scheme based on Haskell
-- Link: https://github.com/fmind/genius
-
-## Alexa History Skill
-
-- Date: Jun 2017 - Jun 2017
-- Description: A Alexa skill that provides year-dated facts on demand
-- Link: https://github.com/fmind/AIND-VUI-Alexa
-
-## Air Cargo Planning System
-
-- Date: Feb 2017 - Apr 2017
-- Description: An automated Air Cargo transport system based on AI planning
-- Link: https://github.com/fmind/AIND-Planning
-
-## Sign Language Recognition System
-
-- Date: Feb 2017 - Apr 2017
-- Description: A sign recognition system based on Hidden Markov Model
-- Link: https://github.com/fmind/AIND-Recognizer
-
-## AI Agent for the Isolation Game
-
-- Date: Mar 2017 - Mar 2017
-- Description: An AI game agent to play the Isolation game
-- Link: https://github.com/fmind/AIND-Isolation
-
-## Sudoku Solver
-
-- Date: Jan 2017 - Feb 2017
-- Description: A Diagonal Sudoku solver implemented with Python
-- Link: https://github.com/fmind/AIND-Sudoku
-
-## lkml
-
-- Date: Nov 2016 - Jan 2017
-- Description: Gather emails from https://lkml.org/
-- Link: https://github.com/fmind/lkml
-
-## Master 2 School Projects
-
-- Date: Sep 2013 - Jun 2014
-- Description: School projects from 2013 to 2014 - Master 2 Sécurité des Systèmes d'Information (Metz)
-- Link: https://github.com/fmind/master2-projects
-
-## chattail
-
-- Date: Dec 2013 - Mar 2014
-- Description: Send log streams over XMPP to monitor your systems
-- Link: https://github.com/fmind/chattail
-
-## Master 1 School Projects
-
-- Date: Jun 2012 - Sep 2013
-- Link: https://github.com/fmind/master1-projects
-
-## Bachelor School Projects
-
-- Date: Jun 2011 - Sep 2012
-- Link: https://github.com/fmind/bachelor-projects
-
-## Professional Bachelor School Project
-
-- Date: Sep 2009 - Jun 2010
-- Link: https://github.com/fmind/professional-bachelor-project
-
-# Skills
-
-## Artificial Intelligence / Machine Learning
-
-- Artificial Intelligence (AI)
-- Machine Learning
-- MLOps
-- Deep Learning
-- Data Science
-- Statistics
-- ChatGPT
-- Scikit-Learn
-- Tensorflow
-- KubeFlow
-- MLflow
-- Jupyter
-- Pandas
-- Keras
-- DVC
-- Transformers
-- Natural Language Processing (NLP)
-
-## Software Engineering
-
-- Python
-- Functional Programming (FP)
-- Object-Oriented Programming (OOP)
-- API REST
-- Android
-- Docker
-- JSON
-- Git
-
-## Cloud Platforms
-
-- AWS SageMaker
-- Vertex AI (GCP)
-- Google Cloud Platform (GCP)
-- Microsoft Azure Machine Learning
-- Apache Airflow
-- Kubernetes
-- DataBricks
-- Terraform
-- Ansible
-- Linux
-
-## Computer Security
-
-- ISO 27001
-- Cybersecurity
-
-## Data Management
-
-- NoSQL
-- MongoDB
-- Big Data
-- PostgreSQL
-- Apache Spark
-- ElasticSearch
-- Data Engineering
-
-## Project Management
-
-- Agile Methodology
-- Project Management
-- Jira
-- UML
-
-# Languages
-
-## Français
-
-- Proficiency: Native or bilingual proficiency
-
-## English
-
-- Proficiency: Full professional proficiency
diff --git a/spaces/gabibi7am/rvc-models/infer_pack/models_onnx.py b/spaces/gabibi7am/rvc-models/infer_pack/models_onnx.py
deleted file mode 100644
index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000
--- a/spaces/gabibi7am/rvc-models/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.emb_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0,
- ):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- mean_only=True,
- )
- )
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=gin_channels,
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=0,
- ):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
- def __init__(
- self,
- initial_channel,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = Conv1d(
- initial_channel, upsample_initial_channel, 7, 1, padding=3
- )
- resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel // (2 ** (i + 1))
- for j, (k, d) in enumerate(
- zip(resblock_kernel_sizes, resblock_dilation_sizes)
- ):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
- return x
-
- def remove_weight_norm(self):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- self.enc_q = PosteriorEncoder(
- spec_channels,
- inter_channels,
- hidden_channels,
- 5,
- 1,
- 16,
- gin_channels=gin_channels,
- )
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
- return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(
- self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- spk_embed_dim,
- # hop_length,
- gin_channels=0,
- use_sdp=True,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256Sim(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- is_half=kwargs["is_half"],
- )
-
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
- ): # y是spec不需要了现在
- g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
- return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [
- DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
- ]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = [] #
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ]
- )
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList(
- [
- norm_f(
- Conv2d(
- 1,
- 32,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 32,
- 128,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 128,
- 512,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 512,
- 1024,
- (kernel_size, 1),
- (stride, 1),
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- norm_f(
- Conv2d(
- 1024,
- 1024,
- (kernel_size, 1),
- 1,
- padding=(get_padding(kernel_size, 1), 0),
- )
- ),
- ]
- )
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
diff --git a/spaces/giulio98/codebleu/parsercode/build.py b/spaces/giulio98/codebleu/parsercode/build.py
deleted file mode 100644
index 6cf4271ac1cc906202ad0a56c27b0de1e671d73e..0000000000000000000000000000000000000000
--- a/spaces/giulio98/codebleu/parsercode/build.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-from tree_sitter import Language, Parser
-
-Language.build_library(
- # Store the library in the `build` directory
- 'my-languages.so',
-
- # Include one or more languages
- [
- 'tree-sitter-python',
- 'tree-sitter-cpp'
- ]
-)
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/How to Download Final Fantasy XIV for Mac and Enjoy the Free Trial.md b/spaces/gotiQspiryo/whisper-ui/examples/How to Download Final Fantasy XIV for Mac and Enjoy the Free Trial.md
deleted file mode 100644
index 3633dcc5d34412680ebce9e6e3a9d5f0acd42fec..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/How to Download Final Fantasy XIV for Mac and Enjoy the Free Trial.md
+++ /dev/null
@@ -1,12 +0,0 @@
-Customers who have already purchased these titles will still be able to play or re-download them, but please be aware that there may be situations where we are unable to guarantee correct function if the environment they are used on changes.
-The newest expansion, Final Fantasy XIV Endwalker, will be out this December 3, 2021, and several players are encountering the "Unable to download patch files" error, so they can't get the update - Here's how you could try to fix it to enjoy FF14 Endwalker at launch.
-final fantasy xiv download mac Download ⚙⚙⚙ https://urlgoal.com/2uyNeX
The "unable to download patch files" error is a pretty common problem with FF14 and a recurrent issue happening with each update. Players who've encountered this issue in the past regularly mentioned it's a firewall issue or a permissions issue. So here are the methods you could try to fix the problem.
-To disable the Windows firewall, go to Control Panel, System and Security, and then Windows firewall. Click "Turn off Windows Firewall (not recommended) for both Private and Public network settings". And then try downloading the FF14 Endwalker update again. Once you're done be sure to reactivate the firewall.
-You can easily do that by deleting the "4e9a232b" folder of FF14. You can find it in Users, -name of the user you're logged on with on Windows-, Documents, My Games, Final Fantasy XIV A Realm Reborn, Downloads, and then Patch. In there, delete the folder "4e9a232b". And then try downloading the update again.
-Disclosures: This game was obtained via paid digital download and reviewed on the PC . Approximately 80 hours of play was devoted to multiplayer modes, and there is no offline option available.
-In the Official MMORPG.com Review, our own Adam Tingle takes a look at the tank simulation MMO that's taking the world by storm and divulges whether you should even bother with the download. The short answer? Yes. Read on!
-
aaccfb2cb3
-data=
-lm_model=
-lexicon=
-```
-
-Example usage for decoding a fine-tuned Wav2Vec model:
-
-```bash
-python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \
- task=audio_pretraining \
- task.data=$data \
- task.labels=ltr \
- common_eval.path=$checkpoint \
- decoding.type=kenlm \
- decoding.lexicon=$lexicon \
- decoding.lmpath=$lm_model \
- dataset.gen_subset=dev_clean,dev_other,test_clean,test_other
-```
-
-Example usage for using Ax to sweep WER parameters (requires `pip install hydra-ax-sweeper`):
-
-```bash
-python $FAIRSEQ_ROOT/examples/speech_recognition/new/infer.py --multirun \
- hydra/sweeper=ax \
- task=audio_pretraining \
- task.data=$data \
- task.labels=ltr \
- common_eval.path=$checkpoint \
- decoding.type=kenlm \
- decoding.lexicon=$lexicon \
- decoding.lmpath=$lm_model \
- dataset.gen_subset=dev_other
-```
diff --git a/spaces/gryhkn/free-fast-youtube-url-video-to-text-using-openai-whisper/README.md b/spaces/gryhkn/free-fast-youtube-url-video-to-text-using-openai-whisper/README.md
deleted file mode 100644
index ece5586a3ae3a4682cd9db3337a33250c589b479..0000000000000000000000000000000000000000
--- a/spaces/gryhkn/free-fast-youtube-url-video-to-text-using-openai-whisper/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Free Youtube URL Video-to-Text Using OpenAI Whisper
-emoji: 📚
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: SteveDigital/free-fast-youtube-url-video-to-text-using-openai-whisper
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/MemoizedChatMessage.tsx b/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/MemoizedChatMessage.tsx
deleted file mode 100644
index 125d23d876450d5a49852f13d32a866f29dcc111..0000000000000000000000000000000000000000
--- a/spaces/gsaivinay/Llama-2-13B-GGML-UI/components/Chat/MemoizedChatMessage.tsx
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FC, memo } from "react";
-import { ChatMessage, Props } from "./ChatMessage";
-
-export const MemoizedChatMessage: FC = memo(
- ChatMessage,
- (prevProps, nextProps) => (
- prevProps.message.content === nextProps.message.content
- )
-);
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py b/spaces/gyugnsu/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py
deleted file mode 100644
index b9cce427e5374c5ddce90199e1184f84a13d30c5..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import wandb
-import numpy as np
-import torch
-import torch.nn.functional as F
-from tqdm import tqdm
-from configs import global_config, hyperparameters
-import dnnlib
-from utils.log_utils import log_image_from_w
-
-
-def project(
- G,
- target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
- *,
- num_steps=1000,
- w_avg_samples=10000,
- initial_learning_rate=0.01,
- initial_noise_factor=0.05,
- lr_rampdown_length=0.25,
- lr_rampup_length=0.05,
- noise_ramp_length=0.75,
- regularize_noise_weight=1e5,
- verbose=False,
- device: torch.device,
- use_wandb=False,
- initial_w=None,
- image_log_step=global_config.image_rec_result_log_snapshot,
- w_name: str
-):
- assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution)
-
- def logprint(*args):
- if verbose:
- print(*args)
-
- G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore
-
- # Compute w stats.
- logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
- z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
- w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C]
- w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C]
- w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C]
- w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device)
- w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
- start_w = initial_w if initial_w is not None else w_avg
-
- # Setup noise inputs.
- noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name}
-
- # Load VGG16 feature detector.
- url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
- with dnnlib.util.open_url(url) as f:
- vgg16 = torch.jit.load(f).eval().to(device)
-
- # Features for target image.
- target_images = target.unsqueeze(0).to(device).to(torch.float32)
- if target_images.shape[2] > 256:
- target_images = F.interpolate(target_images, size=(256, 256), mode='area')
- target_features = vgg16(target_images, resize_images=False, return_lpips=True)
-
- start_w = np.repeat(start_w, G.mapping.num_ws, axis=1)
- w_opt = torch.tensor(start_w, dtype=torch.float32, device=device,
- requires_grad=True) # pylint: disable=not-callable
-
- optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999),
- lr=hyperparameters.first_inv_lr)
-
- # Init noise.
- for buf in noise_bufs.values():
- buf[:] = torch.randn_like(buf)
- buf.requires_grad = True
-
- for step in tqdm(range(num_steps)):
-
- # Learning rate schedule.
- t = step / num_steps
- w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2
- lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
- lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
- lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
- lr = initial_learning_rate * lr_ramp
- for param_group in optimizer.param_groups:
- param_group['lr'] = lr
-
- # Synth images from opt_w.
- w_noise = torch.randn_like(w_opt) * w_noise_scale
- ws = (w_opt + w_noise)
-
- synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True)
-
- # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
- synth_images = (synth_images + 1) * (255 / 2)
- if synth_images.shape[2] > 256:
- synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
-
- # Features for synth images.
- synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
- dist = (target_features - synth_features).square().sum()
-
- # Noise regularization.
- reg_loss = 0.0
- for v in noise_bufs.values():
- noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d()
- while True:
- reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2
- reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2
- if noise.shape[2] <= 8:
- break
- noise = F.avg_pool2d(noise, kernel_size=2)
- loss = dist + reg_loss * regularize_noise_weight
-
- if step % image_log_step == 0:
- with torch.no_grad():
- if use_wandb:
- global_config.training_step += 1
- wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step)
- log_image_from_w(w_opt, G, w_name)
-
- # Step
- optimizer.zero_grad(set_to_none=True)
- loss.backward()
- optimizer.step()
- logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
- # Normalize noise.
- with torch.no_grad():
- for buf in noise_bufs.values():
- buf -= buf.mean()
- buf *= buf.square().mean().rsqrt()
-
- del G
- return w_opt
diff --git a/spaces/haakohu/deep_privacy2/dp2/detection/models/cse.py b/spaces/haakohu/deep_privacy2/dp2/detection/models/cse.py
deleted file mode 100644
index 7b9fbf75fc8fd6c1905993316c03383b9e935564..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/dp2/detection/models/cse.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import torch
-from typing import List
-import tops
-from torchvision.transforms.functional import InterpolationMode, resize
-from densepose.data.utils import get_class_to_mesh_name_mapping
-from densepose import add_densepose_config
-from densepose.structures import DensePoseEmbeddingPredictorOutput
-from densepose.vis.extractor import DensePoseOutputsExtractor
-from densepose.modeling import build_densepose_embedder
-from detectron2.config import get_cfg
-from detectron2.data.transforms import ResizeShortestEdge
-from detectron2.checkpoint.detection_checkpoint import DetectionCheckpointer
-from detectron2.modeling import build_model
-
-
-model_urls = {
- "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml": "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x/250713061/model_final_1d3314.pkl",
- "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/densepose_rcnn_R_50_FPN_s1x.yaml": "https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_s1x/251155172/model_final_c4ea5f.pkl",
-}
-
-
-def cse_det_to_global(boxes_XYXY, S: torch.Tensor, imshape):
- assert len(S.shape) == 3
- H, W = imshape
- N = len(boxes_XYXY)
- segmentation = torch.zeros((N, H, W), dtype=torch.bool, device=S.device)
- boxes_XYXY = boxes_XYXY.long()
- for i in range(N):
- x0, y0, x1, y1 = boxes_XYXY[i]
- assert x0 >= 0 and y0 >= 0
- assert x1 <= imshape[1]
- assert y1 <= imshape[0]
- h = y1 - y0
- w = x1 - x0
- segmentation[i:i+1, y0:y1, x0:x1] = resize(S[i:i+1], (h, w), interpolation=InterpolationMode.NEAREST) > 0
- return segmentation
-
-
-class CSEDetector:
-
- def __init__(
- self,
- cfg_url: str = "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml",
- cfg_2_download: List[str] = [
- "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/densepose_rcnn_R_101_FPN_DL_soft_s1x.yaml",
- "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/Base-DensePose-RCNN-FPN.yaml",
- "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/cse/Base-DensePose-RCNN-FPN-Human.yaml"],
- score_thres: float = 0.9,
- nms_thresh: float = None,
- ) -> None:
- with tops.logger.capture_log_stdout():
- cfg = get_cfg()
- self.device = tops.get_device()
- add_densepose_config(cfg)
- cfg_path = tops.download_file(cfg_url)
- for p in cfg_2_download:
- tops.download_file(p)
- with tops.logger.capture_log_stdout():
- cfg.merge_from_file(cfg_path)
- assert cfg_url in model_urls, cfg_url
- model_path = tops.download_file(model_urls[cfg_url])
- cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = score_thres
- if nms_thresh is not None:
- cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = nms_thresh
- cfg.MODEL.WEIGHTS = str(model_path)
- cfg.MODEL.DEVICE = str(self.device)
- cfg.freeze()
- with tops.logger.capture_log_stdout():
- self.model = build_model(cfg)
- self.model.eval()
- DetectionCheckpointer(self.model).load(str(model_path))
- self.input_format = cfg.INPUT.FORMAT
- self.densepose_extractor = DensePoseOutputsExtractor()
- self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg)
-
- self.embedder = build_densepose_embedder(cfg)
- self.mesh_vertex_embeddings = {
- mesh_name: self.embedder(mesh_name).to(self.device)
- for mesh_name in self.class_to_mesh_name.values()
- if self.embedder.has_embeddings(mesh_name)
- }
- self.cfg = cfg
- self.embed_map = self.mesh_vertex_embeddings["smpl_27554"]
- tops.logger.log("CSEDetector built.")
-
- def __call__(self, *args, **kwargs):
- return self.forward(*args, **kwargs)
-
- def resize_im(self, im):
- H, W = im.shape[1:]
- newH, newW = ResizeShortestEdge.get_output_shape(
- H, W, self.cfg.INPUT.MIN_SIZE_TEST, self.cfg.INPUT.MAX_SIZE_TEST)
- return resize(
- im, (newH, newW), InterpolationMode.BILINEAR, antialias=True)
-
- @torch.no_grad()
- def forward(self, im):
- assert im.dtype == torch.uint8
- if self.input_format == "BGR":
- im = im.flip(0)
- H, W = im.shape[1:]
- im = self.resize_im(im)
- output = self.model([{"image": im, "height": H, "width": W}])[0]["instances"]
- scores = output.get("scores")
- if len(scores) == 0:
- return dict(
- instance_segmentation=torch.empty((0, 0, 112, 112), dtype=torch.bool, device=im.device),
- instance_embedding=torch.empty((0, 16, 112, 112), dtype=torch.float32, device=im.device),
- embed_map=self.mesh_vertex_embeddings["smpl_27554"],
- bbox_XYXY=torch.empty((0, 4), dtype=torch.long, device=im.device),
- im_segmentation=torch.empty((0, H, W), dtype=torch.bool, device=im.device),
- scores=torch.empty((0), dtype=torch.float, device=im.device)
- )
- pred_densepose, boxes_xywh, classes = self.densepose_extractor(output)
- assert isinstance(pred_densepose, DensePoseEmbeddingPredictorOutput), pred_densepose
- S = pred_densepose.coarse_segm.argmax(dim=1) # Segmentation channel Nx2xHxW (2 because only 2 classes)
- E = pred_densepose.embedding
- mesh_name = self.class_to_mesh_name[classes[0]]
- assert mesh_name == "smpl_27554"
- x0, y0, w, h = [boxes_xywh[:, i] for i in range(4)]
- boxes_XYXY = torch.stack((x0, y0, x0+w, y0+h), dim=-1)
- boxes_XYXY = boxes_XYXY.round_().long()
-
- non_empty_boxes = (boxes_XYXY[:, :2] == boxes_XYXY[:, 2:]).any(dim=1).logical_not()
- S = S[non_empty_boxes]
- E = E[non_empty_boxes]
- boxes_XYXY = boxes_XYXY[non_empty_boxes]
- scores = scores[non_empty_boxes]
- im_segmentation = cse_det_to_global(boxes_XYXY, S, [H, W])
- return dict(
- instance_segmentation=S, instance_embedding=E,
- bbox_XYXY=boxes_XYXY,
- im_segmentation=im_segmentation,
- scores=scores.view(-1))
diff --git a/spaces/hahahafofo/vits-uma-genshin-honkai/monotonic_align/__init__.py b/spaces/hahahafofo/vits-uma-genshin-honkai/monotonic_align/__init__.py
deleted file mode 100644
index e97eecc595dd3bd97d0104ec62799e2e5efea57c..0000000000000000000000000000000000000000
--- a/spaces/hahahafofo/vits-uma-genshin-honkai/monotonic_align/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
- """ numba optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(float32)
- path = zeros(neg_cent.shape, dtype=int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
- maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
- return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/resnet.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/resnet.py
deleted file mode 100644
index e0a0feec0de1fcbef2ef7c42ea805b87df2c2810..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/modeling/backbone/resnet.py
+++ /dev/null
@@ -1,643 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-"""
-Variant of the resnet module that takes cfg as an argument.
-Example usage. Strings may be specified in the config file.
- model = ResNet(
- "StemWithFixedBatchNorm",
- "BottleneckWithFixedBatchNorm",
- "ResNet50StagesTo4",
- )
-OR:
- model = ResNet(
- "StemWithGN",
- "BottleneckWithGN",
- "ResNet50StagesTo4",
- )
-Custom implementations may be written in user code and hooked in via the
-`register_*` functions.
-"""
-from collections import namedtuple
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import BatchNorm2d, SyncBatchNorm
-
-from maskrcnn_benchmark.layers import FrozenBatchNorm2d, NaiveSyncBatchNorm2d
-from maskrcnn_benchmark.layers import Conv2d, DFConv2d, SELayer
-from maskrcnn_benchmark.modeling.make_layers import group_norm
-from maskrcnn_benchmark.utils.registry import Registry
-
-
-# ResNet stage specification
-StageSpec = namedtuple(
- "StageSpec",
- [
- "index", # Index of the stage, eg 1, 2, ..,. 5
- "block_count", # Number of residual blocks in the stage
- "return_features", # True => return the last feature map from this stage
- ],
-)
-
-# -----------------------------------------------------------------------------
-# Standard ResNet models
-# -----------------------------------------------------------------------------
-# ResNet-50 (including all stages)
-ResNet50StagesTo5 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True))
-)
-# ResNet-50 up to stage 4 (excludes stage 5)
-ResNet50StagesTo4 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True))
-)
-# ResNet-101 (including all stages)
-ResNet101StagesTo5 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True))
-)
-# ResNet-101 up to stage 4 (excludes stage 5)
-ResNet101StagesTo4 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True))
-)
-# ResNet-50-FPN (including all stages)
-ResNet50FPNStagesTo5 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True))
-)
-# ResNet-101-FPN (including all stages)
-ResNet101FPNStagesTo5 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True))
-)
-# ResNet-152-FPN (including all stages)
-ResNet152FPNStagesTo5 = tuple(
- StageSpec(index=i, block_count=c, return_features=r)
- for (i, c, r) in ((1, 3, True), (2, 8, True), (3, 36, True), (4, 3, True))
-)
-
-class ResNet(nn.Module):
- def __init__(self, cfg):
- super(ResNet, self).__init__()
-
- # If we want to use the cfg in forward(), then we should make a copy
- # of it and store it for later use:
- # self.cfg = cfg.clone()
-
- # Translate string names to implementations
- norm_level = None
- stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC]
- stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY]
- transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC]
-
- if cfg.MODEL.BACKBONE.USE_BN:
- stem_module = StemWithBatchNorm
- transformation_module = BottleneckWithBatchNorm
- norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
- elif cfg.MODEL.BACKBONE.USE_NSYNCBN:
- stem_module = StemWithNaiveSyncBatchNorm
- transformation_module = BottleneckWithNaiveSyncBatchNorm
- norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
- elif cfg.MODEL.BACKBONE.USE_SYNCBN:
- stem_module = StemWithSyncBatchNorm
- transformation_module = BottleneckWithSyncBatchNorm
- norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL
-
- # Construct the stem module
- self.stem = stem_module(cfg)
-
- # Constuct the specified ResNet stages
- num_groups = cfg.MODEL.RESNETS.NUM_GROUPS
- width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP
- in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
- stage2_bottleneck_channels = num_groups * width_per_group
- stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
- with_se = cfg.MODEL.RESNETS.WITH_SE
-
- self.stages = []
- self.out_channels = []
- self.return_features = {}
- for stage_spec in stage_specs:
- name = "layer" + str(stage_spec.index)
- stage2_relative_factor = 2 ** (stage_spec.index - 1)
- bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
- out_channels = stage2_out_channels * stage2_relative_factor
- stage_with_dcn = cfg.MODEL.RESNETS.STAGE_WITH_DCN[stage_spec.index - 1]
- if cfg.MODEL.RESNETS.USE_AVG_DOWN:
- avg_down_stride = 1 if stage_spec.index==1 else 2
- else:
- avg_down_stride = 0
- module = _make_stage(
- transformation_module,
- in_channels,
- bottleneck_channels,
- out_channels,
- stage_spec.block_count,
- num_groups,
- cfg.MODEL.RESNETS.STRIDE_IN_1X1,
- first_stride=int(stage_spec.index > 1) + 1,
- dcn_config={
- "stage_with_dcn": stage_with_dcn,
- "with_modulated_dcn": cfg.MODEL.RESNETS.WITH_MODULATED_DCN,
- "deformable_groups": cfg.MODEL.RESNETS.DEFORMABLE_GROUPS,
- },
- norm_level=norm_level,
- with_se=with_se,
- avg_down_stride=avg_down_stride
- )
- in_channels = out_channels
- self.add_module(name, module)
- self.stages.append(name)
- self.out_channels.append(out_channels)
- self.return_features[name] = stage_spec.return_features
-
- # Optionally freeze (requires_grad=False) parts of the backbone
- self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)
-
- def _freeze_backbone(self, freeze_at):
- if freeze_at < 0:
- return
- for stage_index in range(freeze_at):
- if stage_index == 0:
- m = self.stem # stage 0 is the stem
- else:
- m = getattr(self, "layer" + str(stage_index))
- for p in m.parameters():
- p.requires_grad = False
-
- def forward(self, x):
- outputs = []
- x = self.stem(x)
- for stage_name in self.stages:
- x = getattr(self, stage_name)(x)
- if self.return_features[stage_name]:
- outputs.append(x)
- return outputs
-
-
-class ResNetHead(nn.Module):
- def __init__(
- self,
- block_module,
- stages,
- num_groups=1,
- width_per_group=64,
- stride_in_1x1=True,
- stride_init=None,
- res2_out_channels=256,
- dilation=1,
- dcn_config=None
- ):
- super(ResNetHead, self).__init__()
-
- stage2_relative_factor = 2 ** (stages[0].index - 1)
- stage2_bottleneck_channels = num_groups * width_per_group
- out_channels = res2_out_channels * stage2_relative_factor
- in_channels = out_channels // 2
- bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor
-
- block_module = _TRANSFORMATION_MODULES[block_module]
-
- self.stages = []
- stride = stride_init
- for stage in stages:
- name = "layer" + str(stage.index)
- if not stride:
- stride = int(stage.index > 1) + 1
- module = _make_stage(
- block_module,
- in_channels,
- bottleneck_channels,
- out_channels,
- stage.block_count,
- num_groups,
- stride_in_1x1,
- first_stride=stride,
- dilation=dilation,
- dcn_config=dcn_config
- )
- stride = None
- self.add_module(name, module)
- self.stages.append(name)
- self.out_channels = out_channels
-
- def forward(self, x):
- for stage in self.stages:
- x = getattr(self, stage)(x)
- return x
-
-
-def _make_stage(
- transformation_module,
- in_channels,
- bottleneck_channels,
- out_channels,
- block_count,
- num_groups,
- stride_in_1x1,
- first_stride,
- dilation=1,
- dcn_config=None,
- norm_level=None,
- **kwargs
-):
- blocks = []
- stride = first_stride
- for li in range(block_count):
- if norm_level is not None:
- layer_module = BottleneckWithFixedBatchNorm
- if norm_level >= 1 and li == 0:
- layer_module = transformation_module
- if norm_level >= 2 and li == block_count - 1:
- layer_module = transformation_module
- if norm_level >= 3:
- layer_module = transformation_module
- else:
- layer_module = transformation_module
-
- blocks.append(
- layer_module(
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups,
- stride_in_1x1,
- stride,
- dilation=dilation,
- dcn_config=dcn_config,
- **kwargs
- )
- )
- stride = 1
- in_channels = out_channels
- return nn.Sequential(*blocks)
-
-
-class Bottleneck(nn.Module):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups,
- stride_in_1x1,
- stride,
- dilation,
- norm_func,
- dcn_config,
- with_se=False,
- avg_down_stride=0,
- ):
- super(Bottleneck, self).__init__()
-
- self.downsample = None
- if in_channels != out_channels:
- down_stride = stride if dilation == 1 else 1
- if avg_down_stride>0:
- self.downsample = nn.Sequential(
- nn.AvgPool2d(
- kernel_size=avg_down_stride,
- stride=avg_down_stride,
- ceil_mode=True,
- count_include_pad=False
- ),
- nn.Conv2d(
- in_channels, out_channels,
- kernel_size=1, stride=1, bias=False
- ),
- norm_func(out_channels),
- )
- else:
- self.downsample = nn.Sequential(
- Conv2d(
- in_channels, out_channels,
- kernel_size=1, stride=down_stride, bias=False
- ),
- norm_func(out_channels),
- )
- for modules in [self.downsample,]:
- for l in modules.modules():
- if isinstance(l, Conv2d):
- nn.init.kaiming_uniform_(l.weight, a=1)
-
- if dilation > 1:
- stride = 1 # reset to be 1
-
- # The original MSRA ResNet models have stride in the first 1x1 conv
- # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
- # stride in the 3x3 conv
- stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
-
- self.conv1 = Conv2d(
- in_channels,
- bottleneck_channels,
- kernel_size=1,
- stride=stride_1x1,
- bias=False,
- )
- self.bn1 = norm_func(bottleneck_channels)
- # TODO: specify init for the above
- with_dcn = dcn_config.get("stage_with_dcn", False)
- if with_dcn:
- deformable_groups = dcn_config.get("deformable_groups", 1)
- with_modulated_dcn = dcn_config.get("with_modulated_dcn", False)
- self.conv2 = DFConv2d(
- bottleneck_channels,
- bottleneck_channels,
- with_modulated_dcn=with_modulated_dcn,
- kernel_size=3,
- stride=stride_3x3,
- groups=num_groups,
- dilation=dilation,
- deformable_groups=deformable_groups,
- bias=False
- )
- else:
- self.conv2 = Conv2d(
- bottleneck_channels,
- bottleneck_channels,
- kernel_size=3,
- stride=stride_3x3,
- padding=dilation,
- bias=False,
- groups=num_groups,
- dilation=dilation
- )
- nn.init.kaiming_uniform_(self.conv2.weight, a=1)
-
- self.bn2 = norm_func(bottleneck_channels)
-
- self.conv3 = Conv2d(
- bottleneck_channels, out_channels, kernel_size=1, bias=False
- )
- self.bn3 = norm_func(out_channels)
-
- self.se = SELayer(out_channels) if with_se and not with_dcn else None
-
- for l in [self.conv1, self.conv3,]:
- nn.init.kaiming_uniform_(l.weight, a=1)
-
- def forward(self, x):
- identity = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = F.relu_(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = F.relu_(out)
-
- out0 = self.conv3(out)
- out = self.bn3(out0)
-
- if self.se:
- out = self.se(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = F.relu_(out)
-
- return out
-
-
-class BaseStem(nn.Module):
- def __init__(self, cfg, norm_func):
- super(BaseStem, self).__init__()
-
- out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS
- self.stem_3x3 = cfg.MODEL.RESNETS.USE_STEM3X3
-
- if self.stem_3x3:
- self.conv1 = Conv2d(
- 3, out_channels, kernel_size=3, stride=2, padding=1, bias=False
- )
- self.bn1 = norm_func(out_channels)
- self.conv2 = Conv2d(
- out_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False
- )
- self.bn2 = norm_func(out_channels)
- for l in [self.conv1, self.conv2]:
- nn.init.kaiming_uniform_(l.weight, a=1)
- else:
- self.conv1 = Conv2d(
- 3, out_channels, kernel_size=7, stride=2, padding=3, bias=False
- )
- self.bn1 = norm_func(out_channels)
-
- for l in [self.conv1,]:
- nn.init.kaiming_uniform_(l.weight, a=1)
-
- def forward(self, x):
- if self.stem_3x3:
- x = self.conv1(x)
- x = self.bn1(x)
- x = F.relu_(x)
- x = self.conv2(x)
- x = self.bn2(x)
- x = F.relu_(x)
- else:
- x = self.conv1(x)
- x = self.bn1(x)
- x = F.relu_(x)
- x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
- return x
-
-
-class BottleneckWithFixedBatchNorm(Bottleneck):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups=1,
- stride_in_1x1=True,
- stride=1,
- dilation=1,
- dcn_config=None,
- **kwargs
- ):
- super(BottleneckWithFixedBatchNorm, self).__init__(
- in_channels=in_channels,
- bottleneck_channels=bottleneck_channels,
- out_channels=out_channels,
- num_groups=num_groups,
- stride_in_1x1=stride_in_1x1,
- stride=stride,
- dilation=dilation,
- norm_func=FrozenBatchNorm2d,
- dcn_config=dcn_config,
- **kwargs
- )
-
-
-class StemWithFixedBatchNorm(BaseStem):
- def __init__(self, cfg):
- super(StemWithFixedBatchNorm, self).__init__(
- cfg, norm_func=FrozenBatchNorm2d
- )
-
-
-class BottleneckWithBatchNorm(Bottleneck):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups=1,
- stride_in_1x1=True,
- stride=1,
- dilation=1,
- dcn_config=None,
- **kwargs
- ):
- super(BottleneckWithBatchNorm, self).__init__(
- in_channels=in_channels,
- bottleneck_channels=bottleneck_channels,
- out_channels=out_channels,
- num_groups=num_groups,
- stride_in_1x1=stride_in_1x1,
- stride=stride,
- dilation=dilation,
- norm_func=BatchNorm2d,
- dcn_config=dcn_config,
- **kwargs
- )
-
-
-class StemWithBatchNorm(BaseStem):
- def __init__(self, cfg):
- super(StemWithBatchNorm, self).__init__(
- cfg, norm_func=BatchNorm2d
- )
-
-
-class BottleneckWithNaiveSyncBatchNorm(Bottleneck):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups=1,
- stride_in_1x1=True,
- stride=1,
- dilation=1,
- dcn_config=None,
- **kwargs
- ):
- super(BottleneckWithNaiveSyncBatchNorm, self).__init__(
- in_channels=in_channels,
- bottleneck_channels=bottleneck_channels,
- out_channels=out_channels,
- num_groups=num_groups,
- stride_in_1x1=stride_in_1x1,
- stride=stride,
- dilation=dilation,
- norm_func=NaiveSyncBatchNorm2d,
- dcn_config=dcn_config,
- **kwargs
- )
-
-
-class StemWithNaiveSyncBatchNorm(BaseStem):
- def __init__(self, cfg):
- super(StemWithNaiveSyncBatchNorm, self).__init__(
- cfg, norm_func=NaiveSyncBatchNorm2d
- )
-
-
-class BottleneckWithSyncBatchNorm(Bottleneck):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups=1,
- stride_in_1x1=True,
- stride=1,
- dilation=1,
- dcn_config=None,
- **kwargs
- ):
- super(BottleneckWithSyncBatchNorm, self).__init__(
- in_channels=in_channels,
- bottleneck_channels=bottleneck_channels,
- out_channels=out_channels,
- num_groups=num_groups,
- stride_in_1x1=stride_in_1x1,
- stride=stride,
- dilation=dilation,
- norm_func=SyncBatchNorm,
- dcn_config=dcn_config,
- **kwargs
- )
-
-
-class StemWithSyncBatchNorm(BaseStem):
- def __init__(self, cfg):
- super(StemWithSyncBatchNorm, self).__init__(
- cfg, norm_func=SyncBatchNorm
- )
-
-
-class BottleneckWithGN(Bottleneck):
- def __init__(
- self,
- in_channels,
- bottleneck_channels,
- out_channels,
- num_groups=1,
- stride_in_1x1=True,
- stride=1,
- dilation=1,
- dcn_config=None,
- **kwargs
- ):
- super(BottleneckWithGN, self).__init__(
- in_channels=in_channels,
- bottleneck_channels=bottleneck_channels,
- out_channels=out_channels,
- num_groups=num_groups,
- stride_in_1x1=stride_in_1x1,
- stride=stride,
- dilation=dilation,
- norm_func=group_norm,
- dcn_config=dcn_config,
- **kwargs
- )
-
-
-class StemWithGN(BaseStem):
- def __init__(self, cfg):
- super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
-
-
-_TRANSFORMATION_MODULES = Registry({
- "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
- "BottleneckWithGN": BottleneckWithGN,
-})
-
-_STEM_MODULES = Registry({
- "StemWithFixedBatchNorm": StemWithFixedBatchNorm,
- "StemWithGN": StemWithGN,
-})
-
-_STAGE_SPECS = Registry({
- "R-50-C4": ResNet50StagesTo4,
- "R-50-C5": ResNet50StagesTo5,
- "R-50-RETINANET": ResNet50StagesTo5,
- "R-101-C4": ResNet101StagesTo4,
- "R-101-C5": ResNet101StagesTo5,
- "R-101-RETINANET": ResNet101StagesTo5,
- "R-50-FPN": ResNet50FPNStagesTo5,
- "R-50-FPN-RETINANET": ResNet50FPNStagesTo5,
- "R-50-FPN-FCOS": ResNet50FPNStagesTo5,
- "R-101-FPN": ResNet101FPNStagesTo5,
- "R-101-FPN-RETINANET": ResNet101FPNStagesTo5,
- "R-101-FPN-FCOS": ResNet101FPNStagesTo5,
- "R-152-FPN": ResNet152FPNStagesTo5,
-})
\ No newline at end of file
diff --git a/spaces/heiyuan/ChatGPT/llama_func.py b/spaces/heiyuan/ChatGPT/llama_func.py
deleted file mode 100644
index c71027dd4e6f99c0c12626cbbf276f407877be04..0000000000000000000000000000000000000000
--- a/spaces/heiyuan/ChatGPT/llama_func.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import logging
-
-from llama_index import GPTSimpleVectorIndex
-from llama_index import download_loader
-from llama_index import (
- Document,
- LLMPredictor,
- PromptHelper,
- QuestionAnswerPrompt,
- RefinePrompt,
-)
-from langchain.llms import OpenAI
-import colorama
-
-
-from presets import *
-from utils import *
-
-
-def get_documents(file_src):
- documents = []
- index_name = ""
- logging.debug("Loading documents...")
- logging.debug(f"file_src: {file_src}")
- for file in file_src:
- logging.debug(f"file: {file.name}")
- index_name += file.name
- if os.path.splitext(file.name)[1] == ".pdf":
- logging.debug("Loading PDF...")
- CJKPDFReader = download_loader("CJKPDFReader")
- loader = CJKPDFReader()
- documents += loader.load_data(file=file.name)
- elif os.path.splitext(file.name)[1] == ".docx":
- logging.debug("Loading DOCX...")
- DocxReader = download_loader("DocxReader")
- loader = DocxReader()
- documents += loader.load_data(file=file.name)
- elif os.path.splitext(file.name)[1] == ".epub":
- logging.debug("Loading EPUB...")
- EpubReader = download_loader("EpubReader")
- loader = EpubReader()
- documents += loader.load_data(file=file.name)
- else:
- logging.debug("Loading text file...")
- with open(file.name, "r", encoding="utf-8") as f:
- text = add_space(f.read())
- documents += [Document(text)]
- index_name = sha1sum(index_name)
- return documents, index_name
-
-
-def construct_index(
- api_key,
- file_src,
- max_input_size=4096,
- num_outputs=1,
- max_chunk_overlap=20,
- chunk_size_limit=600,
- embedding_limit=None,
- separator=" ",
- num_children=10,
- max_keywords_per_chunk=10,
-):
- os.environ["OPENAI_API_KEY"] = api_key
- chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
- embedding_limit = None if embedding_limit == 0 else embedding_limit
- separator = " " if separator == "" else separator
-
- llm_predictor = LLMPredictor(
- llm=OpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
- )
- prompt_helper = PromptHelper(
- max_input_size,
- num_outputs,
- max_chunk_overlap,
- embedding_limit,
- chunk_size_limit,
- separator=separator,
- )
- documents, index_name = get_documents(file_src)
- if os.path.exists(f"./index/{index_name}.json"):
- logging.info("找到了缓存的索引文件，加载中……")
- return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
- else:
- try:
- logging.debug("构建索引中……")
- index = GPTSimpleVectorIndex(
- documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
- )
- os.makedirs("./index", exist_ok=True)
- index.save_to_disk(f"./index/{index_name}.json")
- return index
- except Exception as e:
- print(e)
- return None
-
-
-def chat_ai(
- api_key,
- index,
- question,
- context,
- chatbot,
-):
- os.environ["OPENAI_API_KEY"] = api_key
-
- logging.info(f"Question: {question}")
-
- response, chatbot_display, status_text = ask_ai(
- api_key,
- index,
- question,
- replace_today(PROMPT_TEMPLATE),
- REFINE_TEMPLATE,
- SIM_K,
- INDEX_QUERY_TEMPRATURE,
- context,
- )
- if response is None:
- status_text = "查询失败，请换个问法试试"
- return context, chatbot
- response = response
-
- context.append({"role": "user", "content": question})
- context.append({"role": "assistant", "content": response})
- chatbot.append((question, chatbot_display))
-
- os.environ["OPENAI_API_KEY"] = ""
- return context, chatbot, status_text
-
-
-def ask_ai(
- api_key,
- index,
- question,
- prompt_tmpl,
- refine_tmpl,
- sim_k=1,
- temprature=0,
- prefix_messages=[],
-):
- os.environ["OPENAI_API_KEY"] = api_key
-
- logging.debug("Index file found")
- logging.debug("Querying index...")
- llm_predictor = LLMPredictor(
- llm=OpenAI(
- temperature=temprature,
- model_name="gpt-3.5-turbo-0301",
- prefix_messages=prefix_messages,
- )
- )
-
- response = None # Initialize response variable to avoid UnboundLocalError
- qa_prompt = QuestionAnswerPrompt(prompt_tmpl)
- rf_prompt = RefinePrompt(refine_tmpl)
- response = index.query(
- question,
- llm_predictor=llm_predictor,
- similarity_top_k=sim_k,
- text_qa_template=qa_prompt,
- refine_template=rf_prompt,
- response_mode="compact",
- )
-
- if response is not None:
- logging.info(f"Response: {response}")
- ret_text = response.response
- nodes = []
- for index, node in enumerate(response.source_nodes):
- brief = node.source_text[:25].replace("\n", "")
- nodes.append(
- f"[{index+1}]\t{brief}... {node.source_text}
{
- const key = `conversations:${botId}`
- const { [key]: value } = await Storage.get(key)
- return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
- const conversations = await loadHistoryConversations(botId)
- const newConversations = conversations.filter((c) => c.id !== cid)
- await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise {
- const key = `conversation:${botId}:${cid}:messages`
- const { [key]: value } = await Storage.get(key)
- return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
- const conversations = await loadHistoryConversations(botId)
- if (!conversations.some((c) => c.id === cid)) {
- conversations.unshift({ id: cid, createdAt: Date.now() })
- await Storage.set({ [`conversations:${botId}`]: conversations })
- }
- const key = `conversation:${botId}:${cid}:messages`
- await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise {
- const conversations = await loadHistoryConversations(botId)
- const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
- return zip(conversations, messagesList).map(([c, messages]) => ({
- id: c!.id,
- createdAt: c!.createdAt,
- messages: messages!,
- }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
- const messages = await loadConversationMessages(botId, conversationId)
- const newMessages = messages.filter((m) => m.id !== messageId)
- await setConversationMessages(botId, conversationId, newMessages)
- if (!newMessages.length) {
- await deleteHistoryConversation(botId, conversationId)
- }
-}
diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/text/symbols.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/hhhyrhe/vits-uma-genshin-honkai/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/huaiji3y/bingo-Public/src/pages/api/sydney.ts b/spaces/huaiji3y/bingo-Public/src/pages/api/sydney.ts
deleted file mode 100644
index 8bd7074bc72bd2803e4acf89d3814908893ff044..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const conversationContext = req.body
- const headers = createHeaders(req.cookies)
- const id = headers['x-forwarded-for']
-
- debug(id, headers)
- res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
- const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
- headers: {
- ...headers,
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- pragma: 'no-cache',
- }
- })
-
- const closeDog = new WatchDog()
- const timeoutDog = new WatchDog()
- ws.onmessage = (event) => {
- timeoutDog.watch(() => {
- debug(id, 'timeout')
- ws.send(websocketUtils.packMessage({ type: 6 }))
- }, 3000)
- closeDog.watch(() => {
- debug(id, 'timeout close')
- ws.close()
- }, 20000)
- res.write(event.data)
- if (/\{"type":([367])\}/.test(String(event.data))) {
- const type = parseInt(RegExp.$1, 10)
- debug(id, 'connection type', type)
- if (type === 3) {
- ws.close()
- } else {
- ws.send(websocketUtils.packMessage({ type }))
- }
- }
- }
-
- ws.onclose = () => {
- timeoutDog.reset()
- closeDog.reset()
- debug(id, 'ws close')
- res.end()
- }
-
- await new Promise((resolve) => ws.onopen = resolve)
- ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
- ws.send(websocketUtils.packMessage({ type: 6 }))
- ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
- req.socket.once('close', () => {
- debug(id, 'connection close')
- ws.close()
- if (!res.closed) {
- res.end()
- }
- })
-}
diff --git a/spaces/hudsonhayes/Vodafone_CRM_Chatbot/app.py b/spaces/hudsonhayes/Vodafone_CRM_Chatbot/app.py
deleted file mode 100644
index 885960e04eae215a2c2455594b54b842b72656a9..0000000000000000000000000000000000000000
--- a/spaces/hudsonhayes/Vodafone_CRM_Chatbot/app.py
+++ /dev/null
@@ -1,296 +0,0 @@
-from pydantic import NoneStr
-import os
-import mimetypes
-import requests
-import tempfile
-import gradio as gr
-import openai
-import re
-import json
-from transformers import pipeline
-import matplotlib.pyplot as plt
-import plotly.express as px
-
-class SentimentAnalyzer:
- def __init__(self):
- self.model="facebook/bart-large-mnli"
- def analyze_sentiment(self, text):
- pipe = pipeline("zero-shot-classification", model=self.model)
- label=["positive","negative","neutral"]
- result = pipe(text, label)
- sentiment_scores= {result['labels'][0]:result['scores'][0],result['labels'][1]:result['scores'][1],result['labels'][2]:result['scores'][2]}
- sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
- return sentiment_scores_str
- def emotion_analysis(self,text):
- prompt = f""" Your task is to analyze {text} and predict the emotion using scores. Emotions are categorized into the following list: Sadness, Happiness, Joy, Fear, Disgust, and Anger. You need to provide the emotion with the highest score. The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
-Please analyze the text and provide the output in the following format: emotion: score [with one result having the highest score]."""
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=prompt,
- temperature=1,
- max_tokens=60,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
-
- message = response.choices[0].text.strip().replace("\n","")
- print(message)
- return message
-
- def analyze_sentiment_for_graph(self, text):
- pipe = pipeline("zero-shot-classification", model=self.model)
- label=["positive", "negative", "neutral"]
- result = pipe(text, label)
- sentiment_scores = {
- result['labels'][0]: result['scores'][0],
- result['labels'][1]: result['scores'][1],
- result['labels'][2]: result['scores'][2]
- }
- return sentiment_scores
-
- def emotion_analysis_for_graph(self,text):
-
- list_of_emotion=text.split(":")
- label=list_of_emotion[0]
- score=list_of_emotion[1]
- score_dict={
- label:float(score)
- }
- print(score_dict)
- return score_dict
-
-
-class Summarizer:
- def __init__(self):
- pass
-
- def generate_summary(self, text):
- model_engine = "text-davinci-003"
- prompt = f"""summarize the following conversation delimited by triple backticks.
- write within 30 words.
- ```{text}``` """
- completions = openai.Completion.create(
- engine=model_engine,
- prompt=prompt,
- max_tokens=60,
- n=1,
- stop=None,
- temperature=0.5,
- )
- message = completions.choices[0].text.strip()
- return message
-
-history_state = gr.State()
-summarizer = Summarizer()
-sentiment = SentimentAnalyzer()
-
-class LangChain_Document_QA:
-
- def __init__(self):
- pass
-
- def _add_text(self,history, text):
- history = history + [(text, None)]
- history_state.value = history
- return history,gr.update(value="", interactive=False)
-
- def _agent_text(self,history, text):
- response = text
- history[-1][1] = response
- history_state.value = history
- return history
-
- def _chat_history(self):
- history = history_state.value
- formatted_history = " "
- for entry in history:
- customer_text, agent_text = entry
- formatted_history += f"Customer: {customer_text}\n"
- if agent_text:
- formatted_history += f"Agent: {agent_text}\n"
- return formatted_history
-
- def _display_history(self):
- formatted_history=self._chat_history()
- summary=summarizer.generate_summary(formatted_history)
- return summary
-
- def _display_graph(self,sentiment_scores):
- labels = sentiment_scores.keys()
- scores = sentiment_scores.values()
- fig = px.bar(x=scores, y=labels, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
- fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
- fig.update_layout(height=500, width=200)
- return fig
-
- def _history_of_chat(self):
- history = history_state.value
- formatted_history = ""
- client=""
- agent=""
- for entry in history:
- customer_text, agent_text = entry
- client+=customer_text
- formatted_history += f"Customer: {customer_text}\n"
- if agent_text:
- agent+=agent_text
- formatted_history += f"Agent: {agent_text}\n"
- return client,agent
-
-
- def _suggested_answer(self,text):
- try:
- history = self._chat_history()
- start_sequence = "\nCustomer:"
- restart_sequence = "\nVodafone Customer Relationship Manager:"
- prompt = 'your task is make a conversation between a customer and vodafone telecom customer relationship manager.'
- file_path = "vodafone_customer_details.json"
- with open(file_path) as file:
- customer_details = json.load(file)
- prompt = f"""{history}{start_sequence}{text}{restart_sequence} if customer ask any information take it from {customer_details}.
- if customer say thanks or thankyou tone related messages You should not ask anything to end the conversation with greetings tone.
- """
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=prompt,
- temperature=0,
- max_tokens=500,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0.6,
- )
-
- message = response.choices[0].text.strip()
- if ":" in message:
- message = re.sub(r'^.*:', '', message)
- return message.strip()
- except:
- return "I can't get the response"
-
-
-
- def _text_box(self,customer_emotion,agent_emotion,agent_sentiment_score,customer_sentiment_score):
- agent_score = ", ".join([f"{key}: {value:.2f}" for key, value in agent_sentiment_score.items()])
- customer_score = ", ".join([f"{key}: {value:.2f}" for key, value in customer_sentiment_score.items()])
- return f"customer_emotion:{customer_emotion}\nagent_emotion:{agent_emotion}\nAgent_Sentiment_score:{agent_score}\nCustomer_sentiment_score:{customer_score}"
-
- def _on_sentiment_btn_click(self):
- client,agent=self._history_of_chat()
-
- customer_emotion=sentiment.emotion_analysis(client)
- customer_sentiment_score = sentiment.analyze_sentiment_for_graph(client)
-
- agent_emotion=sentiment.emotion_analysis(agent)
- agent_sentiment_score = sentiment.analyze_sentiment_for_graph(agent)
-
- scores=self._text_box(customer_emotion,agent_emotion,agent_sentiment_score,customer_sentiment_score)
-
- customer_fig=self._display_graph(customer_sentiment_score)
- customer_fig.update_layout(title="Sentiment Analysis",width=800)
-
- agent_fig=self._display_graph(agent_sentiment_score)
- agent_fig.update_layout(title="Sentiment Analysis",width=800)
-
- agent_emotion_score = sentiment.emotion_analysis_for_graph(agent_emotion)
-
- agent_emotion_fig=self._display_graph(agent_emotion_score)
- agent_emotion_fig.update_layout(title="Emotion Analysis",width=800)
-
- customer_emotion_score = sentiment.emotion_analysis_for_graph(customer_emotion)
-
- customer_emotion_fig=self._display_graph(customer_emotion_score)
- customer_emotion_fig.update_layout(title="Emotion Analysis",width=800)
-
- return scores,customer_fig,agent_fig,customer_emotion_fig,agent_emotion_fig
-
- def clear_func(self):
- history_state.clear()
- def gradio_interface(self):
- with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
- with gr.Row():
- gr.HTML("""Vodafone Generative AI CRM ChatBot Sentiment and Emotion Score Graph adobe after effects keygen cs6 Download >>>>> https://gohhs.com/2uz3s6
diff --git a/spaces/inamXcontru/PoeticTTS/Detonate 1.2 Full Version Free.md b/spaces/inamXcontru/PoeticTTS/Detonate 1.2 Full Version Free.md
deleted file mode 100644
index ba57ead1a8120b2ff91d93de0defe4a938c4978c..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Detonate 1.2 Full Version Free.md
+++ /dev/null
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-Detonate 1.2 Full Version Free Download File ✸ https://gohhs.com/2uz3bk
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/ABCD Any Body Can Dance 2 English Dubbed Torrent Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/ABCD Any Body Can Dance 2 English Dubbed Torrent Download.md
deleted file mode 100644
index 3676c7c00ff1e432218e2524f47ce31cd305b395..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/ABCD Any Body Can Dance 2 English Dubbed Torrent Download.md
+++ /dev/null
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-ABCD Any Body Can Dance 2. TV Shows.. I really enjoy this movie and I want to share it with everyone. Find all the latest TV Shows online. Time in miliseconds. 34.1 77.0 -.3 0.0. 1st Step.. This is the best dance song by Shagun in her very first movie 'ABCD 2'. Write your name in the comment box to download it for free. ABCD Any Body Can Dance 2 - আবার একটা জন্য বাজে 2 ডাইভ অফ কে কারণে. ABCD Any Body Can Dance 2 - আবার একটা জন্য বাজে 2 ডাইভ অফ কে কারণে. . Song Chives is the first video music site to combine fan driven charts and music videos into a single consistent music video site. Download 'Beauty And The Beast' Game King Game and many more programs here! Boys riding giraffes 2nd episode 01 torrent. . Kalam kal khair ho 1 english audio. Comedian who writes humourous monologues and story was teaching dance to the elderly..
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Find all the latest TV Shows online. Download the official movie music video on Gaana for 'Haseena Maathiri (Unreleased)' by Manikanth, Gaana. Watch the official music video for The Way I Are (Dance With Somebody) by Bebe Rexha feat. Lil Wayne from the album All Your Fault: Pt. 2. . Zameen kaun hai title song 2016. Hindi youtube mobile - ABCD Any Body Can Dance 2: Directed by Remo D'Souza. With Prabhu Deva, Varun Dhawan, Shraddha Kapoor, Lauren Gottlieb. Based on the true story of the Fictitious. Anybody can dance (ABCD) - download torrent torrents - download Anybody can dance 2 torrent. Anybody can dance 2: Directed by Remo D'Souza. With Prabhu Deva, Ganesh Acharya, Kay Kay Menon, Lauren Gottlieb. When a capable dancer is provoked by. Whether it's sports, drama or even education shows on TV, they all use this song in a sappy love story kind of way. . Download 'Beauty And The Beast' Game King Game and many more programs here!. Home; Bollywood Top List; Tamil; English; Kannada; Hindi; Hindi Movie. Top List. . Eve song tumse hum sab aata hai english duet song, sung by Sayantika. Any Body Can Dance 2 - আবার একটা জন্য বাজে 2 ডাইভ অফ কে কারণে. .
899543212bAudio Assault Druminator 1.0 Download Zip ✫ https://urlin.us/2uExad
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (vasanthathil Oru Naal Tamil Movie Mp).md b/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (vasanthathil Oru Naal Tamil Movie Mp).md
deleted file mode 100644
index 10f39e2b41c49b42fe0b7afced2257e9116775b5..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (vasanthathil Oru Naal Tamil Movie Mp).md
+++ /dev/null
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-HD Online Player (vasanthathil oru naal tamil movie mp) Download ———>>> https://urlin.us/2uExFf
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Hitman Absolution Keygen PASSWORD.full.20.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Hitman Absolution Keygen PASSWORD.full.20.md
deleted file mode 100644
index 6031c7b2e63ff2be03e205548962001b0a1cfc44..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Hitman Absolution Keygen PASSWORD.full.20.md
+++ /dev/null
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-
-How to Download and Play Hitman Absolution with Keygen PASSWORD.full.20
-
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3cee63e6c2Adobe Photoshop CS3 Crack - Infinite Pirate Serial Key DOWNLOAD →→→ https://tiurll.com/2uClbP
diff --git a/spaces/inreVtussa/clothingai/Examples/Chemistry Form 5 Module Nilam Publication Zip.md b/spaces/inreVtussa/clothingai/Examples/Chemistry Form 5 Module Nilam Publication Zip.md
deleted file mode 100644
index 273fe7d87056a5b9c315e392764e56b8c4ec58e7..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Chemistry Form 5 Module Nilam Publication Zip.md
+++ /dev/null
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-You can download the signed [Form] to your device or share it with other parties involved with a link or by email, as a result. Because of its cross-platform nature, signNow works on any device and any operating system. Select our signature tool and leave behind the old days with efficiency, affordability and security.
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899543212b"
-
- return stored_commit_hash
-
-
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
- if desc is not None:
- print(desc)
-
- if live:
- result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
- if result.returncode != 0:
- raise RuntimeError(f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}""")
-
- return ""
-
- result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
-
- if result.returncode != 0:
-
- message = f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}
-stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''}
-stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''}
-"""
- raise RuntimeError(message)
-
- return result.stdout.decode(encoding="utf8", errors="ignore")
-
-
-def check_run(command):
- result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
- return result.returncode == 0
-
-
-def is_installed(package):
- try:
- spec = importlib.util.find_spec(package)
- except ModuleNotFoundError:
- return False
-
- return spec is not None
-
-
-def repo_dir(name):
- return os.path.join(script_path, dir_repos, name)
-
-
-def run_python(code, desc=None, errdesc=None):
- return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(args, desc=None):
- if skip_install:
- return
-
- index_url_line = f' --index-url {index_url}' if index_url != '' else ''
- return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
-
-
-def check_run_python(code):
- return check_run(f'"{python}" -c "{code}"')
-
-
-def git_clone(url, dir, name, commithash=None):
- # TODO clone into temporary dir and move if successful
-
- if os.path.exists(dir):
- if commithash is None:
- return
-
- current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
- if current_hash == commithash:
- return
-
- run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
- run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
- return
-
- run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
-
- if commithash is not None:
- run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
-
-
-def git_pull_recursive(dir):
- for subdir, _, _ in os.walk(dir):
- if os.path.exists(os.path.join(subdir, '.git')):
- try:
- output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
- print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
- except subprocess.CalledProcessError as e:
- print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
-
-
-def run_extension_installer(extension_dir):
- path_installer = os.path.join(extension_dir, "install.py")
- if not os.path.isfile(path_installer):
- return
-
- try:
- env = os.environ.copy()
- env['PYTHONPATH'] = os.path.abspath(".")
-
- print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
- except Exception as e:
- print(e, file=sys.stderr)
-
-
-def prepare_environment():
- global skip_install
-
- torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113")
-
- ## check windows
- if sys.platform != 'win32':
- requirements_file = os.environ.get('REQS_FILE', "req.txt")
- else:
- requirements_file = os.environ.get('REQS_FILE', "requirements.txt")
-
- commit = commit_hash()
-
- print(f"Python {sys.version}")
- print(f"Commit hash: {commit}")
-
- if not is_installed("torch") or not is_installed("torchvision"):
- run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
-
- run_pip(f"install -r \"{requirements_file}\"", "requirements for SadTalker WebUI (may take longer time in first time)")
-
- if sys.platform != 'win32' and not is_installed('tts'):
- run_pip(f"install TTS", "install TTS individually in SadTalker, which might not work on windows.")
-
-
-def start():
- print(f"Launching SadTalker Web UI")
- from app_sadtalker import sadtalker_demo
- demo = sadtalker_demo()
- demo.queue()
- demo.launch()
-
-if __name__ == "__main__":
- prepare_environment()
- start()
\ No newline at end of file
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/roberta/README.glue.md b/spaces/koajoel/PolyFormer/fairseq/examples/roberta/README.glue.md
deleted file mode 100644
index 4f596d55af99fba3cdf58b1d5ff3d8f8dbf4383d..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/roberta/README.glue.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# Finetuning RoBERTa on GLUE tasks
-
-### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands:
-```bash
-wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
-python download_glue_data.py --data_dir glue_data --tasks all
-```
-
-### 2) Preprocess GLUE task data:
-```bash
-./examples/roberta/preprocess_GLUE_tasks.sh glue_data
-```
-`glue_task_name` is one of the following:
-`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}`
-Use `ALL` for preprocessing all the glue tasks.
-
-### 3) Fine-tuning on GLUE task:
-Example fine-tuning cmd for `RTE` task
-```bash
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \
-task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH
-```
-
-There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory.
-
-**Note:**
-
-a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
-
-b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.
-
-### Inference on GLUE task
-After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet:
-
-```python
-from fairseq.models.roberta import RobertaModel
-
-roberta = RobertaModel.from_pretrained(
- 'checkpoints/',
- checkpoint_file='checkpoint_best.pt',
- data_name_or_path='RTE-bin'
-)
-
-label_fn = lambda label: roberta.task.label_dictionary.string(
- [label + roberta.task.label_dictionary.nspecial]
-)
-ncorrect, nsamples = 0, 0
-roberta.cuda()
-roberta.eval()
-with open('glue_data/RTE/dev.tsv') as fin:
- fin.readline()
- for index, line in enumerate(fin):
- tokens = line.strip().split('\t')
- sent1, sent2, target = tokens[1], tokens[2], tokens[3]
- tokens = roberta.encode(sent1, sent2)
- prediction = roberta.predict('sentence_classification_head', tokens).argmax().item()
- prediction_label = label_fn(prediction)
- ncorrect += int(prediction_label == target)
- nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-
-```
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/squeeze_excitation.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/squeeze_excitation.py
deleted file mode 100644
index d1d902bb30c071acbc0fa919a134c80fed86bd6c..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/squeeze_excitation.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import torch.nn as nn
-
-
-class SELayer(nn.Module):
- def __init__(self, channel, reduction=16):
- super(SELayer, self).__init__()
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
- self.fc = nn.Sequential(
- nn.Linear(channel, channel // reduction, bias=False),
- nn.ReLU(inplace=True),
- nn.Linear(channel // reduction, channel, bias=False),
- nn.Sigmoid()
- )
-
- def forward(self, x):
- b, c, _, _ = x.size()
- y = self.avg_pool(x).view(b, c)
- y = self.fc(y).view(b, c, 1, 1)
- res = x * y.expand_as(x)
- return res
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/__init__.py
deleted file mode 100644
index f27907ce5133c345f686143c155f3e8523ab46d5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-__all__ = (
- "StateInline",
- "text",
- "text_collapse",
- "link_pairs",
- "escape",
- "newline",
- "backtick",
- "emphasis",
- "image",
- "link",
- "autolink",
- "entity",
- "html_inline",
- "strikethrough",
-)
-from . import emphasis, strikethrough
-from .autolink import autolink
-from .backticks import backtick
-from .balance_pairs import link_pairs
-from .entity import entity
-from .escape import escape
-from .html_inline import html_inline
-from .image import image
-from .link import link
-from .newline import newline
-from .state_inline import StateInline
-from .text import text
-from .text_collapse import text_collapse
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_srmd.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_srmd.py
deleted file mode 100644
index 4c414b236ac5986ff9ee3aea651d8ea433047ece..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/models/network_srmd.py
+++ /dev/null
@@ -1,81 +0,0 @@
-
-import torch.nn as nn
-import models.basicblock as B
-import torch
-
-"""
-# --------------------------------------------
-# SRMD (15 conv layers)
-# --------------------------------------------
-Reference:
-@inproceedings{zhang2018learning,
- title={Learning a single convolutional super-resolution network for multiple degradations},
- author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
- booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
- pages={3262--3271},
- year={2018}
-}
-http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Learning_a_Single_CVPR_2018_paper.pdf
-"""
-
-
-# --------------------------------------------
-# SRMD (SRMD, in_nc = 3+15+1 = 19)
-# SRMD (SRMDNF, in_nc = 3+15 = 18)
-# --------------------------------------------
-class SRMD(nn.Module):
- def __init__(self, in_nc=19, out_nc=3, nc=128, nb=12, upscale=4, act_mode='R', upsample_mode='pixelshuffle'):
- """
- # ------------------------------------
- in_nc: channel number of input, default: 3+15
- out_nc: channel number of output
- nc: channel number
- nb: total number of conv layers
- upscale: scale factor
- act_mode: batch norm + activation function; 'BR' means BN+ReLU
- upsample_mode: default 'pixelshuffle' = conv + pixelshuffle
- # ------------------------------------
- """
- super(SRMD, self).__init__()
- assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL'
- bias = True
-
- if upsample_mode == 'upconv':
- upsample_block = B.upsample_upconv
- elif upsample_mode == 'pixelshuffle':
- upsample_block = B.upsample_pixelshuffle
- elif upsample_mode == 'convtranspose':
- upsample_block = B.upsample_convtranspose
- else:
- raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
-
- m_head = B.conv(in_nc, nc, mode='C'+act_mode[-1], bias=bias)
- m_body = [B.conv(nc, nc, mode='C'+act_mode, bias=bias) for _ in range(nb-2)]
- m_tail = upsample_block(nc, out_nc, mode=str(upscale), bias=bias)
-
- self.model = B.sequential(m_head, *m_body, m_tail)
-
-# def forward(self, x, k_pca):
-# m = k_pca.repeat(1, 1, x.size()[-2], x.size()[-1])
-# x = torch.cat((x, m), 1)
-# x = self.body(x)
-
- def forward(self, x):
-
- x = self.model(x)
-
- return x
-
-
-if __name__ == '__main__':
- from utils import utils_model
- model = SRMD(in_nc=18, out_nc=3, nc=64, nb=15, upscale=4, act_mode='R', upsample_mode='pixelshuffle')
- print(utils_model.describe_model(model))
-
- x = torch.randn((2, 3, 100, 100))
- k_pca = torch.randn(2, 15, 1, 1)
- x = model(x, k_pca)
- print(x.shape)
-
- # run models/network_srmd.py
-
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/elevenlabs_tts/script.py b/spaces/leogabraneth/text-generation-webui-main/extensions/elevenlabs_tts/script.py
deleted file mode 100644
index af0c74582e5be9d31effff28328b5b848554bc05..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/elevenlabs_tts/script.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import html
-import re
-from pathlib import Path
-
-import elevenlabs
-import gradio as gr
-
-from modules import chat, shared, ui_chat
-from modules.logging_colors import logger
-from modules.utils import gradio
-
-params = {
- 'activate': True,
- 'api_key': None,
- 'selected_voice': 'None',
- 'autoplay': False,
- 'show_text': True,
- 'model': 'eleven_monolingual_v1',
-}
-
-voices = None
-wav_idx = 0
-LANG_MODELS = ['eleven_monolingual_v1', 'eleven_multilingual_v1']
-
-
-def update_api_key(key):
- params['api_key'] = key
- if key is not None:
- elevenlabs.set_api_key(key)
-
-
-def refresh_voices():
- global params
- your_voices = elevenlabs.voices()
- voice_names = [voice.name for voice in your_voices]
- return voice_names
-
-
-def refresh_voices_dd():
- all_voices = refresh_voices()
- return gr.Dropdown.update(value=all_voices[0], choices=all_voices)
-
-
-def remove_tts_from_history(history):
- for i, entry in enumerate(history['internal']):
- history['visible'][i] = [history['visible'][i][0], entry[1]]
-
- return history
-
-
-def toggle_text_in_history(history):
- for i, entry in enumerate(history['visible']):
- visible_reply = entry[1]
- if visible_reply.startswith('')[0]}\n\n{reply}"]
- else:
- history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('')[0]}"]
-
- return history
-
-
-def remove_surrounded_chars(string):
- # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
- # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
- return re.sub('\*[^\*]*?(\*|$)', '', string)
-
-
-def state_modifier(state):
- if not params['activate']:
- return state
-
- state['stream'] = False
- return state
-
-
-def input_modifier(string):
- if not params['activate']:
- return string
-
- shared.processing_message = "*Is recording a voice message...*"
- return string
-
-
-def history_modifier(history):
- # Remove autoplay from the last reply
- if len(history['internal']) > 0:
- history['visible'][-1] = [
- history['visible'][-1][0],
- history['visible'][-1][1].replace('controls autoplay>', 'controls>')
- ]
-
- return history
-
-
-def output_modifier(string):
- global params, wav_idx
-
- if not params['activate']:
- return string
-
- original_string = string
- string = remove_surrounded_chars(string)
- string = string.replace('"', '')
- string = string.replace('“', '')
- string = string.replace('\n', ' ')
- string = string.strip()
- if string == '':
- string = 'empty reply, try regenerating'
-
- output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.mp3'.format(wav_idx))
- print(f'Outputting audio to {str(output_file)}')
- try:
- audio = elevenlabs.generate(text=html.unescape(string), voice=params['selected_voice'], model=params['model'])
- elevenlabs.save(audio, str(output_file))
-
- autoplay = 'autoplay' if params['autoplay'] else ''
- string = f'ansys fluent 14.5 torrent download with 132 DOWNLOAD 🔗 https://bytlly.com/2uGwoO
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Hard Disk Sentinel Pro 5.30.6 Build 9417 Crack Key.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Hard Disk Sentinel Pro 5.30.6 Build 9417 Crack Key.md
deleted file mode 100644
index f9f99ea5c67cb86fe16ada7d35cd42c1af1b4055..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Hard Disk Sentinel Pro 5.30.6 Build 9417 Crack Key.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Hard Disk Sentinel Pro 5.30.6 Build 9417 Crack Key DOWNLOAD ✒ ✒ ✒ https://bytlly.com/2uGvTG
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Hitman Absolution V 1.0 433.1 Trainer By Fling ((HOT)).md b/spaces/lincquiQcaudo/Top-20-Diffusion/Hitman Absolution V 1.0 433.1 Trainer By Fling ((HOT)).md
deleted file mode 100644
index ddafe3a7f65eb3d7e77f36477904e6fff66bc300..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Hitman Absolution V 1.0 433.1 Trainer By Fling ((HOT)).md
+++ /dev/null
@@ -1,108 +0,0 @@
-## Hitman Absolution V 1.0 433.1 Trainer By Fling
-
-
-
-
-
-
-
-
-
-**DOWNLOAD ->->->-> [https://fienislile.blogspot.com/?download=2txySU](https://fienislile.blogspot.com/?download=2txySU)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# How to Use Hitman Absolution V 1.0 433.1 Trainer By Fling
-
-
-
-Hitman Absolution is a stealth action game that lets you play as Agent 47, a professional assassin who can use various weapons, disguises and gadgets to complete his missions. However, if you want to make the game easier or more fun, you can use a trainer that modifies some aspects of the game, such as health, ammo, detection and more.
-
-
-
-One of the trainers available for Hitman Absolution is made by Fling, a well-known trainer maker who has created trainers for many other games. This trainer works with version 1.0 433.1 of the game and has 10 options that you can activate by pressing certain keys on your keyboard. Here are the options and their effects:
-
-
-
-- Numpad 1: Infinite Health - You won't die from any damage.
-
-- Numpad 2: Infinite Ammo - You won't run out of bullets or grenades.
-
-- Numpad 3: No Reload - You don't need to reload your weapons.
-
-- Numpad 4: Infinite Instinct - You can use your instinct mode as much as you want.
-
-- Numpad 5: No Detection - You won't be spotted by enemies or cameras.
-
-- Numpad 6: Silent Kill - You can kill enemies silently without alerting others.
-
-- Numpad 7: Super Accuracy - Your shots will always hit the target.
-
-- Numpad 8: Rapid Fire - Your weapons will fire faster.
-
-- Numpad 9: No Recoil - Your weapons won't have any recoil.
-
-- Numpad 0: One Hit Kill - You can kill enemies with one shot or melee attack.
-
-
-
-To use this trainer, you need to follow these steps:
-
-
-
-1. Download the trainer from one of the web search results[^2^] [^3^].
-
-2. Unzip the trainer file using a software like 7-Zip or WinRAR.
-
-3. Run the trainer as administrator before launching the game.
-
-4. Press F1 at the main menu of the game to activate the trainer.
-
-5. Start a new game or load a saved game.
-
-6. Press the numpad keys to toggle the options on or off during gameplay.
-
-
-
-Note: The trainer may not work with other versions of the game or after updating it. It may also cause some glitches or crashes, so use it at your own risk. It is recommended to disable your antivirus software before using the trainer, as some antivirus programs may detect it as a virus or malware. The trainer is only for single-player mode and should not be used online or in multiplayer mode, as it may result in a ban or other consequences.
-
-
-
-## Tips and Tricks for Playing Hitman Absolution
-
-
-
-Hitman Absolution is a game that requires you to be stealthy, creative and adaptable. You can approach each mission in different ways, using various weapons, disguises and tactics. However, some methods are more effective than others, and some challenges are harder than they seem. Here are some tips and tricks that can help you become a better assassin and master the game.
-
-
-
-- Use your instinct mode wisely. Instinct mode allows you to see enemies through walls, predict their movements, blend in with crowds and activate point shooting. However, it is a limited resource that depletes quickly and regenerates slowly. You should use it sparingly and only when necessary, such as when you need to avoid detection or take out multiple enemies quickly.
-
-- Explore your surroundings. Each level in Hitman Absolution is full of hidden paths, items, weapons and opportunities. You should always look around for alternative routes, useful objects and environmental hazards that you can use to your advantage. For example, you can find disguises that allow you to access restricted areas, weapons that suit your play style, or objects that can distract or kill enemies.
-
-- Be careful with your actions. Everything you do in Hitman Absolution has consequences. If you kill someone, you need to hide the body or risk raising an alarm. If you use a loud weapon, you will attract attention and lose your score. If you wear a disguise, you will be suspicious to people who know the real person. You should always think before you act and weigh the risks and rewards of your choices.
-
-- Complete challenges. Challenges are optional objectives that reward you with points and unlock new abilities, weapons and disguises. They also add replay value to the game and encourage you to try different approaches and strategies. You can view the challenges for each level in the pause menu or the main menu. Some challenges are easy to complete, while others require more skill and creativity.
-
-- Watch online videos. If you are stuck on a particular level or challenge, or if you want to see how other players have completed the game, you can watch online videos on YouTube or other platforms. There are many videos that show different ways to complete each mission, achieve silent assassin rating, or find Easter eggs and secrets. You can learn a lot from watching other players' techniques and tips.
-
-
-
-Hitman Absolution is a game that offers a lot of freedom and fun for players who enjoy stealth action games. By following these tips and tricks, you can improve your skills and enjoy the game even more.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Logo Tiger Plus Full Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Logo Tiger Plus Full Crack.md
deleted file mode 100644
index 78796139abcd57c19f1dc10f36dcbaa2eb8c8320..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Logo Tiger Plus Full Crack.md
+++ /dev/null
@@ -1,116 +0,0 @@
-
-Logo Tiger Plus Full Crack: The Ultimate Logo Design Software
-
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-logo tiger plus full crack Download Zip ✺✺✺ https://bytlly.com/2uGvYJ
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-PDF
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3cee63e6c2
-#include
-
-namespace thrust
-{
-
-
-/*! \addtogroup iterators
- * \{
- */
-
-/*! \p distance finds the distance between \p first and \p last, i.e. the
- * number of times that \p first must be incremented until it is equal to
- * \p last.
- *
- * \param first The beginning of an input range of interest.
- * \param last The end of an input range of interest.
- * \return The distance between the beginning and end of the input range.
- *
- * \tparam InputIterator is a model of Input Iterator .
- *
- * \pre If \c InputIterator meets the requirements of random access iterator, \p last shall be reachable from \p first or
- * \p first shall be reachable from \p last; otherwise, \p last shall be reachable from \p first.
- *
- * The following code snippet demonstrates how to use \p distance to compute
- * the distance to one iterator from another.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector vec(13);
- * thrust::device_vector::iterator iter1 = vec.begin();
- * thrust::device_vector::iterator iter2 = iter1 + 7;
- *
- * int d = thrust::distance(iter1, iter2);
- *
- * // d is 7
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/distance.html
- */
-template
-inline __host__ __device__
- typename thrust::iterator_traits::difference_type
- distance(InputIterator first, InputIterator last);
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
-#include
-
diff --git a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/data/__init__.py b/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/data/__init__.py
deleted file mode 100644
index 10a141788da65ea6527a4eecc9628603824d732b..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/data/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import importlib
-import torch.utils.data
-from data.base_dataset import BaseDataset
-from data.face_dataset import FaceTestDataset
-
-
-def create_dataloader(opt):
-
- instance = FaceTestDataset()
- instance.initialize(opt)
- print("dataset [%s] of size %d was created" % (type(instance).__name__, len(instance)))
- dataloader = torch.utils.data.DataLoader(
- instance,
- batch_size=opt.batchSize,
- shuffle=not opt.serial_batches,
- num_workers=int(opt.nThreads),
- drop_last=opt.isTrain,
- )
- return dataloader
diff --git a/spaces/mascIT/AgeGuesser/yolov5/utils/general.c b/spaces/mascIT/AgeGuesser/yolov5/utils/general.c
deleted file mode 100644
index d7753bd0db92b6b31d9497dad1f3fdddccd1a24f..0000000000000000000000000000000000000000
--- a/spaces/mascIT/AgeGuesser/yolov5/utils/general.c
+++ /dev/null
@@ -1,29360 +0,0 @@
-/* Generated by Cython 3.0.0a10 */
-
-/* BEGIN: Cython Metadata
-{
- "distutils": {
- "name": "pdf_toolbox.lib.dia_yolov5.utils.general",
- "sources": [
- "pdf_toolbox\\lib\\dia_yolov5\\utils\\general.py"
- ]
- },
- "module_name": "pdf_toolbox.lib.dia_yolov5.utils.general"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
- #ifndef Py_LIMITED_API
- #if CYTHON_LIMITED_API+0 > 0x03030000
- #define Py_LIMITED_API CYTHON_LIMITED_API
- #else
- #define Py_LIMITED_API 0x03030000
- #endif
- #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
- #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
- #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#define CYTHON_ABI "3_0_0a10"
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000AA
-#define CYTHON_FUTURE_DIVISION 1
-#include
-#ifndef offsetof
- #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
- #ifndef __stdcall
- #define __stdcall
- #endif
- #ifndef __cdecl
- #define __cdecl
- #endif
- #ifndef __fastcall
- #define __fastcall
- #endif
-#endif
-#ifndef DL_IMPORT
- #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
- #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
- #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
- #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
- #define Py_HUGE_VAL HUGE_VAL
-#endif
-#if defined(GRAALVM_PYTHON)
- /* For very preliminary testing purposes. Most variables are set the same as PyPy.
- The existence of this section does not imply that anything works or is even tested */
- #define CYTHON_COMPILING_IN_PYPY 0
- #define CYTHON_COMPILING_IN_CPYTHON 0
- #define CYTHON_COMPILING_IN_LIMITED_API 0
- #define CYTHON_COMPILING_IN_GRAAL 1
- #undef CYTHON_USE_TYPE_SLOTS
- #define CYTHON_USE_TYPE_SLOTS 0
- #undef CYTHON_USE_TYPE_SPECS
- #define CYTHON_USE_TYPE_SPECS 0
- #undef CYTHON_USE_PYTYPE_LOOKUP
- #define CYTHON_USE_PYTYPE_LOOKUP 0
- #if PY_VERSION_HEX < 0x03050000
- #undef CYTHON_USE_ASYNC_SLOTS
- #define CYTHON_USE_ASYNC_SLOTS 0
- #elif !defined(CYTHON_USE_ASYNC_SLOTS)
- #define CYTHON_USE_ASYNC_SLOTS 1
- #endif
- #undef CYTHON_USE_PYLIST_INTERNALS
- #define CYTHON_USE_PYLIST_INTERNALS 0
- #undef CYTHON_USE_UNICODE_INTERNALS
- #define CYTHON_USE_UNICODE_INTERNALS 0
- #undef CYTHON_USE_UNICODE_WRITER
- #define CYTHON_USE_UNICODE_WRITER 0
- #undef CYTHON_USE_PYLONG_INTERNALS
- #define CYTHON_USE_PYLONG_INTERNALS 0
- #undef CYTHON_AVOID_BORROWED_REFS
- #define CYTHON_AVOID_BORROWED_REFS 1
- #undef CYTHON_ASSUME_SAFE_MACROS
- #define CYTHON_ASSUME_SAFE_MACROS 0
- #undef CYTHON_UNPACK_METHODS
- #define CYTHON_UNPACK_METHODS 0
- #undef CYTHON_FAST_THREAD_STATE
- #define CYTHON_FAST_THREAD_STATE 0
- #undef CYTHON_FAST_GIL
- #define CYTHON_FAST_GIL 0
- #undef CYTHON_METH_FASTCALL
- #define CYTHON_METH_FASTCALL 0
- #undef CYTHON_FAST_PYCALL
- #define CYTHON_FAST_PYCALL 0
- #ifndef CYTHON_PEP487_INIT_SUBCLASS
- #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
- #endif
- #undef CYTHON_PEP489_MULTI_PHASE_INIT
- #define CYTHON_PEP489_MULTI_PHASE_INIT 1
- #undef CYTHON_USE_MODULE_STATE
- #define CYTHON_USE_MODULE_STATE 0
- #undef CYTHON_USE_TP_FINALIZE
- #define CYTHON_USE_TP_FINALIZE 0
- #undef CYTHON_USE_DICT_VERSIONS
- #define CYTHON_USE_DICT_VERSIONS 0
- #undef CYTHON_USE_EXC_INFO_STACK
- #define CYTHON_USE_EXC_INFO_STACK 0
-#elif defined(PYPY_VERSION)
- #define CYTHON_COMPILING_IN_PYPY 1
- #define CYTHON_COMPILING_IN_CPYTHON 0
- #define CYTHON_COMPILING_IN_LIMITED_API 0
- #define CYTHON_COMPILING_IN_GRAAL 0
- #undef CYTHON_USE_TYPE_SLOTS
- #define CYTHON_USE_TYPE_SLOTS 0
- #undef CYTHON_USE_TYPE_SPECS
- #define CYTHON_USE_TYPE_SPECS 0
- #undef CYTHON_USE_PYTYPE_LOOKUP
- #define CYTHON_USE_PYTYPE_LOOKUP 0
- #if PY_VERSION_HEX < 0x03050000
- #undef CYTHON_USE_ASYNC_SLOTS
- #define CYTHON_USE_ASYNC_SLOTS 0
- #elif !defined(CYTHON_USE_ASYNC_SLOTS)
- #define CYTHON_USE_ASYNC_SLOTS 1
- #endif
- #undef CYTHON_USE_PYLIST_INTERNALS
- #define CYTHON_USE_PYLIST_INTERNALS 0
- #undef CYTHON_USE_UNICODE_INTERNALS
- #define CYTHON_USE_UNICODE_INTERNALS 0
- #undef CYTHON_USE_UNICODE_WRITER
- #define CYTHON_USE_UNICODE_WRITER 0
- #undef CYTHON_USE_PYLONG_INTERNALS
- #define CYTHON_USE_PYLONG_INTERNALS 0
- #undef CYTHON_AVOID_BORROWED_REFS
- #define CYTHON_AVOID_BORROWED_REFS 1
- #undef CYTHON_ASSUME_SAFE_MACROS
- #define CYTHON_ASSUME_SAFE_MACROS 0
- #undef CYTHON_UNPACK_METHODS
- #define CYTHON_UNPACK_METHODS 0
- #undef CYTHON_FAST_THREAD_STATE
- #define CYTHON_FAST_THREAD_STATE 0
- #undef CYTHON_FAST_GIL
- #define CYTHON_FAST_GIL 0
- #undef CYTHON_METH_FASTCALL
- #define CYTHON_METH_FASTCALL 0
- #undef CYTHON_FAST_PYCALL
- #define CYTHON_FAST_PYCALL 0
- #ifndef CYTHON_PEP487_INIT_SUBCLASS
- #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
- #endif
- #undef CYTHON_PEP489_MULTI_PHASE_INIT
- #define CYTHON_PEP489_MULTI_PHASE_INIT 0
- #undef CYTHON_USE_MODULE_STATE
- #define CYTHON_USE_MODULE_STATE 0
- #undef CYTHON_USE_TP_FINALIZE
- #define CYTHON_USE_TP_FINALIZE 0
- #undef CYTHON_USE_DICT_VERSIONS
- #define CYTHON_USE_DICT_VERSIONS 0
- #undef CYTHON_USE_EXC_INFO_STACK
- #define CYTHON_USE_EXC_INFO_STACK 0
-#elif defined(CYTHON_LIMITED_API)
- #define CYTHON_COMPILING_IN_PYPY 0
- #define CYTHON_COMPILING_IN_CPYTHON 0
- #define CYTHON_COMPILING_IN_LIMITED_API 1
- #define CYTHON_COMPILING_IN_GRAAL 0
- #undef CYTHON_USE_TYPE_SLOTS
- #define CYTHON_USE_TYPE_SLOTS 0
- #undef CYTHON_USE_TYPE_SPECS
- #define CYTHON_USE_TYPE_SPECS 1
- #undef CYTHON_USE_PYTYPE_LOOKUP
- #define CYTHON_USE_PYTYPE_LOOKUP 0
- #undef CYTHON_USE_ASYNC_SLOTS
- #define CYTHON_USE_ASYNC_SLOTS 0
- #undef CYTHON_USE_PYLIST_INTERNALS
- #define CYTHON_USE_PYLIST_INTERNALS 0
- #undef CYTHON_USE_UNICODE_INTERNALS
- #define CYTHON_USE_UNICODE_INTERNALS 0
- #ifndef CYTHON_USE_UNICODE_WRITER
- #define CYTHON_USE_UNICODE_WRITER 0
- #endif
- #undef CYTHON_USE_PYLONG_INTERNALS
- #define CYTHON_USE_PYLONG_INTERNALS 0
- #ifndef CYTHON_AVOID_BORROWED_REFS
- #define CYTHON_AVOID_BORROWED_REFS 0
- #endif
- #undef CYTHON_ASSUME_SAFE_MACROS
- #define CYTHON_ASSUME_SAFE_MACROS 0
- #undef CYTHON_UNPACK_METHODS
- #define CYTHON_UNPACK_METHODS 0
- #undef CYTHON_FAST_THREAD_STATE
- #define CYTHON_FAST_THREAD_STATE 0
- #undef CYTHON_FAST_GIL
- #define CYTHON_FAST_GIL 0
- #undef CYTHON_METH_FASTCALL
- #define CYTHON_METH_FASTCALL 0
- #undef CYTHON_FAST_PYCALL
- #define CYTHON_FAST_PYCALL 0
- #ifndef CYTHON_PEP487_INIT_SUBCLASS
- #define CYTHON_PEP487_INIT_SUBCLASS 1
- #endif
- #undef CYTHON_PEP489_MULTI_PHASE_INIT
- #define CYTHON_PEP489_MULTI_PHASE_INIT 0
- #undef CYTHON_USE_MODULE_STATE
- #define CYTHON_USE_MODULE_STATE 1
- #ifndef CYTHON_USE_TP_FINALIZE
- #define CYTHON_USE_TP_FINALIZE 1
- #endif
- #undef CYTHON_USE_DICT_VERSIONS
- #define CYTHON_USE_DICT_VERSIONS 0
- #undef CYTHON_USE_EXC_INFO_STACK
- #define CYTHON_USE_EXC_INFO_STACK 0
-#else
- #define CYTHON_COMPILING_IN_PYPY 0
- #define CYTHON_COMPILING_IN_CPYTHON 1
- #define CYTHON_COMPILING_IN_LIMITED_API 0
- #define CYTHON_COMPILING_IN_GRAAL 0
- #ifndef CYTHON_USE_TYPE_SLOTS
- #define CYTHON_USE_TYPE_SLOTS 1
- #endif
- #ifndef CYTHON_USE_TYPE_SPECS
- #define CYTHON_USE_TYPE_SPECS 0
- #endif
- #ifndef CYTHON_USE_PYTYPE_LOOKUP
- #define CYTHON_USE_PYTYPE_LOOKUP 1
- #endif
- #if PY_MAJOR_VERSION < 3
- #undef CYTHON_USE_ASYNC_SLOTS
- #define CYTHON_USE_ASYNC_SLOTS 0
- #elif !defined(CYTHON_USE_ASYNC_SLOTS)
- #define CYTHON_USE_ASYNC_SLOTS 1
- #endif
- #ifndef CYTHON_USE_PYLONG_INTERNALS
- #define CYTHON_USE_PYLONG_INTERNALS 1
- #endif
- #ifndef CYTHON_USE_PYLIST_INTERNALS
- #define CYTHON_USE_PYLIST_INTERNALS 1
- #endif
- #ifndef CYTHON_USE_UNICODE_INTERNALS
- #define CYTHON_USE_UNICODE_INTERNALS 1
- #endif
- #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
- #undef CYTHON_USE_UNICODE_WRITER
- #define CYTHON_USE_UNICODE_WRITER 0
- #elif !defined(CYTHON_USE_UNICODE_WRITER)
- #define CYTHON_USE_UNICODE_WRITER 1
- #endif
- #ifndef CYTHON_AVOID_BORROWED_REFS
- #define CYTHON_AVOID_BORROWED_REFS 0
- #endif
- #ifndef CYTHON_ASSUME_SAFE_MACROS
- #define CYTHON_ASSUME_SAFE_MACROS 1
- #endif
- #ifndef CYTHON_UNPACK_METHODS
- #define CYTHON_UNPACK_METHODS 1
- #endif
- #ifndef CYTHON_FAST_THREAD_STATE
- #define CYTHON_FAST_THREAD_STATE 1
- #endif
- #ifndef CYTHON_FAST_GIL
- #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000)
- #endif
- #ifndef CYTHON_METH_FASTCALL
- #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
- #endif
- #ifndef CYTHON_FAST_PYCALL
- #define CYTHON_FAST_PYCALL 1
- #endif
- #ifndef CYTHON_PEP487_INIT_SUBCLASS
- #define CYTHON_PEP487_INIT_SUBCLASS 1
- #endif
- #if PY_VERSION_HEX < 0x03050000
- #undef CYTHON_PEP489_MULTI_PHASE_INIT
- #define CYTHON_PEP489_MULTI_PHASE_INIT 0
- #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
- #define CYTHON_PEP489_MULTI_PHASE_INIT 1
- #endif
- #ifndef CYTHON_USE_MODULE_STATE
- #define CYTHON_USE_MODULE_STATE 0
- #endif
- #if PY_VERSION_HEX < 0x030400a1
- #undef CYTHON_USE_TP_FINALIZE
- #define CYTHON_USE_TP_FINALIZE 0
- #elif !defined(CYTHON_USE_TP_FINALIZE)
- #define CYTHON_USE_TP_FINALIZE 1
- #endif
- #if PY_VERSION_HEX < 0x030600B1
- #undef CYTHON_USE_DICT_VERSIONS
- #define CYTHON_USE_DICT_VERSIONS 0
- #elif !defined(CYTHON_USE_DICT_VERSIONS)
- #define CYTHON_USE_DICT_VERSIONS 1
- #endif
- #if PY_VERSION_HEX < 0x030700A3
- #undef CYTHON_USE_EXC_INFO_STACK
- #define CYTHON_USE_EXC_INFO_STACK 0
- #elif !defined(CYTHON_USE_EXC_INFO_STACK)
- #define CYTHON_USE_EXC_INFO_STACK 1
- #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
- #if PY_MAJOR_VERSION < 3
- #include "longintrepr.h"
- #endif
- #undef SHIFT
- #undef BASE
- #undef MASK
- #ifdef SIZEOF_VOID_P
- enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
- #endif
-#endif
-#ifndef __has_attribute
- #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
- #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
- #if defined(__GNUC__)
- #define CYTHON_RESTRICT __restrict__
- #elif defined(_MSC_VER) && _MSC_VER >= 1400
- #define CYTHON_RESTRICT __restrict
- #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define CYTHON_RESTRICT restrict
- #else
- #define CYTHON_RESTRICT
- #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-# define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-# define CYTHON_UNUSED
-# endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-# define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-# define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-# if defined(__cplusplus)
- template void CYTHON_UNUSED_VAR( const T& ) { }
-# else
-# define CYTHON_UNUSED_VAR(x) (void)(x)
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
- #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-# define CYTHON_NCP_UNUSED
-# else
-# define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
- #ifndef _MSC_STDINT_H_
- #if _MSC_VER < 1300
- typedef unsigned char uint8_t;
- typedef unsigned short uint16_t;
- typedef unsigned int uint32_t;
- #else
- typedef unsigned __int8 uint8_t;
- typedef unsigned __int16 uint16_t;
- typedef unsigned __int32 uint32_t;
- #endif
- #endif
- #if _MSC_VER < 1300
- #ifdef _WIN64
- typedef unsigned long long __pyx_uintptr_t;
- #else
- typedef unsigned int __pyx_uintptr_t;
- #endif
- #else
- #ifdef _WIN64
- typedef unsigned __int64 __pyx_uintptr_t;
- #else
- typedef unsigned __int32 __pyx_uintptr_t;
- #endif
- #endif
-#else
- #include
- typedef uintptr_t __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
- #if defined(__cplusplus) && __cplusplus >= 201103L
- #if __has_cpp_attribute(fallthrough)
- #define CYTHON_FALLTHROUGH [[fallthrough]]
- #elif __has_cpp_attribute(clang::fallthrough)
- #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
- #elif __has_cpp_attribute(gnu::fallthrough)
- #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
- #endif
- #endif
- #ifndef CYTHON_FALLTHROUGH
- #if __has_attribute(fallthrough)
- #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
- #else
- #define CYTHON_FALLTHROUGH
- #endif
- #endif
- #if defined(__clang__ ) && defined(__apple_build_version__)
- #if __apple_build_version__ < 7000000
- #undef CYTHON_FALLTHROUGH
- #define CYTHON_FALLTHROUGH
- #endif
- #endif
-#endif
-
-#ifndef CYTHON_INLINE
- #if defined(__clang__)
- #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
- #elif defined(__GNUC__)
- #define CYTHON_INLINE __inline__
- #elif defined(_MSC_VER)
- #define CYTHON_INLINE __inline
- #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define CYTHON_INLINE inline
- #else
- #define CYTHON_INLINE
- #endif
-#endif
-
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
- #define Py_OptimizeFlag 0
-#endif
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
- #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
- #define __Pyx_DefaultClassType PyClass_Type
- #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
- PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
- #define __Pyx_BUILTIN_MODULE_NAME "builtins"
- #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
- static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
- PyObject *code, PyObject *c, PyObject* n, PyObject *v,
- PyObject *fv, PyObject *cell, PyObject* fn,
- PyObject *name, int fline, PyObject *lnos) {
- PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
- PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;
- const char *fn_cstr=NULL;
- const char *name_cstr=NULL;
- PyCodeObject* co=NULL;
- PyObject *type, *value, *traceback;
- PyErr_Fetch(&type, &value, &traceback);
- if (!(kwds=PyDict_New())) goto end;
- if (!(argcount=PyLong_FromLong(a))) goto end;
- if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
- if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
- if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
- if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
- if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
- if (!(nlocals=PyLong_FromLong(l))) goto end;
- if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
- if (!(stacksize=PyLong_FromLong(s))) goto end;
- if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
- if (!(flags=PyLong_FromLong(f))) goto end;
- if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
- if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
- if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
- if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
- if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too;
- if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here
- if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;
- Py_XDECREF((PyObject*)co);
- co = (PyCodeObject*)call_result;
- call_result = NULL;
- if (0) {
- cleanup_code_too:
- Py_XDECREF((PyObject*)co);
- co = NULL;
- }
- end:
- Py_XDECREF(kwds);
- Py_XDECREF(argcount);
- Py_XDECREF(posonlyargcount);
- Py_XDECREF(kwonlyargcount);
- Py_XDECREF(nlocals);
- Py_XDECREF(stacksize);
- Py_XDECREF(replace);
- Py_XDECREF(call_result);
- Py_XDECREF(empty);
- if (type) {
- PyErr_Restore(type, value, traceback);
- }
- return co;
- }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
- #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
- PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
- #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
- PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
- #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
- #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
- #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
- #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
- #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
- #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef METH_STACKLESS
- #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
- #ifndef METH_FASTCALL
- #define METH_FASTCALL 0x80
- #endif
- typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
- typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
- Py_ssize_t nargs, PyObject *kwnames);
-#else
- #define __Pyx_PyCFunctionFast _PyCFunctionFast
- #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
- #define __Pyx_METH_FASTCALL METH_FASTCALL
- #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
- #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
- #define __Pyx_METH_FASTCALL METH_VARARGS
- #define __Pyx_PyCFunction_FastCall PyCFunction
- #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
- #define __pyx_vectorcallfunc vectorcallfunc
- #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET
- #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
- typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
- size_t nargsf, PyObject *kwnames);
- #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1))
- #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET))
-#else
- #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0
- #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n))
-#endif
-#if PY_VERSION_HEX < 0x030900B1
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-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
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-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
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-#else
-#define __Pyx_PyInt_SubtractCObj(op1, op2, intval, inplace, zerodivision_check)\
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-/* DictGetItem.proto */
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key);
-#define __Pyx_PyObject_Dict_GetItem(obj, name)\
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-#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name)
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-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace, zerodivision_check)\
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-#endif
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
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-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-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)\
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-/* SliceObject.proto */
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- PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
- int has_cstart, int has_cstop, int wraparound);
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-/* ValidateBasesTuple.proto */
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-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
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-/* PyType_Ready.proto */
-static CYTHON_UNUSED int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* PyObject_GenericGetAttrNoDict.proto */
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-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
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-struct __Pyx_CodeObjectCache {
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-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
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-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
- int py_line, const char *filename);
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-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
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-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* FormatTypeName.proto */
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-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
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-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
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-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, 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)
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-#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))
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-/* PyObjectCall2Args.proto */
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- int resume_label;
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-#define __Pyx_Coroutine_SwapException(self)
-#define __Pyx_Coroutine_ResetAndClearException(self) __Pyx_Coroutine_ExceptionClear(&(self)->gi_exc_state)
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-#define __Pyx_Coroutine_SwapException(self) {\
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- __Pyx_Coroutine_ResetFrameBackpointer(&(self)->gi_exc_state);\
- }
-#define __Pyx_Coroutine_ResetAndClearException(self) {\
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-#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
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-/* Generator.proto */
-#define __Pyx_Generator_USED
-static PyTypeObject *__pyx_GeneratorType = 0;
-#define __Pyx_Generator_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_GeneratorType)
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-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
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-/* InitStrings.proto */
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-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str);
-#else
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-#endif
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-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr = 0;
-static PyTypeObject *__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr = 0;
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-static const char __pyx_k_93m[] = "\033[93m";
-static const char __pyx_k_94m[] = "\033[94m";
-static const char __pyx_k_95m[] = "\033[95m";
-static const char __pyx_k_96m[] = "\033[96m";
-static const char __pyx_k_97m[] = "\033[97m";
-static const char __pyx_k__10[] = ".";
-static const char __pyx_k__11[] = "";
-static const char __pyx_k__14[] = "\344\272\272\345\267\245\346\231\272\350\203\275";
-static const char __pyx_k__15[] = "[\344\270\200-\351\277\277]";
-static const char __pyx_k__16[] = "**/*";
-static const char __pyx_k__17[] = ":/";
-static const char __pyx_k__18[] = "://";
-static const char __pyx_k__20[] = "?";
-static const char __pyx_k__21[] = "[|@#!\302\241\302\267$\342\202\254%&()=?\302\277^*;:,\302\250\302\264><+]";
-static const char __pyx_k__22[] = "_";
-static const char __pyx_k_any[] = "any";
-static const char __pyx_k_box[] = "box";
-static const char __pyx_k_cat[] = "cat";
-static const char __pyx_k_cfg[] = "cfg";
-static const char __pyx_k_cos[] = "cos";
-static const char __pyx_k_cv2[] = "cv2";
-static const char __pyx_k_d_2[] = "d";
-static const char __pyx_k_dir[] = "dir";
-static const char __pyx_k_end[] = "end";
-static const char __pyx_k_env[] = "env";
-static const char __pyx_k_eps[] = "eps";
-static const char __pyx_k_get[] = "get";
-static const char __pyx_k_int[] = "int";
-static const char __pyx_k_iou[] = "iou";
-static const char __pyx_k_key[] = "key";
-static const char __pyx_k_max[] = "max";
-static const char __pyx_k_nms[] = "nms";
-static const char __pyx_k_ops[] = "ops";
-static const char __pyx_k_opt[] = "opt";
-static const char __pyx_k_pad[] = "pad";
-static const char __pyx_k_red[] = "red";
-static const char __pyx_k_s_2[] = "%s";
-static const char __pyx_k_sep[] = "sep";
-static const char __pyx_k_str[] = "str";
-static const char __pyx_k_sub[] = "sub";
-static const char __pyx_k_sum[] = "sum";
-static const char __pyx_k_tmp[] = "/tmp";
-static const char __pyx_k_url[] = "url";
-static const char __pyx_k_FILE[] = "FILE";
-static const char __pyx_k_INFO[] = "INFO";
-static const char __pyx_k_Path[] = "Path";
-static const char __pyx_k_RANK[] = "RANK";
-static const char __pyx_k_ROOT[] = "ROOT";
-static const char __pyx_k_R_OK[] = "R_OK";
-static const char __pyx_k_args[] = "args";
-static const char __pyx_k_blue[] = "blue";
-static const char __pyx_k_bold[] = "bold";
-static const char __pyx_k_ceil[] = "ceil";
-static const char __pyx_k_clip[] = "clip";
-static const char __pyx_k_conf[] = "conf";
-static const char __pyx_k_copy[] = "copy";
-static const char __pyx_k_cyan[] = "cyan";
-static const char __pyx_k_dirs[] = "dirs";
-static const char __pyx_k_exit[] = "__exit__";
-static const char __pyx_k_file[] = "__file__";
-static const char __pyx_k_func[] = "func";
-static const char __pyx_k_gain[] = "gain";
-static const char __pyx_k_glob[] = "glob";
-static const char __pyx_k_hard[] = "hard";
-static const char __pyx_k_home[] = "home";
-static const char __pyx_k_info[] = "info";
-static const char __pyx_k_long[] = "long";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_math[] = "math";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ones[] = "ones";
-static const char __pyx_k_open[] = "open";
-static const char __pyx_k_padh[] = "padh";
-static const char __pyx_k_padw[] = "padw";
-static const char __pyx_k_path[] = "path";
-static const char __pyx_k_rank[] = "rank";
-static const char __pyx_k_repl[] = "repl";
-static const char __pyx_k_seed[] = "seed";
-static const char __pyx_k_send[] = "send";
-static const char __pyx_k_spec[] = "__spec__";
-static const char __pyx_k_stat[] = "stat";
-static const char __pyx_k_stem[] = "stem";
-static const char __pyx_k_test[] = "test";
-static const char __pyx_k_time[] = "time";
-static const char __pyx_k_true[] = "true";
-static const char __pyx_k_vars[] = "vars";
-static const char __pyx_k_view[] = "view";
-static const char __pyx_k_yaml[] = "yaml";
-static const char __pyx_k_0_0_0[] = "0.0.0";
-static const char __pyx_k_11_5g[] = "{:11.5g}";
-static const char __pyx_k_3_6_2[] = "3.6.2";
-static const char __pyx_k_Linux[] = "Linux";
-static const char __pyx_k_NCOLS[] = "NCOLS";
-static const char __pyx_k_array[] = "array";
-static const char __pyx_k_ascii[] = "ascii";
-static const char __pyx_k_black[] = "black";
-static const char __pyx_k_boxes[] = "boxes";
-static const char __pyx_k_clamp[] = "clamp_";
-static const char __pyx_k_clone[] = "clone";
-static const char __pyx_k_close[] = "close";
-static const char __pyx_k_cudnn[] = "cudnn";
-static const char __pyx_k_enter[] = "__enter__";
-static const char __pyx_k_float[] = "float";
-static const char __pyx_k_floor[] = "floor";
-static const char __pyx_k_green[] = "green";
-static const char __pyx_k_imgsz[] = "imgsz";
-static const char __pyx_k_input[] = "input";
-static const char __pyx_k_items[] = "items";
-static const char __pyx_k_level[] = "level";
-static const char __pyx_k_lower[] = "lower";
-static const char __pyx_k_merge[] = "merge";
-static const char __pyx_k_mkdir[] = "mkdir";
-static const char __pyx_k_numpy[] = "numpy";
-static const char __pyx_k_parse[] = "parse";
-static const char __pyx_k_print[] = "print";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_split[] = "split";
-static const char __pyx_k_steps[] = "steps";
-static const char __pyx_k_throw[] = "throw";
-static const char __pyx_k_torch[] = "torch";
-static const char __pyx_k_white[] = "white";
-static const char __pyx_k_zeros[] = "zeros";
-static const char __pyx_k_Darwin[] = "Darwin";
-static const char __pyx_k_LOGGER[] = "LOGGER";
-static const char __pyx_k_Python[] = "Python ";
-static const char __pyx_k_Tensor[] = "Tensor";
-static const char __pyx_k_access[] = "access";
-static const char __pyx_k_astype[] = "astype";
-static const char __pyx_k_colors[] = "colors";
-static const char __pyx_k_config[] = ".config";
-static const char __pyx_k_coords[] = "coords";
-static const char __pyx_k_decode[] = "decode";
-static const char __pyx_k_device[] = "device";
-static const char __pyx_k_emojis[] = "emojis";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_exists[] = "exists";
-static const char __pyx_k_file_2[] = "file";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_getenv[] = "getenv";
-static const char __pyx_k_groups[] = "groups";
-static const char __pyx_k_ignore[] = "ignore";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_is_dir[] = "is_dir";
-static const char __pyx_k_kwargs[] = "kwargs";
-static const char __pyx_k_labels[] = "labels";
-static const char __pyx_k_max_wh[] = "max_wh";
-static const char __pyx_k_min_wh[] = "min_wh";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_output[] = "output";
-static const char __pyx_k_pandas[] = "pandas";
-static const char __pyx_k_pinned[] = "pinned";
-static const char __pyx_k_random[] = "random";
-static const char __pyx_k_scores[] = "scores";
-static const char __pyx_k_search[] = "search";
-static const char __pyx_k_shutil[] = "shutil";
-static const char __pyx_k_string[] = "string";
-static const char __pyx_k_suffix[] = "suffix";
-static const char __pyx_k_system[] = "system";
-static const char __pyx_k_tensor[] = "tensor";
-static const char __pyx_k_test_2[] = "__test__";
-static const char __pyx_k_unlink[] = "unlink";
-static const char __pyx_k_urllib[] = "urllib";
-static const char __pyx_k_xyn2xy[] = "xyn2xy";
-static const char __pyx_k_yellow[] = "yellow";
-static const char __pyx_k_yolov5[] = "yolov5";
-static const char __pyx_k_OSError[] = "OSError";
-static const char __pyx_k_VERBOSE[] = "VERBOSE";
-static const char __pyx_k_WARNING[] = "WARNING";
-static const char __pyx_k_Windows[] = "Windows";
-static const char __pyx_k_argsort[] = "argsort";
-static const char __pyx_k_box_iou[] = "box_iou";
-static const char __pyx_k_classes[] = "classes";
-static const char __pyx_k_columns[] = "columns";
-static const char __pyx_k_current[] = "current";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_display[] = "display";
-static const char __pyx_k_divisor[] = "divisor";
-static const char __pyx_k_env_var[] = "env_var";
-static const char __pyx_k_environ[] = "environ";
-static const char __pyx_k_exclude[] = "exclude";
-static const char __pyx_k_fitness[] = "fitness";
-static const char __pyx_k_genexpr[] = "genexpr";
-static const char __pyx_k_handler[] = "handler";
-static const char __pyx_k_is_file[] = "is_file";
-static const char __pyx_k_keepdim[] = "keepdim";
-static const char __pyx_k_last_pt[] = "/**/last*.pt";
-static const char __pyx_k_logging[] = "logging";
-static const char __pyx_k_magenta[] = "magenta";
-static const char __pyx_k_matches[] = "matches";
-static const char __pyx_k_max_det[] = "max_det";
-static const char __pyx_k_max_nms[] = "max_nms";
-static const char __pyx_k_methods[] = "methods";
-static const char __pyx_k_minimum[] = "minimum";
-static const char __pyx_k_nonzero[] = "nonzero";
-static const char __pyx_k_options[] = "options";
-static const char __pyx_k_parents[] = "parents";
-static const char __pyx_k_pathlib[] = "pathlib";
-static const char __pyx_k_pattern[] = "pattern";
-static const char __pyx_k_profile[] = "profile";
-static const char __pyx_k_replace[] = "replace";
-static const char __pyx_k_reshape[] = "reshape";
-static const char __pyx_k_resolve[] = "resolve";
-static const char __pyx_k_st_size[] = "st_size";
-static const char __pyx_k_tmp_txt[] = "tmp.txt";
-static const char __pyx_k_unquote[] = "unquote";
-static const char __pyx_k_verbose[] = "verbose";
-static const char __pyx_k_version[] = "version ";
-static const char __pyx_k_warning[] = "warning";
-static const char __pyx_k_weights[] = "weights";
-static const char __pyx_k_agnostic[] = "agnostic";
-static const char __pyx_k_as_tuple[] = "as_tuple";
-static const char __pyx_k_backends[] = "backends";
-static const char __pyx_k_bincount[] = "bincount";
-static const char __pyx_k_colorstr[] = "colorstr";
-static const char __pyx_k_exist_ok[] = "exist_ok";
-static const char __pyx_k_getctime[] = "getctime";
-static const char __pyx_k_instance[] = "instance";
-static const char __pyx_k_is_ascii[] = "is_ascii";
-static const char __pyx_k_new_size[] = "new_size";
-static const char __pyx_k_platform[] = "platform";
-static const char __pyx_k_url2file[] = "url2file";
-static const char __pyx_k_benchmark[] = "benchmark";
-static const char __pyx_k_clean_str[] = "clean_str";
-static const char __pyx_k_cpu_count[] = "cpu_count";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_file_size[] = "file_size";
-static const char __pyx_k_formatter[] = "formatter";
-static const char __pyx_k_getLogger[] = "getLogger";
-static const char __pyx_k_iou_thres[] = "iou_thres";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_last_list[] = "last_list";
-static const char __pyx_k_linewidth[] = "linewidth";
-static const char __pyx_k_message_s[] = "%(message)s";
-static const char __pyx_k_minlength[] = "minlength";
-static const char __pyx_k_one_cycle[] = "one_cycle";
-static const char __pyx_k_precision[] = "precision";
-static const char __pyx_k_ratio_pad[] = "ratio_pad";
-static const char __pyx_k_recursive[] = "recursive";
-static const char __pyx_k_redundant[] = "redundant";
-static const char __pyx_k_underline[] = "underline";
-static const char __pyx_k_xywh2xyxy[] = "xywh2xyxy";
-static const char __pyx_k_xyxy2xywh[] = "xyxy2xywh";
-static const char __pyx_k_bright_red[] = "bright_red";
-static const char __pyx_k_conf_thres[] = "conf_thres";
-static const char __pyx_k_descending[] = "descending";
-static const char __pyx_k_float_kind[] = "float_kind";
-static const char __pyx_k_from_numpy[] = "from_numpy";
-static const char __pyx_k_img0_shape[] = "img0_shape";
-static const char __pyx_k_img1_shape[] = "img1_shape";
-static const char __pyx_k_init_seeds[] = "init_seeds";
-static const char __pyx_k_is_chinese[] = "is_chinese";
-static const char __pyx_k_prediction[] = "prediction";
-static const char __pyx_k_print_args[] = "print_args";
-static const char __pyx_k_s_exceeded[] = "s exceeded";
-static const char __pyx_k_search_dir[] = "search_dir";
-static const char __pyx_k_startswith[] = "startswith";
-static const char __pyx_k_time_limit[] = "time_limit";
-static const char __pyx_k_try_except[] = "try_except";
-static const char __pyx_k_xywhn2xyxy[] = "xywhn2xyxy";
-static const char __pyx_k_xyxy2xywhn[] = "xyxy2xywhn";
-static const char __pyx_k_Invalid_IoU[] = "Invalid IoU ";
-static const char __pyx_k_NUM_THREADS[] = "NUM_THREADS";
-static const char __pyx_k_Ultralytics[] = "Ultralytics";
-static const char __pyx_k_basicConfig[] = "basicConfig";
-static const char __pyx_k_bright_blue[] = "bright_blue";
-static const char __pyx_k_bright_cyan[] = "bright_cyan";
-static const char __pyx_k_clip_coords[] = "clip_coords";
-static const char __pyx_k_concatenate[] = "concatenate";
-static const char __pyx_k_manual_seed[] = "manual_seed";
-static const char __pyx_k_max_columns[] = "max_columns";
-static const char __pyx_k_multi_label[] = "multi_label";
-static const char __pyx_k_set_logging[] = "set_logging";
-static const char __pyx_k_torchvision[] = "torchvision";
-static const char __pyx_k_updating_to[] = ", updating to ";
-static const char __pyx_k_with_suffix[] = "with_suffix";
-static const char __pyx_k_bright_black[] = "bright_black";
-static const char __pyx_k_bright_green[] = "bright_green";
-static const char __pyx_k_bright_white[] = "bright_white";
-static const char __pyx_k_check_python[] = "check_python";
-static const char __pyx_k_class_counts[] = "class_counts";
-static const char __pyx_k_initializing[] = "_initializing";
-static const char __pyx_k_is_coroutine[] = "_is_coroutine";
-static const char __pyx_k_is_writeable[] = "is_writeable";
-static const char __pyx_k_scale_coords[] = "scale_coords";
-static const char __pyx_k_General_utils[] = "\nGeneral utils\n";
-static const char __pyx_k_bright_yellow[] = "bright_yellow";
-static const char __pyx_k_check_version[] = "check_version";
-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_class_weights[] = "class_weights";
-static const char __pyx_k_deterministic[] = "deterministic";
-static const char __pyx_k_image_weights[] = "image_weights";
-static const char __pyx_k_setNumThreads[] = "setNumThreads";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_YOLOv5_VERBOSE[] = "YOLOv5_VERBOSE";
-static const char __pyx_k_bright_magenta[] = "bright_magenta";
-static const char __pyx_k_check_img_size[] = "check_img_size";
-static const char __pyx_k_get_latest_run[] = "get_latest_run";
-static const char __pyx_k_increment_path[] = "increment_path";
-static const char __pyx_k_make_divisible[] = "make_divisible";
-static const char __pyx_k_python_version[] = "python_version";
-static const char __pyx_k_AppData_Roaming[] = "AppData/Roaming";
-static const char __pyx_k_intersect_dicts[] = "intersect_dicts";
-static const char __pyx_k_user_config_dir[] = "user_config_dir";
-static const char __pyx_k_WARNING_img_size[] = "WARNING: --img-size ";
-static const char __pyx_k_set_printoptions[] = "set_printoptions";
-static const char __pyx_k_YOLOV5_CONFIG_DIR[] = "YOLOV5_CONFIG_DIR";
-static const char __pyx_k_get_terminal_size[] = "get_terminal_size";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_NUMEXPR_MAX_THREADS[] = "NUMEXPR_MAX_THREADS";
-static const char __pyx_k_non_max_suppression[] = "non_max_suppression";
-static const char __pyx_k_torch_backends_cudnn[] = "torch.backends.cudnn";
-static const char __pyx_k_WARNING_NMS_time_limit[] = "WARNING: NMS time limit ";
-static const char __pyx_k_colorstr_locals_genexpr[] = "colorstr..genexpr";
-static const char __pyx_k_labels_to_class_weights[] = "labels_to_class_weights";
-static const char __pyx_k_labels_to_image_weights[] = "labels_to_image_weights";
-static const char __pyx_k_one_cycle_locals_lambda[] = "one_cycle..";
-static const char __pyx_k_file_size_locals_genexpr[] = "file_size..genexpr";
-static const char __pyx_k_print_args_locals_genexpr[] = "print_args..genexpr";
-static const char __pyx_k_try_except_locals_handler[] = "try_except..handler";
-static const char __pyx_k_Library_Application_Support[] = "Library/Application Support";
-static const char __pyx_k_Invalid_Confidence_threshold[] = "Invalid Confidence threshold ";
-static const char __pyx_k_intersect_dicts_locals_genexpr[] = "intersect_dicts..genexpr";
-static const char __pyx_k_must_be_multiple_of_max_stride[] = " must be multiple of max stride ";
-static const char __pyx_k_valid_values_are_between_0_0_an[] = ", valid values are between 0.0 and 1.0";
-static const char __pyx_k_pdf_toolbox_lib_dia_yolov5_utils[] = "pdf_toolbox\\lib\\dia_yolov5\\utils\\general.py";
-static const char __pyx_k_pdf_toolbox_lib_dia_yolov5_utils_2[] = "pdf_toolbox.lib.dia_yolov5.utils.general";
-static const char __pyx_k_pdf_toolbox_lib_dia_yolov5_utils_3[] = "pdf_toolbox.lib.dia_yolov5.utils.metrics";
-#if !CYTHON_USE_MODULE_STATE
-static PyObject *__pyx_kp_u_0_0_0;
-static PyObject *__pyx_kp_u_0m;
-static PyObject *__pyx_kp_u_11_5g;
-static PyObject *__pyx_kp_u_1m;
-static PyObject *__pyx_kp_u_30m;
-static PyObject *__pyx_kp_u_31m;
-static PyObject *__pyx_kp_u_32m;
-static PyObject *__pyx_kp_u_33m;
-static PyObject *__pyx_kp_u_34m;
-static PyObject *__pyx_kp_u_35m;
-static PyObject *__pyx_kp_u_36m;
-static PyObject *__pyx_kp_u_37m;
-static PyObject *__pyx_kp_u_3_6_2;
-static PyObject *__pyx_kp_u_4m;
-static PyObject *__pyx_kp_u_90m;
-static PyObject *__pyx_kp_u_91m;
-static PyObject *__pyx_kp_u_92m;
-static PyObject *__pyx_kp_u_93m;
-static PyObject *__pyx_kp_u_94m;
-static PyObject *__pyx_kp_u_95m;
-static PyObject *__pyx_kp_u_96m;
-static PyObject *__pyx_kp_u_97m;
-static PyObject *__pyx_kp_u_AppData_Roaming;
-static PyObject *__pyx_n_s_AssertionError;
-static PyObject *__pyx_n_u_Darwin;
-static PyObject *__pyx_n_s_FILE;
-static PyObject *__pyx_n_s_INFO;
-static PyObject *__pyx_kp_u_Invalid_Confidence_threshold;
-static PyObject *__pyx_kp_u_Invalid_IoU;
-static PyObject *__pyx_n_s_LOGGER;
-static PyObject *__pyx_kp_u_Library_Application_Support;
-static PyObject *__pyx_n_u_Linux;
-static PyObject *__pyx_n_s_NCOLS;
-static PyObject *__pyx_n_u_NUMEXPR_MAX_THREADS;
-static PyObject *__pyx_n_s_NUM_THREADS;
-static PyObject *__pyx_n_s_OSError;
-static PyObject *__pyx_n_s_Path;
-static PyObject *__pyx_kp_u_Python;
-static PyObject *__pyx_n_u_RANK;
-static PyObject *__pyx_n_s_ROOT;
-static PyObject *__pyx_n_s_R_OK;
-static PyObject *__pyx_n_s_T;
-static PyObject *__pyx_n_s_Tensor;
-static PyObject *__pyx_n_u_Ultralytics;
-static PyObject *__pyx_n_s_VERBOSE;
-static PyObject *__pyx_n_s_WARNING;
-static PyObject *__pyx_kp_u_WARNING_NMS_time_limit;
-static PyObject *__pyx_kp_u_WARNING_img_size;
-static PyObject *__pyx_n_u_Windows;
-static PyObject *__pyx_n_u_YOLOV5_CONFIG_DIR;
-static PyObject *__pyx_n_u_YOLOv5_VERBOSE;
-static PyObject *__pyx_kp_u__10;
-static PyObject *__pyx_kp_u__11;
-static PyObject *__pyx_n_u__14;
-static PyObject *__pyx_kp_u__15;
-static PyObject *__pyx_kp_u__16;
-static PyObject *__pyx_kp_u__17;
-static PyObject *__pyx_kp_u__18;
-static PyObject *__pyx_n_s__20;
-static PyObject *__pyx_kp_u__20;
-static PyObject *__pyx_kp_u__21;
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-static PyObject *__pyx_n_s_file_2;
-static PyObject *__pyx_n_s_file_size;
-static PyObject *__pyx_n_s_file_size_locals_genexpr;
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-static PyObject *__pyx_n_s_format;
-static PyObject *__pyx_n_s_formatter;
-static PyObject *__pyx_n_s_from_numpy;
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-static PyObject *__pyx_n_s_get_latest_run;
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-static PyObject *__pyx_n_s_home;
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-static PyObject *__pyx_n_s_image_weights;
-static PyObject *__pyx_n_s_img0_shape;
-static PyObject *__pyx_n_s_img1_shape;
-static PyObject *__pyx_n_s_imgsz;
-static PyObject *__pyx_n_s_import;
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-static PyObject *__pyx_n_s_iou;
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-static PyObject *__pyx_n_s_is_file;
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-static PyObject *__pyx_kp_u_last_pt;
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-static PyObject *__pyx_n_s_linewidth;
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-static PyObject *__pyx_n_s_long;
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-static PyObject *__pyx_n_u_magenta;
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-static PyObject *__pyx_n_s_matches;
-static PyObject *__pyx_n_s_math;
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-static PyObject *__pyx_n_s_max_det;
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-static PyObject *__pyx_kp_u_message_s;
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-static PyObject *__pyx_n_s_one_cycle_locals_lambda;
-static PyObject *__pyx_n_s_ones;
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-static PyObject *__pyx_n_s_pattern;
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-static PyObject *__pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_2;
-static PyObject *__pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_3;
-static PyObject *__pyx_n_s_pi;
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-static PyObject *__pyx_n_s_torchvision;
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-static PyObject *__pyx_n_s_try_except_locals_handler;
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-static PyObject *__pyx_n_s_urllib;
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-static PyObject *__pyx_n_s_v;
-static PyObject *__pyx_kp_u_valid_values_are_between_0_0_an;
-static PyObject *__pyx_n_s_vars;
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-static PyObject *__pyx_kp_u_version;
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-static PyObject *__pyx_n_u_w;
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-static PyObject *__pyx_n_s_weights;
-static PyObject *__pyx_n_u_white;
-static PyObject *__pyx_n_s_with_suffix;
-static PyObject *__pyx_n_s_x;
-static PyObject *__pyx_n_s_xc;
-static PyObject *__pyx_n_s_xi;
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-static PyObject *__pyx_n_s_xywhn2xyxy;
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-static PyObject *__pyx_n_s_xyxy2xywhn;
-static PyObject *__pyx_n_s_y;
-static PyObject *__pyx_n_s_y1;
-static PyObject *__pyx_n_s_y2;
-static PyObject *__pyx_n_s_yaml;
-static PyObject *__pyx_n_u_yellow;
-static PyObject *__pyx_n_u_yolov5;
-static PyObject *__pyx_n_s_zeros;
-#endif
-/* #### Code section: decls ### */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_64__defaults__(CYTHON_UNUSED PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_set_logging(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_name, PyObject *__pyx_v_verbose); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_10try_except_handler(PyObject *__pyx_self, PyObject *__pyx_v_args, PyObject *__pyx_v_kwargs); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_2try_except(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_func); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_4methods(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_instance); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_10print_args_genexpr(PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_6print_args(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_name, PyObject *__pyx_v_opt); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_8init_seeds(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_seed); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_15intersect_dicts_8genexpr2_genexpr(PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_10intersect_dicts(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_da, PyObject *__pyx_v_db, PyObject *__pyx_v_exclude); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_12get_latest_run(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_search_dir); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_14user_config_dir(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dir, PyObject *__pyx_v_env_var); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_16is_writeable(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_dir, PyObject *__pyx_v_test); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_18is_ascii(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_s); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_20is_chinese(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_s); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_22emojis(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_str); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_9file_size_genexpr(PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_24file_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_path); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_26check_python(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_minimum); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_28check_version(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_current, CYTHON_UNUSED PyObject *__pyx_v_minimum, CYTHON_UNUSED PyObject *__pyx_v_name, CYTHON_UNUSED PyObject *__pyx_v_pinned, CYTHON_UNUSED PyObject *__pyx_v_hard, CYTHON_UNUSED PyObject *__pyx_v_verbose); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_30check_img_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_imgsz, PyObject *__pyx_v_s, PyObject *__pyx_v_floor); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_32url2file(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_url); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_34make_divisible(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_divisor); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_36clean_str(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_s); /* proto */
-static PyObject *__pyx_lambda_funcdef_lambda3(PyObject *__pyx_self, PyObject *__pyx_v_x); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_38one_cycle(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_y1, PyObject *__pyx_v_y2, PyObject *__pyx_v_steps); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_8colorstr_genexpr(PyObject *__pyx_self); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_40colorstr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_input); /* proto */
-static PyObject *__pyx_pf_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general_42labels_to_class_weights(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_labels, PyObject *__pyx_v_nc); /* proto */
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- Py_CLEAR(clear_module_state->__pyx_n_u_Ultralytics);
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- Py_CLEAR(clear_module_state->__pyx_kp_u_isenabled);
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- Py_CLEAR(clear_module_state->__pyx_n_s_keepdim);
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- Py_CLEAR(clear_module_state->__pyx_n_s_urllib);
- Py_CLEAR(clear_module_state->__pyx_n_s_user_config_dir);
- Py_CLEAR(clear_module_state->__pyx_n_s_v);
- Py_CLEAR(clear_module_state->__pyx_kp_u_valid_values_are_between_0_0_an);
- Py_CLEAR(clear_module_state->__pyx_n_s_vars);
- Py_CLEAR(clear_module_state->__pyx_n_s_verbose);
- Py_CLEAR(clear_module_state->__pyx_kp_u_version);
- Py_CLEAR(clear_module_state->__pyx_n_s_view);
- Py_CLEAR(clear_module_state->__pyx_n_s_w);
- Py_CLEAR(clear_module_state->__pyx_n_u_w);
- Py_CLEAR(clear_module_state->__pyx_n_s_warning);
- Py_CLEAR(clear_module_state->__pyx_n_s_weights);
- Py_CLEAR(clear_module_state->__pyx_n_u_white);
- Py_CLEAR(clear_module_state->__pyx_n_s_with_suffix);
- Py_CLEAR(clear_module_state->__pyx_n_s_x);
- Py_CLEAR(clear_module_state->__pyx_n_s_xc);
- Py_CLEAR(clear_module_state->__pyx_n_s_xi);
- Py_CLEAR(clear_module_state->__pyx_n_s_xyn2xy);
- Py_CLEAR(clear_module_state->__pyx_n_s_xywh2xyxy);
- Py_CLEAR(clear_module_state->__pyx_n_s_xywhn2xyxy);
- Py_CLEAR(clear_module_state->__pyx_n_s_xyxy2xywh);
- Py_CLEAR(clear_module_state->__pyx_n_s_xyxy2xywhn);
- Py_CLEAR(clear_module_state->__pyx_n_s_y);
- Py_CLEAR(clear_module_state->__pyx_n_s_y1);
- Py_CLEAR(clear_module_state->__pyx_n_s_y2);
- Py_CLEAR(clear_module_state->__pyx_n_s_yaml);
- Py_CLEAR(clear_module_state->__pyx_n_u_yellow);
- Py_CLEAR(clear_module_state->__pyx_n_u_yolov5);
- Py_CLEAR(clear_module_state->__pyx_n_s_zeros);
- Py_CLEAR(clear_module_state->__pyx_float_0_0);
- Py_CLEAR(clear_module_state->__pyx_float_1_0);
- Py_CLEAR(clear_module_state->__pyx_float_1E6);
- Py_CLEAR(clear_module_state->__pyx_float_3E3);
- Py_CLEAR(clear_module_state->__pyx_float_0_25);
- Py_CLEAR(clear_module_state->__pyx_float_0_45);
- Py_CLEAR(clear_module_state->__pyx_int_0);
- Py_CLEAR(clear_module_state->__pyx_int_1);
- Py_CLEAR(clear_module_state->__pyx_int_2);
- Py_CLEAR(clear_module_state->__pyx_int_3);
- Py_CLEAR(clear_module_state->__pyx_int_4);
- Py_CLEAR(clear_module_state->__pyx_int_5);
- Py_CLEAR(clear_module_state->__pyx_int_6);
- Py_CLEAR(clear_module_state->__pyx_int_10);
- Py_CLEAR(clear_module_state->__pyx_int_32);
- Py_CLEAR(clear_module_state->__pyx_int_80);
- Py_CLEAR(clear_module_state->__pyx_int_100);
- Py_CLEAR(clear_module_state->__pyx_int_300);
- Py_CLEAR(clear_module_state->__pyx_int_320);
- Py_CLEAR(clear_module_state->__pyx_int_640);
- Py_CLEAR(clear_module_state->__pyx_int_neg_1);
- Py_CLEAR(clear_module_state->__pyx_tuple_);
- Py_CLEAR(clear_module_state->__pyx_tuple__2);
- Py_CLEAR(clear_module_state->__pyx_tuple__8);
- Py_CLEAR(clear_module_state->__pyx_slice__23);
- Py_CLEAR(clear_module_state->__pyx_slice__28);
- Py_CLEAR(clear_module_state->__pyx_slice__33);
- Py_CLEAR(clear_module_state->__pyx_slice__36);
- Py_CLEAR(clear_module_state->__pyx_slice__38);
- Py_CLEAR(clear_module_state->__pyx_slice__42);
- Py_CLEAR(clear_module_state->__pyx_tuple__12);
- Py_CLEAR(clear_module_state->__pyx_tuple__13);
- Py_CLEAR(clear_module_state->__pyx_tuple__19);
- Py_CLEAR(clear_module_state->__pyx_tuple__24);
- Py_CLEAR(clear_module_state->__pyx_tuple__25);
- Py_CLEAR(clear_module_state->__pyx_tuple__26);
- Py_CLEAR(clear_module_state->__pyx_tuple__27);
- Py_CLEAR(clear_module_state->__pyx_tuple__29);
- Py_CLEAR(clear_module_state->__pyx_tuple__30);
- Py_CLEAR(clear_module_state->__pyx_tuple__31);
- Py_CLEAR(clear_module_state->__pyx_tuple__32);
- Py_CLEAR(clear_module_state->__pyx_tuple__34);
- Py_CLEAR(clear_module_state->__pyx_tuple__35);
- Py_CLEAR(clear_module_state->__pyx_tuple__37);
- Py_CLEAR(clear_module_state->__pyx_tuple__39);
- Py_CLEAR(clear_module_state->__pyx_tuple__40);
- Py_CLEAR(clear_module_state->__pyx_tuple__41);
- Py_CLEAR(clear_module_state->__pyx_tuple__43);
- Py_CLEAR(clear_module_state->__pyx_tuple__44);
- Py_CLEAR(clear_module_state->__pyx_tuple__45);
- Py_CLEAR(clear_module_state->__pyx_tuple__46);
- Py_CLEAR(clear_module_state->__pyx_tuple__48);
- Py_CLEAR(clear_module_state->__pyx_tuple__49);
- Py_CLEAR(clear_module_state->__pyx_tuple__51);
- Py_CLEAR(clear_module_state->__pyx_tuple__53);
- Py_CLEAR(clear_module_state->__pyx_tuple__55);
- Py_CLEAR(clear_module_state->__pyx_tuple__57);
- Py_CLEAR(clear_module_state->__pyx_tuple__58);
- Py_CLEAR(clear_module_state->__pyx_tuple__60);
- Py_CLEAR(clear_module_state->__pyx_tuple__61);
- Py_CLEAR(clear_module_state->__pyx_tuple__63);
- Py_CLEAR(clear_module_state->__pyx_tuple__64);
- Py_CLEAR(clear_module_state->__pyx_tuple__66);
- Py_CLEAR(clear_module_state->__pyx_tuple__67);
- Py_CLEAR(clear_module_state->__pyx_tuple__69);
- Py_CLEAR(clear_module_state->__pyx_tuple__70);
- Py_CLEAR(clear_module_state->__pyx_tuple__72);
- Py_CLEAR(clear_module_state->__pyx_tuple__74);
- Py_CLEAR(clear_module_state->__pyx_tuple__75);
- Py_CLEAR(clear_module_state->__pyx_tuple__77);
- Py_CLEAR(clear_module_state->__pyx_tuple__78);
- Py_CLEAR(clear_module_state->__pyx_tuple__80);
- Py_CLEAR(clear_module_state->__pyx_tuple__82);
- Py_CLEAR(clear_module_state->__pyx_tuple__83);
- Py_CLEAR(clear_module_state->__pyx_tuple__85);
- Py_CLEAR(clear_module_state->__pyx_tuple__86);
- Py_CLEAR(clear_module_state->__pyx_tuple__88);
- Py_CLEAR(clear_module_state->__pyx_tuple__89);
- Py_CLEAR(clear_module_state->__pyx_tuple__91);
- Py_CLEAR(clear_module_state->__pyx_tuple__94);
- Py_CLEAR(clear_module_state->__pyx_tuple__96);
- Py_CLEAR(clear_module_state->__pyx_tuple__97);
- Py_CLEAR(clear_module_state->__pyx_tuple__99);
- Py_CLEAR(clear_module_state->__pyx_codeobj__3);
- Py_CLEAR(clear_module_state->__pyx_tuple__101);
- Py_CLEAR(clear_module_state->__pyx_tuple__102);
- Py_CLEAR(clear_module_state->__pyx_tuple__104);
- Py_CLEAR(clear_module_state->__pyx_tuple__105);
- Py_CLEAR(clear_module_state->__pyx_tuple__108);
- Py_CLEAR(clear_module_state->__pyx_tuple__110);
- Py_CLEAR(clear_module_state->__pyx_tuple__111);
- Py_CLEAR(clear_module_state->__pyx_tuple__113);
- Py_CLEAR(clear_module_state->__pyx_tuple__115);
- Py_CLEAR(clear_module_state->__pyx_tuple__116);
- Py_CLEAR(clear_module_state->__pyx_tuple__118);
- Py_CLEAR(clear_module_state->__pyx_tuple__119);
- Py_CLEAR(clear_module_state->__pyx_tuple__121);
- Py_CLEAR(clear_module_state->__pyx_tuple__123);
- Py_CLEAR(clear_module_state->__pyx_tuple__124);
- Py_CLEAR(clear_module_state->__pyx_tuple__126);
- Py_CLEAR(clear_module_state->__pyx_codeobj__47);
- Py_CLEAR(clear_module_state->__pyx_codeobj__50);
- Py_CLEAR(clear_module_state->__pyx_codeobj__52);
- Py_CLEAR(clear_module_state->__pyx_codeobj__54);
- Py_CLEAR(clear_module_state->__pyx_codeobj__56);
- Py_CLEAR(clear_module_state->__pyx_codeobj__59);
- Py_CLEAR(clear_module_state->__pyx_codeobj__62);
- Py_CLEAR(clear_module_state->__pyx_codeobj__65);
- Py_CLEAR(clear_module_state->__pyx_codeobj__68);
- Py_CLEAR(clear_module_state->__pyx_codeobj__71);
- Py_CLEAR(clear_module_state->__pyx_codeobj__73);
- Py_CLEAR(clear_module_state->__pyx_codeobj__76);
- Py_CLEAR(clear_module_state->__pyx_codeobj__79);
- Py_CLEAR(clear_module_state->__pyx_codeobj__81);
- Py_CLEAR(clear_module_state->__pyx_codeobj__84);
- Py_CLEAR(clear_module_state->__pyx_codeobj__87);
- Py_CLEAR(clear_module_state->__pyx_codeobj__90);
- Py_CLEAR(clear_module_state->__pyx_codeobj__92);
- Py_CLEAR(clear_module_state->__pyx_codeobj__93);
- Py_CLEAR(clear_module_state->__pyx_codeobj__95);
- Py_CLEAR(clear_module_state->__pyx_codeobj__98);
- Py_CLEAR(clear_module_state->__pyx_codeobj__100);
- Py_CLEAR(clear_module_state->__pyx_codeobj__103);
- Py_CLEAR(clear_module_state->__pyx_codeobj__106);
- Py_CLEAR(clear_module_state->__pyx_codeobj__107);
- Py_CLEAR(clear_module_state->__pyx_codeobj__109);
- Py_CLEAR(clear_module_state->__pyx_codeobj__112);
- Py_CLEAR(clear_module_state->__pyx_codeobj__114);
- Py_CLEAR(clear_module_state->__pyx_codeobj__117);
- Py_CLEAR(clear_module_state->__pyx_codeobj__120);
- Py_CLEAR(clear_module_state->__pyx_codeobj__122);
- Py_CLEAR(clear_module_state->__pyx_codeobj__125);
- return 0;
-}
-#endif
-/* #### Code section: module_state_traverse ### */
-#if CYTHON_USE_MODULE_STATE
-static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
- __pyx_mstate *traverse_module_state = __pyx_mstate(m);
- if (!traverse_module_state) return 0;
- Py_VISIT(traverse_module_state->__pyx_d);
- Py_VISIT(traverse_module_state->__pyx_b);
- Py_VISIT(traverse_module_state->__pyx_cython_runtime);
- Py_VISIT(traverse_module_state->__pyx_empty_tuple);
- Py_VISIT(traverse_module_state->__pyx_empty_bytes);
- Py_VISIT(traverse_module_state->__pyx_empty_unicode);
- #ifdef __Pyx_CyFunction_USED
- Py_VISIT(traverse_module_state->__pyx_CyFunctionType);
- #endif
- #ifdef __Pyx_FusedFunction_USED
- Py_VISIT(traverse_module_state->__pyx_FusedFunctionType);
- #endif
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr);
- Py_VISIT(traverse_module_state->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr);
- Py_VISIT(traverse_module_state->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr);
- Py_VISIT(traverse_module_state->__pyx_kp_u_0_0_0);
- Py_VISIT(traverse_module_state->__pyx_kp_u_0m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_11_5g);
- Py_VISIT(traverse_module_state->__pyx_kp_u_1m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_30m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_31m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_32m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_33m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_34m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_35m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_36m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_37m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_3_6_2);
- Py_VISIT(traverse_module_state->__pyx_kp_u_4m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_90m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_91m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_92m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_93m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_94m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_95m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_96m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_97m);
- Py_VISIT(traverse_module_state->__pyx_kp_u_AppData_Roaming);
- Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError);
- Py_VISIT(traverse_module_state->__pyx_n_u_Darwin);
- Py_VISIT(traverse_module_state->__pyx_n_s_FILE);
- Py_VISIT(traverse_module_state->__pyx_n_s_INFO);
- Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_Confidence_threshold);
- Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_IoU);
- Py_VISIT(traverse_module_state->__pyx_n_s_LOGGER);
- Py_VISIT(traverse_module_state->__pyx_kp_u_Library_Application_Support);
- Py_VISIT(traverse_module_state->__pyx_n_u_Linux);
- Py_VISIT(traverse_module_state->__pyx_n_s_NCOLS);
- Py_VISIT(traverse_module_state->__pyx_n_u_NUMEXPR_MAX_THREADS);
- Py_VISIT(traverse_module_state->__pyx_n_s_NUM_THREADS);
- Py_VISIT(traverse_module_state->__pyx_n_s_OSError);
- Py_VISIT(traverse_module_state->__pyx_n_s_Path);
- Py_VISIT(traverse_module_state->__pyx_kp_u_Python);
- Py_VISIT(traverse_module_state->__pyx_n_u_RANK);
- Py_VISIT(traverse_module_state->__pyx_n_s_ROOT);
- Py_VISIT(traverse_module_state->__pyx_n_s_R_OK);
- Py_VISIT(traverse_module_state->__pyx_n_s_T);
- Py_VISIT(traverse_module_state->__pyx_n_s_Tensor);
- Py_VISIT(traverse_module_state->__pyx_n_u_Ultralytics);
- Py_VISIT(traverse_module_state->__pyx_n_s_VERBOSE);
- Py_VISIT(traverse_module_state->__pyx_n_s_WARNING);
- Py_VISIT(traverse_module_state->__pyx_kp_u_WARNING_NMS_time_limit);
- Py_VISIT(traverse_module_state->__pyx_kp_u_WARNING_img_size);
- Py_VISIT(traverse_module_state->__pyx_n_u_Windows);
- Py_VISIT(traverse_module_state->__pyx_n_u_YOLOV5_CONFIG_DIR);
- Py_VISIT(traverse_module_state->__pyx_n_u_YOLOv5_VERBOSE);
- Py_VISIT(traverse_module_state->__pyx_kp_u__10);
- Py_VISIT(traverse_module_state->__pyx_kp_u__11);
- Py_VISIT(traverse_module_state->__pyx_n_u__14);
- Py_VISIT(traverse_module_state->__pyx_kp_u__15);
- Py_VISIT(traverse_module_state->__pyx_kp_u__16);
- Py_VISIT(traverse_module_state->__pyx_kp_u__17);
- Py_VISIT(traverse_module_state->__pyx_kp_u__18);
- Py_VISIT(traverse_module_state->__pyx_n_s__20);
- Py_VISIT(traverse_module_state->__pyx_kp_u__20);
- Py_VISIT(traverse_module_state->__pyx_kp_u__21);
- Py_VISIT(traverse_module_state->__pyx_n_u__22);
- Py_VISIT(traverse_module_state->__pyx_n_u__4);
- Py_VISIT(traverse_module_state->__pyx_kp_u__5);
- Py_VISIT(traverse_module_state->__pyx_kp_u__6);
- Py_VISIT(traverse_module_state->__pyx_kp_u__7);
- Py_VISIT(traverse_module_state->__pyx_n_s__9);
- Py_VISIT(traverse_module_state->__pyx_kp_u__9);
- Py_VISIT(traverse_module_state->__pyx_n_s_access);
- Py_VISIT(traverse_module_state->__pyx_n_s_agnostic);
- Py_VISIT(traverse_module_state->__pyx_n_s_any);
- Py_VISIT(traverse_module_state->__pyx_n_s_args);
- Py_VISIT(traverse_module_state->__pyx_n_s_argsort);
- Py_VISIT(traverse_module_state->__pyx_n_s_array);
- Py_VISIT(traverse_module_state->__pyx_n_s_as_tuple);
- Py_VISIT(traverse_module_state->__pyx_n_u_ascii);
- Py_VISIT(traverse_module_state->__pyx_n_s_astype);
- Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines);
- Py_VISIT(traverse_module_state->__pyx_n_s_backends);
- Py_VISIT(traverse_module_state->__pyx_n_s_basicConfig);
- Py_VISIT(traverse_module_state->__pyx_n_s_benchmark);
- Py_VISIT(traverse_module_state->__pyx_n_s_bincount);
- Py_VISIT(traverse_module_state->__pyx_n_u_black);
- Py_VISIT(traverse_module_state->__pyx_n_u_blue);
- Py_VISIT(traverse_module_state->__pyx_n_u_bold);
- Py_VISIT(traverse_module_state->__pyx_n_s_box);
- Py_VISIT(traverse_module_state->__pyx_n_s_box_iou);
- Py_VISIT(traverse_module_state->__pyx_n_s_boxes);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_black);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_blue);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_cyan);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_green);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_magenta);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_red);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_white);
- Py_VISIT(traverse_module_state->__pyx_n_u_bright_yellow);
- Py_VISIT(traverse_module_state->__pyx_n_s_c);
- Py_VISIT(traverse_module_state->__pyx_n_s_cat);
- Py_VISIT(traverse_module_state->__pyx_n_s_ceil);
- Py_VISIT(traverse_module_state->__pyx_n_s_cfg);
- Py_VISIT(traverse_module_state->__pyx_n_s_check_img_size);
- Py_VISIT(traverse_module_state->__pyx_n_s_check_python);
- Py_VISIT(traverse_module_state->__pyx_n_s_check_version);
- Py_VISIT(traverse_module_state->__pyx_n_s_clamp);
- Py_VISIT(traverse_module_state->__pyx_n_s_class_counts);
- Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem);
- Py_VISIT(traverse_module_state->__pyx_n_s_class_weights);
- Py_VISIT(traverse_module_state->__pyx_n_s_classes);
- Py_VISIT(traverse_module_state->__pyx_n_s_clean_str);
- Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback);
- Py_VISIT(traverse_module_state->__pyx_n_s_clip);
- Py_VISIT(traverse_module_state->__pyx_n_s_clip_coords);
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-}
-#endif
-/* #### Code section: module_state_defines ### */
-#if CYTHON_USE_MODULE_STATE
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
-#endif
-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
-#endif
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct__try_except
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_1_print_args
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_2_genexpr
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_3_intersect_dicts
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_6_genexpr
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_7_one_cycle
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_8_colorstr
-#define __pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr __pyx_mstate_global->__pyx_ptype_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr
-#define __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr __pyx_mstate_global->__pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_9_genexpr
-#define __pyx_kp_u_0_0_0 __pyx_mstate_global->__pyx_kp_u_0_0_0
-#define __pyx_kp_u_0m __pyx_mstate_global->__pyx_kp_u_0m
-#define __pyx_kp_u_11_5g __pyx_mstate_global->__pyx_kp_u_11_5g
-#define __pyx_kp_u_1m __pyx_mstate_global->__pyx_kp_u_1m
-#define __pyx_kp_u_30m __pyx_mstate_global->__pyx_kp_u_30m
-#define __pyx_kp_u_31m __pyx_mstate_global->__pyx_kp_u_31m
-#define __pyx_kp_u_32m __pyx_mstate_global->__pyx_kp_u_32m
-#define __pyx_kp_u_33m __pyx_mstate_global->__pyx_kp_u_33m
-#define __pyx_kp_u_34m __pyx_mstate_global->__pyx_kp_u_34m
-#define __pyx_kp_u_35m __pyx_mstate_global->__pyx_kp_u_35m
-#define __pyx_kp_u_36m __pyx_mstate_global->__pyx_kp_u_36m
-#define __pyx_kp_u_37m __pyx_mstate_global->__pyx_kp_u_37m
-#define __pyx_kp_u_3_6_2 __pyx_mstate_global->__pyx_kp_u_3_6_2
-#define __pyx_kp_u_4m __pyx_mstate_global->__pyx_kp_u_4m
-#define __pyx_kp_u_90m __pyx_mstate_global->__pyx_kp_u_90m
-#define __pyx_kp_u_91m __pyx_mstate_global->__pyx_kp_u_91m
-#define __pyx_kp_u_92m __pyx_mstate_global->__pyx_kp_u_92m
-#define __pyx_kp_u_93m __pyx_mstate_global->__pyx_kp_u_93m
-#define __pyx_kp_u_94m __pyx_mstate_global->__pyx_kp_u_94m
-#define __pyx_kp_u_95m __pyx_mstate_global->__pyx_kp_u_95m
-#define __pyx_kp_u_96m __pyx_mstate_global->__pyx_kp_u_96m
-#define __pyx_kp_u_97m __pyx_mstate_global->__pyx_kp_u_97m
-#define __pyx_kp_u_AppData_Roaming __pyx_mstate_global->__pyx_kp_u_AppData_Roaming
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_n_u_Darwin __pyx_mstate_global->__pyx_n_u_Darwin
-#define __pyx_n_s_FILE __pyx_mstate_global->__pyx_n_s_FILE
-#define __pyx_n_s_INFO __pyx_mstate_global->__pyx_n_s_INFO
-#define __pyx_kp_u_Invalid_Confidence_threshold __pyx_mstate_global->__pyx_kp_u_Invalid_Confidence_threshold
-#define __pyx_kp_u_Invalid_IoU __pyx_mstate_global->__pyx_kp_u_Invalid_IoU
-#define __pyx_n_s_LOGGER __pyx_mstate_global->__pyx_n_s_LOGGER
-#define __pyx_kp_u_Library_Application_Support __pyx_mstate_global->__pyx_kp_u_Library_Application_Support
-#define __pyx_n_u_Linux __pyx_mstate_global->__pyx_n_u_Linux
-#define __pyx_n_s_NCOLS __pyx_mstate_global->__pyx_n_s_NCOLS
-#define __pyx_n_u_NUMEXPR_MAX_THREADS __pyx_mstate_global->__pyx_n_u_NUMEXPR_MAX_THREADS
-#define __pyx_n_s_NUM_THREADS __pyx_mstate_global->__pyx_n_s_NUM_THREADS
-#define __pyx_n_s_OSError __pyx_mstate_global->__pyx_n_s_OSError
-#define __pyx_n_s_Path __pyx_mstate_global->__pyx_n_s_Path
-#define __pyx_kp_u_Python __pyx_mstate_global->__pyx_kp_u_Python
-#define __pyx_n_u_RANK __pyx_mstate_global->__pyx_n_u_RANK
-#define __pyx_n_s_ROOT __pyx_mstate_global->__pyx_n_s_ROOT
-#define __pyx_n_s_R_OK __pyx_mstate_global->__pyx_n_s_R_OK
-#define __pyx_n_s_T __pyx_mstate_global->__pyx_n_s_T
-#define __pyx_n_s_Tensor __pyx_mstate_global->__pyx_n_s_Tensor
-#define __pyx_n_u_Ultralytics __pyx_mstate_global->__pyx_n_u_Ultralytics
-#define __pyx_n_s_VERBOSE __pyx_mstate_global->__pyx_n_s_VERBOSE
-#define __pyx_n_s_WARNING __pyx_mstate_global->__pyx_n_s_WARNING
-#define __pyx_kp_u_WARNING_NMS_time_limit __pyx_mstate_global->__pyx_kp_u_WARNING_NMS_time_limit
-#define __pyx_kp_u_WARNING_img_size __pyx_mstate_global->__pyx_kp_u_WARNING_img_size
-#define __pyx_n_u_Windows __pyx_mstate_global->__pyx_n_u_Windows
-#define __pyx_n_u_YOLOV5_CONFIG_DIR __pyx_mstate_global->__pyx_n_u_YOLOV5_CONFIG_DIR
-#define __pyx_n_u_YOLOv5_VERBOSE __pyx_mstate_global->__pyx_n_u_YOLOv5_VERBOSE
-#define __pyx_kp_u__10 __pyx_mstate_global->__pyx_kp_u__10
-#define __pyx_kp_u__11 __pyx_mstate_global->__pyx_kp_u__11
-#define __pyx_n_u__14 __pyx_mstate_global->__pyx_n_u__14
-#define __pyx_kp_u__15 __pyx_mstate_global->__pyx_kp_u__15
-#define __pyx_kp_u__16 __pyx_mstate_global->__pyx_kp_u__16
-#define __pyx_kp_u__17 __pyx_mstate_global->__pyx_kp_u__17
-#define __pyx_kp_u__18 __pyx_mstate_global->__pyx_kp_u__18
-#define __pyx_n_s__20 __pyx_mstate_global->__pyx_n_s__20
-#define __pyx_kp_u__20 __pyx_mstate_global->__pyx_kp_u__20
-#define __pyx_kp_u__21 __pyx_mstate_global->__pyx_kp_u__21
-#define __pyx_n_u__22 __pyx_mstate_global->__pyx_n_u__22
-#define __pyx_n_u__4 __pyx_mstate_global->__pyx_n_u__4
-#define __pyx_kp_u__5 __pyx_mstate_global->__pyx_kp_u__5
-#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6
-#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7
-#define __pyx_n_s__9 __pyx_mstate_global->__pyx_n_s__9
-#define __pyx_kp_u__9 __pyx_mstate_global->__pyx_kp_u__9
-#define __pyx_n_s_access __pyx_mstate_global->__pyx_n_s_access
-#define __pyx_n_s_agnostic __pyx_mstate_global->__pyx_n_s_agnostic
-#define __pyx_n_s_any __pyx_mstate_global->__pyx_n_s_any
-#define __pyx_n_s_args __pyx_mstate_global->__pyx_n_s_args
-#define __pyx_n_s_argsort __pyx_mstate_global->__pyx_n_s_argsort
-#define __pyx_n_s_array __pyx_mstate_global->__pyx_n_s_array
-#define __pyx_n_s_as_tuple __pyx_mstate_global->__pyx_n_s_as_tuple
-#define __pyx_n_u_ascii __pyx_mstate_global->__pyx_n_u_ascii
-#define __pyx_n_s_astype __pyx_mstate_global->__pyx_n_s_astype
-#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines
-#define __pyx_n_s_backends __pyx_mstate_global->__pyx_n_s_backends
-#define __pyx_n_s_basicConfig __pyx_mstate_global->__pyx_n_s_basicConfig
-#define __pyx_n_s_benchmark __pyx_mstate_global->__pyx_n_s_benchmark
-#define __pyx_n_s_bincount __pyx_mstate_global->__pyx_n_s_bincount
-#define __pyx_n_u_black __pyx_mstate_global->__pyx_n_u_black
-#define __pyx_n_u_blue __pyx_mstate_global->__pyx_n_u_blue
-#define __pyx_n_u_bold __pyx_mstate_global->__pyx_n_u_bold
-#define __pyx_n_s_box __pyx_mstate_global->__pyx_n_s_box
-#define __pyx_n_s_box_iou __pyx_mstate_global->__pyx_n_s_box_iou
-#define __pyx_n_s_boxes __pyx_mstate_global->__pyx_n_s_boxes
-#define __pyx_n_u_bright_black __pyx_mstate_global->__pyx_n_u_bright_black
-#define __pyx_n_u_bright_blue __pyx_mstate_global->__pyx_n_u_bright_blue
-#define __pyx_n_u_bright_cyan __pyx_mstate_global->__pyx_n_u_bright_cyan
-#define __pyx_n_u_bright_green __pyx_mstate_global->__pyx_n_u_bright_green
-#define __pyx_n_u_bright_magenta __pyx_mstate_global->__pyx_n_u_bright_magenta
-#define __pyx_n_u_bright_red __pyx_mstate_global->__pyx_n_u_bright_red
-#define __pyx_n_u_bright_white __pyx_mstate_global->__pyx_n_u_bright_white
-#define __pyx_n_u_bright_yellow __pyx_mstate_global->__pyx_n_u_bright_yellow
-#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c
-#define __pyx_n_s_cat __pyx_mstate_global->__pyx_n_s_cat
-#define __pyx_n_s_ceil __pyx_mstate_global->__pyx_n_s_ceil
-#define __pyx_n_s_cfg __pyx_mstate_global->__pyx_n_s_cfg
-#define __pyx_n_s_check_img_size __pyx_mstate_global->__pyx_n_s_check_img_size
-#define __pyx_n_s_check_python __pyx_mstate_global->__pyx_n_s_check_python
-#define __pyx_n_s_check_version __pyx_mstate_global->__pyx_n_s_check_version
-#define __pyx_n_s_clamp __pyx_mstate_global->__pyx_n_s_clamp
-#define __pyx_n_s_class_counts __pyx_mstate_global->__pyx_n_s_class_counts
-#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem
-#define __pyx_n_s_class_weights __pyx_mstate_global->__pyx_n_s_class_weights
-#define __pyx_n_s_classes __pyx_mstate_global->__pyx_n_s_classes
-#define __pyx_n_s_clean_str __pyx_mstate_global->__pyx_n_s_clean_str
-#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback
-#define __pyx_n_s_clip __pyx_mstate_global->__pyx_n_s_clip
-#define __pyx_n_s_clip_coords __pyx_mstate_global->__pyx_n_s_clip_coords
-#define __pyx_n_s_clone __pyx_mstate_global->__pyx_n_s_clone
-#define __pyx_n_s_close __pyx_mstate_global->__pyx_n_s_close
-#define __pyx_n_s_colors __pyx_mstate_global->__pyx_n_s_colors
-#define __pyx_n_s_colorstr __pyx_mstate_global->__pyx_n_s_colorstr
-#define __pyx_n_s_colorstr_locals_genexpr __pyx_mstate_global->__pyx_n_s_colorstr_locals_genexpr
-#define __pyx_n_s_columns __pyx_mstate_global->__pyx_n_s_columns
-#define __pyx_n_s_concatenate __pyx_mstate_global->__pyx_n_s_concatenate
-#define __pyx_n_s_conf __pyx_mstate_global->__pyx_n_s_conf
-#define __pyx_n_s_conf_thres __pyx_mstate_global->__pyx_n_s_conf_thres
-#define __pyx_kp_u_config __pyx_mstate_global->__pyx_kp_u_config
-#define __pyx_n_s_coords __pyx_mstate_global->__pyx_n_s_coords
-#define __pyx_n_s_copy __pyx_mstate_global->__pyx_n_s_copy
-#define __pyx_n_s_cos __pyx_mstate_global->__pyx_n_s_cos
-#define __pyx_n_s_cpu_count __pyx_mstate_global->__pyx_n_s_cpu_count
-#define __pyx_n_s_cudnn __pyx_mstate_global->__pyx_n_s_cudnn
-#define __pyx_n_s_current __pyx_mstate_global->__pyx_n_s_current
-#define __pyx_n_s_cv2 __pyx_mstate_global->__pyx_n_s_cv2
-#define __pyx_n_u_cyan __pyx_mstate_global->__pyx_n_u_cyan
-#define __pyx_kp_u_d __pyx_mstate_global->__pyx_kp_u_d
-#define __pyx_n_s_d_2 __pyx_mstate_global->__pyx_n_s_d_2
-#define __pyx_n_s_da __pyx_mstate_global->__pyx_n_s_da
-#define __pyx_n_s_db __pyx_mstate_global->__pyx_n_s_db
-#define __pyx_n_s_decode __pyx_mstate_global->__pyx_n_s_decode
-#define __pyx_n_s_descending __pyx_mstate_global->__pyx_n_s_descending
-#define __pyx_n_s_deterministic __pyx_mstate_global->__pyx_n_s_deterministic
-#define __pyx_n_s_device __pyx_mstate_global->__pyx_n_s_device
-#define __pyx_n_s_dir __pyx_mstate_global->__pyx_n_s_dir
-#define __pyx_n_s_dirs __pyx_mstate_global->__pyx_n_s_dirs
-#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable
-#define __pyx_n_s_display __pyx_mstate_global->__pyx_n_s_display
-#define __pyx_n_s_divisor __pyx_mstate_global->__pyx_n_s_divisor
-#define __pyx_n_s_e __pyx_mstate_global->__pyx_n_s_e
-#define __pyx_n_s_emojis __pyx_mstate_global->__pyx_n_s_emojis
-#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable
-#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode
-#define __pyx_n_u_end __pyx_mstate_global->__pyx_n_u_end
-#define __pyx_n_s_enter __pyx_mstate_global->__pyx_n_s_enter
-#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate
-#define __pyx_n_s_env __pyx_mstate_global->__pyx_n_s_env
-#define __pyx_n_s_env_var __pyx_mstate_global->__pyx_n_s_env_var
-#define __pyx_n_s_environ __pyx_mstate_global->__pyx_n_s_environ
-#define __pyx_n_s_eps __pyx_mstate_global->__pyx_n_s_eps
-#define __pyx_n_s_exclude __pyx_mstate_global->__pyx_n_s_exclude
-#define __pyx_n_s_exist_ok __pyx_mstate_global->__pyx_n_s_exist_ok
-#define __pyx_n_s_exists __pyx_mstate_global->__pyx_n_s_exists
-#define __pyx_n_s_exit __pyx_mstate_global->__pyx_n_s_exit
-#define __pyx_n_s_f __pyx_mstate_global->__pyx_n_s_f
-#define __pyx_n_s_file __pyx_mstate_global->__pyx_n_s_file
-#define __pyx_n_s_file_2 __pyx_mstate_global->__pyx_n_s_file_2
-#define __pyx_n_s_file_size __pyx_mstate_global->__pyx_n_s_file_size
-#define __pyx_n_s_file_size_locals_genexpr __pyx_mstate_global->__pyx_n_s_file_size_locals_genexpr
-#define __pyx_n_s_fitness __pyx_mstate_global->__pyx_n_s_fitness
-#define __pyx_n_s_float __pyx_mstate_global->__pyx_n_s_float
-#define __pyx_n_u_float_kind __pyx_mstate_global->__pyx_n_u_float_kind
-#define __pyx_n_s_floor __pyx_mstate_global->__pyx_n_s_floor
-#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format
-#define __pyx_n_s_formatter __pyx_mstate_global->__pyx_n_s_formatter
-#define __pyx_n_s_from_numpy __pyx_mstate_global->__pyx_n_s_from_numpy
-#define __pyx_n_s_func __pyx_mstate_global->__pyx_n_s_func
-#define __pyx_n_s_gain __pyx_mstate_global->__pyx_n_s_gain
-#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc
-#define __pyx_n_s_genexpr __pyx_mstate_global->__pyx_n_s_genexpr
-#define __pyx_n_s_get __pyx_mstate_global->__pyx_n_s_get
-#define __pyx_n_s_getLogger __pyx_mstate_global->__pyx_n_s_getLogger
-#define __pyx_n_s_get_latest_run __pyx_mstate_global->__pyx_n_s_get_latest_run
-#define __pyx_n_s_get_terminal_size __pyx_mstate_global->__pyx_n_s_get_terminal_size
-#define __pyx_n_s_getctime __pyx_mstate_global->__pyx_n_s_getctime
-#define __pyx_n_s_getenv __pyx_mstate_global->__pyx_n_s_getenv
-#define __pyx_n_s_glob __pyx_mstate_global->__pyx_n_s_glob
-#define __pyx_n_u_green __pyx_mstate_global->__pyx_n_u_green
-#define __pyx_n_s_groups __pyx_mstate_global->__pyx_n_s_groups
-#define __pyx_n_s_h __pyx_mstate_global->__pyx_n_s_h
-#define __pyx_n_s_handler __pyx_mstate_global->__pyx_n_s_handler
-#define __pyx_n_s_hard __pyx_mstate_global->__pyx_n_s_hard
-#define __pyx_n_s_home __pyx_mstate_global->__pyx_n_s_home
-#define __pyx_n_s_i __pyx_mstate_global->__pyx_n_s_i
-#define __pyx_n_u_ignore __pyx_mstate_global->__pyx_n_u_ignore
-#define __pyx_n_s_image_weights __pyx_mstate_global->__pyx_n_s_image_weights
-#define __pyx_n_s_img0_shape __pyx_mstate_global->__pyx_n_s_img0_shape
-#define __pyx_n_s_img1_shape __pyx_mstate_global->__pyx_n_s_img1_shape
-#define __pyx_n_s_imgsz __pyx_mstate_global->__pyx_n_s_imgsz
-#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import
-#define __pyx_n_s_increment_path __pyx_mstate_global->__pyx_n_s_increment_path
-#define __pyx_n_s_info __pyx_mstate_global->__pyx_n_s_info
-#define __pyx_n_s_init_seeds __pyx_mstate_global->__pyx_n_s_init_seeds
-#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing
-#define __pyx_n_s_input __pyx_mstate_global->__pyx_n_s_input
-#define __pyx_n_s_instance __pyx_mstate_global->__pyx_n_s_instance
-#define __pyx_n_s_int __pyx_mstate_global->__pyx_n_s_int
-#define __pyx_n_s_intersect_dicts __pyx_mstate_global->__pyx_n_s_intersect_dicts
-#define __pyx_n_s_intersect_dicts_locals_genexpr __pyx_mstate_global->__pyx_n_s_intersect_dicts_locals_genexpr
-#define __pyx_n_s_iou __pyx_mstate_global->__pyx_n_s_iou
-#define __pyx_n_s_iou_thres __pyx_mstate_global->__pyx_n_s_iou_thres
-#define __pyx_n_s_is_ascii __pyx_mstate_global->__pyx_n_s_is_ascii
-#define __pyx_n_s_is_chinese __pyx_mstate_global->__pyx_n_s_is_chinese
-#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine
-#define __pyx_n_s_is_dir __pyx_mstate_global->__pyx_n_s_is_dir
-#define __pyx_n_s_is_file __pyx_mstate_global->__pyx_n_s_is_file
-#define __pyx_n_s_is_writeable __pyx_mstate_global->__pyx_n_s_is_writeable
-#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled
-#define __pyx_n_s_items __pyx_mstate_global->__pyx_n_s_items
-#define __pyx_n_s_j __pyx_mstate_global->__pyx_n_s_j
-#define __pyx_n_s_k __pyx_mstate_global->__pyx_n_s_k
-#define __pyx_n_s_keepdim __pyx_mstate_global->__pyx_n_s_keepdim
-#define __pyx_n_s_key __pyx_mstate_global->__pyx_n_s_key
-#define __pyx_n_s_kwargs __pyx_mstate_global->__pyx_n_s_kwargs
-#define __pyx_n_s_l __pyx_mstate_global->__pyx_n_s_l
-#define __pyx_n_s_labels __pyx_mstate_global->__pyx_n_s_labels
-#define __pyx_n_s_labels_to_class_weights __pyx_mstate_global->__pyx_n_s_labels_to_class_weights
-#define __pyx_n_s_labels_to_image_weights __pyx_mstate_global->__pyx_n_s_labels_to_image_weights
-#define __pyx_n_s_last_list __pyx_mstate_global->__pyx_n_s_last_list
-#define __pyx_kp_u_last_pt __pyx_mstate_global->__pyx_kp_u_last_pt
-#define __pyx_n_s_level __pyx_mstate_global->__pyx_n_s_level
-#define __pyx_n_s_linewidth __pyx_mstate_global->__pyx_n_s_linewidth
-#define __pyx_n_s_logging __pyx_mstate_global->__pyx_n_s_logging
-#define __pyx_n_s_long __pyx_mstate_global->__pyx_n_s_long
-#define __pyx_n_u_long __pyx_mstate_global->__pyx_n_u_long
-#define __pyx_n_s_lower __pyx_mstate_global->__pyx_n_s_lower
-#define __pyx_n_s_m __pyx_mstate_global->__pyx_n_s_m
-#define __pyx_n_u_magenta __pyx_mstate_global->__pyx_n_u_magenta
-#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main
-#define __pyx_n_s_make_divisible __pyx_mstate_global->__pyx_n_s_make_divisible
-#define __pyx_n_s_manual_seed __pyx_mstate_global->__pyx_n_s_manual_seed
-#define __pyx_n_s_matches __pyx_mstate_global->__pyx_n_s_matches
-#define __pyx_n_s_math __pyx_mstate_global->__pyx_n_s_math
-#define __pyx_n_s_max __pyx_mstate_global->__pyx_n_s_max
-#define __pyx_n_s_max_columns __pyx_mstate_global->__pyx_n_s_max_columns
-#define __pyx_n_s_max_det __pyx_mstate_global->__pyx_n_s_max_det
-#define __pyx_n_s_max_nms __pyx_mstate_global->__pyx_n_s_max_nms
-#define __pyx_n_s_max_wh __pyx_mstate_global->__pyx_n_s_max_wh
-#define __pyx_n_s_merge __pyx_mstate_global->__pyx_n_s_merge
-#define __pyx_kp_u_message_s __pyx_mstate_global->__pyx_kp_u_message_s
-#define __pyx_n_s_methods __pyx_mstate_global->__pyx_n_s_methods
-#define __pyx_n_s_min_wh __pyx_mstate_global->__pyx_n_s_min_wh
-#define __pyx_n_s_minimum __pyx_mstate_global->__pyx_n_s_minimum
-#define __pyx_n_s_minlength __pyx_mstate_global->__pyx_n_s_minlength
-#define __pyx_n_s_mkdir __pyx_mstate_global->__pyx_n_s_mkdir
-#define __pyx_n_s_mm __pyx_mstate_global->__pyx_n_s_mm
-#define __pyx_n_s_multi_label __pyx_mstate_global->__pyx_n_s_multi_label
-#define __pyx_kp_u_must_be_multiple_of_max_stride __pyx_mstate_global->__pyx_kp_u_must_be_multiple_of_max_stride
-#define __pyx_n_s_n __pyx_mstate_global->__pyx_n_s_n
-#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name
-#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2
-#define __pyx_n_s_nc __pyx_mstate_global->__pyx_n_s_nc
-#define __pyx_n_s_new_size __pyx_mstate_global->__pyx_n_s_new_size
-#define __pyx_n_s_nms __pyx_mstate_global->__pyx_n_s_nms
-#define __pyx_n_s_non_max_suppression __pyx_mstate_global->__pyx_n_s_non_max_suppression
-#define __pyx_n_s_nonzero __pyx_mstate_global->__pyx_n_s_nonzero
-#define __pyx_n_s_np __pyx_mstate_global->__pyx_n_s_np
-#define __pyx_n_s_numpy __pyx_mstate_global->__pyx_n_s_numpy
-#define __pyx_n_s_one_cycle __pyx_mstate_global->__pyx_n_s_one_cycle
-#define __pyx_n_s_one_cycle_locals_lambda __pyx_mstate_global->__pyx_n_s_one_cycle_locals_lambda
-#define __pyx_n_s_ones __pyx_mstate_global->__pyx_n_s_ones
-#define __pyx_n_s_open __pyx_mstate_global->__pyx_n_s_open
-#define __pyx_n_s_ops __pyx_mstate_global->__pyx_n_s_ops
-#define __pyx_n_s_opt __pyx_mstate_global->__pyx_n_s_opt
-#define __pyx_n_s_options __pyx_mstate_global->__pyx_n_s_options
-#define __pyx_n_s_os __pyx_mstate_global->__pyx_n_s_os
-#define __pyx_n_s_output __pyx_mstate_global->__pyx_n_s_output
-#define __pyx_n_s_pad __pyx_mstate_global->__pyx_n_s_pad
-#define __pyx_n_s_padh __pyx_mstate_global->__pyx_n_s_padh
-#define __pyx_n_s_padw __pyx_mstate_global->__pyx_n_s_padw
-#define __pyx_n_s_pandas __pyx_mstate_global->__pyx_n_s_pandas
-#define __pyx_n_s_parents __pyx_mstate_global->__pyx_n_s_parents
-#define __pyx_n_s_parse __pyx_mstate_global->__pyx_n_s_parse
-#define __pyx_n_s_path __pyx_mstate_global->__pyx_n_s_path
-#define __pyx_n_s_pathlib __pyx_mstate_global->__pyx_n_s_pathlib
-#define __pyx_n_s_pattern __pyx_mstate_global->__pyx_n_s_pattern
-#define __pyx_n_s_pd __pyx_mstate_global->__pyx_n_s_pd
-#define __pyx_kp_s_pdf_toolbox_lib_dia_yolov5_utils __pyx_mstate_global->__pyx_kp_s_pdf_toolbox_lib_dia_yolov5_utils
-#define __pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_2 __pyx_mstate_global->__pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_2
-#define __pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_3 __pyx_mstate_global->__pyx_n_s_pdf_toolbox_lib_dia_yolov5_utils_3
-#define __pyx_n_s_pi __pyx_mstate_global->__pyx_n_s_pi
-#define __pyx_n_s_pinned __pyx_mstate_global->__pyx_n_s_pinned
-#define __pyx_n_s_platform __pyx_mstate_global->__pyx_n_s_platform
-#define __pyx_n_s_precision __pyx_mstate_global->__pyx_n_s_precision
-#define __pyx_n_s_prediction __pyx_mstate_global->__pyx_n_s_prediction
-#define __pyx_n_s_print __pyx_mstate_global->__pyx_n_s_print
-#define __pyx_n_s_print_args __pyx_mstate_global->__pyx_n_s_print_args
-#define __pyx_n_s_print_args_locals_genexpr __pyx_mstate_global->__pyx_n_s_print_args_locals_genexpr
-#define __pyx_n_s_profile __pyx_mstate_global->__pyx_n_s_profile
-#define __pyx_n_s_python_version __pyx_mstate_global->__pyx_n_s_python_version
-#define __pyx_n_s_random __pyx_mstate_global->__pyx_n_s_random
-#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range
-#define __pyx_n_s_rank __pyx_mstate_global->__pyx_n_s_rank
-#define __pyx_n_s_ratio_pad __pyx_mstate_global->__pyx_n_s_ratio_pad
-#define __pyx_n_s_re __pyx_mstate_global->__pyx_n_s_re
-#define __pyx_n_s_recursive __pyx_mstate_global->__pyx_n_s_recursive
-#define __pyx_n_u_red __pyx_mstate_global->__pyx_n_u_red
-#define __pyx_n_s_redundant __pyx_mstate_global->__pyx_n_s_redundant
-#define __pyx_n_s_repl __pyx_mstate_global->__pyx_n_s_repl
-#define __pyx_n_s_replace __pyx_mstate_global->__pyx_n_s_replace
-#define __pyx_n_s_reshape __pyx_mstate_global->__pyx_n_s_reshape
-#define __pyx_n_s_resolve __pyx_mstate_global->__pyx_n_s_resolve
-#define __pyx_n_s_s __pyx_mstate_global->__pyx_n_s_s
-#define __pyx_kp_u_s_2 __pyx_mstate_global->__pyx_kp_u_s_2
-#define __pyx_kp_u_s_exceeded __pyx_mstate_global->__pyx_kp_u_s_exceeded
-#define __pyx_n_s_scale_coords __pyx_mstate_global->__pyx_n_s_scale_coords
-#define __pyx_n_s_scores __pyx_mstate_global->__pyx_n_s_scores
-#define __pyx_n_s_search __pyx_mstate_global->__pyx_n_s_search
-#define __pyx_n_s_search_dir __pyx_mstate_global->__pyx_n_s_search_dir
-#define __pyx_n_s_seed __pyx_mstate_global->__pyx_n_s_seed
-#define __pyx_n_s_send __pyx_mstate_global->__pyx_n_s_send
-#define __pyx_n_s_sep __pyx_mstate_global->__pyx_n_s_sep
-#define __pyx_n_s_setNumThreads __pyx_mstate_global->__pyx_n_s_setNumThreads
-#define __pyx_n_s_set_logging __pyx_mstate_global->__pyx_n_s_set_logging
-#define __pyx_n_s_set_printoptions __pyx_mstate_global->__pyx_n_s_set_printoptions
-#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape
-#define __pyx_n_s_shutil __pyx_mstate_global->__pyx_n_s_shutil
-#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec
-#define __pyx_n_s_split __pyx_mstate_global->__pyx_n_s_split
-#define __pyx_n_s_st_size __pyx_mstate_global->__pyx_n_s_st_size
-#define __pyx_n_s_startswith __pyx_mstate_global->__pyx_n_s_startswith
-#define __pyx_n_s_stat __pyx_mstate_global->__pyx_n_s_stat
-#define __pyx_n_s_stem __pyx_mstate_global->__pyx_n_s_stem
-#define __pyx_n_s_steps __pyx_mstate_global->__pyx_n_s_steps
-#define __pyx_n_s_str __pyx_mstate_global->__pyx_n_s_str
-#define __pyx_n_s_string __pyx_mstate_global->__pyx_n_s_string
-#define __pyx_n_s_sub __pyx_mstate_global->__pyx_n_s_sub
-#define __pyx_n_s_suffix __pyx_mstate_global->__pyx_n_s_suffix
-#define __pyx_n_s_sum __pyx_mstate_global->__pyx_n_s_sum
-#define __pyx_n_s_system __pyx_mstate_global->__pyx_n_s_system
-#define __pyx_n_s_t __pyx_mstate_global->__pyx_n_s_t
-#define __pyx_n_s_tensor __pyx_mstate_global->__pyx_n_s_tensor
-#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test
-#define __pyx_n_s_test_2 __pyx_mstate_global->__pyx_n_s_test_2
-#define __pyx_n_s_throw __pyx_mstate_global->__pyx_n_s_throw
-#define __pyx_n_s_time __pyx_mstate_global->__pyx_n_s_time
-#define __pyx_n_s_time_limit __pyx_mstate_global->__pyx_n_s_time_limit
-#define __pyx_kp_u_tmp __pyx_mstate_global->__pyx_kp_u_tmp
-#define __pyx_kp_u_tmp_txt __pyx_mstate_global->__pyx_kp_u_tmp_txt
-#define __pyx_n_s_torch __pyx_mstate_global->__pyx_n_s_torch
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-static PyObject *__pyx_tp_new_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
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-static void __pyx_tp_dealloc_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr(PyObject *o) {
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- (*Py_TYPE(o)->tp_free)(o);
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-
-static int __pyx_tp_traverse_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr(PyObject *o, visitproc v, void *a) {
- int e;
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-static PyTypeObject __pyx_type_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_4_genexpr = {
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-static PyObject *__pyx_tp_new_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
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-static int __pyx_tp_traverse_11pdf_toolbox_3lib_10dia_yolov5_5utils_7general___pyx_scope_struct_5_file_size(PyObject *o, visitproc v, void *a) {
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- {0, __pyx_k_url, sizeof(__pyx_k_url), 0, 0, 1, 1},
- {0, __pyx_k_url2file, sizeof(__pyx_k_url2file), 0, 0, 1, 1},
- {0, __pyx_k_urllib, sizeof(__pyx_k_urllib), 0, 0, 1, 1},
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- {0, __pyx_k_valid_values_are_between_0_0_an, sizeof(__pyx_k_valid_values_are_between_0_0_an), 0, 1, 0, 0},
- {0, __pyx_k_vars, sizeof(__pyx_k_vars), 0, 0, 1, 1},
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- {0, __pyx_k_xywh2xyxy, sizeof(__pyx_k_xywh2xyxy), 0, 0, 1, 1},
- {0, __pyx_k_xywhn2xyxy, sizeof(__pyx_k_xywhn2xyxy), 0, 0, 1, 1},
- {0, __pyx_k_xyxy2xywh, sizeof(__pyx_k_xyxy2xywh), 0, 0, 1, 1},
- {0, __pyx_k_xyxy2xywhn, sizeof(__pyx_k_xyxy2xywhn), 0, 0, 1, 1},
- {0, __pyx_k_y, sizeof(__pyx_k_y), 0, 0, 1, 1},
- {0, __pyx_k_y1, sizeof(__pyx_k_y1), 0, 0, 1, 1},
- {0, __pyx_k_y2, sizeof(__pyx_k_y2), 0, 0, 1, 1},
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- {0, __pyx_k_yolov5, sizeof(__pyx_k_yolov5), 0, 1, 0, 1},
- {0, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1},
- #else
- {&__pyx_kp_u_0_0_0, __pyx_k_0_0_0, sizeof(__pyx_k_0_0_0), 0, 1, 0, 0},
- {&__pyx_kp_u_0m, __pyx_k_0m, sizeof(__pyx_k_0m), 0, 1, 0, 0},
- {&__pyx_kp_u_11_5g, __pyx_k_11_5g, sizeof(__pyx_k_11_5g), 0, 1, 0, 0},
- {&__pyx_kp_u_1m, __pyx_k_1m, sizeof(__pyx_k_1m), 0, 1, 0, 0},
- {&__pyx_kp_u_30m, __pyx_k_30m, sizeof(__pyx_k_30m), 0, 1, 0, 0},
- {&__pyx_kp_u_31m, __pyx_k_31m, sizeof(__pyx_k_31m), 0, 1, 0, 0},
- {&__pyx_kp_u_32m, __pyx_k_32m, sizeof(__pyx_k_32m), 0, 1, 0, 0},
- {&__pyx_kp_u_33m, __pyx_k_33m, sizeof(__pyx_k_33m), 0, 1, 0, 0},
- {&__pyx_kp_u_34m, __pyx_k_34m, sizeof(__pyx_k_34m), 0, 1, 0, 0},
- {&__pyx_kp_u_35m, __pyx_k_35m, sizeof(__pyx_k_35m), 0, 1, 0, 0},
- {&__pyx_kp_u_36m, __pyx_k_36m, sizeof(__pyx_k_36m), 0, 1, 0, 0},
- {&__pyx_kp_u_37m, __pyx_k_37m, sizeof(__pyx_k_37m), 0, 1, 0, 0},
- {&__pyx_kp_u_3_6_2, __pyx_k_3_6_2, sizeof(__pyx_k_3_6_2), 0, 1, 0, 0},
- {&__pyx_kp_u_4m, __pyx_k_4m, sizeof(__pyx_k_4m), 0, 1, 0, 0},
- {&__pyx_kp_u_90m, __pyx_k_90m, sizeof(__pyx_k_90m), 0, 1, 0, 0},
- {&__pyx_kp_u_91m, __pyx_k_91m, sizeof(__pyx_k_91m), 0, 1, 0, 0},
- {&__pyx_kp_u_92m, __pyx_k_92m, sizeof(__pyx_k_92m), 0, 1, 0, 0},
- {&__pyx_kp_u_93m, __pyx_k_93m, sizeof(__pyx_k_93m), 0, 1, 0, 0},
- {&__pyx_kp_u_94m, __pyx_k_94m, sizeof(__pyx_k_94m), 0, 1, 0, 0},
- {&__pyx_kp_u_95m, __pyx_k_95m, sizeof(__pyx_k_95m), 0, 1, 0, 0},
- {&__pyx_kp_u_96m, __pyx_k_96m, sizeof(__pyx_k_96m), 0, 1, 0, 0},
- {&__pyx_kp_u_97m, __pyx_k_97m, sizeof(__pyx_k_97m), 0, 1, 0, 0},
- {&__pyx_kp_u_AppData_Roaming, __pyx_k_AppData_Roaming, sizeof(__pyx_k_AppData_Roaming), 0, 1, 0, 0},
- {&__pyx_n_s_AssertionError, __pyx_k_AssertionError, sizeof(__pyx_k_AssertionError), 0, 0, 1, 1},
- {&__pyx_n_u_Darwin, __pyx_k_Darwin, sizeof(__pyx_k_Darwin), 0, 1, 0, 1},
- {&__pyx_n_s_FILE, __pyx_k_FILE, sizeof(__pyx_k_FILE), 0, 0, 1, 1},
- {&__pyx_n_s_INFO, __pyx_k_INFO, sizeof(__pyx_k_INFO), 0, 0, 1, 1},
- {&__pyx_kp_u_Invalid_Confidence_threshold, __pyx_k_Invalid_Confidence_threshold, sizeof(__pyx_k_Invalid_Confidence_threshold), 0, 1, 0, 0},
- {&__pyx_kp_u_Invalid_IoU, __pyx_k_Invalid_IoU, sizeof(__pyx_k_Invalid_IoU), 0, 1, 0, 0},
- {&__pyx_n_s_LOGGER, __pyx_k_LOGGER, sizeof(__pyx_k_LOGGER), 0, 0, 1, 1},
- {&__pyx_kp_u_Library_Application_Support, __pyx_k_Library_Application_Support, sizeof(__pyx_k_Library_Application_Support), 0, 1, 0, 0},
- {&__pyx_n_u_Linux, __pyx_k_Linux, sizeof(__pyx_k_Linux), 0, 1, 0, 1},
- {&__pyx_n_s_NCOLS, __pyx_k_NCOLS, sizeof(__pyx_k_NCOLS), 0, 0, 1, 1},
- {&__pyx_n_u_NUMEXPR_MAX_THREADS, __pyx_k_NUMEXPR_MAX_THREADS, sizeof(__pyx_k_NUMEXPR_MAX_THREADS), 0, 1, 0, 1},
- {&__pyx_n_s_NUM_THREADS, __pyx_k_NUM_THREADS, sizeof(__pyx_k_NUM_THREADS), 0, 0, 1, 1},
- {&__pyx_n_s_OSError, __pyx_k_OSError, sizeof(__pyx_k_OSError), 0, 0, 1, 1},
- {&__pyx_n_s_Path, __pyx_k_Path, sizeof(__pyx_k_Path), 0, 0, 1, 1},
- {&__pyx_kp_u_Python, __pyx_k_Python, sizeof(__pyx_k_Python), 0, 1, 0, 0},
- {&__pyx_n_u_RANK, __pyx_k_RANK, sizeof(__pyx_k_RANK), 0, 1, 0, 1},
- {&__pyx_n_s_ROOT, __pyx_k_ROOT, sizeof(__pyx_k_ROOT), 0, 0, 1, 1},
- {&__pyx_n_s_R_OK, __pyx_k_R_OK, sizeof(__pyx_k_R_OK), 0, 0, 1, 1},
- {&__pyx_n_s_T, __pyx_k_T, sizeof(__pyx_k_T), 0, 0, 1, 1},
- {&__pyx_n_s_Tensor, __pyx_k_Tensor, sizeof(__pyx_k_Tensor), 0, 0, 1, 1},
- {&__pyx_n_u_Ultralytics, __pyx_k_Ultralytics, sizeof(__pyx_k_Ultralytics), 0, 1, 0, 1},
- {&__pyx_n_s_VERBOSE, __pyx_k_VERBOSE, sizeof(__pyx_k_VERBOSE), 0, 0, 1, 1},
- {&__pyx_n_s_WARNING, __pyx_k_WARNING, sizeof(__pyx_k_WARNING), 0, 0, 1, 1},
- {&__pyx_kp_u_WARNING_NMS_time_limit, __pyx_k_WARNING_NMS_time_limit, sizeof(__pyx_k_WARNING_NMS_time_limit), 0, 1, 0, 0},
- {&__pyx_kp_u_WARNING_img_size, __pyx_k_WARNING_img_size, sizeof(__pyx_k_WARNING_img_size), 0, 1, 0, 0},
- {&__pyx_n_u_Windows, __pyx_k_Windows, sizeof(__pyx_k_Windows), 0, 1, 0, 1},
- {&__pyx_n_u_YOLOV5_CONFIG_DIR, __pyx_k_YOLOV5_CONFIG_DIR, sizeof(__pyx_k_YOLOV5_CONFIG_DIR), 0, 1, 0, 1},
- {&__pyx_n_u_YOLOv5_VERBOSE, __pyx_k_YOLOv5_VERBOSE, sizeof(__pyx_k_YOLOv5_VERBOSE), 0, 1, 0, 1},
- {&__pyx_kp_u__10, __pyx_k__10, sizeof(__pyx_k__10), 0, 1, 0, 0},
- {&__pyx_kp_u__11, __pyx_k__11, sizeof(__pyx_k__11), 0, 1, 0, 0},
- {&__pyx_n_u__14, __pyx_k__14, sizeof(__pyx_k__14), 0, 1, 0, 1},
- {&__pyx_kp_u__15, __pyx_k__15, sizeof(__pyx_k__15), 0, 1, 0, 0},
- {&__pyx_kp_u__16, __pyx_k__16, sizeof(__pyx_k__16), 0, 1, 0, 0},
- {&__pyx_kp_u__17, __pyx_k__17, sizeof(__pyx_k__17), 0, 1, 0, 0},
- {&__pyx_kp_u__18, __pyx_k__18, sizeof(__pyx_k__18), 0, 1, 0, 0},
- {&__pyx_n_s__20, __pyx_k__20, sizeof(__pyx_k__20), 0, 0, 1, 1},
- {&__pyx_kp_u__20, __pyx_k__20, sizeof(__pyx_k__20), 0, 1, 0, 0},
- {&__pyx_kp_u__21, __pyx_k__21, sizeof(__pyx_k__21), 0, 1, 0, 0},
- {&__pyx_n_u__22, __pyx_k__22, sizeof(__pyx_k__22), 0, 1, 0, 1},
- {&__pyx_n_u__4, __pyx_k__4, sizeof(__pyx_k__4), 0, 1, 0, 1},
- {&__pyx_kp_u__5, __pyx_k__5, sizeof(__pyx_k__5), 0, 1, 0, 0},
- {&__pyx_kp_u__6, __pyx_k__6, sizeof(__pyx_k__6), 0, 1, 0, 0},
- {&__pyx_kp_u__7, __pyx_k__7, sizeof(__pyx_k__7), 0, 1, 0, 0},
- {&__pyx_n_s__9, __pyx_k__9, sizeof(__pyx_k__9), 0, 0, 1, 1},
- {&__pyx_kp_u__9, __pyx_k__9, sizeof(__pyx_k__9), 0, 1, 0, 0},
- {&__pyx_n_s_access, __pyx_k_access, sizeof(__pyx_k_access), 0, 0, 1, 1},
- {&__pyx_n_s_agnostic, __pyx_k_agnostic, sizeof(__pyx_k_agnostic), 0, 0, 1, 1},
- {&__pyx_n_s_any, __pyx_k_any, sizeof(__pyx_k_any), 0, 0, 1, 1},
- {&__pyx_n_s_args, __pyx_k_args, sizeof(__pyx_k_args), 0, 0, 1, 1},
- {&__pyx_n_s_argsort, __pyx_k_argsort, sizeof(__pyx_k_argsort), 0, 0, 1, 1},
- {&__pyx_n_s_array, __pyx_k_array, sizeof(__pyx_k_array), 0, 0, 1, 1},
- {&__pyx_n_s_as_tuple, __pyx_k_as_tuple, sizeof(__pyx_k_as_tuple), 0, 0, 1, 1},
- {&__pyx_n_u_ascii, __pyx_k_ascii, sizeof(__pyx_k_ascii), 0, 1, 0, 1},
- {&__pyx_n_s_astype, __pyx_k_astype, sizeof(__pyx_k_astype), 0, 0, 1, 1},
- {&__pyx_n_s_asyncio_coroutines, __pyx_k_asyncio_coroutines, sizeof(__pyx_k_asyncio_coroutines), 0, 0, 1, 1},
- {&__pyx_n_s_backends, __pyx_k_backends, sizeof(__pyx_k_backends), 0, 0, 1, 1},
- {&__pyx_n_s_basicConfig, __pyx_k_basicConfig, sizeof(__pyx_k_basicConfig), 0, 0, 1, 1},
- {&__pyx_n_s_benchmark, __pyx_k_benchmark, sizeof(__pyx_k_benchmark), 0, 0, 1, 1},
- {&__pyx_n_s_bincount, __pyx_k_bincount, sizeof(__pyx_k_bincount), 0, 0, 1, 1},
- {&__pyx_n_u_black, __pyx_k_black, sizeof(__pyx_k_black), 0, 1, 0, 1},
- {&__pyx_n_u_blue, __pyx_k_blue, sizeof(__pyx_k_blue), 0, 1, 0, 1},
- {&__pyx_n_u_bold, __pyx_k_bold, sizeof(__pyx_k_bold), 0, 1, 0, 1},
- {&__pyx_n_s_box, __pyx_k_box, sizeof(__pyx_k_box), 0, 0, 1, 1},
- {&__pyx_n_s_box_iou, __pyx_k_box_iou, sizeof(__pyx_k_box_iou), 0, 0, 1, 1},
- {&__pyx_n_s_boxes, __pyx_k_boxes, sizeof(__pyx_k_boxes), 0, 0, 1, 1},
- {&__pyx_n_u_bright_black, __pyx_k_bright_black, sizeof(__pyx_k_bright_black), 0, 1, 0, 1},
- {&__pyx_n_u_bright_blue, __pyx_k_bright_blue, sizeof(__pyx_k_bright_blue), 0, 1, 0, 1},
- {&__pyx_n_u_bright_cyan, __pyx_k_bright_cyan, sizeof(__pyx_k_bright_cyan), 0, 1, 0, 1},
- {&__pyx_n_u_bright_green, __pyx_k_bright_green, sizeof(__pyx_k_bright_green), 0, 1, 0, 1},
- {&__pyx_n_u_bright_magenta, __pyx_k_bright_magenta, sizeof(__pyx_k_bright_magenta), 0, 1, 0, 1},
- {&__pyx_n_u_bright_red, __pyx_k_bright_red, sizeof(__pyx_k_bright_red), 0, 1, 0, 1},
- {&__pyx_n_u_bright_white, __pyx_k_bright_white, sizeof(__pyx_k_bright_white), 0, 1, 0, 1},
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- } else {
- num_expected = num_max;
- more_or_less = "at most";
- }
- if (exact) {
- more_or_less = "exactly";
- }
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
- func_name, more_or_less, num_expected,
- (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* 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;
- }
-#elif CYTHON_COMPILING_IN_LIMITED_API
- if (unlikely(!__pyx_m)) {
- return NULL;
- }
- result = PyObject_GetAttr(__pyx_m, name);
- if (likely(result)) {
- return result;
- }
-#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);
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
- PyObject *result;
- ternaryfunc call = Py_TYPE(func)->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
-
-/* PyIntCompare */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, long inplace) {
- CYTHON_MAYBE_UNUSED_VAR(intval);
- CYTHON_UNUSED_VAR(inplace);
- if (op1 == op2) {
- Py_RETURN_TRUE;
- }
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op1))) {
- const long b = intval;
- long a = PyInt_AS_LONG(op1);
- if (a == b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
- }
- #endif
- #if CYTHON_USE_PYLONG_INTERNALS
- if (likely(PyLong_CheckExact(op1))) {
- int unequal;
- unsigned long uintval;
- Py_ssize_t size = Py_SIZE(op1);
- const digit* digits = ((PyLongObject*)op1)->ob_digit;
- if (intval == 0) {
- if (size == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
- } else if (intval < 0) {
- if (size >= 0)
- Py_RETURN_FALSE;
- intval = -intval;
- size = -size;
- } else {
- if (size <= 0)
- Py_RETURN_FALSE;
- }
- uintval = (unsigned long) intval;
-#if PyLong_SHIFT * 4 < SIZEOF_LONG*8
- if (uintval >> (PyLong_SHIFT * 4)) {
- unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
- | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
- } else
-#endif
-#if PyLong_SHIFT * 3 < SIZEOF_LONG*8
- if (uintval >> (PyLong_SHIFT * 3)) {
- unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
- | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
- } else
-#endif
-#if PyLong_SHIFT * 2 < SIZEOF_LONG*8
- if (uintval >> (PyLong_SHIFT * 2)) {
- unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
- | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
- } else
-#endif
-#if PyLong_SHIFT * 1 < SIZEOF_LONG*8
- if (uintval >> (PyLong_SHIFT * 1)) {
- unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
- | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK));
- } else
-#endif
- unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
- if (unequal == 0) Py_RETURN_TRUE; else Py_RETURN_FALSE;
- }
- #endif
- if (PyFloat_CheckExact(op1)) {
- const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
- double a = __pyx_PyFloat_AsDouble(op1);
-#else
- double a = PyFloat_AS_DOUBLE(op1);
-#endif
- if ((double)a == (double)b) Py_RETURN_TRUE; else Py_RETURN_FALSE;
- }
- return (
- PyObject_RichCompare(op1, op2, Py_EQ));
-}
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-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;
-}
-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 (unlikely(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
-
-/* 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
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
- PyObject *argstuple;
- PyObject *result;
- size_t i;
- argstuple = PyTuple_New((Py_ssize_t)nargs);
- if (unlikely(!argstuple)) return NULL;
- for (i = 0; i < nargs; i++) {
- Py_INCREF(args[i]);
- PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
- }
- result = __Pyx_PyObject_Call(func, argstuple, kwargs);
- Py_DECREF(argstuple);
- return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
- Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
- if (nargs == 0 && kwargs == NULL) {
-#ifdef __Pyx_CyFunction_USED
- if (__Pyx_IsCyOrPyCFunction(func))
-#else
- if (PyCFunction_Check(func))
-#endif
- {
- if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
- return __Pyx_PyObject_CallMethO(func, NULL);
- }
- }
- }
- else if (nargs == 1 && kwargs == NULL) {
- if (PyCFunction_Check(func))
- {
- if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
- return __Pyx_PyObject_CallMethO(func, args[0]);
- }
- }
- }
-#endif
- #if PY_VERSION_HEX < 0x030800B1
- #if CYTHON_FAST_PYCCALL
- if (PyCFunction_Check(func)) {
- if (kwargs) {
- return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
- } else {
- return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
- }
- }
- #if PY_VERSION_HEX >= 0x030700A1
- if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
- return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
- }
- #endif
- #endif
- #if CYTHON_FAST_PYCALL
- if (PyFunction_Check(func)) {
- return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
- }
- #endif
- #endif
- #if CYTHON_VECTORCALL
- vectorcallfunc f = _PyVectorcall_Function(func);
- if (f) {
- return f(func, args, (size_t)nargs, kwargs);
- }
- #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
- if (__Pyx_CyFunction_CheckExact(func)) {
- __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
- if (f) return f(func, args, (size_t)nargs, kwargs);
- }
- #endif
- if (nargs == 0) {
- return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
- }
- return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* KeywordStringCheck */
-static int __Pyx_CheckKeywordStrings(
- PyObject *kw,
- const char* function_name,
- int kw_allowed)
-{
- PyObject* key = 0;
- Py_ssize_t pos = 0;
-#if CYTHON_COMPILING_IN_PYPY
- if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0))
- goto invalid_keyword;
- return 1;
-#else
- if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) {
- if (unlikely(PyTuple_GET_SIZE(kw) == 0))
- return 1;
- if (!kw_allowed) {
- key = PyTuple_GET_ITEM(kw, 0);
- goto invalid_keyword;
- }
-#if PY_VERSION_HEX < 0x03090000
- for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) {
- key = PyTuple_GET_ITEM(kw, pos);
- if (unlikely(!PyUnicode_Check(key)))
- goto invalid_keyword_type;
- }
-#endif
- return 1;
- }
- while (PyDict_Next(kw, &pos, &key, 0)) {
- #if PY_MAJOR_VERSION < 3
- if (unlikely(!PyString_Check(key)))
- #endif
- if (unlikely(!PyUnicode_Check(key)))
- goto invalid_keyword_type;
- }
- if (!kw_allowed && unlikely(key))
- goto invalid_keyword;
- return 1;
-invalid_keyword_type:
- PyErr_Format(PyExc_TypeError,
- "%.200s() keywords must be strings", function_name);
- return 0;
-#endif
-invalid_keyword:
- #if PY_MAJOR_VERSION < 3
- PyErr_Format(PyExc_TypeError,
- "%.200s() got an unexpected keyword argument '%.200s'",
- function_name, PyString_AsString(key));
- #else
- PyErr_Format(PyExc_TypeError,
- "%s() got an unexpected keyword argument '%U'",
- function_name, key);
- #endif
- return 0;
-}
-
-/* RaiseClosureNameError */
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) {
- PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname);
-}
-
-/* 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;
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
- PyObject *args[2] = {NULL, arg};
- return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* 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
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
- (void) spec;
- (void) type;
-#else
- const PyType_Slot *slot = spec->slots;
- while (slot && slot->slot && slot->slot != Py_tp_members)
- slot++;
- if (slot && slot->slot == Py_tp_members) {
- int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
- const
-#endif
- PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
- while (memb && memb->name) {
- if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
- if (strcmp(memb->name, "__weaklistoffset__") == 0) {
- assert(memb->type == T_PYSSIZET);
- assert(memb->flags == READONLY);
- type->tp_weaklistoffset = memb->offset;
- changed = 1;
- }
- else if (strcmp(memb->name, "__dictoffset__") == 0) {
- assert(memb->type == T_PYSSIZET);
- assert(memb->flags == READONLY);
- type->tp_dictoffset = memb->offset;
- changed = 1;
- }
-#if CYTHON_METH_FASTCALL
- else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
- assert(memb->type == T_PYSSIZET);
- assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
- type->tp_vectorcall_offset = memb->offset;
-#else
- type->tp_print = (printfunc) memb->offset;
-#endif
- changed = 1;
- }
-#endif
-#else
- if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
- else if (strcmp(memb->name, "__module__") == 0) {
- PyObject *descr;
- assert(memb->type == T_OBJECT);
- assert(memb->flags == 0 || memb->flags == READONLY);
- descr = PyDescr_NewMember(type, memb);
- if (unlikely(!descr))
- return -1;
- if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
- Py_DECREF(descr);
- return -1;
- }
- Py_DECREF(descr);
- changed = 1;
- }
-#endif
- }
- memb++;
- }
- if (changed)
- PyType_Modified(type);
- }
-#endif
- return 0;
-}
-#endif
-
-/* FetchCommonType */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
- PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
- if (!abi_module) return NULL;
- Py_INCREF(abi_module);
- return abi_module;
-}
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
- const char *name,
- Py_ssize_t basicsize,
- Py_ssize_t expected_basicsize) {
- if (!PyType_Check(cached_type)) {
- PyErr_Format(PyExc_TypeError,
- "Shared Cython type %.200s is not a type object", name);
- return -1;
- }
- if (basicsize != expected_basicsize) {
- PyErr_Format(PyExc_TypeError,
- "Shared Cython type %.200s has the wrong size, try recompiling",
- name);
- return -1;
- }
- return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
- PyObject* abi_module;
- const char* object_name;
- PyTypeObject *cached_type = NULL;
- abi_module = __Pyx_FetchSharedCythonABIModule();
- if (!abi_module) return NULL;
- object_name = strrchr(type->tp_name, '.');
- object_name = object_name ? object_name+1 : type->tp_name;
- cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
- if (cached_type) {
- if (__Pyx_VerifyCachedType(
- (PyObject *)cached_type,
- object_name,
- cached_type->tp_basicsize,
- type->tp_basicsize) < 0) {
- goto bad;
- }
- goto done;
- }
- if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
- PyErr_Clear();
- if (PyType_Ready(type) < 0) goto bad;
- if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
- goto bad;
- Py_INCREF(type);
- cached_type = type;
-done:
- Py_DECREF(abi_module);
- return cached_type;
-bad:
- Py_XDECREF(cached_type);
- cached_type = NULL;
- goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
- PyObject *abi_module, *cached_type = NULL;
- const char* object_name = strrchr(spec->name, '.');
- object_name = object_name ? object_name+1 : spec->name;
- abi_module = __Pyx_FetchSharedCythonABIModule();
- if (!abi_module) return NULL;
- cached_type = PyObject_GetAttrString(abi_module, object_name);
- if (cached_type) {
- Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
- PyObject *py_basicsize;
- py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
- if (unlikely(!py_basicsize)) goto bad;
- basicsize = PyLong_AsSsize_t(py_basicsize);
- Py_DECREF(py_basicsize);
- py_basicsize = 0;
- if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
- basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
- if (__Pyx_VerifyCachedType(
- cached_type,
- object_name,
- basicsize,
- spec->basicsize) < 0) {
- goto bad;
- }
- goto done;
- }
- if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
- PyErr_Clear();
- (void) module;
- cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
- if (unlikely(!cached_type)) goto bad;
- if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
- if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
- Py_DECREF(abi_module);
- assert(cached_type == NULL || PyType_Check(cached_type));
- return (PyTypeObject *) cached_type;
-bad:
- Py_XDECREF(cached_type);
- cached_type = NULL;
- goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
- PyObject *res = NULL;
- PyObject *kwnames;
- PyObject **newargs;
- PyObject **kwvalues;
- Py_ssize_t i, pos;
- size_t j;
- PyObject *key, *value;
- unsigned long keys_are_strings;
- Py_ssize_t nkw = PyDict_GET_SIZE(kw);
- newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
- if (unlikely(newargs == NULL)) {
- PyErr_NoMemory();
- return NULL;
- }
- for (j = 0; j < nargs; j++) newargs[j] = args[j];
- kwnames = PyTuple_New(nkw);
- if (unlikely(kwnames == NULL)) {
- PyMem_Free(newargs);
- return NULL;
- }
- kwvalues = newargs + nargs;
- pos = i = 0;
- keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
- while (PyDict_Next(kw, &pos, &key, &value)) {
- keys_are_strings &= Py_TYPE(key)->tp_flags;
- Py_INCREF(key);
- Py_INCREF(value);
- PyTuple_SET_ITEM(kwnames, i, key);
- kwvalues[i] = value;
- i++;
- }
- if (unlikely(!keys_are_strings)) {
- PyErr_SetString(PyExc_TypeError, "keywords must be strings");
- goto cleanup;
- }
- res = vc(func, newargs, nargs, kwnames);
-cleanup:
- Py_DECREF(kwnames);
- for (i = 0; i < nkw; i++)
- Py_DECREF(kwvalues[i]);
- PyMem_Free(newargs);
- return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
- if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
- return vc(func, args, nargs, NULL);
- }
- return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
- __Pyx_Py_XDECREF_SET(
- __Pyx_CyFunction_GetClassObj(f),
- ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
- __Pyx_Py_XDECREF_SET(
- ((PyCMethodObject *) (f))->mm_class,
- (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
- CYTHON_UNUSED_VAR(closure);
- if (unlikely(op->func_doc == NULL)) {
- if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
- op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
- op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
- if (unlikely(op->func_doc == NULL))
- return NULL;
- } else {
- Py_INCREF(Py_None);
- return Py_None;
- }
- }
- Py_INCREF(op->func_doc);
- return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- if (value == NULL) {
- value = Py_None;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->func_doc, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
- op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
- op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
- if (unlikely(op->func_name == NULL))
- return NULL;
- }
- Py_INCREF(op->func_name);
- return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
- if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
- if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
- {
- PyErr_SetString(PyExc_TypeError,
- "__name__ must be set to a string object");
- return -1;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->func_name, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- Py_INCREF(op->func_qualname);
- return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
- if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
- if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
- {
- PyErr_SetString(PyExc_TypeError,
- "__qualname__ must be set to a string object");
- return -1;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->func_qualname, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- if (unlikely(op->func_dict == NULL)) {
- op->func_dict = PyDict_New();
- if (unlikely(op->func_dict == NULL))
- return NULL;
- }
- Py_INCREF(op->func_dict);
- return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- if (unlikely(value == NULL)) {
- PyErr_SetString(PyExc_TypeError,
- "function's dictionary may not be deleted");
- return -1;
- }
- if (unlikely(!PyDict_Check(value))) {
- PyErr_SetString(PyExc_TypeError,
- "setting function's dictionary to a non-dict");
- return -1;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->func_dict, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
- CYTHON_UNUSED_VAR(context);
- Py_INCREF(op->func_globals);
- return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
- CYTHON_UNUSED_VAR(op);
- CYTHON_UNUSED_VAR(context);
- Py_INCREF(Py_None);
- return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
- PyObject* result = (op->func_code) ? op->func_code : Py_None;
- CYTHON_UNUSED_VAR(context);
- Py_INCREF(result);
- return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
- int result = 0;
- PyObject *res = op->defaults_getter((PyObject *) op);
- if (unlikely(!res))
- return -1;
- #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
- Py_INCREF(op->defaults_tuple);
- op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
- Py_INCREF(op->defaults_kwdict);
- #else
- op->defaults_tuple = PySequence_ITEM(res, 0);
- if (unlikely(!op->defaults_tuple)) result = -1;
- else {
- op->defaults_kwdict = PySequence_ITEM(res, 1);
- if (unlikely(!op->defaults_kwdict)) result = -1;
- }
- #endif
- Py_DECREF(res);
- return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
- CYTHON_UNUSED_VAR(context);
- if (!value) {
- value = Py_None;
- } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
- PyErr_SetString(PyExc_TypeError,
- "__defaults__ must be set to a tuple object");
- return -1;
- }
- PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
- "currently affect the values used in function calls", 1);
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
- PyObject* result = op->defaults_tuple;
- CYTHON_UNUSED_VAR(context);
- if (unlikely(!result)) {
- if (op->defaults_getter) {
- if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
- result = op->defaults_tuple;
- } else {
- result = Py_None;
- }
- }
- Py_INCREF(result);
- return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
- CYTHON_UNUSED_VAR(context);
- if (!value) {
- value = Py_None;
- } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
- PyErr_SetString(PyExc_TypeError,
- "__kwdefaults__ must be set to a dict object");
- return -1;
- }
- PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
- "currently affect the values used in function calls", 1);
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
- PyObject* result = op->defaults_kwdict;
- CYTHON_UNUSED_VAR(context);
- if (unlikely(!result)) {
- if (op->defaults_getter) {
- if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
- result = op->defaults_kwdict;
- } else {
- result = Py_None;
- }
- }
- Py_INCREF(result);
- return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
- CYTHON_UNUSED_VAR(context);
- if (!value || value == Py_None) {
- value = NULL;
- } else if (unlikely(!PyDict_Check(value))) {
- PyErr_SetString(PyExc_TypeError,
- "__annotations__ must be set to a dict object");
- return -1;
- }
- Py_XINCREF(value);
- __Pyx_Py_XDECREF_SET(op->func_annotations, value);
- return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
- PyObject* result = op->func_annotations;
- CYTHON_UNUSED_VAR(context);
- if (unlikely(!result)) {
- result = PyDict_New();
- if (unlikely(!result)) return NULL;
- op->func_annotations = result;
- }
- Py_INCREF(result);
- return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
- int is_coroutine;
- CYTHON_UNUSED_VAR(context);
- if (op->func_is_coroutine) {
- return __Pyx_NewRef(op->func_is_coroutine);
- }
- is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
- if (is_coroutine) {
- PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
- fromlist = PyList_New(1);
- if (unlikely(!fromlist)) return NULL;
- Py_INCREF(marker);
- PyList_SET_ITEM(fromlist, 0, marker);
- module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
- Py_DECREF(fromlist);
- if (unlikely(!module)) goto ignore;
- op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
- Py_DECREF(module);
- if (likely(op->func_is_coroutine)) {
- return __Pyx_NewRef(op->func_is_coroutine);
- }
-ignore:
- PyErr_Clear();
- }
-#endif
- op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
- return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
- {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
- {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
- {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
- {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
- {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
- {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
- {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
- {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
- {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
- {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
- {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
- {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
- {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
- {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
- {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
- {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
- {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
- {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
- {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
- {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
- {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
- {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
- {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
- {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
- {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
- {0, 0, 0, 0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
- CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
- Py_INCREF(m->func_qualname);
- return m->func_qualname;
-#else
- return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
- {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
- {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
- PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
- PyCFunctionObject *cf = (PyCFunctionObject*) op;
- if (unlikely(op == NULL))
- return NULL;
- op->flags = flags;
- __Pyx_CyFunction_weakreflist(op) = NULL;
- cf->m_ml = ml;
- cf->m_self = (PyObject *) op;
- Py_XINCREF(closure);
- op->func_closure = closure;
- Py_XINCREF(module);
- cf->m_module = module;
- op->func_dict = NULL;
- op->func_name = NULL;
- Py_INCREF(qualname);
- op->func_qualname = qualname;
- op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
- op->func_classobj = NULL;
-#else
- ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
- op->func_globals = globals;
- Py_INCREF(op->func_globals);
- Py_XINCREF(code);
- op->func_code = code;
- op->defaults_pyobjects = 0;
- op->defaults_size = 0;
- op->defaults = NULL;
- op->defaults_tuple = NULL;
- op->defaults_kwdict = NULL;
- op->defaults_getter = NULL;
- op->func_annotations = NULL;
- op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
- switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
- case METH_NOARGS:
- __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
- break;
- case METH_O:
- __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
- break;
- case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
- __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
- break;
- case METH_FASTCALL | METH_KEYWORDS:
- __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
- break;
- case METH_VARARGS | METH_KEYWORDS:
- __Pyx_CyFunction_func_vectorcall(op) = NULL;
- break;
- default:
- PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
- Py_DECREF(op);
- return NULL;
- }
-#endif
- return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
- Py_CLEAR(m->func_closure);
- Py_CLEAR(((PyCFunctionObject*)m)->m_module);
- Py_CLEAR(m->func_dict);
- Py_CLEAR(m->func_name);
- Py_CLEAR(m->func_qualname);
- Py_CLEAR(m->func_doc);
- Py_CLEAR(m->func_globals);
- Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
- Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
- {
- PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
- ((PyCMethodObject *) (m))->mm_class = NULL;
- Py_XDECREF(cls);
- }
-#endif
- Py_CLEAR(m->defaults_tuple);
- Py_CLEAR(m->defaults_kwdict);
- Py_CLEAR(m->func_annotations);
- Py_CLEAR(m->func_is_coroutine);
- if (m->defaults) {
- PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
- int i;
- for (i = 0; i < m->defaults_pyobjects; i++)
- Py_XDECREF(pydefaults[i]);
- PyObject_Free(m->defaults);
- m->defaults = NULL;
- }
- return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
- if (__Pyx_CyFunction_weakreflist(m) != NULL)
- PyObject_ClearWeakRefs((PyObject *) m);
- __Pyx_CyFunction_clear(m);
- __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
- PyObject_GC_UnTrack(m);
- __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
- Py_VISIT(m->func_closure);
- Py_VISIT(((PyCFunctionObject*)m)->m_module);
- Py_VISIT(m->func_dict);
- Py_VISIT(m->func_name);
- Py_VISIT(m->func_qualname);
- Py_VISIT(m->func_doc);
- Py_VISIT(m->func_globals);
- Py_VISIT(m->func_code);
- Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
- Py_VISIT(m->defaults_tuple);
- Py_VISIT(m->defaults_kwdict);
- Py_VISIT(m->func_is_coroutine);
- if (m->defaults) {
- PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
- int i;
- for (i = 0; i < m->defaults_pyobjects; i++)
- Py_VISIT(pydefaults[i]);
- }
- return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
- return PyUnicode_FromFormat("",
- op->func_qualname, (void *)op);
-#else
- return PyString_FromFormat("",
- PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
- PyCFunctionObject* f = (PyCFunctionObject*)func;
- PyCFunction meth = f->m_ml->ml_meth;
- Py_ssize_t size;
- switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
- case METH_VARARGS:
- if (likely(kw == NULL || PyDict_Size(kw) == 0))
- return (*meth)(self, arg);
- break;
- case METH_VARARGS | METH_KEYWORDS:
- return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
- case METH_NOARGS:
- if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
- size = PyTuple_GET_SIZE(arg);
- if (likely(size == 0))
- return (*meth)(self, NULL);
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
- f->m_ml->ml_name, size);
- return NULL;
- }
- break;
- case METH_O:
- if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
- size = PyTuple_GET_SIZE(arg);
- if (likely(size == 1)) {
- PyObject *result, *arg0;
- #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- arg0 = PyTuple_GET_ITEM(arg, 0);
- #else
- arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
- #endif
- result = (*meth)(self, arg0);
- #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
- Py_DECREF(arg0);
- #endif
- return result;
- }
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
- f->m_ml->ml_name, size);
- return NULL;
- }
- break;
- default:
- PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
- return NULL;
- }
- PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
- f->m_ml->ml_name);
- return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
- return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
- PyObject *result;
- __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
- __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
- if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
- return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
- (void) &__Pyx_PyVectorcall_FastCallDict;
- return PyVectorcall_Call(func, args, kw);
-#endif
- }
-#endif
- if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
- Py_ssize_t argc;
- PyObject *new_args;
- PyObject *self;
- argc = PyTuple_GET_SIZE(args);
- new_args = PyTuple_GetSlice(args, 1, argc);
- if (unlikely(!new_args))
- return NULL;
- self = PyTuple_GetItem(args, 0);
- if (unlikely(!self)) {
- Py_DECREF(new_args);
- return NULL;
- }
- result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
- Py_DECREF(new_args);
- } else {
- result = __Pyx_CyFunction_Call(func, args, kw);
- }
- return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
- int ret = 0;
- if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
- if (unlikely(nargs < 1)) {
- PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
- ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
- return -1;
- }
- ret = 1;
- }
- if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
- return -1;
- }
- return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
- __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
- PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
- Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
- Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
- PyObject *self;
- switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
- case 1:
- self = args[0];
- args += 1;
- nargs -= 1;
- break;
- case 0:
- self = ((PyCFunctionObject*)cyfunc)->m_self;
- break;
- default:
- return NULL;
- }
- if (unlikely(nargs != 0)) {
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
- def->ml_name, nargs);
- return NULL;
- }
- return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
- __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
- PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
- Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
- Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
- PyObject *self;
- switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
- case 1:
- self = args[0];
- args += 1;
- nargs -= 1;
- break;
- case 0:
- self = ((PyCFunctionObject*)cyfunc)->m_self;
- break;
- default:
- return NULL;
- }
- if (unlikely(nargs != 1)) {
- PyErr_Format(PyExc_TypeError,
- "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
- def->ml_name, nargs);
- return NULL;
- }
- return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
- __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
- PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
- Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
- Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
- PyObject *self;
- switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
- case 1:
- self = args[0];
- args += 1;
- nargs -= 1;
- break;
- case 0:
- self = ((PyCFunctionObject*)cyfunc)->m_self;
- break;
- default:
- return NULL;
- }
- return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
- __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
- PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
- PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
- Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
- Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
- PyObject *self;
- switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
- case 1:
- self = args[0];
- args += 1;
- nargs -= 1;
- break;
- case 0:
- self = ((PyCFunctionObject*)cyfunc)->m_self;
- break;
- default:
- return NULL;
- }
- return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
- {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
- {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
- {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
- {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
- {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
- {Py_tp_methods, (void *)__pyx_CyFunction_methods},
- {Py_tp_members, (void *)__pyx_CyFunction_members},
- {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
- {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
- {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
- __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
- sizeof(__pyx_CyFunctionObject),
- 0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
- Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
- _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
- Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
- __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
- PyVarObject_HEAD_INIT(0, 0)
- __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
- sizeof(__pyx_CyFunctionObject),
- 0,
- (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
- 0,
-#elif CYTHON_BACKPORT_VECTORCALL
- (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
- offsetof(PyCFunctionObject, vectorcall),
-#endif
- 0,
- 0,
-#if PY_MAJOR_VERSION < 3
- 0,
-#else
- 0,
-#endif
- (reprfunc) __Pyx_CyFunction_repr,
- 0,
- 0,
- 0,
- 0,
- __Pyx_CyFunction_CallAsMethod,
- 0,
- 0,
- 0,
- 0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
- Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
- _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
- Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
- 0,
- (traverseproc) __Pyx_CyFunction_traverse,
- (inquiry) __Pyx_CyFunction_clear,
- 0,
-#if PY_VERSION_HEX < 0x030500A0
- offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
- offsetof(PyCFunctionObject, m_weakreflist),
-#endif
- 0,
- 0,
- __pyx_CyFunction_methods,
- __pyx_CyFunction_members,
- __pyx_CyFunction_getsets,
- 0,
- 0,
- __Pyx_PyMethod_New,
- 0,
- offsetof(__pyx_CyFunctionObject, func_dict),
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
-#if PY_VERSION_HEX >= 0x030400a1
- 0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
- 0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
- 0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000
- 0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
- __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
- (void) module;
- __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
- if (unlikely(__pyx_CyFunctionType == NULL)) {
- return -1;
- }
- return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
- __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
- m->defaults = PyObject_Malloc(size);
- if (unlikely(!m->defaults))
- return PyErr_NoMemory();
- memset(m->defaults, 0, size);
- m->defaults_pyobjects = pyobjects;
- m->defaults_size = size;
- return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
- __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
- m->defaults_tuple = tuple;
- Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
- __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
- m->defaults_kwdict = dict;
- Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
- __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
- m->func_annotations = dict;
- Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
- PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
- PyObject *op = __Pyx_CyFunction_Init(
- PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
- ml, flags, qualname, closure, module, globals, code
- );
- if (likely(op)) {
- PyObject_GC_Track(op);
- }
- return op;
-}
-
-/* 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);
-}
-
-/* IterFinish */
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-#if CYTHON_FAST_THREAD_STATE
- PyThreadState *tstate = __Pyx_PyThreadState_Current;
- PyObject* exc_type = tstate->curexc_type;
- if (unlikely(exc_type)) {
- if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {
- PyObject *exc_value, *exc_tb;
- exc_value = tstate->curexc_value;
- exc_tb = tstate->curexc_traceback;
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
- Py_DECREF(exc_type);
- Py_XDECREF(exc_value);
- Py_XDECREF(exc_tb);
- return 0;
- } else {
- return -1;
- }
- }
- return 0;
-#else
- if (unlikely(PyErr_Occurred())) {
- if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
- PyErr_Clear();
- return 0;
- } else {
- return -1;
- }
- }
- return 0;
-#endif
-}
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
- PyObject *arg = NULL;
- return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
- PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
- __Pyx_TypeName type_name;
- PyTypeObject *tp = Py_TYPE(obj);
- PyObject *descr;
- descrgetfunc f = NULL;
- PyObject **dictptr, *dict;
- int meth_found = 0;
- assert (*method == NULL);
- if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
- attr = __Pyx_PyObject_GetAttrStr(obj, name);
- goto try_unpack;
- }
- if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
- return 0;
- }
- descr = _PyType_Lookup(tp, name);
- if (likely(descr != NULL)) {
- Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
- if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
- #ifdef __Pyx_CyFunction_USED
- if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
- #else
- if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
- #endif
-#else
- #ifdef __Pyx_CyFunction_USED
- if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
- #else
- if (likely(PyFunction_Check(descr)))
- #endif
-#endif
- {
- meth_found = 1;
- } else {
- f = Py_TYPE(descr)->tp_descr_get;
- if (f != NULL && PyDescr_IsData(descr)) {
- attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
- Py_DECREF(descr);
- goto try_unpack;
- }
- }
- }
- dictptr = _PyObject_GetDictPtr(obj);
- if (dictptr != NULL && (dict = *dictptr) != NULL) {
- Py_INCREF(dict);
- attr = __Pyx_PyDict_GetItemStr(dict, name);
- if (attr != NULL) {
- Py_INCREF(attr);
- Py_DECREF(dict);
- Py_XDECREF(descr);
- goto try_unpack;
- }
- Py_DECREF(dict);
- }
- if (meth_found) {
- *method = descr;
- return 1;
- }
- if (f != NULL) {
- attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
- Py_DECREF(descr);
- goto try_unpack;
- }
- if (likely(descr != NULL)) {
- *method = descr;
- return 0;
- }
- type_name = __Pyx_PyType_GetName(tp);
- PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
- "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
- type_name, name);
-#else
- "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
- type_name, PyString_AS_STRING(name));
-#endif
- __Pyx_DECREF_TypeName(type_name);
- return 0;
-#else
- attr = __Pyx_PyObject_GetAttrStr(obj, name);
- goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
- if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
- PyObject *function = PyMethod_GET_FUNCTION(attr);
- Py_INCREF(function);
- Py_DECREF(attr);
- *method = function;
- return 1;
- }
-#endif
- *method = attr;
- return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
- PyObject *method = NULL, *result = NULL;
- int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
- if (likely(is_method)) {
- result = __Pyx_PyObject_CallOneArg(method, obj);
- Py_DECREF(method);
- return result;
- }
- if (unlikely(!method)) goto bad;
- result = __Pyx_PyObject_CallNoArg(method);
- Py_DECREF(method);
-bad:
- return result;
-}
-
-/* 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");
-}
-
-/* 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);
-}
-
-/* UnpackItemEndCheck */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
- if (unlikely(retval)) {
- Py_DECREF(retval);
- __Pyx_RaiseTooManyValuesError(expected);
- return -1;
- }
- return __Pyx_IterFinish();
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
- PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* UnpackTupleError */
-static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) {
- if (t == Py_None) {
- __Pyx_RaiseNoneNotIterableError();
- } else if (PyTuple_GET_SIZE(t) < index) {
- __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t));
- } else {
- __Pyx_RaiseTooManyValuesError(index);
- }
-}
-
-/* UnpackTuple2 */
-static CYTHON_INLINE int __Pyx_unpack_tuple2_exact(
- PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2, int decref_tuple) {
- PyObject *value1 = NULL, *value2 = NULL;
-#if CYTHON_COMPILING_IN_PYPY
- value1 = PySequence_ITEM(tuple, 0); if (unlikely(!value1)) goto bad;
- value2 = PySequence_ITEM(tuple, 1); if (unlikely(!value2)) goto bad;
-#else
- value1 = PyTuple_GET_ITEM(tuple, 0); Py_INCREF(value1);
- value2 = PyTuple_GET_ITEM(tuple, 1); Py_INCREF(value2);
-#endif
- if (decref_tuple) {
- Py_DECREF(tuple);
- }
- *pvalue1 = value1;
- *pvalue2 = value2;
- return 0;
-#if CYTHON_COMPILING_IN_PYPY
-bad:
- Py_XDECREF(value1);
- Py_XDECREF(value2);
- if (decref_tuple) { Py_XDECREF(tuple); }
- return -1;
-#endif
-}
-static int __Pyx_unpack_tuple2_generic(PyObject* tuple, PyObject** pvalue1, PyObject** pvalue2,
- int has_known_size, int decref_tuple) {
- Py_ssize_t index;
- PyObject *value1 = NULL, *value2 = NULL, *iter = NULL;
- iternextfunc iternext;
- iter = PyObject_GetIter(tuple);
- if (unlikely(!iter)) goto bad;
- if (decref_tuple) { Py_DECREF(tuple); tuple = NULL; }
- iternext = __Pyx_PyObject_GetIterNextFunc(iter);
- value1 = iternext(iter); if (unlikely(!value1)) { index = 0; goto unpacking_failed; }
- value2 = iternext(iter); if (unlikely(!value2)) { index = 1; goto unpacking_failed; }
- if (!has_known_size && unlikely(__Pyx_IternextUnpackEndCheck(iternext(iter), 2))) goto bad;
- Py_DECREF(iter);
- *pvalue1 = value1;
- *pvalue2 = value2;
- return 0;
-unpacking_failed:
- if (!has_known_size && __Pyx_IterFinish() == 0)
- __Pyx_RaiseNeedMoreValuesError(index);
-bad:
- Py_XDECREF(iter);
- Py_XDECREF(value1);
- Py_XDECREF(value2);
- if (decref_tuple) { Py_XDECREF(tuple); }
- return -1;
-}
-
-/* dict_iter */
-static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* iterable, int is_dict, PyObject* method_name,
- Py_ssize_t* p_orig_length, int* p_source_is_dict) {
- is_dict = is_dict || likely(PyDict_CheckExact(iterable));
- *p_source_is_dict = is_dict;
- if (is_dict) {
-#if !CYTHON_COMPILING_IN_PYPY
- *p_orig_length = PyDict_Size(iterable);
- Py_INCREF(iterable);
- return iterable;
-#elif PY_MAJOR_VERSION >= 3
- static PyObject *py_items = NULL, *py_keys = NULL, *py_values = NULL;
- PyObject **pp = NULL;
- if (method_name) {
- const char *name = PyUnicode_AsUTF8(method_name);
- if (strcmp(name, "iteritems") == 0) pp = &py_items;
- else if (strcmp(name, "iterkeys") == 0) pp = &py_keys;
- else if (strcmp(name, "itervalues") == 0) pp = &py_values;
- if (pp) {
- if (!*pp) {
- *pp = PyUnicode_FromString(name + 4);
- if (!*pp)
- return NULL;
- }
- method_name = *pp;
- }
- }
-#endif
- }
- *p_orig_length = 0;
- if (method_name) {
- PyObject* iter;
- iterable = __Pyx_PyObject_CallMethod0(iterable, method_name);
- if (!iterable)
- return NULL;
-#if !CYTHON_COMPILING_IN_PYPY
- if (PyTuple_CheckExact(iterable) || PyList_CheckExact(iterable))
- return iterable;
-#endif
- iter = PyObject_GetIter(iterable);
- Py_DECREF(iterable);
- return iter;
- }
- return PyObject_GetIter(iterable);
-}
-static CYTHON_INLINE int __Pyx_dict_iter_next(
- PyObject* iter_obj, CYTHON_NCP_UNUSED Py_ssize_t orig_length, CYTHON_NCP_UNUSED Py_ssize_t* ppos,
- PyObject** pkey, PyObject** pvalue, PyObject** pitem, int source_is_dict) {
- PyObject* next_item;
-#if !CYTHON_COMPILING_IN_PYPY
- if (source_is_dict) {
- PyObject *key, *value;
- if (unlikely(orig_length != PyDict_Size(iter_obj))) {
- PyErr_SetString(PyExc_RuntimeError, "dictionary changed size during iteration");
- return -1;
- }
- if (unlikely(!PyDict_Next(iter_obj, ppos, &key, &value))) {
- return 0;
- }
- if (pitem) {
- PyObject* tuple = PyTuple_New(2);
- if (unlikely(!tuple)) {
- return -1;
- }
- Py_INCREF(key);
- Py_INCREF(value);
- PyTuple_SET_ITEM(tuple, 0, key);
- PyTuple_SET_ITEM(tuple, 1, value);
- *pitem = tuple;
- } else {
- if (pkey) {
- Py_INCREF(key);
- *pkey = key;
- }
- if (pvalue) {
- Py_INCREF(value);
- *pvalue = value;
- }
- }
- return 1;
- } else if (PyTuple_CheckExact(iter_obj)) {
- Py_ssize_t pos = *ppos;
- if (unlikely(pos >= PyTuple_GET_SIZE(iter_obj))) return 0;
- *ppos = pos + 1;
- next_item = PyTuple_GET_ITEM(iter_obj, pos);
- Py_INCREF(next_item);
- } else if (PyList_CheckExact(iter_obj)) {
- Py_ssize_t pos = *ppos;
- if (unlikely(pos >= PyList_GET_SIZE(iter_obj))) return 0;
- *ppos = pos + 1;
- next_item = PyList_GET_ITEM(iter_obj, pos);
- Py_INCREF(next_item);
- } else
-#endif
- {
- next_item = PyIter_Next(iter_obj);
- if (unlikely(!next_item)) {
- return __Pyx_IterFinish();
- }
- }
- if (pitem) {
- *pitem = next_item;
- } else if (pkey && pvalue) {
- if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1))
- return -1;
- } else if (pkey) {
- *pkey = next_item;
- } else {
- *pvalue = next_item;
- }
- return 1;
-}
-
-/* JoinPyUnicode */
-static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength,
- Py_UCS4 max_char) {
-#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- PyObject *result_uval;
- int result_ukind, kind_shift;
- Py_ssize_t i, char_pos;
- void *result_udata;
- CYTHON_MAYBE_UNUSED_VAR(max_char);
-#if CYTHON_PEP393_ENABLED
- result_uval = PyUnicode_New(result_ulength, max_char);
- if (unlikely(!result_uval)) return NULL;
- result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND;
- kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1;
- result_udata = PyUnicode_DATA(result_uval);
-#else
- result_uval = PyUnicode_FromUnicode(NULL, result_ulength);
- if (unlikely(!result_uval)) return NULL;
- result_ukind = sizeof(Py_UNICODE);
- kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1;
- result_udata = PyUnicode_AS_UNICODE(result_uval);
-#endif
- assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0);
- char_pos = 0;
- for (i=0; i < value_count; i++) {
- int ukind;
- Py_ssize_t ulength;
- void *udata;
- PyObject *uval = PyTuple_GET_ITEM(value_tuple, i);
- if (unlikely(__Pyx_PyUnicode_READY(uval)))
- goto bad;
- ulength = __Pyx_PyUnicode_GET_LENGTH(uval);
- if (unlikely(!ulength))
- continue;
- if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos))
- goto overflow;
- ukind = __Pyx_PyUnicode_KIND(uval);
- udata = __Pyx_PyUnicode_DATA(uval);
- if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) {
- memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift));
- } else {
- #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters)
- _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength);
- #else
- Py_ssize_t j;
- for (j=0; j < ulength; j++) {
- Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j);
- __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar);
- }
- #endif
- }
- char_pos += ulength;
- }
- return result_uval;
-overflow:
- PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string");
-bad:
- Py_DECREF(result_uval);
- return NULL;
-#else
- CYTHON_UNUSED_VAR(max_char);
- CYTHON_UNUSED_VAR(result_ulength);
- CYTHON_UNUSED_VAR(value_count);
- return PyUnicode_Join(__pyx_empty_unicode, value_tuple);
-#endif
-}
-
-/* pep479 */
-static void __Pyx_Generator_Replace_StopIteration(int in_async_gen) {
- PyObject *exc, *val, *tb, *cur_exc;
- __Pyx_PyThreadState_declare
- #ifdef __Pyx_StopAsyncIteration_USED
- int is_async_stopiteration = 0;
- #endif
- CYTHON_MAYBE_UNUSED_VAR(in_async_gen);
- cur_exc = PyErr_Occurred();
- if (likely(!__Pyx_PyErr_GivenExceptionMatches(cur_exc, PyExc_StopIteration))) {
- #ifdef __Pyx_StopAsyncIteration_USED
- if (in_async_gen && unlikely(__Pyx_PyErr_GivenExceptionMatches(cur_exc, __Pyx_PyExc_StopAsyncIteration))) {
- is_async_stopiteration = 1;
- } else
- #endif
- return;
- }
- __Pyx_PyThreadState_assign
- __Pyx_GetException(&exc, &val, &tb);
- Py_XDECREF(exc);
- Py_XDECREF(val);
- Py_XDECREF(tb);
- PyErr_SetString(PyExc_RuntimeError,
- #ifdef __Pyx_StopAsyncIteration_USED
- is_async_stopiteration ? "async generator raised StopAsyncIteration" :
- in_async_gen ? "async generator raised StopIteration" :
- #endif
- "generator raised StopIteration");
-}
-
-/* UnicodeConcatInPlace */
-# if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-static int
-__Pyx_unicode_modifiable(PyObject *unicode)
-{
- if (Py_REFCNT(unicode) != 1)
- return 0;
- if (!PyUnicode_CheckExact(unicode))
- return 0;
- if (PyUnicode_CHECK_INTERNED(unicode))
- return 0;
- return 1;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_ConcatInPlaceImpl(PyObject **p_left, PyObject *right
- #if CYTHON_REFNANNY
- , void* __pyx_refnanny
- #endif
- ) {
- PyObject *left = *p_left;
- Py_ssize_t left_len, right_len, new_len;
- if (unlikely(__Pyx_PyUnicode_READY(left) == -1))
- return NULL;
- if (unlikely(__Pyx_PyUnicode_READY(right) == -1))
- return NULL;
- left_len = PyUnicode_GET_LENGTH(left);
- if (left_len == 0) {
- Py_INCREF(right);
- return right;
- }
- right_len = PyUnicode_GET_LENGTH(right);
- if (right_len == 0) {
- Py_INCREF(left);
- return left;
- }
- if (unlikely(left_len > PY_SSIZE_T_MAX - right_len)) {
- PyErr_SetString(PyExc_OverflowError,
- "strings are too large to concat");
- return NULL;
- }
- new_len = left_len + right_len;
- if (__Pyx_unicode_modifiable(left)
- && PyUnicode_CheckExact(right)
- && PyUnicode_KIND(right) <= PyUnicode_KIND(left)
- && !(PyUnicode_IS_ASCII(left) && !PyUnicode_IS_ASCII(right))) {
- __Pyx_GIVEREF(*p_left);
- if (unlikely(PyUnicode_Resize(p_left, new_len) != 0)) {
- __Pyx_GOTREF(*p_left);
- return NULL;
- }
- __Pyx_INCREF(*p_left);
- _PyUnicode_FastCopyCharacters(*p_left, left_len, right, 0, right_len);
- return *p_left;
- } else {
- return __Pyx_PyUnicode_Concat(left, right);
- }
- }
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
- PyObject *module = 0;
- PyObject *empty_dict = 0;
- PyObject *empty_list = 0;
- #if PY_MAJOR_VERSION < 3
- PyObject *py_import;
- py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
- if (unlikely(!py_import))
- goto bad;
- if (!from_list) {
- empty_list = PyList_New(0);
- if (unlikely(!empty_list))
- goto bad;
- from_list = empty_list;
- }
- #endif
- empty_dict = PyDict_New();
- if (unlikely(!empty_dict))
- goto bad;
- {
- #if PY_MAJOR_VERSION >= 3
- if (level == -1) {
- if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
- #if CYTHON_COMPILING_IN_LIMITED_API
- module = PyImport_ImportModuleLevelObject(
- name, empty_dict, empty_dict, from_list, 1);
- #else
- module = PyImport_ImportModuleLevelObject(
- name, __pyx_d, empty_dict, from_list, 1);
- #endif
- if (unlikely(!module)) {
- if (unlikely(!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 (unlikely(!py_level))
- goto bad;
- module = PyObject_CallFunctionObjArgs(py_import,
- name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
- Py_DECREF(py_level);
- #else
- #if CYTHON_COMPILING_IN_LIMITED_API
- module = PyImport_ImportModuleLevelObject(
- name, empty_dict, empty_dict, from_list, level);
- #else
- module = PyImport_ImportModuleLevelObject(
- name, __pyx_d, empty_dict, from_list, level);
- #endif
- #endif
- }
- }
-bad:
- Py_XDECREF(empty_dict);
- Py_XDECREF(empty_list);
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(py_import);
- #endif
- return module;
-}
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
- PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
- if (unlikely(PyErr_Occurred())) {
- PyErr_Clear();
- }
- if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
- partial_name = name;
- } else {
- slice = PySequence_GetSlice(parts_tuple, 0, count);
- if (unlikely(!slice))
- goto bad;
- sep = PyUnicode_FromStringAndSize(".", 1);
- if (unlikely(!sep))
- goto bad;
- partial_name = PyUnicode_Join(sep, slice);
- }
- PyErr_Format(
-#if PY_MAJOR_VERSION < 3
- PyExc_ImportError,
- "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
- PyExc_ModuleNotFoundError,
-#else
- PyExc_ImportError,
-#endif
- "No module named '%U'", partial_name);
-#endif
-bad:
- Py_XDECREF(sep);
- Py_XDECREF(slice);
- Py_XDECREF(partial_name);
- return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
- PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400)
- PyObject *modules = PyImport_GetModuleDict();
- if (unlikely(!modules))
- return NULL;
- imported_module = __Pyx_PyDict_GetItemStr(modules, name);
- Py_XINCREF(imported_module);
-#else
- imported_module = PyImport_GetModule(name);
-#endif
- return imported_module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
- PyObject *module, *from_list, *star = __pyx_n_s__9;
- CYTHON_UNUSED_VAR(parts_tuple);
- from_list = PyList_New(1);
- if (unlikely(!from_list))
- return NULL;
- Py_INCREF(star);
- PyList_SET_ITEM(from_list, 0, star);
- module = __Pyx_Import(name, from_list, 0);
- Py_DECREF(from_list);
- return module;
-#else
- Py_ssize_t i, nparts;
- PyObject *imported_module;
- PyObject *module = __Pyx_Import(name, NULL, 0);
- if (!parts_tuple || unlikely(!module))
- return module;
- imported_module = __Pyx__ImportDottedModule_Lookup(name);
- if (likely(imported_module)) {
- Py_DECREF(module);
- return imported_module;
- }
- PyErr_Clear();
- nparts = PyTuple_GET_SIZE(parts_tuple);
- for (i=1; i < nparts && module; i++) {
- PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
- part = PySequence_ITEM(parts_tuple, i);
-#endif
- submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
- Py_DECREF(part);
-#endif
- Py_DECREF(module);
- module = submodule;
- }
- if (likely(module))
- return module;
- return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
- PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
- if (likely(module)) {
- PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
- if (likely(spec)) {
- PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
- if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
- Py_DECREF(spec);
- spec = NULL;
- }
- Py_XDECREF(unsafe);
- }
- if (likely(!spec)) {
- PyErr_Clear();
- return module;
- }
- Py_DECREF(spec);
- Py_DECREF(module);
- } else if (PyErr_Occurred()) {
- PyErr_Clear();
- }
-#endif
- return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* PyObjectSetAttrStr */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
- PyTypeObject* tp = Py_TYPE(obj);
- if (likely(tp->tp_setattro))
- return tp->tp_setattro(obj, attr_name, value);
-#if PY_MAJOR_VERSION < 3
- if (likely(tp->tp_setattr))
- return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
-#endif
- return PyObject_SetAttr(obj, attr_name, value);
-}
-#endif
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
- PyObject *r;
- if (unlikely(!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 {
- PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
- PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
- if (mm && mm->mp_subscript) {
- PyObject *r, *key = PyInt_FromSsize_t(i);
- if (unlikely(!key)) return NULL;
- r = mm->mp_subscript(o, key);
- Py_DECREF(key);
- return r;
- }
- if (likely(sm && sm->sq_item)) {
- if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
- Py_ssize_t l = sm->sq_length(o);
- if (likely(l >= 0)) {
- i += l;
- } else {
- if (!PyErr_ExceptionMatches(PyExc_OverflowError))
- return NULL;
- PyErr_Clear();
- }
- }
- return sm->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;
- 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)) {
- __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
- PyErr_Clear();
- PyErr_Format(PyExc_IndexError,
- "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
- __Pyx_DECREF_TypeName(index_type_name);
- }
- return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
- __Pyx_TypeName obj_type_name;
- if (likely(PyType_Check(obj))) {
- PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
- if (meth) {
- PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
- Py_DECREF(meth);
- return result;
- }
- }
- obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
- PyErr_Format(PyExc_TypeError,
- "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
- __Pyx_DECREF_TypeName(obj_type_name);
- return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
- PyTypeObject *tp = Py_TYPE(obj);
- PyMappingMethods *mm = tp->tp_as_mapping;
- PySequenceMethods *sm = tp->tp_as_sequence;
- if (likely(mm && mm->mp_subscript)) {
- return mm->mp_subscript(obj, key);
- }
- if (likely(sm && sm->sq_item)) {
- return __Pyx_PyObject_GetIndex(obj, key);
- }
- return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* UnpackUnboundCMethod */
-static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) {
- PyObject *method;
- method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name);
- if (unlikely(!method))
- return -1;
- target->method = method;
-#if CYTHON_COMPILING_IN_CPYTHON
- #if PY_MAJOR_VERSION >= 3
- if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type)))
- #endif
- {
- PyMethodDescrObject *descr = (PyMethodDescrObject*) method;
- target->func = descr->d_method->ml_meth;
- target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_STACKLESS);
- }
-#endif
- return 0;
-}
-
-/* CallUnboundCMethod1 */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_CallUnboundCMethod1(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg) {
- if (likely(cfunc->func)) {
- int flag = cfunc->flag;
- if (flag == METH_O) {
- return (*(cfunc->func))(self, arg);
- } else if ((PY_VERSION_HEX >= 0x030600B1) && flag == METH_FASTCALL) {
- if ((PY_VERSION_HEX >= 0x030700A0)) {
- return (*(__Pyx_PyCFunctionFast)(void*)(PyCFunction)cfunc->func)(self, &arg, 1);
- } else {
- return (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)cfunc->func)(self, &arg, 1, NULL);
- }
- } else if ((PY_VERSION_HEX >= 0x030700A0) && flag == (METH_FASTCALL | METH_KEYWORDS)) {
- return (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)cfunc->func)(self, &arg, 1, NULL);
- }
- }
- return __Pyx__CallUnboundCMethod1(cfunc, self, arg);
-}
-#endif
-static PyObject* __Pyx__CallUnboundCMethod1(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg){
- PyObject *args, *result = NULL;
- if (unlikely(!cfunc->func && !cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
-#if CYTHON_COMPILING_IN_CPYTHON
- if (cfunc->func && (cfunc->flag & METH_VARARGS)) {
- args = PyTuple_New(1);
- if (unlikely(!args)) goto bad;
- Py_INCREF(arg);
- PyTuple_SET_ITEM(args, 0, arg);
- if (cfunc->flag & METH_KEYWORDS)
- result = (*(PyCFunctionWithKeywords)(void*)(PyCFunction)cfunc->func)(self, args, NULL);
- else
- result = (*cfunc->func)(self, args);
- } else {
- args = PyTuple_New(2);
- if (unlikely(!args)) goto bad;
- Py_INCREF(self);
- PyTuple_SET_ITEM(args, 0, self);
- Py_INCREF(arg);
- PyTuple_SET_ITEM(args, 1, arg);
- result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
- }
-#else
- args = PyTuple_Pack(2, self, arg);
- if (unlikely(!args)) goto bad;
- result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
-#endif
-bad:
- Py_XDECREF(args);
- return result;
-}
-
-/* CallUnboundCMethod2 */
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030600B1
-static CYTHON_INLINE PyObject *__Pyx_CallUnboundCMethod2(__Pyx_CachedCFunction *cfunc, PyObject *self, PyObject *arg1, PyObject *arg2) {
- if (likely(cfunc->func)) {
- PyObject *args[2] = {arg1, arg2};
- if (cfunc->flag == METH_FASTCALL) {
- #if PY_VERSION_HEX >= 0x030700A0
- return (*(__Pyx_PyCFunctionFast)(void*)(PyCFunction)cfunc->func)(self, args, 2);
- #else
- return (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)cfunc->func)(self, args, 2, NULL);
- #endif
- }
- #if PY_VERSION_HEX >= 0x030700A0
- if (cfunc->flag == (METH_FASTCALL | METH_KEYWORDS))
- return (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)cfunc->func)(self, args, 2, NULL);
- #endif
- }
- return __Pyx__CallUnboundCMethod2(cfunc, self, arg1, arg2);
-}
-#endif
-static PyObject* __Pyx__CallUnboundCMethod2(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg1, PyObject* arg2){
- PyObject *args, *result = NULL;
- if (unlikely(!cfunc->func && !cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
-#if CYTHON_COMPILING_IN_CPYTHON
- if (cfunc->func && (cfunc->flag & METH_VARARGS)) {
- args = PyTuple_New(2);
- if (unlikely(!args)) goto bad;
- Py_INCREF(arg1);
- PyTuple_SET_ITEM(args, 0, arg1);
- Py_INCREF(arg2);
- PyTuple_SET_ITEM(args, 1, arg2);
- if (cfunc->flag & METH_KEYWORDS)
- result = (*(PyCFunctionWithKeywords)(void*)(PyCFunction)cfunc->func)(self, args, NULL);
- else
- result = (*cfunc->func)(self, args);
- } else {
- args = PyTuple_New(3);
- if (unlikely(!args)) goto bad;
- Py_INCREF(self);
- PyTuple_SET_ITEM(args, 0, self);
- Py_INCREF(arg1);
- PyTuple_SET_ITEM(args, 1, arg1);
- Py_INCREF(arg2);
- PyTuple_SET_ITEM(args, 2, arg2);
- result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
- }
-#else
- args = PyTuple_Pack(3, self, arg1, arg2);
- if (unlikely(!args)) goto bad;
- result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
-#endif
-bad:
- Py_XDECREF(args);
- return result;
-}
-
-/* dict_getitem_default */
-static PyObject* __Pyx_PyDict_GetItemDefault(PyObject* d, PyObject* key, PyObject* default_value) {
- PyObject* value;
-#if PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000)
- value = PyDict_GetItemWithError(d, key);
- if (unlikely(!value)) {
- if (unlikely(PyErr_Occurred()))
- return NULL;
- value = default_value;
- }
- Py_INCREF(value);
- if ((1));
-#else
- if (PyString_CheckExact(key) || PyUnicode_CheckExact(key) || PyInt_CheckExact(key)) {
- value = PyDict_GetItem(d, key);
- if (unlikely(!value)) {
- value = default_value;
- }
- Py_INCREF(value);
- }
-#endif
- else {
- if (default_value == Py_None)
- value = __Pyx_CallUnboundCMethod1(&__pyx_umethod_PyDict_Type_get, d, key);
- else
- value = __Pyx_CallUnboundCMethod2(&__pyx_umethod_PyDict_Type_get, d, key, default_value);
- }
- return value;
-}
-
-/* PyObjectLookupSpecial */
-#if CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx__PyObject_LookupSpecial(PyObject* obj, PyObject* attr_name, int with_error) {
- PyObject *res;
- PyTypeObject *tp = Py_TYPE(obj);
-#if PY_MAJOR_VERSION < 3
- if (unlikely(PyInstance_Check(obj)))
- return with_error ? __Pyx_PyObject_GetAttrStr(obj, attr_name) : __Pyx_PyObject_GetAttrStrNoError(obj, attr_name);
-#endif
- res = _PyType_Lookup(tp, attr_name);
- if (likely(res)) {
- descrgetfunc f = Py_TYPE(res)->tp_descr_get;
- if (!f) {
- Py_INCREF(res);
- } else {
- res = f(res, obj, (PyObject *)tp);
- }
- } else if (with_error) {
- PyErr_SetObject(PyExc_AttributeError, attr_name);
- }
- return res;
-}
-#endif
-
-/* PyFloatBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyFloat_TrueDivideObjC(PyObject *op1, PyObject *op2, double floatval, int inplace, int zerodivision_check) {
- const double b = floatval;
- double a, result;
- (void)inplace; (void)zerodivision_check;
- if (likely(PyFloat_CheckExact(op1))) {
-#if CYTHON_COMPILING_IN_LIMITED_API
- a = __pyx_PyFloat_AsDouble(op1);
-#else
- a = PyFloat_AS_DOUBLE(op1);
-#endif
-
- } else
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op1))) {
- a = (double) PyInt_AS_LONG(op1);
-
- } else
- #endif
- if (likely(PyLong_CheckExact(op1))) {
- #if CYTHON_USE_PYLONG_INTERNALS
- const digit* digits = ((PyLongObject*)op1)->ob_digit;
- const Py_ssize_t size = Py_SIZE(op1);
- switch (size) {
- case 0: a = 0.0; break;
- case -1: a = -(double) digits[0]; break;
- case 1: a = (double) digits[0]; break;
- case -2:
- case 2:
- if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT && ((8 * sizeof(unsigned long) < 53) || (1 * PyLong_SHIFT < 53))) {
- a = (double) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- if ((8 * sizeof(unsigned long) < 53) || (2 * PyLong_SHIFT < 53) || (a < (double) ((PY_LONG_LONG)1 << 53))) {
- if (size == -2)
- a = -a;
- break;
- }
- }
- CYTHON_FALLTHROUGH;
- case -3:
- case 3:
- if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT && ((8 * sizeof(unsigned long) < 53) || (2 * PyLong_SHIFT < 53))) {
- a = (double) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- if ((8 * sizeof(unsigned long) < 53) || (3 * PyLong_SHIFT < 53) || (a < (double) ((PY_LONG_LONG)1 << 53))) {
- if (size == -3)
- a = -a;
- break;
- }
- }
- CYTHON_FALLTHROUGH;
- case -4:
- case 4:
- if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT && ((8 * sizeof(unsigned long) < 53) || (3 * PyLong_SHIFT < 53))) {
- a = (double) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- if ((8 * sizeof(unsigned long) < 53) || (4 * PyLong_SHIFT < 53) || (a < (double) ((PY_LONG_LONG)1 << 53))) {
- if (size == -4)
- a = -a;
- break;
- }
- }
- CYTHON_FALLTHROUGH;
- default:
- #else
- {
- #endif
- a = PyLong_AsDouble(op1);
- if (unlikely(a == -1.0 && PyErr_Occurred())) return NULL;
- }
- } else {
- return (inplace ? PyNumber_InPlaceTrueDivide : PyNumber_TrueDivide)(op1, op2);
- }
- PyFPE_START_PROTECT("divide", return NULL)
- result = a / b;
- PyFPE_END_PROTECT(result)
- return PyFloat_FromDouble(result);
-}
-#endif
-
-/* PyIntBinop */
- #if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_TrueDivideObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
- CYTHON_MAYBE_UNUSED_VAR(intval);
- CYTHON_MAYBE_UNUSED_VAR(inplace);
- CYTHON_UNUSED_VAR(zerodivision_check);
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op1))) {
- const long b = intval;
- long a = PyInt_AS_LONG(op1);
-
- if (8 * sizeof(long) <= 53 || likely(labs(a) <= ((PY_LONG_LONG)1 << 53))) {
- return PyFloat_FromDouble((double)a / (double)b);
- }
- return PyInt_Type.tp_as_number->nb_true_divide(op1, op2);
- }
- #endif
- #if CYTHON_USE_PYLONG_INTERNALS
- if (likely(PyLong_CheckExact(op1))) {
- const long b = intval;
- long a, x;
- const digit* digits = ((PyLongObject*)op1)->ob_digit;
- const Py_ssize_t size = Py_SIZE(op1);
- if (unlikely(size == 0)) {
- }
- 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 && 1 * PyLong_SHIFT < 53) {
- a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
- }
- CYTHON_FALLTHROUGH;
- case 2:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT && 1 * PyLong_SHIFT < 53) {
- a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
- }
- CYTHON_FALLTHROUGH;
- case -3:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT && 2 * PyLong_SHIFT < 53) {
- a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
- }
- CYTHON_FALLTHROUGH;
- case 3:
- if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT && 2 * PyLong_SHIFT < 53) {
- a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
- break;
- }
- CYTHON_FALLTHROUGH;
- case -4:
- if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT && 3 * PyLong_SHIFT < 53) {
- 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;
- }
- CYTHON_FALLTHROUGH;
- case 4:
- if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT && 3 * PyLong_SHIFT < 53) {
- 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;
- }
- CYTHON_FALLTHROUGH;
- default: return PyLong_Type.tp_as_number->nb_true_divide(op1, op2);
- }
- }
- if ((8 * sizeof(long) <= 53 || likely(labs(a) <= ((PY_LONG_LONG)1 << 53)))
- || __Pyx_sst_abs(size) <= 52 / PyLong_SHIFT) {
- return PyFloat_FromDouble((double)a / (double)b);
- }
- return PyLong_Type.tp_as_number->nb_true_divide(op1, op2);
- return PyLong_FromLong(x);
-
- }
- #endif
- if (PyFloat_CheckExact(op1)) {
- const long b = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
- double a = __pyx_PyFloat_AsDouble(op1);
-#else
- double a = PyFloat_AS_DOUBLE(op1);
-#endif
- double result;
-
- PyFPE_START_PROTECT("divide", return NULL)
- result = ((double)a) / (double)b;
- PyFPE_END_PROTECT(result)
- return PyFloat_FromDouble(result);
- }
- return (inplace ? PyNumber_InPlaceTrueDivide : PyNumber_TrueDivide)(op1, op2);
-}
-#endif
-
-/* PyIntBinop */
- #if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
- CYTHON_MAYBE_UNUSED_VAR(intval);
- CYTHON_MAYBE_UNUSED_VAR(inplace);
- CYTHON_UNUSED_VAR(zerodivision_check);
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op2))) {
- const long a = intval;
- long x;
- long b = PyInt_AS_LONG(op2);
-
- x = (long)((unsigned long)a - b);
- if (likely((x^a) >= 0 || (x^~b) >= 0))
- return PyInt_FromLong(x);
- return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
- }
- #endif
- #if CYTHON_USE_PYLONG_INTERNALS
- if (likely(PyLong_CheckExact(op2))) {
- const long a = intval;
- long b, x;
-#ifdef HAVE_LONG_LONG
- const PY_LONG_LONG lla = intval;
- PY_LONG_LONG llb, llx;
-#endif
- const digit* digits = ((PyLongObject*)op2)->ob_digit;
- const Py_ssize_t size = Py_SIZE(op2);
- if (unlikely(size == 0)) {
- return __Pyx_NewRef(op1);
- }
- if (likely(__Pyx_sst_abs(size) <= 1)) {
- b = likely(size) ? digits[0] : 0;
- if (size == -1) b = -b;
- } else {
- switch (size) {
- case -2:
- if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
- b = -(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) {
- llb = -(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) {
- b = (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) {
- llb = (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) {
- b = -(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) {
- llb = -(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) {
- b = (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) {
- llb = (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) {
- b = -(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) {
- llb = -(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) {
- b = (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) {
- llb = (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_subtract(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(op2)) {
- const long a = intval;
-#if CYTHON_COMPILING_IN_LIMITED_API
- double b = __pyx_PyFloat_AsDouble(op2);
-#else
- double b = PyFloat_AS_DOUBLE(op2);
-#endif
- double result;
-
- PyFPE_START_PROTECT("subtract", return NULL)
- result = ((double)a) - (double)b;
- PyFPE_END_PROTECT(result)
- return PyFloat_FromDouble(result);
- }
- return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* DictGetItem */
- #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
- PyObject *value;
- value = PyDict_GetItemWithError(d, key);
- if (unlikely(!value)) {
- if (!PyErr_Occurred()) {
- if (unlikely(PyTuple_Check(key))) {
- PyObject* args = PyTuple_Pack(1, key);
- if (likely(args)) {
- PyErr_SetObject(PyExc_KeyError, args);
- Py_DECREF(args);
- }
- } else {
- PyErr_SetObject(PyExc_KeyError, key);
- }
- }
- return NULL;
- }
- Py_INCREF(value);
- return value;
-}
-#endif
-
-/* PyIntBinop */
- #if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
- CYTHON_MAYBE_UNUSED_VAR(intval);
- CYTHON_MAYBE_UNUSED_VAR(inplace);
- CYTHON_UNUSED_VAR(zerodivision_check);
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op1))) {
- const long b = intval;
- long x;
- long a = PyInt_AS_LONG(op1);
-
- x = (long)((unsigned long)a - b);
- if (likely((x^a) >= 0 || (x^~b) >= 0))
- return PyInt_FromLong(x);
- return PyLong_Type.tp_as_number->nb_subtract(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 (unlikely(size == 0)) {
- return PyLong_FromLong(-intval);
- }
- 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_subtract(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;
-#if CYTHON_COMPILING_IN_LIMITED_API
- double a = __pyx_PyFloat_AsDouble(op1);
-#else
- double a = PyFloat_AS_DOUBLE(op1);
-#endif
- double result;
-
- PyFPE_START_PROTECT("subtract", return NULL)
- result = ((double)a) - (double)b;
- PyFPE_END_PROTECT(result)
- return PyFloat_FromDouble(result);
- }
- return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
-}
-#endif
-
-/* RaiseException */
- #if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
- __Pyx_PyThreadState_declare
- CYTHON_UNUSED_VAR(cause);
- 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 || CYTHON_COMPILING_IN_LIMITED_API
- 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
-
-/* PyIntBinop */
- #if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) {
- CYTHON_MAYBE_UNUSED_VAR(intval);
- CYTHON_MAYBE_UNUSED_VAR(inplace);
- CYTHON_UNUSED_VAR(zerodivision_check);
- #if PY_MAJOR_VERSION < 3
- if (likely(PyInt_CheckExact(op1))) {
- const long b = intval;
- long x;
- long a = PyInt_AS_LONG(op1);
-
- x = (long)((unsigned long)a + b);
- if (likely((x^a) >= 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 (unlikely(size == 0)) {
- return __Pyx_NewRef(op2);
- }
- 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;
-#if CYTHON_COMPILING_IN_LIMITED_API
- double a = __pyx_PyFloat_AsDouble(op1);
-#else
- double a = PyFloat_AS_DOUBLE(op1);
-#endif
- 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
-
-/* SliceObject */
- static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(PyObject* obj,
- Py_ssize_t cstart, Py_ssize_t cstop,
- PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
- int has_cstart, int has_cstop, int wraparound) {
- __Pyx_TypeName obj_type_name;
-#if CYTHON_USE_TYPE_SLOTS
- PyMappingMethods* mp;
-#if PY_MAJOR_VERSION < 3
- PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
- if (likely(ms && ms->sq_slice)) {
- if (!has_cstart) {
- if (_py_start && (*_py_start != Py_None)) {
- cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
- if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
- } else
- cstart = 0;
- }
- if (!has_cstop) {
- if (_py_stop && (*_py_stop != Py_None)) {
- cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
- if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
- } else
- cstop = PY_SSIZE_T_MAX;
- }
- if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
- Py_ssize_t l = ms->sq_length(obj);
- if (likely(l >= 0)) {
- if (cstop < 0) {
- cstop += l;
- if (cstop < 0) cstop = 0;
- }
- if (cstart < 0) {
- cstart += l;
- if (cstart < 0) cstart = 0;
- }
- } else {
- if (!PyErr_ExceptionMatches(PyExc_OverflowError))
- goto bad;
- PyErr_Clear();
- }
- }
- return ms->sq_slice(obj, cstart, cstop);
- }
-#else
- CYTHON_UNUSED_VAR(wraparound);
-#endif
- mp = Py_TYPE(obj)->tp_as_mapping;
- if (likely(mp && mp->mp_subscript))
-#else
- CYTHON_UNUSED_VAR(wraparound);
-#endif
- {
- PyObject* result;
- PyObject *py_slice, *py_start, *py_stop;
- if (_py_slice) {
- py_slice = *_py_slice;
- } else {
- PyObject* owned_start = NULL;
- PyObject* owned_stop = NULL;
- if (_py_start) {
- py_start = *_py_start;
- } else {
- if (has_cstart) {
- owned_start = py_start = PyInt_FromSsize_t(cstart);
- if (unlikely(!py_start)) goto bad;
- } else
- py_start = Py_None;
- }
- if (_py_stop) {
- py_stop = *_py_stop;
- } else {
- if (has_cstop) {
- owned_stop = py_stop = PyInt_FromSsize_t(cstop);
- if (unlikely(!py_stop)) {
- Py_XDECREF(owned_start);
- goto bad;
- }
- } else
- py_stop = Py_None;
- }
- py_slice = PySlice_New(py_start, py_stop, Py_None);
- Py_XDECREF(owned_start);
- Py_XDECREF(owned_stop);
- if (unlikely(!py_slice)) goto bad;
- }
-#if CYTHON_USE_TYPE_SLOTS
- result = mp->mp_subscript(obj, py_slice);
-#else
- result = PyObject_GetItem(obj, py_slice);
-#endif
- if (!_py_slice) {
- Py_DECREF(py_slice);
- }
- return result;
- }
- obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
- PyErr_Format(PyExc_TypeError,
- "'" __Pyx_FMT_TYPENAME "' object is unsliceable", obj_type_name);
- __Pyx_DECREF_TypeName(obj_type_name);
-bad:
- return NULL;
-}
-
-/* ValidateBasesTuple */
- #if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
- Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
- for (i = 1; i < n; i++)
- {
- PyObject *b0 = PyTuple_GET_ITEM(bases, i);
- PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
- if (PyClass_Check(b0))
- {
- PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
- PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
- return -1;
- }
-#endif
- b = (PyTypeObject*) b0;
- if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
- {
- __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
- PyErr_Format(PyExc_TypeError,
- "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
- __Pyx_DECREF_TypeName(b_name);
- return -1;
- }
- if (dictoffset == 0 && b->tp_dictoffset)
- {
- __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
- PyErr_Format(PyExc_TypeError,
- "extension type '%.200s' has no __dict__ slot, "
- "but base type '" __Pyx_FMT_TYPENAME "' has: "
- "either add 'cdef dict __dict__' to the extension type "
- "or add '__slots__ = [...]' to the base type",
- type_name, b_name);
- __Pyx_DECREF_TypeName(b_name);
- return -1;
- }
- }
- return 0;
-}
-#endif
-
-/* PyType_Ready */
- static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
- (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
- (void)__Pyx_validate_bases_tuple;
-#endif
- return PyType_Ready(t);
-#else
- int r;
- PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
- if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
- return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
- {
- int gc_was_enabled;
- #if PY_VERSION_HEX >= 0x030A00b1
- gc_was_enabled = PyGC_Disable();
- (void)__Pyx_PyObject_CallMethod0;
- #else
- PyObject *ret, *py_status;
- PyObject *gc = NULL;
- #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
- gc = PyImport_GetModule(__pyx_kp_u_gc);
- #endif
- if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
- if (unlikely(!gc)) return -1;
- py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
- if (unlikely(!py_status)) {
- Py_DECREF(gc);
- return -1;
- }
- gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
- Py_DECREF(py_status);
- if (gc_was_enabled > 0) {
- ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
- if (unlikely(!ret)) {
- Py_DECREF(gc);
- return -1;
- }
- Py_DECREF(ret);
- } else if (unlikely(gc_was_enabled == -1)) {
- Py_DECREF(gc);
- return -1;
- }
- #endif
- t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#else
- (void)__Pyx_PyObject_CallMethod0;
-#endif
- r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
- t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
- #if PY_VERSION_HEX >= 0x030A00b1
- if (gc_was_enabled)
- PyGC_Enable();
- #else
- if (gc_was_enabled) {
- PyObject *tp, *v, *tb;
- PyErr_Fetch(&tp, &v, &tb);
- ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
- if (likely(ret || r == -1)) {
- Py_XDECREF(ret);
- PyErr_Restore(tp, v, tb);
- } else {
- Py_XDECREF(tp);
- Py_XDECREF(v);
- Py_XDECREF(tb);
- r = -1;
- }
- }
- Py_DECREF(gc);
- #endif
- }
-#endif
- return r;
-#endif
-}
-
-/* PyObject_GenericGetAttrNoDict */
- #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
- __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
- PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
- "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
- type_name, attr_name);
-#else
- "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
- type_name, PyString_AS_STRING(attr_name));
-#endif
- __Pyx_DECREF_TypeName(type_name);
- 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
-
-/* ImportFrom */
- static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
- PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
- if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
- const char* module_name_str = 0;
- PyObject* module_name = 0;
- PyObject* module_dot = 0;
- PyObject* full_name = 0;
- PyErr_Clear();
- module_name_str = PyModule_GetName(module);
- if (unlikely(!module_name_str)) { goto modbad; }
- module_name = PyUnicode_FromString(module_name_str);
- if (unlikely(!module_name)) { goto modbad; }
- module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__10);
- if (unlikely(!module_dot)) { goto modbad; }
- full_name = PyUnicode_Concat(module_dot, name);
- if (unlikely(!full_name)) { goto modbad; }
- #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400)
- {
- PyObject *modules = PyImport_GetModuleDict();
- if (unlikely(!modules))
- goto modbad;
- value = PyObject_GetItem(modules, full_name);
- }
- #else
- value = PyImport_GetModule(full_name);
- #endif
- modbad:
- Py_XDECREF(full_name);
- Py_XDECREF(module_dot);
- Py_XDECREF(module_name);
- }
- if (unlikely(!value)) {
- 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;
-}
-
-/* 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_GetAttrStrNoError(__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;
- (void) 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 */
- #if !CYTHON_COMPILING_IN_LIMITED_API
-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);
-}
-#endif
-
-/* AddTraceback */
- #include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
- int py_line, const char *filename) {
- if (c_line) {
- (void) __pyx_cfilenm;
- c_line = __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
- }
- _PyTraceback_Add(funcname, filename, c_line ? -c_line : py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
- const char *funcname, int c_line,
- int py_line, const char *filename) {
- PyCodeObject *py_code = NULL;
- PyObject *py_funcname = NULL;
- #if PY_MAJOR_VERSION < 3
- PyObject *py_srcfile = NULL;
- py_srcfile = PyString_FromString(filename);
- if (!py_srcfile) goto bad;
- #endif
- if (c_line) {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- if (!py_funcname) goto bad;
- #else
- py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- if (!py_funcname) goto bad;
- funcname = PyUnicode_AsUTF8(py_funcname);
- if (!funcname) goto bad;
- #endif
- }
- else {
- #if PY_MAJOR_VERSION < 3
- py_funcname = PyString_FromString(funcname);
- if (!py_funcname) goto bad;
- #endif
- }
- #if PY_MAJOR_VERSION < 3
- py_code = __Pyx_PyCode_New(
- 0,
- 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);
- #else
- py_code = PyCode_NewEmpty(filename, funcname, py_line);
- #endif
- Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline
- return py_code;
-bad:
- Py_XDECREF(py_funcname);
- #if PY_MAJOR_VERSION < 3
- Py_XDECREF(py_srcfile);
- #endif
- 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);
-}
-#endif
-
-/* CIntToPy */
- static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- 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);
- }
-}
-
-/* FormatTypeName */
- #if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
- PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
- __pyx_n_s_name_2);
- if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
- PyErr_Clear();
- Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__20));
- }
- return name;
-}
-#endif
-
-/* 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;\
- }
-
-/* CIntFromPy */
- static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- 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 || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available, 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 int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
- const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
- 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 || CYTHON_COMPILING_IN_LIMITED_API) && !defined(_PyLong_AsByteArray)
- PyErr_SetString(PyExc_RuntimeError,
- "_PyLong_AsByteArray() not available, 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;
-}
-
-/* FastTypeChecks */
- #if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
- while (a) {
- a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
- 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);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
- PyObject *mro;
- if (cls == a || cls == b) return 1;
- mro = cls->tp_mro;
- if (likely(mro)) {
- Py_ssize_t i, n;
- n = PyTuple_GET_SIZE(mro);
- for (i = 0; i < n; i++) {
- PyObject *base = PyTuple_GET_ITEM(mro, i);
- if (base == (PyObject *)a || base == (PyObject *)b)
- return 1;
- }
- return 0;
- }
- return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, 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) {
- if (exc_type1) {
- return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
- } else {
- return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
- }
-}
-#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
-#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *__pyx_tstate, PyObject **pvalue) {
- PyObject *et, *ev, *tb;
- PyObject *value = NULL;
- CYTHON_UNUSED_VAR(__pyx_tstate);
- __Pyx_ErrFetch(&et, &ev, &tb);
- if (!et) {
- Py_XDECREF(tb);
- Py_XDECREF(ev);
- Py_INCREF(Py_None);
- *pvalue = Py_None;
- return 0;
- }
- if (likely(et == PyExc_StopIteration)) {
- if (!ev) {
- Py_INCREF(Py_None);
- value = Py_None;
- }
-#if PY_VERSION_HEX >= 0x030300A0
- else if (likely(__Pyx_IS_TYPE(ev, (PyTypeObject*)PyExc_StopIteration))) {
- value = ((PyStopIterationObject *)ev)->value;
- Py_INCREF(value);
- Py_DECREF(ev);
- }
-#endif
- else if (unlikely(PyTuple_Check(ev))) {
- if (PyTuple_GET_SIZE(ev) >= 1) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
- value = PyTuple_GET_ITEM(ev, 0);
- Py_INCREF(value);
-#else
- value = PySequence_ITEM(ev, 0);
-#endif
- } else {
- Py_INCREF(Py_None);
- value = Py_None;
- }
- Py_DECREF(ev);
- }
- else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
- value = ev;
- }
- if (likely(value)) {
- Py_XDECREF(tb);
- Py_DECREF(et);
- *pvalue = value;
- return 0;
- }
- } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
- __Pyx_ErrRestore(et, ev, tb);
- return -1;
- }
- PyErr_NormalizeException(&et, &ev, &tb);
- if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
- __Pyx_ErrRestore(et, ev, tb);
- return -1;
- }
- Py_XDECREF(tb);
- Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
- value = ((PyStopIterationObject *)ev)->value;
- Py_INCREF(value);
- Py_DECREF(ev);
-#else
- {
- PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args);
- Py_DECREF(ev);
- if (likely(args)) {
- value = PySequence_GetItem(args, 0);
- Py_DECREF(args);
- }
- if (unlikely(!value)) {
- __Pyx_ErrRestore(NULL, NULL, NULL);
- Py_INCREF(Py_None);
- value = Py_None;
- }
- }
-#endif
- *pvalue = value;
- return 0;
-}
-static CYTHON_INLINE
-void __Pyx_Coroutine_ExceptionClear(__Pyx_ExcInfoStruct *exc_state) {
- PyObject *t, *v, *tb;
- t = exc_state->exc_type;
- v = exc_state->exc_value;
- tb = exc_state->exc_traceback;
- exc_state->exc_type = NULL;
- exc_state->exc_value = NULL;
- exc_state->exc_traceback = NULL;
- Py_XDECREF(t);
- Py_XDECREF(v);
- Py_XDECREF(tb);
-}
-#define __Pyx_Coroutine_AlreadyRunningError(gen) (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyRunningError(__pyx_CoroutineObject *gen) {
- const char *msg;
- CYTHON_MAYBE_UNUSED_VAR(gen);
- if ((0)) {
- #ifdef __Pyx_Coroutine_USED
- } else if (__Pyx_Coroutine_Check((PyObject*)gen)) {
- msg = "coroutine already executing";
- #endif
- #ifdef __Pyx_AsyncGen_USED
- } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
- msg = "async generator already executing";
- #endif
- } else {
- msg = "generator already executing";
- }
- PyErr_SetString(PyExc_ValueError, msg);
-}
-#define __Pyx_Coroutine_NotStartedError(gen) (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
-static void __Pyx__Coroutine_NotStartedError(PyObject *gen) {
- const char *msg;
- CYTHON_MAYBE_UNUSED_VAR(gen);
- if ((0)) {
- #ifdef __Pyx_Coroutine_USED
- } else if (__Pyx_Coroutine_Check(gen)) {
- msg = "can't send non-None value to a just-started coroutine";
- #endif
- #ifdef __Pyx_AsyncGen_USED
- } else if (__Pyx_AsyncGen_CheckExact(gen)) {
- msg = "can't send non-None value to a just-started async generator";
- #endif
- } else {
- msg = "can't send non-None value to a just-started generator";
- }
- PyErr_SetString(PyExc_TypeError, msg);
-}
-#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing) (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
-static void __Pyx__Coroutine_AlreadyTerminatedError(PyObject *gen, PyObject *value, int closing) {
- CYTHON_MAYBE_UNUSED_VAR(gen);
- CYTHON_MAYBE_UNUSED_VAR(closing);
- #ifdef __Pyx_Coroutine_USED
- if (!closing && __Pyx_Coroutine_Check(gen)) {
- PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
- } else
- #endif
- if (value) {
- #ifdef __Pyx_AsyncGen_USED
- if (__Pyx_AsyncGen_CheckExact(gen))
- PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
- else
- #endif
- PyErr_SetNone(PyExc_StopIteration);
- }
-}
-static
-PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
- __Pyx_PyThreadState_declare
- PyThreadState *tstate;
- __Pyx_ExcInfoStruct *exc_state;
- PyObject *retval;
- assert(!self->is_running);
- if (unlikely(self->resume_label == 0)) {
- if (unlikely(value && value != Py_None)) {
- return __Pyx_Coroutine_NotStartedError((PyObject*)self);
- }
- }
- if (unlikely(self->resume_label == -1)) {
- return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
- }
-#if CYTHON_FAST_THREAD_STATE
- __Pyx_PyThreadState_assign
- tstate = __pyx_tstate;
-#else
- tstate = __Pyx_PyThreadState_Current;
-#endif
- exc_state = &self->gi_exc_state;
- if (exc_state->exc_type) {
- #if CYTHON_COMPILING_IN_PYPY
- #else
- if (exc_state->exc_traceback) {
- PyTracebackObject *tb = (PyTracebackObject *) exc_state->exc_traceback;
- PyFrameObject *f = tb->tb_frame;
- assert(f->f_back == NULL);
- #if PY_VERSION_HEX >= 0x030B00A1
- f->f_back = PyThreadState_GetFrame(tstate);
- #else
- Py_XINCREF(tstate->frame);
- f->f_back = tstate->frame;
- #endif
- }
- #endif
- }
-#if CYTHON_USE_EXC_INFO_STACK
- exc_state->previous_item = tstate->exc_info;
- tstate->exc_info = exc_state;
-#else
- if (exc_state->exc_type) {
- __Pyx_ExceptionSwap(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
- } else {
- __Pyx_Coroutine_ExceptionClear(exc_state);
- __Pyx_ExceptionSave(&exc_state->exc_type, &exc_state->exc_value, &exc_state->exc_traceback);
- }
-#endif
- self->is_running = 1;
- retval = self->body(self, tstate, value);
- self->is_running = 0;
-#if CYTHON_USE_EXC_INFO_STACK
- exc_state = &self->gi_exc_state;
- tstate->exc_info = exc_state->previous_item;
- exc_state->previous_item = NULL;
- __Pyx_Coroutine_ResetFrameBackpointer(exc_state);
-#endif
- return retval;
-}
-static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__Pyx_ExcInfoStruct *exc_state) {
- PyObject *exc_tb = exc_state->exc_traceback;
- if (likely(exc_tb)) {
-#if CYTHON_COMPILING_IN_PYPY
-#else
- PyTracebackObject *tb = (PyTracebackObject *) exc_tb;
- PyFrameObject *f = tb->tb_frame;
- Py_CLEAR(f->f_back);
-#endif
- }
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_MethodReturn(PyObject* gen, PyObject *retval) {
- CYTHON_MAYBE_UNUSED_VAR(gen);
- if (unlikely(!retval)) {
- __Pyx_PyThreadState_declare
- __Pyx_PyThreadState_assign
- if (!__Pyx_PyErr_Occurred()) {
- PyObject *exc = PyExc_StopIteration;
- #ifdef __Pyx_AsyncGen_USED
- if (__Pyx_AsyncGen_CheckExact(gen))
- exc = __Pyx_PyExc_StopAsyncIteration;
- #endif
- __Pyx_PyErr_SetNone(exc);
- }
- }
- return retval;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
-static CYTHON_INLINE
-PyObject *__Pyx_PyGen_Send(PyGenObject *gen, PyObject *arg) {
-#if PY_VERSION_HEX <= 0x030A00A1
- return _PyGen_Send(gen, arg);
-#else
- PyObject *result;
- if (PyIter_Send((PyObject*)gen, arg ? arg : Py_None, &result) == PYGEN_RETURN) {
- if (PyAsyncGen_CheckExact(gen)) {
- assert(result == Py_None);
- PyErr_SetNone(PyExc_StopAsyncIteration);
- }
- else if (result == Py_None) {
- PyErr_SetNone(PyExc_StopIteration);
- }
- else {
- _PyGen_SetStopIterationValue(result);
- }
- Py_CLEAR(result);
- }
- return result;
-#endif
-}
-#endif
-static CYTHON_INLINE
-PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
- PyObject *ret;
- PyObject *val = NULL;
- __Pyx_Coroutine_Undelegate(gen);
- __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
- ret = __Pyx_Coroutine_SendEx(gen, val, 0);
- Py_XDECREF(val);
- return ret;
-}
-static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
- PyObject *retval;
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
- PyObject *yf = gen->yieldfrom;
- if (unlikely(gen->is_running))
- return __Pyx_Coroutine_AlreadyRunningError(gen);
- if (yf) {
- PyObject *ret;
- gen->is_running = 1;
- #ifdef __Pyx_Generator_USED
- if (__Pyx_Generator_CheckExact(yf)) {
- ret = __Pyx_Coroutine_Send(yf, value);
- } else
- #endif
- #ifdef __Pyx_Coroutine_USED
- if (__Pyx_Coroutine_Check(yf)) {
- ret = __Pyx_Coroutine_Send(yf, value);
- } else
- #endif
- #ifdef __Pyx_AsyncGen_USED
- if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
- ret = __Pyx_async_gen_asend_send(yf, value);
- } else
- #endif
- #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
- if (PyGen_CheckExact(yf)) {
- ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
- } else
- #endif
- #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
- if (PyCoro_CheckExact(yf)) {
- ret = __Pyx_PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
- } else
- #endif
- {
- if (value == Py_None)
- ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
- else
- ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
- }
- gen->is_running = 0;
- if (likely(ret)) {
- return ret;
- }
- retval = __Pyx_Coroutine_FinishDelegation(gen);
- } else {
- retval = __Pyx_Coroutine_SendEx(gen, value, 0);
- }
- return __Pyx_Coroutine_MethodReturn(self, retval);
-}
-static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
- PyObject *retval = NULL;
- int err = 0;
- #ifdef __Pyx_Generator_USED
- if (__Pyx_Generator_CheckExact(yf)) {
- retval = __Pyx_Coroutine_Close(yf);
- if (!retval)
- return -1;
- } else
- #endif
- #ifdef __Pyx_Coroutine_USED
- if (__Pyx_Coroutine_Check(yf)) {
- retval = __Pyx_Coroutine_Close(yf);
- if (!retval)
- return -1;
- } else
- if (__Pyx_CoroutineAwait_CheckExact(yf)) {
- retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf, NULL);
- if (!retval)
- return -1;
- } else
- #endif
- #ifdef __Pyx_AsyncGen_USED
- if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
- retval = __Pyx_async_gen_asend_close(yf, NULL);
- } else
- if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
- retval = __Pyx_async_gen_athrow_close(yf, NULL);
- } else
- #endif
- {
- PyObject *meth;
- gen->is_running = 1;
- meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_close);
- if (unlikely(!meth)) {
- if (unlikely(PyErr_Occurred())) {
- PyErr_WriteUnraisable(yf);
- }
- } else {
- retval = __Pyx_PyObject_CallNoArg(meth);
- Py_DECREF(meth);
- if (unlikely(!retval))
- err = -1;
- }
- gen->is_running = 0;
- }
- Py_XDECREF(retval);
- return err;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
- PyObject *yf = gen->yieldfrom;
- if (unlikely(gen->is_running))
- return __Pyx_Coroutine_AlreadyRunningError(gen);
- if (yf) {
- PyObject *ret;
- gen->is_running = 1;
- #ifdef __Pyx_Generator_USED
- if (__Pyx_Generator_CheckExact(yf)) {
- ret = __Pyx_Generator_Next(yf);
- } else
- #endif
- #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
- if (PyGen_CheckExact(yf)) {
- ret = __Pyx_PyGen_Send((PyGenObject*)yf, NULL);
- } else
- #endif
- #ifdef __Pyx_Coroutine_USED
- if (__Pyx_Coroutine_Check(yf)) {
- ret = __Pyx_Coroutine_Send(yf, Py_None);
- } else
- #endif
- ret = __Pyx_PyObject_GetIterNextFunc(yf)(yf);
- gen->is_running = 0;
- if (likely(ret)) {
- return ret;
- }
- return __Pyx_Coroutine_FinishDelegation(gen);
- }
- return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
-}
-static PyObject *__Pyx_Coroutine_Close_Method(PyObject *self, PyObject *arg) {
- CYTHON_UNUSED_VAR(arg);
- return __Pyx_Coroutine_Close(self);
-}
-static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
- PyObject *retval, *raised_exception;
- PyObject *yf = gen->yieldfrom;
- int err = 0;
- if (unlikely(gen->is_running))
- return __Pyx_Coroutine_AlreadyRunningError(gen);
- if (yf) {
- Py_INCREF(yf);
- err = __Pyx_Coroutine_CloseIter(gen, yf);
- __Pyx_Coroutine_Undelegate(gen);
- Py_DECREF(yf);
- }
- if (err == 0)
- PyErr_SetNone(PyExc_GeneratorExit);
- retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
- if (unlikely(retval)) {
- const char *msg;
- Py_DECREF(retval);
- if ((0)) {
- #ifdef __Pyx_Coroutine_USED
- } else if (__Pyx_Coroutine_Check(self)) {
- msg = "coroutine ignored GeneratorExit";
- #endif
- #ifdef __Pyx_AsyncGen_USED
- } else if (__Pyx_AsyncGen_CheckExact(self)) {
-#if PY_VERSION_HEX < 0x03060000
- msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
-#else
- msg = "async generator ignored GeneratorExit";
-#endif
- #endif
- } else {
- msg = "generator ignored GeneratorExit";
- }
- PyErr_SetString(PyExc_RuntimeError, msg);
- return NULL;
- }
- raised_exception = PyErr_Occurred();
- if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
- if (raised_exception) PyErr_Clear();
- Py_INCREF(Py_None);
- return Py_None;
- }
- return NULL;
-}
-static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
- PyObject *args, int close_on_genexit) {
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
- PyObject *yf = gen->yieldfrom;
- if (unlikely(gen->is_running))
- return __Pyx_Coroutine_AlreadyRunningError(gen);
- if (yf) {
- PyObject *ret;
- Py_INCREF(yf);
- if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
- int err = __Pyx_Coroutine_CloseIter(gen, yf);
- Py_DECREF(yf);
- __Pyx_Coroutine_Undelegate(gen);
- if (err < 0)
- return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
- goto throw_here;
- }
- gen->is_running = 1;
- if (0
- #ifdef __Pyx_Generator_USED
- || __Pyx_Generator_CheckExact(yf)
- #endif
- #ifdef __Pyx_Coroutine_USED
- || __Pyx_Coroutine_Check(yf)
- #endif
- ) {
- ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
- #ifdef __Pyx_Coroutine_USED
- } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
- ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
- #endif
- } else {
- PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(yf, __pyx_n_s_throw);
- if (unlikely(!meth)) {
- Py_DECREF(yf);
- if (unlikely(PyErr_Occurred())) {
- gen->is_running = 0;
- return NULL;
- }
- __Pyx_Coroutine_Undelegate(gen);
- gen->is_running = 0;
- goto throw_here;
- }
- if (likely(args)) {
- ret = __Pyx_PyObject_Call(meth, args, NULL);
- } else {
- PyObject *cargs[4] = {NULL, typ, val, tb};
- ret = __Pyx_PyObject_FastCall(meth, cargs+1, 3 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
- }
- Py_DECREF(meth);
- }
- gen->is_running = 0;
- Py_DECREF(yf);
- if (!ret) {
- ret = __Pyx_Coroutine_FinishDelegation(gen);
- }
- return __Pyx_Coroutine_MethodReturn(self, ret);
- }
-throw_here:
- __Pyx_Raise(typ, val, tb, NULL);
- return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
-}
-static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
- PyObject *typ;
- PyObject *val = NULL;
- PyObject *tb = NULL;
- if (unlikely(!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)))
- return NULL;
- return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
-}
-static CYTHON_INLINE int __Pyx_Coroutine_traverse_excstate(__Pyx_ExcInfoStruct *exc_state, visitproc visit, void *arg) {
- Py_VISIT(exc_state->exc_type);
- Py_VISIT(exc_state->exc_value);
- Py_VISIT(exc_state->exc_traceback);
- return 0;
-}
-static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
- Py_VISIT(gen->closure);
- Py_VISIT(gen->classobj);
- Py_VISIT(gen->yieldfrom);
- return __Pyx_Coroutine_traverse_excstate(&gen->gi_exc_state, visit, arg);
-}
-static int __Pyx_Coroutine_clear(PyObject *self) {
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
- Py_CLEAR(gen->closure);
- Py_CLEAR(gen->classobj);
- Py_CLEAR(gen->yieldfrom);
- __Pyx_Coroutine_ExceptionClear(&gen->gi_exc_state);
-#ifdef __Pyx_AsyncGen_USED
- if (__Pyx_AsyncGen_CheckExact(self)) {
- Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
- }
-#endif
- Py_CLEAR(gen->gi_code);
- Py_CLEAR(gen->gi_frame);
- Py_CLEAR(gen->gi_name);
- Py_CLEAR(gen->gi_qualname);
- Py_CLEAR(gen->gi_modulename);
- return 0;
-}
-static void __Pyx_Coroutine_dealloc(PyObject *self) {
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
- PyObject_GC_UnTrack(gen);
- if (gen->gi_weakreflist != NULL)
- PyObject_ClearWeakRefs(self);
- if (gen->resume_label >= 0) {
- PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
- if (unlikely(PyObject_CallFinalizerFromDealloc(self)))
-#else
- Py_TYPE(gen)->tp_del(self);
- if (unlikely(Py_REFCNT(self) > 0))
-#endif
- {
- return;
- }
- PyObject_GC_UnTrack(self);
- }
-#ifdef __Pyx_AsyncGen_USED
- if (__Pyx_AsyncGen_CheckExact(self)) {
- /* We have to handle this case for asynchronous generators
- right here, because this code has to be between UNTRACK
- and GC_Del. */
- Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
- }
-#endif
- __Pyx_Coroutine_clear(self);
- __Pyx_PyHeapTypeObject_GC_Del(gen);
-}
-static void __Pyx_Coroutine_del(PyObject *self) {
- PyObject *error_type, *error_value, *error_traceback;
- __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
- __Pyx_PyThreadState_declare
- if (gen->resume_label < 0) {
- return;
- }
-#if !CYTHON_USE_TP_FINALIZE
- assert(self->ob_refcnt == 0);
- __Pyx_SET_REFCNT(self, 1);
-#endif
- __Pyx_PyThreadState_assign
- __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-#ifdef __Pyx_AsyncGen_USED
- if (__Pyx_AsyncGen_CheckExact(self)) {
- __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
- PyObject *finalizer = agen->ag_finalizer;
- if (finalizer && !agen->ag_closed) {
- PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
- if (unlikely(!res)) {
- PyErr_WriteUnraisable(self);
- } else {
- Py_DECREF(res);
- }
- __Pyx_ErrRestore(error_type, error_value, error_traceback);
- return;
- }
- }
-#endif
- if (unlikely(gen->resume_label == 0 && !error_value)) {
-#ifdef __Pyx_Coroutine_USED
-#ifdef __Pyx_Generator_USED
- if (!__Pyx_Generator_CheckExact(self))
-#endif
- {
- PyObject_GC_UnTrack(self);
-#if PY_MAJOR_VERSION >= 3 || defined(PyErr_WarnFormat)
- if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
- PyErr_WriteUnraisable(self);
-#else
- {PyObject *msg;
- char *cmsg;
- #if CYTHON_COMPILING_IN_PYPY
- msg = NULL;
- cmsg = (char*) "coroutine was never awaited";
- #else
- char *cname;
- PyObject *qualname;
- qualname = gen->gi_qualname;
- cname = PyString_AS_STRING(qualname);
- msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
- if (unlikely(!msg)) {
- PyErr_Clear();
- cmsg = (char*) "coroutine was never awaited";
- } else {
- cmsg = PyString_AS_STRING(msg);
- }
- #endif
- if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
- PyErr_WriteUnraisable(self);
- Py_XDECREF(msg);}
-#endif
- PyObject_GC_Track(self);
- }
-#endif
- } else {
- PyObject *res = __Pyx_Coroutine_Close(self);
- if (unlikely(!res)) {
- if (PyErr_Occurred())
- PyErr_WriteUnraisable(self);
- } else {
- Py_DECREF(res);
- }
- }
- __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if !CYTHON_USE_TP_FINALIZE
- assert(Py_REFCNT(self) > 0);
- if (likely(--self->ob_refcnt == 0)) {
- return;
- }
- {
- Py_ssize_t refcnt = Py_REFCNT(self);
- _Py_NewReference(self);
- __Pyx_SET_REFCNT(self, refcnt);
- }
-#if CYTHON_COMPILING_IN_CPYTHON
- assert(PyType_IS_GC(Py_TYPE(self)) &&
- _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
- _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
- --Py_TYPE(self)->tp_frees;
- --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self, void *context)
-{
- PyObject *name = self->gi_name;
- CYTHON_UNUSED_VAR(context);
- if (unlikely(!name)) name = Py_None;
- Py_INCREF(name);
- return name;
-}
-static int
-__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
- if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
- if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
- {
- PyErr_SetString(PyExc_TypeError,
- "__name__ must be set to a string object");
- return -1;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(self->gi_name, value);
- return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self, void *context)
-{
- PyObject *name = self->gi_qualname;
- CYTHON_UNUSED_VAR(context);
- if (unlikely(!name)) name = Py_None;
- Py_INCREF(name);
- return name;
-}
-static int
-__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value, void *context)
-{
- CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
- if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
- if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
- {
- PyErr_SetString(PyExc_TypeError,
- "__qualname__ must be set to a string object");
- return -1;
- }
- Py_INCREF(value);
- __Pyx_Py_XDECREF_SET(self->gi_qualname, value);
- return 0;
-}
-static PyObject *
-__Pyx_Coroutine_get_frame(__pyx_CoroutineObject *self, void *context)
-{
- PyObject *frame = self->gi_frame;
- CYTHON_UNUSED_VAR(context);
- if (!frame) {
- if (unlikely(!self->gi_code)) {
- Py_RETURN_NONE;
- }
- frame = (PyObject *) PyFrame_New(
- PyThreadState_Get(), /*PyThreadState *tstate,*/
- (PyCodeObject*) self->gi_code, /*PyCodeObject *code,*/
- __pyx_d, /*PyObject *globals,*/
- 0 /*PyObject *locals*/
- );
- if (unlikely(!frame))
- return NULL;
- self->gi_frame = frame;
- }
- Py_INCREF(frame);
- return frame;
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_New(
- PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
- PyObject *name, PyObject *qualname, PyObject *module_name) {
- __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
- if (unlikely(!gen))
- return NULL;
- return __Pyx__Coroutine_NewInit(gen, body, code, closure, name, qualname, module_name);
-}
-static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
- __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *code, PyObject *closure,
- PyObject *name, PyObject *qualname, PyObject *module_name) {
- gen->body = body;
- gen->closure = closure;
- Py_XINCREF(closure);
- gen->is_running = 0;
- gen->resume_label = 0;
- gen->classobj = NULL;
- gen->yieldfrom = NULL;
- gen->gi_exc_state.exc_type = NULL;
- gen->gi_exc_state.exc_value = NULL;
- gen->gi_exc_state.exc_traceback = NULL;
-#if CYTHON_USE_EXC_INFO_STACK
- gen->gi_exc_state.previous_item = NULL;
-#endif
- gen->gi_weakreflist = NULL;
- Py_XINCREF(qualname);
- gen->gi_qualname = qualname;
- Py_XINCREF(name);
- gen->gi_name = name;
- Py_XINCREF(module_name);
- gen->gi_modulename = module_name;
- Py_XINCREF(code);
- gen->gi_code = code;
- gen->gi_frame = NULL;
- PyObject_GC_Track(gen);
- return gen;
-}
-
-/* PatchModuleWithCoroutine */
- static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
- int result;
- PyObject *globals, *result_obj;
- globals = PyDict_New(); if (unlikely(!globals)) goto ignore;
- result = PyDict_SetItemString(globals, "_cython_coroutine_type",
- #ifdef __Pyx_Coroutine_USED
- (PyObject*)__pyx_CoroutineType);
- #else
- Py_None);
- #endif
- if (unlikely(result < 0)) goto ignore;
- result = PyDict_SetItemString(globals, "_cython_generator_type",
- #ifdef __Pyx_Generator_USED
- (PyObject*)__pyx_GeneratorType);
- #else
- Py_None);
- #endif
- if (unlikely(result < 0)) goto ignore;
- if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
- if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
- result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
- if (unlikely(!result_obj)) goto ignore;
- Py_DECREF(result_obj);
- Py_DECREF(globals);
- return module;
-ignore:
- Py_XDECREF(globals);
- PyErr_WriteUnraisable(module);
- if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
- Py_DECREF(module);
- module = NULL;
- }
-#else
- py_code++;
-#endif
- return module;
-}
-
-/* PatchGeneratorABC */
- #ifndef CYTHON_REGISTER_ABCS
-#define CYTHON_REGISTER_ABCS 1
-#endif
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
-static PyObject* __Pyx_patch_abc_module(PyObject *module);
-static PyObject* __Pyx_patch_abc_module(PyObject *module) {
- module = __Pyx_Coroutine_patch_module(
- module, ""
-"if _cython_generator_type is not None:\n"
-" try: Generator = _module.Generator\n"
-" except AttributeError: pass\n"
-" else: Generator.register(_cython_generator_type)\n"
-"if _cython_coroutine_type is not None:\n"
-" try: Coroutine = _module.Coroutine\n"
-" except AttributeError: pass\n"
-" else: Coroutine.register(_cython_coroutine_type)\n"
- );
- return module;
-}
-#endif
-static int __Pyx_patch_abc(void) {
-#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
- static int abc_patched = 0;
- if (CYTHON_REGISTER_ABCS && !abc_patched) {
- PyObject *module;
- module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
- if (unlikely(!module)) {
- PyErr_WriteUnraisable(NULL);
- if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
- ((PY_MAJOR_VERSION >= 3) ?
- "Cython module failed to register with collections.abc module" :
- "Cython module failed to register with collections module"), 1) < 0)) {
- return -1;
- }
- } else {
- module = __Pyx_patch_abc_module(module);
- abc_patched = 1;
- if (unlikely(!module))
- return -1;
- Py_DECREF(module);
- }
- module = PyImport_ImportModule("backports_abc");
- if (module) {
- module = __Pyx_patch_abc_module(module);
- Py_XDECREF(module);
- }
- if (!module) {
- PyErr_Clear();
- }
- }
-#else
- if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
-#endif
- return 0;
-}
-
-/* Generator */
- static PyMethodDef __pyx_Generator_methods[] = {
- {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
- (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
- {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
- (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
- {"close", (PyCFunction) __Pyx_Coroutine_Close_Method, METH_NOARGS,
- (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
- {0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
- {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
- {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
- (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
- {(char*) "gi_code", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_code), READONLY, NULL},
- {(char *) "__module__", T_OBJECT, offsetof(__pyx_CoroutineObject, gi_modulename), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
- {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CoroutineObject, gi_weakreflist), READONLY, 0},
-#endif
- {0, 0, 0, 0, 0}
-};
-static PyGetSetDef __pyx_Generator_getsets[] = {
- {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
- (char*) PyDoc_STR("name of the generator"), 0},
- {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
- (char*) PyDoc_STR("qualified name of the generator"), 0},
- {(char *) "gi_frame", (getter)__Pyx_Coroutine_get_frame, NULL,
- (char*) PyDoc_STR("Frame of the generator"), 0},
- {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_GeneratorType_slots[] = {
- {Py_tp_dealloc, (void *)__Pyx_Coroutine_dealloc},
- {Py_tp_traverse, (void *)__Pyx_Coroutine_traverse},
- {Py_tp_iter, (void *)PyObject_SelfIter},
- {Py_tp_iternext, (void *)__Pyx_Generator_Next},
- {Py_tp_methods, (void *)__pyx_Generator_methods},
- {Py_tp_members, (void *)__pyx_Generator_memberlist},
- {Py_tp_getset, (void *)__pyx_Generator_getsets},
- {Py_tp_getattro, (void *) __Pyx_PyObject_GenericGetAttrNoDict},
-#if CYTHON_USE_TP_FINALIZE
- {Py_tp_finalize, (void *)__Pyx_Coroutine_del},
-#endif
- {0, 0},
-};
-static PyType_Spec __pyx_GeneratorType_spec = {
- __PYX_TYPE_MODULE_PREFIX "generator",
- sizeof(__pyx_CoroutineObject),
- 0,
- Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
- __pyx_GeneratorType_slots
-};
-#else
-static PyTypeObject __pyx_GeneratorType_type = {
- PyVarObject_HEAD_INIT(0, 0)
- __PYX_TYPE_MODULE_PREFIX "generator",
- sizeof(__pyx_CoroutineObject),
- 0,
- (destructor) __Pyx_Coroutine_dealloc,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
- 0,
- (traverseproc) __Pyx_Coroutine_traverse,
- 0,
- 0,
- offsetof(__pyx_CoroutineObject, gi_weakreflist),
- 0,
- (iternextfunc) __Pyx_Generator_Next,
- __pyx_Generator_methods,
- __pyx_Generator_memberlist,
- __pyx_Generator_getsets,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
-#if CYTHON_USE_TP_FINALIZE
- 0,
-#else
- __Pyx_Coroutine_del,
-#endif
- 0,
-#if CYTHON_USE_TP_FINALIZE
- __Pyx_Coroutine_del,
-#elif PY_VERSION_HEX >= 0x030400a1
- 0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
- 0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
- 0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000
- 0,
-#endif
-};
-#endif
-static int __pyx_Generator_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
- __pyx_GeneratorType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_GeneratorType_spec, NULL);
-#else
- (void) module;
- __pyx_GeneratorType_type.tp_getattro = __Pyx_PyObject_GenericGetAttrNoDict;
- __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
- __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
-#endif
- if (unlikely(!__pyx_GeneratorType)) {
- return -1;
- }
- return 0;
-}
-
-/* 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),
- "compile time 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 */
- #if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
- if (t.is_unicode | t.is_str) {
- if (t.intern) {
- *str = PyUnicode_InternFromString(t.s);
- } else if (t.encoding) {
- *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
- } else {
- *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
- }
- } else {
- *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
- }
- if (!*str)
- return -1;
- if (PyObject_Hash(*str) == -1)
- return -1;
- return 0;
-}
-#endif
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
- while (t->p) {
- #if PY_MAJOR_VERSION >= 3
- __Pyx_InitString(*t, t->p);
- #else
- 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);
- }
- if (!*t->p)
- return -1;
- if (PyObject_Hash(*t->p) == -1)
- return -1;
- #endif
- ++t;
- }
- return 0;
-}
-#endif
-
-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 && !CYTHON_COMPILING_IN_LIMITED_API) || (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) {
- __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
- if (PyLong_Check(result)) {
- if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
- "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). "
- "The ability to return an instance of a strict subclass of int is deprecated, "
- "and may be removed in a future version of Python.",
- result_type_name)) {
- __Pyx_DECREF_TypeName(result_type_name);
- Py_DECREF(result);
- return NULL;
- }
- __Pyx_DECREF_TypeName(result_type_name);
- return result;
- }
-#endif
- PyErr_Format(PyExc_TypeError,
- "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
- type_name, type_name, result_type_name);
- __Pyx_DECREF_TypeName(result_type_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 Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
- if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
- return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
- } else if (likely(PyInt_CheckExact(o))) {
- return PyInt_AS_LONG(o);
-#endif
- } else {
- Py_ssize_t ival;
- PyObject *x;
- x = PyNumber_Index(o);
- if (!x) return -1;
- ival = PyInt_AsLong(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);
-}
-
-
-/* #### Code section: utility_code_pragmas_end ### */
-#if _MSV_VER
-#pragma warning( pop )
-#endif
-
-
-
-/* #### Code section: end ### */
-#endif /* Py_PYTHON_H */
diff --git a/spaces/matthoffner/chatbot-mini/components/Chatbar/components/ClearConversations.tsx b/spaces/matthoffner/chatbot-mini/components/Chatbar/components/ClearConversations.tsx
deleted file mode 100644
index 5f17aacc511b7f531dc9df14e9e24bbaaf6c7fe9..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/components/Chatbar/components/ClearConversations.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import { IconCheck, IconTrash, IconX } from '@tabler/icons-react';
-import { FC, useState } from 'react';
-
-import { useTranslation } from 'next-i18next';
-
-
-interface Props {
- onClearConversations: () => void;
-}
-
-export const ClearConversations: FC = ({ onClearConversations }) => {
- const [isConfirming, setIsConfirming] = useState(false);
-
- const { t } = useTranslation('sidebar');
-
- const handleClearConversations = () => {
- onClearConversations();
- setIsConfirming(false);
- };
-
- return isConfirming ? (
-
-
-
-
- {t('Are you sure?')}
-
-
-
- {
- e.stopPropagation();
- handleClearConversations();
- }}
- />
-
- {
- e.stopPropagation();
- setIsConfirming(false);
- }}
- />
-
-
;
-
-export function randomUUID(): UUID {
- // Only on old safari / ios
- if (!("randomUUID" in crypto)) {
- return "10000000-1000-4000-8000-100000000000".replace(/[018]/g, (c) =>
- (
- Number(c) ^
- (crypto.getRandomValues(new Uint8Array(1))[0] & (15 >> (Number(c) / 4)))
- ).toString(16)
- ) as UUID;
- }
- return crypto.randomUUID();
-}
diff --git a/spaces/ml6team/post-processing-summarization/custom_renderer.py b/spaces/ml6team/post-processing-summarization/custom_renderer.py
deleted file mode 100644
index 2610ce8df612751f8d007da7d52e51dcaf474550..0000000000000000000000000000000000000000
--- a/spaces/ml6team/post-processing-summarization/custom_renderer.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from typing import Dict
-from PIL import ImageFont
-
-TPL_DEP_WORDS = """
-
- {text}
- {tag}
-
-"""
-
-TPL_DEP_SVG = """
-{content}
-"""
-
-TPL_DEP_ARCS = """
-
-
- {label}
-
-
-"""
-
-
-def get_pil_text_size(text, font_size, font_name):
- font = ImageFont.truetype(font_name, font_size)
- size = font.getsize(text)
- return size
-
-
-def render_arrow(
- label: str, start: int, end: int, direction: str, i: int
-) -> str:
- """Render individual arrow.
-
- label (str): Dependency label.
- start (int): Index of start word.
- end (int): Index of end word.
- direction (str): Arrow direction, 'left' or 'right'.
- i (int): Unique ID, typically arrow index.
- RETURNS (str): Rendered SVG markup.
- """
-
- arc = get_arc(start + 10, 50, 5, end + 10)
- arrowhead = get_arrowhead(direction, start + 10, 50, end + 10)
- label_side = "right" if direction == "rtl" else "left"
- return TPL_DEP_ARCS.format(
- id=0,
- i=0,
- stroke=2,
- head=arrowhead,
- label=label,
- label_side=label_side,
- arc=arc,
- )
-
-
-def get_arc(x_start: int, y: int, y_curve: int, x_end: int) -> str:
- """Render individual arc.
-
- x_start (int): X-coordinate of arrow start point.
- y (int): Y-coordinate of arrow start and end point.
- y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
- x_end (int): X-coordinate of arrow end point.
- RETURNS (str): Definition of the arc path ('d' attribute).
- """
- template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
- return template.format(x=x_start, y=y, c=y_curve, e=x_end)
-
-
-def get_arrowhead(direction: str, x: int, y: int, end: int) -> str:
- """Render individual arrow head.
-
- direction (str): Arrow direction, 'left' or 'right'.
- x (int): X-coordinate of arrow start point.
- y (int): Y-coordinate of arrow start and end point.
- end (int): X-coordinate of arrow end point.
- RETURNS (str): Definition of the arrow head path ('d' attribute).
- """
- arrow_width = 6
- if direction == "left":
- p1, p2, p3 = (x, x - arrow_width + 2, x + arrow_width - 2)
- else:
- p1, p2, p3 = (end, end + arrow_width - 2, end - arrow_width + 2)
- return f"M{p1},{y + 2} L{p2},{y - arrow_width} {p3},{y - arrow_width}"
-
-
-def render_sentence_custom(unmatched_list: Dict, nlp):
- arcs_svg = []
- doc = nlp(unmatched_list["sentence"])
-
- x_value_counter = 10
- index_counter = 0
- svg_words = []
- words_under_arc = []
- direction_current = "rtl"
-
- if unmatched_list["cur_word_index"] < unmatched_list["target_word_index"]:
- min_index = unmatched_list["cur_word_index"]
- max_index = unmatched_list["target_word_index"]
- direction_current = "left"
- else:
- max_index = unmatched_list["cur_word_index"]
- min_index = unmatched_list["target_word_index"]
- for i, token in enumerate(doc):
- word = str(token)
- word = word + " "
- pixel_x_length = get_pil_text_size(word, 16, 'arial.ttf')[0]
- svg_words.append(TPL_DEP_WORDS.format(text=word, tag="", x=x_value_counter, y=70))
- if min_index <= index_counter <= max_index:
- words_under_arc.append(x_value_counter)
- if index_counter < max_index - 1:
- x_value_counter += 50
- index_counter += 1
- x_value_counter += pixel_x_length + 4
-
- arcs_svg.append(render_arrow(unmatched_list['dep'], words_under_arc[0], words_under_arc[-1], direction_current, i))
-
- content = "".join(svg_words) + "".join(arcs_svg)
-
- full_svg = TPL_DEP_SVG.format(
- id=0,
- width=1200, # 600
- height=75, # 125
- color="#00000",
- bg="#ffffff",
- font="Arial",
- content=content,
- dir="ltr",
- lang="en",
- )
- return full_svg
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/criterion.py b/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/criterion.py
deleted file mode 100644
index f4d5b71242f87c6f67463f9c31f873a742f3e5c7..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/criterion.py
+++ /dev/null
@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-"""
-MaskFormer criterion.
-"""
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.utils.comm import get_world_size
-
-from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list
-
-
-def dice_loss(inputs, targets, num_masks):
- """
- Compute the DICE loss, similar to generalized IOU for masks
- Args:
- inputs: A float tensor of arbitrary shape.
- The predictions for each example.
- targets: A float tensor with the same shape as inputs. Stores the binary
- classification label for each element in inputs
- (0 for the negative class and 1 for the positive class).
- """
- inputs = inputs.sigmoid()
- inputs = inputs.flatten(1)
- numerator = 2 * (inputs * targets).sum(-1)
- denominator = inputs.sum(-1) + targets.sum(-1)
- loss = 1 - (numerator + 1) / (denominator + 1)
- return loss.sum() / num_masks
-
-
-def sigmoid_focal_loss(
- inputs, targets, num_masks, alpha: float = 0.25, gamma: float = 2
-):
- """
- Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
- Args:
- inputs: A float tensor of arbitrary shape.
- The predictions for each example.
- targets: A float tensor with the same shape as inputs. Stores the binary
- classification label for each element in inputs
- (0 for the negative class and 1 for the positive class).
- alpha: (optional) Weighting factor in range (0,1) to balance
- positive vs negative examples. Default = -1 (no weighting).
- gamma: Exponent of the modulating factor (1 - p_t) to
- balance easy vs hard examples.
- Returns:
- Loss tensor
- """
- prob = inputs.sigmoid()
- ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
- p_t = prob * targets + (1 - prob) * (1 - targets)
- loss = ce_loss * ((1 - p_t) ** gamma)
-
- if alpha >= 0:
- alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
- loss = alpha_t * loss
-
- return loss.mean(1).sum() / num_masks
-
-
-class SetCriterion(nn.Module):
- """This class computes the loss for DETR.
- The process happens in two steps:
- 1) we compute hungarian assignment between ground truth boxes and the outputs of the model
- 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
- """
-
- def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses):
- """Create the criterion.
- Parameters:
- num_classes: number of object categories, omitting the special no-object category
- matcher: module able to compute a matching between targets and proposals
- weight_dict: dict containing as key the names of the losses and as values their relative weight.
- eos_coef: relative classification weight applied to the no-object category
- losses: list of all the losses to be applied. See get_loss for list of available losses.
- """
- super().__init__()
- self.num_classes = num_classes
- self.matcher = matcher
- self.weight_dict = weight_dict
- self.eos_coef = eos_coef
- self.losses = losses
- if eos_coef > 0:
-
- empty_weight = torch.ones(self.num_classes + 1)
-
- empty_weight[-1] = self.eos_coef
- self.register_buffer("empty_weight", empty_weight)
- self.use_ignore_idx = False
- else:
- self.use_ignore_idx = True
- self.cur_target = []
-
- def loss_labels(self, outputs, targets, indices, num_masks):
- """Classification loss (NLL)
- targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
- """
- assert "pred_logits" in outputs
- src_logits = outputs["pred_logits"]
-
- idx = self._get_src_permutation_idx(indices)
- target_classes_o = torch.cat(
- [t["labels"][J] for t, (_, J) in zip(targets, indices)]
- )
- target_classes = torch.full(
- src_logits.shape[:2],
- self.num_classes,
- dtype=torch.int64,
- device=src_logits.device,
- )
- target_classes[idx] = target_classes_o
- if self.use_ignore_idx:
- loss_ce = F.cross_entropy(
- src_logits.transpose(1, 2),
- target_classes,
- ignore_index=self.num_classes,
- )
- else:
- if "empty_weight" in outputs:
- empty_weight = torch.cat(
- [outputs["empty_weight"], self.empty_weight[-1:]]
- ).detach()
- else:
- empty_weight = self.empty_weight
- loss_ce = F.cross_entropy(
- src_logits.transpose(1, 2), target_classes, empty_weight
- )
- losses = {"loss_ce": loss_ce}
- return losses
-
- def loss_masks(self, outputs, targets, indices, num_masks):
- """Compute the losses related to the masks: the focal loss and the dice loss.
- targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
- """
- assert "pred_masks" in outputs
-
- src_idx = self._get_src_permutation_idx(indices)
- tgt_idx = self._get_tgt_permutation_idx(indices)
- src_masks = outputs["pred_masks"]
- src_masks = src_masks[src_idx]
- masks = [t["masks"] for t in targets]
- # TODO use valid to mask invalid areas due to padding in loss
- target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
- target_masks = target_masks.to(src_masks)
- target_masks = target_masks[tgt_idx]
-
- # upsample predictions to the target size
- src_masks = F.interpolate(
- src_masks[:, None],
- size=target_masks.shape[-2:],
- mode="bilinear",
- align_corners=False,
- )
- src_masks = src_masks[:, 0].flatten(1)
-
- target_masks = target_masks.flatten(1)
- target_masks = target_masks.view(src_masks.shape)
- losses = {
- "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_masks),
- "loss_dice": dice_loss(src_masks, target_masks, num_masks),
- }
- return losses
-
- def _get_src_permutation_idx(self, indices):
- # permute predictions following indices
- batch_idx = torch.cat(
- [torch.full_like(src, i) for i, (src, _) in enumerate(indices)]
- )
- src_idx = torch.cat([src for (src, _) in indices])
- return batch_idx, src_idx
-
- def _get_tgt_permutation_idx(self, indices):
- # permute targets following indices
- batch_idx = torch.cat(
- [torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]
- )
- tgt_idx = torch.cat([tgt for (_, tgt) in indices])
- return batch_idx, tgt_idx
-
- def get_loss(self, loss, outputs, targets, indices, num_masks):
- loss_map = {"labels": self.loss_labels, "masks": self.loss_masks}
- assert loss in loss_map, f"do you really want to compute {loss} loss?"
- return loss_map[loss](outputs, targets, indices, num_masks)
-
- def forward(self, outputs, targets):
- """This performs the loss computation.
- Parameters:
- outputs: dict of tensors, see the output specification of the model for the format
- targets: list of dicts, such that len(targets) == batch_size.
- The expected keys in each dict depends on the losses applied, see each loss' doc
- """
- outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
-
- # Retrieve the matching between the outputs of the last layer and the targets
- indices = self.matcher(outputs_without_aux, targets)
-
- # Compute the average number of target boxes accross all nodes, for normalization purposes
- num_masks = sum(len(t["labels"]) for t in targets)
- num_masks = torch.as_tensor(
- [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device
- )
- if is_dist_avail_and_initialized():
- torch.distributed.all_reduce(num_masks)
- num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
-
- # Compute all the requested losses
- losses = {}
- for loss in self.losses:
- losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))
-
- # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
- if "aux_outputs" in outputs:
- for i, aux_outputs in enumerate(outputs["aux_outputs"]):
- indices = self.matcher(aux_outputs, targets)
- for loss in self.losses:
- l_dict = self.get_loss(
- loss, aux_outputs, targets, indices, num_masks
- )
- l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
- losses.update(l_dict)
-
- return losses
-
- def clean_buffer(self):
- self.cur_target = []
diff --git a/spaces/mohitmayank/EmojiFinder/README.md b/spaces/mohitmayank/EmojiFinder/README.md
deleted file mode 100644
index 87afcccbf8e4d4a5a77f2c1e581b1f81b4ff5028..0000000000000000000000000000000000000000
--- a/spaces/mohitmayank/EmojiFinder/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: EmojiFinder 🕵
-emoji: 🐨
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/mueller-franzes/medfusion-app/tests/losses/test_lpips.py b/spaces/mueller-franzes/medfusion-app/tests/losses/test_lpips.py
deleted file mode 100644
index 606fbac8362a0a491fc35d3e12a0394a7509829d..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/tests/losses/test_lpips.py
+++ /dev/null
@@ -1,37 +0,0 @@
-
-
-import torch
-from medical_diffusion.loss.perceivers import LPIPS
-from medical_diffusion.data.datasets import AIROGSDataset, SimpleDataset3D
-
-loss = LPIPS(normalize=False)
-torch.manual_seed(0)
-
-# input = torch.randn((1, 3, 16, 128, 128)) # 3D - 1 channel
-# input = torch.randn((1, 1, 128, 128)) # 2D - 1 channel
-# input = torch.randn((1, 3, 128, 128)) # 2D - 3 channel
-
-# target = input/2
-
-# print(loss(input, target))
-
-
-# ds = AIROGSDataset(
-# crawler_ext='jpg',
-# image_resize=(256, 256),
-# image_crop=(256, 256),
-# path_root='/mnt/hdd/datasets/eye/AIROGS/data/', # '/home/gustav/Documents/datasets/AIROGS/dataset', '/mnt/hdd/datasets/eye/AIROGS/data/'
-# )
-ds = SimpleDataset3D(
- crawler_ext='nii.gz',
- image_resize=None,
- image_crop=None,
- flip=True,
- path_root='/mnt/hdd/datasets/breast/DUKE/dataset_lr_256_256_32',
- use_znorm=True
- )
-
-input = ds[0]['source'][None]
-
-target = torch.randn_like(input)
-print(loss(input, target))
\ No newline at end of file
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/transforms.py b/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/transforms.py
deleted file mode 100644
index 20251ef28bc8a9c2b599ee7365a5394ae568720e..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/transforms.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""
-Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
-Source url: https://github.com/MarcoForte/FBA_Matting
-License: MIT License
-"""
-import cv2
-import numpy as np
-
-group_norm_std = [0.229, 0.224, 0.225]
-group_norm_mean = [0.485, 0.456, 0.406]
-
-
-def dt(a):
- return cv2.distanceTransform((a * 255).astype(np.uint8), cv2.DIST_L2, 0)
-
-
-def trimap_transform(trimap):
- h, w = trimap.shape[0], trimap.shape[1]
-
- clicks = np.zeros((h, w, 6))
- for k in range(2):
- if np.count_nonzero(trimap[:, :, k]) > 0:
- dt_mask = -dt(1 - trimap[:, :, k]) ** 2
- L = 320
- clicks[:, :, 3 * k] = np.exp(dt_mask / (2 * ((0.02 * L) ** 2)))
- clicks[:, :, 3 * k + 1] = np.exp(dt_mask / (2 * ((0.08 * L) ** 2)))
- clicks[:, :, 3 * k + 2] = np.exp(dt_mask / (2 * ((0.16 * L) ** 2)))
-
- return clicks
-
-
-def groupnorm_normalise_image(img, format="nhwc"):
- """
- Accept rgb in range 0,1
- """
- if format == "nhwc":
- for i in range(3):
- img[..., i] = (img[..., i] - group_norm_mean[i]) / group_norm_std[i]
- else:
- for i in range(3):
- img[..., i, :, :] = (
- img[..., i, :, :] - group_norm_mean[i]
- ) / group_norm_std[i]
-
- return img
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/web/routers/__init__.py b/spaces/mygyasir/genious_bgremover/carvekit/web/routers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/nazneen/model-usage/README.md b/spaces/nazneen/model-usage/README.md
deleted file mode 100644
index f2f55f35fc8cf4dec94e38cc3308a0db4c0d390b..0000000000000000000000000000000000000000
--- a/spaces/nazneen/model-usage/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Model Usage
-emoji: ⏱️
-colorFrom: pink
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/AutoCAD Plant 3D 2015 X86 X64 Torrent [PATCHED] Download.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/AutoCAD Plant 3D 2015 X86 X64 Torrent [PATCHED] Download.md
deleted file mode 100644
index 9aa0795eefeb4e94924315a580758c461a2b4c3e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/AutoCAD Plant 3D 2015 X86 X64 Torrent [PATCHED] Download.md
+++ /dev/null
@@ -1,135 +0,0 @@
-AutoCAD Plant 3D 2015 X86 X64 Torrent Download
-Are you looking for a powerful and easy-to-use tool for designing and modeling plants of all sizes? Do you want to create accurate and realistic plant layouts, piping diagrams, isometrics, and reports? If yes, then you might be interested in AutoCAD Plant 3D 2015, a software that is specially designed for plant design and engineering.
-But how can you get this software without spending a fortune? One way is to use torrent download, which is a method of sharing files over the internet. Torrent download can help you access AutoCAD Plant 3D 2015 for free or at a low cost. However, torrent download also comes with some disadvantages and risks that you should be aware of before using it.
-AutoCAD Plant 3D 2015 X86 X64 Torrent Download DOWNLOAD ✵ https://urlcod.com/2uI9zb
In this article, we will explain what AutoCAD Plant 3D 2015 is, what are its features and system requirements, why use torrent download for it, how to download and install it from torrent, and how to use it for plant design. We will also answer some frequently asked questions about AutoCAD Plant 3D 2015 and torrent download. By the end of this article, you will have a clear idea of whether AutoCAD Plant 3D 2015 X86 X64 Torrent Download is worth it or not.
- What is AutoCAD Plant 3D 2015?
-AutoCAD Plant 3D 2015 is a software that is part of the Autodesk AutoCAD family. It is an industry-specific toolset that allows you to create and edit plant layouts, piping diagrams, orthographics, isometrics, and reports. It also enables you to collaborate with other project teams in a cloud-based common data environment. With AutoCAD Plant 3D 2015, you can:
-
-Create and modify P&IDs (piping and instrumentation diagrams) with in-context commands and industry-standard symbol libraries.
-Create and edit 3D models of plants with parametric equipment, structural elements, piping components, valves, supports, and more.
-Extract piping orthographics and isometrics automatically from the 3D model with customizable settings and annotations.
-Generate reports such as bills of materials, line lists, valve lists, etc. from the P&ID or the 3D model.
-Validate data consistency across P&IDs and the 3D model with user-definable rules.
-Collaborate securely with other project stakeholders in a cloud-based common data environment using BIM (building information modeling) workflows.
-
- Features of AutoCAD Plant 3D 2015
-Some of the features of AutoCAD Plant 3D 2015 are:
-
-Powerful plant design tool: You can create realistic and accurate plant layouts and models with a comprehensive set of tools that are tailored for plant design. You can also customize the tools according to your preferences and standards.
-Flexible and affordable plans: You can subscribe to AutoCAD Plant 3D 2015 for one or multiple years with different term options. You can also add or remove seats as needed. You can access the latest updates and support as well as cloud services with your subscription.
-Cloud collaboration: You can share and manage your plant design data in a cloud-based common data environment using BIM 360 Design. You can also access your data from any device and location with the Autodesk Desktop Connector.
-Data exchange: You can import and export data from various sources and formats, such as AutoCAD P&ID, Revit, Navisworks, Excel, PDF, etc. You can also use open APIs (application programming interfaces) to integrate AutoCAD Plant 3D 2015 with other applications.
-Customization and extensibility: You can customize AutoCAD Plant 3D 2015 to suit your specific needs and standards. You can create and edit catalogs, specs, templates, symbols, rules, etc. You can also use Autodesk Exchange Apps to find and download additional content and tools for AutoCAD Plant 3D 2015.
-
- System requirements for AutoCAD Plant 3D 2015
-To run AutoCAD Plant 3D 2015 smoothly on your computer, you need to meet the following minimum system requirements:
-
-
-Operating system
-Memory (RAM)
-Disk space
-Processor
-Display resolution
-Graphics card
-
-
-Windows 7/8/8.1 (32-bit or 64-bit)
-4 GB (8 GB recommended)
-8 GB (12 GB recommended)
-Intel Pentium 4 or AMD Athlon 64 (3 GHz or higher)
-1280 x 1024 (1600 x 1050 or higher recommended)
-Windows display adapter capable of DirectX 9.0c (DirectX 11 compliant card recommended)
-
-
- Why use torrent download for AutoCAD Plant 3D 2015?
-Torrent download is a way of downloading files from the internet using a peer-to-peer (P2P) network. A P2P network is a network of computers that share files with each other without relying on a central server. A torrent file is a small file that contains information about the larger file that you want to download, such as its name, size, location, etc. A torrent client is a software that reads the torrent file and connects you to other computers that have the file or parts of it. A torrent client then downloads the file or parts of it from those computers and assembles them into the complete file on your computer.
-Torrent download can be used for downloading any type of file, including software like AutoCAD Plant 3D 2015. There are some advantages and disadvantages of using torrent download for AutoCAD Plant 3D 2015, which we will discuss below.
- Advantages of torrent download
-Some of the advantages of using torrent download for AutoCAD Plant 3D 2015 are:
-
-Fast and efficient: Torrent download can be faster and more efficient than direct download because it uses multiple sources and connections to download the file. You can also resume or pause the download at any time without losing the progress.
-Cheap or free: Torrent download can help you save money by accessing AutoCAD Plant 3D 2015 for free or at a low cost. You do not need to pay for a subscription or a license to use the software.
-Easy and convenient: Torrent download can be easy and convenient because you only need a torrent client and a torrent file to start the download. You do not need to register or log in to any website or service to use the software.
-Variety and availability: Torrent download can offer you a variety of options and versions of AutoCAD Plant 3D 2015 to choose from. You can also find older or newer versions of the software that may not be available on the official website or other sources.
-
- Disadvantages and risks of torrent download
-Some of the disadvantages and risks of using torrent download for AutoCAD Plant 3D 2015 are:
-
-
-Illegal and unethical: Torrent download can be illegal and unethical because it violates the intellectual property rights of the software developer. By downloading AutoCAD Plant 3D 2015 from torrent, you are not supporting the developer or paying for their work. You may also face legal consequences if you are caught downloading or using pirated software.
-Unsafe and unreliable: Torrent download can be unsafe and unreliable because it exposes your computer to viruses, malware, spyware, etc. that may harm your system or steal your data. You may also download corrupted, incomplete, or fake files that may not work properly or at all. You may also face technical issues or errors while downloading or installing the software.
-Unfair and unprofessional: Torrent download can be unfair and unprofessional because it deprives you of the benefits and features that come with the official version of AutoCAD Plant 3D 2015. You may not be able to access the latest updates, support, cloud services, etc. that are available for the subscribers or licensed users. You may also face compatibility or performance issues with other software or hardware that are not tested or certified for the pirated version of AutoCAD Plant 3D 2015.
-
- How to download and install AutoCAD Plant 3D 2015 from torrent?
-If you still want to use torrent download for AutoCAD Plant 3D 2015, you need to follow some steps to download and install it from torrent. Here are the steps:
- Steps to download AutoCAD Plant 3D 2015 from torrent
-
-Find a reliable and reputable torrent website that offers AutoCAD Plant 3D 2015 X86 X64 Torrent Download. Some examples are The Pirate Bay, Kickass Torrents, RARBG, etc. However, be careful of the risks and legality of using these websites.
-Search for AutoCAD Plant 3D 2015 on the torrent website and choose the torrent file that matches your system requirements and preferences. Check the file size, seeders, leechers, comments, ratings, etc. to ensure the quality and safety of the file.
-Download the torrent file to your computer or copy the magnet link to your clipboard.
-Download and install a torrent client on your computer if you do not have one already. Some examples are uTorrent, BitTorrent, qBittorrent, etc. However, be careful of the risks and legality of using these software.
-Open the torrent file with your torrent client or paste the magnet link into your torrent client.
-Select the destination folder for the downloaded file and start the download process.
-Wait for the download to complete. The time may vary depending on your internet speed, file size, seeders, leechers, etc.
-
- Steps to install AutoCAD Plant 3D 2015 from torrent
-
-Locate the downloaded file on your computer and extract it if it is compressed in a ZIP or RAR format.
-Open the extracted folder and look for the setup file or the ISO file of AutoCAD Plant 3D 2015.
-If it is a setup file, double-click on it and follow the instructions on the screen to install AutoCAD Plant 3D 2015 on your computer.
-If it is an ISO file, you need to mount it on a virtual drive using a software like Daemon Tools or PowerISO. Then, open the virtual drive and run the setup file as above.
-During the installation process, you may need to enter a serial number or a product key for AutoCAD Plant 3D 2015. You can find these in a text file or a readme file in the downloaded folder. However, be careful of the risks and legality of using these codes.
-You may also need to apply a crack or a patch for AutoCAD Plant 3D 2015 to bypass the activation or registration process. You can find these in a separate folder or a subfolder in the downloaded folder. However, be careful of the risks and legality of using these files.
-After the installation is complete, you can launch AutoCAD Plant 3D 2015 from your desktop or start menu and start using it for plant design.
-
- How to use AutoCAD Plant 3D 2015 for plant design?
-Now that you have downloaded and installed AutoCAD Plant 3D 2015 from torrent, you may wonder how to use it for plant design. Here are some basic steps to get you started:
- Basic workflow of AutoCAD Plant 3D 2015
-
-Create a new project or open an existing project in AutoCAD Plant 3D 2015. A project is a collection of files and settings that define your plant design data and environment.
-Create or edit P&IDs using the P&ID workspace. A P&ID is a schematic diagram that shows the piping and instrumentation of a plant system. You can use various tools and commands to draw P&ID objects such as lines, valves, equipment, instruments, etc. You can also add data and annotations to your P &ID objects. You can also validate and check your P&ID for errors and inconsistencies.
-Create or edit 3D models using the Plant 3D workspace. A 3D model is a realistic representation of the physical layout and components of a plant system. You can use various tools and commands to create and modify 3D objects such as equipment, piping, structural elements, supports, etc. You can also add data and annotations to your 3D objects. You can also link your 3D model to your P&ID and synchronize the data between them.
-Extract orthographics and isometrics from your 3D model using the Ortho and Iso workspace. Orthographics are 2D drawings that show the elevation, plan, and section views of your 3D model. Isometrics are 2D drawings that show the piping layout of your 3D model in a 3D perspective. You can use various tools and commands to generate, edit, and annotate orthographics and isometrics from your 3D model. You can also customize the settings and styles of your orthographics and isometrics.
-Generate reports from your P&ID or your 3D model using the Report Creator. Reports are documents that contain information about your plant design data, such as bills of materials, line lists, valve lists, etc. You can use various tools and commands to create, edit, and export reports from your P&ID or your 3D model. You can also customize the settings and formats of your reports.
-Collaborate with other project teams using BIM 360 Design. BIM 360 Design is a cloud-based service that allows you to share and manage your plant design data in a common data environment. You can use various tools and commands to upload, download, view, comment, and compare your plant design data with other project stakeholders. You can also use BIM 360 Design to access your plant design data from any device and location.
-
- Tips and tricks for using AutoCAD Plant 3D 2015
-Here are some tips and tricks that can help you use AutoCAD Plant 3D 2015 more effectively and efficiently:
-
-Use the Project Manager to organize and manage your project files and settings. You can use the Project Manager to create, open, close, copy, rename, delete, or backup your project files. You can also use the Project Manager to edit your project settings, such as units, catalogs, specs, templates, etc.
-Use the Properties Palette to view and edit the properties of your P&ID or 3D objects. You can use the Properties Palette to change the attributes, data, dimensions, styles, etc. of your P&ID or 3D objects. You can also use the Properties Palette to filter or select objects based on their properties.
-Use the Data Manager to view and edit the data of your P&ID or 3D objects in a spreadsheet format. You can use the Data Manager to add, modify, delete, or export data of your P&ID or 3D objects. You can also use the Data Manager to run queries or reports on your data.
-Use the Spec Editor to create and edit catalogs and specs for your piping components. You can use the Spec Editor to define the properties, dimensions, materials, connections, etc. of your piping components. You can also use the Spec Editor to import or export catalogs and specs from other sources.
-Use the Project Setup to customize the rules, styles, settings, etc. of your project. You can use the Project Setup to define the rules for validating your P&ID or 3D model data consistency. You can also use the Project Setup to define the styles for generating orthographics and isometrics from your 3D model. You can also use the Project Setup to define the settings for generating reports from your P&ID or 3D model.
-Use the Help menu to access the online documentation, tutorials, videos, forums, etc. for AutoCAD Plant 3D 2015. You can use the Help menu to learn more about the features and functions of AutoCAD Plant 3D 2015. You can also use the Help menu to find answers to your questions or problems with AutoCAD Plant 3D 2015.
-
- Conclusion
-In conclusion, AutoCAD Plant 3D 2015 is a software that is designed for plant design and engineering. It allows you to create and edit plant layouts, piping diagrams, orthographics, isometrics, and reports. It also enables you to collaborate with other project teams in a cloud-based common data environment.
-Torrent download is a way of downloading files from the internet using a peer-to-peer network. Torrent download can help you access AutoCAD Plant 3D 2015 for free or at a low cost. However, torrent download also has some disadvantages and risks that you should consider before using it.
-If you decide to use torrent download for AutoCAD Plant 3D 2015, you need to follow some steps to download and install it from torrent. You also need to follow some steps to use it for plant design. You can also use some tips and tricks to improve your experience with AutoCAD Plant 3D 2015.
-We hope that this article has given you a clear idea of whether AutoCAD Plant 3D 2015 X86 X64 Torrent Download is worth it or not. If you have any questions or feedback, please feel free to leave a comment below.
- FAQs
-Here are some frequently asked questions about AutoCAD Plant 3D 2015 and torrent download:
-
-Q: Is AutoCAD Plant 3D 2015 compatible with other Autodesk software? A: Yes, AutoCAD Plant 3D 2015 is compatible with other Autodesk software, such as AutoCAD P&ID, Revit, Navisworks, BIM 360 Design, etc. You can exchange data and information between these software using various formats and methods.
-Q: How much does AutoCAD Plant 3D 2015 cost? A: The official price of AutoCAD Plant 3D 2015 is $6,295 for a one-year subscription or $16,065 for a three-year subscription. However, you may find different prices or discounts from other sources or resellers.
-Q: Is torrent download legal? A: Torrent download itself is not illegal, but downloading or sharing copyrighted content without permission or license is illegal and unethical. You may face legal actions or penalties if you are caught downloading or using pirated software.
-Q: How can I protect my computer from viruses or malware when using torrent download? A: You can protect your computer from viruses or malware when using torrent download by following some precautions, such as:
-
-Use a reliable and reputable torrent website and torrent client.
-Check the file size, seeders, leechers, comments, ratings, etc. of the torrent file before downloading it.
-Scan the downloaded file with an antivirus or anti-malware software before opening it.
-Avoid opening or running any suspicious or unknown files or programs that come with the downloaded file.
-Use a VPN (virtual private network) or a proxy to hide your IP address and location when using torrent download.
-
-Q: How can I update AutoCAD Plant 3D 2015 if I download it from torrent? A: You may not be able to update AutoCAD Plant 3D 2015 if you download it from torrent, because the official updates may not be compatible with the pirated version of the software. You may also lose the crack or patch that you applied to activate the software. You may need to find and download another torrent file that contains the updated version of AutoCAD Plant 3D 2015.
- b2dd77e56b{sim}
', unsafe_allow_html=True)
- else :
- # Вывод изображения в контейнер
- col.markdown(comment)
-
- col.image(resized_image, width=thumbnail_width)
- i = i + 1
-
- return
-
-def show_landing() :
-
- st.title('Find my pic!')
-
- search_request = st.text_input('Search for images',
- 'Search ...')
-
-
- col1, col2 = st.columns(2)
-
- if col1.button('Find!') and os.path.exists(IMAGE_DIR) :
- results = st.container()
- start_time = time.time()
- search_result = search(search_request)
- end_time = time.time()
- show_result(search_request,
- search_result,
- IMAGE_DIR+'/',
- results,
- end_time - start_time)
-
- if col2.button('Find with faiss!') and os.path.exists(IMAGE_DIR) :
- results = st.container()
- start_time = time.time()
- search_result = searchWithFaiss(search_request)
- end_time = time.time()
- show_result(search_request,
- search_result,
- IMAGE_DIR+'/',
- results,
- end_time - start_time)
- return
-
-
-downlad_images()
-
-show_landing()
\ No newline at end of file
diff --git a/spaces/patgpt4/MusicGen/CODE_OF_CONDUCT.md b/spaces/patgpt4/MusicGen/CODE_OF_CONDUCT.md
deleted file mode 100644
index 83f431e8feeb7e80d571f39c9f6c1b96857b5f85..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at . All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
diff --git a/spaces/perilli/tortoise-tts-v2/models/classifier.py b/spaces/perilli/tortoise-tts-v2/models/classifier.py
deleted file mode 100644
index c8997738a2b689cb4bd744323339e5e8b46035ae..0000000000000000000000000000000000000000
--- a/spaces/perilli/tortoise-tts-v2/models/classifier.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.checkpoint import checkpoint
-
-from models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
-
-
-class ResBlock(nn.Module):
- def __init__(
- self,
- channels,
- dropout,
- out_channels=None,
- use_conv=False,
- use_scale_shift_norm=False,
- dims=2,
- up=False,
- down=False,
- kernel_size=3,
- do_checkpoint=True,
- ):
- super().__init__()
- self.channels = channels
- self.dropout = dropout
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_scale_shift_norm = use_scale_shift_norm
- self.do_checkpoint = do_checkpoint
- padding = 1 if kernel_size == 3 else 2
-
- self.in_layers = nn.Sequential(
- normalization(channels),
- nn.SiLU(),
- nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
- )
-
- self.updown = up or down
-
- if up:
- self.h_upd = Upsample(channels, False, dims)
- self.x_upd = Upsample(channels, False, dims)
- elif down:
- self.h_upd = Downsample(channels, False, dims)
- self.x_upd = Downsample(channels, False, dims)
- else:
- self.h_upd = self.x_upd = nn.Identity()
-
- self.out_layers = nn.Sequential(
- normalization(self.out_channels),
- nn.SiLU(),
- nn.Dropout(p=dropout),
- zero_module(
- nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
- ),
- )
-
- if self.out_channels == channels:
- self.skip_connection = nn.Identity()
- elif use_conv:
- self.skip_connection = nn.Conv1d(
- dims, channels, self.out_channels, kernel_size, padding=padding
- )
- else:
- self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)
-
- def forward(self, x):
- if self.do_checkpoint:
- return checkpoint(
- self._forward, x
- )
- else:
- return self._forward(x)
-
- def _forward(self, x):
- if self.updown:
- in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
- h = in_rest(x)
- h = self.h_upd(h)
- x = self.x_upd(x)
- h = in_conv(h)
- else:
- h = self.in_layers(x)
- h = self.out_layers(h)
- return self.skip_connection(x) + h
-
-
-class AudioMiniEncoder(nn.Module):
- def __init__(self,
- spec_dim,
- embedding_dim,
- base_channels=128,
- depth=2,
- resnet_blocks=2,
- attn_blocks=4,
- num_attn_heads=4,
- dropout=0,
- downsample_factor=2,
- kernel_size=3):
- super().__init__()
- self.init = nn.Sequential(
- nn.Conv1d(spec_dim, base_channels, 3, padding=1)
- )
- ch = base_channels
- res = []
- self.layers = depth
- for l in range(depth):
- for r in range(resnet_blocks):
- res.append(ResBlock(ch, dropout, do_checkpoint=False, kernel_size=kernel_size))
- res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
- ch *= 2
- self.res = nn.Sequential(*res)
- self.final = nn.Sequential(
- normalization(ch),
- nn.SiLU(),
- nn.Conv1d(ch, embedding_dim, 1)
- )
- attn = []
- for a in range(attn_blocks):
- attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=False))
- self.attn = nn.Sequential(*attn)
- self.dim = embedding_dim
-
- def forward(self, x):
- h = self.init(x)
- h = self.res(h)
- h = self.final(h)
- for blk in self.attn:
- h = checkpoint(blk, h)
- return h[:, :, 0]
-
-
-class AudioMiniEncoderWithClassifierHead(nn.Module):
- def __init__(self, classes, distribute_zero_label=True, **kwargs):
- super().__init__()
- self.enc = AudioMiniEncoder(**kwargs)
- self.head = nn.Linear(self.enc.dim, classes)
- self.num_classes = classes
- self.distribute_zero_label = distribute_zero_label
-
- def forward(self, x, labels=None):
- h = self.enc(x)
- logits = self.head(h)
- if labels is None:
- return logits
- else:
- if self.distribute_zero_label:
- oh_labels = nn.functional.one_hot(labels, num_classes=self.num_classes)
- zeros_indices = (labels == 0).unsqueeze(-1)
- # Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.
- zero_extra_mass = torch.full_like(oh_labels, dtype=torch.float, fill_value=.2/(self.num_classes-1))
- zero_extra_mass[:, 0] = -.2
- zero_extra_mass = zero_extra_mass * zeros_indices
- oh_labels = oh_labels + zero_extra_mass
- else:
- oh_labels = labels
- loss = nn.functional.cross_entropy(logits, oh_labels)
- return loss
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
deleted file mode 100644
index 259b15ba194db7c02fbcbf170d522230b4418933..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from ._compat import Protocol
-from typing import Any, Dict, Iterator, List, TypeVar, Union
-
-
-_T = TypeVar("_T")
-
-
-class PackageMetadata(Protocol):
- def __len__(self) -> int:
- ... # pragma: no cover
-
- def __contains__(self, item: str) -> bool:
- ... # pragma: no cover
-
- def __getitem__(self, key: str) -> str:
- ... # pragma: no cover
-
- def __iter__(self) -> Iterator[str]:
- ... # pragma: no cover
-
- def get_all(self, name: str, failobj: _T = ...) -> Union[List[Any], _T]:
- """
- Return all values associated with a possibly multi-valued key.
- """
-
- @property
- def json(self) -> Dict[str, Union[str, List[str]]]:
- """
- A JSON-compatible form of the metadata.
- """
-
-
-class SimplePath(Protocol[_T]):
- """
- A minimal subset of pathlib.Path required by PathDistribution.
- """
-
- def joinpath(self) -> _T:
- ... # pragma: no cover
-
- def __truediv__(self, other: Union[str, _T]) -> _T:
- ... # pragma: no cover
-
- @property
- def parent(self) -> _T:
- ... # pragma: no cover
-
- def read_text(self) -> str:
- ... # pragma: no cover
diff --git a/spaces/poojasree2003/aiproject/README.md b/spaces/poojasree2003/aiproject/README.md
deleted file mode 100644
index 5f99967042c0d4ef1bd3391e880beced5a2c853a..0000000000000000000000000000000000000000
--- a/spaces/poojasree2003/aiproject/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Aiproject
-emoji: 📉
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/prerna9811/Chord/portaudio/src/common/pa_types.h b/spaces/prerna9811/Chord/portaudio/src/common/pa_types.h
deleted file mode 100644
index f628783adf8333122fbdf08b3572e1959e49b78f..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/common/pa_types.h
+++ /dev/null
@@ -1,107 +0,0 @@
-#ifndef PA_TYPES_H
-#define PA_TYPES_H
-
-/*
- * Portable Audio I/O Library
- * integer type definitions
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2006 Ross Bencina, Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file
- @ingroup common_src
-
- @brief Definition of 16 and 32 bit integer types (PaInt16, PaInt32 etc)
-
- SIZEOF_SHORT, SIZEOF_INT and SIZEOF_LONG are set by the configure script
- when it is used. Otherwise we default to the common 32 bit values, if your
- platform doesn't use configure, and doesn't use the default values below
- you will need to explicitly define these symbols in your make file.
-
- A PA_VALIDATE_SIZES macro is provided to assert that the values set in this
- file are correct.
-*/
-
-#ifndef SIZEOF_SHORT
-#define SIZEOF_SHORT 2
-#endif
-
-#ifndef SIZEOF_INT
-#define SIZEOF_INT 4
-#endif
-
-#ifndef SIZEOF_LONG
-#define SIZEOF_LONG 4
-#endif
-
-
-#if SIZEOF_SHORT == 2
-typedef signed short PaInt16;
-typedef unsigned short PaUint16;
-#elif SIZEOF_INT == 2
-typedef signed int PaInt16;
-typedef unsigned int PaUint16;
-#else
-#error pa_types.h was unable to determine which type to use for 16bit integers on the target platform
-#endif
-
-#if SIZEOF_SHORT == 4
-typedef signed short PaInt32;
-typedef unsigned short PaUint32;
-#elif SIZEOF_INT == 4
-typedef signed int PaInt32;
-typedef unsigned int PaUint32;
-#elif SIZEOF_LONG == 4
-typedef signed long PaInt32;
-typedef unsigned long PaUint32;
-#else
-#error pa_types.h was unable to determine which type to use for 32bit integers on the target platform
-#endif
-
-
-/* PA_VALIDATE_TYPE_SIZES compares the size of the integer types at runtime to
- ensure that PortAudio was configured correctly, and raises an assertion if
- they don't match the expected values. must be included in the
- context in which this macro is used.
-*/
-#define PA_VALIDATE_TYPE_SIZES \
- { \
- assert( "PortAudio: type sizes are not correct in pa_types.h" && sizeof( PaUint16 ) == 2 ); \
- assert( "PortAudio: type sizes are not correct in pa_types.h" && sizeof( PaInt16 ) == 2 ); \
- assert( "PortAudio: type sizes are not correct in pa_types.h" && sizeof( PaUint32 ) == 4 ); \
- assert( "PortAudio: type sizes are not correct in pa_types.h" && sizeof( PaInt32 ) == 4 ); \
- }
-
-
-#endif /* PA_TYPES_H */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/openapi/docs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/openapi/docs.py
deleted file mode 100644
index 69473d19cb2569cfbec9dae86328a675947a465c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/openapi/docs.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import json
-from typing import Any, Dict, Optional
-
-from fastapi.encoders import jsonable_encoder
-from starlette.responses import HTMLResponse
-from typing_extensions import Annotated, Doc # type: ignore [attr-defined]
-
-swagger_ui_default_parameters: Annotated[
- Dict[str, Any],
- Doc(
- """
- Default configurations for Swagger UI.
-
- You can use it as a template to add any other configurations needed.
- """
- ),
-] = {
- "dom_id": "#swagger-ui",
- "layout": "BaseLayout",
- "deepLinking": True,
- "showExtensions": True,
- "showCommonExtensions": True,
-}
-
-
-def get_swagger_ui_html(
- *,
- openapi_url: Annotated[
- str,
- Doc(
- """
- The OpenAPI URL that Swagger UI should load and use.
-
- This is normally done automatically by FastAPI using the default URL
- `/openapi.json`.
- """
- ),
- ],
- title: Annotated[
- str,
- Doc(
- """
- The HTML `` content, normally shown in the browser tab.
- """
- ),
- ],
- swagger_js_url: Annotated[
- str,
- Doc(
- """
- The URL to use to load the Swagger UI JavaScript.
-
- It is normally set to a CDN URL.
- """
- ),
- ] = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5.9.0/swagger-ui-bundle.js",
- swagger_css_url: Annotated[
- str,
- Doc(
- """
- The URL to use to load the Swagger UI CSS.
-
- It is normally set to a CDN URL.
- """
- ),
- ] = "https://cdn.jsdelivr.net/npm/swagger-ui-dist@5.9.0/swagger-ui.css",
- swagger_favicon_url: Annotated[
- str,
- Doc(
- """
- The URL of the favicon to use. It is normally shown in the browser tab.
- """
- ),
- ] = "https://fastapi.tiangolo.com/img/favicon.png",
- oauth2_redirect_url: Annotated[
- Optional[str],
- Doc(
- """
- The OAuth2 redirect URL, it is normally automatically handled by FastAPI.
- """
- ),
- ] = None,
- init_oauth: Annotated[
- Optional[Dict[str, Any]],
- Doc(
- """
- A dictionary with Swagger UI OAuth2 initialization configurations.
- """
- ),
- ] = None,
- swagger_ui_parameters: Annotated[
- Optional[Dict[str, Any]],
- Doc(
- """
- Configuration parameters for Swagger UI.
-
- It defaults to [swagger_ui_default_parameters][fastapi.openapi.docs.swagger_ui_default_parameters].
- """
- ),
- ] = None,
-) -> HTMLResponse:
- """
- Generate and return the HTML that loads Swagger UI for the interactive
- API docs (normally served at `/docs`).
-
- You would only call this function yourself if you needed to override some parts,
- for example the URLs to use to load Swagger UI's JavaScript and CSS.
-
- Read more about it in the
- [FastAPI docs for Configure Swagger UI](https://fastapi.tiangolo.com/how-to/configure-swagger-ui/)
- and the [FastAPI docs for Custom Docs UI Static Assets (Self-Hosting)](https://fastapi.tiangolo.com/how-to/custom-docs-ui-assets/).
- """
- current_swagger_ui_parameters = swagger_ui_default_parameters.copy()
- if swagger_ui_parameters:
- current_swagger_ui_parameters.update(swagger_ui_parameters)
-
- html = f"""
- <!DOCTYPE html>
- <html>
- <head>
- <link type="text/css" rel="stylesheet" href="{swagger_css_url}">
- <link rel="shortcut icon" href="{swagger_favicon_url}">
- <title>{title}
-
-
-
-
-
-
-
-
-
- 
-
- 
-
-
-
- 
""")
-
- with gr.Row():
- gr.HTML("""